This dataset contains data for last 10 seasons of Spanish La Liga including current season. The data is updated on weekly basis via Travis-CI. The dataset is sourced from http://www.football-data.co.u...

This dataset consists in 22 JSON files representing a season of the Spanish Football League ("La Liga").

The dataset represents several hierarchically related elements, however, only the Match, Event and Player elements contain relevant information for analysis. The rest of the elements simply serve to keep the data structured, by seasons and matchdays. The dataset collects information from several seasons between the years 2000 and 2022. The attributes of each of the elements that make up the dataset are described below:

Season: JSON documents represent a season, their root contains the following information:

• competition: Name by which the competition is known
• country: Country where the competition is held
• season_id: Identifier of the season, example: Season 2021/22
• season_url: Relative URL of the season's web page
• rounds: List of Round elements, the days into which the championship is divided

Rounds: (or matchdays) Collection of matches:

• number: Name of the matchday, e.g.: Matchday 1.
• matches: List of Match elements, matches that are played on the same day/s of the championship.

Match: contains relevant match information.

• id: Match identifier used at BeSoccer.com
• status: Code representing the status of the match: Played (1), Not Played (0)
• home_team: Name of the home team
• away_team: Name of the away team
• result: List of two integers representing the match score
• date_time: Date and time at which the match started
• referee: First and last name of the referee of the match
• href: URL relative to the match page
• home_tactic: Tactical arrangement of the home team, e.g.: 4-3-3
• home_lineup: List of players in the starting lineup of the home team
• home_bench: List of the home team's substitute players
• away_tactic: Tactical arrangement of the away team, e.g. 4-3-3
• away_lineup: List of players in the home team's starting lineup
• away_bench: List of substitute players of the away team

Event: contains information that defines each of the relevant actions that occur during a soccer match. Events can be described by the following attributes:

• player: Player identifier. Relative URL
• team: Team of the player who participates in the event
• minute: Minute of the match in which the event occurs
• type: Event type (Enumeration)

Players: Player information:

• name: First name
• fullname: Player's full name
• dob: Date of birth
• country: Nationality
• position: Position the player usually occupies: GOA (GoalKeeper), DF (Defender), MID (Midfielder), STR (Striker)
• foot: Dominant Foot: Right-footed, Left-footed, Two-footed, Unknown
• weight: Weight of player in kilograms
• height: Player height in centimeters
• elo: Measurement of the player's skills on a scale of 1 to 100
• potential: Estimate of the maximum ELO that a player can reach on a scale of 1 to 100.
• href: Relative URL of the player's record

Context

I love football. As I am a fan of Barcelona, one of the teams of La Liga, I created this dataset in order to get the insights of the title race in the margin of points earned throughout the entire season.

Content

The table consists of 94 rows and 7 columns. Each row contains specific season, top three names of the table and their respective points from 1929 to 2021.

Inspiration

How many points are required to win La liga in the upcoming years? What is the possible range of points in order to get a position in the top three?

This dataset contains a set of statistics regarding the spanish first division teams.

Each field value has been computed as the mean for the last 30 games played, for the following statistics:

1. Possession
2. Passes
3. Tackles
4. Corners
5. Shots - Total
6. Shots - On target
7. Shots - Off target
8. Shots - Blocked
9. Shots - Outside Box
10. Shots - Inside Box
11. Fouls
12. Offsides
13. Yellow Card
14. Red Card
15. Penalties

Data has been obtained from https://playerstats.football

Dataset

This dataset was created by PrimE_GameR

Contents

La Liga, Spain’s top soccer competition, is widely considered the second biggest domestic soccer property in the world behind the Premier League, with its top sides Barcelona and Real Madrid the most followed and supported around the world. Having been able to ensure a completed season, a worst case scenario of a $1.16 billion loss has been avoided, but challenges still remain in ensuring La Liga’s product remains competitive and desirable to an international audience Read More

Complete database of football players with most titles including Champions League, La Liga, Premier League, Serie A, Bundesliga, and international tournaments

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

La Liga Omri Alean And Abo Shah Haetm Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

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

This dataset provides information about the number of properties, residents, and average property values for Three League Road cross streets in Natchitoches, LA.

