Football

In this example, we use the football dataset to predict the outcomes of games between various teams. You can download the dataset here.

• date: Date of the game.

• home_team: Home Team.

• home_score: Home Team number of goals.

• away_team: Away Team.

• away_score: Away Team number of goals.

• tournament: Game Type (World Cup, Friendly…).

• city: City where the game took place.

• country: Country where the game took place.

• neutral: If the event took place to a neutral location.

We will follow the data science cycle (Data Exploration - Data Preparation - Data Modeling - Model Evaluation - Model Deployment) to solve this problem.

Initialization

This example uses the following version of vastorbit:

import vastorbit as vo

vo.__version__

Connect to VAST. This example uses an existing connection called VASTDSN. For details on how to create a connection, see the Connection tutorial. You can skip the below cell if you already have an established connection.

vo.connect("VASTDSN")

Let’s create a VastFrame of the dataset.

football = vo.read_csv("games.csv")
football

	📅 date Date 100%	Abc home_team Varchar(50) 100%	Abc away_team Varchar(50) 100%	123 home_score Integer 100%	123 away_score Integer 100%	Abc tournament Varchar(50) 100%	Abc city Varchar(50) 100%	Abc country Varchar(50) 100%	0|1 neutral Boolean 100%
1	1872-11-30	Scotland	England	0	0	Friendly	Glasgow	Scotland	✗
2	1873-03-08	England	Scotland	4	2	Friendly	London	England	✗
3	1874-03-07	Scotland	England	2	1	Friendly	Glasgow	Scotland	✗
4	1875-03-06	England	Scotland	2	2	Friendly	London	England	✗
5	1876-03-04	Scotland	England	3	0	Friendly	Glasgow	Scotland	✗
6	1876-03-25	Scotland	Wales	4	0	Friendly	Glasgow	Scotland	✗
7	1877-03-03	England	Scotland	1	3	Friendly	London	England	✗
8	1877-03-05	Wales	Scotland	0	2	Friendly	Wrexham	Wales	✗
9	1878-03-02	Scotland	England	7	2	Friendly	Glasgow	Scotland	✗
10	1878-03-23	Scotland	Wales	9	0	Friendly	Glasgow	Scotland	✗
11	1879-01-18	England	Wales	2	1	Friendly	London	England	✗
12	1879-04-05	England	Scotland	5	4	Friendly	London	England	✗
13	1879-04-07	Wales	Scotland	0	3	Friendly	Wrexham	Wales	✗
14	1880-03-13	Scotland	England	5	4	Friendly	Glasgow	Scotland	✗
15	1880-03-15	Wales	England	2	3	Friendly	Wrexham	Wales	✗
16	1880-03-27	Scotland	Wales	5	1	Friendly	Glasgow	Scotland	✗
17	1881-02-26	England	Wales	0	1	Friendly	Blackburn	England	✗
18	1881-03-12	England	Scotland	1	6	Friendly	London	England	✗
19	1881-03-14	Wales	Scotland	1	5	Friendly	Wrexham	Wales	✗
20	1882-02-18	Northern Ireland	England	0	13	Friendly	Belfast	Republic of Ireland	✗

Data Exploration and Preparation

Let’s explore the data by displaying descriptive statistics of all the columns.

football["date"].describe()

	value
name	"date"
dtype	date
count	41586
min	1872-11-30
max	2020-02-01

The dataset includes a total of 41,586 games, which take place between 1872 and 2020. Let’s look at our game types and teams.

football["tournament"].describe()

	value
name	"tournament"
dtype	varchar(50)
unique	112.0
count	41586.0
Friendly	17029
Others	10630
FIFA World Cup qualification	7236
UEFA Euro qualification	2582
African Cup of Nations qualification	1672
FIFA World Cup	900
Copa América	813

Different types of tournaments took place (FIFA World Cup, UEFA Euro, etc.) aand most of the games in our data are friendlies or qualifiers for international tournaments.

football.describe()

	count	mean	std	min	approx_25%	approx_50%	approx_75%	max
"home_score"	41586.0	1.745755783196268	1.753780340476994	0.0	1.0	1.0	2.0	31.0
"away_score"	41586.0	1.187587168758717	1.4053234683358853	0.0	0.0	1.0	2.0	21.0
"neutral"	41586.0	0.24724666955225316	0.43141653827564974	0.0	0.0	0.0	0.0	1.0

football.describe(method = "categorical")

	dtype	count	top	top_percent
"date"	date	41586	2012-02-29	0.159
"home_team"	varchar(50)	41586	Brazil	1.366
"away_team"	varchar(50)	41586	Uruguay	1.301
"home_score"	integer	41586	1	29.57
"away_score"	integer	41586	0	37.135
"tournament"	varchar(50)	41586	Friendly	40.949
"city"	varchar(50)	41586	Kuala Lumpur	1.416
"country"	varchar(50)	41586	United States	2.787
"neutral"	boolean	41586	✗	75.275

The dataset includes 308 national teams. For most of the games, the home team scores better than the away team. Since some games take place in a neutral location, we can ensure this hypothesis using the variable neutral. Notice also that the number of goals per match is pretty low (median of 1 for both away and home teams).

Goal

Our goal for the study will be to predict the outcomes of games after 2015. Before doing the study, we can notice that some teams names have changed over time. We need to change the old names by the new names otherwise it will add too much bias in the data.

for team in ["home_team", "away_team"]:
    football[team].decode(
        'German DR', 'Germany',
        'Czechoslovakia', 'Czech Republic',
        'Yugoslavia', 'Serbia',
        'Yemen DPR', 'Yemen',
        football[team],
    )

Let’s just consider teams that have played more than five home and away games.

football["cnt_games_1"] = "COUNT(*) OVER (PARTITION BY home_team)"
football["cnt_games_2"] = "COUNT(*) OVER (PARTITION BY away_team)"
football.filter((football["cnt_games_2"] > 5) & (football["cnt_games_1"] > 5))
vo.drop("football_clean", method = "table")
football.to_db(
    name = "football_clean",
    usecols = [
        "date",
        "home_score",
        "home_team",
        "tournament",
        "away_team",
        "away_score",
        "neutral",
        "country",
        "city",
    ],
    relation_type = "table",
    inplace = True,
)

	📅 date Date 100%	123 home_score Integer 100%	Abc home_team Varchar(50) 100%	Abc tournament Varchar(50) 100%	Abc away_team Varchar(50) 100%	123 away_score Integer 100%	0|1 neutral Boolean 100%	Abc country Varchar(50) 100%	Abc city Varchar(50) 100%
1	1993-12-16	0	Mexico	Friendly	Brazil	1	✗	Mexico	Guadalajara
2	2009-06-21	0	Italy	Confederations Cup	Brazil	3	✓	South Africa	Pretoria
3	2011-11-10	0	Gabon	Friendly	Brazil	2	✗	Gabon	Libreville
4	2017-10-05	0	Bolivia	FIFA World Cup qualification	Brazil	0	✗	Bolivia	La Paz
5	1979-08-23	2	Argentina	Copa América	Brazil	2	✗	Argentina	Buenos Aires
6	2005-10-09	1	Bolivia	FIFA World Cup qualification	Brazil	1	✗	Bolivia	La Paz
7	1959-03-21	2	Bolivia	Copa América	Brazil	4	✓	Argentina	Buenos Aires
8	1988-07-17	0	Australia	Friendly	Brazil	2	✗	Australia	Sydney
9	1997-12-14	0	Australia	Confederations Cup	Brazil	0	✓	Saudi Arabia	Riyadh
10	2017-06-13	0	Australia	Friendly	Brazil	4	✗	Australia	Melbourne
11	1987-07-03	4	Chile	Copa América	Brazil	0	✓	Argentina	Córdoba
12	1988-07-07	0	Australia	Friendly	Brazil	1	✗	Australia	Melbourne
13	1985-05-21	2	Chile	Friendly	Brazil	1	✗	Chile	Santiago
14	1957-09-15	1	Chile	Copa Bernardo O'Higgins	Brazil	0	✗	Chile	Santiago
15	2001-06-09	1	Australia	Confederations Cup	Brazil	0	✓	South Korea	Ulsan
16	1957-09-18	1	Chile	Copa Bernardo O'Higgins	Brazil	1	✗	Chile	Santiago
17	1999-07-14	0	Mexico	Copa América	Brazil	2	✓	Paraguay	Ciudad del Este
18	2000-08-15	3	Chile	FIFA World Cup qualification	Brazil	0	✗	Chile	Santiago
19	2013-10-12	0	South Korea	Friendly	Brazil	2	✗	South Korea	Seoul
20	2006-08-16	1	Norway	Friendly	Brazil	1	✗	Norway	Oslo

A lot of things could influence the outcome of a game. Since we only have access to the score, teams, and type of game, we can’t consider external factors like, weather or temperature, which would otherwise help our prediction.

To create a good model using this dataset, we could compute each team’s key performance indicator (KPI), ranking (clusters computed using the number of games in important tournaments like the World Cup, the percentage of victory…), shape (moving windows using the last games information), and other factors.

Here’s our plan: - Identify cup winners - Rank the teams with clustering - Compute teams’ KPIs - Create a machine learning model

Data Preparation for Clustering

To create clusters, we need to find which teams are the winners of main tournaments (mainly the World Cups and Continental Cups). Since all tournaments took place the same year, we could partition by tournament and year to identify the last game of the tournament.

We’ll ignore ties for our analysis since there’s no way to determine a winner.

Cup Winner

Let’s start by creating the feature winner to indicate the winner of a game.

import vastorbit.sql.functions as fun

football.filter(fun.year(football["date"]) <= 2015)
football.case_when(
    "winner",
    football["home_score"] > football["away_score"], football["home_team"],
    football["home_score"] < football["away_score"], football["away_team"],
    None,
)

	📅 date Date 100%	123 home_score Integer 100%	Abc home_team Varchar(50) 100%	Abc tournament Varchar(50) 100%	Abc away_team Varchar(50) 100%	123 away_score Integer 100%	0|1 neutral Boolean 100%	Abc country Varchar(50) 100%	Abc city Varchar(50) 100%	Abc winner Varchar(50) 77%
1	1993-12-16	0	Mexico	Friendly	Brazil	1	✗	Mexico	Guadalajara	Brazil
2	2009-06-21	0	Italy	Confederations Cup	Brazil	3	✓	South Africa	Pretoria	Brazil
3	2011-11-10	0	Gabon	Friendly	Brazil	2	✗	Gabon	Libreville	Brazil
4	1979-08-23	2	Argentina	Copa América	Brazil	2	✗	Argentina	Buenos Aires	[null]
5	2005-10-09	1	Bolivia	FIFA World Cup qualification	Brazil	1	✗	Bolivia	La Paz	[null]
6	1959-03-21	2	Bolivia	Copa América	Brazil	4	✓	Argentina	Buenos Aires	Brazil
7	1988-07-17	0	Australia	Friendly	Brazil	2	✗	Australia	Sydney	Brazil
8	1997-12-14	0	Australia	Confederations Cup	Brazil	0	✓	Saudi Arabia	Riyadh	[null]
9	1987-07-03	4	Chile	Copa América	Brazil	0	✓	Argentina	Córdoba	Chile
10	1988-07-07	0	Australia	Friendly	Brazil	1	✗	Australia	Melbourne	Brazil
11	1985-05-21	2	Chile	Friendly	Brazil	1	✗	Chile	Santiago	Chile
12	1957-09-15	1	Chile	Copa Bernardo O'Higgins	Brazil	0	✗	Chile	Santiago	Chile
13	2001-06-09	1	Australia	Confederations Cup	Brazil	0	✓	South Korea	Ulsan	Australia
14	1957-09-18	1	Chile	Copa Bernardo O'Higgins	Brazil	1	✗	Chile	Santiago	[null]
15	1999-07-14	0	Mexico	Copa América	Brazil	2	✓	Paraguay	Ciudad del Este	Brazil
16	2000-08-15	3	Chile	FIFA World Cup qualification	Brazil	0	✗	Chile	Santiago	Chile
17	2013-10-12	0	South Korea	Friendly	Brazil	2	✗	South Korea	Seoul	Brazil
18	2006-08-16	1	Norway	Friendly	Brazil	1	✗	Norway	Oslo	[null]
19	1988-07-28	1	Norway	Friendly	Brazil	1	✗	Norway	Oslo	[null]
20	1995-08-12	0	South Korea	Friendly	Brazil	1	✗	South Korea	Suwon	Brazil

Let’s analyze the last game of each tournament.

football["year"] = fun.year(football["date"])
football.analytic(
    "row_number",
    order_by = {"date": "desc"},
    by = ["tournament", "year"] ,
    name = "order_tournament",
)

	📅 date Date 100%	123 home_score Integer 100%	Abc home_team Varchar(50) 100%	Abc tournament Varchar(50) 100%	Abc away_team Varchar(50) 100%	123 away_score Integer 100%	0|1 neutral Boolean 100%	Abc country Varchar(50) 100%	Abc city Varchar(50) 100%	Abc winner Varchar(50) 77%	123 year Bigint 100%	123 order_tournament Bigint 100%
1	1998-12-27	1	Kuwait	Friendly	Egypt	1	✗	Kuwait	Kuwait City	[null]	1998	1
2	1998-12-23	2	Israel	Friendly	Serbia	0	✗	Israel	Ramat-Gan	Israel	1998	2
3	1998-12-22	5	Basque Country	Friendly	Uruguay	1	✗	Spain	San Sebastián	Basque Country	1998	3
4	1998-12-22	5	Catalonia	Friendly	Nigeria	0	✗	Spain	Barcelona	Catalonia	1998	4
5	1998-12-16	2	South Africa	Friendly	Egypt	1	✗	South Africa	Johannesburg	South Africa	1998	5
6	1998-12-06	0	British Virgin Islands	Friendly	Saint Vincent and the Grenadines	5	✗	British Virgin Islands	Road Town	Saint Vincent and the Grenadines	1998	6
7	1998-12-03	0	Mali	Friendly	Ghana	2	✗	Mali	Bamako	Ghana	1998	7
8	1998-12-02	3	United Arab Emirates	Friendly	North Korea	3	✓	Thailand	Songkhla	[null]	1998	8
9	1998-11-28	2	Azerbaijan	Friendly	Estonia	1	✗	Azerbaijan	Gəncə	Azerbaijan	1998	9
10	1998-11-27	2	Thailand	Friendly	Nepal	0	✗	Thailand	Bangkok	Thailand	1998	10
11	1998-11-25	0	Singapore	Friendly	United Arab Emirates	4	✗	Singapore	Singapore	United Arab Emirates	1998	11
12	1998-11-22	0	China PR	Friendly	South Korea	0	✗	China PR	Shanghai	[null]	1998	12
13	1998-11-21	2	Armenia	Friendly	Estonia	1	✗	Armenia	Abovyan	Armenia	1998	13
14	1998-11-21	3	Thailand	Friendly	Turkmenistan	3	✗	Thailand	Bangkok	[null]	1998	14
15	1998-11-19	0	India	Friendly	Uzbekistan	4	✗	India	Calcutta	Uzbekistan	1998	15
16	1998-11-19	1	Hong Kong	Friendly	Vietnam	1	✗	Hong Kong	Victoria	[null]	1998	16
17	1998-11-18	2	Hungary	Friendly	Switzerland	0	✗	Hungary	Budapest	Hungary	1998	17
18	1998-11-18	1	El Salvador	Friendly	Honduras	2	✓	United States	Los Angeles	Honduras	1998	18
19	1998-11-18	5	Brazil	Friendly	Russia	1	✗	Brazil	Fortaleza	Brazil	1998	19
20	1998-11-18	2	England	Friendly	Czech Republic	0	✗	England	London	England	1998	20

We can filter the data by only considering the last games and top tournaments.

football.filter(
    conditions = [
        football["order_tournament"] == 1,
        football["winner"] != None,
        football["tournament"]._in(
            [
                "FIFA World Cup",
                "UEFA Euro",
                "Copa América",
                "African Cup of Nations",
                "AFC Asian Cup",
                "Gold Cup",
            ]
        )
    ]
)

	📅 date Date 101%	123 home_score Integer 101%	Abc home_team Varchar(50) 101%	Abc tournament Varchar(50) 101%	Abc away_team Varchar(50) 101%	123 away_score Integer 101%	0|1 neutral Boolean 101%	Abc country Varchar(50) 101%	Abc city Varchar(50) 101%	Abc winner Varchar(50) 101%	123 year Bigint 101%	123 order_tournament Bigint 101%
1	1988-06-25	0	Russia	UEFA Euro	Netherlands	2	✓	Germany	Munich	Netherlands	1988	1
2	2009-07-26	0	United States	Gold Cup	Mexico	5	✗	United States	East Rutherford	Mexico	2009	1
3	1996-02-03	2	South Africa	African Cup of Nations	Tunisia	0	✗	South Africa	Johannesburg	South Africa	1996	1
4	1962-01-21	4	Ethiopia	African Cup of Nations	Egypt	2	✗	Ethiopia	Addis Abeba	Ethiopia	1962	1
5	1941-03-04	0	Chile	Copa América	Argentina	1	✗	Chile	Santiago	Argentina	1941	1
6	2007-07-15	3	Brazil	Copa América	Argentina	0	✓	Venezuela	Maracaibo	Brazil	2007	1
7	1970-02-16	3	Egypt	African Cup of Nations	Ivory Coast	1	✓	Sudan	Khartoum	Egypt	1970	1
8	1947-12-31	3	Bolivia	Copa América	Chile	4	✓	Ecuador	Guayaquil	Chile	1947	1
9	1959-12-25	3	Ecuador	Copa América	Paraguay	1	✗	Ecuador	Guayaquil	Ecuador	1959	1
10	1980-09-30	3	Kuwait	AFC Asian Cup	South Korea	0	✗	Kuwait	Kuwait City	Kuwait	1980	1
11	1958-06-29	2	Sweden	FIFA World Cup	Brazil	5	✗	Sweden	Solna	Brazil	1958	1
12	1978-03-16	2	Nigeria	African Cup of Nations	Tunisia	0	✓	Ghana	Accra	Nigeria	1978	1
13	1942-02-07	1	Uruguay	Copa América	Argentina	0	✗	Uruguay	Montevideo	Uruguay	1942	1
14	2002-02-02	2	Canada	Gold Cup	South Korea	1	✓	United States	Pasadena	Canada	2002	1
15	1939-02-12	2	Peru	Copa América	Uruguay	1	✗	Peru	Lima	Peru	1939	1
16	2001-07-29	1	Colombia	Copa América	Mexico	0	✗	Colombia	Bogotá	Colombia	2001	1
17	1990-07-08	1	Germany	FIFA World Cup	Argentina	0	✓	Italy	Rome	Germany	1990	1
18	2013-02-10	1	Nigeria	African Cup of Nations	Burkina Faso	0	✓	South Africa	Johannesburg	Nigeria	2013	1
19	1982-07-11	3	Italy	FIFA World Cup	Germany	1	✓	Spain	Madrid	Italy	1982	1
20	1965-11-21	1	Ivory Coast	African Cup of Nations	Senegal	0	✓	Tunisia	Tunis	Ivory Coast	1965	1

Let’s consider the World Cup as a special tournament. It is the only one where the confrontations between the top teams is possible.

football["Word_Cup"] = fun.decode(
    football["tournament"], "FIFA World Cup",
    1, 0,
)
football["Word_Cup"]

	123 Word_Cup Integer
1	0
2	0
3	0
4	0
5	0
6	0
7	0
8	0
9	0
10	0
11	1
12	0
13	0
14	1
15	0
16	0
17	1
18	0
19	0
20	0

We can compute all the number of cup-wins by team. As expected, Brazil and Germany are the top football teams.

agg = [
    fun.sum(football["Word_Cup"])._as("nb_World_Cup"),
    fun.sum(1 - football["Word_Cup"])._as("nb_Continental_Cup"),
]
football_cup_winners = football.groupby(["winner"], agg)
football_cup_winners.sort(
    {
        "nb_World_Cup": "desc",
        "nb_Continental_Cup": "desc",
    }
).head(10)

	Abc winner Varchar(50) 92%	123 nb_World_Cup Bigint 92%	123 nb_Continental_Cup Bigint 92%
1	Brazil	4	9
2	Germany	4	3
3	Italy	3	1
4	Uruguay	2	8
5	Argentina	2	8
6	Spain	1	3
7	France	1	2
8	England	1	0
9	Mexico	0	6
10	Egypt	0	6

Let’s export the result to our VAST DataBase.

vo.drop(
    "football_cup_winners",
    method = "table",
)
football_cup_winners.to_db(
    "football_cup_winners",
    relation_type = "table",
)

	Abc winner Varchar(50) 92%	123 nb_World_Cup Bigint 92%	123 nb_Continental_Cup Bigint 92%
1	Brazil	4	10
2	Germany	4	3
3	Italy	3	1
4	Uruguay	2	8
5	Argentina	2	8
6	Spain	1	3
7	France	1	2
8	England	1	0
9	Mexico	0	6
10	Egypt	0	6
11	Ivory Coast	0	3
12	Peru	0	3
13	United States	0	3
14	Japan	0	3
15	Nigeria	0	3
16	Paraguay	0	2
17	South Korea	0	2
18	Ghana	0	2
19	Iran	0	2
20	Cameroon	0	2

Team Confederations

Looking into team confederations could help our analysis. For example, this might help us quantify skill differences between different continents. A team that had played a qualification of a specific location can only belong to that tournament confederation.

First let’s encode the different continents so we can compute the correct aggregations.

football = vo.read_csv("games.csv")
football.case_when(
    'confederation',
    football["tournament"] == 'UEFA Euro qualification', 5,
    football["tournament"] == 'African Cup of Nations qualification', 4,
    football["tournament"] == 'AFC Asian Cup qualification', 3,
    football["tournament"] == 'Copa América', 2,
    football["tournament"] == 'Gold Cup', 1, 0,
)

	📅 date Date 100%	Abc home_team Varchar(50) 100%	Abc away_team Varchar(50) 100%	123 home_score Integer 100%	123 away_score Integer 100%	Abc tournament Varchar(50) 100%	Abc city Varchar(50) 100%	Abc country Varchar(50) 100%	0|1 neutral Boolean 100%	123 confederation Integer 100%
1	1872-11-30	Scotland	England	0	0	Friendly	Glasgow	Scotland	✗	0
2	1873-03-08	England	Scotland	4	2	Friendly	London	England	✗	0
3	1874-03-07	Scotland	England	2	1	Friendly	Glasgow	Scotland	✗	0
4	1875-03-06	England	Scotland	2	2	Friendly	London	England	✗	0
5	1876-03-04	Scotland	England	3	0	Friendly	Glasgow	Scotland	✗	0
6	1876-03-25	Scotland	Wales	4	0	Friendly	Glasgow	Scotland	✗	0
7	1877-03-03	England	Scotland	1	3	Friendly	London	England	✗	0
8	1877-03-05	Wales	Scotland	0	2	Friendly	Wrexham	Wales	✗	0
9	1878-03-02	Scotland	England	7	2	Friendly	Glasgow	Scotland	✗	0
10	1878-03-23	Scotland	Wales	9	0	Friendly	Glasgow	Scotland	✗	0
11	1879-01-18	England	Wales	2	1	Friendly	London	England	✗	0
12	1879-04-05	England	Scotland	5	4	Friendly	London	England	✗	0
13	1879-04-07	Wales	Scotland	0	3	Friendly	Wrexham	Wales	✗	0
14	1880-03-13	Scotland	England	5	4	Friendly	Glasgow	Scotland	✗	0
15	1880-03-15	Wales	England	2	3	Friendly	Wrexham	Wales	✗	0
16	1880-03-27	Scotland	Wales	5	1	Friendly	Glasgow	Scotland	✗	0
17	1881-02-26	England	Wales	0	1	Friendly	Blackburn	England	✗	0
18	1881-03-12	England	Scotland	1	6	Friendly	London	England	✗	0
19	1881-03-14	Wales	Scotland	1	5	Friendly	Wrexham	Wales	✗	0
20	1882-02-18	Northern Ireland	England	0	13	Friendly	Belfast	Republic of Ireland	✗	0

We can aggregate the data and get each team’s continent.

confederation = football.groupby(
    ["home_team"],
    [fun.max(football["confederation"])._as("confederation")],
)
confederation.head(100)

	Abc home_team Varchar(50) 100%	123 confederation Integer 100%
1	Niger	4
2	Iceland	5
3	Honduras	2
4	South Korea	3
5	New Zealand	0
6	Sudan	4
7	Saint Lucia	0
8	Ecuador	2
9	Catalonia	0
10	Gambia	4
11	Nepal	3
12	Manchukuo	0
13	Isle of Man	0
14	Eritrea	4
15	Saint Pierre and Miquelon	0
16	Felvidék	0
17	Namibia	4
18	Japan	3
19	Corsica	0
20	Moldova	5
21	Iraqi Kurdistan	0
22	Albania	5
23	Luxembourg	5
24	Bosnia and Herzegovina	5
25	Senegal	4
26	Thailand	3
27	Guatemala	1
28	Liberia	4
29	United Arab Emirates	3
30	Syria	3
31	Montserrat	0
32	Comoros	4
33	Hong Kong	3
34	Samoa	0
35	Shetland	0
36	Falkland Islands	0
37	South Sudan	4
38	United Koreans in Japan	0
39	Tamil Eelam	0
40	Kabylia	0
41	Madrid	0
42	Sark	0
43	Paraguay	2
44	Croatia	5
45	England	5
46	Portugal	5
47	Greece	5
48	Ukraine	5
49	Belarus	5
50	Saudi Arabia	3
51	Bahrain	3
52	North Macedonia	5
53	Palestine	3
54	East Timor	0
55	Pakistan	3
56	Bangladesh	3
57	Solomon Islands	0
58	Menorca	0
59	Wallis Islands and Futuna	0
60	Romani people	0
61	Palau	0
62	Yemen	3
63	Turkey	5
64	Netherlands	5
65	Uruguay	2
66	Serbia	5
67	Madagascar	4
68	New Caledonia	0
69	Rwanda	4
70	Philippines	3
71	Curaçao	1
72	Macau	3
73	Maldives	3
74	Sápmi	0
75	Northern Cyprus	0
76	Mexico	2
77	Germany	5
78	Gabon	4
79	Armenia	5
80	Republic of Ireland	5
81	Montenegro	5
82	Poland	5
83	Togo	4
84	Malawi	4
85	India	3
86	French Guiana	1
87	Iraq	3
88	Kosovo	5
89	County of Nice	0
90	Republic of St. Pauli	0
91	Găgăuzia	0
92	Chile	2
93	Argentina	2
94	Brittany	0
95	Switzerland	5
96	Chad	4
97	Uganda	4
98	Scotland	5
99	Vietnam	3
100	Sierra Leone	4

We can decode the previous label encoding.

confederation["confederation"].decode(
    5, "UEFA",
    4, "CAF",
    3, "AFC",
    2, "CONMEBOL",
    1, "CONCACAF",
    "OFC",
)

	Abc home_team Varchar(50) 100%	Abc confederation Varchar(8) 100%
1	Lithuania	UEFA
2	Kuwait	AFC
3	Saint Martin	OFC
4	Brazil	CONMEBOL
5	Cameroon	CAF
6	Nigeria	CAF
7	Slovenia	UEFA
8	Iran	AFC
9	Canada	CONCACAF
10	Cayman Islands	OFC
11	Gotland	OFC
12	Gozo	OFC
13	Chinese Taipei	AFC
14	Kyrgyzstan	AFC
15	San Marino	UEFA
16	Colombia	CONMEBOL
17	Israel	UEFA
18	Basque Country	OFC
19	Benin	CAF
20	Seychelles	CAF

Let’s export the result to our VAST DataBase.

vo.drop("confederation")
confederation["home_team"].rename("team")
confederation.to_db(
    name = "confederation",
    relation_type = "table",
)

	Abc confederation Varchar(8) 100%	Abc team Varchar(50) 100%
1	CAF	Kenya
2	CAF	Burkina Faso
3	CAF	Mali
4	UEFA	Hungary
5	UEFA	Romania
6	UEFA	Finland
7	CONMEBOL	Bolivia
8	AFC	Australia
9	UEFA	France
10	OFC	Antigua and Barbuda
11	CAF	Central African Republic
12	AFC	Oman
13	UEFA	Andorra
14	AFC	Afghanistan
15	AFC	Bhutan
16	OFC	Anguilla
17	OFC	Panjab
18	OFC	Provence
19	OFC	Niue
20	OFC	Somaliland

Team KPIs

We use just two variables to track teams: away_team and home_team. This makes it a bit difficult to compute new features. We need to duplicate the dataset and intervert the two teams. This way, we can compute KPIs using a partition by the first team to avoid double-counting any games.

football = vo.VastFrame("football_clean")
football.filter(fun.year(football["date"]) <= 2015)
football["home_team"].rename("team1")
football["home_score"].rename("team1_score")
football["away_team"].rename("team2")
football["away_score"].rename("team2_score")
football["neutral"].decode(True, 0, 1)

football2 = vo.VastFrame("football_clean")
football2.filter(fun.year(football["date"]) <= 2015)
football2["home_team"].rename("team2")
football2["home_score"].rename("team2_score")
football2["away_team"].rename("team1")
football2["away_score"].rename("team1_score")
football2["neutral"].decode(True, 0, 2)

# Merging the 2 interverted datasets
all_matchs = football.append(football2)
all_matchs["neutral"].rename("home_team_id")

	📅 date Date 100%	Abc tournament Varchar(50) 100%	Abc country Varchar(50) 100%	Abc city Varchar(50) 100%	Abc team1 Varchar(50) 100%	123 team1_score Integer 100%	Abc team2 Varchar(50) 100%	123 team2_score Integer 100%	123 home_team_id Integer 100%
1	1912-02-10	Friendly	France	Paris	France	7	Catalonia	0	1
2	1915-01-03	Friendly	Spain	Bilbao	Basque Country	6	Catalonia	1	1
3	1931-01-01	Friendly	Spain	Bilbao	Basque Country	3	Catalonia	2	1
4	1926-07-07	Friendly	Czech Republic	Prague	Czech Republic	2	Catalonia	1	1
5	1915-05-13	Friendly	Spain	Madrid	Basque Country	1	Catalonia	0	1
6	1971-02-21	Friendly	Spain	Bilbao	Basque Country	1	Catalonia	2	1
7	2007-12-29	Friendly	Spain	Bilbao	Basque Country	1	Catalonia	1	1
8	2014-12-28	Friendly	Spain	Bilbao	Basque Country	1	Catalonia	1	1
9	1916-06-04	Friendly	Spain	Bilbao	Basque Country	5	Catalonia	0	1
10	2005-02-16	Friendly	Costa Rica	Heredia	Costa Rica	1	Ecuador	2	1
11	2009-05-27	Friendly	United States	Los Angeles	El Salvador	3	Ecuador	1	0
12	1997-05-28	Friendly	El Salvador	San Salvador	El Salvador	0	Ecuador	2	1
13	1984-12-12	Friendly	El Salvador	San Salvador	El Salvador	0	Ecuador	0	1
14	1945-02-21	Copa América	Chile	Santiago	Brazil	9	Ecuador	2	0
15	2001-07-02	Friendly	United States	East Rutherford	El Salvador	0	Ecuador	1	0
16	1949-04-03	Copa América	Brazil	Rio de Janeiro	Brazil	9	Ecuador	1	1
17	1942-01-31	Copa América	Uruguay	Montevideo	Brazil	5	Ecuador	1	0
18	1996-02-11	Friendly	Lebanon	Bourj Hammoud	Lebanon	1	Ecuador	0	1
19	2011-06-01	Friendly	Canada	Toronto	Canada	2	Ecuador	2	1
20	1973-05-15	Friendly	Haiti	Port-au-Prince	Haiti	1	Ecuador	0	1

To compute the different aggregations, we need to add dummies which indicate the type of game and winner.

all_matchs["World_Tournament"] = fun.case_when(all_matchs["tournament"]._in(
    [
        "FIFA World Cup",
        "Confederations Cup"
    ],
), 1, 0)
all_matchs["Continental_Tournament"] = fun.case_when(
    all_matchs["tournament"]._in(
        [
            "UEFA Euro",
            "Copa América",
            "African Cup of Nations",
            "AFC Asian Cup",
            "Gold Cup",
            "FIFA World Cup qualification",
        ]
    ), 1, 0)
all_matchs["Victory_team1"] = (all_matchs["team1_score"] > all_matchs["team2_score"])
all_matchs["Victory_team1"].astype("int")
all_matchs["Draw"] = (all_matchs["team1_score"] == all_matchs["team2_score"])
all_matchs["Draw"].astype("int")

	📅 date Date 100%	Abc tournament Varchar(50) 100%	Abc country Varchar(50) 100%	Abc city Varchar(50) 100%	Abc team1 Varchar(50) 100%	123 team1_score Integer 100%	Abc team2 Varchar(50) 100%	123 team2_score Integer 100%	123 home_team_id Integer 100%	123 World_Tournament Integer 100%	123 Continental_Tournament Integer 100%	123 Victory_team1 Int 100%	123 Draw Int 100%
1	1999-11-17	UEFA Euro qualification	Ukraine	Kyïv	Ukraine	1	Slovenia	1	1	0	0	0	1
2	2015-11-14	UEFA Euro qualification	Ukraine	L'viv	Ukraine	2	Slovenia	0	1	0	0	1	0
3	1994-10-12	UEFA Euro qualification	Ukraine	Kyïv	Ukraine	0	Slovenia	0	1	0	0	0	1
4	2005-10-08	FIFA World Cup qualification	Italy	Palermo	Italy	1	Slovenia	0	1	0	1	1	0
5	2002-08-21	Friendly	Italy	Trieste	Italy	0	Slovenia	1	1	0	0	0	0
6	1997-03-18	Friendly	Austria	Linz	Austria	0	Slovenia	2	1	0	0	0	0
7	2008-10-15	FIFA World Cup qualification	Czech Republic	Teplice	Czech Republic	1	Slovenia	0	1	0	1	1	0
8	1995-09-06	UEFA Euro qualification	Italy	Udine	Italy	1	Slovenia	0	1	0	0	1	0
9	2000-08-16	Friendly	Czech Republic	Ostrava	Czech Republic	0	Slovenia	1	1	0	0	0	0
10	2011-09-06	UEFA Euro qualification	Italy	Florence	Italy	1	Slovenia	0	1	0	0	1	0
11	1999-06-05	UEFA Euro qualification	Latvia	Riga	Latvia	1	Slovenia	2	1	0	0	0	0
12	2014-06-07	Friendly	Argentina	La Plata	Argentina	2	Slovenia	0	1	0	0	1	0
13	2008-09-06	FIFA World Cup qualification	Poland	Wrocław	Poland	1	Slovenia	1	1	0	1	0	1
14	2004-02-18	Friendly	Spain	San Fernando	Poland	2	Slovenia	0	0	0	0	1	0
15	1998-03-25	Friendly	Poland	Warsaw	Poland	2	Slovenia	0	1	0	0	1	0
16	2010-06-13	FIFA World Cup	South Africa	Polokwane	Algeria	0	Slovenia	1	0	1	0	0	0
17	2002-06-08	FIFA World Cup	South Korea	Daegu	South Africa	1	Slovenia	0	0	1	0	1	0
18	2014-03-05	Friendly	Algeria	Blida	Algeria	2	Slovenia	0	1	0	0	1	0
19	2014-09-08	UEFA Euro qualification	Estonia	Tallinn	Estonia	1	Slovenia	0	1	0	0	1	0
20	1995-06-11	UEFA Euro qualification	Estonia	Tallinn	Estonia	1	Slovenia	3	1	0	0	0	0

Now we can compute each team’s KPI.

teams_kpi = all_matchs.groupby(
    ["team1"],
    [
        fun.sum(all_matchs["World_Tournament"])._as("Number_Games_World_Tournament"),
        fun.sum(all_matchs["Continental_Tournament"])._as("Number_Games_Continental_Tournament"),
        fun.avg(fun.decode(all_matchs["World_Tournament"], 1, all_matchs["Victory_team1"]))._as("Percent_Victory_World_Tournament"),
        fun.avg(fun.decode(all_matchs["Continental_Tournament"], 1, all_matchs["Victory_team1"]))._as("Percent_Victory_Continental_Tournament"),
        fun.avg(fun.case_when((all_matchs["home_team_id"] == 1) & (all_matchs["World_Tournament"] == 0) & (all_matchs["Continental_Tournament"] == 0), all_matchs["Victory_team1"], None))._as("Percent_Victory_Home"),
        fun.avg(fun.case_when((all_matchs["home_team_id"] != 1) & (all_matchs["World_Tournament"] == 0) & (all_matchs["Continental_Tournament"] == 0), all_matchs["Victory_team1"], None))._as("Percent_Victory_Away"),
        fun.avg(all_matchs["Victory_team1"])._as("Percent_Victory"),
        fun.avg(all_matchs["Draw"])._as("Percent_Draw"),
        fun.avg(all_matchs["team1_score"])._as("Avg_goals"),
        fun.avg(all_matchs["team2_score"])._as("Avg_goals_conceded"),
    ],
).sort({"Number_Games_World_Tournament": "desc"})
teams_kpi.head(100)

