Women's Football (European Leagues) by Kaggle | Sports

About this Dataset

Women's Football (European Leagues)

Team and Player Performance Statistics

About this dataset

This dataset includes comprehensive female football-related performance data and player statistics from the top 5 European leagues: Serie A in Italy, Liga Femenina in Spain, Women's Super League in England, Bundesliga Frauen in Germany, and Division 1 Feminin in France. Gathered throughout each season of the respective leagues, the dataset tracks teams, players, matches and a range of important performance metrics. The recently released data provides intriguing insight into team success and player form - covering parameters such as goals scored per game (xGHome), clean sheets (CS), number of opponents' passes allowed (Sweeper_#OPA) as well as individual performance stats such as tackles made per goal kick (Crosses_Stp). Analyze this insightful data to gain further insight on how female football is developing across Europe's major leagues!

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How to use the dataset

This dataset can be used to analyze and compare the performance of teams and players across the top five European leagues: Serie A in Italy, Liga Femenina in Spain, Women's Super League in England, Bundesliga Frauen in Germany, and Division 1 Feminin in France. The dataset provides records of each individual match that occurred within these leagues during the tracked season(s), as well as a range of performance metrics for both teams and players.

To use this dataset effectively it is important to understand which columns are available, as described above. By exploring different combinations of team-level versus player-level data you will be able to identify correlations between certain performance metrics for teams or players that provide insights about female football success across Europe.

Once you’re ready to start exploring the data there are several approaches you may take from visualizing your data via bar or line graphs with Python Matplotlib or Seaborn packages; correlating team-level versus player-level statistics such as number of wins (W) compared against goalkeeper saves (Saves); or performing more complicated regression analyses on your data that explore how different features like time played (Min) can predict goals scored (Goals_FK). Each approach provides unique insights into trends within female football success.

No matter how you choose to analyze this dataset it is important to note that trendlines may shift from year-to year -- so make sure you use consistent periods when comparing changes between seasons! It is also helpful to break down aggregate results by country when analyzing different trends across Europe so consider running separate analyses for each country instead aggregating them all together at once. Using this stepwise approach we hope that through careful exploration of the female football success will begin ‘uncovering’!

Research Ideas

Analyzing the effect of player performance metrics on team success and vice versa: Using this dataset, it is possible to analyze how changes in different player performance metrics might affect overall team performance (e.g. goals scored or allowed, clean sheets). With further analysis, correlations can be drawn between teams’ and players’ performances under different match-day conditions such as travel distance or surface type.

Examining trends in the development of female football: This data set spans multiple seasons, making it possible to evaluate any general trends in aspects such as the average age of the players across countries and how that affects their performances; or identifying any underused opportunities available for young talented footballers in specific countries which could be benefitted from improvisations by these countries' governing bodies;

Benchmark positions used among teams versus outside experts’ opinions: One clever use for this dataset can be to compare positional performances between expert opinions from scouts with actual field results from teams using those positions within each country's top leagues and analyzing areas where consensus is reached upon versus discrepancies found throughout the analyzed data samples . For example, one may cross-examine national team call up rosters with squad selections for clubs’ top female divisions - finding anomalies not spotted prior by those making roster decisions - thereby potentially deriving more informed decisions with regards to selecting position holders based on tangible facts rather than focusing merely on biased subjective eye tests over which player should officially take a particular spot on a lineup card due to proximity or existing relationships attracted through media attention etc..

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: matches-checkpoint.csv

Column name	Description
Wk	Week of the season. (Integer)
Day	Day of the week. (String)
Date	Date of the match. (Date)
Time	Time of the match. (Time)
Home	Home team. (String)
xGHome	Expected goals for the home team. (Float)
Score	Final score of the match. (String)
xGAway	Expected goals for the away team. (Float)
xPHome	Expected points for the home team. (Float)
xPAway	Expected points for the away team. (Float)
ScoreHome	Final score of the home team. (Integer)
ScoreAway	Final score of the away team. (Integer)

