Scraped player stats per game from Understat from 2014/2015 to 2024/2025 (still in progress) seasons.

This contains more detailed information than the dataset from https://www.kaggle.com/datasets/codytipton/understat-data, which includes the individual player stats per game for the English Premier League, La Liga, Bundesliga, Serie A, Ligue 1, and the Russian Football Premier League. In particular, it contains each player's xG, xGBuildup, goals, and shots per game. Furthermore, it has the events for each shot in the events table, clubs and their stats per season in the clubs table, and each game with who lost, won, shots, possession, probabilities of who wins, ect..

This is for educational purposes in our data science bootcamp project.

lineup_stats

• match_id: the id for the match they played
• goals: number of goals for this match
• own_goals: number of own goals for this match
• shots: number of shots for this match
• xG: players xG for this match
• **time*: total amount of time this player played in this match
• player_id: player id
• team_id: id for the players team
• position: players position in this match (SUB means they were substituted in)
• player: player's name
• h_a: 'h' if they are in the home team and 'a' if they are in the away team
• yellow_card: number of yellow cards for this match
• red_card: number of red cards for this match
• **roster_in*: (there is roster information in another table that I did not get, will update later)
• roster_out: (same as roster_in)
• key_passes: number of key passes for this match
• assists: number of assists for this match
• xA: expected assists for this match
• xGChain: total xG for every possession the player is involved in this match
• xGBuildup: Total xG for every possession the player is involved in without key passes and shots in this match
• positionOrder: ordering in the lineup

general_game_stats

• id: this game id
• fid: not sure what this is
• h_id: home team id
• a_id: away team id
• date: date of this game
• league_id: id for the league
• season: which season which game was for
• h_goals: number of goals for the home team
• a_goals: number of goals for the away team
• team_h: home team name
• team_a: away team name
• h_xg: home xG
• a_xg: away xG
• h_w: home win probability
• h_d: home draw probability
• h_l: home loss probability
• league: league name
• h_shot: number of shots by the home team
• a_shot: number of shots by the away team
• h_shotOnTarget: number of shots on target by the home team
• a_shotOnTarget: number of shots on target by the away team
• h_deep:home team passes completed within an estimated 20 yards of goal (crosses excluded) -deap_allowed: opponent passes completed within an estimated 20 yards of goal (crosses excluded)
• a_deep: away team passes completed within an estimated 20 yards of goal (crosses excluded) -deap_allowed: opponent passes completed within an estimated 20 yards of goal (crosses excluded)
• h_ppda: home team passes allowed per defensive action in the opposition half.
• a_ppda:away team passes allowed per defensive action in the opposition half.

game_events

• id: id for event
• minute: minute the event happend
• result: result (blocked shot, saved shot, ect..)
• X: x-coordinate where the player took the shot
• Y: y-coordinate where the player took the shot
• xG: the xG for the shot
• player: player's name
• h_a: h for home team or a for away team
• player_id: player's id
• situation: situation where this shot happend (direct free kicks, set piece, open play, ect..)
• season: the match season
• shotType: what type of shot (left foot, right foot, head, ect..)
• ** match_id**: id for the match
• h_team: home team name
• ** a_team**: away team name
• ** h_goals**: number of home goals at this time
• ** a_goals**: number of away goals at this time
• date: date of the match
• ** player_assisted**: player who assisted
• lastAction: the last action before this shot

clubs

• club_id: id for the club
• ** club**: club name
• ** league_id** : league id
• ** league**: league name
• ** season**: which season these stats are from
• ** wins**: number of wins that season
• ** draws**: number of draws that season
• ** losses**: number of losses that season
• ** pts**: number of points for that season
• ** avg_xG**: average xG throughout the season
• ** total_goals**: total amount of goals for this season
• total_goals_cond: total amount of goals conceded this season

This dataset consists of the Premier League team stats for seasons 2022/2023, 2021/2022 and 2022/2021. The data was scraped from fbref.com and formatted into a csv file.

Columns:

date = Date of the match time = Kick-off time of the match comp = Competition of the match (i.e English Premier League) round = The match week the match took place on day = The day the match took place on (i.e Monday, Tuesday etc) venue = Whether team was Home, Away or Neutral venue result = Whether the team Won, Lost or Drew (W, L, D) gf = How many goals the team scored ga = How many goals the team conceded opponent = Who the team faced that day xg = Expected goals xa = Expected goals allowed poss = Possession attendance = How many people attended the match captain = Captain of the team for match formation = Formation the team used for match referee = The referee for the match match report = Please ignore notes = Please ignore sh = Shots total sot = Shots on target dist = average distance by shot fk = shots from free kicks pk = Penalty kicks made pkatt= Penalty kicks attempted season = The year the season took place (i.e for 2022/2023 season year would be 2023) team = The team the stats belong to (i.e Manchester City)

I didn't realise how many soccer games are played each year until I started collecting data. I've been collecting data for about two years now and have nearly 25,000 rows of data. Thats nearly 25,000 soccer games from all leagues all over the world

