Our NFL Data product offers extensive access to historic and current National Football League statistics and results, available in multiple formats. Whether you're a sports analyst, data scientist, fantasy football enthusiast, or a developer building sports-related apps, this dataset provides everything you need to dive deep into NFL performance insights.

Key Benefits:

Comprehensive Coverage: Includes historic and real-time data on NFL stats, game results, team performance, player metrics, and more.

Multiple Formats: Datasets are available in various formats (CSV, JSON, XML) for easy integration into your tools and applications.

User-Friendly Access: Whether you are an advanced analyst or a beginner, you can easily access and manipulate data to suit your needs.

Free Trial: Explore the full range of data with our free trial before committing, ensuring the product meets your expectations.

Customizable: Filter and download only the data you need, tailored to specific seasons, teams, or players.

API Access: Developers can integrate real-time NFL data into their apps with API support, allowing seamless updates and user engagement.

Use Cases:

Fantasy Football Players: Use the data to analyze player performance, helping to draft winning teams and make better game-day decisions.

Sports Analysts: Dive deep into historical and current NFL stats for research, articles, and game predictions.

Developers: Build custom sports apps and dashboards by integrating NFL data directly through API access.

Betting & Prediction Models: Use data to create accurate predictions for NFL games, helping sportsbooks and bettors alike.

Media Outlets: Enhance game previews, post-game analysis, and highlight reels with accurate, detailed NFL stats.

Our NFL Data product ensures you have the most reliable, up-to-date information to drive your projects, whether it's enhancing user experiences, creating predictive models, or simply enjoying in-depth football analysis.

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...

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

Notes for Football Data from football-data.co.uk.

All data is in csv format, ready for use within standard spreadsheet applications. Please note that some abbreviations are no longer in use (in particular odds from specific bookmakers no longer used) and refer to data collected in earlier seasons. For a current list of what bookmakers are included in the dataset please visit http://www.football-data.co.uk/matches.php

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)

Note that 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).

Note also that 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 han...

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...

Context

I was working on FPL points prediction model and I thought that opponent team stats matters a lot, so I started collecting this data. But, this data is stored in around 550 different csv files and I wanted the data in a single csv file. So, I merged all the csv files according to the stats. And now I'm sharing these files with all of you so that you could save your precious time.

Content

These files contain data of three seasons 17/18, 18/19 and 19/20. If you want the data for more seasons you can visit to this link mentioned below. - https://fbref.com/en/comps/9/1631/2017-2018-Premier-League-Stats

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 ---

This dataset contains data for last 10 seasons of French Ligue 1 including current season. The data is updated on weekly basis via Travis-CI. The dataset is sourced from http://www.football-data.co.uk...

This datasets contains information about all the matches in the 11 football league (Primera Catalana, group 1, Season 2022-2023).

There are 4 datasets containing:

• primera-catalana-2223-grup-1-matches.csv: General data about the match
• primera-catalana-2223-grup-1-penalties.csv: Data about the penalties in a match
• primera-catalana-2223-grup-1-goals.csv: Data about the goals in a match
• primera-catalana-2223-grup-1-substitutions.csv: Data about the substitutions in a match

All these datasets share the field match_id, unique identifier for a match.

⚽ 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! 📈🥅⚽

La Liga 2023/24: Match, Player, and Team Performance Insights

Dataset Description

This dataset provides an in-depth look at the 2023/24 La Liga season, covering various aspects of team and player performances across all matchdays. With over 50 individual CSV files, the dataset includes statistics on passing accuracy, goal-scoring, defensive actions, possession metrics, and player ratings, among others. Whether you're interested in analyzing top scorers, understanding team strengths, or delving into player-specific contributions, this dataset offers a rich foundation for football analytics enthusiasts and professionals.

In addition to the core dataset, we have now added more files related to the league table, expanding the dataset with essential information on match outcomes, league standings, and advanced metrics.

Contents

The dataset contains the following types of data:

• Team Performance Metrics: Information on accurate passes, crosses, goals conceded, interceptions, and other team stats.
• Player Performance Metrics: Individual stats including expected goals (xG), assists, clearances, fouls committed, and tackles won.
• Match-Specific Insights: Detailed metrics on goals scored, scoring attempts, possession percentages, and cards issued per match.
• Match Details (New): Information about rounds, match IDs, teams, scores, and match statuses.
• League Tables (New):
  • Overall standings including matches played, wins, draws, losses, goals scored, goal differences, and points.
  • Separate breakdowns for home and away performances.
  • Advanced metrics including expected goals (xG), expected goals conceded, and expected points.