	Abc team1 Varchar(50) 100%	123 Number_Games_World_Tournament Bigint 100%	123 Number_Games_Continental_Tournament Bigint 100%	123 Percent_Victory_World_Tournament Double 28%	123 Percent_Victory_Continental_Tournament Double 78%	123 Percent_Victory_Home Double 89%	123 Percent_Victory_Away Double 96%	123 Percent_Victory Double 100%	123 Percent_Draw Double 100%	123 Avg_goals Double 100%	123 Avg_goals_conceded Double 100%
1	Brazil	137	279	0.6788321167883211	0.5770609318996416	0.7333333333333333	0.59375	0.635091496232508	0.20021528525296017	2.193756727664155	0.93756727664155
2	Germany	120	173	0.6	0.6242774566473989	0.6083916083916084	0.4702127659574468	0.5553691275167785	0.2080536912751678	2.0998322147651005	1.179530201342282
3	Italy	91	130	0.5274725274725275	0.6153846153846154	0.6438848920863309	0.3464566929133858	0.5245683930942895	0.28286852589641437	1.6958831341301461	0.9827357237715804
4	Argentina	87	302	0.5402298850574713	0.6059602649006622	0.638095238095238	0.40606060606060607	0.5360602798708288	0.24865446716899892	1.8654467168998923	1.0505920344456405
5	Mexico	75	258	0.30666666666666664	0.6085271317829457	0.5424836601307189	0.42356687898089174	0.495	0.2375	1.7475	1.07625
6	Spain	69	143	0.5217391304347826	0.6223776223776224	0.6768558951965066	0.46568627450980393	0.5813953488372093	0.2248062015503876	1.9674418604651163	0.9085271317829458
7	France	69	133	0.5362318840579711	0.5488721804511278	0.5321100917431193	0.36363636363636365	0.48081841432225064	0.21483375959079284	1.758312020460358	1.3427109974424551
8	England	62	129	0.41935483870967744	0.5968992248062015	0.6187845303867403	0.5308641975308642	0.5657620041753654	0.24112734864300625	2.19937369519833	0.9926931106471816
9	Uruguay	61	332	0.4098360655737705	0.5060240963855421	0.5	0.3007246376811594	0.4302741358760429	0.24553039332538737	1.5756853396901074	1.264600715137068
10	Netherlands	50	150	0.54	0.62	0.5422535211267606	0.3852140077821012	0.5033738191632928	0.22807017543859648	2.0620782726045883	1.2496626180836707
11	United States	48	215	0.2916666666666667	0.5674418604651162	0.40654205607476634	0.2620689655172414	0.41961414790996787	0.21221864951768488	1.4196141479099678	1.3713826366559485
12	Sweden	46	136	0.34782608695652173	0.5735294117647058	0.5805555555555556	0.41092636579572445	0.49428868120456904	0.21703011422637591	2.005192107995846	1.3063343717549325
13	Serbia	43	132	0.3953488372093023	0.5454545454545454	0.5445026178010471	0.3815028901734104	0.45646067415730335	0.21910112359550563	1.8160112359550562	1.3792134831460674
14	Belgium	41	135	0.34146341463414637	0.5333333333333333	0.4785714285714286	0.2846153846153846	0.4106145251396648	0.21927374301675978	1.6843575418994414	1.606145251396648
15	Russia	40	144	0.425	0.5902777777777778	0.5932203389830508	0.45614035087719296	0.521671826625387	0.2647058823529412	1.7198142414860682	0.93343653250774
16	Czech Republic	38	155	0.3684210526315789	0.5290322580645161	0.654320987654321	0.34185303514376997	0.4833110814419226	0.22162883845126835	1.843791722296395	1.2349799732977302
17	South Korea	34	192	0.20588235294117646	0.5625	0.5789473684210527	0.5122615803814714	0.5286783042394015	0.256857855361596	1.7830423940149627	0.8977556109725686
18	Chile	33	301	0.3333333333333333	0.3754152823920266	0.558282208588957	0.2571428571428571	0.38048090523338046	0.2065063649222065	1.4214992927864214	1.4653465346534653
19	Japan	33	152	0.2727272727272727	0.5526315789473685	0.5263157894736842	0.4009433962264151	0.47183098591549294	0.23415492957746478	1.7200704225352113	1.1602112676056338
20	Switzerland	33	129	0.3333333333333333	0.4186046511627907	0.41333333333333333	0.22758620689655173	0.3390957446808511	0.21675531914893617	1.4414893617021276	1.7313829787234043
21	Hungary	32	115	0.46875	0.46956521739130436	0.5809248554913294	0.3822784810126582	0.4740990990990991	0.2195945945945946	2.0720720720720722	1.4954954954954955
22	Poland	31	112	0.4838709677419355	0.4642857142857143	0.5	0.352112676056338	0.42597402597402595	0.2519480519480519	1.6844155844155844	1.37012987012987
23	Cameroon	31	154	0.25806451612903225	0.564935064935065	0.5882352941176471	0.32057416267942584	0.44258872651356995	0.30062630480167013	1.4217118997912317	1.0542797494780793
24	Austria	29	116	0.41379310344827586	0.47413793103448276	0.4837662337662338	0.30633802816901406	0.41112618724559025	0.2198100407055631	1.7978290366350067	1.5929443690637721
25	Paraguay	27	304	0.25925925925925924	0.40460526315789475	0.4423076923076923	0.29098360655737704	0.3637702503681885	0.2621502209131075	1.3357879234167893	1.438880706921944
26	Portugal	26	157	0.5	0.5222929936305732	0.5445026178010471	0.34615384615384615	0.4712230215827338	0.2302158273381295	1.631294964028777	1.2014388489208634
27	Australia	26	137	0.2692307692307692	0.583941605839416	0.4393939393939394	0.5112359550561798	0.4989429175475687	0.21141649048625794	2.031712473572939	1.1120507399577166
28	Bulgaria	26	124	0.11538461538461539	0.4596774193548387	0.4810810810810811	0.29276315789473684	0.37245696400625977	0.2519561815336463	1.431924882629108	1.4710485133020343
29	Saudi Arabia	25	149	0.2	0.5302013422818792	0.5604395604395604	0.41706161137440756	0.48324514991181655	0.2275132275132275	1.5961199294532629	1.0458553791887126
30	Nigeria	24	182	0.2916666666666667	0.5439560439560439	0.6203703703703703	0.319047619047619	0.4580152671755725	0.2958015267175573	1.4961832061068703	1.0038167938931297
31	Colombia	23	259	0.391304347826087	0.3667953667953668	0.46875	0.35714285714285715	0.378	0.272	1.206	1.21
32	Scotland	23	121	0.17391304347826086	0.48760330578512395	0.5847750865051903	0.3942307692307692	0.47651006711409394	0.2174496644295302	1.7503355704697987	1.225503355704698
33	Romania	21	130	0.38095238095238093	0.5	0.5922330097087378	0.31186440677966104	0.4401840490797546	0.2561349693251534	1.6457055214723926	1.2883435582822085
34	Denmark	19	137	0.5263157894736842	0.4233576642335766	0.5525423728813559	0.3466666666666667	0.44607190412782954	0.20372836218375498	1.77762982689747	1.4287616511318242
35	South Africa	17	85	0.17647058823529413	0.4823529411764706	0.5275590551181102	0.3709677419354839	0.4447592067988669	0.2747875354107649	1.339943342776204	1.0084985835694051
36	Croatia	16	64	0.4375	0.546875	0.6705882352941176	0.42574257425742573	0.5338345864661654	0.2706766917293233	1.7518796992481203	0.981203007518797
37	New Zealand	15	72	0.0	0.5416666666666666	0.4868421052631579	0.3468208092485549	0.40476190476190477	0.18154761904761904	1.7470238095238095	1.6130952380952381
38	Costa Rica	15	217	0.3333333333333333	0.43317972350230416	0.6238532110091743	0.3317307692307692	0.42987249544626593	0.2568306010928962	1.6703096539162112	1.1675774134790529
39	Tunisia	15	161	0.13333333333333333	0.4472049689440994	0.531578947368421	0.2777777777777778	0.4166666666666667	0.2916666666666667	1.433712121212121	1.0643939393939394
40	Turkey	15	130	0.4666666666666667	0.35384615384615387	0.4539877300613497	0.3380281690140845	0.381957773512476	0.2380038387715931	1.3320537428023032	1.4280230326295584
41	Peru	15	277	0.26666666666666666	0.3176895306859206	0.42857142857142855	0.2236024844720497	0.3129370629370629	0.243006993006993	1.229020979020979	1.4685314685314685
42	Northern Ireland	13	122	0.23076923076923078	0.30327868852459017	0.29017857142857145	0.1721311475409836	0.24378109452736318	0.23217247097844113	1.043117744610282	1.9535655058043118
43	Republic of Ireland	13	135	0.15384615384615385	0.37777777777777777	0.48756218905472637	0.3103448275862069	0.3919694072657744	0.27724665391969405	1.401529636711281	1.2466539196940727
44	Algeria	13	146	0.23076923076923078	0.410958904109589	0.5806451612903226	0.3163841807909605	0.4152173913043478	0.28043478260869564	1.3543478260869566	1.0304347826086957
45	Greece	13	132	0.15384615384615385	0.4090909090909091	0.45901639344262296	0.26262626262626265	0.3650190114068441	0.25285171102661597	1.2395437262357414	1.420152091254753
46	Morocco	13	161	0.15384615384615385	0.422360248447205	0.6122448979591837	0.3548387096774194	0.45168067226890757	0.3004201680672269	1.388655462184874	0.8739495798319328
47	Ghana	12	164	0.3333333333333333	0.5304878048780488	0.6727272727272727	0.38405797101449274	0.4822064056939502	0.25622775800711745	1.6423487544483986	1.0409252669039146
48	Iran	12	175	0.08333333333333333	0.5942857142857143	0.6415094339622641	0.43037974683544306	0.5343680709534369	0.2616407982261641	1.8403547671840355	0.835920177383592
49	Ivory Coast	11	160	0.2727272727272727	0.49375	0.6917293233082706	0.4	0.5029469548133595	0.24950884086444008	1.6345776031434185	1.0530451866404715
50	Ecuador	10	244	0.4	0.2459016393442623	0.59375	0.24025974025974026	0.2944915254237288	0.2457627118644068	1.194915254237288	1.6504237288135593
51	Egypt	10	174	0.1	0.5632183908045977	0.5823529411764706	0.3739130434782609	0.4863013698630137	0.25	1.643835616438356	1.0359589041095891
52	Honduras	9	175	0.0	0.44	0.5483870967741935	0.3316062176165803	0.4085106382978723	0.26170212765957446	1.4914893617021276	1.2234042553191489
53	Bolivia	9	243	0.0	0.2222222222222222	0.3787878787878788	0.13861386138613863	0.22195704057279236	0.24821002386634844	1.0381861575178997	1.9427207637231503
54	Norway	8	118	0.25	0.3474576271186441	0.3948220064724919	0.3343465045592705	0.3599476439790576	0.22774869109947643	1.5013089005235603	1.6845549738219896
55	North Korea	7	97	0.14285714285714285	0.4329896907216495	0.7333333333333333	0.4088397790055249	0.4266666666666667	0.27	1.61	1.0366666666666666
56	Iraq	6	124	0.0	0.4596774193548387	0.6666666666666666	0.436241610738255	0.4676113360323887	0.2773279352226721	1.6214574898785425	0.9453441295546559
57	El Salvador	6	156	0.0	0.4166666666666667	0.4819277108433735	0.2009132420091324	0.32112068965517243	0.22629310344827586	1.2262931034482758	1.4870689655172413
58	United Arab Emirates	6	123	0.16666666666666666	0.3983739837398374	0.4768211920529801	0.3368421052631579	0.39574468085106385	0.24893617021276596	1.4148936170212767	1.2851063829787235
59	Slovenia	6	55	0.16666666666666666	0.38181818181818183	0.4264705882352941	0.2857142857142857	0.352112676056338	0.2347417840375587	1.2394366197183098	1.272300469483568
60	Canada	6	144	0.0	0.3888888888888889	0.3387096774193548	0.2814814814814815	0.3314121037463977	0.23919308357348704	1.0489913544668588	1.3976945244956773
61	Ukraine	5	63	0.4	0.49206349206349204	0.5285714285714286	0.33707865168539325	0.44052863436123346	0.2907488986784141	1.39647577092511	0.973568281938326
62	Wales	5	110	0.2	0.3	0.3670886075949367	0.26515151515151514	0.31006493506493504	0.21266233766233766	1.2532467532467533	1.6931818181818181
63	Senegal	5	106	0.4	0.3584905660377358	0.648	0.3360655737704918	0.42291666666666666	0.26875	1.2875	1.0020833333333334
64	Slovakia	4	54	0.25	0.48148148148148145	0.45	0.3333333333333333	0.40160642570281124	0.23694779116465864	1.4417670682730923	1.3012048192771084
65	DR Congo	3	135	0.0	0.362962962962963	0.6153846153846154	0.3081081081081081	0.391304347826087	0.26811594202898553	1.502415458937198	1.2536231884057971
66	Jamaica	3	128	0.3333333333333333	0.34375	0.5304878048780488	0.30973451327433627	0.3877159309021113	0.22840690978886757	1.3186180422264875	1.3378119001919386
67	China PR	3	147	0.0	0.5510204081632653	0.5796178343949044	0.3958333333333333	0.48811700182815354	0.226691042047532	1.8372943327239488	1.0877513711151736
68	Cuba	3	92	0.3333333333333333	0.25	0.45	0.3728813559322034	0.34615384615384615	0.25	1.3205128205128205	1.439102564102564
69	Bosnia and Herzegovina	3	48	0.3333333333333333	0.4791666666666667	0.42857142857142855	0.3048780487804878	0.38461538461538464	0.21428571428571427	1.4285714285714286	1.39010989010989
70	Tahiti	3	27	0.0	0.2962962962962963	0.4838709677419355	0.5846153846153846	0.518324607329843	0.1256544502617801	2.518324607329843	1.7015706806282722
71	Trinidad and Tobago	3	145	0.0	0.3793103448275862	0.6224489795918368	0.3763440860215054	0.45264847512038525	0.2102728731942215	1.739967897271268	1.274478330658106
72	Togo	3	85	0.0	0.2823529411764706	0.4845360824742268	0.23636363636363636	0.3142857142857143	0.24571428571428572	1.082857142857143	1.3914285714285715
73	Kuwait	3	133	0.0	0.47368421052631576	0.4573170731707317	0.37916666666666665	0.42407407407407405	0.2722222222222222	1.5555555555555556	1.0796296296296297
74	Angola	3	84	0.0	0.3333333333333333	0.5625	0.2535211267605634	0.35275080906148865	0.35275080906148865	1.174757281553398	1.0420711974110033
75	Israel	3	124	0.0	0.3548387096774194	0.38926174496644295	0.30327868852459017	0.3492462311557789	0.25125628140703515	1.4547738693467336	1.4522613065326633
76	Haiti	3	102	0.0	0.4019607843137255	0.5304347826086957	0.35858585858585856	0.4138755980861244	0.23205741626794257	1.562200956937799	1.3181818181818181
77	Indonesia	1	82	0.0	0.25609756097560976	0.49264705882352944	0.35906040268456374	0.3771760154738878	0.18762088974854932	1.6731141199226305	1.6750483558994198
78	Lithuania	0	63	[null]	0.25396825396825395	0.4090909090909091	0.24293785310734464	0.2896341463414634	0.18902439024390244	1.0975609756097562	1.7835365853658536
79	Palestine	0	25	[null]	0.28	0.0	0.25	0.25165562913907286	0.26490066225165565	1.2847682119205297	1.6026490066225165
80	Estonia	0	66	[null]	0.19696969696969696	0.3618421052631579	0.18686868686868688	0.25240384615384615	0.21394230769230768	1.0240384615384615	1.78125
81	Saint Martin	0	0	[null]	[null]	0.75	0.16	0.30303030303030304	0.09090909090909091	1.121212121212121	2.909090909090909
82	North Macedonia	0	49	[null]	0.20408163265306123	0.39705882352941174	0.2	0.26732673267326734	0.2623762376237624	1.0841584158415842	1.381188118811881
83	Cayman Islands	0	14	[null]	0.0	0.3333333333333333	0.13513513513513514	0.18518518518518517	0.16049382716049382	1.037037037037037	2.54320987654321
84	Catalonia	0	0	[null]	[null]	0.4594594594594595	0.1111111111111111	0.391304347826087	0.2391304347826087	1.5217391304347827	1.7391304347826086
85	Mayotte	0	0	[null]	[null]	0.0	0.1875	0.16666666666666666	0.2777777777777778	1.6111111111111112	2.0555555555555554
86	Curaçao	0	60	[null]	0.21666666666666667	0.6666666666666666	0.29444444444444445	0.3564356435643564	0.26732673267326734	1.5478547854785478	1.6765676567656767
87	Gambia	0	24	[null]	0.25	0.34328358208955223	0.16	0.2356020942408377	0.2879581151832461	0.93717277486911	1.450261780104712
88	New Caledonia	0	16	[null]	0.5	0.625	0.4918032786885246	0.5346534653465347	0.09900990099009901	2.633663366336634	1.4752475247524752
89	Rwanda	0	40	[null]	0.2	0.44642857142857145	0.3247863247863248	0.3333333333333333	0.2535211267605634	1.051643192488263	1.2253521126760563
90	Saint Kitts and Nevis	0	26	[null]	0.34615384615384615	0.5076923076923077	0.3064516129032258	0.39869281045751637	0.1895424836601307	1.7973856209150327	1.5947712418300655
91	Philippines	0	19	[null]	0.15789473684210525	0.47619047619047616	0.22727272727272727	0.27461139896373055	0.14507772020725387	1.16580310880829	2.2797927461139897
92	Macau	0	34	[null]	0.08823529411764706	0.21739130434782608	0.14285714285714285	0.14150943396226415	0.09433962264150944	0.7641509433962265	3.2641509433962264
93	Yemen	0	53	[null]	0.22641509433962265	0.42857142857142855	0.13432835820895522	0.20270270270270271	0.17567567567567569	1.0045045045045045	2.3468468468468466
94	Guyana	0	32	[null]	0.21875	0.3870967741935484	0.2564102564102564	0.30165289256198347	0.2066115702479339	1.1983471074380165	1.7479338842975207
95	Madagascar	0	37	[null]	0.32432432432432434	0.4642857142857143	0.3218390804597701	0.36666666666666664	0.19444444444444445	1.3333333333333333	1.6333333333333333
96	Burundi	0	20	[null]	0.35	0.37037037037037035	0.2972972972972973	0.31645569620253167	0.24050632911392406	1.0379746835443038	1.2974683544303798
97	Sápmi	0	0	[null]	[null]	0.375	0.5	0.42857142857142855	0.14285714285714285	4.357142857142857	1.7142857142857142
98	Maldives	0	30	[null]	0.2	0.36666666666666664	0.2777777777777778	0.2803030303030303	0.18181818181818182	1.3409090909090908	2.234848484848485
99	Sudan	0	87	[null]	0.25287356321839083	0.5921052631578947	0.2783018867924528	0.336	0.248	1.1413333333333333	1.368
100	Northern Cyprus	0	0	[null]	[null]	0.8	0.4	0.5333333333333333	0.06666666666666667	2.2666666666666666	1.2

We can join the different information about the cup winners to enrich our dataset. We’ll be using this later, so let’s export it to our VAST DataBase.

vo.drop("teams_kpi", method = "table")
teams_kpi = teams_kpi.join(
    football_cup_winners,
    on = {"team1": "winner"},
    how = "left",
    expr2 = [
        "nb_World_Cup",
        "nb_Continental_Cup",
    ],
).to_db("teams_kpi", relation_type = "table")
teams_kpi.head(100)

	Abc team1 Varchar(50) 100%	123 Number_Games_World_Tournament Bigint 100%	123 Number_Games_Continental_Tournament Bigint 100%	123 Percent_Victory_World_Tournament Double 28%	123 Percent_Victory_Continental_Tournament Double 78%	123 Percent_Victory_Home Double 89%	123 Percent_Victory_Away Double 96%	123 Percent_Victory Double 100%	123 Percent_Draw Double 100%	123 Avg_goals Double 100%	123 Avg_goals_conceded Double 100%	123 nb_World_Cup Bigint 15%	123 nb_Continental_Cup Bigint 15%
1	Honduras	9	175	0.0	0.44	0.5483870967741935	0.3316062176165803	0.4085106382978723	0.26170212765957446	1.4914893617021276	1.2234042553191489	[null]	[null]
2	South Korea	34	192	0.20588235294117646	0.5625	0.5789473684210527	0.5122615803814714	0.5286783042394015	0.256857855361596	1.7830423940149627	0.8977556109725686	0	3
3	New Zealand	15	72	0.0	0.5416666666666666	0.4868421052631579	0.3468208092485549	0.40476190476190477	0.18154761904761904	1.7470238095238095	1.6130952380952381	[null]	[null]
4	Nepal	0	30	[null]	0.13333333333333333	0.39473684210526316	0.14634146341463414	0.20666666666666667	0.14	0.8133333333333334	2.5866666666666664	[null]	[null]
5	Ecuador	10	244	0.4	0.2459016393442623	0.59375	0.24025974025974026	0.2944915254237288	0.2457627118644068	1.194915254237288	1.6504237288135593	0	1
6	Eritrea	0	8	[null]	0.0	0.4166666666666667	0.12	0.15714285714285714	0.22857142857142856	0.6714285714285714	1.7428571428571429	[null]	[null]
7	Isle of Man	0	0	[null]	[null]	0.25	0.5555555555555556	0.525	0.1	2.8	1.625	[null]	[null]
8	Niger	0	36	[null]	0.25	0.4482758620689655	0.0898876404494382	0.23497267759562843	0.2568306010928962	0.9344262295081968	1.644808743169399	[null]	[null]
9	Gambia	0	24	[null]	0.25	0.34328358208955223	0.16	0.2356020942408377	0.2879581151832461	0.93717277486911	1.450261780104712	[null]	[null]
10	Sudan	0	87	[null]	0.25287356321839083	0.5921052631578947	0.2783018867924528	0.336	0.248	1.1413333333333333	1.368	[null]	[null]
11	Catalonia	0	0	[null]	[null]	0.4594594594594595	0.1111111111111111	0.391304347826087	0.2391304347826087	1.5217391304347827	1.7391304347826086	[null]	[null]
12	Iceland	0	96	[null]	0.21875	0.4307692307692308	0.23780487804878048	0.29743589743589743	0.18974358974358974	1.1641025641025642	1.7	[null]	[null]
13	Saint Lucia	0	22	[null]	0.2727272727272727	0.40350877192982454	0.308411214953271	0.3333333333333333	0.13978494623655913	1.4731182795698925	1.9623655913978495	[null]	[null]
14	Saint Pierre and Miquelon	0	0	[null]	[null]	[null]	0.0	0.0	0.0	0.16666666666666666	10.333333333333334	[null]	[null]
15	Felvidék	0	0	[null]	[null]	[null]	0.3333333333333333	0.3333333333333333	0.3333333333333333	1.3333333333333333	2.3333333333333335	[null]	[null]
16	Brazil	137	279	0.6788321167883211	0.5770609318996416	0.7333333333333333	0.59375	0.635091496232508	0.20021528525296017	2.193756727664155	0.93756727664155	4	8
17	Canada	6	144	0.0	0.3888888888888889	0.3387096774193548	0.2814814814814815	0.3314121037463977	0.23919308357348704	1.0489913544668588	1.3976945244956773	0	2
18	Iran	12	175	0.08333333333333333	0.5942857142857143	0.6415094339622641	0.43037974683544306	0.5343680709534369	0.2616407982261641	1.8403547671840355	0.835920177383592	0	1
19	Kuwait	3	133	0.0	0.47368421052631576	0.4573170731707317	0.37916666666666665	0.42407407407407405	0.2722222222222222	1.5555555555555556	1.0796296296296297	0	1
20	Slovenia	6	55	0.16666666666666666	0.38181818181818183	0.4264705882352941	0.2857142857142857	0.352112676056338	0.2347417840375587	1.2394366197183098	1.272300469483568	[null]	[null]
21	Nigeria	24	182	0.2916666666666667	0.5439560439560439	0.6203703703703703	0.319047619047619	0.4580152671755725	0.2958015267175573	1.4961832061068703	1.0038167938931297	0	3
22	Cameroon	31	154	0.25806451612903225	0.564935064935065	0.5882352941176471	0.32057416267942584	0.44258872651356995	0.30062630480167013	1.4217118997912317	1.0542797494780793	0	2
23	Lithuania	0	63	[null]	0.25396825396825395	0.4090909090909091	0.24293785310734464	0.2896341463414634	0.18902439024390244	1.0975609756097562	1.7835365853658536	[null]	[null]
24	Cayman Islands	0	14	[null]	0.0	0.3333333333333333	0.13513513513513514	0.18518518518518517	0.16049382716049382	1.037037037037037	2.54320987654321	[null]	[null]
25	Gotland	0	0	[null]	[null]	0.6	0.2857142857142857	0.34615384615384615	0.15384615384615385	2.4615384615384617	2.0	[null]	[null]
26	Saint Martin	0	0	[null]	[null]	0.75	0.16	0.30303030303030304	0.09090909090909091	1.121212121212121	2.909090909090909	[null]	[null]
27	Japan	33	152	0.2727272727272727	0.5526315789473685	0.5263157894736842	0.4009433962264151	0.47183098591549294	0.23415492957746478	1.7200704225352113	1.1602112676056338	0	2
28	Thailand	0	97	[null]	0.24742268041237114	0.5642201834862385	0.259927797833935	0.36993243243243246	0.22972972972972974	1.5456081081081081	1.5945945945945945	[null]	[null]
29	United Arab Emirates	6	123	0.16666666666666666	0.3983739837398374	0.4768211920529801	0.3368421052631579	0.39574468085106385	0.24893617021276596	1.4148936170212767	1.2851063829787235	[null]	[null]
30	Senegal	5	106	0.4	0.3584905660377358	0.648	0.3360655737704918	0.42291666666666666	0.26875	1.2875	1.0020833333333334	[null]	[null]
31	Liberia	0	64	[null]	0.25	0.4567901234567901	0.1	0.27111111111111114	0.23555555555555555	0.8888888888888888	1.4088888888888889	[null]	[null]
32	Guatemala	0	139	[null]	0.33093525179856115	0.46808510638297873	0.2857142857142857	0.3412322274881517	0.25829383886255924	1.3293838862559242	1.3696682464454977	[null]	[null]
33	Syria	0	85	[null]	0.4470588235294118	0.43137254901960786	0.33181818181818185	0.37359550561797755	0.23314606741573032	1.4803370786516854	1.3314606741573034	[null]	[null]
34	Hong Kong	0	82	[null]	0.2804878048780488	0.43333333333333335	0.3005464480874317	0.3295774647887324	0.21971830985915494	1.5295774647887324	1.684507042253521	[null]	[null]
35	South Sudan	0	2	[null]	0.0	0.5	0.14285714285714285	0.16666666666666666	0.2777777777777778	0.5555555555555556	1.5555555555555556	[null]	[null]
36	Bosnia and Herzegovina	3	48	0.3333333333333333	0.4791666666666667	0.42857142857142855	0.3048780487804878	0.38461538461538464	0.21428571428571427	1.4285714285714286	1.39010989010989	[null]	[null]
37	Albania	0	94	[null]	0.14893617021276595	0.40425531914893614	0.17307692307692307	0.23972602739726026	0.2226027397260274	0.9006849315068494	1.595890410958904	[null]	[null]
38	Luxembourg	0	124	[null]	0.03225806451612903	0.11029411764705882	0.058823529411764705	0.06906077348066299	0.11878453038674033	0.5662983425414365	2.8121546961325965	[null]	[null]
39	Moldova	0	48	[null]	0.10416666666666667	0.26666666666666666	0.23	0.20725388601036268	0.23834196891191708	0.8808290155440415	1.6269430051813472	[null]	[null]
40	Namibia	0	49	[null]	0.1836734693877551	0.35185185185185186	0.19540229885057472	0.23684210526315788	0.28421052631578947	1.0157894736842106	1.5315789473684212	[null]	[null]
41	Iraqi Kurdistan	0	0	[null]	[null]	1.0	0.35294117647058826	0.45	0.25	2.1	1.15	[null]	[null]
42	Tamil Eelam	0	0	[null]	[null]	[null]	0.16666666666666666	0.16666666666666666	0.0	1.0	4.166666666666667	[null]	[null]
43	Comoros	0	8	[null]	0.0	0.0	0.14285714285714285	0.07142857142857142	0.2857142857142857	0.5	1.7142857142857142	[null]	[null]
44	Samoa	0	10	[null]	0.3	0.47058823529411764	0.23529411764705882	0.3409090909090909	0.09090909090909091	1.5454545454545454	2.9545454545454546	[null]	[null]
45	Shetland	0	0	[null]	[null]	0.6	0.34210526315789475	0.37209302325581395	0.16279069767441862	1.627906976744186	1.8604651162790697	[null]	[null]
46	Falkland Islands	0	0	[null]	[null]	[null]	0.24	0.24	0.0	1.0	3.8	[null]	[null]
47	Montserrat	0	9	[null]	0.0	1.0	0.10526315789473684	0.13333333333333333	0.06666666666666667	1.0333333333333334	4.4	[null]	[null]
48	Corsica	0	0	[null]	[null]	0.4	[null]	0.4	0.4	0.8	0.6	[null]	[null]
49	Costa Rica	15	217	0.3333333333333333	0.43317972350230416	0.6238532110091743	0.3317307692307692	0.42987249544626593	0.2568306010928962	1.6703096539162112	1.1675774134790529	[null]	[null]
50	Indonesia	1	82	0.0	0.25609756097560976	0.49264705882352944	0.35906040268456374	0.3771760154738878	0.18762088974854932	1.6731141199226305	1.6750483558994198	[null]	[null]
51	Italy	91	130	0.5274725274725275	0.6153846153846154	0.6438848920863309	0.3464566929133858	0.5245683930942895	0.28286852589641437	1.6958831341301461	0.9827357237715804	3	1
52	Morocco	13	161	0.15384615384615385	0.422360248447205	0.6122448979591837	0.3548387096774194	0.45168067226890757	0.3004201680672269	1.388655462184874	0.8739495798319328	[null]	[null]
53	Somalia	0	11	[null]	0.0	0.3333333333333333	0.08333333333333333	0.0891089108910891	0.1188118811881188	0.504950495049505	2.801980198019802	[null]	[null]
54	Algeria	13	146	0.23076923076923078	0.410958904109589	0.5806451612903226	0.3163841807909605	0.4152173913043478	0.28043478260869564	1.3543478260869566	1.0304347826086957	0	1
55	Belize	0	22	[null]	0.22727272727272727	0.5	0.037037037037037035	0.1864406779661017	0.1694915254237288	1.0338983050847457	2.23728813559322	[null]	[null]
56	Djibouti	0	12	[null]	0.08333333333333333	0.07692307692307693	0.0	0.0273972602739726	0.0547945205479452	0.684931506849315	4.027397260273973	[null]	[null]
57	Nicaragua	0	19	[null]	0.21052631578947367	0.6	0.0989010989010989	0.15833333333333333	0.06666666666666667	0.825	3.191666666666667	[null]	[null]
58	Cook Islands	0	10	[null]	0.0	0.5	0.2413793103448276	0.20930232558139536	0.09302325581395349	0.8837209302325582	5.372093023255814	[null]	[null]
59	Jersey	0	0	[null]	[null]	0.8	0.6346153846153846	0.6805555555555556	0.1388888888888889	2.125	0.9166666666666666	[null]	[null]
60	Hitra	0	0	[null]	[null]	[null]	0.16666666666666666	0.16666666666666666	0.0	1.1666666666666667	4.833333333333333	[null]	[null]
61	Saarland	0	2	[null]	0.0	0.0	[null]	0.0	0.3333333333333333	0.8333333333333334	3.0	[null]	[null]
62	Artsakh	0	0	[null]	[null]	1.0	[null]	1.0	0.0	3.0	0.0	[null]	[null]
63	Oman	0	76	[null]	0.3815789473684211	0.46258503401360546	0.2596685082872928	0.3564356435643564	0.24752475247524752	1.2648514851485149	1.2896039603960396	[null]	[null]
64	Australia	26	137	0.2692307692307692	0.583941605839416	0.4393939393939394	0.5112359550561798	0.4989429175475687	0.21141649048625794	2.031712473572939	1.1120507399577166	0	1
65	France	69	133	0.5362318840579711	0.5488721804511278	0.5321100917431193	0.36363636363636365	0.48081841432225064	0.21483375959079284	1.758312020460358	1.3427109974424551	1	2
66	Hungary	32	115	0.46875	0.46956521739130436	0.5809248554913294	0.3822784810126582	0.4740990990990991	0.2195945945945946	2.0720720720720722	1.4954954954954955	[null]	[null]
67	Burkina Faso	0	90	[null]	0.3111111111111111	0.5053763440860215	0.19753086419753085	0.3101449275362319	0.263768115942029	1.1594202898550725	1.4202898550724639	[null]	[null]
68	Mali	0	75	[null]	0.37333333333333335	0.568	0.32558139534883723	0.39956331877729256	0.24672489082969432	1.2729257641921397	1.1768558951965065	[null]	[null]
69	Antigua and Barbuda	0	36	[null]	0.2777777777777778	0.47058823529411764	0.23333333333333334	0.3107344632768362	0.20903954802259886	1.3954802259887005	1.7005649717514124	[null]	[null]
70	Finland	0	119	[null]	0.25210084033613445	0.30735930735930733	0.20285714285714285	0.24571428571428572	0.21	1.18	2.162857142857143	[null]	[null]
71	Kenya	0	80	[null]	0.3	0.521505376344086	0.32335329341317365	0.38166666666666665	0.23666666666666666	1.4316666666666666	1.3966666666666667	[null]	[null]
72	Andorra	0	42	[null]	0.023809523809523808	0.05714285714285714	0.0	0.022900763358778626	0.07633587786259542	0.2824427480916031	2.8244274809160306	[null]	[null]
73	Romania	21	130	0.38095238095238093	0.5	0.5922330097087378	0.31186440677966104	0.4401840490797546	0.2561349693251534	1.6457055214723926	1.2883435582822085	[null]	[null]
74	Central African Republic	0	10	[null]	0.1	0.35294117647058826	0.12	0.16883116883116883	0.2077922077922078	1.025974025974026	2.064935064935065	[null]	[null]
75	Anguilla	0	9	[null]	0.0	0.0	0.09375	0.0625	0.0625	0.6666666666666666	4.041666666666667	[null]	[null]
76	Bolivia	9	243	0.0	0.2222222222222222	0.3787878787878788	0.13861386138613863	0.22195704057279236	0.24821002386634844	1.0381861575178997	1.9427207637231503	0	1
77	Bhutan	0	9	[null]	0.2222222222222222	0.6666666666666666	0.0392156862745098	0.09523809523809523	0.06349206349206349	0.6190476190476191	3.9523809523809526	[null]	[null]
78	Afghanistan	0	12	[null]	0.16666666666666666	0.4	0.29577464788732394	0.2840909090909091	0.20454545454545456	1.0795454545454546	2.0568181818181817	[null]	[null]
79	Provence	0	0	[null]	[null]	[null]	0.16666666666666666	0.16666666666666666	0.08333333333333333	1.3333333333333333	3.5	[null]	[null]
80	Colombia	23	259	0.391304347826087	0.3667953667953668	0.46875	0.35714285714285715	0.378	0.272	1.206	1.21	0	1
81	Israel	3	124	0.0	0.3548387096774194	0.38926174496644295	0.30327868852459017	0.3492462311557789	0.25125628140703515	1.4547738693467336	1.4522613065326633	0	1
82	Papua New Guinea	0	10	[null]	0.4	0.3333333333333333	0.24	0.26804123711340205	0.17525773195876287	1.907216494845361	2.2268041237113403	[null]	[null]
83	Vietnam Republic	0	9	[null]	0.1111111111111111	0.6153846153846154	0.3904761904761905	0.43137254901960786	0.17647058823529413	1.8366013071895424	1.7320261437908497	[null]	[null]
84	Benin	0	50	[null]	0.24	0.27631578947368424	0.13725490196078433	0.20614035087719298	0.22807017543859648	1.0043859649122806	1.9517543859649122	[null]	[null]
85	Tunisia	15	161	0.13333333333333333	0.4472049689440994	0.531578947368421	0.2777777777777778	0.4166666666666667	0.2916666666666667	1.433712121212121	1.0643939393939394	0	1
86	Eswatini	0	19	[null]	0.21052631578947367	0.24096385542168675	0.17708333333333334	0.20707070707070707	0.2727272727272727	0.8282828282828283	1.7424242424242424	[null]	[null]
87	Mauritius	0	21	[null]	0.047619047619047616	0.35294117647058826	0.2903225806451613	0.2863849765258216	0.2112676056338028	1.3286384976525822	1.699530516431925	[null]	[null]
88	Seychelles	0	14	[null]	0.0	0.4166666666666667	0.0425531914893617	0.1411764705882353	0.1411764705882353	0.7294117647058823	2.0588235294117645	[null]	[null]
89	San Marino	0	56	[null]	0.0	0.024390243902439025	0.0	0.007462686567164179	0.029850746268656716	0.15671641791044777	4.268656716417911	[null]	[null]
90	Saare County	0	0	[null]	[null]	[null]	0.12903225806451613	0.12903225806451613	0.0967741935483871	1.032258064516129	2.6129032258064515	[null]	[null]
91	Greenland	0	0	[null]	[null]	0.0	0.3125	0.30303030303030304	0.10606060606060606	1.7424242424242424	2.090909090909091	[null]	[null]
92	Basque Country	0	0	[null]	[null]	0.6333333333333333	0.6956521739130435	0.660377358490566	0.16981132075471697	2.7547169811320753	1.3396226415094339	[null]	[null]
93	Kyrgyzstan	0	30	[null]	0.36666666666666664	0.4444444444444444	0.15942028985507245	0.24074074074074073	0.1574074074074074	0.9166666666666666	1.8611111111111112	[null]	[null]
94	Orkney	0	0	[null]	[null]	[null]	0.15384615384615385	0.15384615384615385	0.0	1.0	4.3076923076923075	[null]	[null]
95	Chinese Taipei	0	35	[null]	0.11428571428571428	0.5	0.25316455696202533	0.21551724137931033	0.15517241379310345	1.3189655172413792	2.853448275862069	[null]	[null]
96	Székely Land	0	0	[null]	[null]	0.0	[null]	0.0	0.0	1.0	3.0	[null]	[null]
97	Chile	33	301	0.3333333333333333	0.3754152823920266	0.558282208588957	0.2571428571428571	0.38048090523338046	0.2065063649222065	1.4214992927864214	1.4653465346534653	0	2
98	Scotland	23	121	0.17391304347826086	0.48760330578512395	0.5847750865051903	0.3942307692307692	0.47651006711409394	0.2174496644295302	1.7503355704697987	1.225503355704698	[null]	[null]
99	Sierra Leone	0	50	[null]	0.24	0.5416666666666666	0.19491525423728814	0.30833333333333335	0.24166666666666667	0.9583333333333334	1.3625	[null]	[null]
100	Uganda	0	52	[null]	0.3076923076923077	0.5919540229885057	0.3827893175074184	0.4404973357015986	0.2539964476021314	1.6181172291296626	1.211367673179396	[null]	[null]