File: gk_players.csv

Column name	Description
Rk	Rank of the player in the squad. (Integer)
Player	Name of the player. (String)
Nation	Nationality of the player. (String)
Pos	Position of the player. (String)
Squad	Name of the team the player is in. (String)
Age	Age of the player. (Integer)
Born	Year the player was born. (Integer)
MP	Number of matches played. (Integer)
Starts	Number of matches started. (Integer)
Min	Number of minutes played. (Integer)
GA	Number of goals allowed. (Integer)
SoTA	Number of shots on target allowed. (Integer)
Saves	Number of saves made. (Integer)
W	Number of wins. (Integer)
D	Number of draws. (Integer)
L	Number of losses. (Integer)
CS	Number of clean sheets. (Integer)
PKatt	Number of penalties attempted. (Integer)
PKA	Number of penalties allowed. (Integer)
PKsv	Number of penalties saved. (Integer)
PKm	Number of penalties missed. (Integer)
Matches	Number of matches played. (Integer)
Goals_FK	Number of goals scored from free kicks. (Integer)
Goals_CK	Number of goals scored from corner kicks. (Integer)
Goals_OG	Number of goals scored from own goals. (Integer)
PSxG	Expected goals per shot on target. (Float)
PSxG/SoT	Expected goals per shot on target. (Float)
PSxG+/-	Difference between expected goals and actual goals. (Float)
Launched_Cmp	Number of successful launcher passes. (Integer)
Launched_Att	Number of attempted launcher passes. (Integer)
Passes_Att	Number of passes attempted. (Integer)
Passes_Thr
Passes_AvgLen

File: gk_teams.csv

Column name	Description
Squad	Name of the team the player is in. (String)
MP	Number of matches played. (Integer)
Starts	Number of matches started. (Integer)
Min	Number of minutes played. (Integer)
GA	Number of goals allowed. (Integer)
SoTA	Number of shots on target allowed. (Integer)
Saves	Number of saves made. (Integer)
W	Number of wins. (Integer)
D	Number of draws. (Integer)
L	Number of losses. (Integer)
CS	Number of clean sheets. (Integer)
PKatt	Number of penalties attempted. (Integer)
PKA	Number of penalties allowed. (Integer)
PKsv	Number of penalties saved. (Integer)
PKm	Number of penalties missed. (Integer)
Goals_FK	Number of goals scored from free kicks. (Integer)
Goals_CK	Number of goals scored from corner kicks. (Integer)
Goals_OG	Number of goals scored from own goals. (Integer)
PSxG	Expected goals per shot on target. (Float)
PSxG/SoT	Expected goals per shot on target. (Float)
PSxG+/-	Difference between expected goals and actual goals. (Float)
Launched_Cmp	Number of successful launcher passes. (Integer)
Launched_Att	Number of attempted launcher passes. (Integer)
Passes_Att	Number of passes attempted. (Integer)
Passes_Thr
Passes_AvgLen

File: matches.csv

Column name	Description
Wk	Week of the season. (Integer)
Day	Day of the week. (String)
Date	Date of the match. (Date)
Time	Time of the match. (Time)
Home	Home team. (String)
xGHome	Expected goals for the home team. (Float)
Score	Final score of the match. (String)
xGAway	Expected goals for the away team. (Float)
xPHome	Expected points for the home team. (Float)
xPAway	Expected points for the away team. (Float)
ScoreHome	Final score of the home team. (Integer)
ScoreAway	Final score of the away team. (Integer)

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit .

Tables

All Players Checkpoint

@kaggle.thedevastator_uncovering_female_football_success_in_top_europe.all_players_checkpoint

