Use our trusted SportMonks Football API to build your own sports application and be at the forefront of football data today.

Our Football API is designed for iGaming, media, developers and football enthusiasts alike, ensuring you can create a football application that meets your needs.

Over 20,000 sports fanatics make use of our data. We know what data works best for you, so we ensured that our Football API has all the necessary tools you need to create a successful football application.

• Livescores and schedules Our Football API features extremely fast livescores and up-to-date season schedules, meaning your app will be the first to notify its customers about a goal scored. This also works to further improve the look and feel of your website.

• Statistics and line-ups We offer various kinds of football statistics, ranging from (live) player statistics to team, match and season statistics. And that’s not all - we also provide pre-match lineups for all important leagues.

• Coverage and historical data Our Football API covers over 1,200 leagues, all managed by our in-house scouts and data platform. That means there’s up to 14 years of historical data available.

• Bookmakers and odds Build your football sportsbook, odds comparison or betting portal with our pre-match and in-play odds collated from all major bookmakers and markets.

• TV Stations and highlights Show your customers where the football games are broadcasted and provide video highlights of major match events.

• Standings and topscorers Enhance your football website with standings and live standings, and allow your customers to see the top scorers and what the season's standings are.

⚽ Explore an extensive dataset featuring detailed player statistics exclusively from the top 7 European football leagues:

EPL (English Premier League)

Bundesliga 🇩🇪

La Liga 🇪🇸

Serie A 🇮🇹

Ligue 1 🇫🇷

Eredivisie 🇳🇱

Primeira Liga 🇵🇹

This dataset provides comprehensive insights into player performances, including attributes like goals, assists, minutes played, and other key metrics. Uncover in-depth player analyses and comparisons across leagues to fuel your football data-driven strategies and player evaluations! 📈🥅⚽

Football is not only the most popular sport to watch and spectate in the United Kingdom (UK) and England, but also the most popular team sport to participate in. Between November 2023 and November 2024, roughly 2.2 million people in England played the sport. Football nation Being home to not only the biggest football league but the biggest and most successful sports league in the world, the Premier League, England has many football fans who support the sport with famous clubs such as Manchester United, Liverpool FC, Arsenal FC or Manchester City. Champions League Some of these top tier clubs compete in the UEFA Champions League with other high division teams, primarily from the other ’Big Five’ football leagues in Europe, Germany, Spain, Italy and France. In 2023/24, Real Madrid came out as the victor, winning their 15th Champions League title that season.

Use our trusted SportMonks Football API to build your own sports application and be at the forefront of football data today.

Our Football API is designed for iGaming, media, developers and football enthusiasts alike, ensuring you can create a football application that meets your needs.

Over 20,000 sports fanatics make use of our data. We know what data works best for you, so we ensured that our Football API has all the necessary tools you need to create a successful football application.

• Livescores and schedules Our Football API features extremely fast livescores and up-to-date season schedules, meaning your app will be the first to notify its customers about a goal scored. This also works to further improve the look and feel of your website.

• Statistics and line-ups We offer various kinds of football statistics, ranging from (live) player statistics to team, match and season statistics. And that’s not all - we also provide pre-match lineups for all important leagues.

• Coverage and historical data Our Football API covers over 1,200 leagues, all managed by our in-house scouts and data platform. That means there’s up to 14 years of historical data available.

• Bookmakers and odds Build your football sportsbook, odds comparison or betting portal with our pre-match and in-play odds collated from all major bookmakers and markets.

• TV Stations and highlights Show your customers where the football games are broadcasted and provide video highlights of major match events.

• Standings and topscorers Enhance your football website with standings and live standings, and allow your customers to see the top scorers and what the season's standings are.

Context

Most publicly available football (soccer) statistics are limited to aggregated data such as Goals, Shots, Fouls, Cards. When assessing performance or building predictive models, this simple aggregation, without any context, can be misleading. For example, a team that produced 10 shots on target from long range has a lower chance of scoring than a club that produced the same amount of shots from inside the box. However, metrics derived from this simple count of shots will similarly asses the two teams.

A football game generates much more events and it is very important and interesting to take into account the context in which those events were generated. This dataset should keep sports analytics enthusiasts awake for long hours as the number of questions that can be asked is huge.