What makes this data set so detailed is that is contains 1) Statistics on the home and away teams 2) Home win, draw, away win odds and 3) Final result

The fields in the data set are: Columns A to E contains information about the league, home and away teams, date etc Columns F, G and H contain the odds for the home win, draw and away win Columns I to BQ contain the team statistics. Home team stats are prefixed with a "h" similarly, away team stats are prefixed with an "a". Examples include ladder position, games played, goals conceded, away games won etc
Columns BR to CA contain final result information. That is the result, the full time result and if available, the half time score aswell

The dataset ranges from January 2016 to October 2017 and the statistics have been sourced from a few different websites. Odds come from BET365 and the results have been manually entered from http://www.soccerstats.com

The motivations for publishing this data set is twofold: 1) Predictive Model - I am curious to know if a predictive model can be created from this dataset, or are results completely random! 2) Probability - Is it possible to calculate the probability of a home win, draw or away win based on this dataset.

Context The dataset is scraped from many resources and edited by me the top website is Infogol Infogol has league tables and statistics from some of the top competitions from all around the world, including the English Premier League, English Championship, Spanish La Liga, Italian Serie A, German Bundesliga, French Ligue 1, US MLS and Brazilian Série A, . Choose the competition you are interested in to get the actual league table, plus expected and forecast positions based on the Infogol model, along with top scorers and betting odds. Content This dataset includes top football leagues scorers their goals ,Country, Club, matches played ,substitution, min ,Goals, xG,... Note : xG & xG Per Avg Match is a statistical value that is supported by the website I scraped the data from (Infogol) Acknowledgements The data in this dataset has been scraped using Selenium from Infogol website Some leagues in some seasons ate not forund right now because the website not supporting it so in the next update all the seasons will be found

Content

Latest Premier League team standings as of October 13th 2021.

Attributes Information

Team: The team names Played: How many matches played so far Won: How many games won Drawn: How many games drawn Lost: How many games lost For: How many goals scored Against: How many goals conceded Goal Difference: The goal difference is calculated as For minus Against. In case there is tie in total points between two teams, the team with greater goal difference stands above. Points: Total points each team has Form Last 5 Games: Form of last five games

Source

Link - https://www.bbc.com/sport/football/tables robots.txt permissions can be found in https://www.bbc.co.uk/robots.txt

Banner Image: Photo by Tim Bechervaise on Unsplash

Inspiration

• To get a rough idea of the next 7 games and points based on the current standings
• To visualize all team performances based on number of goals scored, conceded and goal difference

Context

Find insights that might help inform betting decisions.

Content

Betting odds and results for European football/soccer leagues from 1993 - 2021. Key to leagues:

England E0 - Premier league E1,E2,E3 - Divisions 1, 2 & 3 respectively

Scotland SC0 - Premier league SC1,SC2,SC3 - Divisions 1, 2 & 3 respectively

Germany D1,D2 - Bundesliga 1 & 2 respectively

Spain SP1,SP2 - La Liga Premera & Segunda respectively

Italy I1,I2 - Serie A & B respectively

France F1,F2 - Le Championnat & Division 2

Netherlands N1 - KPN Eredivisie

Belgium B1 - Jupiler League

Portugal P1 - Liga I

Turkey T1 - Ligi 1

Greece G1 - Ethniki Katigoria

Key to results data:

Div = League Division Date = Match Date (dd/mm/yy) Time = Time of match kick off HomeTeam = Home Team AwayTeam = Away Team FTHG and HG = Full Time Home Team Goals FTAG and AG = Full Time Away Team Goals FTR and Res = Full Time Result (H=Home Win, D=Draw, A=Away Win) HTHG = Half Time Home Team Goals HTAG = Half Time Away Team Goals HTR = Half Time Result (H=Home Win, D=Draw, A=Away Win)

Match Statistics (where available) Attendance = Crowd Attendance Referee = Match Referee HS = Home Team Shots AS = Away Team Shots HST = Home Team Shots on Target AST = Away Team Shots on Target HHW = Home Team Hit Woodwork AHW = Away Team Hit Woodwork HC = Home Team Corners AC = Away Team Corners HF = Home Team Fouls Committed AF = Away Team Fouls Committed HFKC = Home Team Free Kicks Conceded AFKC = Away Team Free Kicks Conceded HO = Home Team Offsides AO = Away Team Offsides HY = Home Team Yellow Cards AY = Away Team Yellow Cards HR = Home Team Red Cards AR = Away Team Red Cards HBP = Home Team Bookings Points (10 = yellow, 25 = red) ABP = Away Team Bookings Points (10 = yellow, 25 = red)

Free Kicks Conceeded includes fouls, offsides and any other offense commmitted and will always be equal to or higher than the number of fouls. Fouls make up the vast majority of Free Kicks Conceded. Free Kicks Conceded are shown when specific data on Fouls are not available (France 2nd, Belgium 1st and Greece 1st divisions).