252.96 KB
1328 rows
111 columns


CREATE TABLE all_players_checkpoint (
  "player" VARCHAR,
  "nation" VARCHAR,
  "pos" VARCHAR,
  "squad" VARCHAR,
  "age" VARCHAR,
  "born" DOUBLE,
  "mp" BIGINT,
  "starts" BIGINT,
  "min" BIGINT,
  "gls" BIGINT,
  "ast" BIGINT,
  "g_pk" BIGINT,
  "pk" BIGINT,
  "pkatt" BIGINT,
  "crdy" BIGINT,
  "crdr" BIGINT,
  "xg" DOUBLE,
  "npxg" DOUBLE,
  "xag" DOUBLE,
  "player_id" VARCHAR,
  "squad_id" VARCHAR,
  "sh" DOUBLE,
  "sot" DOUBLE,
  "g_by_sh" DOUBLE,
  "g_by_sot" DOUBLE,
  "dist" DOUBLE,
  "fk" DOUBLE,
  "npxg_by_sh" DOUBLE,
  "g_xg" DOUBLE,
  "np_g_xg" DOUBLE,
  "total_cmp" DOUBLE,
  "total_att" DOUBLE,
  "total_totdist" DOUBLE,
  "total_prgdist" DOUBLE,
  "xa" DOUBLE,
  "a_xag" DOUBLE,
  "kp" DOUBLE,
  "lastthird" DOUBLE,
  "ppa" DOUBLE,
  "crspa" DOUBLE,
  "prog" DOUBLE,
  "sca" DOUBLE,
  "sca_passlive" DOUBLE,
  "sca_passdead" DOUBLE,
  "sca_drib" DOUBLE,
  "sca_sh" DOUBLE,
  "sca_fld" DOUBLE,
  "sca_def" DOUBLE,
  "gca" DOUBLE,
  "gca_passlive" DOUBLE,
  "gca_passdead" DOUBLE,
  "gca_drib" DOUBLE,
  "gca_sh" DOUBLE,
  "gca_fld" DOUBLE,
  "gca_def" DOUBLE,
  "tackles_tkl" DOUBLE,
  "tackles_tklw" DOUBLE,
  "tackles_def3rd" DOUBLE,
  "tackles_mid3rd" DOUBLE,
  "tackles_att3rd" DOUBLE,
  "vsdribbles_tkl" DOUBLE,
  "vsdribbles_att" DOUBLE,
  "vsdribbles_past" DOUBLE,
  "blocks_blocks" DOUBLE,
  "blocks_sh" DOUBLE,
  "blocks_pass" DOUBLE,
  "int" DOUBLE,
  "tkl_plus_int" DOUBLE,
  "clr" DOUBLE,
  "err" DOUBLE,
  "touches_touches" DOUBLE,
  "touches_defpen" DOUBLE,
  "touches_def3rd" DOUBLE,
  "touches_mid3rd" DOUBLE,
  "touches_att3rd" DOUBLE,
  "touches_attpen" DOUBLE,
  "touches_live" DOUBLE,
  "dribbles_succ" DOUBLE,
  "dribbles_att" DOUBLE,
  "dribbles_mis" DOUBLE,
  "dribbles_dis" DOUBLE,
  "receiving_rec" DOUBLE,
  "receiving_prog" DOUBLE,
  "mn_by_mp" DOUBLE,
  "mn_by_start" DOUBLE,
  "compl" BIGINT,
  "subs" BIGINT,
  "mn_by_sub" DOUBLE,
  "unsub" DOUBLE,
  "ts_ppm" DOUBLE,
  "ts_ong" DOUBLE,
  "ts_onga" DOUBLE,
  "ts_diff" DOUBLE,
  "ts_on_off" DOUBLE,
  "ts_xg_onxg" DOUBLE,
  "ts_xg_onxga" DOUBLE,
  "ts_xg_xgdiff" DOUBLE,
  "ts_xg_on_off" DOUBLE,
  "n_2crdy" BIGINT,
  "fls" DOUBLE
);

All Players

@kaggle.thedevastator_uncovering_female_football_success_in_top_europe.all_players