Let’s add each team’s confederation to our dataset.

teams_kpi = teams_kpi.join(
    confederation,
    how = "left",
    on = {"team1": "team"},
    expr2 = ["confederation"],
)
teams_kpi.head(100)

	Abc team1 Varchar(50) 100%	123 Number_Games_World_Tournament Bigint 100%	123 Number_Games_Continental_Tournament Bigint 100%	123 Percent_Victory_World_Tournament Double 28%	123 Percent_Victory_Continental_Tournament Double 78%	123 Percent_Victory_Home Double 89%	123 Percent_Victory_Away Double 96%	123 Percent_Victory Double 100%	123 Percent_Draw Double 100%	123 Avg_goals Double 100%	123 Avg_goals_conceded Double 100%	123 nb_World_Cup Bigint 14%	123 nb_Continental_Cup Bigint 14%	Abc confederation Varchar(8) 99%
1	Liberia	0	64	[null]	0.25	0.4567901234567901	0.1	0.27111111111111114	0.23555555555555555	0.8888888888888888	1.4088888888888889	[null]	[null]	CAF
2	Moldova	0	48	[null]	0.10416666666666667	0.26666666666666666	0.23	0.20725388601036268	0.23834196891191708	0.8808290155440415	1.6269430051813472	[null]	[null]	UEFA
3	Senegal	5	106	0.4	0.3584905660377358	0.648	0.3360655737704918	0.42291666666666666	0.26875	1.2875	1.0020833333333334	[null]	[null]	CAF
4	Guatemala	0	139	[null]	0.33093525179856115	0.46808510638297873	0.2857142857142857	0.3412322274881517	0.25829383886255924	1.3293838862559242	1.3696682464454977	[null]	[null]	CONCACAF
5	Hong Kong	0	82	[null]	0.2804878048780488	0.43333333333333335	0.3005464480874317	0.3295774647887324	0.21971830985915494	1.5295774647887324	1.684507042253521	[null]	[null]	AFC
6	Japan	33	152	0.2727272727272727	0.5526315789473685	0.5263157894736842	0.4009433962264151	0.47183098591549294	0.23415492957746478	1.7200704225352113	1.1602112676056338	0	4	AFC
7	Thailand	0	97	[null]	0.24742268041237114	0.5642201834862385	0.259927797833935	0.36993243243243246	0.22972972972972974	1.5456081081081081	1.5945945945945945	[null]	[null]	AFC
8	United Arab Emirates	6	123	0.16666666666666666	0.3983739837398374	0.4768211920529801	0.3368421052631579	0.39574468085106385	0.24893617021276596	1.4148936170212767	1.2851063829787235	[null]	[null]	AFC
9	Namibia	0	49	[null]	0.1836734693877551	0.35185185185185186	0.19540229885057472	0.23684210526315788	0.28421052631578947	1.0157894736842106	1.5315789473684212	[null]	[null]	CAF
10	South Sudan	0	2	[null]	0.0	0.5	0.14285714285714285	0.16666666666666666	0.2777777777777778	0.5555555555555556	1.5555555555555556	[null]	[null]	CAF
11	Shetland	0	0	[null]	[null]	0.6	0.34210526315789475	0.37209302325581395	0.16279069767441862	1.627906976744186	1.8604651162790697	[null]	[null]	OFC
12	Falkland Islands	0	0	[null]	[null]	[null]	0.24	0.24	0.0	1.0	3.8	[null]	[null]	OFC
13	Bosnia and Herzegovina	3	48	0.3333333333333333	0.4791666666666667	0.42857142857142855	0.3048780487804878	0.38461538461538464	0.21428571428571427	1.4285714285714286	1.39010989010989	[null]	[null]	UEFA
14	Syria	0	85	[null]	0.4470588235294118	0.43137254901960786	0.33181818181818185	0.37359550561797755	0.23314606741573032	1.4803370786516854	1.3314606741573034	[null]	[null]	AFC
15	Luxembourg	0	124	[null]	0.03225806451612903	0.11029411764705882	0.058823529411764705	0.06906077348066299	0.11878453038674033	0.5662983425414365	2.8121546961325965	[null]	[null]	UEFA
16	Albania	0	94	[null]	0.14893617021276595	0.40425531914893614	0.17307692307692307	0.23972602739726026	0.2226027397260274	0.9006849315068494	1.595890410958904	[null]	[null]	UEFA
17	Tamil Eelam	0	0	[null]	[null]	[null]	0.16666666666666666	0.16666666666666666	0.0	1.0	4.166666666666667	[null]	[null]	OFC
18	Samoa	0	10	[null]	0.3	0.47058823529411764	0.23529411764705882	0.3409090909090909	0.09090909090909091	1.5454545454545454	2.9545454545454546	[null]	[null]	OFC
19	Comoros	0	8	[null]	0.0	0.0	0.14285714285714285	0.07142857142857142	0.2857142857142857	0.5	1.7142857142857142	[null]	[null]	CAF
20	Montserrat	0	9	[null]	0.0	1.0	0.10526315789473684	0.13333333333333333	0.06666666666666667	1.0333333333333334	4.4	[null]	[null]	OFC
21	Iraqi Kurdistan	0	0	[null]	[null]	1.0	0.35294117647058826	0.45	0.25	2.1	1.15	[null]	[null]	OFC
22	Corsica	0	0	[null]	[null]	0.4	[null]	0.4	0.4	0.8	0.6	[null]	[null]	OFC
23	Zanzibar	0	0	[null]	[null]	0.1724137931034483	0.19047619047619047	0.18781725888324874	0.19289340101522842	0.8883248730964467	2.1624365482233503	[null]	[null]	OFC
24	Ivory Coast	11	160	0.2727272727272727	0.49375	0.6917293233082706	0.4	0.5029469548133595	0.24950884086444008	1.6345776031434185	1.0530451866404715	0	2	CAF
25	Lesotho	0	24	[null]	0.08333333333333333	0.21686746987951808	0.1509433962264151	0.16901408450704225	0.30985915492957744	0.7793427230046949	1.619718309859155	[null]	[null]	CAF
26	Silesia	0	0	[null]	[null]	0.375	[null]	0.375	0.25	3.0	2.125	[null]	[null]	OFC
27	Bermuda	0	33	[null]	0.42424242424242425	0.40384615384615385	0.24444444444444444	0.35384615384615387	0.18461538461538463	1.676923076923077	1.5615384615384615	[null]	[null]	CONCACAF
28	Grenada	0	27	[null]	0.25925925925925924	0.4	0.35398230088495575	0.3526315789473684	0.22105263157894736	1.7894736842105263	1.763157894736842	[null]	[null]	CONCACAF
29	Jamaica	3	128	0.3333333333333333	0.34375	0.5304878048780488	0.30973451327433627	0.3877159309021113	0.22840690978886757	1.3186180422264875	1.3378119001919386	[null]	[null]	CONMEBOL
30	Dominica	0	17	[null]	0.11764705882352941	0.475	0.1574074074074074	0.23030303030303031	0.20606060606060606	1.1393939393939394	2.090909090909091	[null]	[null]	OFC
31	Malaysia	0	65	[null]	0.27692307692307694	0.42702702702702705	0.3023255813953488	0.3411978221415608	0.25226860254083483	1.4972776769509981	1.5698729582577133	[null]	[null]	AFC
32	Cambodia	0	30	[null]	0.1	0.391304347826087	0.18461538461538463	0.19672131147540983	0.15300546448087432	1.1092896174863387	2.6557377049180326	[null]	[null]	AFC
33	Abkhazia	0	0	[null]	[null]	[null]	0.16666666666666666	0.16666666666666666	0.5	1.0	1.5	[null]	[null]	OFC
34	Bonaire	0	0	[null]	[null]	[null]	0.3076923076923077	0.3076923076923077	0.15384615384615385	1.6153846153846154	3.3846153846153846	[null]	[null]	OFC
35	Nigeria	24	182	0.2916666666666667	0.5439560439560439	0.6203703703703703	0.319047619047619	0.4580152671755725	0.2958015267175573	1.4961832061068703	1.0038167938931297	0	5	CAF
36	Kuwait	3	133	0.0	0.47368421052631576	0.4573170731707317	0.37916666666666665	0.42407407407407405	0.2722222222222222	1.5555555555555556	1.0796296296296297	0	1	AFC
37	Cayman Islands	0	14	[null]	0.0	0.3333333333333333	0.13513513513513514	0.18518518518518517	0.16049382716049382	1.037037037037037	2.54320987654321	[null]	[null]	OFC
38	Slovenia	6	55	0.16666666666666666	0.38181818181818183	0.4264705882352941	0.2857142857142857	0.352112676056338	0.2347417840375587	1.2394366197183098	1.272300469483568	[null]	[null]	UEFA
39	Brazil	137	279	0.6788321167883211	0.5770609318996416	0.7333333333333333	0.59375	0.635091496232508	0.20021528525296017	2.193756727664155	0.93756727664155	4	9	CONMEBOL
40	Canada	6	144	0.0	0.3888888888888889	0.3387096774193548	0.2814814814814815	0.3314121037463977	0.23919308357348704	1.0489913544668588	1.3976945244956773	0	1	CONCACAF
41	Iran	12	175	0.08333333333333333	0.5942857142857143	0.6415094339622641	0.43037974683544306	0.5343680709534369	0.2616407982261641	1.8403547671840355	0.835920177383592	0	2	AFC
42	Cameroon	31	154	0.25806451612903225	0.564935064935065	0.5882352941176471	0.32057416267942584	0.44258872651356995	0.30062630480167013	1.4217118997912317	1.0542797494780793	0	2	CAF
43	Gotland	0	0	[null]	[null]	0.6	0.2857142857142857	0.34615384615384615	0.15384615384615385	2.4615384615384617	2.0	[null]	[null]	OFC
44	Lithuania	0	63	[null]	0.25396825396825395	0.4090909090909091	0.24293785310734464	0.2896341463414634	0.18902439024390244	1.0975609756097562	1.7835365853658536	[null]	[null]	UEFA
45	Saint Martin	0	0	[null]	[null]	0.75	0.16	0.30303030303030304	0.09090909090909091	1.121212121212121	2.909090909090909	[null]	[null]	OFC
46	Trinidad and Tobago	3	145	0.0	0.3793103448275862	0.6224489795918368	0.3763440860215054	0.45264847512038525	0.2102728731942215	1.739967897271268	1.274478330658106	[null]	[null]	CONCACAF
47	United States	48	215	0.2916666666666667	0.5674418604651162	0.40654205607476634	0.2620689655172414	0.41961414790996787	0.21221864951768488	1.4196141479099678	1.3713826366559485	0	4	CONMEBOL
48	Western Australia	0	0	[null]	[null]	[null]	0.3103448275862069	0.3103448275862069	0.10344827586206896	1.896551724137931	2.3793103448275863	[null]	[null]	[null]
49	Western Isles	0	0	[null]	[null]	[null]	0.45	0.45	0.1	1.75	2.2	[null]	[null]	OFC
50	Ynys Môn	0	0	[null]	[null]	[null]	0.47058823529411764	0.47058823529411764	0.21568627450980393	1.7058823529411764	1.4901960784313726	[null]	[null]	OFC
51	British Virgin Islands	0	9	[null]	0.0	0.36363636363636365	0.16	0.19753086419753085	0.16049382716049382	0.8765432098765432	3.111111111111111	[null]	[null]	OFC
52	Equatorial Guinea	0	29	[null]	0.27586206896551724	0.4482758620689655	0.06666666666666667	0.23300970873786409	0.22330097087378642	0.7961165048543689	1.6504854368932038	[null]	[null]	CAF
53	Estonia	0	66	[null]	0.19696969696969696	0.3618421052631579	0.18686868686868688	0.25240384615384615	0.21394230769230768	1.0240384615384615	1.78125	[null]	[null]	UEFA
54	Guinea	0	110	[null]	0.41818181818181815	0.65	0.2542372881355932	0.37089201877934275	0.29107981220657275	1.363849765258216	1.2206572769953052	[null]	[null]	CAF
55	Northern Ireland	13	122	0.23076923076923078	0.30327868852459017	0.29017857142857145	0.1721311475409836	0.24378109452736318	0.23217247097844113	1.043117744610282	1.9535655058043118	[null]	[null]	UEFA
56	Northern Mariana Islands	0	0	[null]	[null]	0.0	0.13333333333333333	0.1111111111111111	0.05555555555555555	0.8888888888888888	4.166666666666667	[null]	[null]	OFC
57	Slovakia	4	54	0.25	0.48148148148148145	0.45	0.3333333333333333	0.40160642570281124	0.23694779116465864	1.4417670682730923	1.3012048192771084	[null]	[null]	UEFA
58	Tahiti	3	27	0.0	0.2962962962962963	0.4838709677419355	0.5846153846153846	0.518324607329843	0.1256544502617801	2.518324607329843	1.7015706806282722	[null]	[null]	OFC
59	Tanzania	0	40	[null]	0.2	0.3860759493670886	0.23024054982817868	0.278118609406953	0.28016359918200406	1.1513292433537832	1.5501022494887526	[null]	[null]	CAF
60	Spain	69	143	0.5217391304347826	0.6223776223776224	0.6768558951965066	0.46568627450980393	0.5813953488372093	0.2248062015503876	1.9674418604651163	0.9085271317829458	1	3	UEFA
61	Kernow	0	0	[null]	[null]	0.2857142857142857	[null]	0.2857142857142857	0.2857142857142857	1.7142857142857142	2.2857142857142856	[null]	[null]	OFC
62	Scotland	23	121	0.17391304347826086	0.48760330578512395	0.5847750865051903	0.3942307692307692	0.47651006711409394	0.2174496644295302	1.7503355704697987	1.225503355704698	[null]	[null]	UEFA
63	Argentina	87	302	0.5402298850574713	0.6059602649006622	0.638095238095238	0.40606060606060607	0.5360602798708288	0.24865446716899892	1.8654467168998923	1.0505920344456405	2	8	CONMEBOL
64	Chile	33	301	0.3333333333333333	0.3754152823920266	0.558282208588957	0.2571428571428571	0.38048090523338046	0.2065063649222065	1.4214992927864214	1.4653465346534653	0	2	CONMEBOL
65	Switzerland	33	129	0.3333333333333333	0.4186046511627907	0.41333333333333333	0.22758620689655173	0.3390957446808511	0.21675531914893617	1.4414893617021276	1.7313829787234043	[null]	[null]	UEFA
66	Uganda	0	52	[null]	0.3076923076923077	0.5919540229885057	0.3827893175074184	0.4404973357015986	0.2539964476021314	1.6181172291296626	1.211367673179396	[null]	[null]	CAF
67	Isle of Wight	0	0	[null]	[null]	0.7777777777777778	0.4411764705882353	0.5116279069767442	0.16279069767441862	2.0	1.302325581395349	[null]	[null]	OFC
68	Cuba	3	92	0.3333333333333333	0.25	0.45	0.3728813559322034	0.34615384615384615	0.25	1.3205128205128205	1.439102564102564	[null]	[null]	CONCACAF
69	Vietnam	0	39	[null]	0.23076923076923078	0.46551724137931033	0.38461538461538464	0.3765432098765432	0.2037037037037037	1.6666666666666667	1.5925925925925926	[null]	[null]	AFC
70	Suriname	0	61	[null]	0.3114754098360656	0.6320754716981132	0.35185185185185186	0.43465045592705165	0.23708206686930092	1.9331306990881458	1.452887537993921	[null]	[null]	OFC
71	Sierra Leone	0	50	[null]	0.24	0.5416666666666666	0.19491525423728814	0.30833333333333335	0.24166666666666667	0.9583333333333334	1.3625	[null]	[null]	CAF
72	Chad	0	16	[null]	0.375	0.3181818181818182	0.16176470588235295	0.22641509433962265	0.24528301886792453	0.9339622641509434	1.7358490566037736	[null]	[null]	CAF
73	Bahamas	0	13	[null]	0.3076923076923077	0.3333333333333333	0.18181818181818182	0.25925925925925924	0.14814814814814814	1.1481481481481481	3.3333333333333335	[null]	[null]	OFC
74	Monaco	0	0	[null]	[null]	[null]	0.16666666666666666	0.16666666666666666	0.16666666666666666	1.1666666666666667	8.333333333333334	[null]	[null]	OFC
75	Galicia	0	0	[null]	[null]	0.5	[null]	0.5	0.5	2.0	1.5	[null]	[null]	OFC
76	Brittany	0	0	[null]	[null]	0.5555555555555556	[null]	0.5555555555555556	0.2222222222222222	1.4444444444444444	1.2222222222222223	[null]	[null]	OFC
77	Burundi	0	20	[null]	0.35	0.37037037037037035	0.2972972972972973	0.31645569620253167	0.24050632911392406	1.0379746835443038	1.2974683544303798	[null]	[null]	CAF
78	Angola	3	84	0.0	0.3333333333333333	0.5625	0.2535211267605634	0.35275080906148865	0.35275080906148865	1.174757281553398	1.0420711974110033	[null]	[null]	CAF
79	Saint Kitts and Nevis	0	26	[null]	0.34615384615384615	0.5076923076923077	0.3064516129032258	0.39869281045751637	0.1895424836601307	1.7973856209150327	1.5947712418300655	[null]	[null]	OFC
80	Guyana	0	32	[null]	0.21875	0.3870967741935484	0.2564102564102564	0.30165289256198347	0.2066115702479339	1.1983471074380165	1.7479338842975207	[null]	[null]	CONCACAF
81	China PR	3	147	0.0	0.5510204081632653	0.5796178343949044	0.3958333333333333	0.48811700182815354	0.226691042047532	1.8372943327239488	1.0877513711151736	[null]	[null]	AFC
82	Jordan	0	78	[null]	0.38461538461538464	0.411214953271028	0.28662420382165604	0.347953216374269	0.28654970760233917	1.1842105263157894	1.1140350877192982	[null]	[null]	AFC
83	Turkmenistan	0	41	[null]	0.36585365853658536	0.5384615384615384	0.36507936507936506	0.38461538461538464	0.17094017094017094	1.6324786324786325	1.4786324786324787	[null]	[null]	AFC
84	Cyprus	0	104	[null]	0.11538461538461539	0.3014705882352941	0.09090909090909091	0.18597560975609756	0.1676829268292683	0.8689024390243902	2.2469512195121952	[null]	[null]	UEFA
85	Faroe Islands	0	60	[null]	0.08333333333333333	0.25925925925925924	0.1875	0.17525773195876287	0.08762886597938144	0.8350515463917526	2.5257731958762886	[null]	[null]	UEFA
86	Russia	40	144	0.425	0.5902777777777778	0.5932203389830508	0.45614035087719296	0.521671826625387	0.2647058823529412	1.7198142414860682	0.93343653250774	0	1	UEFA
87	Norway	8	118	0.25	0.3474576271186441	0.3948220064724919	0.3343465045592705	0.3599476439790576	0.22774869109947643	1.5013089005235603	1.6845549738219896	[null]	[null]	UEFA
88	Mayotte	0	0	[null]	[null]	0.0	0.1875	0.16666666666666666	0.2777777777777778	1.6111111111111112	2.0555555555555554	[null]	[null]	OFC
89	Réunion	0	0	[null]	[null]	0.21212121212121213	0.4166666666666667	0.3333333333333333	0.16049382716049382	1.6666666666666667	2.3580246913580245	[null]	[null]	OFC
90	Mongolia	0	14	[null]	0.21428571428571427	1.0	0.2413793103448276	0.26666666666666666	0.08888888888888889	1.0888888888888888	3.2222222222222223	[null]	[null]	OFC
91	Malta	0	92	[null]	0.021739130434782608	0.20666666666666667	0.1016949152542373	0.125	0.16666666666666666	0.6277777777777778	2.3055555555555554	[null]	[null]	UEFA
92	Peru	15	277	0.26666666666666666	0.3176895306859206	0.42857142857142855	0.2236024844720497	0.3129370629370629	0.243006993006993	1.229020979020979	1.4685314685314685	0	2	CONMEBOL
93	Cape Verde	0	35	[null]	0.34285714285714286	0.5806451612903226	0.27848101265822783	0.3586206896551724	0.23448275862068965	1.0620689655172413	1.1586206896551725	[null]	[null]	CAF
94	Libya	0	62	[null]	0.3709677419354839	0.5876288659793815	0.23357664233576642	0.3783783783783784	0.24662162162162163	1.2905405405405406	1.2162162162162162	[null]	[null]	CAF
95	Rhodes	0	0	[null]	[null]	0.6666666666666666	0.6	0.6111111111111112	0.1111111111111111	1.6111111111111112	1.0555555555555556	[null]	[null]	OFC
96	Dominican Republic	0	28	[null]	0.35714285714285715	0.42857142857142855	0.3333333333333333	0.3626373626373626	0.14285714285714285	1.5714285714285714	1.8571428571428572	[null]	[null]	OFC
97	Ellan Vannin	0	0	[null]	[null]	[null]	0.0	0.0	0.6666666666666666	0.6666666666666666	1.0	[null]	[null]	OFC
98	Kiribati	0	0	[null]	[null]	[null]	0.0	0.0	0.0	0.3333333333333333	9.666666666666666	[null]	[null]	OFC
99	Mauritania	0	18	[null]	0.1111111111111111	0.3	0.08571428571428572	0.15028901734104047	0.26011560693641617	0.7341040462427746	1.6416184971098267	[null]	[null]	CAF
100	São Tomé and Príncipe	0	8	[null]	0.25	0.2222222222222222	0.0625	0.15151515151515152	0.15151515151515152	0.7575757575757576	2.5757575757575757	[null]	[null]	CAF

Since clustering will use different statistics, we need to normalize the data. We’ll also create a dummy that will equal 1 if the team won at least one World Cup.

teams_kpi.normalize(
    columns = [
        "Number_Games_Continental_Tournament",
        "Number_Games_World_Tournament",
        "nb_Continental_Cup",
    ],
    method = "minmax",
)
teams_kpi["Word_Cup_Victory"] = teams_kpi["nb_World_Cup"] > 0
teams_kpi["Word_Cup_Victory"].astype("int")

	Abc team1 Varchar(50) 100%	123 Number_Games_World_Tournament Real 100%	123 Number_Games_Continental_Tournament Real 100%	123 Percent_Victory_World_Tournament Double 28%	123 Percent_Victory_Continental_Tournament Double 78%	123 Percent_Victory_Home Double 89%	123 Percent_Victory_Away Double 96%	123 Percent_Victory Double 100%	123 Percent_Draw Double 100%	123 Avg_goals Double 100%	123 Avg_goals_conceded Double 100%	123 nb_World_Cup Bigint 14%	123 nb_Continental_Cup Real 14%	Abc confederation Varchar(8) 99%	123 Word_Cup_Victory Int 15%
1	Brazil	1.0	0.8403614	0.6788321167883211	0.5770609318996416	0.7333333333333333	0.59375	0.635091496232508	0.20021528525296017	2.193756727664155	0.93756727664155	4	0.8	CONMEBOL	1
2	Nigeria	0.1751825	0.5481928	0.2916666666666667	0.5439560439560439	0.6203703703703703	0.319047619047619	0.4580152671755725	0.2958015267175573	1.4961832061068703	1.0038167938931297	0	0.3	CAF	0
3	Cameroon	0.2262774	0.4638554	0.25806451612903225	0.564935064935065	0.5882352941176471	0.32057416267942584	0.44258872651356995	0.30062630480167013	1.4217118997912317	1.0542797494780793	0	0.2	CAF	0
4	Kuwait	0.0218978	0.4006024	0.0	0.47368421052631576	0.4573170731707317	0.37916666666666665	0.42407407407407405	0.2722222222222222	1.5555555555555556	1.0796296296296297	0	0.1	AFC	0
5	Iran	0.0875912	0.5271084	0.08333333333333333	0.5942857142857143	0.6415094339622641	0.43037974683544306	0.5343680709534369	0.2616407982261641	1.8403547671840355	0.835920177383592	0	0.2	AFC	0
6	Slovenia	0.0437956	0.1656627	0.16666666666666666	0.38181818181818183	0.4264705882352941	0.2857142857142857	0.352112676056338	0.2347417840375587	1.2394366197183098	1.272300469483568	[null]	[null]	UEFA	[null]
7	Canada	0.0437956	0.4337349	0.0	0.3888888888888889	0.3387096774193548	0.2814814814814815	0.3314121037463977	0.23919308357348704	1.0489913544668588	1.3976945244956773	0	0.2	CONCACAF	0
8	Lithuania	0.0	0.189759	[null]	0.25396825396825395	0.4090909090909091	0.24293785310734464	0.2896341463414634	0.18902439024390244	1.0975609756097562	1.7835365853658536	[null]	[null]	UEFA	[null]
9	Cayman Islands	0.0	0.0421687	[null]	0.0	0.3333333333333333	0.13513513513513514	0.18518518518518517	0.16049382716049382	1.037037037037037	2.54320987654321	[null]	[null]	OFC	[null]
10	Saint Martin	0.0	0.0	[null]	[null]	0.75	0.16	0.30303030303030304	0.09090909090909091	1.121212121212121	2.909090909090909	[null]	[null]	OFC	[null]
11	Gotland	0.0	0.0	[null]	[null]	0.6	0.2857142857142857	0.34615384615384615	0.15384615384615385	2.4615384615384617	2.0	[null]	[null]	OFC	[null]
12	Sudan	0.0	0.2620482	[null]	0.25287356321839083	0.5921052631578947	0.2783018867924528	0.336	0.248	1.1413333333333333	1.368	[null]	[null]	CAF	[null]
13	Niger	0.0	0.1084337	[null]	0.25	0.4482758620689655	0.0898876404494382	0.23497267759562843	0.2568306010928962	0.9344262295081968	1.644808743169399	[null]	[null]	CAF	[null]
14	New Zealand	0.1094891	0.2168675	0.0	0.5416666666666666	0.4868421052631579	0.3468208092485549	0.40476190476190477	0.18154761904761904	1.7470238095238095	1.6130952380952381	[null]	[null]	OFC	[null]
15	Catalonia	0.0	0.0	[null]	[null]	0.4594594594594595	0.1111111111111111	0.391304347826087	0.2391304347826087	1.5217391304347827	1.7391304347826086	[null]	[null]	OFC	[null]
16	Ecuador	0.0729927	0.7349398	0.4	0.2459016393442623	0.59375	0.24025974025974026	0.2944915254237288	0.2457627118644068	1.194915254237288	1.6504237288135593	0	0.1	CONMEBOL	0
17	South Korea	0.2481752	0.5783133	0.20588235294117646	0.5625	0.5789473684210527	0.5122615803814714	0.5286783042394015	0.256857855361596	1.7830423940149627	0.8977556109725686	0	0.3	AFC	0
18	Nepal	0.0	0.0903614	[null]	0.13333333333333333	0.39473684210526316	0.14634146341463414	0.20666666666666667	0.14	0.8133333333333334	2.5866666666666664	[null]	[null]	AFC	[null]
19	Iceland	0.0	0.2891566	[null]	0.21875	0.4307692307692308	0.23780487804878048	0.29743589743589743	0.18974358974358974	1.1641025641025642	1.7	[null]	[null]	UEFA	[null]
20	Eritrea	0.0	0.0240964	[null]	0.0	0.4166666666666667	0.12	0.15714285714285714	0.22857142857142856	0.6714285714285714	1.7428571428571429	[null]	[null]	CAF	[null]

Some data is missing; this is because only top teams won major tournaments. Besides, some non-professional teams may not have a stadium.

teams_kpi.count()

	count
"team1"	272.0
"Number_Games_World_Tournament"	272.0
"Number_Games_Continental_Tournament"	272.0
"Percent_Victory_World_Tournament"	77.0
"Percent_Victory_Continental_Tournament"	213.0
"Percent_Victory_Home"	243.0
"Percent_Victory_Away"	263.0
"Percent_Victory"	272.0
"Percent_Draw"	272.0
"Avg_goals"	272.0
"Avg_goals_conceded"	272.0
"nb_World_Cup"	40.0
"nb_Continental_Cup"	40.0
"confederation"	271.0
"Word_Cup_Victory"	40.0

Let’s impute the missing values by 0.

teams_kpi.fillna(
    {
        "Percent_Victory_Away": 0,
        "Percent_Victory_Home": 0,
        "Percent_Victory_Continental_Tournament": 0,
        "Percent_Victory_World_Tournament": 0,
        "nb_World_Cup": 0,
        "Word_Cup_Victory": 0,
        "nb_Continental_Cup": 0,
        "confederation": "OFC",
    },
)

	Abc team1 Varchar(50) 100%	123 Number_Games_World_Tournament Real 100%	123 Number_Games_Continental_Tournament Real 100%	123 Percent_Victory_World_Tournament Real 100%	123 Percent_Victory_Continental_Tournament Real 100%	123 Percent_Victory_Home Real 100%	123 Percent_Victory_Away Real 100%	123 Percent_Victory Double 100%	123 Percent_Draw Double 100%	123 Avg_goals Double 100%	123 Avg_goals_conceded Double 100%	123 nb_World_Cup Bigint 100%	123 nb_Continental_Cup Real 100%	Abc confederation Varchar(8) 100%	123 Word_Cup_Victory Int 100%
1	El Salvador	0.0437956	0.4698795	0.0	0.4166666666666667	0.4819277108433735	0.2009132420091324	0.32112068965517243	0.22629310344827586	1.2262931034482758	1.4870689655172413	0	0.0	CONCACAF	0
2	Czech Republic	0.2773723	0.4668675	0.3684210526315789	0.5290322580645161	0.654320987654321	0.34185303514376997	0.4833110814419226	0.22162883845126835	1.843791722296395	1.2349799732977302	0	0.0	UEFA	0
3	Wales	0.0364964	0.3313253	0.2	0.3	0.3670886075949367	0.26515151515151514	0.31006493506493504	0.21266233766233766	1.2532467532467533	1.6931818181818181	0	0.0	UEFA	0
4	South Africa	0.1240876	0.2560241	0.17647058823529413	0.4823529411764706	0.5275590551181102	0.3709677419354839	0.4447592067988669	0.2747875354107649	1.339943342776204	1.0084985835694051	0	0.0	CAF	0
5	Austria	0.2116788	0.3493976	0.41379310344827586	0.47413793103448276	0.4837662337662338	0.30633802816901406	0.41112618724559025	0.2198100407055631	1.7978290366350067	1.5929443690637721	0	0.0	UEFA	0
6	Argentina	0.6350365	0.9096386	0.5402298850574713	0.6059602649006622	0.638095238095238	0.40606060606060607	0.5360602798708288	0.24865446716899892	1.8654467168998923	1.0505920344456405	2	0.8	CONMEBOL	1
7	Chile	0.2408759	0.9066265	0.3333333333333333	0.3754152823920266	0.558282208588957	0.2571428571428571	0.38048090523338046	0.2065063649222065	1.4214992927864214	1.4653465346534653	0	0.2	CONMEBOL	0
8	Scotland	0.1678832	0.3644578	0.17391304347826086	0.48760330578512395	0.5847750865051903	0.3942307692307692	0.47651006711409394	0.2174496644295302	1.7503355704697987	1.225503355704698	0	0.0	UEFA	0
9	Switzerland	0.2408759	0.3885542	0.3333333333333333	0.4186046511627907	0.41333333333333333	0.22758620689655173	0.3390957446808511	0.21675531914893617	1.4414893617021276	1.7313829787234043	0	0.0	UEFA	0
10	Uganda	0.0	0.1566265	0.0	0.3076923076923077	0.5919540229885057	0.3827893175074184	0.4404973357015986	0.2539964476021314	1.6181172291296626	1.211367673179396	0	0.0	CAF	0
11	Sierra Leone	0.0	0.1506024	0.0	0.24	0.5416666666666666	0.19491525423728814	0.30833333333333335	0.24166666666666667	0.9583333333333334	1.3625	0	0.0	CAF	0
12	Galicia	0.0	0.0	0.0	0.0	0.5	0.0	0.5	0.5	2.0	1.5	0	0.0	OFC	0
13	Cuba	0.0218978	0.2771084	0.3333333333333333	0.25	0.45	0.3728813559322034	0.34615384615384615	0.25	1.3205128205128205	1.439102564102564	0	0.0	CONCACAF	0
14	Chad	0.0	0.0481928	0.0	0.375	0.3181818181818182	0.16176470588235295	0.22641509433962265	0.24528301886792453	0.9339622641509434	1.7358490566037736	0	0.0	CAF	0
15	Brittany	0.0	0.0	0.0	0.0	0.5555555555555556	0.0	0.5555555555555556	0.2222222222222222	1.4444444444444444	1.2222222222222223	0	0.0	OFC	0
16	Bahamas	0.0	0.0391566	0.0	0.3076923076923077	0.3333333333333333	0.18181818181818182	0.25925925925925924	0.14814814814814814	1.1481481481481481	3.3333333333333335	0	0.0	OFC	0
17	Suriname	0.0	0.1837349	0.0	0.3114754098360656	0.6320754716981132	0.35185185185185186	0.43465045592705165	0.23708206686930092	1.9331306990881458	1.452887537993921	0	0.0	OFC	0
18	Vietnam	0.0	0.1174699	0.0	0.23076923076923078	0.46551724137931033	0.38461538461538464	0.3765432098765432	0.2037037037037037	1.6666666666666667	1.5925925925925926	0	0.0	AFC	0
19	Isle of Wight	0.0	0.0	0.0	0.0	0.7777777777777778	0.4411764705882353	0.5116279069767442	0.16279069767441862	2.0	1.302325581395349	0	0.0	OFC	0
20	Monaco	0.0	0.0	0.0	0.0	0.0	0.16666666666666666	0.16666666666666666	0.16666666666666666	1.1666666666666667	8.333333333333334	0	0.0	OFC	0

Let’s export the result to our VAST DataBase.

vo.drop("football_clustering", method = "table")
teams_kpi.to_db(
    "football_clustering",
    relation_type = "table",
    inplace = True,
)