ABSTRACT The aim of the paper is to elaborate an alternative measure to compare football leagues based on Accumulated Points Difference (APD). The sample includes eight seasons (2006-2007 to 2013-2014) from nine football leagues: German Bundesliga, Campeonato Brasileiro (Brazil), La Liga BBVA (Spain), French Ligue 1, Dutch Eredivise, English Premier League, Italian Serie A, Portuguese Primeira Liga and Russian Premier League. We have employed the ANOVA one way with Tukey post hoc to compare the results. We have confirmed the robustness of the model comparing it with two traditional measures: Herfindahl Index of Competitive Balance (HICB) and C4 Index of Competitive Balance (C4ICB). As a result, we have found that the Brazilian League was the most balanced tournament in this period and there are no statistical differences between European leagues.

Sprint profile of professional football players and differences between playing positions.

ESPN's Soccer Power Index (SPI) is an international and club rating system designed to be the best possible representation of a team's current overall skill level. As opposed to other systems like the FIFA rankings, SPI is forward-looking and predictive. It is designed to project which teams will have the most success going forward, not rate the success of teams in the past - though the two are obviously related.

SPI differs from other rating systems in a number of ways.

• For international matches it weights each match based on the lineup each team is playing. This is often a problem for international ranking systems, as most teams do not put out their best lineup in every match. SPI recognizes that and weights the importance of each match based on how seriously both teams are taking the match

• Mexico is a great example of how this works. Coming out of the 2011 CONCACAF Gold Cup Mexico was 9th in the FIFA rankings and 10th in SPI. Then Mexico proceeded to lose all three matches in Copa America, which dropped its FIFA ranking to 20th. However, Mexico brought nothing more than a glorified U-23 squad to that tournament, which did not include any of its top players that had just competed in the Gold Cup. Undoubtedly the world learned very little about how Mexico would perform in matches it actually cares about based on that performance. SPI detected Mexico's lack of first-teamers and de-weighted the matches such that Mexico did not move in the rankings at all.

• There is a time effect such that a match played yesterday is weighted more toward a team's rating than a match played two days ago, which is weighted higher than a match played three days ago, and so on. For clubs the cut off for the "assessment period" is four years. For international matches, the cut off depends on how much data we have for the particular team. For countries that play a relatively large number of matches per year (say, Spain), the cut off will usually be somewhere between 4-5 years. For countries that almost never play (Belize, for example) the assessment period could go back six years or more.

• It weights the result of each match based on the quality of the opponent. Clearly, beating San Marino 1-0 is not the same as beating Germany 1-0. SPI identifies this and gives each team the appropriate amount of credit (or blame) based on how the team performed relative to how well it should have performed given its opponent.

• Home-field advantage is also factored into this "expected" performance for each team. France beating England is more likely to happen in Paris than in London. So France would be rewarded more heavily for an away win, and conversely, penalized more harshly for a loss at home. Based on historical evidence, the home pitch advantage is slightly higher in international matches compared to major club competitions. This is taken into account as well.

• It uses club play as a small indicator for international performance. SPI very carefully looks at how players in the top European leagues are performing and adjusts those players' international teams accordingly. For example, if Robin van Persie goes on a scoring spree for Manchester United in the Premier League we would expect him to at least somewhat continue that form for the Netherlands. Strong play for a club incrementally increases the rating for that player's country. Conversely, poor play for a club could adversely affect country ratings as well. Keep in mind that this is a small part of the rating. The vast majority of a country's rating comes from its performance on the field as a team. And for countries that have no players playing in the top European leagues, all of their ratings come from their international matches.