252.76 KB
1328 rows
111 columns


CREATE TABLE all_players (
  "player" VARCHAR,
  "nation" VARCHAR,
  "pos" VARCHAR,
  "squad" VARCHAR,
  "age" VARCHAR,
  "born" DOUBLE,
  "mp" BIGINT,
  "starts" BIGINT,
  "min" BIGINT,
  "gls" BIGINT,
  "ast" BIGINT,
  "g_pk" BIGINT,
  "pk" BIGINT,
  "pkatt" BIGINT,
  "crdy" BIGINT,
  "crdr" BIGINT,
  "xg" DOUBLE,
  "npxg" DOUBLE,
  "xag" DOUBLE,
  "player_id" VARCHAR,
  "squad_id" VARCHAR,
  "sh" DOUBLE,
  "sot" DOUBLE,
  "g_by_sh" DOUBLE,
  "g_by_sot" DOUBLE,
  "dist" DOUBLE,
  "fk" DOUBLE,
  "npxg_by_sh" DOUBLE,
  "g_xg" DOUBLE,
  "np_g_xg" DOUBLE,
  "total_cmp" BIGINT,
  "total_att" DOUBLE,
  "total_totdist" DOUBLE,
  "total_prgdist" DOUBLE,
  "xa" DOUBLE,
  "a_xag" DOUBLE,
  "kp" DOUBLE,
  "lastthird" DOUBLE,
  "ppa" DOUBLE,
  "crspa" DOUBLE,
  "prog" DOUBLE,
  "sca" DOUBLE,
  "sca_passlive" DOUBLE,
  "sca_passdead" DOUBLE,
  "sca_drib" DOUBLE,
  "sca_sh" DOUBLE,
  "sca_fld" DOUBLE,
  "sca_def" DOUBLE,
  "gca" DOUBLE,
  "gca_passlive" DOUBLE,
  "gca_passdead" DOUBLE,
  "gca_drib" DOUBLE,
  "gca_sh" DOUBLE,
  "gca_fld" DOUBLE,
  "gca_def" DOUBLE,
  "tackles_tkl" DOUBLE,
  "tackles_tklw" DOUBLE,
  "tackles_def3rd" DOUBLE,
  "tackles_mid3rd" DOUBLE,
  "tackles_att3rd" DOUBLE,
  "vsdribbles_tkl" DOUBLE,
  "vsdribbles_att" DOUBLE,
  "vsdribbles_past" DOUBLE,
  "blocks_blocks" DOUBLE,
  "blocks_sh" DOUBLE,
  "blocks_pass" DOUBLE,
  "int" DOUBLE,
  "tkl_plus_int" DOUBLE,
  "clr" DOUBLE,
  "err" DOUBLE,
  "touches_touches" DOUBLE,
  "touches_defpen" DOUBLE,
  "touches_def3rd" DOUBLE,
  "touches_mid3rd" DOUBLE,
  "touches_att3rd" DOUBLE,
  "touches_attpen" DOUBLE,
  "touches_live" DOUBLE,
  "dribbles_succ" DOUBLE,
  "dribbles_att" DOUBLE,
  "dribbles_mis" DOUBLE,
  "dribbles_dis" DOUBLE,
  "receiving_rec" DOUBLE,
  "receiving_prog" DOUBLE,
  "mn_by_mp" DOUBLE,
  "mn_by_start" DOUBLE,
  "compl" BIGINT,
  "subs" BIGINT,
  "mn_by_sub" DOUBLE,
  "unsub" DOUBLE,
  "ts_ppm" DOUBLE,
  "ts_ong" DOUBLE,
  "ts_onga" DOUBLE,
  "ts_diff" DOUBLE,
  "ts_on_off" DOUBLE,
  "ts_xg_onxg" DOUBLE,
  "ts_xg_onxga" DOUBLE,
  "ts_xg_xgdiff" DOUBLE,
  "ts_xg_on_off" DOUBLE,
  "n_2crdy" BIGINT,
  "fls" DOUBLE
);