	Abc team1 Varchar(50) 100%	123 number_games_world_tournament Decimal(28,7) 100%	123 number_games_continental_tournament Decimal(28,7) 100%	123 percent_victory_world_tournament Double 100%	123 percent_victory_continental_tournament Double 100%	123 percent_victory_home Double 100%	123 percent_victory_away Double 100%	123 percent_victory Double 100%	123 percent_draw Double 100%	123 avg_goals Double 100%	123 avg_goals_conceded Double 100%	123 nb_world_cup Bigint 100%	123 nb_continental_cup Decimal(27,6) 100%	Abc confederation Varchar(8) 100%	123 word_cup_victory Integer 100%
1	Brazil	1.0	0.8403614	0.6788321167883211	0.5770609318996416	0.7333333333333333	0.59375	0.635091496232508	0.20021528525296017	2.193756727664155	0.93756727664155	4	0.9	CONMEBOL	1
2	Slovenia	0.0437956	0.1656627	0.16666666666666666	0.38181818181818183	0.4264705882352941	0.2857142857142857	0.352112676056338	0.2347417840375587	1.2394366197183098	1.272300469483568	0	0.0	UEFA	0
3	Canada	0.0437956	0.4337349	0.0	0.3888888888888889	0.3387096774193548	0.2814814814814815	0.3314121037463977	0.23919308357348704	1.0489913544668588	1.3976945244956773	0	0.1	CONCACAF	0
4	Gotland	0.0	0.0	0.0	0.0	0.6	0.2857142857142857	0.34615384615384615	0.15384615384615385	2.4615384615384617	2.0	0	0.0	OFC	0
5	Saint Martin	0.0	0.0	0.0	0.0	0.75	0.16	0.30303030303030304	0.09090909090909091	1.121212121212121	2.909090909090909	0	0.0	OFC	0
6	Cayman Islands	0.0	0.0421687	0.0	0.0	0.3333333333333333	0.13513513513513514	0.18518518518518517	0.16049382716049382	1.037037037037037	2.54320987654321	0	0.0	OFC	0
7	Iran	0.0875912	0.5271084	0.08333333333333333	0.5942857142857143	0.6415094339622641	0.43037974683544306	0.5343680709534369	0.2616407982261641	1.8403547671840355	0.835920177383592	0	0.3	AFC	0
8	Kuwait	0.0218978	0.4006024	0.0	0.47368421052631576	0.4573170731707317	0.37916666666666665	0.42407407407407405	0.2722222222222222	1.5555555555555556	1.0796296296296297	0	0.1	AFC	0
9	Lithuania	0.0	0.189759	0.0	0.25396825396825395	0.4090909090909091	0.24293785310734464	0.2896341463414634	0.18902439024390244	1.0975609756097562	1.7835365853658536	0	0.0	UEFA	0
10	Nigeria	0.1751825	0.5481928	0.2916666666666667	0.5439560439560439	0.6203703703703703	0.319047619047619	0.4580152671755725	0.2958015267175573	1.4961832061068703	1.0038167938931297	0	0.3	CAF	0
11	Cameroon	0.2262774	0.4638554	0.25806451612903225	0.564935064935065	0.5882352941176471	0.32057416267942584	0.44258872651356995	0.30062630480167013	1.4217118997912317	1.0542797494780793	0	0.2	CAF	0
12	South Korea	0.2481752	0.5783133	0.20588235294117646	0.5625	0.5789473684210527	0.5122615803814714	0.5286783042394015	0.256857855361596	1.7830423940149627	0.8977556109725686	0	0.3	AFC	0
13	New Zealand	0.1094891	0.2168675	0.0	0.5416666666666666	0.4868421052631579	0.3468208092485549	0.40476190476190477	0.18154761904761904	1.7470238095238095	1.6130952380952381	0	0.0	OFC	0
14	Iceland	0.0	0.2891566	0.0	0.21875	0.4307692307692308	0.23780487804878048	0.29743589743589743	0.18974358974358974	1.1641025641025642	1.7	0	0.0	UEFA	0
15	Honduras	0.0656934	0.5271084	0.0	0.44	0.5483870967741935	0.3316062176165803	0.4085106382978723	0.26170212765957446	1.4914893617021276	1.2234042553191489	0	0.0	CONMEBOL	0
16	Ecuador	0.0729927	0.7349398	0.4	0.2459016393442623	0.59375	0.24025974025974026	0.2944915254237288	0.2457627118644068	1.194915254237288	1.6504237288135593	0	0.2	CONMEBOL	0
17	Isle of Man	0.0	0.0	0.0	0.0	0.25	0.5555555555555556	0.525	0.1	2.8	1.625	0	0.0	OFC	0
18	Saint Lucia	0.0	0.0662651	0.0	0.2727272727272727	0.40350877192982454	0.308411214953271	0.3333333333333333	0.13978494623655913	1.4731182795698925	1.9623655913978495	0	0.0	OFC	0
19	Catalonia	0.0	0.0	0.0	0.0	0.4594594594594595	0.1111111111111111	0.391304347826087	0.2391304347826087	1.5217391304347827	1.7391304347826086	0	0.0	OFC	0
20	Sudan	0.0	0.2620482	0.0	0.25287356321839083	0.5921052631578947	0.2783018867924528	0.336	0.248	1.1413333333333333	1.368	0	0.0	CAF	0

Team Rankings with k-means

To compute a KMeans model, we need to find a value for k. Let’s draw an elbow() curve to find a suitable number of clusters.

from vastorbit.machine_learning.model_selection import elbow

predictors = [
    'Word_Cup_Victory',
    'nb_Continental_Cup',
    'Number_Games_World_Tournament',
    'Number_Games_Continental_Tournament',
    'Percent_Victory_World_Tournament',
    'Percent_Victory_Continental_Tournament',
    'Percent_Victory_Home',
    'Percent_Victory_Away',
]
elbow(
    "football_clustering",
    predictors,
    n_clusters = (1, 11),
)

6 seems to be a good number of clusters. To help the algorithm to converge to meaningful clusters, we can initialize the clusters with different types of centroid levels. For example, we can associate very good teams (champions) to World Cups Winners, good teams to continental Cup Winners, etc. This will let us to properly weigh the performance of each team relatve to the strength of their region.

from vastorbit.machine_learning.vast import KMeans

    # w_cup c_cup w_games c_games w_vict c_vict h_vict a_vict
init =  [
    (0,    0,       0,  0.05,      0,    0,      0, 0.05), # very bad
    (0,    0,       0,  0.30,      0, 0.25,   0.30, 0.10), # bad
    (0,    0,    0.05,  0.40,   0.15, 0.35,   0.40, 0.20), # outsiders
    (0, 0.10,    0.15,  0.50,   0.20, 0.45,   0.50, 0.30), # good
    (0, 0.20,    0.30,  0.40,   0.40, 0.55,   0.60, 0.40), # strong
    (1,  0.5,       1,  0.80,   0.70, 0.65,   0.75, 0.55), # champions
]
model_kmeans = KMeans(
    n_clusters = 6,
    init = init,
)
model_kmeans.fit("football_clustering", predictors)
model_kmeans.clusters_

Let’s add the prediction to the VastFrame.

model_kmeans.predict(
    teams_kpi,
    name = "fifa_rank",
)

	Abc team1 Varchar(50) 100%	123 number_games_world_tournament Decimal(28,7) 100%	123 number_games_continental_tournament Decimal(28,7) 100%	123 percent_victory_world_tournament Double 100%	123 percent_victory_continental_tournament Double 100%	123 percent_victory_home Double 100%	123 percent_victory_away Double 100%	123 percent_victory Double 100%	123 percent_draw Double 100%	123 avg_goals Double 100%	123 avg_goals_conceded Double 100%	123 nb_world_cup Bigint 100%	123 nb_continental_cup Decimal(27,6) 100%	Abc confederation Varchar(8) 100%	123 word_cup_victory Integer 100%	123 fifa_rank Integer 100%
1	Brazil	1.0	0.8403614	0.6788321167883211	0.5770609318996416	0.7333333333333333	0.59375	0.635091496232508	0.20021528525296017	2.193756727664155	0.93756727664155	4	0.9	CONMEBOL	1	5
2	Slovenia	0.0437956	0.1656627	0.16666666666666666	0.38181818181818183	0.4264705882352941	0.2857142857142857	0.352112676056338	0.2347417840375587	1.2394366197183098	1.272300469483568	0	0.0	UEFA	0	2
3	Canada	0.0437956	0.4337349	0.0	0.3888888888888889	0.3387096774193548	0.2814814814814815	0.3314121037463977	0.23919308357348704	1.0489913544668588	1.3976945244956773	0	0.1	CONCACAF	0	3
4	Gotland	0.0	0.0	0.0	0.0	0.6	0.2857142857142857	0.34615384615384615	0.15384615384615385	2.4615384615384617	2.0	0	0.0	OFC	0	1
5	Saint Martin	0.0	0.0	0.0	0.0	0.75	0.16	0.30303030303030304	0.09090909090909091	1.121212121212121	2.909090909090909	0	0.0	OFC	0	1
6	Cayman Islands	0.0	0.0421687	0.0	0.0	0.3333333333333333	0.13513513513513514	0.18518518518518517	0.16049382716049382	1.037037037037037	2.54320987654321	0	0.0	OFC	0	0
7	Iran	0.0875912	0.5271084	0.08333333333333333	0.5942857142857143	0.6415094339622641	0.43037974683544306	0.5343680709534369	0.2616407982261641	1.8403547671840355	0.835920177383592	0	0.3	AFC	0	4
8	Kuwait	0.0218978	0.4006024	0.0	0.47368421052631576	0.4573170731707317	0.37916666666666665	0.42407407407407405	0.2722222222222222	1.5555555555555556	1.0796296296296297	0	0.1	AFC	0	3
9	Lithuania	0.0	0.189759	0.0	0.25396825396825395	0.4090909090909091	0.24293785310734464	0.2896341463414634	0.18902439024390244	1.0975609756097562	1.7835365853658536	0	0.0	UEFA	0	2
10	Nigeria	0.1751825	0.5481928	0.2916666666666667	0.5439560439560439	0.6203703703703703	0.319047619047619	0.4580152671755725	0.2958015267175573	1.4961832061068703	1.0038167938931297	0	0.3	CAF	0	4
11	Cameroon	0.2262774	0.4638554	0.25806451612903225	0.564935064935065	0.5882352941176471	0.32057416267942584	0.44258872651356995	0.30062630480167013	1.4217118997912317	1.0542797494780793	0	0.2	CAF	0	4
12	South Korea	0.2481752	0.5783133	0.20588235294117646	0.5625	0.5789473684210527	0.5122615803814714	0.5286783042394015	0.256857855361596	1.7830423940149627	0.8977556109725686	0	0.3	AFC	0	4
13	New Zealand	0.1094891	0.2168675	0.0	0.5416666666666666	0.4868421052631579	0.3468208092485549	0.40476190476190477	0.18154761904761904	1.7470238095238095	1.6130952380952381	0	0.0	OFC	0	3
14	Iceland	0.0	0.2891566	0.0	0.21875	0.4307692307692308	0.23780487804878048	0.29743589743589743	0.18974358974358974	1.1641025641025642	1.7	0	0.0	UEFA	0	2
15	Honduras	0.0656934	0.5271084	0.0	0.44	0.5483870967741935	0.3316062176165803	0.4085106382978723	0.26170212765957446	1.4914893617021276	1.2234042553191489	0	0.0	CONMEBOL	0	3
16	Ecuador	0.0729927	0.7349398	0.4	0.2459016393442623	0.59375	0.24025974025974026	0.2944915254237288	0.2457627118644068	1.194915254237288	1.6504237288135593	0	0.2	CONMEBOL	0	4
17	Isle of Man	0.0	0.0	0.0	0.0	0.25	0.5555555555555556	0.525	0.1	2.8	1.625	0	0.0	OFC	0	0
18	Saint Lucia	0.0	0.0662651	0.0	0.2727272727272727	0.40350877192982454	0.308411214953271	0.3333333333333333	0.13978494623655913	1.4731182795698925	1.9623655913978495	0	0.0	OFC	0	2
19	Catalonia	0.0	0.0	0.0	0.0	0.4594594594594595	0.1111111111111111	0.391304347826087	0.2391304347826087	1.5217391304347827	1.7391304347826086	0	0.0	OFC	0	1
20	Sudan	0.0	0.2620482	0.0	0.25287356321839083	0.5921052631578947	0.2783018867924528	0.336	0.248	1.1413333333333333	1.368	0	0.0	CAF	0	2

Let’s look at the strongest group, which includes well-known teams like Argentina, Brazil, and France.

teams_kpi.search(
    conditions = [teams_kpi["fifa_rank"] == 5],
    usecols = ["team1", "fifa_rank"],
    order_by = ["fifa_rank"],
).head(10)

	Abc team1 Varchar(50) 100%	123 fifa_rank Integer 100%
1	Uruguay	5
2	Sweden	5
3	Italy	5
4	Argentina	5
5	Germany	5
6	England	5
7	Brazil	5
8	Spain	5
9	France	5

The weakest group includes less well-known teams.

teams_kpi.search(
    conditions = [teams_kpi["fifa_rank"] == 0],
    usecols = ["team1", "fifa_rank"],
    order_by = ["fifa_rank"],
).head(10)

	Abc team1 Varchar(50) 100%	123 fifa_rank Integer 100%
1	Western Australia	0
2	Andorra	0
3	Provence	0
4	Anguilla	0
5	Ynys Môn	0
6	Faroe Islands	0
7	Malta	0
8	Kernow	0
9	Western Isles	0
10	Northern Mariana Islands	0

A bubble plot will let us visualize the differences in strength between each confederation.

We can see the strongest group at the top right of the graphic and weakest teams at the bottom left. Some teams may be very good in their location but very bad in World Tournaments. They are mainly at the bottom right of the graph.

teams_kpi.scatter(
    [
        "Percent_Victory_Continental_Tournament",
        "Percent_Victory_World_Tournament",
    ],
    size = "fifa_rank",
    by = "confederation",
)

We can also look at the Percent of Victory by rank to confirm our hypothesis.

teams_kpi.scatter(
    [
        "Percent_Victory_Continental_Tournament",
        "Percent_Victory_World_Tournament",
    ],
    size = "Percent_Victory",
    by = "fifa_rank",
)

A box plot can also show us the differences in skill between teams. We can look at rank 1, where the percent of victory is high because of the confederation.

Note that the best team in a weaker confederation might not be particularly strong, but still have a high Percent of Victory.

teams_kpi["Percent_Victory"].boxplot(by = "fifa_rank")

Let’s export the KPIs to our VAST DataBase.

vo.drop(
    "team_kpi",
    method = "table",
)
teams_kpi.to_db(
    name = "team_kpi",
    relation_type = "table",
    inplace = True,
)

	Abc team1 Varchar(50) 100%	123 number_games_world_tournament Decimal(28,7) 100%	123 number_games_continental_tournament Decimal(28,7) 100%	123 percent_victory_world_tournament Double 100%	123 percent_victory_continental_tournament Double 100%	123 percent_victory_home Double 100%	123 percent_victory_away Double 100%	123 percent_victory Double 100%	123 percent_draw Double 100%	123 avg_goals Double 100%	123 avg_goals_conceded Double 100%	123 nb_world_cup Bigint 100%	123 nb_continental_cup Decimal(27,6) 100%	Abc confederation Varchar(8) 100%	123 word_cup_victory Integer 100%	123 fifa_rank Integer 100%
1	Israel	0.0218978	0.373494	0.0	0.3548387096774194	0.38926174496644295	0.30327868852459017	0.3492462311557789	0.25125628140703515	1.4547738693467336	1.4522613065326633	0	0.1	UEFA	0	3
2	Colombia	0.1678832	0.7801205	0.391304347826087	0.3667953667953668	0.46875	0.35714285714285715	0.378	0.272	1.206	1.21	0	0.1	CONMEBOL	0	4
3	Vietnam Republic	0.0	0.0271084	0.0	0.1111111111111111	0.6153846153846154	0.3904761904761905	0.43137254901960786	0.17647058823529413	1.8366013071895424	1.7320261437908497	0	0.0	AFC	0	1
4	Saare County	0.0	0.0	0.0	0.0	0.0	0.12903225806451613	0.12903225806451613	0.0967741935483871	1.032258064516129	2.6129032258064515	0	0.0	OFC	0	0
5	Greenland	0.0	0.0	0.0	0.0	0.0	0.3125	0.30303030303030304	0.10606060606060606	1.7424242424242424	2.090909090909091	0	0.0	OFC	0	0
6	Orkney	0.0	0.0	0.0	0.0	0.0	0.15384615384615385	0.15384615384615385	0.0	1.0	4.3076923076923075	0	0.0	OFC	0	0
7	Kyrgyzstan	0.0	0.0903614	0.0	0.36666666666666664	0.4444444444444444	0.15942028985507245	0.24074074074074073	0.1574074074074074	0.9166666666666666	1.8611111111111112	0	0.0	AFC	0	2
8	Papua New Guinea	0.0	0.0301205	0.0	0.4	0.3333333333333333	0.24	0.26804123711340205	0.17525773195876287	1.907216494845361	2.2268041237113403	0	0.0	OFC	0	2
9	Tunisia	0.1094891	0.4849398	0.13333333333333333	0.4472049689440994	0.531578947368421	0.2777777777777778	0.4166666666666667	0.2916666666666667	1.433712121212121	1.0643939393939394	0	0.1	CAF	0	3
10	San Marino	0.0	0.1686747	0.0	0.0	0.024390243902439025	0.0	0.007462686567164179	0.029850746268656716	0.15671641791044777	4.268656716417911	0	0.0	UEFA	0	0
11	Benin	0.0	0.1506024	0.0	0.24	0.27631578947368424	0.13725490196078433	0.20614035087719298	0.22807017543859648	1.0043859649122806	1.9517543859649122	0	0.0	CAF	0	2
12	Eswatini	0.0	0.0572289	0.0	0.21052631578947367	0.24096385542168675	0.17708333333333334	0.20707070707070707	0.2727272727272727	0.8282828282828283	1.7424242424242424	0	0.0	CAF	0	0
13	Mauritius	0.0	0.063253	0.0	0.047619047619047616	0.35294117647058826	0.2903225806451613	0.2863849765258216	0.2112676056338028	1.3286384976525822	1.699530516431925	0	0.0	CAF	0	1
14	Seychelles	0.0	0.0421687	0.0	0.0	0.4166666666666667	0.0425531914893617	0.1411764705882353	0.1411764705882353	0.7294117647058823	2.0588235294117645	0	0.0	CAF	0	1
15	Basque Country	0.0	0.0	0.0	0.0	0.6333333333333333	0.6956521739130435	0.660377358490566	0.16981132075471697	2.7547169811320753	1.3396226415094339	0	0.0	OFC	0	1
16	Székely Land	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	3.0	0	0.0	OFC	0	0
17	Chinese Taipei	0.0	0.1054217	0.0	0.11428571428571428	0.5	0.25316455696202533	0.21551724137931033	0.15517241379310345	1.3189655172413792	2.853448275862069	0	0.0	AFC	0	1
18	South Korea	0.2481752	0.5783133	0.20588235294117646	0.5625	0.5789473684210527	0.5122615803814714	0.5286783042394015	0.256857855361596	1.7830423940149627	0.8977556109725686	0	0.3	AFC	0	4
19	New Zealand	0.1094891	0.2168675	0.0	0.5416666666666666	0.4868421052631579	0.3468208092485549	0.40476190476190477	0.18154761904761904	1.7470238095238095	1.6130952380952381	0	0.0	OFC	0	3
20	Iceland	0.0	0.2891566	0.0	0.21875	0.4307692307692308	0.23780487804878048	0.29743589743589743	0.18974358974358974	1.1641025641025642	1.7	0	0.0	UEFA	0	2

Features Engineering

Many very interesting features can be to use to evaluate each team. Moving windows of the previous games can drastically improve our model.

Since a team can by a home or away team, we’ll intervert the away and home teams. By using this technique, we will never get twice the same game and we will get the proper moving windows.

football = vo.VastFrame("football_clean")
football["home_team"].rename("team1");
football["home_score"].rename("team1_score");
football["away_team"].rename("team2");
football["away_score"].rename("team2_score");
# will be to use to filter the data after the features engineering
football["match_sample"] = "1";

football2 = vo.VastFrame("football_clean");
football2["home_team"].rename("team2");
football2["home_score"].rename("team2_score");
football2["away_team"].rename("team1");
football2["away_score"].rename("team1_score");
# will be to use to filter the data after the features engineering
football2["match_sample"] = "2";

# Merging the 2 interverted datasets
all_matchs = football.append(football2);

Let’s add the different KPIs to our dataset.

all_matchs = all_matchs.join(
    teams_kpi,
    on = {"team1": "team1"},
    how = "left",
    expr2 = [
        "nb_World_Cup AS nb_World_Cup_1",
        "fifa_rank AS fifa_rank_1",
        "Avg_goals AS Avg_goals_1",
        "Percent_Draw AS Percent_Draw_1",
        "Number_Games_World_Tournament AS Number_Games_World_Tournament_1",
        "Percent_Victory_World_Tournament AS Percent_Victory_World_Tournament_1",
        "Percent_Victory_Away AS Percent_Victory_Away_1",
        "Percent_Victory_Continental_Tournament AS Percent_Victory_Continental_Tournament_1",
        "confederation AS confederation_1",
        "Percent_Victory_Home AS Percent_Victory_Home_1",
        "Avg_goals_conceded AS Avg_goals_conceded_1",
        "Number_Games_Continental_Tournament AS Number_Games_Continental_Tournament_1",
        "nb_Continental_Cup AS nb_Continental_Cup_1",
        "Percent_Victory AS Percent_Victory_1",
    ],
)
all_matchs = all_matchs.join(
    teams_kpi,
    on = {"team2": "team1"},
    how = "left",
    expr2 = [
        "nb_World_Cup AS nb_World_Cup_2",
        "fifa_rank AS fifa_rank_2",
        "Avg_goals AS Avg_goals_2",
        "Percent_Draw AS Percent_Draw_2",
        "Number_Games_World_Tournament AS Number_Games_World_Tournament_2",
        "Percent_Victory_World_Tournament AS Percent_Victory_World_Tournament_2",
        "Percent_Victory_Away AS Percent_Victory_Away_2",
        "Percent_Victory_Continental_Tournament AS Percent_Victory_Continental_Tournament_2",
        "confederation AS confederation_2",
        "Percent_Victory_Home AS Percent_Victory_Home_2",
        "Avg_goals_conceded AS Avg_goals_conceded_2",
        "Number_Games_Continental_Tournament AS Number_Games_Continental_Tournament_2",
        "nb_Continental_Cup AS nb_Continental_Cup_2",
        "Percent_Victory AS Percent_Victory_2",
    ],
)

We can add dumies to do aggregations on the different games.

all_matchs["victory_team1"] = all_matchs["team1_score"] > all_matchs["team2_score"]
all_matchs["victory_team1"].astype("int")
all_matchs["draw"] = all_matchs["team1_score"] == all_matchs["team2_score"]
all_matchs["draw"].astype("int")
all_matchs["victory_team2"] = all_matchs["team1_score"] < all_matchs["team2_score"]
all_matchs["victory_team2"].astype("int")

	📅 date Date 100%	Abc tournament Varchar(50) 100%	0|1 neutral Boolean 100%	Abc country Varchar(50) 100%	Abc city Varchar(50) 100%	Abc team1 Varchar(50) 100%	123 team1_score Integer 100%	Abc team2 Varchar(50) 100%	123 team2_score Integer 100%	123 match_sample Integer 100%	123 nb_World_Cup_1 Bigint 99%	123 fifa_rank_1 Integer 99%	123 Avg_goals_1 Double 99%	123 Percent_Draw_1 Double 99%	123 Number_Games_World_Tournament_1 Decimal(28,7) 99%	123 Percent_Victory_World_Tournament_1 Double 99%	123 Percent_Victory_Away_1 Double 99%	123 Percent_Victory_Continental_Tournament_1 Double 99%	Abc confederation_1 Varchar(8) 99%	123 Percent_Victory_Home_1 Double 99%	123 Avg_goals_conceded_1 Double 99%	123 Number_Games_Continental_Tournament_1 Decimal(28,7) 99%	123 nb_Continental_Cup_1 Decimal(27,6) 99%	123 Percent_Victory_1 Double 99%	123 nb_World_Cup_2 Bigint 99%	123 fifa_rank_2 Integer 99%	123 Avg_goals_2 Double 99%	123 Percent_Draw_2 Double 99%	123 Number_Games_World_Tournament_2 Decimal(28,7) 99%	123 Percent_Victory_World_Tournament_2 Double 99%	123 Percent_Victory_Away_2 Double 99%	123 Percent_Victory_Continental_Tournament_2 Double 99%	Abc confederation_2 Varchar(8) 99%	123 Percent_Victory_Home_2 Double 99%	123 Avg_goals_conceded_2 Double 99%	123 Number_Games_Continental_Tournament_2 Decimal(28,7) 99%	123 nb_Continental_Cup_2 Decimal(27,6) 99%	123 Percent_Victory_2 Double 99%	123 victory_team1 Int 100%	123 draw Int 100%	123 victory_team2 Int 100%
1	1993-12-16	Friendly	✗	Mexico	Guadalajara	Mexico	0	Brazil	1	1	0	4	1.7475	0.2375	0.5474453	0.30666666666666664	0.42356687898089174	0.6085271317829457	CONMEBOL	0.5424836601307189	1.07625	0.7771084	0.5	0.495	4	5	2.193756727664155	0.20021528525296017	1.0	0.6788321167883211	0.59375	0.5770609318996416	CONMEBOL	0.7333333333333333	0.93756727664155	0.8403614	0.9	0.635091496232508	0	0	1
2	2009-06-21	Confederations Cup	✓	South Africa	Pretoria	Italy	0	Brazil	3	1	3	5	1.6958831341301461	0.28286852589641437	0.6642336	0.5274725274725275	0.3464566929133858	0.6153846153846154	UEFA	0.6438848920863309	0.9827357237715804	0.3915663	0.1	0.5245683930942895	4	5	2.193756727664155	0.20021528525296017	1.0	0.6788321167883211	0.59375	0.5770609318996416	CONMEBOL	0.7333333333333333	0.93756727664155	0.8403614	0.9	0.635091496232508	0	0	1
3	2011-11-10	Friendly	✗	Gabon	Libreville	Gabon	0	Brazil	2	1	0	2	1.1926605504587156	0.2782874617737003	0.0	0.0	0.2702702702702703	0.37681159420289856	CAF	0.4727272727272727	1.1314984709480123	0.2078313	0.0	0.36085626911314983	4	5	2.193756727664155	0.20021528525296017	1.0	0.6788321167883211	0.59375	0.5770609318996416	CONMEBOL	0.7333333333333333	0.93756727664155	0.8403614	0.9	0.635091496232508	0	0	1
4	2017-10-05	FIFA World Cup qualification	✗	Bolivia	La Paz	Bolivia	0	Brazil	0	1	0	3	1.0381861575178997	0.24821002386634844	0.0656934	0.0	0.13861386138613863	0.2222222222222222	CONMEBOL	0.3787878787878788	1.9427207637231503	0.7319277	0.0	0.22195704057279236	4	5	2.193756727664155	0.20021528525296017	1.0	0.6788321167883211	0.59375	0.5770609318996416	CONMEBOL	0.7333333333333333	0.93756727664155	0.8403614	0.9	0.635091496232508	0	1	0
5	1979-08-23	Copa América	✗	Argentina	Buenos Aires	Argentina	2	Brazil	2	1	2	5	1.8654467168998923	0.24865446716899892	0.6350365	0.5402298850574713	0.40606060606060607	0.6059602649006622	CONMEBOL	0.638095238095238	1.0505920344456405	0.9096386	0.8	0.5360602798708288	4	5	2.193756727664155	0.20021528525296017	1.0	0.6788321167883211	0.59375	0.5770609318996416	CONMEBOL	0.7333333333333333	0.93756727664155	0.8403614	0.9	0.635091496232508	0	1	0
6	2005-10-09	FIFA World Cup qualification	✗	Bolivia	La Paz	Bolivia	1	Brazil	1	1	0	3	1.0381861575178997	0.24821002386634844	0.0656934	0.0	0.13861386138613863	0.2222222222222222	CONMEBOL	0.3787878787878788	1.9427207637231503	0.7319277	0.0	0.22195704057279236	4	5	2.193756727664155	0.20021528525296017	1.0	0.6788321167883211	0.59375	0.5770609318996416	CONMEBOL	0.7333333333333333	0.93756727664155	0.8403614	0.9	0.635091496232508	0	1	0
7	1959-03-21	Copa América	✓	Argentina	Buenos Aires	Bolivia	2	Brazil	4	1	0	3	1.0381861575178997	0.24821002386634844	0.0656934	0.0	0.13861386138613863	0.2222222222222222	CONMEBOL	0.3787878787878788	1.9427207637231503	0.7319277	0.0	0.22195704057279236	4	5	2.193756727664155	0.20021528525296017	1.0	0.6788321167883211	0.59375	0.5770609318996416	CONMEBOL	0.7333333333333333	0.93756727664155	0.8403614	0.9	0.635091496232508	0	0	1
8	1988-07-17	Friendly	✗	Australia	Sydney	Australia	0	Brazil	2	1	0	4	2.031712473572939	0.21141649048625794	0.189781	0.2692307692307692	0.5112359550561798	0.583941605839416	AFC	0.4393939393939394	1.1120507399577166	0.4126506	0.1	0.4989429175475687	4	5	2.193756727664155	0.20021528525296017	1.0	0.6788321167883211	0.59375	0.5770609318996416	CONMEBOL	0.7333333333333333	0.93756727664155	0.8403614	0.9	0.635091496232508	0	0	1
9	1997-12-14	Confederations Cup	✓	Saudi Arabia	Riyadh	Australia	0	Brazil	0	1	0	4	2.031712473572939	0.21141649048625794	0.189781	0.2692307692307692	0.5112359550561798	0.583941605839416	AFC	0.4393939393939394	1.1120507399577166	0.4126506	0.1	0.4989429175475687	4	5	2.193756727664155	0.20021528525296017	1.0	0.6788321167883211	0.59375	0.5770609318996416	CONMEBOL	0.7333333333333333	0.93756727664155	0.8403614	0.9	0.635091496232508	0	1	0
10	2017-06-13	Friendly	✗	Australia	Melbourne	Australia	0	Brazil	4	1	0	4	2.031712473572939	0.21141649048625794	0.189781	0.2692307692307692	0.5112359550561798	0.583941605839416	AFC	0.4393939393939394	1.1120507399577166	0.4126506	0.1	0.4989429175475687	4	5	2.193756727664155	0.20021528525296017	1.0	0.6788321167883211	0.59375	0.5770609318996416	CONMEBOL	0.7333333333333333	0.93756727664155	0.8403614	0.9	0.635091496232508	0	0	1
11	1987-07-03	Copa América	✓	Argentina	Córdoba	Chile	4	Brazil	0	1	0	4	1.4214992927864214	0.2065063649222065	0.2408759	0.3333333333333333	0.2571428571428571	0.3754152823920266	CONMEBOL	0.558282208588957	1.4653465346534653	0.9066265	0.2	0.38048090523338046	4	5	2.193756727664155	0.20021528525296017	1.0	0.6788321167883211	0.59375	0.5770609318996416	CONMEBOL	0.7333333333333333	0.93756727664155	0.8403614	0.9	0.635091496232508	1	0	0
12	1988-07-07	Friendly	✗	Australia	Melbourne	Australia	0	Brazil	1	1	0	4	2.031712473572939	0.21141649048625794	0.189781	0.2692307692307692	0.5112359550561798	0.583941605839416	AFC	0.4393939393939394	1.1120507399577166	0.4126506	0.1	0.4989429175475687	4	5	2.193756727664155	0.20021528525296017	1.0	0.6788321167883211	0.59375	0.5770609318996416	CONMEBOL	0.7333333333333333	0.93756727664155	0.8403614	0.9	0.635091496232508	0	0	1
13	1985-05-21	Friendly	✗	Chile	Santiago	Chile	2	Brazil	1	1	0	4	1.4214992927864214	0.2065063649222065	0.2408759	0.3333333333333333	0.2571428571428571	0.3754152823920266	CONMEBOL	0.558282208588957	1.4653465346534653	0.9066265	0.2	0.38048090523338046	4	5	2.193756727664155	0.20021528525296017	1.0	0.6788321167883211	0.59375	0.5770609318996416	CONMEBOL	0.7333333333333333	0.93756727664155	0.8403614	0.9	0.635091496232508	1	0	0
14	1957-09-15	Copa Bernardo O'Higgins	✗	Chile	Santiago	Chile	1	Brazil	0	1	0	4	1.4214992927864214	0.2065063649222065	0.2408759	0.3333333333333333	0.2571428571428571	0.3754152823920266	CONMEBOL	0.558282208588957	1.4653465346534653	0.9066265	0.2	0.38048090523338046	4	5	2.193756727664155	0.20021528525296017	1.0	0.6788321167883211	0.59375	0.5770609318996416	CONMEBOL	0.7333333333333333	0.93756727664155	0.8403614	0.9	0.635091496232508	1	0	0
15	2001-06-09	Confederations Cup	✓	South Korea	Ulsan	Australia	1	Brazil	0	1	0	4	2.031712473572939	0.21141649048625794	0.189781	0.2692307692307692	0.5112359550561798	0.583941605839416	AFC	0.4393939393939394	1.1120507399577166	0.4126506	0.1	0.4989429175475687	4	5	2.193756727664155	0.20021528525296017	1.0	0.6788321167883211	0.59375	0.5770609318996416	CONMEBOL	0.7333333333333333	0.93756727664155	0.8403614	0.9	0.635091496232508	1	0	0
16	1957-09-18	Copa Bernardo O'Higgins	✗	Chile	Santiago	Chile	1	Brazil	1	1	0	4	1.4214992927864214	0.2065063649222065	0.2408759	0.3333333333333333	0.2571428571428571	0.3754152823920266	CONMEBOL	0.558282208588957	1.4653465346534653	0.9066265	0.2	0.38048090523338046	4	5	2.193756727664155	0.20021528525296017	1.0	0.6788321167883211	0.59375	0.5770609318996416	CONMEBOL	0.7333333333333333	0.93756727664155	0.8403614	0.9	0.635091496232508	0	1	0
17	1999-07-14	Copa América	✓	Paraguay	Ciudad del Este	Mexico	0	Brazil	2	1	0	4	1.7475	0.2375	0.5474453	0.30666666666666664	0.42356687898089174	0.6085271317829457	CONMEBOL	0.5424836601307189	1.07625	0.7771084	0.5	0.495	4	5	2.193756727664155	0.20021528525296017	1.0	0.6788321167883211	0.59375	0.5770609318996416	CONMEBOL	0.7333333333333333	0.93756727664155	0.8403614	0.9	0.635091496232508	0	0	1
18	2000-08-15	FIFA World Cup qualification	✗	Chile	Santiago	Chile	3	Brazil	0	1	0	4	1.4214992927864214	0.2065063649222065	0.2408759	0.3333333333333333	0.2571428571428571	0.3754152823920266	CONMEBOL	0.558282208588957	1.4653465346534653	0.9066265	0.2	0.38048090523338046	4	5	2.193756727664155	0.20021528525296017	1.0	0.6788321167883211	0.59375	0.5770609318996416	CONMEBOL	0.7333333333333333	0.93756727664155	0.8403614	0.9	0.635091496232508	1	0	0
19	2013-10-12	Friendly	✗	South Korea	Seoul	South Korea	0	Brazil	2	1	0	4	1.7830423940149627	0.256857855361596	0.2481752	0.20588235294117646	0.5122615803814714	0.5625	AFC	0.5789473684210527	0.8977556109725686	0.5783133	0.3	0.5286783042394015	4	5	2.193756727664155	0.20021528525296017	1.0	0.6788321167883211	0.59375	0.5770609318996416	CONMEBOL	0.7333333333333333	0.93756727664155	0.8403614	0.9	0.635091496232508	0	0	1
20	2006-08-16	Friendly	✗	Norway	Oslo	Norway	1	Brazil	1	1	0	3	1.5013089005235603	0.22774869109947643	0.0583942	0.25	0.3343465045592705	0.3474576271186441	UEFA	0.3948220064724919	1.6845549738219896	0.3554217	0.0	0.3599476439790576	4	5	2.193756727664155	0.20021528525296017	1.0	0.6788321167883211	0.59375	0.5770609318996416	CONMEBOL	0.7333333333333333	0.93756727664155	0.8403614	0.9	0.635091496232508	0	1	0

Let’s use moving windows to compute some additional features.

