Introduction

The lack of publicly available National Football League (NFL) data sources has been a major obstacle in the creation of modern, reproducible research in football analytics. While clean play-by-play data is available via open-source software packages in other sports (e.g. nhlscrapr for hockey; PitchF/x data in baseball; the Basketball Reference for basketball), the equivalent datasets are not freely available for researchers interested in the statistical analysis of the NFL. To solve this issue, a group of Carnegie Mellon University statistical researchers including Maksim Horowitz, Ron Yurko, and Sam Ventura, built and released nflscrapR an R package which uses an API maintained by the NFL to scrape, clean, parse, and output clean datasets at the individual play, player, game, and season levels. Using the data outputted by the package, the trio went on to develop reproducible methods for building expected point and win probability models for the NFL. The outputs of these models are included in this dataset and can be accessed using the nflscrapR package.

Content

The dataset made available on Kaggle contains all the regular season plays from the 2009-2016 NFL seasons. The dataset has 356,768 rows and 100 columns. Each play is broken down into great detail containing information on: game situation, players involved, results, and advanced metrics such as expected point and win probability values. Detailed information about the dataset can be found at the following web page, along with more NFL data: https://github.com/ryurko/nflscrapR-data.

Acknowledgements

This dataset was compiled by Ron Yurko, Sam Ventura, and myself. Special shout-out to Ron for improving our current expected points and win probability models and compiling this dataset. All three of us are proud founders of the Carnegie Mellon Sports Analytics Club.

Inspiration

This dataset is meant to both grow and bring together the community of sports analytics by providing clean and easily accessible NFL data that has never been availabe on this scale for free.

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Introduction

The lack of publicly available National Football League (NFL) data sources has been a major obstacle in the creation of modern, reproducible research in football analytics. While clean play-by-play data is available via open-source software packages in other sports (e.g. nhlscrapr for hockey; PitchF/x data in baseball; the NBA API for basketball), the equivalent datasets are not freely available for researchers interested in the statistical analysis of the NFL. To solve this issue, a group of Carnegie Mellon University statistical researchers led by recent graduate, Maksim Horowitz, built and released nflscrapR an R package which uses an API maintained by the NFL to scrape, clean, parse, and output clean datasets at the individual play, player, game, and season levels. These datasets allow for the advancement of NFL research in the public domain by allowing analysts to develop from a common source in order to create reproducible NFL research, similar to what is being done currently in other professional sports.

2015 NFL Play-by-Play Dataset

The dataset made available on Kaggle contains all the regular season plays from the 2015-2016 NFL season. The dataset contain 46,129 rows and 63 columns. Each play is broken down into great detail containing information on; game situation, players involved and results. Detailed information about the dataset can be found in the nflscrapR documentation.

Downloading and Installing nflscrapR:

Use the following code in your R console:

# Must install the devtools package using the below code
install.packages('devtools')
library(devtools)
# For now you must install nflscrapR from github
if (!is.element("nflscrapR", installed.packages())) {
  # Print Installing nflscrapR
  devtools::install_github(repo = "maksimhorowitz/nflscrapR")
}

library(nflscrapR)

Content

NFL Wide Receivers

This folder contains data behind the story The Football Hall Of Fame Has A Receiver Problem.

advanced-historical.csv contains advanced career stats for NFL receivers, 1932-2013.

try-per-game-aging-curve.csv contains receiver aging curve definitions.

Context

This is a dataset from FiveThirtyEight hosted on their GitHub. Explore FiveThirtyEight data using Kaggle and all of the data sources available through the FiveThirtyEight organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using GitHub's API and Kaggle's API.

This dataset is distributed under the Attribution 4.0 International (CC BY 4.0) license.

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European Football Leagues Database 2023-2024

Overview This dataset provides comprehensive information about the top 5 European football leagues for the 2023-2024 season. It includes detailed statistics about matches, players, teams, coaches, referees, and more, making it an invaluable resource for sports analysts, researchers, and football enthusiasts.