All Teams

@kaggle.thedevastator_uncovering_female_football_success_in_top_europe.all_teams

89.33 KB
62 rows
106 columns


CREATE TABLE all_teams (
  "squad" VARCHAR,
  "n__pl" BIGINT,
  "age" DOUBLE,
  "poss" DOUBLE,
  "mp" BIGINT,
  "starts" BIGINT,
  "min" BIGINT,
  "gls" BIGINT,
  "ast" BIGINT,
  "g_pk" BIGINT,
  "pk" BIGINT,
  "pkatt" BIGINT,
  "crdy" BIGINT,
  "crdr" BIGINT,
  "xg" DOUBLE,
  "npxg" DOUBLE,
  "xag" DOUBLE,
  "squad_id" VARCHAR,
  "league_id" BIGINT,
  "sh" BIGINT,
  "sot" BIGINT,
  "g_by_sh" DOUBLE,
  "g_by_sot" DOUBLE,
  "dist" DOUBLE,
  "fk" BIGINT,
  "npxg_by_sh" DOUBLE,
  "g_xg" DOUBLE,
  "np_g_xg" DOUBLE,
  "total_cmp" BIGINT,
  "total_att" BIGINT,
  "total_totdist" BIGINT,
  "total_prgdist" BIGINT,
  "xa" DOUBLE,
  "a_xag" DOUBLE,
  "kp" BIGINT,
  "lastthird" BIGINT,
  "ppa" BIGINT,
  "crspa" BIGINT,
  "prog" BIGINT,
  "sca" BIGINT,
  "sca_passlive" BIGINT,
  "sca_passdead" BIGINT,
  "sca_drib" BIGINT,
  "sca_sh" BIGINT,
  "sca_fld" BIGINT,
  "sca_def" BIGINT,
  "gca" BIGINT,
  "gca_passlive" BIGINT,
  "gca_passdead" BIGINT,
  "gca_drib" BIGINT,
  "gca_sh" BIGINT,
  "gca_fld" BIGINT,
  "gca_def" BIGINT,
  "tackles_tkl" BIGINT,
  "tackles_tklw" BIGINT,
  "tackles_def3rd" BIGINT,
  "tackles_mid3rd" BIGINT,
  "tackles_att3rd" BIGINT,
  "vsdribbles_tkl" BIGINT,
  "vsdribbles_att" BIGINT,
  "vsdribbles_past" BIGINT,
  "blocks_blocks" BIGINT,
  "blocks_sh" BIGINT,
  "blocks_pass" BIGINT,
  "int" BIGINT,
  "tkl_plus_int" BIGINT,
  "clr" BIGINT,
  "err" BIGINT,
  "touches_touches" BIGINT,
  "touches_defpen" BIGINT,
  "touches_def3rd" BIGINT,
  "touches_mid3rd" BIGINT,
  "touches_att3rd" BIGINT,
  "touches_attpen" BIGINT,
  "touches_live" BIGINT,
  "dribbles_succ" BIGINT,
  "dribbles_att" BIGINT,
  "dribbles_mis" BIGINT,
  "dribbles_dis" BIGINT,
  "receiving_rec" BIGINT,
  "receiving_prog" BIGINT,
  "mn_by_mp" BIGINT,
  "mn_by_start" BIGINT,
  "compl" BIGINT,
  "subs" BIGINT,
  "mn_by_sub" BIGINT,
  "unsub" BIGINT,
  "ts_ppm" DOUBLE,
  "ts_ong" BIGINT,
  "ts_onga" BIGINT,
  "ts_diff" BIGINT,
  "ts_xg_onxg" DOUBLE,
  "ts_xg_onxga" DOUBLE,
  "ts_xg_xgdiff" DOUBLE,
  "n_2crdy" BIGINT,
  "fls" BIGINT,
  "fld" BIGINT,
  "off" BIGINT,
  "crs" BIGINT,
  "tklw" BIGINT
);

Gk Players

@kaggle.thedevastator_uncovering_female_football_success_in_top_europe.gk_players