Content

This dataset is a result of a very tiresome effort of webscraping and integrating different data sources. The central element is the text commentary. All the events were derived by reverse engineering the text commentary, using regex. Using this, I was able to derive 11 types of events, as well as the main player and secondary player involved in those events and many other statistics. In case I've missed extracting some useful information, you are gladly invited to do so and share your findings. The dataset provides a granular view of 9,074 games, totaling 941,009 events from the biggest 5 European football (soccer) leagues: England, Spain, Germany, Italy, France from 2011/2012 season to 2016/2017 season as of 25.01.2017. There are games that have been played during these seasons for which I could not collect detailed data. Overall, over 90% of the played games during these seasons have event data.

The dataset is organized in 3 files:

• events.csv contains event data about each game. Text commentary was scraped from: bbc.com, espn.com and onefootball.com
• ginf.csv - contains metadata and market odds about each game. odds were collected from oddsportal.com
• dictionary.txt contains a dictionary with the textual description of each categorical variable coded with integers

Past Research

I have used this data to:

• create predictive models for football games in order to bet on football outcomes.
• make visualizations about upcoming games
• build expected goals models and compare players

Inspiration

There are tons of interesting questions a sports enthusiast can answer with this dataset. For example:

• What is the value of a shot? Or what is the probability of a shot being a goal given it's location, shooter, league, assist method, gamestate, number of players on the pitch, time - known as expected goals (xG) models
• When are teams more likely to score?
• Which teams are the best or sloppiest at holding the lead?
• Which teams or players make the best use of set pieces?
• In which leagues is the referee more likely to give a card?
• How do players compare when they shoot with their week foot versus strong foot? Or which players are ambidextrous?
• Identify different styles of plays (shooting from long range vs shooting from the box, crossing the ball vs passing the ball, use of headers)
• Which teams have a bias for attacking on a particular flank?

And many many more...

As of February 28, 2023, Cristiano Ronaldo and Lionel Messi were the most followed football players on social media, with the former having a total of 832 million followers across all platforms. Meanwhile, four out of the ten most followed soccer players played for Paris Saint-Germain Football on different online networks As of 2023, football clubs and players received the most engagement from fans on Instagram. This Meta platform was home to 63 percent of the social media audience of football players and 34 percent of followers of football clubs. Furthermore, football clubs also saw high followings on Facebook, X (formerly known as Twitter), and the China-based network Weibo.   Football stars, social media sensations In addition to being the most followed football players on social media, Lionel Messi and Cristiano Ronaldo have achieved other important milestones on online networks. As of April 2024, Cristiano Ronaldo did not only have the largest social media following in relation to other football players, but he was also the individual with the most Instagram followers in general, ranking second in total following only after Instagram’s official page. Messi ranked third after Ronaldo with 502 million followers on Instagram, placing him above celebrities such as Selena Gomez and Kylie Jenner on the Meta-owned platform. In addition, the most liked Instagram post on the platform as of April 2024 was of Lionel Messi and his teammates after winning the FIFA 2022 World Cup, which generated over 75 million likes. As of 2024, Messi was behind five of the top ten most popular posts of all time on Instagram.  

We exploit the natural experimental setting provided by the Covid-19 lockdown to analyse how performance is affected by a friendly audience. Specifically, we use data on all football matches in the top-level competitions across France, Germany, Italy, Spain, and the United Kingdom over the 2019/2020 season. We compare the difference between the number of points gained by teams playing at home and teams competing away before the Covid-19 outbreak, when supporters could attend any match, with the same difference after the lockdown, when all matches took place behind closed doors. We find that the performance of the home team is halved when stadiums are empty. Further analyses indicate that offensive (defensive) actions taken by the home team are drastically reduced (increased) once games are played behind closed doors. The referee is affected too, as she changes her behaviour in games without spectators. Finally, the home advantage is entirely driven by teams that do not have international experience. Taken together, our findings corroborate the hypothesis that social pressure influences individual behaviour.

The presened data are used to determine how the change of teams’ efficiency affects the level of competitive balance in the top European football leagues. The data about valuation of teams were collected from Transfermarket, while the number of goals and points were collected from the sites of the national leagues.

This dataset contains detailed league performance statistics for the nominees of the 2024 Ballon D'Or across major European football leagues. The stats cover the 2023-2024 season, showcasing metrics such as goals, assists, expected goals (xG), expected assists (xAG), progression metrics, and more.