The file details provide an overview of each dataset, including a brief description of the data structure and potential uses for analysis. This helps users quickly navigate and understand the data available for analysis.

This dataset is ideal for statistical analysis, data visualization, and machine learning applications to uncover patterns in football performance.

Suggested Analysis

This dataset opens up multiple avenues for data analysis and visualization. Here are some ideas:

1. Team Performance Analysis

• Analyze team performance trends, such as comparing passing accuracy, possession, and expected goals (xG) across teams.
• Visualize which teams generate the most scoring opportunities and miss the most big chances.
• Identify the strongest and weakest defenses based on goals conceded, clean sheets, and clearances.

2. Player Performance Analysis

• Identify top-performing players by goals scored, assists, expected goals, and expected assists.
• Explore defensive contributions by analyzing tackles won, interceptions, and clearances per player.
• Assess attacking efficiency by comparing total attempts vs. on-target attempts for each player.

3. Goalkeeping and Defensive Analysis

• Compare goalkeepers on metrics like saves made, goals conceded, and clean sheets to highlight the top performers of the season.
• Evaluate defensive strength by analyzing interception rates and clearances by both teams and players.

4. League Table Insights (New)

• Analyze overall league standings to determine team performance trends.
• Explore home and away performance and identify strengths and weaknesses in different scenarios.
• Utilize advanced metrics to evaluate under- and overperforming teams.

5. Advanced Metrics Exploration

• Examine possession-based metrics, such as possession percentage and possessions won in the attacking third, to identify possession-dominant teams.
• Use expected goals and expected assists data to build profiles highlighting efficient playmaking and finishing among players and teams.

This dataset is a valuable resource for football enthusiasts, data scientists, and analysts interested in uncovering patterns, building predictive models, or generating insights for La Liga 2023/24.

License and Disclaimer

License

This dataset is shared for non-commercial, educational, and personal analysis purposes only. It is not intended for redistribution, commercial use, or integration into other public datasets.

Disclaimer

This dataset was sourced from FotMob, a proprietary provider of football statistics. All rights to the original data belong to FotMob. The dataset is a restructured collection of publicly viewable data and does not claim ownership over FotMob's data. Users should reference FotMob as the original source when using this dataset for research or analysis.

Terms of Use

By using this dataset, you agree to the following: - Non-commercial Use: This dataset is only for educational, analytical, and personal use. It may not be used for commercial purposes or integrated into other public datasets. - Proper Attribution: Please attribu...

Analysis of ‘LaLiga Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sdelquin/laliga-data on 28 January 2022.

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

Context

LaLiga is the trademark behind the Spanish Football Competitions and their data can be viewed on laliga.com but there is no way to download a csv file with the whole information.

To that end, I've built a Python scraper to retrieve and public these data. Data is updated weekly.

Content

So far, here you have the available contents:

• Player Data: You'll find all available player data from LaLiga with a huge amount of columns for these competitions: female first division, male first division and male second division. Each file is identified by SXX-YY at the beginning meaning the season XX-YY.

Acknowledgements

Thanks Python!

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

Analysis of ‘Barclays Premiere League for last 12 seasons’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/lumierebatalong/english-premiere-league-team-datasets on 28 January 2022.

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

Context

Barclay premier league is the best league in the world 💯 . It has 20 teams that qualified for the title. Among these 20 teams there are 5 teams which have already won the title in the last 12 seasons namely Man City, Liverpool, Man United, Chelsea, Leicester with two outsiders Arsenal and Tottenham. Who is your favorite team and how can you predict their title victory for the current or next season? The ball is in your camp 👀 .

Content

Notes for Football Data

All data is in csv format, ready for use within standard spreadsheet applications. Please note that some abbreviations are no longer in use and refer to data collected in earlier seasons. Each data contains last 12 seasons of English Premier League.

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

I remove some features.

Acknowledgements

This dataset contains data for last 12 seasons of English Premier League. The dataset is sourced from http://www.football-data.co.uk/ website and contains various statistical data such as final and half time result, corners, yellow and red cards etc

Inspiration

Can you explain why Man United has not won the title for last 12 seasons?. Can you predict the victory of your favorite team in every championship game?.