40.66 KB
106 rows
43 columns


CREATE TABLE gk_players (
  "rk" BIGINT,
  "player" VARCHAR,
  "nation" VARCHAR,
  "pos" VARCHAR,
  "squad" VARCHAR,
  "age" VARCHAR,
  "born" BIGINT,
  "mp" BIGINT,
  "starts" BIGINT,
  "min" BIGINT,
  "ga" DOUBLE,
  "sota" DOUBLE,
  "saves" DOUBLE,
  "w" BIGINT,
  "d" BIGINT,
  "l" BIGINT,
  "cs" BIGINT,
  "pkatt" BIGINT,
  "pka" BIGINT,
  "pksv" BIGINT,
  "pkm" BIGINT,
  "matches" VARCHAR,
  "goals_fk" DOUBLE,
  "goals_ck" DOUBLE,
  "goals_og" DOUBLE,
  "psxg" DOUBLE,
  "psxg_sot" DOUBLE,
  "psxg_3588aa" DOUBLE,
  "launched_cmp" DOUBLE,
  "launched_att" DOUBLE,
  "passes_att" DOUBLE,
  "passes_thr" DOUBLE,
  "passes_avglen" DOUBLE,
  "goal_kicks_att" DOUBLE,
  "goal_kicks_avglen" DOUBLE,
  "crosses_opp" DOUBLE,
  "crosses_stp" DOUBLE,
  "sweeper_opa" DOUBLE,
  "sweeper_avgdist" DOUBLE,
  "player_id" VARCHAR,
  "nation_id" VARCHAR,
  "squad_id" VARCHAR,
  "matches_id" VARCHAR
);

Gk Teams

@kaggle.thedevastator_uncovering_female_football_success_in_top_europe.gk_teams

30.11 KB
62 rows
37 columns


CREATE TABLE gk_teams (
  "squad" VARCHAR,
  "n__pl" BIGINT,
  "mp" BIGINT,
  "starts" BIGINT,
  "min" BIGINT,
  "ga" BIGINT,
  "sota" BIGINT,
  "saves" BIGINT,
  "w" BIGINT,
  "d" BIGINT,
  "l" BIGINT,
  "cs" BIGINT,
  "pkatt" BIGINT,
  "pka" BIGINT,
  "pksv" BIGINT,
  "pkm" BIGINT,
  "squad_id" VARCHAR,
  "league_id" BIGINT,
  "goals_ga" BIGINT,
  "goals_pka" BIGINT,
  "goals_fk" BIGINT,
  "goals_ck" BIGINT,
  "goals_og" BIGINT,
  "psxg" DOUBLE,
  "psxg_sot" DOUBLE,
  "psxg_3588aa" DOUBLE,
  "launched_cmp" BIGINT,
  "launched_att" BIGINT,
  "passes_att" BIGINT,
  "passes_thr" BIGINT,
  "passes_avglen" DOUBLE,
  "goal_kicks_att" BIGINT,
  "goal_kicks_avglen" DOUBLE,
  "crosses_opp" BIGINT,
  "crosses_stp" BIGINT,
  "sweeper_opa" BIGINT,
  "sweeper_avgdist" DOUBLE
);

Matches Checkpoint

@kaggle.thedevastator_uncovering_female_football_success_in_top_europe.matches_checkpoint

22.92 KB
306 rows
17 columns


CREATE TABLE matches_checkpoint (
  "wk" BIGINT,
  "day" VARCHAR,
  "date" TIMESTAMP,
  "time" VARCHAR,
  "home" VARCHAR,
  "xghome" DOUBLE,
  "score" VARCHAR,
  "xgaway" DOUBLE,
  "away" VARCHAR,
  "home_id" VARCHAR,
  "away_id" VARCHAR,
  "match_id" VARCHAR,
  "league_id" BIGINT,
  "xphome" DOUBLE,
  "xpaway" DOUBLE,
  "scorehome" BIGINT,
  "scoreaway" BIGINT
);

Matches

@kaggle.thedevastator_uncovering_female_football_success_in_top_europe.matches

23 KB
306 rows
17 columns


CREATE TABLE matches (
  "wk" BIGINT,
  "day" VARCHAR,
  "date" TIMESTAMP,
  "time" VARCHAR,
  "home" VARCHAR,
  "xghome" DOUBLE,
  "score" VARCHAR,
  "xgaway" DOUBLE,
  "away" VARCHAR,
  "home_id" VARCHAR,
  "away_id" VARCHAR,
  "match_id" VARCHAR,
  "league_id" BIGINT,
  "xphome" DOUBLE,
  "xpaway" DOUBLE,
  "scorehome" BIGINT,
  "scoreaway" BIGINT
);