Men's Ballon d'Or 2024 Nominees:

• Jude Bellingham (England, Real Madrid)
• Hakan Çalhanoğlu (Turkey, Inter)
• Dani Carvajal (Spain, Real Madrid)
• Rúben Dias (Portugal, Manchester City)
• Artem Dovbyk (Ukraine, Dnipro / Girona / Roma)
• Phil Foden (England, Manchester City)
• Alejandro Grimaldo (Spain, Bayer Leverkusen)
• Erling Haaland (Norway, Manchester City)
• Mats Hummels (Germany, Borussia Dortmund)
• Harry Kane (England, Bayern Munich)
• Toni Kroos (Germany, Real Madrid)
• Ademola Lookman (Nigeria, Atalanta)
• Emiliano Martínez (Argentina, Aston Villa)
• Lautaro Martínez (Argentina, Inter )
• Kylian Mbappé (France, Paris Saint-Germain / Real Madrid)
• Martin Ødegaard (Norway, Arsenal)
• Dani Olmo (Spain, Leipzig / Barcelona)
• Cole Palmer (England, Manchester City / Chelsea)
• Declan Rice (England, Arsenal)
• Rodri (Spain, Manchester City)
• Antonio Rüdiger (Germany, Real Madrid)
• Bukayo Saka (England, Arsenal)
• William Saliba (France, Arsenal)
• Federico Valverde (Uruguay, Real Madrid)
• Vinícius Júnior (Brazil, Real Madrid)
• Vitinha (Portugal, Paris Saint-Germain)
• Nico Williams (Spain, Athletic Club)
• Florian Wirtz (Germany, Bayer Leverkusen)
• Granit Xhaka (Switzerland, Bayer Leverkusen)
• Lamine Yamal (Spain, Barcelona)

The winner of the Men's Ballon d'Or goes to the best male player voted by a panel of soccer journalists representing the top 100 countries in the FIFA Men's Rankings.

The Ballon d'Or ceremony will be held on Oct. 28, 2024.

For the first time since 2003, though, Cristiano Ronaldo and Lionel Messi were not included among the nominees!

This statistic shows the share of expatriate football players in top divisions clubs in Europe in 2019, by league. In 2019, approximately **** percent of the football players in Premier League clubs in England are expatriates.

Real Madrid CF was the most visited soccer club website worldwide as of June 2021, with over *** million unique visitors per month. The website of Manchester United followed second in the list, with online traffic of more than *** thousand visitors. All top ten websites included in the global ranking belong to European soccer clubs.

As of 2024, the combined value of all Premier League players amounted to over 11.3 billion euros, significantly more than any other league in the world. England's second-tier, the EFL Championship, had a combined player value of over 1.5 billion euros - more than any other top-tier league outside of the Big Five.

The SoccerTrack dataset comprises top-view and wide-view video footage annotated with bounding boxes. GNSS coordinates of each player are also provided. We hope that the SoccerTrack dataset will help advance the state of the art in multi-object tracking, especially in team sports.

Dataset Details

'https://openaccess.thecvf.com/content/CVPR2022W/CVSports/papers/Scott_SoccerTrack_A_Dataset_and_Tracking_Algorithm_for_Soccer_With_Fish-Eye_CVPRW_2022_paper.pdf'> https://img.shields.io/badge/Paper-PDF-red?style=for-the-badge&logo=adobe-acrobat-reader'/> 'https://github.com/AtomScott/SoccerTrack'> https://img.shields.io/badge/Code-Page-blue?style=for-the-badge&logo=github'/> 'https://soccertrack.readthedocs.io/'> https://img.shields.io/badge/Documentation-Page-blue?style=for-the-badge&logo=read-the-docs'/>

All data in SoccerTrack was obtained from 11-vs-11 soccer games between college-aged athletes. Measurements were conducted after we received the approval of Tsukuba university’s ethics committee, and all participants provided signed informed permission. After recording several soccer matches, the videos were semi-automatically annotated based on the GNSS coordinates of each player.