Dataset Description Leagues Covered: - English Premier League - Spanish La Liga - German Bundesliga - Italian Serie A - French Ligue 1

Database Schema

The database follows a normalized schema design with proper relationships between tables. Here's a simplified view of the main relationships:

leagues
 ↑
teams → matches ← referees
 ↓     ↑
players   scores
 ↑
coaches

Usage Examples

SQL Queries

Here are some example SQL queries to get you started:

1. Get all matches for a specific team: sql SELECT m.*, t1.name as home_team, t2.name as away_team FROM matches m JOIN teams t1 ON m.home_team_id = t1.team_id JOIN teams t2 ON m.away_team_id = t2.team_id WHERE t1.team_id = [team_id] OR t2.team_id = [team_id];

2. Get current league standings: sql SELECT t.name, s.* FROM standings s JOIN teams t ON s.team_id = t.team_id WHERE s.league_id = [league_id] ORDER BY s.points DESC;

3. Get top scorers: sql SELECT p.name, p.team_id, COUNT(*) as goals FROM scores s JOIN players p ON s.scorer_id = p.player_id GROUP BY p.player_id, p.name, p.team_id ORDER BY goals DESC;

Python Example

import pandas as pd
import sqlite3

# Connect to the SQLite database
conn = sqlite3.connect('sports_league.sqlite')

# Read data into pandas DataFrames
matches_df = pd.read_sql('SELECT * FROM matches', conn)
players_df = pd.read_sql('SELECT * FROM players', conn)
teams_df = pd.read_sql('SELECT * FROM teams', conn)

# Analyze data
team_stats = matches_df.groupby('home_team_id')['home_team_goals'].agg(['mean', 'sum'])

Applications

This dataset can be used for: 1. Match outcome prediction 2. Player performance analysis 3. Team strategy analysis 4. Historical trend analysis 5. Sports betting research 6. Fantasy football insights 7. Statistical modeling 8. Machine learning projects

Data Files:

1. matches.csv

   • Match ID, Date, Home Team, Away Team
   • Final Score, Half-time Score
   • Stadium, Referee
   • League and Season information

2. players.csv

   • Player ID, Name, Position
   • Date of Birth, Nationality
   • Team affiliation
   • Personal details

3. teams.csv

   • Team ID, Name, Founded Year
   • Stadium information
   • League affiliation
   • Coach information
   • Team crest URL

4. coaches.csv

   • Coach ID, Name
   • Team affiliation
   • Nationality

5. referees.csv

   • Referee ID, Name
   • Nationality
   • Matches officiated

6. stadiums.csv

   • Stadium ID, Name
   • Location
   • Capacity

7. standings.csv

   • Current league positions
   • Points, Wins, Draws, Losses
   • Goals For/Against
   • Form and Performance metrics

8. scores.csv

   • Detailed match scores
   • Goal statistics
   • Match events

9. seasons.csv

   • Season information
   • League details
   • Year

10. sports_league.sqlite

    • Complete database in SQLite format
    • All tables and relationships included
    • Ready for immediate use

Data Quality

• Data is sourced from football-data.org API
• Regular weekly updates
• Consistent format across all leagues
• Complete historical record for the 2023-2024 season
• Verified and cleaned data

License

This dataset is released under the Creative Commons Zero v1.0 Universal license

Updates and Maintenance

• Dataset is updated weekly
• Last update: March 20, 2024
• Check the version history for detailed changes

Contributing

If you find any issues or have suggestions for improvements, please: 1. Open an issue on the dataset's GitHub repository 2. Submit a pull request with your proposed changes 3. Contact the maintainer directly

Acknowledgments

• Data provided by football-data.org
• Community contributions and feedback
• Open-source tools and libraries used in data collection and processing

Github

Project: https://github.com/kaimg/Sports-League-Management-System

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Content

NFL Favorite Team

This folder contains data behind the story The Rams Are Dead To Me, So I Answered 3,352 Questions To Find A New NFL Team.

team-picking-categories.csv contains grades for each NFL franchise in 16 categories, to be used to pick a new favorite team.

Should be used in conjunction with weights derived from a survey structured like this: http://www.allourideas.org/nflteampickingsample.