The teams’ performance in their recent games

# TEAM 1

# Victory 10 previous games
all_matchs.rolling(
    func = "avg",
    window = (-10, -1),
    columns = "victory_team1",
    by = ["team1"],
    order_by = ["date"],
    name = "avg_victory_team1_1_10",
)
# Victory 3 previous games
all_matchs.rolling(
    func = "avg",
    window = (-3, -1),
    columns = "victory_team1",
    by = ["team1"],
    order_by = ["date"],
    name = "avg_victory_team1_1_3",
)
# Draw 5 previous games
all_matchs.rolling(
    func = "avg",
    window = (-5, -1),
    columns = "draw",
    by = ["team1"],
    order_by = ["date"],
    name = "avg_draw_team1_1_5",
)

# TEAM 2

# Victory 10 previous games
all_matchs.rolling(
    func = "avg",
    window = (-10, -1),
    columns = "victory_team2",
    by = ["team2"],
    order_by = ["date"],
    name = "avg_victory_team2_1_10",
)
# Victory 3 previous games
all_matchs.rolling(
    func = "avg",
    window = (-3, -1),
    columns = "victory_team2",
    by = ["team2"],
    order_by = ["date"],
    name = "avg_victory_team2_1_3",
)
# Draw 5 previous games
all_matchs.rolling(
    func = "avg",
    window = (-5, -1),
    columns = "draw",
    by = ["team2"],
    order_by = ["date"],
    name = "avg_draw_team2_1_5",
)

	📅 date Date 100%	Abc tournament Varchar(50) 100%	0|1 neutral Boolean 100%	Abc country Varchar(50) 100%	Abc city Varchar(50) 100%	Abc team1 Varchar(50) 100%	123 team1_score Integer 100%	Abc team2 Varchar(50) 100%	123 team2_score Integer 100%	123 match_sample Integer 100%	123 nb_World_Cup_1 Bigint 99%	123 fifa_rank_1 Integer 99%	123 Avg_goals_1 Double 99%	123 Percent_Draw_1 Double 99%	123 Number_Games_World_Tournament_1 Decimal(28,7) 99%	123 Percent_Victory_World_Tournament_1 Double 99%	123 Percent_Victory_Away_1 Double 99%	123 Percent_Victory_Continental_Tournament_1 Double 99%	Abc confederation_1 Varchar(8) 99%	123 Percent_Victory_Home_1 Double 99%	123 Avg_goals_conceded_1 Double 99%	123 Number_Games_Continental_Tournament_1 Decimal(28,7) 99%	123 nb_Continental_Cup_1 Decimal(27,6) 99%	123 Percent_Victory_1 Double 99%	123 nb_World_Cup_2 Bigint 99%	123 fifa_rank_2 Integer 99%	123 Avg_goals_2 Double 99%	123 Percent_Draw_2 Double 99%	123 Number_Games_World_Tournament_2 Decimal(28,7) 99%	123 Percent_Victory_World_Tournament_2 Double 99%	123 Percent_Victory_Away_2 Double 99%	123 Percent_Victory_Continental_Tournament_2 Double 99%	Abc confederation_2 Varchar(8) 99%	123 Percent_Victory_Home_2 Double 99%	123 Avg_goals_conceded_2 Double 99%	123 Number_Games_Continental_Tournament_2 Decimal(28,7) 99%	123 nb_Continental_Cup_2 Decimal(27,6) 99%	123 Percent_Victory_2 Double 99%	123 victory_team1 Int 100%	123 draw Int 100%	123 victory_team2 Int 100%	123 avg_victory_team1_1_10 Double 99%	123 avg_victory_team1_1_3 Double 99%	123 avg_draw_team1_1_5 Double 99%	123 avg_victory_team2_1_10 Double 99%	123 avg_victory_team2_1_3 Double 99%	123 avg_draw_team2_1_5 Double 99%
1	2012-10-21	Friendly	✗	Azerbaijan	Stepanakert	Artsakh	3	Abkhazia	0	1	0	1	3.0	0.0	0.0	0.0	0.0	0.0	OFC	1.0	0.0	0.0	0.0	1.0	0	0	1.0	0.5	0.0	0.0	0.16666666666666666	0.0	OFC	0.0	1.5	0.0	0.0	0.16666666666666666	1	0	0	[null]	[null]	[null]	[null]	[null]	[null]
2	2014-06-01	CONIFA World Football Cup	✓	Sweden	Östersund	Occitania	1	Abkhazia	1	2	0	0	1.25	0.16666666666666666	0.0	0.0	0.3	0.0	OFC	0.0	1.8333333333333333	0.0	0.0	0.25	0	0	1.0	0.5	0.0	0.0	0.16666666666666666	0.0	OFC	0.0	1.5	0.0	0.0	0.16666666666666666	0	1	0	0.125	0.3333333333333333	0.0	0.0	0.0	0.0
3	2014-06-02	CONIFA World Football Cup	✗	Sweden	Östersund	Sápmi	1	Abkhazia	2	1	0	1	4.357142857142857	0.14285714285714285	0.0	0.0	0.5	0.0	OFC	0.375	1.7142857142857142	0.0	0.0	0.42857142857142855	0	0	1.0	0.5	0.0	0.0	0.16666666666666666	0.0	OFC	0.0	1.5	0.0	0.0	0.16666666666666666	0	0	1	0.4	0.0	0.2	0.0	0.0	0.5
4	2014-06-04	CONIFA World Football Cup	✓	Sweden	Östersund	South Ossetia	0	Abkhazia	0	2	0	0	0.6666666666666666	0.3333333333333333	0.0	0.0	0.0	0.0	OFC	0.0	2.3333333333333335	0.0	0.0	0.0	0	0	1.0	0.5	0.0	0.0	0.16666666666666666	0.0	OFC	0.0	1.5	0.0	0.0	0.16666666666666666	0	1	0	0.0	0.0	0.0	0.3333333333333333	0.3333333333333333	0.3333333333333333
5	2014-06-05	CONIFA World Football Cup	✓	Sweden	Östersund	Padania	3	Abkhazia	3	1	0	1	2.5	0.125	0.0	0.0	0.8333333333333334	0.0	OFC	1.0	0.5625	0.0	0.0	0.875	0	0	1.0	0.5	0.0	0.0	0.16666666666666666	0.0	OFC	0.0	1.5	0.0	0.0	0.16666666666666666	0	1	0	1.0	1.0	0.0	0.25	0.3333333333333333	0.5
6	2014-06-07	CONIFA World Football Cup	✓	Sweden	Östersund	Occitania	1	Abkhazia	0	2	0	0	1.25	0.16666666666666666	0.0	0.0	0.3	0.0	OFC	0.0	1.8333333333333333	0.0	0.0	0.25	0	0	1.0	0.5	0.0	0.0	0.16666666666666666	0.0	OFC	0.0	1.5	0.0	0.0	0.16666666666666666	1	0	0	0.2	0.3333333333333333	0.4	0.2	0.3333333333333333	0.6
7	2016-05-29	CONIFA World Football Cup	✗	Georgia	Suhkumi	Chagos Islands	0	Abkhazia	9	2	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]	0	0	1.0	0.5	0.0	0.0	0.16666666666666666	0.0	OFC	0.0	1.5	0.0	0.0	0.16666666666666666	0	0	1	[null]	[null]	[null]	0.16666666666666666	0.0	0.6
8	2016-05-31	CONIFA World Football Cup	✗	Georgia	Suhkumi	Western Armenia	0	Abkhazia	1	2	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]	0	0	1.0	0.5	0.0	0.0	0.16666666666666666	0.0	OFC	0.0	1.5	0.0	0.0	0.16666666666666666	0	0	1	[null]	[null]	[null]	0.2857142857142857	0.3333333333333333	0.4
9	2016-06-01	CONIFA World Football Cup	✗	Georgia	Suhkumi	Sápmi	0	Abkhazia	2	2	0	1	4.357142857142857	0.14285714285714285	0.0	0.0	0.5	0.0	OFC	0.375	1.7142857142857142	0.0	0.0	0.42857142857142855	0	0	1.0	0.5	0.0	0.0	0.16666666666666666	0.0	OFC	0.0	1.5	0.0	0.0	0.16666666666666666	0	0	1	0.3	0.3333333333333333	0.2	0.375	0.6666666666666666	0.4
10	2016-06-04	CONIFA World Football Cup	✗	Georgia	Suhkumi	Northern Cyprus	0	Abkhazia	2	2	0	1	2.2666666666666666	0.06666666666666667	0.0	0.0	0.4	0.0	OFC	0.8	1.2	0.0	0.0	0.5333333333333333	0	0	1.0	0.5	0.0	0.0	0.16666666666666666	0.0	OFC	0.0	1.5	0.0	0.0	0.16666666666666666	0	0	1	0.6	0.6666666666666666	0.2	0.4444444444444444	1.0	0.2
11	2016-06-05	CONIFA World Football Cup	✗	Georgia	Suhkumi	Panjab	1	Abkhazia	1	2	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]	0	0	1.0	0.5	0.0	0.0	0.16666666666666666	0.0	OFC	0.0	1.5	0.0	0.0	0.16666666666666666	0	1	0	0.5	0.5	0.0	0.5	1.0	0.0
12	2017-06-05	CONIFA European Football Cup	✓	Northern Cyprus	Kyrenia	South Ossetia	1	Abkhazia	2	2	0	0	0.6666666666666666	0.3333333333333333	0.0	0.0	0.0	0.0	OFC	0.0	2.3333333333333335	0.0	0.0	0.0	0	0	1.0	0.5	0.0	0.0	0.16666666666666666	0.0	OFC	0.0	1.5	0.0	0.0	0.16666666666666666	0	0	1	0.0	0.0	0.3333333333333333	0.5	0.6666666666666666	0.2
13	2017-06-07	CONIFA European Football Cup	✗	Northern Cyprus	Morphou	Northern Cyprus	0	Abkhazia	0	1	0	1	2.2666666666666666	0.06666666666666667	0.0	0.0	0.4	0.0	OFC	0.8	1.2	0.0	0.0	0.5333333333333333	0	0	1.0	0.5	0.0	0.0	0.16666666666666666	0.0	OFC	0.0	1.5	0.0	0.0	0.16666666666666666	0	1	0	0.5	0.6666666666666666	0.2	0.6	0.6666666666666666	0.2
14	2017-06-09	CONIFA European Football Cup	✓	Northern Cyprus	Kyrenia	Padania	0	Abkhazia	0	1	0	1	2.5	0.125	0.0	0.0	0.8333333333333334	0.0	OFC	1.0	0.5625	0.0	0.0	0.875	0	0	1.0	0.5	0.0	0.0	0.16666666666666666	0.0	OFC	0.0	1.5	0.0	0.0	0.16666666666666666	0	1	0	0.5	0.6666666666666666	0.2	0.5	0.3333333333333333	0.4
15	2017-06-10	CONIFA European Football Cup	✓	Northern Cyprus	Kyrenia	Székely Land	3	Abkhazia	1	1	0	0	1.0	0.0	0.0	0.0	0.0	0.0	OFC	0.0	3.0	0.0	0.0	0.0	0	0	1.0	0.5	0.0	0.0	0.16666666666666666	0.0	OFC	0.0	1.5	0.0	0.0	0.16666666666666666	1	0	0	0.2857142857142857	0.3333333333333333	0.2	0.5	0.3333333333333333	0.6
16	2018-05-31	CONIFA World Football Cup	✓	England	Enfield	Tibet	0	Abkhazia	3	2	0	0	0.25	0.0	0.0	0.0	0.0	0.0	OFC	0.0	5.5	0.0	0.0	0.0	0	0	1.0	0.5	0.0	0.0	0.16666666666666666	0.0	OFC	0.0	1.5	0.0	0.0	0.16666666666666666	0	0	1	0.0	0.0	0.0	0.5	0.0	0.6
17	2018-06-03	CONIFA World Football Cup	✓	England	Enfield	Northern Cyprus	2	Abkhazia	2	2	0	1	2.2666666666666666	0.06666666666666667	0.0	0.0	0.4	0.0	OFC	0.8	1.2	0.0	0.0	0.5333333333333333	0	0	1.0	0.5	0.0	0.0	0.16666666666666666	0.0	OFC	0.0	1.5	0.0	0.0	0.16666666666666666	0	1	0	0.6	0.6666666666666666	0.4	0.6	0.3333333333333333	0.4
18	2018-06-05	CONIFA World Football Cup	✓	England	Aveley	Tamil Eelam	0	Abkhazia	6	2	0	0	1.0	0.0	0.0	0.0	0.16666666666666666	0.0	OFC	0.0	4.166666666666667	0.0	0.0	0.16666666666666666	0	0	1.0	0.5	0.0	0.0	0.16666666666666666	0.0	OFC	0.0	1.5	0.0	0.0	0.16666666666666666	0	0	1	0.2222222222222222	0.3333333333333333	0.0	0.5	0.3333333333333333	0.6
19	2019-06-03	CONIFA European Football Cup	✓	Azerbaijan	Martakert	Sápmi	0	Abkhazia	1	1	0	1	4.357142857142857	0.14285714285714285	0.0	0.0	0.5	0.0	OFC	0.375	1.7142857142857142	0.0	0.0	0.42857142857142855	0	0	1.0	0.5	0.0	0.0	0.16666666666666666	0.0	OFC	0.0	1.5	0.0	0.0	0.16666666666666666	0	0	1	0.3	0.6666666666666666	0.0	0.5	0.6666666666666666	0.4
20	2019-06-04	CONIFA European Football Cup	✗	Azerbaijan	Martuni	Artsakh	1	Abkhazia	1	1	0	1	3.0	0.0	0.0	0.0	0.0	0.0	OFC	1.0	0.0	0.0	0.0	1.0	0	0	1.0	0.5	0.0	0.0	0.16666666666666666	0.0	OFC	0.0	1.5	0.0	0.0	0.16666666666666666	0	1	0	1.0	1.0	0.0	0.5	0.6666666666666666	0.2

The teams’ performance in the last same tournament

# TEAM 1

# Victory 10 previous games
all_matchs.rolling(
    func = "avg",
    window = (-10, -1),
    columns = "victory_team1",
    by = ["team1", "tournament"],
    order_by = ["date"],
    name = "avg_victory_same_tournament_team1_1_10",
)
# Victory 3 previous games
all_matchs.rolling(
    func = "avg",
    window = (-3, -1),
    columns = "victory_team1",
    by = ["team1", "tournament"],
    order_by = ["date"],
    name = "avg_victory_same_tournament_team1_1_3",
)
# Draw 5 previous games
all_matchs.rolling(
    func = "avg",
    window = (-5, -1),
    columns = "draw",
    by = ["team1", "tournament"],
    order_by = ["date"],
    name = "avg_draw_same_tournament_team1_1_5",
)

# TEAM 2

# Victory 10 previous games
all_matchs.rolling(
    func = "avg",
    window = (-10, -1),
    columns = "victory_team2",
    by = ["team2", "tournament"],
    order_by = ["date"],
    name = "avg_victory_same_tournament_team2_1_10",
)
# Victory 3 previous games
all_matchs.rolling(
    func = "avg",
    window = (-3, -1),
    columns = "victory_team2",
    by = ["team2", "tournament"],
    order_by = ["date"],
    name = "avg_victory_same_tournament_team2_1_3",
)
# Draw 5 previous games
all_matchs.rolling(
    func = "avg",
    window = (-5, -1),
    columns = "draw",
    by = ["team2", "tournament"],
    order_by = ["date"],
    name = "avg_draw_same_tournament_team2_1_5",
)

	📅 date Date 100%	Abc tournament Varchar(50) 100%	0|1 neutral Boolean 100%	Abc country Varchar(50) 100%	Abc city Varchar(50) 100%	Abc team1 Varchar(50) 100%	123 team1_score Integer 100%	Abc team2 Varchar(50) 100%	123 team2_score Integer 100%	123 match_sample Integer 100%	123 nb_World_Cup_1 Bigint 99%	123 fifa_rank_1 Integer 99%	123 Avg_goals_1 Double 99%	123 Percent_Draw_1 Double 99%	123 Number_Games_World_Tournament_1 Decimal(28,7) 99%	123 Percent_Victory_World_Tournament_1 Double 99%	123 Percent_Victory_Away_1 Double 99%	123 Percent_Victory_Continental_Tournament_1 Double 99%	Abc confederation_1 Varchar(8) 99%	123 Percent_Victory_Home_1 Double 99%	123 Avg_goals_conceded_1 Double 99%	123 Number_Games_Continental_Tournament_1 Decimal(28,7) 99%	123 nb_Continental_Cup_1 Decimal(27,6) 99%	123 Percent_Victory_1 Double 99%	123 nb_World_Cup_2 Bigint 99%	...	123 Number_Games_World_Tournament_2 Decimal(28,7) 99%	123 Percent_Victory_World_Tournament_2 Double 99%	123 Percent_Victory_Away_2 Double 99%	123 Percent_Victory_Continental_Tournament_2 Double 99%	Abc confederation_2 Varchar(8) 99%	123 Percent_Victory_Home_2 Double 99%	123 Avg_goals_conceded_2 Double 99%	123 Number_Games_Continental_Tournament_2 Decimal(28,7) 99%	123 nb_Continental_Cup_2 Decimal(27,6) 99%	123 Percent_Victory_2 Double 99%	123 victory_team1 Int 100%	123 draw Int 100%	123 victory_team2 Int 100%	123 avg_victory_team1_1_10 Double 99%	123 avg_victory_team1_1_3 Double 99%	123 avg_draw_team1_1_5 Double 99%	123 avg_victory_team2_1_10 Double 99%	123 avg_victory_team2_1_3 Double 99%	123 avg_draw_team2_1_5 Double 99%	123 avg_victory_same_tournament_team1_1_10 Double 97%	123 avg_victory_same_tournament_team1_1_3 Double 97%	123 avg_draw_same_tournament_team1_1_5 Double 97%	123 avg_victory_same_tournament_team2_1_10 Double 97%	123 avg_victory_same_tournament_team2_1_3 Double 97%	123 avg_draw_same_tournament_team2_1_5 Double 97%
1	1963-06-29	UEFA Euro qualification	✗	Denmark	Copenhagen	Denmark	4	Albania	0	1	0	4	1.77762982689747	0.20372836218375498	0.1386861	0.5263157894736842	0.3466666666666667	0.4233576642335766	UEFA	0.5525423728813559	1.4287616511318242	0.4126506	0.1	0.44607190412782954	0	...	0.0	0.0	0.17307692307692307	0.14893617021276595	UEFA	0.40425531914893614	1.595890410958904	0.2831325	0.0	0.23972602739726026	1	0	0	0.6	0.0	0.4	0.3	0.3333333333333333	0.2	0.5	0.6666666666666666	0.25	[null]	[null]	[null]
2	1963-10-30	UEFA Euro qualification	✗	Albania	Tirana	Denmark	0	Albania	1	2	0	4	1.77762982689747	0.20372836218375498	0.1386861	0.5263157894736842	0.3466666666666667	0.4233576642335766	UEFA	0.5525423728813559	1.4287616511318242	0.4126506	0.1	0.44607190412782954	0	...	0.0	0.0	0.17307692307692307	0.14893617021276595	UEFA	0.40425531914893614	1.595890410958904	0.2831325	0.0	0.23972602739726026	0	0	1	0.5	0.6666666666666666	0.4	0.3	0.0	0.2	0.6	1.0	0.2	0.0	0.0	0.0
3	1967-04-08	UEFA Euro qualification	✗	Germany	Dortmund	Germany	6	Albania	0	1	4	5	2.0998322147651005	0.2080536912751678	0.8759124	0.6	0.4702127659574468	0.6242774566473989	UEFA	0.6083916083916084	1.179530201342282	0.5210843	0.3	0.5553691275167785	0	...	0.0	0.0	0.17307692307692307	0.14893617021276595	UEFA	0.40425531914893614	1.595890410958904	0.2831325	0.0	0.23972602739726026	1	0	0	0.7	1.0	0.2	0.1	0.0	0.2	0.2857142857142857	0.3333333333333333	0.4	0.5	0.5	0.0
4	1967-05-14	UEFA Euro qualification	✗	Albania	Tirana	Serbia	2	Albania	0	2	0	4	1.8160112359550562	0.21910112359550563	0.3138686	0.3953488372093023	0.3815028901734104	0.5454545454545454	UEFA	0.5445026178010471	1.3792134831460674	0.3975904	0.0	0.45646067415730335	0	...	0.0	0.0	0.17307692307692307	0.14893617021276595	UEFA	0.40425531914893614	1.595890410958904	0.2831325	0.0	0.23972602739726026	1	0	0	0.4	0.3333333333333333	0.0	0.1	0.0	0.2	0.5555555555555556	0.3333333333333333	0.2	0.3333333333333333	0.3333333333333333	0.0
5	1967-11-12	UEFA Euro qualification	✗	Yugoslavia	Belgrade	Serbia	4	Albania	0	1	0	4	1.8160112359550562	0.21910112359550563	0.3138686	0.3953488372093023	0.3815028901734104	0.5454545454545454	UEFA	0.5445026178010471	1.3792134831460674	0.3975904	0.0	0.45646067415730335	0	...	0.0	0.0	0.17307692307692307	0.14893617021276595	UEFA	0.40425531914893614	1.595890410958904	0.2831325	0.0	0.23972602739726026	1	0	0	0.5	0.6666666666666666	0.0	0.1	0.0	0.2	0.5	0.6666666666666666	0.2	0.25	0.3333333333333333	0.0
6	1967-12-17	UEFA Euro qualification	✗	Albania	Tirana	Germany	0	Albania	0	2	4	5	2.0998322147651005	0.2080536912751678	0.8759124	0.6	0.4702127659574468	0.6242774566473989	UEFA	0.6083916083916084	1.179530201342282	0.5210843	0.3	0.5553691275167785	0	...	0.0	0.0	0.17307692307692307	0.14893617021276595	UEFA	0.40425531914893614	1.595890410958904	0.2831325	0.0	0.23972602739726026	0	1	0	0.5	0.6666666666666666	0.0	0.1	0.0	0.2	0.5	1.0	0.0	0.2	0.0	0.0
7	1970-10-14	UEFA Euro qualification	✗	Poland	Chorzów	Poland	3	Albania	0	1	0	4	1.6844155844155844	0.2519480519480519	0.2262774	0.4838709677419355	0.352112676056338	0.4642857142857143	UEFA	0.5	1.37012987012987	0.3373494	0.0	0.42597402597402595	0	...	0.0	0.0	0.17307692307692307	0.14893617021276595	UEFA	0.40425531914893614	1.595890410958904	0.2831325	0.0	0.23972602739726026	1	0	0	0.6	0.6666666666666666	0.0	0.0	0.0	0.4	0.3	0.6666666666666666	0.2	0.16666666666666666	0.0	0.2
8	1970-12-13	UEFA Euro qualification	✗	Turkey	Istanbul	Turkey	2	Albania	1	1	0	4	1.3320537428023032	0.2380038387715931	0.1094891	0.4666666666666667	0.3380281690140845	0.35384615384615387	UEFA	0.4539877300613497	1.4280230326295584	0.3915663	0.0	0.381957773512476	0	...	0.0	0.0	0.17307692307692307	0.14893617021276595	UEFA	0.40425531914893614	1.595890410958904	0.2831325	0.0	0.23972602739726026	1	0	0	0.5	0.3333333333333333	0.4	0.0	0.0	0.2	0.2	0.0	0.4	0.14285714285714285	0.0	0.2
9	1971-02-17	UEFA Euro qualification	✗	Albania	Tirana	Germany	1	Albania	0	2	4	5	2.0998322147651005	0.2080536912751678	0.8759124	0.6	0.4702127659574468	0.6242774566473989	UEFA	0.6083916083916084	1.179530201342282	0.5210843	0.3	0.5553691275167785	0	...	0.0	0.0	0.17307692307692307	0.14893617021276595	UEFA	0.40425531914893614	1.595890410958904	0.2831325	0.0	0.23972602739726026	1	0	0	0.6	0.6666666666666666	0.2	0.0	0.0	0.2	0.5	0.6666666666666666	0.4	0.125	0.0	0.2
10	1971-05-12	UEFA Euro qualification	✗	Albania	Tirana	Poland	1	Albania	1	2	0	4	1.6844155844155844	0.2519480519480519	0.2262774	0.4838709677419355	0.352112676056338	0.4642857142857143	UEFA	0.5	1.37012987012987	0.3373494	0.0	0.42597402597402595	0	...	0.0	0.0	0.17307692307692307	0.14893617021276595	UEFA	0.40425531914893614	1.595890410958904	0.2831325	0.0	0.23972602739726026	0	1	0	0.7	0.6666666666666666	0.2	0.0	0.0	0.2	0.4	0.6666666666666666	0.2	0.1111111111111111	0.0	0.2
11	1971-06-12	UEFA Euro qualification	✗	Germany	Karlsruhe	Germany	2	Albania	0	1	4	5	2.0998322147651005	0.2080536912751678	0.8759124	0.6	0.4702127659574468	0.6242774566473989	UEFA	0.6083916083916084	1.179530201342282	0.5210843	0.3	0.5553691275167785	0	...	0.0	0.0	0.17307692307692307	0.14893617021276595	UEFA	0.40425531914893614	1.595890410958904	0.2831325	0.0	0.23972602739726026	1	0	0	0.6	0.6666666666666666	0.2	0.0	0.0	0.4	0.7	0.6666666666666666	0.0	0.1	0.0	0.4
12	1971-11-14	UEFA Euro qualification	✗	Albania	Tirana	Turkey	0	Albania	3	2	0	4	1.3320537428023032	0.2380038387715931	0.1094891	0.4666666666666667	0.3380281690140845	0.35384615384615387	UEFA	0.4539877300613497	1.4280230326295584	0.3915663	0.0	0.381957773512476	0	...	0.0	0.0	0.17307692307692307	0.14893617021276595	UEFA	0.40425531914893614	1.595890410958904	0.2831325	0.0	0.23972602739726026	0	0	1	0.3	0.0	0.2	0.0	0.0	0.2	0.2	0.3333333333333333	0.4	0.1	0.0	0.2
13	1982-09-22	UEFA Euro qualification	✗	Austria	Vienna	Austria	5	Albania	0	1	0	4	1.7978290366350067	0.2198100407055631	0.2116788	0.41379310344827586	0.30633802816901406	0.47413793103448276	UEFA	0.4837662337662338	1.5929443690637721	0.3493976	0.0	0.41112618724559025	0	...	0.0	0.0	0.17307692307692307	0.14893617021276595	UEFA	0.40425531914893614	1.595890410958904	0.2831325	0.0	0.23972602739726026	1	0	0	0.5	0.0	0.2	0.2	0.0	0.0	0.5	0.6666666666666666	0.6	0.1	0.3333333333333333	0.2
14	1982-10-27	UEFA Euro qualification	✗	Turkey	Izmir	Turkey	1	Albania	0	1	0	4	1.3320537428023032	0.2380038387715931	0.1094891	0.4666666666666667	0.3380281690140845	0.35384615384615387	UEFA	0.4539877300613497	1.4280230326295584	0.3915663	0.0	0.381957773512476	0	...	0.0	0.0	0.17307692307692307	0.14893617021276595	UEFA	0.40425531914893614	1.595890410958904	0.2831325	0.0	0.23972602739726026	1	0	0	0.1	0.0	0.0	0.2	0.0	0.0	0.4	0.6666666666666666	0.2	0.1	0.3333333333333333	0.2
15	1982-12-15	UEFA Euro qualification	✗	Albania	Tirana	Northern Ireland	0	Albania	0	2	0	3	1.043117744610282	0.23217247097844113	0.0948905	0.23076923076923078	0.1721311475409836	0.30327868852459017	UEFA	0.29017857142857145	1.9535655058043118	0.3674699	0.0	0.24378109452736318	0	...	0.0	0.0	0.17307692307692307	0.14893617021276595	UEFA	0.40425531914893614	1.595890410958904	0.2831325	0.0	0.23972602739726026	0	1	0	0.2	0.3333333333333333	0.2	0.1	0.0	0.0	0.5	0.6666666666666666	0.0	0.1	0.3333333333333333	0.2
16	1983-03-30	UEFA Euro qualification	✗	Albania	Tirana	Germany	2	Albania	1	2	4	5	2.0998322147651005	0.2080536912751678	0.8759124	0.6	0.4702127659574468	0.6242774566473989	UEFA	0.6083916083916084	1.179530201342282	0.5210843	0.3	0.5553691275167785	0	...	0.0	0.0	0.17307692307692307	0.14893617021276595	UEFA	0.40425531914893614	1.595890410958904	0.2831325	0.0	0.23972602739726026	1	0	0	0.5	0.6666666666666666	0.0	0.0	0.0	0.2	0.5	0.0	0.0	0.1	0.0	0.2
17	1983-04-27	UEFA Euro qualification	✗	Northern Ireland	Belfast	Northern Ireland	1	Albania	0	1	0	3	1.043117744610282	0.23217247097844113	0.0948905	0.23076923076923078	0.1721311475409836	0.30327868852459017	UEFA	0.29017857142857145	1.9535655058043118	0.3674699	0.0	0.24378109452736318	0	...	0.0	0.0	0.17307692307692307	0.14893617021276595	UEFA	0.40425531914893614	1.595890410958904	0.2831325	0.0	0.23972602739726026	1	0	0	0.3	0.6666666666666666	0.2	0.0	0.0	0.2	0.5	0.6666666666666666	0.2	0.1	0.0	0.2
18	1983-05-11	UEFA Euro qualification	✗	Albania	Tirana	Turkey	1	Albania	1	2	0	4	1.3320537428023032	0.2380038387715931	0.1094891	0.4666666666666667	0.3380281690140845	0.35384615384615387	UEFA	0.4539877300613497	1.4280230326295584	0.3915663	0.0	0.381957773512476	0	...	0.0	0.0	0.17307692307692307	0.14893617021276595	UEFA	0.40425531914893614	1.595890410958904	0.2831325	0.0	0.23972602739726026	0	1	0	0.1	0.0	0.2	0.0	0.0	0.2	0.4	0.0	0.0	0.1	0.0	0.2
19	1983-06-08	UEFA Euro qualification	✗	Albania	Tirana	Austria	2	Albania	1	2	0	4	1.7978290366350067	0.2198100407055631	0.2116788	0.41379310344827586	0.30633802816901406	0.47413793103448276	UEFA	0.4837662337662338	1.5929443690637721	0.3493976	0.0	0.41112618724559025	0	...	0.0	0.0	0.17307692307692307	0.14893617021276595	UEFA	0.40425531914893614	1.595890410958904	0.2831325	0.0	0.23972602739726026	1	0	0	0.5	0.3333333333333333	0.4	0.0	0.0	0.4	0.5	0.6666666666666666	0.2	0.1	0.0	0.4
20	1983-11-20	UEFA Euro qualification	✗	Germany	Saarbrücken	Germany	2	Albania	1	1	4	5	2.0998322147651005	0.2080536912751678	0.8759124	0.6	0.4702127659574468	0.6242774566473989	UEFA	0.6083916083916084	1.179530201342282	0.5210843	0.3	0.5553691275167785	0	...	0.0	0.0	0.17307692307692307	0.14893617021276595	UEFA	0.40425531914893614	1.595890410958904	0.2831325	0.0	0.23972602739726026	1	0	0	0.5	0.6666666666666666	0.0	0.0	0.0	0.4	0.6	0.6666666666666666	0.0	0.1	0.0	0.4

Direct Confrontation

# Victory 5 previous games
all_matchs.rolling(
    func = "avg",
    window = (-5, -1),
    columns = "victory_team1",
    by = ["team1", "team2"],
    order_by = ["date"],
    name = "avg_victory_direct_team1_1_5",
)
# Victory 3 previous games
all_matchs.rolling(
    func = "avg",
    window = (-3, -1),
    columns = "victory_team1",
    by = ["team1", "team2"],
    order_by = ["date"],
    name = "avg_victory_direct_team1_1_3",
)
# Draw 5 previous games
all_matchs.rolling(
    func = "avg",
    window = (-5, -1),
    columns = "draw",
    by = ["team1", "team2"],
    order_by = ["date"],
    name = "avg_draw_direct_team1_1_5",
)