Citation @inproceedings{scott2022soccertrack, title={SoccerTrack: A Dataset and Tracking Algorithm for Soccer With Fish-Eye and Drone Videos}, author={Scott, Atom and Uchida, Ikuma and Onishi, Masaki and Kameda, Yoshinari and Fukui, Kazuhiro and Fujii, Keisuke}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={3569--3579}, year={2022} }

This dataset was obtained as part of my project to rate player performances in a game and use it to model game outcomes. I was looking for an open dataset which included important in-game stats for players but couldn't find one. Hence I ended up scraping data myself. Subsequently, it has been successfully used to predict player performances in future games and build an optimum fantasy league team. I would be updating the dataset monthly to include newer games of the current season.

The dataset includes 2 JSON files. One of the files describes in-game match stats for every match of the past 4 seasons (current season included) like player touches, passes, shots, yellow cards, saves etc. Some of the stats are available as aggregate stats for the entire team and some of them are player specific. Second, file describes general match outcomes like the full time and half-time score etc.

Data snapshot --

{
  "1190174":{
    "13":{
      "team_details":{
        "team_id":"13",
        "team_name":"Arsenal",
        "team_rating":"7.30714285714286",
        "date":"11/08/2017"
      },
      "aggregate_stats":{
        "fk_foul_lost":"9",
        "won_contest":"16",
        "possession_percentage":"70",
        "total_throws":"21",
         .............
       },
      "Player_stats":{
        "Petr Cech":{
          "player_details":{
            "player_id":"6775",
            "player_name":"Petr Cech",
            "player_position_value":"1",
            "player_position_info":"GK",
            "player_rating":"5.78"
          },
          "Match_stats":{
            "good_high_claim":"1",
            "touches":"27",
            "total_tackle":"1",
            "total_pass":"20",
            "formation_place":"1",
            "accurate_pass":"16"
          },

This dataset could be used to predict player performances and how a particular player/team plays against another. Can a game outcome be modeled on the player composition of the participating teams? Are goals the most important factor that determines season outcomes or something other than historical goals be used to predict the future team performance in the league?

The National Football League comprises 32 teams from across the United States competing in two conferences split roughly by region. The NFL is one of the most popular professional sports leagues in the United States, with televised games attracting millions of viewers each week. A January 2025 survey found that 32 percent of Americans aged 34 to 54 considered the NFL to be their top interest.

Context

I had the need to collect Europe's top 5 leagues' dataset for my own undergraduate project. The idea was to eliminate human bias from the player scouting process.

More Details: https://github.com/Suwadith/Winning-Eleven-Scout-Evaluation-and-Analysis-to-Enhance-Football-Player-Recommendations-ML-Flask

Content

This dataset contains league table data from 2009 - 2018. Leagues included: La Liga, Bundesliga, Serie A, Ligue 1, Premier League

Acknowledgements

This dataset was compiled from the https://www.whoscored.com website

Includes data for over 23000 matches and over 2 million events for those matches!

Content

This dataset contains information on 6 of the top European football/soccer leagues. I plan on updating this dataset weekly/biweekly with data for new matches played as well as gradually going backwards for game data as well.

(All data listed below is through roughly present during the current season.)

Data start years:

• English Premier League ** Game Data - 2001 ** Aggregate Stats - 2002 ** Tables - 2001

• Spanish La Liga ** Game Data - 2004 ** Aggregate Stats - 2002 ** Tables - 2000

• German Bundesliga ** Game Data - 2002 ** Aggregate Stats - 2002 ** Tables - 2000

• Italian Serie A ** Game Data - 2016 ** Aggregate Stats - 2001 ** Tables - 2000

• Dutch Eredivisie ** Game Data - 2018 ** Aggregate Stats - 2001 ** Tables - 2000

• French Ligue 1 ** Game Data - 2018 ** Aggregate Stats - 2002 ** Tables - 2002

Some notes: * Year as a column refers to the year a season started in. So if a match was played in January 2021, it's value for year would be 2020 because that season started in 2020. * Some older matches have no commentary, but they do have one row in events.csv to denote such

Acknowledgements

ESPN, as that's where this data is scraped from Image

Inspiration

• How do the leagues compare in things like goals per game and red cards per team per season?
• Which teams across the leagues foul/get fouled the most and the least per year?
• SkillCorner has some interesting data here that may be worth a bit of your time to check out.