Context

This is a dataset from FiveThirtyEight hosted on their GitHub. Explore FiveThirtyEight data using Kaggle and all of the data sources available through the FiveThirtyEight organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using GitHub's API and Kaggle's API.

This dataset is distributed under the Attribution 4.0 International (CC BY 4.0) license.

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Context

"These are arrests, charges and citations of NFL players for crimes more serious than common traffic violations. Almost all of the players belonged to an NFL roster at the time of the incident. In rare cases, a free agent is included only if that player later signs with an NFL team. The data comes from media reports and public records. It cannot be considered fully complete because records of some player arrests might not have been found for various reasons, including lack of media coverage or accessible public records. Many resolutions to these cases also are pending or could not be immediately found." (Source)

Content

This data covers January 2000 to March 2017. Like mentioned above, it is not fully complete. In the future I hope to add files to add dimensions like USA crime rates, team info, player info, team season records

• Column Name | Description | Example data
• DATE | Date of the Incident | 3/7/2017
• TEAM | Team Identifier at time of incident | SEA (35 total)
• NAME | Player Name | Aldon Smith (627 total)
• POSITION | Player's Position at time of incident | TE (18 total)
• CASE | Incident Type | Cited (10 total)
• CATEGORY | Incident Crime Categories, a comma separated list of crime types | DUI (103 unique sets)
• DESCRIPTION | A short text description of the incident | Suspected of stealing golf cart, driving drunk, resisting arrest in Scottsdale, Ariz.
• OUTCOME | Incident outcome description | Resolution undetermined.

Acknowledgements

The original database was conceived and created by sports writer Brent Schrotenboer of USA Today. http://www.usatoday.com/sports/nfl/arrests/

Past Research:

The Rate of Domestic Violence Arrests Among NFL Players - Benjamin Morris (FiveThirtyEight)

I found this data set August of 2015 and created http://nflarrest.com/ that attempts to provide a visual tool to explore the data set and a RESTful API.

Inspiration

• Can the next arrest team or crime or date be predicted?
• Does the number of arrests in the previous season, pre-season, in season effect overall Team season record(Wins,losses,playoff progression).
• How does the NFL arrest rate compare to the nation on average?
• How does the NFL arrest rate compare to populations with similar affluency?
• How do crime rates (e.g DUI rates) compare to the geographic area the team represents?

Content

NFL Fandom

This folder contains data behind the story How Every NFL Team’s Fans Lean Politically.

Google Trends Data

Google Trends data was derived from comparing 5-year search traffic for the 7 sports leagues we analyzed:

https://g.co/trends/5P8aa

Results are listed by designated market area (DMA).

The percentages are the approximate percentage of major-sports searches that were conducted for each league.

Trump's percentage is his share of the vote within the DMA in the 2016 presidential election.

SurveyMonkey Data

SurveyMonkey data was derived from a poll of American adults ages 18 and older, conducted between Sept. 1-7, 2017.

Listed numbers are the raw totals for respondents who ranked a given NFL team among their three favorites, and how many identified with a given party (further broken down by race). We also list the percentages of the entire sample that identified with each party, and were of each race.

Context

This is a dataset from FiveThirtyEight hosted on their GitHub. Explore FiveThirtyEight data using Kaggle and all of the data sources available through the FiveThirtyEight organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using GitHub's API and Kaggle's API.

This dataset is distributed under the Attribution 4.0 International (CC BY 4.0) license.

Have you ever found yourself with a football dataset that almost had it all, but left you short of happiness? Time after time, promising datasets failed to deliver the statistics that truly matter – match events, player performances, team results, and season standings.

That time is over!

This in-depth football dataset, curated straight from a RapidAPI endpoint, brings you the data points we've all been waiting for. From fixtures and injuries to goals, assists, and tactical breakdowns, this dataset unlocks the full picture of the beautiful game.

What You Get 🏆 - Fixture Stats & Events: Goals, assists, fouls, and match-defining moments across leagues up to 2024. - Player Performances: From tackles to dribbles, passes, and shots – every stat that makes a difference. - Season Stats & League Standings: Discover how teams dominate, stumble, or rise to glory each season. - Team Insights: Analyze home/away performance, goal-scoring patterns, and defensive strengths. - Match Highlights: Real-time events like own goals, red cards, and critical substitutions. - Injuries & Suspensions: Missing players and their impact on team dynamics. - Iconic Stadiums: Explore venues, capacities, and surfaces that set the stage for football's greatest moments.

