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 hundreds of events and it is very important and interesting to take into account the context in which those events were generated. This incredibly rich data set should keep football analytics enthusiasts awake for long hours as the size of the data set and number of questions that can be asked is huge.

Content

There are 4 main files containing the data: 1) Competition data: Contains information regarding competetion id, competition name, season id, season name, country and gender.

2)Match data: Match information for each match including competition and season information, stadium and referee information, home and away team information as well as the data version the match was collected under.

3) Lineup data: Records the lineup information for the players, managers and referees involved with each match. The following variables are collected in the lineups of each match - team id, team name and lineup. The lineup array is a nested data frame inside of the lineup object, the lineup array contains the following information for each team- player id, player name, player nickname, jersey number and country

4) Event data: Event Data comprises of general attributes and event specific attributes. General attributes are recorded for most event types, depending only on applicability. Event specific attributes help describe the event type in more detail as well as describe the outcome of the event type.

The open data specification document in the doc folder describes the structure of the data along with all attributes in great detail. Take a look at this file for deeper understanding of the data.

Acknowledgements

This data is from the StatsBomb Open Data repository. StatsBomb are committed to sharing new data and research publicly to enhance understanding of the game of Football. They want to actively encourage new research and analysis at all levels. Therefore they have made certain leagues of StatsBomb Data freely available for public use for research projects and genuine interest in football analytics.

Inspiration

There are many many questions we can ask with such detailed event data. Here are just a few examples: 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? 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?

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.

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.

Description

This dataset contains detailed player performance statistics for the 2023-2024 season from the Big 5 European soccer leagues: Premier League, La Liga, Serie A, Bundesliga, and Ligue 1. The data has been meticulously scraped from FBref.com, a comprehensive source for soccer statistics.

Columns and Metrics:

• Rank: The rank of the player based on performance metrics.
• Player: Name of the player.
• Nation: Nationality of the player.
• Position: Playing position of the player.
• Squad: Club the player belongs to.
• Competition: League the player is competing in.
• Age: Age of the player.
• Year_Born: Year the player was born.
• Playing Time_MP: Matches played.
• Playing Time_Starts: Matches started.
• Playing Time_Min: Minutes played.
• Playing Time_90s: Equivalent of 90-minute matches played.
• Performance_Gls: Goals scored.
• Performance_Ast: Assists.
• Performance_G+A: Goals plus assists.
• Performance_G-PK: Goals excluding penalties.
• Performance_PK: Penalty kicks made.
• Performance_PKatt: Penalty kicks attempted.
• Performance_CrdY: Yellow cards.
• Performance_CrdR: Red cards.
• Expected_xG: Expected goals.
• Expected_npxG: Non-penalty expected goals.
• Expected_xAG: Expected assists.
• Expected_npxG+xAG: Non-penalty expected goals plus expected assists.
• Progression_PrgC: Progressive carries.
• Progression_PrgP: Progressive passes.
• Progression_PrgR: Progressive dribbles.
• Per 90 Minutes_Gls: Goals per 90 minutes.
• Per 90 Minutes_Ast: Assists per 90 minutes.
• Per 90 Minutes_G+A: Goals plus assists per 90 minutes.
• Per 90 Minutes_G-PK: Goals excluding penalties per 90 minutes.
• Per 90 Minutes_G+A-PK: Goals plus assists excluding penalties per 90 minutes.
• Per 90 Minutes_xG: Expected goals per 90 minutes.
• Per 90 Minutes_xAG: Expected assists per 90 minutes.
• Per 90 Minutes_xG+xAG: Expected goals plus expected assists per 90 minutes.
• Per 90 Minutes_npxG: Non-penalty expected goals per 90 minutes.
• Per 90 Minutes_npxG+xAG: Non-penalty expected goals plus expected assists per 90 minutes.

I am passionate about soccer and have created this dataset in the hope that it can be useful for others who share my love for the game. Whether you're conducting analysis, building models, or just exploring player stats, I hope this dataset provides valuable insights and serves as a helpful resource.

Analysis of ‘Premier League’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/zaeemnalla/premier-league on 12 November 2021.

--- Dataset description provided by original source is as follows ---

Context

Official football data organised and formatted in csv files ready for download is quite hard to come by. Stats providers are hesitant to release their data to anyone and everyone, even if it's for academic purposes. That was my exact dilemma which prompted me to scrape and extract it myself. Now that it's at your disposal, have fun with it.

Content

The data was acquired from the Premier League website and is representative of seasons 2006/2007 to 2017/2018. Visit both sets to get a detailed description of what each entails.

Inspiration

Use it to the best of your ability to predict match outcomes or for a thorough data analysis to uncover some intriguing insights. Be safe and only use this dataset for personal projects. If you'd like to use this type of data for a commercial project, contact Opta to access it through their API instead.

--- Original source retains full ownership of the source dataset ---

Premier League Players Performance Dataset

This dataset provides a comprehensive overview of player performance in the Premier League 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.

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.

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!

Analysis of ‘Women's International Football Results’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/martj42/womens-international-football-results on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

This is a work-in-progress sister data set to the men's international football results dataset. If you're interested in helping out, submit a pull request here.