• It is based on goals scored and allowed, not wins, losses, and draws. This goes back to SPI's definition of being a forward-looking system. Since the 1998-99 season, Premier League teams that enter a match with a better goal differential in league play, but fewer league points, have a record of 179 wins, 138 losses and 130 draws. Similar trends show up in other leagues like La Liga and Serie A as well as other team sports across the world. Because SPI is trying to predict future events, it uses the stat that correlates better future success - scoring margin. Other systems like the FIFA rankings and the ELO ratings are solely based on wins and losses, which is why SPI is better at projecting future success than these two methods.

The outputs of SPI are offensive and defensive ratings for all 217 international countries and every club that participates in the Barclays Premier League, La Liga, Serie A, Bundesliga, Ligue 1 and the Champions League. The offensive (or defensive) ratings are interpreted as the number of goals a team would be expected to score (or allow) against a league-average team at a neutral site. For example, if Manchester Uni...

Description of acceleration and sprint profile variables.

Abstract Aim: In this study, we sought to analyze the influence of the quality of opposition on players’ performance of Club Atlético de Madrid (ATM) 2016/2017. For that, the Golden Index (GI) formula was applied to identify and rank the Golden Players of ATM when playing against opponents of different quality levels. Methods: GI formula variables were collected through notational analysis and three global adjacency matrices were constructed to record all successful ball-passing actions performed. Next, the matrices were imported to SocNetV to collect the network centrality metrics. To uniformize each variable, the statistical standardization technique was applied to all variables. ATM opponents were classified into three groups: high-level (n=4), medium-level (n=2) and low-level (n=2), according to their classification in La Liga and participation in the final stage of UEFA Champions League 2016/2017. Results: Koke was considered the Golden Player playing against high-level teams, while opposing low-level opponents he was ranked as second. Against medium- and low-level teams, Antoine Griezmann was considered the Golden Player, but was not classified in the three first positions against high-level opponents. Yannick Carrasco and Filipe Luís were ranked in the second and third positions, respectively, when playing against high- and medium-level opponents. Also, Saúl Ñíguez obtained the third higher index against low-level teams. Conclusions: This study evidenced that players’ individual performance is influenced by the quality of the opposition. Additionally, the GI formula proved to be a potent tool in analyzing player’s performance in attacking plays in Football.

📈 Mejores jugadores de La Liga Fantasy 2025/2026. ⚽️ Mejores delanteros, centrocampistas, defensas y porteros en clave Fantasy Marca, Biwenger, Comunio, Mister y Futmondo

Earning a weekly salary of 450,000 euros in 2020, Oscar dos Santos Emboaba Júnior, better known as Oscar from Shanghai SIPG was the highest paid soccer player in the Chinese Super League. Globally, Lionel Messi ranked first with 96 million euros per year before taxes in 2020. Cristiano Ronaldo followed as second with 54 million euros, while Oscar took the tenth spot with 24 million euro.

Chinese top tier soccer league

The Chinese Super League (CSL), China’s premier professional soccer league, has contributed significantly to the booming sports industry. Since its introduction in 2004, the League has attracted millions of spectators to live matches and received over 300 million yuan in sponsorships each year from the likes of Shell, DHL, and Nike. In recent years, CSL has paid record-breaking fees for top player transfers, an act in line with the national goal - making China a global soccer powerhouse by 2030.

The soccer craze in China

In a country with 200 million soccer fans, football marketing is a thriving business in China. A 2019 survey found that the majority of Chinese soccer fans were inclined to pay for sports streaming platforms. Almost 74 percent of football enthusiasts streamed football matches at least once a week. Apart from CSL, Chinese fans closely follow the English Premier League, the UEFA Champions League, and the Spanish La Liga.

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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In 2024, the most valuable MLS team was Los Angeles FC, with the club being worth an estimated 1.2 billion U.S. dollars. Meanwhile, Inter Miami ranked in second place, with a value of just over one billion U.S. dollars.