	📅 date Date 100%	Abc tournament Varchar(50) 100%	0|1 neutral Boolean 100%	Abc country Varchar(50) 100%	Abc city Varchar(50) 100%	Abc team1 Varchar(50) 100%	123 team1_score Integer 100%	Abc team2 Varchar(50) 100%	123 team2_score Integer 100%	123 match_sample Integer 100%	123 nb_World_Cup_1 Bigint 99%	123 fifa_rank_1 Integer 99%	123 Avg_goals_1 Double 99%	123 Percent_Draw_1 Double 99%	123 Number_Games_World_Tournament_1 Decimal(28,7) 99%	123 Percent_Victory_World_Tournament_1 Double 99%	123 Percent_Victory_Away_1 Double 99%	123 Percent_Victory_Continental_Tournament_1 Double 99%	Abc confederation_1 Varchar(8) 99%	123 Percent_Victory_Home_1 Double 99%	123 Avg_goals_conceded_1 Double 99%	123 Number_Games_Continental_Tournament_1 Decimal(28,7) 99%	123 nb_Continental_Cup_1 Decimal(27,6) 99%	123 Percent_Victory_1 Double 99%	123 nb_World_Cup_2 Bigint 99%	...	123 Percent_Victory_Continental_Tournament_2 Double 99%	Abc confederation_2 Varchar(8) 99%	123 Percent_Victory_Home_2 Double 99%	123 Avg_goals_conceded_2 Double 99%	123 Number_Games_Continental_Tournament_2 Decimal(28,7) 99%	123 nb_Continental_Cup_2 Decimal(27,6) 99%	123 Percent_Victory_2 Double 99%	123 victory_team1 Int 100%	123 draw Int 100%	123 victory_team2 Int 100%	123 avg_victory_team1_1_10 Double 99%	123 avg_victory_team1_1_3 Double 99%	123 avg_draw_team1_1_5 Double 99%	123 avg_victory_team2_1_10 Double 99%	123 avg_victory_team2_1_3 Double 99%	123 avg_draw_team2_1_5 Double 99%	123 avg_victory_same_tournament_team1_1_10 Double 97%	123 avg_victory_same_tournament_team1_1_3 Double 97%	123 avg_draw_same_tournament_team1_1_5 Double 97%	123 avg_victory_same_tournament_team2_1_10 Double 97%	123 avg_victory_same_tournament_team2_1_3 Double 97%	123 avg_draw_same_tournament_team2_1_5 Double 97%	123 avg_victory_direct_team1_1_5 Double 84%	123 avg_victory_direct_team1_1_3 Double 84%	123 avg_draw_direct_team1_1_5 Double 84%
1	2014-06-01	CONIFA World Football Cup	✓	Sweden	Östersund	Abkhazia	1	Occitania	1	1	0	0	1.0	0.5	0.0	0.0	0.16666666666666666	0.0	OFC	0.0	1.5	0.0	0.0	0.16666666666666666	0	...	0.0	OFC	0.0	1.8333333333333333	0.0	0.0	0.25	0	1	0	0.0	0.0	0.0	0.125	0.3333333333333333	0.0	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]
2	2014-06-07	CONIFA World Football Cup	✓	Sweden	Östersund	Abkhazia	0	Occitania	1	1	0	0	1.0	0.5	0.0	0.0	0.16666666666666666	0.0	OFC	0.0	1.5	0.0	0.0	0.16666666666666666	0	...	0.0	OFC	0.0	1.8333333333333333	0.0	0.0	0.25	0	0	1	0.2	0.3333333333333333	0.6	0.2	0.3333333333333333	0.4	0.25	0.3333333333333333	0.75	0.3333333333333333	0.3333333333333333	0.6666666666666666	0.0	0.0	1.0
3	2018-05-31	CONIFA World Football Cup	✓	England	Enfield	Abkhazia	3	Tibet	0	1	0	0	1.0	0.5	0.0	0.0	0.16666666666666666	0.0	OFC	0.0	1.5	0.0	0.0	0.16666666666666666	0	...	0.0	OFC	0.0	5.5	0.0	0.0	0.0	1	0	0	0.5	0.0	0.6	0.0	0.0	0.0	0.5	0.6666666666666666	0.2	[null]	[null]	[null]	[null]	[null]	[null]
4	2015-06-16	FIFA World Cup qualification	✗	Cambodia	Phnom Penh	Afghanistan	1	Cambodia	0	2	0	2	1.0795454545454546	0.20454545454545456	0.0	0.0	0.29577464788732394	0.16666666666666666	AFC	0.4	2.0568181818181817	0.0361446	0.0	0.2840909090909091	0	...	0.1	AFC	0.391304347826087	2.6557377049180326	0.0903614	0.0	0.19672131147540983	1	0	0	0.2	0.3333333333333333	0.4	0.4	0.3333333333333333	0.4	0.0	0.0	0.2	0.2	0.3333333333333333	0.2	[null]	[null]	[null]
5	2015-11-12	FIFA World Cup qualification	✓	Iran	Teheran	Afghanistan	3	Cambodia	0	1	0	2	1.0795454545454546	0.20454545454545456	0.0	0.0	0.29577464788732394	0.16666666666666666	AFC	0.4	2.0568181818181817	0.0361446	0.0	0.2840909090909091	0	...	0.1	AFC	0.391304347826087	2.6557377049180326	0.0903614	0.0	0.19672131147540983	1	0	0	0.2	0.0	0.0	0.3	0.3333333333333333	0.0	0.1	0.0	0.0	0.2	0.0	0.0	1.0	1.0	0.0
6	2017-06-13	AFC Asian Cup qualification	✗	Cambodia	Phnom Penh	Afghanistan	0	Cambodia	1	2	0	2	1.0795454545454546	0.20454545454545456	0.0	0.0	0.29577464788732394	0.16666666666666666	AFC	0.4	2.0568181818181817	0.0361446	0.0	0.2840909090909091	0	...	0.1	AFC	0.391304347826087	2.6557377049180326	0.0903614	0.0	0.19672131147540983	0	0	1	0.4	0.6666666666666666	0.4	0.2	0.0	0.0	0.1	0.3333333333333333	0.4	0.3	0.3333333333333333	0.0	1.0	1.0	0.0
7	2018-03-27	AFC Asian Cup qualification	✓	Tajikistan	Dushanbe	Afghanistan	2	Cambodia	1	1	0	2	1.0795454545454546	0.20454545454545456	0.0	0.0	0.29577464788732394	0.16666666666666666	AFC	0.4	2.0568181818181817	0.0361446	0.0	0.2840909090909091	0	...	0.1	AFC	0.391304347826087	2.6557377049180326	0.0903614	0.0	0.19672131147540983	1	0	0	0.2	0.0	0.4	0.2	0.3333333333333333	0.0	0.1	0.0	0.6	0.2	0.0	0.0	0.6666666666666666	0.6666666666666666	0.0
8	1984-09-12	AFC Asian Cup qualification	✗	China PR	Guangzhou	Afghanistan	0	China PR	6	2	0	2	1.0795454545454546	0.20454545454545456	0.0	0.0	0.29577464788732394	0.16666666666666666	AFC	0.4	2.0568181818181817	0.0361446	0.0	0.2840909090909091	0	...	0.5510204081632653	AFC	0.5796178343949044	1.0877513711151736	0.4427711	0.0	0.48811700182815354	0	0	1	0.1	0.0	0.2	0.4	0.3333333333333333	0.0	0.0	0.0	0.2	0.75	0.6666666666666666	0.0	[null]	[null]	[null]
9	2013-03-06	AFC Challenge Cup qualification	✗	Laos	Vientiane	Afghanistan	1	Laos	1	2	0	2	1.0795454545454546	0.20454545454545456	0.0	0.0	0.29577464788732394	0.16666666666666666	AFC	0.4	2.0568181818181817	0.0361446	0.0	0.2840909090909091	0	...	0.08	AFC	0.42857142857142855	3.3587786259541983	0.0753012	0.0	0.183206106870229	0	1	0	0.5	0.6666666666666666	0.0	0.2	0.3333333333333333	0.2	0.6666666666666666	0.6666666666666666	0.0	0.3333333333333333	0.3333333333333333	0.6666666666666666	[null]	[null]	[null]
10	2014-05-24	AFC Challenge Cup	✓	Maldives	Addu City	Afghanistan	0	Laos	0	1	0	2	1.0795454545454546	0.20454545454545456	0.0	0.0	0.29577464788732394	0.16666666666666666	AFC	0.4	2.0568181818181817	0.0361446	0.0	0.2840909090909091	0	...	0.08	AFC	0.42857142857142855	3.3587786259541983	0.0753012	0.0	0.183206106870229	0	1	0	0.6	0.3333333333333333	0.4	0.1	0.0	0.2	0.14285714285714285	0.3333333333333333	0.2	0.0	0.0	0.0	0.0	0.0	1.0
11	2015-05-29	Friendly	✗	Laos	Vientiane	Afghanistan	2	Laos	0	2	0	2	1.0795454545454546	0.20454545454545456	0.0	0.0	0.29577464788732394	0.16666666666666666	AFC	0.4	2.0568181818181817	0.0361446	0.0	0.2840909090909091	0	...	0.08	AFC	0.42857142857142855	3.3587786259541983	0.0753012	0.0	0.183206106870229	1	0	0	0.3	0.0	0.4	0.3	0.0	0.0	0.2	0.3333333333333333	0.2	0.1111111111111111	0.0	0.0	0.0	0.0	1.0
12	2005-12-07	SAFF Cup	✓	Pakistan	Karachi	Afghanistan	1	Maldives	9	2	0	2	1.0795454545454546	0.20454545454545456	0.0	0.0	0.29577464788732394	0.16666666666666666	AFC	0.4	2.0568181818181817	0.0361446	0.0	0.2840909090909091	0	...	0.2	AFC	0.36666666666666664	2.234848484848485	0.0903614	0.0	0.2803030303030303	0	0	1	0.1	0.0	0.0	0.1	0.3333333333333333	0.2	0.0	0.0	0.0	0.5	0.6666666666666666	0.2	[null]	[null]	[null]
13	2009-12-07	SAFF Cup	✓	Bangladesh	Dhaka	Afghanistan	1	Maldives	3	2	0	2	1.0795454545454546	0.20454545454545456	0.0	0.0	0.29577464788732394	0.16666666666666666	AFC	0.4	2.0568181818181817	0.0361446	0.0	0.2840909090909091	0	...	0.2	AFC	0.36666666666666664	2.234848484848485	0.0903614	0.0	0.2803030303030303	0	0	1	0.1	0.0	0.2	0.6	0.6666666666666666	0.4	0.1111111111111111	0.0	0.4	0.6	0.6666666666666666	0.2	0.0	0.0	0.0
14	2013-09-06	SAFF Cup	✓	Nepal	Kathmandu	Afghanistan	0	Maldives	0	1	0	2	1.0795454545454546	0.20454545454545456	0.0	0.0	0.29577464788732394	0.16666666666666666	AFC	0.4	2.0568181818181817	0.0361446	0.0	0.2840909090909091	0	...	0.2	AFC	0.36666666666666664	2.234848484848485	0.0903614	0.0	0.2803030303030303	0	1	0	0.7	1.0	0.2	0.6	1.0	0.2	0.5	0.6666666666666666	0.0	0.6	0.6666666666666666	0.2	0.0	0.0	0.0
15	2014-05-29	AFC Challenge Cup	✗	Maldives	Malé	Afghanistan	1	Maldives	1	2	0	2	1.0795454545454546	0.20454545454545456	0.0	0.0	0.29577464788732394	0.16666666666666666	AFC	0.4	2.0568181818181817	0.0361446	0.0	0.2840909090909091	0	...	0.2	AFC	0.36666666666666664	2.234848484848485	0.0903614	0.0	0.2803030303030303	0	1	0	0.4	0.3333333333333333	0.4	0.3	0.3333333333333333	0.2	0.1111111111111111	0.3333333333333333	0.4	0.2857142857142857	0.3333333333333333	0.2	0.0	0.0	0.3333333333333333
16	2015-12-28	SAFF Cup	✓	India	Thiruvananthapuram	Afghanistan	4	Maldives	1	1	0	2	1.0795454545454546	0.20454545454545456	0.0	0.0	0.29577464788732394	0.16666666666666666	AFC	0.4	2.0568181818181817	0.0361446	0.0	0.2840909090909091	0	...	0.2	AFC	0.36666666666666664	2.234848484848485	0.0903614	0.0	0.2803030303030303	1	0	0	0.4	1.0	0.0	0.3	0.6666666666666666	0.0	0.8	1.0	0.2	0.5	0.6666666666666666	0.2	0.0	0.0	0.5
17	2017-06-06	Friendly	✓	United Arab Emirates	Dubai	Afghanistan	2	Maldives	1	1	0	2	1.0795454545454546	0.20454545454545456	0.0	0.0	0.29577464788732394	0.16666666666666666	AFC	0.4	2.0568181818181817	0.0361446	0.0	0.2840909090909091	0	...	0.2	AFC	0.36666666666666664	2.234848484848485	0.0903614	0.0	0.2803030303030303	1	0	0	0.4	0.3333333333333333	0.4	0.4	0.3333333333333333	0.2	0.3	0.3333333333333333	0.2	0.3	0.6666666666666666	0.0	0.2	0.3333333333333333	0.4
18	2013-03-04	AFC Challenge Cup qualification	✓	Laos	Vientiane	Afghanistan	1	Mongolia	0	2	0	2	1.0795454545454546	0.20454545454545456	0.0	0.0	0.29577464788732394	0.16666666666666666	AFC	0.4	2.0568181818181817	0.0361446	0.0	0.2840909090909091	0	...	0.21428571428571427	OFC	1.0	3.2222222222222223	0.0421687	0.0	0.26666666666666666	1	0	0	0.5	0.6666666666666666	0.0	0.5	0.3333333333333333	0.2	0.625	0.6666666666666666	0.0	0.4	0.3333333333333333	0.2	[null]	[null]	[null]
19	1975-04-06	AFC Asian Cup qualification	✓	Iraq	Baghdad	Afghanistan	1	Qatar	2	2	0	2	1.0795454545454546	0.20454545454545456	0.0	0.0	0.29577464788732394	0.16666666666666666	AFC	0.4	2.0568181818181817	0.0361446	0.0	0.2840909090909091	0	...	0.41044776119402987	AFC	0.4936708860759494	1.1829787234042553	0.4036145	0.0	0.4021276595744681	0	0	1	0.0	0.0	0.0	0.1	0.0	0.2	0.0	0.0	0.0	0.0	0.0	0.0	[null]	[null]	[null]
20	1975-04-12	AFC Asian Cup qualification	✓	Iraq	Baghdad	Afghanistan	1	Qatar	1	2	0	2	1.0795454545454546	0.20454545454545456	0.0	0.0	0.29577464788732394	0.16666666666666666	AFC	0.4	2.0568181818181817	0.0361446	0.0	0.2840909090909091	0	...	0.41044776119402987	AFC	0.4936708860759494	1.1829787234042553	0.4036145	0.0	0.4021276595744681	0	1	0	0.0	0.0	0.0	0.2	0.3333333333333333	0.2	0.0	0.0	0.0	0.25	0.3333333333333333	0.0	0.0	0.0	0.0

Games against an opponents with the same rank

# TEAM 1

# Victory 5 previous games
all_matchs.rolling(
    func = "avg",
    window = (-5, -1),
    columns = "victory_team1",
    by = ["team1", "fifa_rank_2"],
    order_by = ["date"],
    name = "avg_victory_rank2_team1_1_5",
)
# Draw 5 previous games
all_matchs.rolling(
    func = "avg",
    window = (-5, -1),
    columns = "draw",
    by = ["team1", "fifa_rank_2"],
    order_by = ["date"],
    name = "avg_draw_rank2_team1_1_5",
)

# TEAM 2

# Victory 5 previous games
all_matchs.rolling(
    func = "avg",
    window = (-5, -1),
    columns = "victory_team2",
    by = ["team2", "fifa_rank_1"],
    order_by = ["date"],
    name = "avg_victory_rank1_team2_1_5",
)
# Draw 5 previous games
all_matchs.rolling(
    func = "avg",
    window = (-5, -1),
    columns = "draw",
    by = ["team2", "fifa_rank_1"],
    order_by = ["date"],
    name = "avg_draw_rank1_team2_1_5",
)

	📅 date Date 100%	Abc tournament Varchar(50) 100%	0|1 neutral Boolean 100%	Abc country Varchar(50) 100%	Abc city Varchar(50) 100%	Abc team1 Varchar(50) 100%	123 team1_score Integer 100%	Abc team2 Varchar(50) 100%	123 team2_score Integer 100%	123 match_sample Integer 100%	123 nb_World_Cup_1 Bigint 99%	123 fifa_rank_1 Integer 99%	123 Avg_goals_1 Double 99%	123 Percent_Draw_1 Double 99%	123 Number_Games_World_Tournament_1 Decimal(28,7) 99%	123 Percent_Victory_World_Tournament_1 Double 99%	123 Percent_Victory_Away_1 Double 99%	123 Percent_Victory_Continental_Tournament_1 Double 99%	Abc confederation_1 Varchar(8) 99%	123 Percent_Victory_Home_1 Double 99%	123 Avg_goals_conceded_1 Double 99%	123 Number_Games_Continental_Tournament_1 Decimal(28,7) 99%	123 nb_Continental_Cup_1 Decimal(27,6) 99%	123 Percent_Victory_1 Double 99%	123 nb_World_Cup_2 Bigint 99%	...	123 Number_Games_Continental_Tournament_2 Decimal(28,7) 99%	123 nb_Continental_Cup_2 Decimal(27,6) 99%	123 Percent_Victory_2 Double 99%	123 victory_team1 Int 100%	123 draw Int 100%	123 victory_team2 Int 100%	123 avg_victory_team1_1_10 Double 99%	123 avg_victory_team1_1_3 Double 99%	123 avg_draw_team1_1_5 Double 99%	123 avg_victory_team2_1_10 Double 99%	123 avg_victory_team2_1_3 Double 99%	123 avg_draw_team2_1_5 Double 99%	123 avg_victory_same_tournament_team1_1_10 Double 97%	123 avg_victory_same_tournament_team1_1_3 Double 97%	123 avg_draw_same_tournament_team1_1_5 Double 97%	123 avg_victory_same_tournament_team2_1_10 Double 97%	123 avg_victory_same_tournament_team2_1_3 Double 97%	123 avg_draw_same_tournament_team2_1_5 Double 97%	123 avg_victory_direct_team1_1_5 Double 84%	123 avg_victory_direct_team1_1_3 Double 84%	123 avg_draw_direct_team1_1_5 Double 84%	123 avg_victory_rank2_team1_1_5 Double 98%	123 avg_draw_rank2_team1_1_5 Double 98%	123 avg_victory_rank1_team2_1_5 Double 98%	123 avg_draw_rank1_team2_1_5 Double 98%
1	1998-06-03	Friendly	✓	France	Saint-Ouen	Brazil	3	Andorra	0	2	4	5	2.193756727664155	0.20021528525296017	1.0	0.6788321167883211	0.59375	0.5770609318996416	CONMEBOL	0.7333333333333333	0.93756727664155	0.8403614	0.9	0.635091496232508	0	...	0.126506	0.0	0.022900763358778626	1	0	0	0.6	0.6666666666666666	0.0	0.0	0.0	0.0	0.8	0.6666666666666666	0.0	0.0	0.0	0.0	[null]	[null]	[null]	[null]	[null]	[null]	[null]
2	1998-10-14	UEFA Euro qualification	✗	France	Saint-Denis	France	2	Andorra	0	1	1	5	1.758312020460358	0.21483375959079284	0.5036496	0.5362318840579711	0.36363636363636365	0.5488721804511278	UEFA	0.5321100917431193	1.3427109974424551	0.4006024	0.2	0.48081841432225064	0	...	0.126506	0.0	0.022900763358778626	1	0	0	0.7	0.3333333333333333	0.4	0.0	0.0	0.2	0.5	0.6666666666666666	0.2	0.0	0.0	0.0	[null]	[null]	[null]	1.0	0.0	0.0	0.0
3	1999-06-09	UEFA Euro qualification	✓	Spain	Barcelona	France	1	Andorra	0	2	1	5	1.758312020460358	0.21483375959079284	0.5036496	0.5362318840579711	0.36363636363636365	0.5488721804511278	UEFA	0.5321100917431193	1.3427109974424551	0.4006024	0.2	0.48081841432225064	0	...	0.126506	0.0	0.022900763358778626	1	0	0	0.6	0.3333333333333333	0.2	0.0	0.0	0.0	0.6	0.3333333333333333	0.2	0.0	0.0	0.0	1.0	1.0	0.0	1.0	0.0	0.0	0.0
4	2004-05-28	Friendly	✗	France	Montpellier	France	4	Andorra	0	1	1	5	1.758312020460358	0.21483375959079284	0.5036496	0.5362318840579711	0.36363636363636365	0.5488721804511278	UEFA	0.5321100917431193	1.3427109974424551	0.4006024	0.2	0.48081841432225064	0	...	0.126506	0.0	0.022900763358778626	1	0	0	0.8	0.3333333333333333	0.4	0.0	0.0	0.2	0.6	0.3333333333333333	0.4	0.2	0.0	0.2	1.0	1.0	0.0	1.0	0.0	0.0	0.0
5	2004-06-05	Friendly	✗	Spain	Getafe	Spain	4	Andorra	0	1	1	5	1.9674418604651163	0.2248062015503876	0.5036496	0.5217391304347826	0.46568627450980393	0.6223776223776224	UEFA	0.6768558951965066	0.9085271317829458	0.4307229	0.3	0.5813953488372093	0	...	0.126506	0.0	0.022900763358778626	1	0	0	0.7	0.6666666666666666	0.2	0.0	0.0	0.2	0.7	0.6666666666666666	0.2	0.2	0.0	0.2	[null]	[null]	[null]	1.0	0.0	0.0	0.0
6	2006-09-02	UEFA Euro qualification	✗	England	Manchester	England	5	Andorra	0	1	1	5	2.19937369519833	0.24112734864300625	0.4525547	0.41935483870967744	0.5308641975308642	0.5968992248062015	UEFA	0.6187845303867403	0.9926931106471816	0.3885542	0.0	0.5657620041753654	0	...	0.126506	0.0	0.022900763358778626	1	0	0	0.8	0.6666666666666666	0.4	0.0	0.0	0.2	0.7	0.6666666666666666	0.2	0.0	0.0	0.0	[null]	[null]	[null]	1.0	0.0	0.0	0.0
7	2007-03-28	UEFA Euro qualification	✓	Spain	Barcelona	England	3	Andorra	0	2	1	5	2.19937369519833	0.24112734864300625	0.4525547	0.41935483870967744	0.5308641975308642	0.5968992248062015	UEFA	0.6187845303867403	0.9926931106471816	0.3885542	0.0	0.5657620041753654	0	...	0.126506	0.0	0.022900763358778626	1	0	0	0.4	0.0	0.6	0.0	0.0	0.2	0.6	0.0	0.4	0.0	0.0	0.0	1.0	1.0	0.0	1.0	0.0	0.0	0.0
8	2008-09-06	FIFA World Cup qualification	✓	Spain	Barcelona	England	2	Andorra	0	2	1	5	2.19937369519833	0.24112734864300625	0.4525547	0.41935483870967744	0.5308641975308642	0.5968992248062015	UEFA	0.6187845303867403	0.9926931106471816	0.3885542	0.0	0.5657620041753654	0	...	0.126506	0.0	0.022900763358778626	1	0	0	0.6	0.6666666666666666	0.2	0.0	0.0	0.0	0.8	0.6666666666666666	0.0	0.0	0.0	0.2	1.0	1.0	0.0	1.0	0.0	0.0	0.0
9	2009-06-10	FIFA World Cup qualification	✗	England	London	England	6	Andorra	0	1	1	5	2.19937369519833	0.24112734864300625	0.4525547	0.41935483870967744	0.5308641975308642	0.5968992248062015	UEFA	0.6187845303867403	0.9926931106471816	0.3885542	0.0	0.5657620041753654	0	...	0.126506	0.0	0.022900763358778626	1	0	0	0.8	1.0	0.0	0.0	0.0	0.0	0.9	1.0	0.0	0.0	0.0	0.0	1.0	1.0	0.0	1.0	0.0	0.0	0.0
10	2019-06-11	UEFA Euro qualification	✗	Andorra	Andorra la Vella	France	4	Andorra	0	2	1	5	1.758312020460358	0.21483375959079284	0.5036496	0.5362318840579711	0.36363636363636365	0.5488721804511278	UEFA	0.5321100917431193	1.3427109974424551	0.4006024	0.2	0.48081841432225064	0	...	0.126506	0.0	0.022900763358778626	1	0	0	0.6	0.6666666666666666	0.0	0.0	0.0	0.4	0.6	0.6666666666666666	0.2	0.0	0.0	0.0	1.0	1.0	0.0	0.8	0.2	0.0	0.0
11	2019-09-10	UEFA Euro qualification	✗	France	Paris	France	3	Andorra	0	1	1	5	1.758312020460358	0.21483375959079284	0.5036496	0.5362318840579711	0.36363636363636365	0.5488721804511278	UEFA	0.5321100917431193	1.3427109974424551	0.4006024	0.2	0.48081841432225064	0	...	0.126506	0.0	0.022900763358778626	1	0	0	0.7	0.6666666666666666	0.0	0.0	0.0	0.0	0.6	0.6666666666666666	0.0	0.0	0.0	0.0	1.0	1.0	0.0	0.8	0.2	0.0	0.0
12	1991-05-16	CFU Caribbean Cup qualification	✗	Saint Lucia	Castries	Saint Lucia	6	Anguilla	0	1	0	2	1.4731182795698925	0.13978494623655913	0.0	0.0	0.308411214953271	0.2727272727272727	OFC	0.40350877192982454	1.9623655913978495	0.0662651	0.0	0.3333333333333333	0	...	0.0271084	0.0	0.0625	1	0	0	0.3	0.6666666666666666	0.2	0.0	0.0	0.5	0.3333333333333333	0.6666666666666666	0.2	0.0	0.0	1.0	[null]	[null]	[null]	[null]	[null]	[null]	[null]
13	1993-04-04	CFU Caribbean Cup qualification	✗	Anguilla	The Valley	Antigua and Barbuda	4	Anguilla	0	2	0	2	1.3954802259887005	0.20903954802259886	0.0	0.0	0.23333333333333334	0.2777777777777778	OFC	0.47058823529411764	1.7005649717514124	0.1084337	0.0	0.3107344632768362	0	...	0.0271084	0.0	0.0625	1	0	0	0.3	0.0	0.2	0.0	0.0	0.2	0.5	0.6666666666666666	0.2	0.0	0.0	0.25	[null]	[null]	[null]	0.0	0.0	0.0	0.0
14	1994-03-04	CFU Caribbean Cup qualification	✓	Saint Vincent and the Grenadines	Kingstown	Guadeloupe	9	Anguilla	0	1	0	2	1.6712328767123288	0.182648401826484	0.0	0.0	0.38028169014084506	0.3333333333333333	CONCACAF	0.5076923076923077	1.4840182648401827	0.0361446	0.0	0.4155251141552511	0	...	0.0271084	0.0	0.0625	1	0	0	0.1	0.0	0.0	0.0	0.0	0.0	0.2	0.0	0.2	0.0	0.0	0.0	[null]	[null]	[null]	0.6	0.0	0.0	0.0
15	1994-03-06	CFU Caribbean Cup qualification	✗	Saint Vincent and the Grenadines	Kingstown	Saint Vincent and the Grenadines	2	Anguilla	0	1	0	2	1.7619047619047619	0.2698412698412698	0.0	0.0	0.4050632911392405	0.5384615384615384	OFC	0.5294117647058824	1.3174603174603174	0.0391566	0.0	0.4523809523809524	0	...	0.0271084	0.0	0.0625	1	0	0	0.4	0.6666666666666666	0.2	0.0	0.0	0.0	0.4	0.3333333333333333	0.4	0.0	0.0	0.0	[null]	[null]	[null]	0.5	0.5	0.0	0.0
16	1996-03-27	CFU Caribbean Cup qualification	✗	Saint Kitts and Nevis	Basseterre	Saint Kitts and Nevis	8	Anguilla	0	1	0	2	1.7973856209150327	0.1895424836601307	0.0	0.0	0.3064516129032258	0.34615384615384615	OFC	0.5076923076923077	1.5947712418300655	0.0783133	0.0	0.39869281045751637	0	...	0.0271084	0.0	0.0625	1	0	0	0.3	0.0	0.4	0.0	0.0	0.0	0.5	0.0	0.4	0.0	0.0	0.0	[null]	[null]	[null]	0.4	0.4	0.0	0.0
17	1998-04-15	CFU Caribbean Cup qualification	✓	Antigua and Barbuda	St. John's	Grenada	14	Anguilla	1	1	0	2	1.7894736842105263	0.22105263157894736	0.0	0.0	0.35398230088495575	0.25925925925925924	CONCACAF	0.4	1.763157894736842	0.0813253	0.0	0.3526315789473684	0	...	0.0271084	0.0	0.0625	1	0	0	0.4	0.3333333333333333	0.2	0.0	0.0	0.0	0.4	0.6666666666666666	0.2	0.0	0.0	0.0	[null]	[null]	[null]	0.5	0.5	0.0	0.0
18	1998-04-19	CFU Caribbean Cup qualification	✓	Antigua and Barbuda	St. John's	Guyana	14	Anguilla	0	1	0	2	1.1983471074380165	0.2066115702479339	0.0	0.0	0.2564102564102564	0.21875	CONCACAF	0.3870967741935484	1.7479338842975207	0.0963855	0.0	0.30165289256198347	0	...	0.0271084	0.0	0.0625	1	0	0	0.2	0.0	0.2	0.0	0.0	0.0	0.2	0.0	0.2	0.0	0.0	0.0	[null]	[null]	[null]	0.4	0.4	0.0	0.0
19	2000-03-05	FIFA World Cup qualification	✗	Anguilla	The Valley	Bahamas	3	Anguilla	1	2	0	2	1.1481481481481481	0.14814814814814814	0.0	0.0	0.18181818181818182	0.3076923076923077	OFC	0.3333333333333333	3.3333333333333335	0.0391566	0.0	0.25925925925925924	0	...	0.0271084	0.0	0.0625	1	0	0	0.14285714285714285	0.0	0.2	0.1	0.3333333333333333	0.0	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]	0.25	0.25	0.0	0.0
20	2000-03-19	FIFA World Cup qualification	✗	Bahamas	Nassau	Bahamas	2	Anguilla	1	1	0	2	1.1481481481481481	0.14814814814814814	0.0	0.0	0.18181818181818182	0.3076923076923077	OFC	0.3333333333333333	3.3333333333333335	0.0391566	0.0	0.25925925925925924	0	...	0.0271084	0.0	0.0625	1	0	0	0.25	0.3333333333333333	0.0	0.1	0.3333333333333333	0.0	1.0	1.0	0.0	0.0	0.0	0.0	1.0	1.0	0.0	0.4	0.2	0.0	0.0

Games between teams with rank 1 and rank 2

# Victory 5 previous games
all_matchs.rolling(
    func = "avg",
    window = (-5, -1),
    columns = "victory_team1",
    by = ["fifa_rank_1", "fifa_rank_2"],
    order_by = ["date"],
    name = "avg_victory_rank1_rank2_team1_1_5",
)
# Draw 5 previous games
all_matchs.rolling(
    func = "avg",
    window = (-5, -1),
    columns = "draw",
    by = ["fifa_rank_1", "fifa_rank_2"],
    order_by = ["date"],
    name = "avg_draw_rank1_rank2_team1_1_5",
)

	📅 date Date 100%	Abc tournament Varchar(50) 100%	0|1 neutral Boolean 100%	Abc country Varchar(50) 100%	Abc city Varchar(50) 100%	Abc team1 Varchar(50) 100%	123 team1_score Integer 100%	Abc team2 Varchar(50) 100%	123 team2_score Integer 100%	123 match_sample Integer 100%	123 nb_World_Cup_1 Bigint 99%	123 fifa_rank_1 Integer 99%	123 Avg_goals_1 Double 99%	123 Percent_Draw_1 Double 99%	123 Number_Games_World_Tournament_1 Decimal(28,7) 99%	123 Percent_Victory_World_Tournament_1 Double 99%	123 Percent_Victory_Away_1 Double 99%	123 Percent_Victory_Continental_Tournament_1 Double 99%	Abc confederation_1 Varchar(8) 99%	123 Percent_Victory_Home_1 Double 99%	123 Avg_goals_conceded_1 Double 99%	123 Number_Games_Continental_Tournament_1 Decimal(28,7) 99%	123 nb_Continental_Cup_1 Decimal(27,6) 99%	123 Percent_Victory_1 Double 99%	123 nb_World_Cup_2 Bigint 99%	...	123 Percent_Victory_2 Double 99%	123 victory_team1 Int 100%	123 draw Int 100%	123 victory_team2 Int 100%	123 avg_victory_team1_1_10 Double 99%	123 avg_victory_team1_1_3 Double 99%	123 avg_draw_team1_1_5 Double 99%	123 avg_victory_team2_1_10 Double 99%	123 avg_victory_team2_1_3 Double 99%	123 avg_draw_team2_1_5 Double 99%	123 avg_victory_same_tournament_team1_1_10 Double 97%	123 avg_victory_same_tournament_team1_1_3 Double 97%	123 avg_draw_same_tournament_team1_1_5 Double 97%	123 avg_victory_same_tournament_team2_1_10 Double 97%	123 avg_victory_same_tournament_team2_1_3 Double 97%	123 avg_draw_same_tournament_team2_1_5 Double 97%	123 avg_victory_direct_team1_1_5 Double 84%	123 avg_victory_direct_team1_1_3 Double 84%	123 avg_draw_direct_team1_1_5 Double 84%	123 avg_victory_rank2_team1_1_5 Double 98%	123 avg_draw_rank2_team1_1_5 Double 98%	123 avg_victory_rank1_team2_1_5 Double 98%	123 avg_draw_rank1_team2_1_5 Double 98%	123 avg_victory_rank1_rank2_team1_1_5 Double 99%	123 avg_draw_rank1_rank2_team1_1_5 Double 99%
1	1911-10-29	Friendly	✗	Luxembourg	Luxembourg	France	4	Luxembourg	1	2	1	5	1.758312020460358	0.21483375959079284	0.5036496	0.5362318840579711	0.36363636363636365	0.5488721804511278	UEFA	0.5321100917431193	1.3427109974424551	0.4006024	0.2	0.48081841432225064	0	...	0.06906077348066299	1	0	0	0.1	0.0	0.2	[null]	[null]	[null]	0.1	0.0	0.2	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]
2	1913-04-20	Friendly	✗	France	Saint-Ouen	France	8	Luxembourg	0	1	1	5	1.758312020460358	0.21483375959079284	0.5036496	0.5362318840579711	0.36363636363636365	0.5488721804511278	UEFA	0.5321100917431193	1.3427109974424551	0.4006024	0.2	0.48081841432225064	0	...	0.06906077348066299	1	0	0	0.6	0.6666666666666666	0.0	0.0	0.0	0.0	0.6	0.6666666666666666	0.0	0.0	0.0	0.0	1.0	1.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0
3	1914-02-08	Friendly	✗	Luxembourg	Luxembourg	France	4	Luxembourg	5	2	1	5	1.758312020460358	0.21483375959079284	0.5036496	0.5362318840579711	0.36363636363636365	0.5488721804511278	UEFA	0.5321100917431193	1.3427109974424551	0.4006024	0.2	0.48081841432225064	0	...	0.06906077348066299	0	0	1	0.7	1.0	0.0	0.0	0.0	0.0	0.7	1.0	0.0	0.0	0.0	0.0	1.0	1.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0
4	1927-05-21	Friendly	✗	Luxembourg	Esch-sur-Alzette	England	5	Luxembourg	2	2	1	5	2.19937369519833	0.24112734864300625	0.4525547	0.41935483870967744	0.5308641975308642	0.5968992248062015	UEFA	0.6187845303867403	0.9926931106471816	0.3885542	0.0	0.5657620041753654	0	...	0.06906077348066299	1	0	0	0.4	0.6666666666666666	0.4	0.2	0.3333333333333333	0.2	0.9	1.0	0.0	0.2	0.3333333333333333	0.2	[null]	[null]	[null]	[null]	[null]	0.3333333333333333	0.0	0.6666666666666666	0.0
5	1934-03-11	FIFA World Cup qualification	✗	Luxembourg	Luxembourg	Germany	9	Luxembourg	1	2	4	5	2.0998322147651005	0.2080536912751678	0.8759124	0.6	0.4702127659574468	0.6242774566473989	UEFA	0.6083916083916084	1.179530201342282	0.5210843	0.3	0.5553691275167785	0	...	0.06906077348066299	1	0	0	0.5	1.0	0.2	0.14285714285714285	0.0	0.4	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]	0.25	0.0	0.75	0.0
6	1934-04-15	FIFA World Cup qualification	✗	Luxembourg	Luxembourg	France	6	Luxembourg	1	2	1	5	1.758312020460358	0.21483375959079284	0.5036496	0.5362318840579711	0.36363636363636365	0.5488721804511278	UEFA	0.5321100917431193	1.3427109974424551	0.4006024	0.2	0.48081841432225064	0	...	0.06906077348066299	1	0	0	0.3	0.3333333333333333	0.0	0.125	0.0	0.4	[null]	[null]	[null]	0.0	0.0	0.0	0.6666666666666666	0.6666666666666666	0.0	0.6666666666666666	0.0	0.2	0.0	0.8	0.0
7	1935-08-18	Friendly	✗	Luxembourg	Luxembourg	Germany	1	Luxembourg	0	2	4	5	2.0998322147651005	0.2080536912751678	0.8759124	0.6	0.4702127659574468	0.6242774566473989	UEFA	0.6083916083916084	1.179530201342282	0.5210843	0.3	0.5553691275167785	0	...	0.06906077348066299	1	0	0	0.7	0.3333333333333333	0.2	0.1111111111111111	0.0	0.4	0.7	0.3333333333333333	0.2	0.14285714285714285	0.0	0.4	1.0	1.0	0.0	1.0	0.0	0.2	0.0	0.8	0.0
8	1936-09-27	Friendly	✗	Germany	Krefeld	Germany	7	Luxembourg	2	1	4	5	2.0998322147651005	0.2080536912751678	0.8759124	0.6	0.4702127659574468	0.6242774566473989	UEFA	0.6083916083916084	1.179530201342282	0.5210843	0.3	0.5553691275167785	0	...	0.06906077348066299	1	0	0	0.7	0.3333333333333333	0.2	0.1	0.0	0.2	0.7	0.3333333333333333	0.2	0.1111111111111111	0.0	0.4	1.0	1.0	0.0	1.0	0.0	0.2	0.0	0.8	0.0
9	1937-03-21	Friendly	✗	Luxembourg	Luxembourg	Germany	3	Luxembourg	2	2	4	5	2.0998322147651005	0.2080536912751678	0.8759124	0.6	0.4702127659574468	0.6242774566473989	UEFA	0.6083916083916084	1.179530201342282	0.5210843	0.3	0.5553691275167785	0	...	0.06906077348066299	1	0	0	0.4	0.3333333333333333	0.4	0.1	0.0	0.0	0.4	0.3333333333333333	0.4	0.1	0.0	0.2	1.0	1.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0
10	1938-03-20	Friendly	✗	Germany	Wuppertal	Germany	2	Luxembourg	1	1	4	5	2.0998322147651005	0.2080536912751678	0.8759124	0.6	0.4702127659574468	0.6242774566473989	UEFA	0.6083916083916084	1.179530201342282	0.5210843	0.3	0.5553691275167785	0	...	0.06906077348066299	1	0	0	0.9	0.6666666666666666	0.2	0.0	0.0	0.0	0.7	0.3333333333333333	0.4	0.1	0.0	0.0	1.0	1.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0
11	1939-03-26	Friendly	✗	Luxembourg	Differdange	Germany	1	Luxembourg	2	2	4	5	2.0998322147651005	0.2080536912751678	0.8759124	0.6	0.4702127659574468	0.6242774566473989	UEFA	0.6083916083916084	1.179530201342282	0.5210843	0.3	0.5553691275167785	0	...	0.06906077348066299	0	0	1	0.5	1.0	0.0	0.0	0.0	0.0	0.6	1.0	0.0	0.0	0.0	0.0	1.0	1.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0
12	1951-12-23	Friendly	✗	Germany	Essen	Germany	4	Luxembourg	1	1	4	5	2.0998322147651005	0.2080536912751678	0.8759124	0.6	0.4702127659574468	0.6242774566473989	UEFA	0.6083916083916084	1.179530201342282	0.5210843	0.3	0.5553691275167785	0	...	0.06906077348066299	1	0	0	0.7	0.6666666666666666	0.0	0.2	0.3333333333333333	0.4	0.7	0.6666666666666666	0.0	0.4	0.3333333333333333	0.4	0.8	0.6666666666666666	0.0	0.8	0.0	0.2	0.0	0.8	0.0
13	1952-04-20	Friendly	✗	Luxembourg	Luxembourg	Germany	3	Luxembourg	0	2	4	5	2.0998322147651005	0.2080536912751678	0.8759124	0.6	0.4702127659574468	0.6242774566473989	UEFA	0.6083916083916084	1.179530201342282	0.5210843	0.3	0.5553691275167785	0	...	0.06906077348066299	1	0	0	0.8	0.6666666666666666	0.0	0.2	0.3333333333333333	0.2	0.8	0.6666666666666666	0.0	0.3	0.3333333333333333	0.4	0.8	0.6666666666666666	0.0	0.8	0.0	0.2	0.0	0.8	0.0
14	1953-09-20	FIFA World Cup qualification	✗	Luxembourg	Luxembourg	France	6	Luxembourg	1	2	1	5	1.758312020460358	0.21483375959079284	0.5036496	0.5362318840579711	0.36363636363636365	0.5488721804511278	UEFA	0.5321100917431193	1.3427109974424551	0.4006024	0.2	0.48081841432225064	0	...	0.06906077348066299	1	0	0	0.6	0.3333333333333333	0.2	0.2	0.3333333333333333	0.2	0.25	0.0	0.5	0.0	0.0	0.0	0.75	0.6666666666666666	0.0	0.75	0.0	0.2	0.0	0.8	0.0
15	1953-12-17	FIFA World Cup qualification	✗	France	Paris	France	8	Luxembourg	0	1	1	5	1.758312020460358	0.21483375959079284	0.5036496	0.5362318840579711	0.36363636363636365	0.5488721804511278	UEFA	0.5321100917431193	1.3427109974424551	0.4006024	0.2	0.48081841432225064	0	...	0.06906077348066299	1	0	0	0.5	0.3333333333333333	0.0	0.1	0.0	0.0	0.5714285714285714	1.0	0.2	0.0	0.0	0.0	0.8	0.6666666666666666	0.0	0.8	0.0	0.2	0.0	0.8	0.0
16	1954-03-28	FIFA World Cup qualification	✗	Saarland	Saarbrücken	Germany	3	Saarland	1	2	4	5	2.0998322147651005	0.2080536912751678	0.8759124	0.6	0.4702127659574468	0.6242774566473989	UEFA	0.6083916083916084	1.179530201342282	0.5210843	0.3	0.5553691275167785	0	...	0.0	1	0	0	0.3	0.3333333333333333	0.8	0.0	0.0	1.0	0.8333333333333334	0.6666666666666666	0.2	0.0	0.0	1.0	[null]	[null]	[null]	0.8	0.0	[null]	[null]	0.8	0.0
17	1954-06-05	Friendly	✗	Saarland	Saarbrücken	Uruguay	7	Saarland	1	2	1	5	1.5756853396901074	0.24553039332538737	0.4452555	0.4098360655737705	0.3007246376811594	0.5060240963855421	CONMEBOL	0.5	1.264600715137068	1.0	0.8	0.4302741358760429	0	...	0.0	1	0	0	0.4	0.0	0.4	0.0	0.0	0.5	0.2	0.0	0.4	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]	0.0	0.0	1.0	0.0
18	1957-03-10	Friendly	✗	German DR	Berlin	Germany	3	Luxembourg	0	1	4	5	2.0998322147651005	0.2080536912751678	0.8759124	0.6	0.4702127659574468	0.6242774566473989	UEFA	0.6083916083916084	1.179530201342282	0.5210843	0.3	0.5553691275167785	0	...	0.06906077348066299	1	0	0	0.5	0.6666666666666666	0.0	0.1	0.0	0.0	0.4	0.3333333333333333	0.0	0.2	0.3333333333333333	0.4	0.8	0.6666666666666666	0.0	0.8	0.0	0.2	0.0	1.0	0.0
19	1960-10-19	FIFA World Cup qualification	✗	Luxembourg	Luxembourg	England	9	Luxembourg	0	2	1	5	2.19937369519833	0.24112734864300625	0.4525547	0.41935483870967744	0.5308641975308642	0.5968992248062015	UEFA	0.6187845303867403	0.9926931106471816	0.3885542	0.0	0.5657620041753654	0	...	0.06906077348066299	1	0	0	0.3	0.3333333333333333	0.4	0.0	0.0	0.0	0.9	0.6666666666666666	0.2	0.0	0.0	0.0	1.0	1.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0
20	1961-09-28	FIFA World Cup qualification	✗	England	London	England	4	Luxembourg	1	1	1	5	2.19937369519833	0.24112734864300625	0.4525547	0.41935483870967744	0.5308641975308642	0.5968992248062015	UEFA	0.6187845303867403	0.9926931106471816	0.3885542	0.0	0.5657620041753654	0	...	0.06906077348066299	1	0	0	0.7	0.3333333333333333	0.2	0.0	0.0	0.0	0.8	0.3333333333333333	0.4	0.0	0.0	0.0	1.0	1.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0

Before we use the neutral variable with our model, we should convert it to an integer.