This statistic shows a ranking of advertising categories based on their television screen time during FIFA World Cup games in the United Kingdom (UK) in June and July 2018. Throughout 30 World Cup games shown in ITV, betting ads were the most prominent with a total of 88 minutes of screen time, followed by motoring ads with 68 minutes and grooming ads with 39 minutes.

La Liga Players Performance Dataset

This dataset provides a comprehensive overview of player performance in the La Liga capturing a wide array of metrics related to gameplay, scoring, passing, and defensive actions. With records detailing individual player statistics across different teams, this dataset is a valuable resource for analysts, data scientists, and fans who are interested in diving into player performance data from one of the world’s top soccer leagues.

Each entry represents a single player's profile, featuring data on expected goals (xG), expected assists (xAG), touches, dribbles, tackles, and more. This dataset is ideal for analyzing various aspects of player contribution, both offensively and defensively, and understanding their impact on team performance.

Dataset Columns

Player: Name of the player Team: Team the player belongs to '#' : Player's jersey number Nation: Nationality of the player Position: Primary playing position on the field Age: Age of the player Minutes: Total minutes played Goals: Number of goals scored Assists: Number of assists Penalty Shoot on Goal: Penalty shots taken on goal Penalty Shoot: Total penalty shots attempted Total Shoot: Total shots attempted Shoot on Target: Shots successfully on target Yellow Cards: Number of yellow cards received Red Cards: Number of red cards received Touches: Total ball touches Dribbles: Total dribbles attempted Tackles: Total tackles made Blocks: Total blocks Expected Goals (xG): Expected goals, calculated based on shooting positions and likelihood of scoring Non-Penalty xG (npxG): Expected goals excluding penalties Expected Assists (xAG): Expected assists, based on actions leading to an expected goal (xG) Shot-Creating Actions: Actions leading to a shot attempt Goal-Creating Actions: Actions leading to a goal Passes Completed: Successful passes completed Passes Attempted: Total passes attempted Pass Completion %: Pass completion rate, expressed as a percentage (some entries have missing values here) Progressive Passes: Passes advancing the ball significantly toward the opponent’s goal Carries: Total ball carries Progressive Carries: Carries advancing the ball significantly toward the opponent’s goal Dribble Attempts: Total dribbles attempted Successful Dribbles: Total successful dribbles Date: Date of record collection or game date

Potential Use Cases

Data Visualization: Explore relationships between various performance metrics to identify patterns.

Player Comparisons: Compare individual players based on goals, assists, xG, xAG, and other metrics.

Team Analysis: Evaluate contributions of players within the same team to gain insights into team dynamics.

Predictive Modeling: Use the dataset to build models for predicting game outcomes, goals, or assists based on player performance metrics.

Context

We all love the climatic last-minute goal that tilts the game in favor of one side or the other, after an intense nail-biting game that seems equally balanced until that point, but what lies beyond what appears on the final score-line?

This dataset presents in-depth detailed statistics including thousands of players of Europe's top 5 leagues from the season 2014-15 to 2019-20. In addition, it includes expected goals, assists and other expected stats which can be used to garner insights about the expected outcomes of games, based on understat.com's algorithm that takes goal-scoring positions, probability of conversion, and other metrics that define how likely a player is to score a goal given the situation.

Content

This dataset contains data for all seasons from 2014-15 upto the latest 2019-20 season. For ease of use, each season is stored in a seperate .csv file. Inside each file, there are multiple metrics:

• player_name: Name of the player
• teams_ played_for: Club(s) the player was associated with during that season
• league: The league that player played in. Europe's top 5 leagues (EPL,La_liga,Bundesliga,Ligue_1,Serie_A)
• games: The number of appearances made by the player in that season
• minutes: Number of minutes played
• goals: Total goals scored
• npg: Non-penalty goals
• assists: Total assists
• xG: Expected goals
• xA: Expected assists
• npxG: Expected non-penalty goals
• xG90,xA90,npxG90: Expected stats per 90 minutes
• position: Positions the player has played in during that season
• shots: Total shots by player
• key_passes: Total no of key passes
• yellow_cards: Total yellow cards during the season
• red_cards: Total red cards during the season
• xGBuildup: Total xG of every possession the player is involved in without key passes and shots
• xGChain: Total xG of every possession the player is involved in

Acknowledgements

All data is fetched from Understat.com, massive thanks to them for making their intelligent insights of expected stats publicly available