English and Scottish yellow cards do not include the initial yellow card when a second is shown to a player converting it into a red, but this is included as a yellow (plus red) for European games.

Key to 1X2 (match) betting odds data:

B365H = Bet365 home win odds B365D = Bet365 draw odds B365A = Bet365 away win odds BSH = Blue Square home win odds BSD = Blue Square draw odds BSA = Blue Square away win odds BWH = Bet&Win home win odds BWD = Bet&Win draw odds BWA = Bet&Win away win odds GBH = Gamebookers home win odds GBD = Gamebookers draw odds GBA = Gamebookers away win odds IWH = Interwetten home win odds IWD = Interwetten draw odds IWA = Interwetten away win odds LBH = Ladbrokes home win odds LBD = Ladbrokes draw odds LBA = Ladbrokes away win odds PSH and PH = Pinnacle home win odds PSD and PD = Pinnacle draw odds PSA and PA = Pinnacle away win odds SOH = Sporting Odds home win odds SOD = Sporting Odds draw odds SOA = Sporting Odds away win odds SBH = Sportingbet home win odds SBD = Sportingbet draw odds SBA = Sportingbet away win odds SJH = Stan James home win odds SJD = Stan James draw odds SJA = Stan James away win odds SYH = Stanleybet home win odds SYD = Stanleybet draw odds SYA = Stanleybet away win odds VCH = VC Bet home win odds VCD = VC Bet draw odds VCA = VC Bet away win odds WHH = William Hill home win odds WHD = William Hill draw odds WHA = William Hill away win odds

Bb1X2 = Number of BetBrain bookmakers used to calculate match odds averages and maximums BbMxH = Betbrain maximum home win odds BbAvH = Betbrain average home win odds BbMxD = Betbrain maximum draw odds BbAvD = Betbrain average draw win odds BbMxA = Betbrain maximum away win odds BbAvA = Betbrain average away win odds

MaxH = Market maximum home win odds MaxD = Market maximum draw win odds MaxA = Market maximum away win odds AvgH = Market average home win odds AvgD = Market average draw win odds AvgA = Market average away win odds

Key to total goals betting odds:

BbOU = Number of BetBrain bookmakers used to calculate over/under 2.5 goals (total goals) averages and maximums BbMx>2.5 = Betbrain maximum over 2.5 goals BbAv>2.5 = Betbrain average over 2.5 goals BbMx<2.5 = Betbrain maximum under 2.5 goals BbAv<2.5 = Betbrain average under 2.5 goals

GB>2.5 = Gamebookers over 2.5 goals GB<2.5 = Gamebookers under 2.5 goals B365>2.5 = Bet365 over 2.5 goals B365<2.5 = Bet365 under 2.5 goals P>2.5 = Pinnacle over 2.5 goals P<2.5 = Pinnacle under 2.5 goals Max>2.5 = Market maximum over 2.5 goals Max<2.5 = Market maximum under 2.5 goals Avg>2.5 = Market average over 2.5 goals Avg<2.5 = Market average under 2.5 goals

Key to Asian handicap betting odds:

BbAH = Number of BetBrain bookmakers used to Asian handicap averages and maximums BbAHh = Betbrain size of handicap (home team) AHh = Market size of handicap (home team) (since 2019/2020) BbMxAHH = Betbrain maximum Asian handicap home team odds BbAvAHH = Betbrain average Asian handicap home team odds BbMxAHA = Betbrain maximum Asian handicap away team odds BbAvAHA = Betbrain average Asian handicap away team odds

GBAHH = Gamebookers Asian handicap home team odds GBAHA = Gamebookers Asian handicap away team odds GBAH = Gamebookers size of handicap (home team) LBAHH = Ladbrokes Asian handicap home team odds LBAHA = Ladbrokes Asian handicap away team odds LBAH = Ladbrokes size of handicap (home team) B365AHH = Bet365 Asian handicap home team odds B365AHA = Bet365 Asian handicap away team odds B365AH = Bet365 size of handicap (home team) PAHH = Pinnacle Asian handicap home team odds PAHA = Pinnacle Asian handicap away team odds MaxAHH = Market maximum Asian handicap home team odds MaxAHA = Market maximum Asian handicap away team odds
AvgAHH = Market average Asian handicap home team odds AvgAHA = Market average Asian handicap away team odds

Acknowledgements

Data obtained from Football-Data Photo by Waldemar Brandt on Unsplash

Inspiration

1. What's the proportion of luck (if any) in positive bet results?

2. What's the proportion of misfortune (if any) in negative bet results?

3. Is there a relationship between game odds and the actual game outcome?

4. Comparing single-game (separate stakes) vs multiple game (single stake) bets. Which is more likely to win or lose given a fixed amount to stake?

5. How much influence does form (trend) have in deciding the team's outcome of their next game?

6. Are some leagues more difficult to predict than others?

Please help suggest any other insights I might have missed.

Remember to BeGambleAware

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