We need also to create our response column: the outcome of the game.

all_matchs["neutral"].astype("int")
all_matchs.case_when(
    "result",
    all_matchs["team1_score"] > all_matchs["team2_score"], "1",
    all_matchs["team1_score"] < all_matchs["team2_score"], "2",
    "X",
)

	📅 date Date 100%	Abc tournament Varchar(50) 100%	123 neutral Int 100%	Abc country Varchar(50) 100%	Abc city Varchar(50) 100%	Abc team1 Varchar(50) 100%	123 team1_score Integer 100%	Abc team2 Varchar(50) 100%	123 team2_score Integer 100%	123 match_sample Integer 100%	123 nb_World_Cup_1 Bigint 99%	123 fifa_rank_1 Integer 99%	123 Avg_goals_1 Double 99%	123 Percent_Draw_1 Double 99%	123 Number_Games_World_Tournament_1 Decimal(28,7) 99%	123 Percent_Victory_World_Tournament_1 Double 99%	123 Percent_Victory_Away_1 Double 99%	123 Percent_Victory_Continental_Tournament_1 Double 99%	Abc confederation_1 Varchar(8) 99%	123 Percent_Victory_Home_1 Double 99%	123 Avg_goals_conceded_1 Double 99%	123 Number_Games_Continental_Tournament_1 Decimal(28,7) 99%	123 nb_Continental_Cup_1 Decimal(27,6) 99%	123 Percent_Victory_1 Double 99%	123 nb_World_Cup_2 Bigint 99%	...	123 victory_team1 Int 100%	123 draw Int 100%	123 victory_team2 Int 100%	123 avg_victory_team1_1_10 Double 99%	123 avg_victory_team1_1_3 Double 99%	123 avg_draw_team1_1_5 Double 99%	123 avg_victory_team2_1_10 Double 99%	123 avg_victory_team2_1_3 Double 99%	123 avg_draw_team2_1_5 Double 99%	123 avg_victory_same_tournament_team1_1_10 Double 97%	123 avg_victory_same_tournament_team1_1_3 Double 97%	123 avg_draw_same_tournament_team1_1_5 Double 97%	123 avg_victory_same_tournament_team2_1_10 Double 97%	123 avg_victory_same_tournament_team2_1_3 Double 97%	123 avg_draw_same_tournament_team2_1_5 Double 97%	123 avg_victory_direct_team1_1_5 Double 84%	123 avg_victory_direct_team1_1_3 Double 84%	123 avg_draw_direct_team1_1_5 Double 84%	123 avg_victory_rank2_team1_1_5 Double 98%	123 avg_draw_rank2_team1_1_5 Double 98%	123 avg_victory_rank1_team2_1_5 Double 98%	123 avg_draw_rank1_team2_1_5 Double 98%	123 avg_victory_rank1_rank2_team1_1_5 Double 99%	123 avg_draw_rank1_rank2_team1_1_5 Double 99%	Abc result Varchar(1) 100%
1	1923-11-01	Friendly	0	France	Rennes	Norway	5	Brittany	1	2	0	3	1.5013089005235603	0.22774869109947643	0.0583942	0.25	0.3343465045592705	0.3474576271186441	UEFA	0.3948220064724919	1.6845549738219896	0.3554217	0.0	0.3599476439790576	0	...	1	0	0	0.4	0.3333333333333333	0.2	1.0	1.0	0.0	0.4	0.3333333333333333	0.2	1.0	1.0	0.0	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]	1
2	1929-05-01	Friendly	0	El Salvador	San Salvador	El Salvador	9	Nicaragua	0	1	0	3	1.2262931034482758	0.22629310344827586	0.0437956	0.0	0.2009132420091324	0.4166666666666667	CONCACAF	0.4819277108433735	1.4870689655172413	0.4698795	0.0	0.32112068965517243	0	...	1	0	0	0.25	0.3333333333333333	0.25	[null]	[null]	[null]	0.25	0.3333333333333333	0.25	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]	1.0	0.0	1
3	1937-08-22	Friendly	0	Norway	Oslo	Norway	1	Basque Country	3	1	0	3	1.5013089005235603	0.22774869109947643	0.0583942	0.25	0.3343465045592705	0.3474576271186441	UEFA	0.3948220064724919	1.6845549738219896	0.3554217	0.0	0.3599476439790576	0	...	0	0	1	0.1	0.0	0.4	0.7	0.3333333333333333	0.0	0.2	0.0	0.2	0.7	0.3333333333333333	0.0	[null]	[null]	[null]	1.0	0.0	[null]	[null]	1.0	0.0	2
4	1938-05-29	Friendly	0	Cuba	Havana	Cuba	0	Basque Country	4	1	0	3	1.3205128205128205	0.25	0.0218978	0.3333333333333333	0.3728813559322034	0.25	CONCACAF	0.45	1.439102564102564	0.2771084	0.0	0.34615384615384615	0	...	0	0	1	0.3333333333333333	0.0	0.2	0.7	0.6666666666666666	0.0	[null]	[null]	[null]	0.7	0.6666666666666666	0.0	[null]	[null]	[null]	[null]	[null]	1.0	0.0	0.6666666666666666	0.0	2
5	1938-06-20	Friendly	0	Cuba	Havana	Cuba	3	Basque Country	4	1	0	3	1.3205128205128205	0.25	0.0218978	0.3333333333333333	0.3728813559322034	0.25	CONCACAF	0.45	1.439102564102564	0.2771084	0.0	0.34615384615384615	0	...	0	0	1	0.3	0.3333333333333333	0.2	0.7	0.6666666666666666	0.0	0.0	0.0	0.0	0.7	0.6666666666666666	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.5	0.0	2
6	1941-05-13	CCCF Championship	1	Costa Rica	San José	El Salvador	8	Nicaragua	0	1	0	3	1.2262931034482758	0.22629310344827586	0.0437956	0.0	0.2009132420091324	0.4166666666666667	CONCACAF	0.4819277108433735	1.4870689655172413	0.4698795	0.0	0.32112068965517243	0	...	1	0	0	0.42857142857142855	0.6666666666666666	0.4	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	1.0	0.0	1.0	0.0	0.0	0.0	0.4	0.0	1
7	1943-12-07	CCCF Championship	0	El Salvador	San Salvador	El Salvador	8	Nicaragua	1	1	0	3	1.2262931034482758	0.22629310344827586	0.0437956	0.0	0.2009132420091324	0.4166666666666667	CONCACAF	0.4819277108433735	1.4870689655172413	0.4698795	0.0	0.32112068965517243	0	...	1	0	0	0.5	0.3333333333333333	0.4	0.0	0.0	0.0	0.4	0.3333333333333333	0.4	0.0	0.0	0.0	1.0	1.0	0.0	1.0	0.0	0.0	0.0	0.4	0.0	1
8	1943-12-09	CCCF Championship	1	El Salvador	San Salvador	Guatemala	6	Nicaragua	2	1	0	3	1.3293838862559242	0.25829383886255924	0.0	0.0	0.2857142857142857	0.33093525179856115	CONCACAF	0.46808510638297873	1.3696682464454977	0.4186747	0.0	0.3412322274881517	0	...	1	0	0	0.2222222222222222	0.3333333333333333	0.4	0.0	0.0	0.0	0.5	0.5	0.5	0.0	0.0	0.0	[null]	[null]	[null]	[null]	[null]	0.0	0.0	0.4	0.0	1
9	1943-12-16	CCCF Championship	0	El Salvador	San Salvador	El Salvador	10	Nicaragua	1	1	0	3	1.2262931034482758	0.22629310344827586	0.0437956	0.0	0.2009132420091324	0.4166666666666667	CONCACAF	0.4819277108433735	1.4870689655172413	0.4698795	0.0	0.32112068965517243	0	...	1	0	0	0.7	1.0	0.2	0.0	0.0	0.0	0.625	1.0	0.2	0.0	0.0	0.0	1.0	1.0	0.0	1.0	0.0	0.0	0.0	0.6	0.0	1
10	1943-12-19	CCCF Championship	1	El Salvador	San Salvador	Guatemala	5	Nicaragua	1	2	0	3	1.3293838862559242	0.25829383886255924	0.0	0.0	0.2857142857142857	0.33093525179856115	CONCACAF	0.46808510638297873	1.3696682464454977	0.4186747	0.0	0.3412322274881517	0	...	1	0	0	0.4	0.6666666666666666	0.2	0.0	0.0	0.0	0.6	0.6666666666666666	0.2	0.0	0.0	0.0	1.0	1.0	0.0	1.0	0.0	0.0	0.0	0.8	0.0	1
11	1946-02-28	CCCF Championship	1	Costa Rica	San José	El Salvador	7	Nicaragua	2	1	0	3	1.2262931034482758	0.22629310344827586	0.0437956	0.0	0.2009132420091324	0.4166666666666667	CONCACAF	0.4819277108433735	1.4870689655172413	0.4698795	0.0	0.32112068965517243	0	...	1	0	0	0.6	0.3333333333333333	0.0	0.0	0.0	0.0	0.6	0.3333333333333333	0.0	0.0	0.0	0.0	1.0	1.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	1
12	1946-03-07	CCCF Championship	1	Costa Rica	San José	Guatemala	7	Nicaragua	0	1	0	3	1.3293838862559242	0.25829383886255924	0.0	0.0	0.2857142857142857	0.33093525179856115	CONCACAF	0.46808510638297873	1.3696682464454977	0.4186747	0.0	0.3412322274881517	0	...	1	0	0	0.5	0.6666666666666666	0.0	0.0	0.0	0.0	0.625	0.6666666666666666	0.0	0.0	0.0	0.0	1.0	1.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	1
13	1946-03-13	CCCF Championship	1	Costa Rica	San José	Honduras	10	Nicaragua	0	1	0	3	1.4914893617021276	0.26170212765957446	0.0656934	0.0	0.3316062176165803	0.44	CONMEBOL	0.5483870967741935	1.2234042553191489	0.5271084	0.0	0.4085106382978723	0	...	1	0	0	0.25	0.3333333333333333	0.0	0.1	0.3333333333333333	0.0	0.25	0.3333333333333333	0.0	0.1	0.3333333333333333	0.0	[null]	[null]	[null]	[null]	[null]	0.0	0.0	1.0	0.0	1
14	1949-05-09	Friendly	1	Suriname	Parmaribo	Cuba	0	Martinique	0	1	0	3	1.3205128205128205	0.25	0.0218978	0.3333333333333333	0.3728813559322034	0.25	CONCACAF	0.45	1.439102564102564	0.2771084	0.0	0.34615384615384615	0	...	0	1	0	0.5	0.0	0.4	0.7	0.6666666666666666	0.0	0.4	0.0	0.4	0.7	0.6666666666666666	0.0	[null]	[null]	[null]	0.0	0.0	[null]	[null]	1.0	0.0	X
15	1949-07-01	Friendly	0	Martinique	Fort-de-France	Trinidad and Tobago	0	Martinique	2	2	0	3	1.739967897271268	0.2102728731942215	0.0218978	0.0	0.3763440860215054	0.3793103448275862	CONCACAF	0.6224489795918368	1.274478330658106	0.436747	0.0	0.45264847512038525	0	...	0	0	1	0.6	1.0	0.0	0.6	0.3333333333333333	0.2	0.6	1.0	0.0	0.6	0.3333333333333333	0.2	[null]	[null]	[null]	[null]	[null]	0.0	1.0	0.8	0.2	2
16	1952-08-24	Friendly	0	Poland	Chorzów	China PR	1	Silesia	5	2	0	3	1.8372943327239488	0.226691042047532	0.0218978	0.0	0.3958333333333333	0.5510204081632653	AFC	0.5796178343949044	1.0877513711151736	0.4427711	0.0	0.48811700182815354	0	...	0	0	1	0.5	0.3333333333333333	0.0	0.3333333333333333	0.3333333333333333	0.0	0.5	0.3333333333333333	0.0	0.3333333333333333	0.3333333333333333	0.0	[null]	[null]	[null]	[null]	[null]	[null]	[null]	0.6	0.2	2
17	1953-03-08	CCCF Championship	1	Costa Rica	San José	Honduras	2	Nicaragua	1	1	0	3	1.4914893617021276	0.26170212765957446	0.0656934	0.0	0.3316062176165803	0.44	CONMEBOL	0.5483870967741935	1.2234042553191489	0.5271084	0.0	0.4085106382978723	0	...	1	0	0	0.3333333333333333	0.3333333333333333	0.0	0.1	0.0	0.0	0.4	0.3333333333333333	0.0	0.1	0.0	0.0	1.0	1.0	0.0	1.0	0.0	0.0	0.0	0.4	0.2	1
18	1953-03-10	CCCF Championship	1	Costa Rica	San José	El Salvador	4	Nicaragua	1	1	0	3	1.2262931034482758	0.22629310344827586	0.0437956	0.0	0.2009132420091324	0.4166666666666667	CONCACAF	0.4819277108433735	1.4870689655172413	0.4698795	0.0	0.32112068965517243	0	...	1	0	0	0.5	0.6666666666666666	0.0	0.1	0.0	0.0	0.2	0.0	0.2	0.1	0.0	0.0	1.0	1.0	0.0	1.0	0.0	0.0	0.0	0.4	0.2	1
19	1953-03-19	CCCF Championship	1	Costa Rica	San José	Guatemala	1	Nicaragua	0	1	0	3	1.3293838862559242	0.25829383886255924	0.0	0.0	0.2857142857142857	0.33093525179856115	CONCACAF	0.46808510638297873	1.3696682464454977	0.4186747	0.0	0.3412322274881517	0	...	1	0	0	0.3	0.6666666666666666	0.4	0.1	0.3333333333333333	0.0	0.4	0.6666666666666666	0.4	0.1	0.3333333333333333	0.0	1.0	1.0	0.0	1.0	0.0	0.0	0.0	0.4	0.2	1
20	1956-09-12	AFC Asian Cup	1	Hong Kong	So Kon Po	Israel	2	Vietnam Republic	1	1	0	3	1.4547738693467336	0.25125628140703515	0.0218978	0.0	0.30327868852459017	0.3548387096774194	UEFA	0.38926174496644295	1.4522613065326633	0.373494	0.1	0.3492462311557789	0	...	1	0	0	0.2	0.6666666666666666	0.2	0.3333333333333333	0.3333333333333333	0.6666666666666666	0.5	0.5	0.0	0.0	0.0	1.0	[null]	[null]	[null]	[null]	[null]	[null]	[null]	0.6	0.0	1

We have some missing values here. This might be because the two teams never played together, the competition was one or both teams’ first, etc.

all_matchs.count()

	count
"date"	82818.0
"tournament"	82818.0
"neutral"	82818.0
"country"	82818.0
"city"	82818.0
"team1"	82818.0
"team1_score"	82818.0
"team2"	82818.0
"team2_score"	82818.0
"match_sample"	82818.0
"nb_World_Cup_1"	82765.0
"fifa_rank_1"	82765.0
"Avg_goals_1"	82765.0
"Percent_Draw_1"	82765.0
"Number_Games_World_Tournament_1"	82765.0
"Percent_Victory_World_Tournament_1"	82765.0
"Percent_Victory_Away_1"	82765.0
"Percent_Victory_Continental_Tournament_1"	82765.0
"confederation_1"	82765.0
"Percent_Victory_Home_1"	82765.0
"Avg_goals_conceded_1"	82765.0
"Number_Games_Continental_Tournament_1"	82765.0
"nb_Continental_Cup_1"	82765.0
"Percent_Victory_1"	82765.0
"nb_World_Cup_2"	82765.0
"fifa_rank_2"	82765.0
"Avg_goals_2"	82765.0
"Percent_Draw_2"	82765.0
"Number_Games_World_Tournament_2"	82765.0
"Percent_Victory_World_Tournament_2"	82765.0
"Percent_Victory_Away_2"	82765.0
"Percent_Victory_Continental_Tournament_2"	82765.0
"confederation_2"	82765.0
"Percent_Victory_Home_2"	82765.0
"Avg_goals_conceded_2"	82765.0
"Number_Games_Continental_Tournament_2"	82765.0
"nb_Continental_Cup_2"	82765.0
"Percent_Victory_2"	82765.0
"victory_team1"	82818.0
"draw"	82818.0
"victory_team2"	82818.0
"avg_victory_team1_1_10"	82539.0
"avg_victory_team1_1_3"	82539.0
"avg_draw_team1_1_5"	82539.0
"avg_victory_team2_1_10"	82539.0
"avg_victory_team2_1_3"	82539.0
"avg_draw_team2_1_5"	82539.0
"avg_victory_same_tournament_team1_1_10"	80706.0
"avg_victory_same_tournament_team1_1_3"	80706.0
"avg_draw_same_tournament_team1_1_5"	80706.0
"avg_victory_same_tournament_team2_1_10"	80706.0
"avg_victory_same_tournament_team2_1_3"	80706.0
"avg_draw_same_tournament_team2_1_5"	80706.0
"avg_victory_direct_team1_1_5"	69759.0
"avg_victory_direct_team1_1_3"	69759.0
"avg_draw_direct_team1_1_5"	69759.0
"avg_victory_rank2_team1_1_5"	81440.0
"avg_draw_rank2_team1_1_5"	81440.0
"avg_victory_rank1_team2_1_5"	81440.0
"avg_draw_rank1_team2_1_5"	81440.0
"avg_victory_rank1_rank2_team1_1_5"	82771.0
"avg_draw_rank1_rank2_team1_1_5"	82771.0
"result"	82818.0

We need to impute these missing values.

all_matchs["avg_victory_direct_team1_1_5"] = fun.coalesce(
    all_matchs["avg_victory_direct_team1_1_5"],
    all_matchs["avg_victory_rank2_team1_1_5"],
    all_matchs["avg_victory_rank1_rank2_team1_1_5"],
)
all_matchs["avg_victory_direct_team1_1_3"] = fun.coalesce(
    all_matchs["avg_victory_direct_team1_1_3"],
    all_matchs["avg_victory_rank2_team1_1_5"],
    all_matchs["avg_victory_rank1_rank2_team1_1_5"],
)
all_matchs["avg_draw_direct_team1_1_5"] = fun.coalesce(
    all_matchs["avg_draw_direct_team1_1_5"],
    all_matchs["avg_draw_rank2_team1_1_5"],
    all_matchs["avg_draw_rank1_rank2_team1_1_5"],
)
all_matchs["avg_victory_same_tournament_team1_1_10"].fillna(expr = "avg_victory_team1_1_10")
all_matchs["avg_victory_same_tournament_team1_1_3"].fillna(expr = "avg_victory_team1_1_3")
all_matchs["avg_draw_same_tournament_team1_1_5"].fillna(expr = "avg_draw_team1_1_5")
all_matchs["avg_victory_same_tournament_team2_1_10"].fillna(expr = "avg_victory_team2_1_10")
all_matchs["avg_victory_same_tournament_team2_1_3"].fillna(expr = "avg_victory_team2_1_3")
all_matchs["avg_draw_same_tournament_team2_1_5"].fillna(expr = "avg_draw_team2_1_5")

	📅 date Date 100%	Abc tournament Varchar(50) 100%	123 neutral Int 100%	Abc country Varchar(50) 100%	Abc city Varchar(50) 100%	Abc team1 Varchar(50) 100%	123 team1_score Integer 100%	Abc team2 Varchar(50) 100%	123 team2_score Integer 100%	123 match_sample Integer 100%	123 nb_World_Cup_1 Bigint 99%	123 fifa_rank_1 Integer 99%	123 Avg_goals_1 Double 99%	123 Percent_Draw_1 Double 99%	123 Number_Games_World_Tournament_1 Decimal(28,7) 99%	123 Percent_Victory_World_Tournament_1 Double 99%	123 Percent_Victory_Away_1 Double 99%	123 Percent_Victory_Continental_Tournament_1 Double 99%	Abc confederation_1 Varchar(8) 99%	123 Percent_Victory_Home_1 Double 99%	123 Avg_goals_conceded_1 Double 99%	123 Number_Games_Continental_Tournament_1 Decimal(28,7) 99%	123 nb_Continental_Cup_1 Decimal(27,6) 99%	123 Percent_Victory_1 Double 99%	123 nb_World_Cup_2 Bigint 99%	...	123 victory_team1 Int 100%	123 draw Int 100%	123 victory_team2 Int 100%	123 avg_victory_team1_1_10 Double 99%	123 avg_victory_team1_1_3 Double 99%	123 avg_draw_team1_1_5 Double 99%	123 avg_victory_team2_1_10 Double 99%	123 avg_victory_team2_1_3 Double 99%	123 avg_draw_team2_1_5 Double 99%	123 avg_victory_same_tournament_team1_1_10 Real 99%	123 avg_victory_same_tournament_team1_1_3 Real 100%	123 avg_draw_same_tournament_team1_1_5 Real 99%	123 avg_victory_same_tournament_team2_1_10 Real 99%	123 avg_victory_same_tournament_team2_1_3 Real 100%	123 avg_draw_same_tournament_team2_1_5 Real 99%	123 avg_victory_direct_team1_1_5 Double 99%	123 avg_victory_direct_team1_1_3 Double 99%	123 avg_draw_direct_team1_1_5 Double 99%	123 avg_victory_rank2_team1_1_5 Double 98%	123 avg_draw_rank2_team1_1_5 Double 98%	123 avg_victory_rank1_team2_1_5 Double 98%	123 avg_draw_rank1_team2_1_5 Double 98%	123 avg_victory_rank1_rank2_team1_1_5 Double 99%	123 avg_draw_rank1_rank2_team1_1_5 Double 99%	Abc result Varchar(1) 100%
1	1911-10-29	Friendly	0	Luxembourg	Luxembourg	France	4	Luxembourg	1	2	1	5	1.758312020460358	0.21483375959079284	0.5036496	0.5362318840579711	0.36363636363636365	0.5488721804511278	UEFA	0.5321100917431193	1.3427109974424551	0.4006024	0.2	0.48081841432225064	0	...	1	0	0	0.1	0.0	0.2	[null]	[null]	[null]	0.1	0.0	0.2	[null]	0.3333333333333333	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]	1
2	1913-04-20	Friendly	0	France	Saint-Ouen	France	8	Luxembourg	0	1	1	5	1.758312020460358	0.21483375959079284	0.5036496	0.5362318840579711	0.36363636363636365	0.5488721804511278	UEFA	0.5321100917431193	1.3427109974424551	0.4006024	0.2	0.48081841432225064	0	...	1	0	0	0.6	0.6666666666666666	0.0	0.0	0.0	0.0	0.6	0.6666666666666666	0.0	0.0	0.0	0.0	1.0	1.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	1
3	1914-02-08	Friendly	0	Luxembourg	Luxembourg	France	4	Luxembourg	5	2	1	5	1.758312020460358	0.21483375959079284	0.5036496	0.5362318840579711	0.36363636363636365	0.5488721804511278	UEFA	0.5321100917431193	1.3427109974424551	0.4006024	0.2	0.48081841432225064	0	...	0	0	1	0.7	1.0	0.0	0.0	0.0	0.0	0.7	1.0	0.0	0.0	0.0	0.0	1.0	1.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	2
4	1927-05-21	Friendly	0	Luxembourg	Esch-sur-Alzette	England	5	Luxembourg	2	2	1	5	2.19937369519833	0.24112734864300625	0.4525547	0.41935483870967744	0.5308641975308642	0.5968992248062015	UEFA	0.6187845303867403	0.9926931106471816	0.3885542	0.0	0.5657620041753654	0	...	1	0	0	0.4	0.6666666666666666	0.4	0.2	0.3333333333333333	0.2	0.9	1.0	0.0	0.2	0.3333333333333333	0.2	0.6666666666666666	0.6666666666666666	0.0	[null]	[null]	0.3333333333333333	0.0	0.6666666666666666	0.0	1
5	1934-03-11	FIFA World Cup qualification	0	Luxembourg	Luxembourg	Germany	9	Luxembourg	1	2	4	5	2.0998322147651005	0.2080536912751678	0.8759124	0.6	0.4702127659574468	0.6242774566473989	UEFA	0.6083916083916084	1.179530201342282	0.5210843	0.3	0.5553691275167785	0	...	1	0	0	0.5	1.0	0.2	0.14285714285714285	0.0	0.4	0.5	0.3333333333333333	0.2	0.14285714285714285	0.3333333333333333	0.4	0.75	0.75	0.0	[null]	[null]	0.25	0.0	0.75	0.0	1
6	1934-04-15	FIFA World Cup qualification	0	Luxembourg	Luxembourg	France	6	Luxembourg	1	2	1	5	1.758312020460358	0.21483375959079284	0.5036496	0.5362318840579711	0.36363636363636365	0.5488721804511278	UEFA	0.5321100917431193	1.3427109974424551	0.4006024	0.2	0.48081841432225064	0	...	1	0	0	0.3	0.3333333333333333	0.0	0.125	0.0	0.4	0.3	0.3333333333333333	0.0	0.0	0.0	0.0	0.6666666666666666	0.6666666666666666	0.0	0.6666666666666666	0.0	0.2	0.0	0.8	0.0	1
7	1935-08-18	Friendly	0	Luxembourg	Luxembourg	Germany	1	Luxembourg	0	2	4	5	2.0998322147651005	0.2080536912751678	0.8759124	0.6	0.4702127659574468	0.6242774566473989	UEFA	0.6083916083916084	1.179530201342282	0.5210843	0.3	0.5553691275167785	0	...	1	0	0	0.7	0.3333333333333333	0.2	0.1111111111111111	0.0	0.4	0.7	0.3333333333333333	0.2	0.14285714285714285	0.0	0.4	1.0	1.0	0.0	1.0	0.0	0.2	0.0	0.8	0.0	1
8	1936-09-27	Friendly	0	Germany	Krefeld	Germany	7	Luxembourg	2	1	4	5	2.0998322147651005	0.2080536912751678	0.8759124	0.6	0.4702127659574468	0.6242774566473989	UEFA	0.6083916083916084	1.179530201342282	0.5210843	0.3	0.5553691275167785	0	...	1	0	0	0.7	0.3333333333333333	0.2	0.1	0.0	0.2	0.7	0.3333333333333333	0.2	0.1111111111111111	0.0	0.4	1.0	1.0	0.0	1.0	0.0	0.2	0.0	0.8	0.0	1
9	1937-03-21	Friendly	0	Luxembourg	Luxembourg	Germany	3	Luxembourg	2	2	4	5	2.0998322147651005	0.2080536912751678	0.8759124	0.6	0.4702127659574468	0.6242774566473989	UEFA	0.6083916083916084	1.179530201342282	0.5210843	0.3	0.5553691275167785	0	...	1	0	0	0.4	0.0	0.4	0.1	0.0	0.0	0.4	0.3333333333333333	0.4	0.1	0.0	0.2	1.0	1.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	1
10	1938-03-20	Friendly	0	Germany	Wuppertal	Germany	2	Luxembourg	1	1	4	5	2.0998322147651005	0.2080536912751678	0.8759124	0.6	0.4702127659574468	0.6242774566473989	UEFA	0.6083916083916084	1.179530201342282	0.5210843	0.3	0.5553691275167785	0	...	1	0	0	0.9	0.6666666666666666	0.2	0.0	0.0	0.0	0.7	0.3333333333333333	0.4	0.1	0.0	0.0	1.0	1.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	1
11	1939-03-26	Friendly	0	Luxembourg	Differdange	Germany	1	Luxembourg	2	2	4	5	2.0998322147651005	0.2080536912751678	0.8759124	0.6	0.4702127659574468	0.6242774566473989	UEFA	0.6083916083916084	1.179530201342282	0.5210843	0.3	0.5553691275167785	0	...	0	0	1	0.5	1.0	0.0	0.0	0.0	0.0	0.5	0.6666666666666666	0.0	0.0	0.0	0.0	1.0	1.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	2
12	1951-12-23	Friendly	0	Germany	Essen	Germany	4	Luxembourg	1	1	4	5	2.0998322147651005	0.2080536912751678	0.8759124	0.6	0.4702127659574468	0.6242774566473989	UEFA	0.6083916083916084	1.179530201342282	0.5210843	0.3	0.5553691275167785	0	...	1	0	0	0.7	0.6666666666666666	0.0	0.2	0.3333333333333333	0.4	0.7	0.6666666666666666	0.0	0.4	0.3333333333333333	0.4	0.8	0.6666666666666666	0.0	0.8	0.0	0.2	0.0	0.8	0.0	1
13	1952-04-20	Friendly	0	Luxembourg	Luxembourg	Germany	3	Luxembourg	0	2	4	5	2.0998322147651005	0.2080536912751678	0.8759124	0.6	0.4702127659574468	0.6242774566473989	UEFA	0.6083916083916084	1.179530201342282	0.5210843	0.3	0.5553691275167785	0	...	1	0	0	0.8	0.6666666666666666	0.0	0.2	0.3333333333333333	0.2	0.8	0.6666666666666666	0.0	0.3	0.3333333333333333	0.4	0.8	0.6666666666666666	0.0	0.8	0.0	0.2	0.0	0.8	0.0	1
14	1953-09-20	FIFA World Cup qualification	0	Luxembourg	Luxembourg	France	6	Luxembourg	1	2	1	5	1.758312020460358	0.21483375959079284	0.5036496	0.5362318840579711	0.36363636363636365	0.5488721804511278	UEFA	0.5321100917431193	1.3427109974424551	0.4006024	0.2	0.48081841432225064	0	...	1	0	0	0.6	0.3333333333333333	0.2	0.2	0.3333333333333333	0.2	0.25	0.0	0.5	0.0	0.0	0.0	0.75	0.6666666666666666	0.0	0.75	0.0	0.2	0.0	0.8	0.0	1
15	1953-12-17	FIFA World Cup qualification	0	France	Paris	France	8	Luxembourg	0	1	1	5	1.758312020460358	0.21483375959079284	0.5036496	0.5362318840579711	0.36363636363636365	0.5488721804511278	UEFA	0.5321100917431193	1.3427109974424551	0.4006024	0.2	0.48081841432225064	0	...	1	0	0	0.5	0.3333333333333333	0.0	0.1	0.0	0.0	0.5714285714285714	1.0	0.2	0.0	0.0	0.0	0.8	0.6666666666666666	0.0	0.8	0.0	0.2	0.0	0.8	0.0	1
16	1954-03-28	FIFA World Cup qualification	0	Saarland	Saarbrücken	Germany	3	Saarland	1	2	4	5	2.0998322147651005	0.2080536912751678	0.8759124	0.6	0.4702127659574468	0.6242774566473989	UEFA	0.6083916083916084	1.179530201342282	0.5210843	0.3	0.5553691275167785	0	...	1	0	0	0.3	0.3333333333333333	0.8	0.0	0.0	1.0	0.8333333333333334	0.6666666666666666	0.2	0.0	0.0	1.0	0.8	0.8	0.0	0.8	0.0	[null]	[null]	0.8	0.0	1
17	1954-06-05	Friendly	0	Saarland	Saarbrücken	Uruguay	7	Saarland	1	2	1	5	1.5756853396901074	0.24553039332538737	0.4452555	0.4098360655737705	0.3007246376811594	0.5060240963855421	CONMEBOL	0.5	1.264600715137068	1.0	0.8	0.4302741358760429	0	...	1	0	0	0.4	0.0	0.4	0.0	0.0	0.5	0.2	0.0	0.4	0.0	0.3333333333333333	0.5	1.0	1.0	0.0	[null]	[null]	0.0	0.0	1.0	0.0	1
18	1957-03-10	Friendly	0	German DR	Berlin	Germany	3	Luxembourg	0	1	4	5	2.0998322147651005	0.2080536912751678	0.8759124	0.6	0.4702127659574468	0.6242774566473989	UEFA	0.6083916083916084	1.179530201342282	0.5210843	0.3	0.5553691275167785	0	...	1	0	0	0.5	0.6666666666666666	0.0	0.1	0.0	0.0	0.5	0.6666666666666666	0.0	0.2	0.3333333333333333	0.4	0.8	0.6666666666666666	0.0	0.8	0.0	0.2	0.0	1.0	0.0	1
19	1960-10-19	FIFA World Cup qualification	0	Luxembourg	Luxembourg	England	9	Luxembourg	0	2	1	5	2.19937369519833	0.24112734864300625	0.4525547	0.41935483870967744	0.5308641975308642	0.5968992248062015	UEFA	0.6187845303867403	0.9926931106471816	0.3885542	0.0	0.5657620041753654	0	...	1	0	0	0.3	0.3333333333333333	0.4	0.0	0.0	0.0	0.9	0.6666666666666666	0.2	0.0	0.0	0.0	1.0	1.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	1
20	1961-09-28	FIFA World Cup qualification	0	England	London	England	4	Luxembourg	1	1	1	5	2.19937369519833	0.24112734864300625	0.4525547	0.41935483870967744	0.5308641975308642	0.5968992248062015	UEFA	0.6187845303867403	0.9926931106471816	0.3885542	0.0	0.5657620041753654	0	...	1	0	0	0.7	0.3333333333333333	0.2	0.0	0.0	0.0	0.8	0.3333333333333333	0.4	0.0	0.0	0.0	1.0	1.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	1

Let’s export the result to our VAST DataBase using the variable match_sample to avoid counting the same game twice.

vo.drop("football_train", method = "view")
all_matchs.to_db(
    name = "football_train",
    relation_type = "view",
    db_filter = (fun.year(all_matchs["date"]) <= 2015) & (fun.year(all_matchs["date"]) > 1980) & (all_matchs["match_sample"] == 1),
)

vo.drop("football_test", method = "view")
all_matchs.to_db(
    name = "football_test",
    relation_type = "view",
    db_filter = (fun.year(all_matchs["date"]) > 2015) & (all_matchs["match_sample"] == 1),
)