Why It’s Exciting 🌟

This isn’t just another football dataset – it’s the ultimate resource for fans, analysts, and strategists who want to dig deeper. Whether you're predicting outcomes, analyzing player form, or crafting the next big football insights project, you now have all the tools you need.

Get ready to unlock stories, trends, and insights like never before – because this time, the stats you actually care about are all here. Let’s kick it off! ⚽✨

In terms of fixture stats for players, the endpoint provides data from 2015 up through the 2024 season and I plan to make one more update at the end of all league/cup seasons in June of 2025.

Disclaimer: This dataset is intended for non-commercial, academic purposes and does not infringe upon any intellectual property rights of the original data providers, including RapidAPI or associated sources. For full details, please refer to the respective terms of use provided by the data sources.

If you have questions about the data or simply want to connect, reach out on LinkedIn and if you plan on using this data for any type of analysis, can you please share that with me!

PS: I am a Ronaldo fan... Suiiiii !!!

Leagues/Cups in datasets: - La Liga - Ligue 1 - Serie A - World Cup - Bundesliga - NWSL Women - Pro League - Championship League - Copa America - Premier League - CONCACAF Gold Cup - Euro Championship - UEFA Europa League - MLS - Africa Cup Of Nations - CONCACAF Champions League

Other Datasets: - Spotify - Zillow

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StatsBomb Open Data

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

StatsBomb are hoping that by making data freely available, we will extend the wider football analytics community and attract new talent to the industry. We would like to collect some basic personal information about users of our data. By giving us your email address, it means we will let you know when we make more data, tutorials and research available. We will store the information in accordance with our Privacy Policy and the GDPR.

Whilst we are keen to share data and facilitate research, we also urge you to be responsible with the data. Please register your details on https://www.statsbomb.com/resource-centre and read our User Agreement carefully.

Terms & Conditions By using this repository, you are agreeing to the user agreement.

If you publish, share or distribute any research, analysis or insights based on this data, please state the data source as StatsBomb and use our logo, available in our Media Pack.

Getting Started The data is provided as JSON files exported from the StatsBomb Data API, in the following structure:

Competition and seasons stored in competitions.json. Matches for each competition and season, stored in matches. Each folder within is named for a competition ID, each file is named for a season ID within that competition. Events and lineups for each match, stored in events and lineups respectively. Each file is named for a match ID. StatsBomb 360 data for selected matches, stored in three-sixty. Each file is named for a match ID. Some documentation about the meaning of different events and the format of the JSON can be found in the doc directory.

Premier League, Serie A, La Liga, Bundesliga, Ligue 1 from 2017-2018 to 2020-2021. 1 collection for each league of a certain season. 1 document for each player. Within each document:
- name, age, nationality, height, weight, team, position. - general stats: games, time, yellow cards, red cards. - offensive stats: goals, assists, xG, xA, shots, key passes, npg, npxG, xGChain, xGBuildup. - defensive stats: Tkl, TklW, Past, Press, Succ, Block, Int. - passing stats: Cmp, Cmp%, 1/3, PPA, CrsPA, Prog.

Three data resources were used: Understat, api-football and Fbref. For more information on the data acquisition phase, I recommend reading the Football players notebook in the Code section.

This dataset is built with the aim of supporting an analysis to try to identify the most probable top performance age range of a player knowing the league in which he plays, his physical characteristics, his role and his nationality.

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Maryland Sports (http://www.marylandsports.us/) has identified sport venues located within the State of Maryland. These venues offer opportunities to participate in free and fee-based, organized and pick-up, indoor and outdoor sports and physical fitness related activities in the area of Football.This is a MD iMAP hosted service layer. Find more information at https://imap.maryland.gov.Feature Service Layer Link:https://mdgeodata.md.gov/imap/rest/services/Society/MD_SportVenues/FeatureServer/26