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

The data and STATA code files included are part of our team's study of the marginal revenue product of elite college football players. Included are: a CSV file containing all of the original data, a .do file with the regressions/models included in this paper, and a .dta file containing recruiting data used later in the paper. Data covers the 2006-2015 seasons.

Data from: FWC, FWWC, UEC, UWC, UCL, UWCL

This dataset contains comprehensive information on the teams participating in the UEFA Euro 2024 tournament. It includes details about each team, their group stage placement, FIFA rankings, captains, head coaches, pre-tournament forms, and average player age.

Columns Description:

• FIFA Ranking: Pre-tournament FIFA ranking of the team
• Team: Name of the team
• Total Points: Points in the pre-tournament FIFA ranking
• Previous Points: Points in the previous FIFA ranking
• Change in Points: Difference between points in Total Points and Previous Points
• Base Camp: Location for training and residency for the duration of UEFA Euro 2024
• Training Ground: Home training ground for the duration of UEFA Euro 2024
• Qualified as: Status under which the team has qualified for UEFA Euro 2024
• Previous appearances: Number of times the team has qualified for previous editions of UEFA Euro Championship
• Manager Name: Name of the manager of the team
• Installation (in years): Number of years the current manager has been coaching the team (rounded down an integer number of years)
• Group: Group in UEFA Euro 2024
• Average Age: Average age of the squad
• Captain: Captain of the team
• Recent Form: Record of wins, draws and losses in the 10 most recent matches played by the team (Friendlies included)

This dataset includes CSV files about players of games. We gathered the list of games from https://en.wikipedia.org/wiki/2020_Summer_Olympics. The data in the CSV files are gathered from the below resources:

http://www.adventurestats.com/tables/8000ergeo.shtmlhttp://www.bbc.com/news/magazine-28363129https://www.statcrunch.com/app/index.php?dataid=614855http://www.stadiumguide.com/figures-and-statistics/lists/europes-largest-football-stadiums/https://data.world/sports/olympics/file/outturn%20sports-related%20costs%20of%20the%20Olympic%20Games%20.csvhttp://www.bbc.com/news/magazine-28363129https://owlcation.com/stem/TopTenMountainRangeshttps://github.com/jokecamp/FootballData/blob/master/MLS/MLS%20-%202015/mls_players_2015-01-12.csvhttp://www.topendsports.com/events/summer/sports/number.htmhttp://newsok.sportsdirectinc.com/golf/pga-players.aspx?page=/data/pga/players/A_players.html

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.

Analysis of ‘International football results from 1872 to 2021’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/martj42/international-football-results-from-1872-to-2017 on 13 February 2022.

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

Context

Well, what happened was that I was looking for a semi-definite easy-to-read list of international football matches and couldn't find anything decent. So I took it upon myself to collect it for my own use. I might as well share it.

Content

This dataset includes 43,170 results of international football matches starting from the very first official match in 1972 up to 2019. The matches range from FIFA World Cup to FIFI Wild Cup to regular friendly matches. The matches are strictly men's full internationals and the data does not include Olympic Games or matches where at least one of the teams was the nation's B-team, U-23 or a league select team.

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

shootouts.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
• winner - winner of the penalty-shootout

Note on team and country names: For home and away teams the current name of the team has been used. For example, when in 1882 a team who called themselves Ireland played against England, in this dataset, it is called Northern Ireland because the current team of Northern Ireland is the successor of the 1882 Ireland team. This is done so it is easier to track the history and statistics of teams.

For country names, the name of the country at the time of the match is used. So when Ghana played in Accra, Gold Coast in the 1950s, even though the names of the home team and the country don't match, it was a home match for Ghana. This is indicated by the neutral column, which says FALSE for those matches, meaning it was not at a neutral venue.

Acknowledgements

The data is gathered from several sources including but not limited to Wikipedia, 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: https://github.com/martj42/international_results

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

This dataset contains information on first-team matches, goals, player appearances, and disciplinary actions for Cheltenham Town AFC from the 1989/90 season through the 2024/25 season.

It covers 1,943 matches, 36 seasons, 14 competitions, and 1,225 players.

Usage is intended for football researchers, football historians, and data scientists, but not limited to these groups.

The data are available for download in UTF-8 encoded CSV format. CSV file includes headers.

.