	📅 date Date 100%	Abc tournament Varchar(50) 100%	123 neutral Int 100%	Abc country Varchar(50) 100%	Abc city Varchar(50) 100%	Abc team1 Varchar(50) 100%	123 team1_score Integer 100%	Abc team2 Varchar(50) 100%	123 team2_score Integer 100%	123 match_sample Integer 100%	123 nb_World_Cup_1 Bigint 99%	123 fifa_rank_1 Integer 99%	123 Avg_goals_1 Double 99%	123 Percent_Draw_1 Double 99%	123 Number_Games_World_Tournament_1 Decimal(28,7) 99%	123 Percent_Victory_World_Tournament_1 Double 99%	123 Percent_Victory_Away_1 Double 99%	123 Percent_Victory_Continental_Tournament_1 Double 99%	Abc confederation_1 Varchar(8) 99%	123 Percent_Victory_Home_1 Double 99%	123 Avg_goals_conceded_1 Double 99%	123 Number_Games_Continental_Tournament_1 Decimal(28,7) 99%	123 nb_Continental_Cup_1 Decimal(27,6) 99%	123 Percent_Victory_1 Double 99%	123 nb_World_Cup_2 Bigint 99%	...	123 victory_team1 Int 100%	123 draw Int 100%	123 victory_team2 Int 100%	123 avg_victory_team1_1_10 Double 99%	123 avg_victory_team1_1_3 Double 99%	123 avg_draw_team1_1_5 Double 99%	123 avg_victory_team2_1_10 Double 99%	123 avg_victory_team2_1_3 Double 99%	123 avg_draw_team2_1_5 Double 99%	123 avg_victory_same_tournament_team1_1_10 Real 99%	123 avg_victory_same_tournament_team1_1_3 Real 100%	123 avg_draw_same_tournament_team1_1_5 Real 99%	123 avg_victory_same_tournament_team2_1_10 Real 99%	123 avg_victory_same_tournament_team2_1_3 Real 100%	123 avg_draw_same_tournament_team2_1_5 Real 99%	123 avg_victory_direct_team1_1_5 Double 99%	123 avg_victory_direct_team1_1_3 Double 99%	123 avg_draw_direct_team1_1_5 Double 99%	123 avg_victory_rank2_team1_1_5 Double 98%	123 avg_draw_rank2_team1_1_5 Double 98%	123 avg_victory_rank1_team2_1_5 Double 98%	123 avg_draw_rank1_team2_1_5 Double 98%	123 avg_victory_rank1_rank2_team1_1_5 Double 99%	123 avg_draw_rank1_rank2_team1_1_5 Double 99%	Abc result Varchar(1) 100%
1	1911-10-29	Friendly	0	Luxembourg	Luxembourg	France	4	Luxembourg	1	2	1	5	1.758312020460358	0.21483375959079284	0.5036496	0.5362318840579711	0.36363636363636365	0.5488721804511278	UEFA	0.5321100917431193	1.3427109974424551	0.4006024	0.2	0.48081841432225064	0	...	1	0	0	0.1	0.0	0.2	[null]	[null]	[null]	0.1	0.0	0.2	[null]	0.3333333333333333	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]	[null]	1
2	1913-04-20	Friendly	0	France	Saint-Ouen	France	8	Luxembourg	0	1	1	5	1.758312020460358	0.21483375959079284	0.5036496	0.5362318840579711	0.36363636363636365	0.5488721804511278	UEFA	0.5321100917431193	1.3427109974424551	0.4006024	0.2	0.48081841432225064	0	...	1	0	0	0.6	0.6666666666666666	0.0	0.0	0.0	0.0	0.6	0.6666666666666666	0.0	0.0	0.0	0.0	1.0	1.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	1
3	1914-02-08	Friendly	0	Luxembourg	Luxembourg	France	4	Luxembourg	5	2	1	5	1.758312020460358	0.21483375959079284	0.5036496	0.5362318840579711	0.36363636363636365	0.5488721804511278	UEFA	0.5321100917431193	1.3427109974424551	0.4006024	0.2	0.48081841432225064	0	...	0	0	1	0.7	1.0	0.0	0.0	0.0	0.0	0.7	1.0	0.0	0.0	0.0	0.0	1.0	1.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	2
4	1927-05-21	Friendly	0	Luxembourg	Esch-sur-Alzette	England	5	Luxembourg	2	2	1	5	2.19937369519833	0.24112734864300625	0.4525547	0.41935483870967744	0.5308641975308642	0.5968992248062015	UEFA	0.6187845303867403	0.9926931106471816	0.3885542	0.0	0.5657620041753654	0	...	1	0	0	0.4	0.6666666666666666	0.4	0.2	0.3333333333333333	0.2	0.9	1.0	0.0	0.2	0.3333333333333333	0.2	0.6666666666666666	0.6666666666666666	0.0	[null]	[null]	0.3333333333333333	0.0	0.6666666666666666	0.0	1
5	1934-03-11	FIFA World Cup qualification	0	Luxembourg	Luxembourg	Germany	9	Luxembourg	1	2	4	5	2.0998322147651005	0.2080536912751678	0.8759124	0.6	0.4702127659574468	0.6242774566473989	UEFA	0.6083916083916084	1.179530201342282	0.5210843	0.3	0.5553691275167785	0	...	1	0	0	0.5	1.0	0.2	0.14285714285714285	0.0	0.4	0.5	0.3333333333333333	0.2	0.14285714285714285	0.3333333333333333	0.4	0.75	0.75	0.0	[null]	[null]	0.25	0.0	0.75	0.0	1
6	1934-04-15	FIFA World Cup qualification	0	Luxembourg	Luxembourg	France	6	Luxembourg	1	2	1	5	1.758312020460358	0.21483375959079284	0.5036496	0.5362318840579711	0.36363636363636365	0.5488721804511278	UEFA	0.5321100917431193	1.3427109974424551	0.4006024	0.2	0.48081841432225064	0	...	1	0	0	0.3	0.3333333333333333	0.0	0.125	0.0	0.4	0.3	0.3333333333333333	0.0	0.0	0.0	0.0	0.6666666666666666	0.6666666666666666	0.0	0.6666666666666666	0.0	0.2	0.0	0.8	0.0	1
7	1935-08-18	Friendly	0	Luxembourg	Luxembourg	Germany	1	Luxembourg	0	2	4	5	2.0998322147651005	0.2080536912751678	0.8759124	0.6	0.4702127659574468	0.6242774566473989	UEFA	0.6083916083916084	1.179530201342282	0.5210843	0.3	0.5553691275167785	0	...	1	0	0	0.7	0.3333333333333333	0.2	0.1111111111111111	0.0	0.4	0.7	0.3333333333333333	0.2	0.14285714285714285	0.0	0.4	1.0	1.0	0.0	1.0	0.0	0.2	0.0	0.8	0.0	1
8	1936-09-27	Friendly	0	Germany	Krefeld	Germany	7	Luxembourg	2	1	4	5	2.0998322147651005	0.2080536912751678	0.8759124	0.6	0.4702127659574468	0.6242774566473989	UEFA	0.6083916083916084	1.179530201342282	0.5210843	0.3	0.5553691275167785	0	...	1	0	0	0.7	0.3333333333333333	0.2	0.1	0.0	0.2	0.7	0.3333333333333333	0.2	0.1111111111111111	0.0	0.4	1.0	1.0	0.0	1.0	0.0	0.2	0.0	0.8	0.0	1
9	1937-03-21	Friendly	0	Luxembourg	Luxembourg	Germany	3	Luxembourg	2	2	4	5	2.0998322147651005	0.2080536912751678	0.8759124	0.6	0.4702127659574468	0.6242774566473989	UEFA	0.6083916083916084	1.179530201342282	0.5210843	0.3	0.5553691275167785	0	...	1	0	0	0.4	0.0	0.4	0.1	0.0	0.0	0.4	0.3333333333333333	0.4	0.1	0.0	0.2	1.0	1.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	1
10	1938-03-20	Friendly	0	Germany	Wuppertal	Germany	2	Luxembourg	1	1	4	5	2.0998322147651005	0.2080536912751678	0.8759124	0.6	0.4702127659574468	0.6242774566473989	UEFA	0.6083916083916084	1.179530201342282	0.5210843	0.3	0.5553691275167785	0	...	1	0	0	0.9	0.6666666666666666	0.2	0.0	0.0	0.0	0.7	0.3333333333333333	0.4	0.1	0.0	0.0	1.0	1.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	1
11	1939-03-26	Friendly	0	Luxembourg	Differdange	Germany	1	Luxembourg	2	2	4	5	2.0998322147651005	0.2080536912751678	0.8759124	0.6	0.4702127659574468	0.6242774566473989	UEFA	0.6083916083916084	1.179530201342282	0.5210843	0.3	0.5553691275167785	0	...	0	0	1	0.5	1.0	0.0	0.0	0.0	0.0	0.6	1.0	0.0	0.0	0.0	0.0	1.0	1.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	2
12	1951-12-23	Friendly	0	Germany	Essen	Germany	4	Luxembourg	1	1	4	5	2.0998322147651005	0.2080536912751678	0.8759124	0.6	0.4702127659574468	0.6242774566473989	UEFA	0.6083916083916084	1.179530201342282	0.5210843	0.3	0.5553691275167785	0	...	1	0	0	0.7	0.6666666666666666	0.0	0.2	0.3333333333333333	0.4	0.7	0.6666666666666666	0.0	0.4	0.3333333333333333	0.4	0.8	0.6666666666666666	0.0	0.8	0.0	0.2	0.0	0.8	0.0	1
13	1952-04-20	Friendly	0	Luxembourg	Luxembourg	Germany	3	Luxembourg	0	2	4	5	2.0998322147651005	0.2080536912751678	0.8759124	0.6	0.4702127659574468	0.6242774566473989	UEFA	0.6083916083916084	1.179530201342282	0.5210843	0.3	0.5553691275167785	0	...	1	0	0	0.8	0.6666666666666666	0.0	0.2	0.3333333333333333	0.2	0.8	0.6666666666666666	0.0	0.3	0.3333333333333333	0.4	0.8	0.6666666666666666	0.0	0.8	0.0	0.2	0.0	0.8	0.0	1
14	1953-09-20	FIFA World Cup qualification	0	Luxembourg	Luxembourg	France	6	Luxembourg	1	2	1	5	1.758312020460358	0.21483375959079284	0.5036496	0.5362318840579711	0.36363636363636365	0.5488721804511278	UEFA	0.5321100917431193	1.3427109974424551	0.4006024	0.2	0.48081841432225064	0	...	1	0	0	0.6	0.3333333333333333	0.2	0.2	0.3333333333333333	0.2	0.25	0.0	0.5	0.0	0.0	0.0	0.75	0.6666666666666666	0.0	0.75	0.0	0.2	0.0	0.8	0.0	1
15	1953-12-17	FIFA World Cup qualification	0	France	Paris	France	8	Luxembourg	0	1	1	5	1.758312020460358	0.21483375959079284	0.5036496	0.5362318840579711	0.36363636363636365	0.5488721804511278	UEFA	0.5321100917431193	1.3427109974424551	0.4006024	0.2	0.48081841432225064	0	...	1	0	0	0.5	0.3333333333333333	0.0	0.1	0.0	0.0	0.5714285714285714	1.0	0.2	0.0	0.0	0.0	0.8	0.6666666666666666	0.0	0.8	0.0	0.2	0.0	0.8	0.0	1
16	1954-03-28	FIFA World Cup qualification	0	Saarland	Saarbrücken	Germany	3	Saarland	1	2	4	5	2.0998322147651005	0.2080536912751678	0.8759124	0.6	0.4702127659574468	0.6242774566473989	UEFA	0.6083916083916084	1.179530201342282	0.5210843	0.3	0.5553691275167785	0	...	1	0	0	0.3	0.3333333333333333	0.8	0.0	0.0	1.0	0.8333333333333334	0.6666666666666666	0.2	0.0	0.0	1.0	0.8	0.8	0.0	0.8	0.0	[null]	[null]	0.8	0.0	1
17	1954-06-05	Friendly	0	Saarland	Saarbrücken	Uruguay	7	Saarland	1	2	1	5	1.5756853396901074	0.24553039332538737	0.4452555	0.4098360655737705	0.3007246376811594	0.5060240963855421	CONMEBOL	0.5	1.264600715137068	1.0	0.8	0.4302741358760429	0	...	1	0	0	0.4	0.0	0.4	0.0	0.0	0.5	0.2	0.0	0.4	0.0	0.3333333333333333	0.5	1.0	1.0	0.0	[null]	[null]	0.0	0.0	1.0	0.0	1
18	1957-03-10	Friendly	0	German DR	Berlin	Germany	3	Luxembourg	0	1	4	5	2.0998322147651005	0.2080536912751678	0.8759124	0.6	0.4702127659574468	0.6242774566473989	UEFA	0.6083916083916084	1.179530201342282	0.5210843	0.3	0.5553691275167785	0	...	1	0	0	0.4	0.3333333333333333	0.0	0.1	0.0	0.0	0.5	0.6666666666666666	0.0	0.2	0.3333333333333333	0.4	0.8	0.6666666666666666	0.0	0.8	0.0	0.2	0.0	1.0	0.0	1
19	1960-10-19	FIFA World Cup qualification	0	Luxembourg	Luxembourg	England	9	Luxembourg	0	2	1	5	2.19937369519833	0.24112734864300625	0.4525547	0.41935483870967744	0.5308641975308642	0.5968992248062015	UEFA	0.6187845303867403	0.9926931106471816	0.3885542	0.0	0.5657620041753654	0	...	1	0	0	0.3	0.3333333333333333	0.4	0.0	0.0	0.0	0.9	0.6666666666666666	0.2	0.0	0.0	0.0	1.0	1.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	1
20	1961-09-28	FIFA World Cup qualification	0	England	London	England	4	Luxembourg	1	1	1	5	2.19937369519833	0.24112734864300625	0.4525547	0.41935483870967744	0.5308641975308642	0.5968992248062015	UEFA	0.6187845303867403	0.9926931106471816	0.3885542	0.0	0.5657620041753654	0	...	1	0	0	0.7	0.3333333333333333	0.2	0.0	0.0	0.0	0.8	0.3333333333333333	0.4	0.0	0.0	0.0	1.0	1.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	1

Machine Learning

It’s time to make predictions about the outcomes of games. We have a lot of variables, so we need trees deep enough to pick up the most important features. We also need to consider a minimum number of games in each leaf to avoid over-fitting.

predictors = all_matchs.get_columns(
    exclude_columns = [
        "match_sample",
        "team2_score",
        "team1_score",
        "date",
        "city",
        "country",
        "result",
        "victory_team1",
        "victory_team2",
        "draw",
        "tournament",
        "team1",
        "team2",
        "confederation_1",
        "confederation_2",
    ],
)

from vastorbit.machine_learning.vast import RandomForestClassifier

model = RandomForestClassifier(
    n_estimators = 4,
    max_depth = 3,
)
model.fit(
    "football_train",
    predictors,
    "result",
    "football_test",
)

model.classification_report()

	1	2	X	avg_macro	avg_weighted	avg_micro
auc	0.6271499375481677	0.7142270975431444	0	0.4471256783637707	0.5013063070834367	[null]
prc_auc	0.6293187232280789	0.5343307662858413	0.6195932028836251	0.5944142307991818	0.5999759085176515	[null]
accuracy	0.6514992109416097	0.7117306680694372	0.759863229879011	0.7076977029633525	0.6946466476709703	0.7076977029633527
log_loss	33.158074422223756	20.947486706798067	49.567234712984636	34.55759861400215	33.62675354507739	[null]
precision	0.5971049457177322	0.49429657794676807	0.0	0.36380050788816676	0.4244872021615245	0.5615465544450289
recall	0.8213495575221239	0.6012950971322849	0.0	0.47421488488480296	0.5615465544450289	0.5615465544450289
f1_score	0.6915017462165308	0.5425709515859767	0.0	0.4113575659341692	0.4831021451404336	0.5615465544450289
mcc	0.3347623387328022	0.3384717883764017	0.0	0.224411375703068	0.2554282776601253	0.3423198316675434
informedness	0.31884203495442076	0.35689965428039216	0.0	0.22524722974493763	0.25309703458040417	0.3423198316675433
markedness	0.3514775692918768	0.32099541188323766	-0.240136770120989	0.14411207035137516	0.20074240252630984	0.3423198316675433
csi	0.5284697508896797	0.3722794959908362	0.0	0.300249748960172	0.35715608752620587	0.39038215395867615

Our model is excellent! 57% of accuracy on 3 categories - it’s almost twice as good as a random model.

model.score(metric = "accuracy")

Looking at the importance of each feature, it seems like direct confrontations and victories against teams of another rank seem to be the strongest indicators of a team’s success.

model.features_importance()

Let’s add the predictions to the VastFrame.

Draws are pretty rare, so we’ll only consider them if a tie was very likely to occur.

test = vo.VastFrame("football_test")
model.predict_proba(test, name = "prob_1", pos_label = "1")
model.predict_proba(test, name = "prob_X", pos_label = "X")
model.predict_proba(test, name = "prob_2", pos_label = "2")
# Materialize the probabilities so the decision rule below compares stored
# columns instead of re-deriving the forest SQL for every reference.
vo.drop("football_pred", method = "table")
test.to_db(name = "football_pred", relation_type = "table")
test = vo.VastFrame("football_pred")
test.case_when(
    "prediction",
    test["prob_1"] > test["prob_2"] + 0.05, "1",
    test["prob_2"] > test["prob_1"] + 0.05, "2",
    (test["prob_X"] > test["prob_1"]) & (test["prob_X"] > test["prob_2"]), "X",
    fun.abs(test["prob_1"] - test["prob_2"]) < 0.03, "X",
    test["prob_1"] > test["prob_2"], "1",
    test["prob_1"] < test["prob_2"], "2",
)

	📅 date Date 100%	Abc tournament Varchar(50) 100%	123 neutral Integer 100%	Abc country Varchar(50) 100%	Abc city Varchar(50) 100%	Abc team1 Varchar(50) 100%	123 team1_score Integer 100%	Abc team2 Varchar(50) 100%	123 team2_score Integer 100%	123 match_sample Integer 100%	123 nb_world_cup_1 Bigint 99%	123 fifa_rank_1 Integer 99%	123 avg_goals_1 Double 99%	123 percent_draw_1 Double 99%	123 number_games_world_tournament_1 Decimal(28,7) 99%	123 percent_victory_world_tournament_1 Double 99%	123 percent_victory_away_1 Double 99%	123 percent_victory_continental_tournament_1 Double 99%	Abc confederation_1 Varchar(8) 99%	123 percent_victory_home_1 Double 99%	123 avg_goals_conceded_1 Double 99%	123 number_games_continental_tournament_1 Decimal(28,7) 99%	123 nb_continental_cup_1 Decimal(27,6) 99%	123 percent_victory_1 Double 99%	123 nb_world_cup_2 Bigint 99%	...	123 avg_victory_team1_1_3 Double 99%	123 avg_draw_team1_1_5 Double 99%	123 avg_victory_team2_1_10 Double 99%	123 avg_victory_team2_1_3 Double 99%	123 avg_draw_team2_1_5 Double 99%	123 avg_victory_same_tournament_team1_1_10 Double 99%	123 avg_victory_same_tournament_team1_1_3 Double 100%	123 avg_draw_same_tournament_team1_1_5 Double 99%	123 avg_victory_same_tournament_team2_1_10 Double 99%	123 avg_victory_same_tournament_team2_1_3 Double 100%	123 avg_draw_same_tournament_team2_1_5 Double 99%	123 avg_victory_direct_team1_1_5 Double 99%	123 avg_victory_direct_team1_1_3 Double 99%	123 avg_draw_direct_team1_1_5 Double 99%	123 avg_victory_rank2_team1_1_5 Double 98%	123 avg_draw_rank2_team1_1_5 Double 98%	123 avg_victory_rank1_team2_1_5 Double 99%	123 avg_draw_rank1_team2_1_5 Double 99%	123 avg_victory_rank1_rank2_team1_1_5 Double 99%	123 avg_draw_rank1_rank2_team1_1_5 Double 99%	Abc result Varchar(1) 100%	123 prob_1 Decimal(16,12) 100%	123 prob_x Decimal(16,12) 100%	123 prob_2 Decimal(16,12) 100%	Abc prediction Varchar(1) 100%
1	2016-03-23	CFU Caribbean Cup qualification	0	Martinique	Fort-de-France	Martinique	3	British Virgin Islands	0	1	0	1	1.8664259927797835	0.259927797833935	0.0	0.0	0.4260355029585799	0.18181818181818182	CONCACAF	0.5463917525773195	1.2743682310469313	0.0331325	0.0	0.4584837545126354	0	...	0.6666666666666666	0.4	0.1	0.0	0.2	0.4	0.0	0.8	0.0	0.0	0.0	0.6666666666666666	0.6666666666666666	0.3333333333333333	1.0	0.0	0.0	0.2	0.2	0.4	1	1.0	1.0	1.0	X
2	2016-03-26	CFU Caribbean Cup qualification	0	British Virgin Islands	Road Town	British Virgin Islands	0	Dominica	7	1	0	1	0.8765432098765432	0.16049382716049382	0.0	0.0	0.16	0.0	OFC	0.36363636363636365	3.111111111111111	0.0271084	0.0	0.19753086419753085	0	...	0.0	0.2	0.2	0.3333333333333333	0.2	0.0	0.0	0.0	0.2	0.0	0.2	0.0	0.0	0.2	0.0	0.2	0.4	0.2	0.4	0.2	2	0.0	0.0	0.0	X
3	2016-03-29	CFU Caribbean Cup qualification	0	Dominica	Roseau	Dominica	1	Martinique	4	1	0	1	1.1393939393939394	0.20606060606060606	0.0	0.0	0.1574074074074074	0.11764705882352941	OFC	0.475	2.090909090909091	0.0512048	0.0	0.23030303030303031	0	...	0.6666666666666666	0.2	0.5	0.6666666666666666	0.2	0.2	0.3333333333333333	0.2	0.5	0.3333333333333333	0.6	0.0	0.0	0.0	0.4	0.2	1.0	0.0	0.6	0.0	2	0.25	0.25	0.25	X
4	2016-05-27	Friendly	0	France	Ajaccio	Corsica	1	Basque Country	1	1	0	1	0.8	0.4	0.0	0.0	0.0	0.0	OFC	0.4	0.6	0.0	0.0	0.4	0	...	0.6666666666666666	0.4	0.7	0.6666666666666666	0.2	0.4	0.6666666666666666	0.4	0.7	0.6666666666666666	0.2	0.6	0.6	0.0	[null]	[null]	0.2	0.6	0.6	0.0	X	0.75	0.75	0.75	X
5	2016-05-29	CONIFA World Football Cup	1	Georgia	Gagra	Padania	1	Northern Cyprus	2	1	0	1	2.5	0.125	0.0	0.0	0.8333333333333334	0.0	OFC	1.0	0.5625	0.0	0.0	0.875	0	...	0.6666666666666666	0.4	0.6	0.3333333333333333	0.0	0.3333333333333333	0.3333333333333333	0.6666666666666666	0.6	0.3333333333333333	0.0	0.8	0.8	0.2	0.8	0.2	0.5	0.0	0.4	0.4	2	0.75	0.75	0.75	X
6	2016-06-02	CONIFA World Football Cup	1	Georgia	Suhkumi	Iraqi Kurdistan	2	Padania	2	1	0	1	2.1	0.25	0.0	0.0	0.35294117647058826	0.0	OFC	1.0	1.15	0.0	0.0	0.45	0	...	0.6666666666666666	0.6	0.7	0.6666666666666666	0.4	0.42857142857142855	0.6666666666666666	0.6	0.25	0.0	0.5	0.0	0.0	0.2	0.2	0.2	0.6	0.2	0.2	0.6	X	0.0	0.0	0.0	X
7	2016-06-05	CONIFA World Football Cup	1	Georgia	Suhkumi	Padania	0	Northern Cyprus	2	1	0	1	2.5	0.125	0.0	0.0	0.8333333333333334	0.0	OFC	1.0	0.5625	0.0	0.0	0.875	0	...	0.3333333333333333	0.2	0.5	0.3333333333333333	0.2	0.3333333333333333	0.3333333333333333	0.6	0.5	0.3333333333333333	0.25	0.0	0.0	0.0	0.4	0.4	0.6666666666666666	0.0	0.4	0.4	2	0.25	0.25	0.25	X
8	2016-07-02	EAFF Championship	1	Guam	Dededo	Mongolia	0	Chinese Taipei	2	1	0	1	1.0888888888888888	0.08888888888888889	0.0	0.0	0.2413793103448276	0.21428571428571427	OFC	1.0	3.2222222222222223	0.0421687	0.0	0.26666666666666666	0	...	0.6666666666666666	0.2	0.2	0.0	0.0	0.4	0.0	0.2	0.42857142857142855	0.0	0.2	0.4	0.4	0.2	0.4	0.2	0.6	0.4	0.2	0.4	2	0.75	0.75	0.75	X
9	2016-10-21	AFF Championship	1	Cambodia	Phnom Penh	Laos	4	Brunei	3	1	0	1	1.0229007633587786	0.15267175572519084	0.0	0.0	0.17391304347826086	0.08	AFC	0.42857142857142855	3.3587786259541983	0.0753012	0.0	0.183206106870229	0	...	0.3333333333333333	0.2	0.2	0.3333333333333333	0.0	0.4	0.3333333333333333	0.0	0.3	0.3333333333333333	0.0	1.0	1.0	0.0	0.6	0.4	0.0	0.0	0.4	0.0	1	1.0	1.0	1.0	X
10	2016-11-09	AFC Challenge Cup	1	Malaysia	Kuching	Mongolia	0	Laos	3	1	0	1	1.0888888888888888	0.08888888888888889	0.0	0.0	0.2413793103448276	0.21428571428571427	OFC	1.0	3.2222222222222223	0.0421687	0.0	0.26666666666666666	0	...	0.6666666666666666	0.2	0.3	0.6666666666666666	0.0	0.5	0.5	0.0	0.2	0.3333333333333333	0.2	0.0	0.0	0.5	0.2	0.2	0.6	0.4	0.6	0.0	2	0.5	0.5	0.5	X
11	2016-11-14	AFC Challenge Cup	1	Malaysia	Kuching	Laos	3	Brunei	2	1	0	1	1.0229007633587786	0.15267175572519084	0.0	0.0	0.17391304347826086	0.08	AFC	0.42857142857142855	3.3587786259541983	0.0753012	0.0	0.183206106870229	0	...	0.3333333333333333	0.2	0.2	0.3333333333333333	0.2	0.2857142857142857	0.3333333333333333	0.4	0.3333333333333333	0.3333333333333333	0.4	1.0	1.0	0.0	0.8	0.2	0.0	0.0	0.4	0.0	1	1.0	1.0	1.0	X
12	2017-04-22	African Nations Championship	0	Mauritius	Belle Vue Maurel	Mauritius	2	Seychelles	1	1	0	1	1.3286384976525822	0.2112676056338028	0.0	0.0	0.2903225806451613	0.047619047619047616	CAF	0.35294117647058826	1.699530516431925	0.063253	0.0	0.2863849765258216	0	...	0.0	0.2	0.1	0.0	0.0	0.2	0.0	0.2	0.0	0.0	0.0	0.4	0.6666666666666666	0.2	0.4	0.2	0.4	0.2	0.4	0.0	1	0.75	0.75	0.75	X
13	2017-04-29	African Nations Championship	0	Seychelles	Victoria	Seychelles	1	Mauritius	1	1	0	1	0.7294117647058823	0.1411764705882353	0.0	0.0	0.0425531914893617	0.0	CAF	0.4166666666666667	2.0588235294117645	0.0421687	0.0	0.1411764705882353	0	...	0.0	0.0	0.3	0.3333333333333333	0.2	0.0	0.0	0.0	0.3333333333333333	0.3333333333333333	0.2	0.2	0.0	0.2	0.2	0.2	0.6	0.2	0.4	0.2	X	0.0	0.0	0.0	X
14	2017-06-10	CONIFA European Football Cup	0	Northern Cyprus	Nicosia	Northern Cyprus	1	Padania	1	1	0	1	2.2666666666666666	0.06666666666666667	0.0	0.0	0.4	0.0	OFC	0.8	1.2	0.0	0.0	0.5333333333333333	0	...	0.6666666666666666	0.2	0.5	0.3333333333333333	0.4	0.6666666666666666	0.6666666666666666	0.3333333333333333	0.6666666666666666	0.3333333333333333	0.4	1.0	1.0	0.0	0.75	0.0	0.2	0.4	0.2	0.6	X	0.75	0.75	0.75	X
15	2017-06-25	Island Games	1	Sweden	Dalhem	Guernsey	1	Åland Islands	1	1	0	1	2.1492537313432836	0.22388059701492538	0.0	0.0	0.4716981132075472	0.0	OFC	0.5	1.2686567164179106	0.0	0.0	0.47761194029850745	0	...	1.0	0.0	0.3	0.0	0.2	0.9	1.0	0.0	0.3	0.0	0.2	0.3333333333333333	0.3333333333333333	0.3333333333333333	0.8	0.0	0.2	0.2	0.2	0.8	X	0.75	0.75	0.75	X
16	2017-06-26	Island Games	1	Sweden	Stenkyrka	Shetland	0	Guernsey	3	1	0	1	1.627906976744186	0.16279069767441862	0.0	0.0	0.34210526315789475	0.0	OFC	0.6	1.8604651162790697	0.0	0.0	0.37209302325581395	0	...	0.3333333333333333	0.0	0.8	0.6666666666666666	0.2	0.4	0.3333333333333333	0.0	0.8	0.6666666666666666	0.2	0.6666666666666666	0.6666666666666666	0.0	0.4	0.2	0.6	0.2	0.0	1.0	2	0.75	0.75	0.75	X
17	2017-06-27	Island Games	1	Sweden	Visby	Åland Islands	2	Shetland	1	1	0	1	1.619047619047619	0.09523809523809523	0.0	0.0	0.42424242424242425	0.0	OFC	0.5555555555555556	1.7380952380952381	0.0	0.0	0.4523809523809524	0	...	0.0	0.6	0.4	0.3333333333333333	0.0	0.2	0.0	0.6	0.4	0.3333333333333333	0.0	0.75	0.6666666666666666	0.0	0.0	0.4	0.4	0.2	0.2	0.6	1	0.5	0.5	0.5	X
18	2017-06-29	Island Games	0	Sweden	Visby	Gotland	2	Jersey	3	1	0	1	2.4615384615384617	0.15384615384615385	0.0	0.0	0.2857142857142857	0.0	OFC	0.6	2.0	0.0	0.0	0.34615384615384615	0	...	0.6666666666666666	0.0	0.7	0.6666666666666666	0.2	0.3	0.6666666666666666	0.0	0.7	0.6666666666666666	0.2	0.0	0.0	0.5	0.0	0.2	0.8	0.0	0.6	0.0	2	0.0	0.0	0.0	X
19	2017-07-08	Gold Cup	1	United States	Nashville	Martinique	2	Nicaragua	0	1	0	1	1.8664259927797835	0.259927797833935	0.0	0.0	0.4260355029585799	0.18181818181818182	CONCACAF	0.5463917525773195	1.2743682310469313	0.0331325	0.0	0.4584837545126354	0	...	0.3333333333333333	0.2	0.2	0.0	0.2	0.2	0.3333333333333333	0.0	0.0	0.0	0.0	1.0	1.0	0.0	1.0	0.0	1.0	0.0	0.6	0.0	1	1.0	1.0	1.0	X
20	2017-10-05	Friendly	0	Chinese Taipei	Taipei	Chinese Taipei	4	Mongolia	2	1	0	1	1.3189655172413792	0.15517241379310345	0.0	0.0	0.25316455696202533	0.11428571428571428	AFC	0.5	2.853448275862069	0.1054217	0.0	0.21551724137931033	0	...	0.3333333333333333	0.2	0.4	0.3333333333333333	0.0	0.1	0.0	0.2	0.0	0.0	0.0	1.0	1.0	0.0	0.6	0.4	0.0	0.2	0.4	0.0	1	1.0	1.0	1.0	X

Let’s look at our predictions for the 2018 World Cup.

test.search(
    conditions = [test["tournament"] == 'FIFA World Cup'],
    usecols = [
        "date",
        "team1",
        "result",
        "prediction",
        "team2",
        "prob_1",
        "prob_X",
        "prob_2",
    ],
    order_by = ["date"],
).head(128)

	📅 date Date 100%	Abc team1 Varchar(50) 100%	Abc result Varchar(1) 100%	Abc prediction Varchar(1) 100%	Abc team2 Varchar(50) 100%	123 prob_1 Decimal(16,12) 100%	123 prob_x Decimal(16,12) 100%	123 prob_2 Decimal(16,12) 100%
1	2018-06-14	Russia	1	X	Saudi Arabia	0.75	0.75	0.75
2	2018-06-15	Morocco	2	X	Iran	0.5	0.5	0.5
3	2018-06-15	Portugal	X	X	Spain	0.75	0.75	0.75
4	2018-06-15	Egypt	2	X	Uruguay	0.75	0.75	0.75
5	2018-06-16	Croatia	1	X	Nigeria	1.0	1.0	1.0
6	2018-06-16	Peru	2	X	Denmark	1.0	1.0	1.0
7	2018-06-16	Argentina	X	X	Iceland	1.0	1.0	1.0
8	2018-06-16	France	1	X	Australia	1.0	1.0	1.0
9	2018-06-17	Brazil	X	X	Switzerland	1.0	1.0	1.0
10	2018-06-17	Costa Rica	2	X	Serbia	0.75	0.75	0.75
11	2018-06-17	Germany	2	X	Mexico	1.0	1.0	1.0
12	2018-06-18	Sweden	1	X	South Korea	0.5	0.5	0.5
13	2018-06-18	Tunisia	2	X	England	0.25	0.25	0.25
14	2018-06-18	Belgium	1	X	Panama	1.0	1.0	1.0
15	2018-06-19	Poland	2	X	Senegal	1.0	1.0	1.0
16	2018-06-19	Russia	1	X	Egypt	1.0	1.0	1.0
17	2018-06-19	Colombia	2	X	Japan	0.75	0.75	0.75
18	2018-06-20	Iran	2	X	Spain	0.5	0.5	0.5
19	2018-06-20	Portugal	1	X	Morocco	0.75	0.75	0.75
20	2018-06-20	Uruguay	1	X	Saudi Arabia	1.0	1.0	1.0
21	2018-06-21	France	1	X	Peru	1.0	1.0	1.0
22	2018-06-21	Denmark	X	X	Australia	1.0	1.0	1.0
23	2018-06-21	Argentina	2	X	Croatia	0.75	0.75	0.75
24	2018-06-22	Serbia	2	X	Switzerland	1.0	1.0	1.0
25	2018-06-22	Brazil	1	X	Costa Rica	1.0	1.0	1.0
26	2018-06-22	Nigeria	1	X	Iceland	1.0	1.0	1.0
27	2018-06-23	Belgium	1	X	Tunisia	1.0	1.0	1.0
28	2018-06-23	Germany	1	X	Sweden	0.75	0.75	0.75
29	2018-06-23	South Korea	2	X	Mexico	1.0	1.0	1.0
30	2018-06-24	Poland	2	X	Colombia	0.75	0.75	0.75
31	2018-06-24	Japan	X	X	Senegal	1.0	1.0	1.0
32	2018-06-24	England	1	X	Panama	1.0	1.0	1.0
33	2018-06-25	Russia	2	X	Uruguay	0.75	0.75	0.75
34	2018-06-25	Saudi Arabia	1	X	Egypt	0.75	0.75	0.75
35	2018-06-25	Iran	X	X	Portugal	0.75	0.75	0.75
36	2018-06-25	Spain	X	X	Morocco	1.0	1.0	1.0
37	2018-06-26	Iceland	2	X	Croatia	0.25	0.25	0.25
38	2018-06-26	Australia	2	X	Peru	1.0	1.0	1.0
39	2018-06-26	Denmark	X	X	France	0.5	0.5	0.5
40	2018-06-26	Nigeria	2	X	Argentina	0.5	0.5	0.5
41	2018-06-27	Serbia	2	X	Brazil	0.25	0.25	0.25
42	2018-06-27	South Korea	1	X	Germany	0.5	0.5	0.5
43	2018-06-27	Switzerland	X	X	Costa Rica	1.0	1.0	1.0
44	2018-06-27	Mexico	2	X	Sweden	1.0	1.0	1.0
45	2018-06-28	Japan	2	X	Poland	1.0	1.0	1.0
46	2018-06-28	Senegal	2	X	Colombia	0.5	0.5	0.5
47	2018-06-28	Panama	2	X	Tunisia	0.75	0.75	0.75
48	2018-06-28	England	2	X	Belgium	1.0	1.0	1.0
49	2018-06-30	France	1	X	Argentina	0.75	0.75	0.75
50	2018-06-30	Uruguay	1	X	Portugal	1.0	1.0	1.0
51	2018-07-01	Russia	X	X	Spain	0.5	0.5	0.5
52	2018-07-01	Croatia	X	X	Denmark	1.0	1.0	1.0
53	2018-07-02	Belgium	1	X	Japan	1.0	1.0	1.0
54	2018-07-02	Brazil	1	X	Mexico	1.0	1.0	1.0
55	2018-07-03	Sweden	1	X	Switzerland	1.0	1.0	1.0
56	2018-07-03	Colombia	X	X	England	0.25	0.25	0.25
57	2018-07-06	Uruguay	2	X	France	0.5	0.5	0.5
58	2018-07-06	Brazil	2	X	Belgium	1.0	1.0	1.0
59	2018-07-07	Sweden	2	X	England	0.75	0.75	0.75
60	2018-07-07	Russia	X	X	Croatia	0.75	0.75	0.75
61	2018-07-10	France	1	X	Belgium	0.75	0.75	0.75
62	2018-07-11	Croatia	1	X	England	0.75	0.75	0.75
63	2018-07-14	Belgium	1	X	England	0.75	0.75	0.75
64	2018-07-15	France	1	X	Croatia	0.75	0.75	0.75

Fantastic: we built a very efficient model which predicted that France will win almost all of its games (except the game against Argentina which is really hard to predict). In reality, France did indeed win the 2018 World Cup!

test.search(
    conditions = [
        test["tournament"] == 'FIFA World Cup',
        (test["team1"] == 'France') | (test["team2"] == 'France'),
    ],
    usecols = [
        "date",
        "team1",
        "result",
        "prediction",
        "team2",
        "prob_1",
        "prob_X",
        "prob_2",
    ],
    order_by = ["date"],
).head(128)

	📅 date Date 100%	Abc team1 Varchar(50) 100%	Abc result Varchar(1) 100%	Abc prediction Varchar(1) 100%	Abc team2 Varchar(50) 100%	123 prob_1 Decimal(16,12) 100%	123 prob_x Decimal(16,12) 100%	123 prob_2 Decimal(16,12) 100%
1	2018-06-16	France	1	X	Australia	1.0	1.0	1.0
2	2018-06-21	France	1	X	Peru	1.0	1.0	1.0
3	2018-06-26	Denmark	X	X	France	0.5	0.5	0.5
4	2018-06-30	France	1	X	Argentina	0.75	0.75	0.75
5	2018-07-06	Uruguay	2	X	France	0.5	0.5	0.5
6	2018-07-10	France	1	X	Belgium	0.75	0.75	0.75
7	2018-07-15	France	1	X	Croatia	0.75	0.75	0.75

Conclusion

We’ve solved our problem in a pandas-like way, all without ever loading data into memory!