Content

Currently, the dataset includes 4,169 women's international football results. All major tournament results should be complete. Some international friendlies, particularly tournaments, are included. A LOT of results are not yet in the dataset.

results.csv includes the following columns:

• date - date of the match
• home_team - the name of the home team
• away_team - the name of the away team
• home_score - full-time home team score including extra time, not including penalty-shootouts
• away_score - full-time away team score including extra time, not including penalty-shootouts
• tournament - the name of the tournament
• city - the name of the city/town/administrative unit where the match was played
• country - the name of the country where the match was played
• neutral - TRUE/FALSE column indicating whether the match was played at a neutral venue

Acknowledgements

The data is gathered from several sources including but not limited to Wikipedia, fifa.com, rsssf.com and individual football associations' websites.

Inspiration

Some directions to take when exploring the data:

• Who is the best team of all time
• Which teams dominated different eras of football
• What trends have there been in international football throughout the ages - home advantage, total goals scored, distribution of teams' strength etc
• Can we say anything about geopolitics from football fixtures - how has the number of countries changed, which teams like to play each other
• Which countries host the most matches where they themselves are not participating in
• How much, if at all, does hosting a major tournament help a country's chances in the tournament
• Which teams are the most active in playing friendlies and friendly tournaments - does it help or hurt them

The world's your oyster, my friend.

Contribute

If you notice a mistake or the results are being updated fast enough for your liking, you can fix that by submitting a pull request on github.

✌🏼✌🏼✌🏼

--- Original source retains full ownership of the source dataset ---

The Youth Football Helmet market is an integral segment of the sports equipment industry, designed to protect young athletes from head injuries while playing American football. As awareness surrounding the risks of concussions and other brain injuries continues to grow, the demand for high-quality, innovative youth

The Intelligent Football market is experiencing significant growth and transformation, fundamentally changing how the sport is played, analyzed, and enjoyed. This innovative sector, leveraging advanced technologies such as AI, IoT, and data analytics, is redefining the dynamics of football by providing insights that

Video analysis of head impacts in top-level female football players during one season. Registration of both header and non-headers as well as analysis of of situation, mechanism and outcome of non-headers. Events that were suspected to be head injuries on video were compared with reports from the team medical staff. Data available in this repository are anonymized for data protection purposes, and not all analyses are reproducible. The codebook file shows which values describe which categories in the variabeles in the data set. Code is available for transparency, in files "descriptives.R", "header_vs_non-header.R" and "player_characteristics_and_age_analysis.R". Appendix file - R code output shows the results of the analyses conducted on the pseudo-anonymous data, and is the same as the one available with the article.

How to Win Football Games: A Data-Driven Approach

A Dataset that contains data on football scenarios and coaching decisions

About this dataset

This dataset captures thousands of different football scenarios and the best coaching decision for each one. Whether it's third down and three yards to go or five seconds left on the clock, this dataset has the scenario and the optimal coaching decision. With this data, football coaches at all levels can make better decisions on game day

How to use the dataset

The file 'Football-Scenarios-DFE-832307.csv' contains data on football scenarios and coaching decisions. The data was collected by presenting contributors with a football scenario and asking them to note what the best coaching decision would be. The scenarios varied in terms of down, distance, time remaining, score, and field position. The decisions presented were punt, pass, run, kick a field goal, kneel down, or don't know. There are thousands of such scenarios in this job.

This dataset can be used to develop a model that can predict the best coaching decision for a given football scenario. This model can be used by coaches to make better game-time decisions that could lead to winning more games

Research Ideas

• Developing a football strategy simulator that allows a coach to input a scenario and see what the best decision is according to data.
• Using the data to develop a football strategy coaching app that gives the user the best coaching decision for different scenarios.
• Analyzing the data to develop football strategy guidelines for coaches to follow in different situations

Acknowledgements

This dataset contains data on football scenarios and coaching decisions. Contributors were asked to note what the best coaching decision would be in each scenario. There are thousands of such scenarios in this dataset.

License

Unknown License - Please check the dataset description for more information.

Columns

File: Football-Scenarios-DFE-832307.csv | Column name | Description | |:-----------------------|:---------------------------------------------------------------------------------------------------------| | _golden | This column is to indicate if the data is gold or not. (Boolean) | | _unit_state | This column is to indicate if the data is gold or not. (Boolean) | | _trusted_judgments | This column is to indicate if the data is gold or not. (Boolean) | | _last_judgment_at | This column is to indicate if the data is gold or not. (Boolean) | | antecedent | The antecedent is the condition that must be met for the coaching decision to be made. (String) | | orig_antecedent | The original antecedent is the condition that must be met for the coaching decision to be made. (String) | | antecedent_gold | This column is to indicate if the antecedent is gold or not. (Boolean) | | option1 | Option 1 is the first coaching decision that can be made. (String) | | option2 | Option 2 is the second coaching decision that can be made. (String) | | option3 | Option 3 is the third coaching decision that can be made. (String) | | option4 | Option 4 is the fourth coaching decision that can be made. (String) | | option5 | Option 5 is the fifth coaching decision that can be made. (String) |

The Children's Luminous Football market is a vibrant segment of the sports and leisure industry, focusing on specially designed footballs that illuminate in the dark, engaging children in outdoor play even after sunset. These luminous footballs enhance the experience of playing soccer, stimulating both physical acti

The Football Socks market plays a significant role in the sports apparel industry, catering to athletes of all levels from amateurs to professionals. Engineered for optimal performance, football socks are designed to provide comfort, support, and protection during intense gameplay. They help in moisture management,

la liga player stats including leaders in goals, assists, yellow and red cards.

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} }

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

MLS stats including leaders in goals, assists, yellow and red cards