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

National Football League historic game and betting info

Content

National Football League (NFL) game results since 1966 with betting odds information since 1979. Dataset was created from a variety of sources including games and scores from a variety of public websites such as ESPN, NFL.com, and Pro Football Reference. Weather information is from NOAA data with NFLweather.com a good cross reference. Betting data was used from http://www.repole.com/sun4cast/data.html for 1978-2013 seasons. Pro-football-reference.com data was then cross referenced for betting lines and odds as well as weather data. From 2013 on betting data reflects lines available at sportsline.com.

Acknowledgements

Helpful sites with interest in football and sports betting include:

https://github.com/fivethirtyeight/nfl-elo-game

http://www.repole.com/sun4cast/data.html

https://www.pro-football-reference.com/

http://www.espn.com/nfl/

http://www.nflweather.com/

http://www.noaa.gov/weather

https://www.sportsline.com/

https://github.com/jp-wright/nfl_betting_market_analysis

http://www.aussportsbetting.com/data/historical-nfl-results-and-odds-data/

Inspiration

Can you build a predictive model to better predict NFL game outcomes and identify successful betting strategies?

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.

Analysis of ‘NFL scores and betting data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/tobycrabtree/nfl-scores-and-betting-data on 12 November 2021.

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

Context

National Football League historic game and betting info

Content

National Football League (NFL) game results since 1966 with betting odds information since 1979. Dataset was created from a variety of sources including games and scores from a variety of public websites such as ESPN, NFL.com, and Pro Football Reference. Weather information is from NOAA data with NFLweather.com a good cross reference. Betting data was used from http://www.repole.com/sun4cast/data.html for 1978-2013 seasons. Pro-football-reference.com data was then cross referenced for betting lines and odds as well as weather data. From 2013 on betting data reflects lines available at sportsline.com.

Acknowledgements

Helpful sites with interest in football and sports betting include:

https://github.com/fivethirtyeight/nfl-elo-game

http://www.repole.com/sun4cast/data.html

https://www.pro-football-reference.com/

http://www.espn.com/nfl/

http://www.nflweather.com/

http://www.noaa.gov/weather

https://www.sportsline.com/

https://github.com/jp-wright/nfl_betting_market_analysis

http://www.aussportsbetting.com/data/historical-nfl-results-and-odds-data/

Inspiration

Can you build a predictive model to better predict NFL game outcomes and identify successful betting strategies?

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

Comprehensive dataset of college football teams ranked by all time record. Includes historical data, statistics, and performance metrics for NCAA Division I FBS teams.

Historical quarterback performance data with era-adjusted metrics for fair cross-era comparisons

Comprehensive dataset of college football teams ranked by nfl draft picks. Includes historical data, statistics, and performance metrics for NCAA Division I FBS teams.

Description:

This comprehensive dataset offers detailed information on approximately 17,000 FIFA football players, meticulously scraped from SoFIFA.com.

It encompasses a wide array of player-specific data points, including but not limited to player names, nationalities, clubs, player ratings, potential, positions, ages, and various skill attributes. This dataset is ideal for football enthusiasts, data analysts, and researchers seeking to conduct in-depth analysis, statistical studies, or machine learning projects related to football players' performance, characteristics, and career progressions.

Features:

• name: Name of the player.
• full_name: Full name of the player.
• birth_date: Date of birth of the player.
• age: Age of the player.
• height_cm: Player's height in centimeters.
• weight_kgs: Player's weight in kilograms.
• positions: Positions the player can play.
• nationality: Player's nationality.
• overall_rating: Overall rating of the player in FIFA.
• potential: Potential rating of the player in FIFA.
• value_euro: Market value of the player in euros.
• wage_euro: Weekly wage of the player in euros.
• preferred_foot: Player's preferred foot.
• international_reputation(1-5): International reputation rating from 1 to 5.
• weak_foot(1-5): Rating of the player's weaker foot from 1 to 5.
• skill_moves(1-5): Skill moves rating from 1 to 5.
• body_type: Player's body type.
• release_clause_euro: Release clause of the player in euros.
• national_team: National team of the player.
• national_rating: Rating in the national team.
• national_team_position: Position in the national team.
• national_jersey_number: Jersey number in the national team.
• crossing: Rating for crossing ability.
• finishing: Rating for finishing ability.
• heading_accuracy: Rating for heading accuracy.
• short_passing: Rating for short passing ability.
• volleys: Rating for volleys.
• dribbling: Rating for dribbling.
• curve: Rating for curve shots.
• freekick_accuracy: Rating for free kick accuracy.
• long_passing: Rating for long passing.
• ball_control: Rating for ball control.
• acceleration: Rating for acceleration.
• sprint_speed: Rating for sprint speed.
• agility: Rating for agility.
• reactions: Rating for reactions.
• balance: Rating for balance.
• shot_power: Rating for shot power.
• jumping: Rating for jumping.
• stamina: Rating for stamina.
• strength: Rating for strength.
• long_shots: Rating for long shots.
• aggression: Rating for aggression.
• interceptions: Rating for interceptions.
• positioning: Rating for positioning.
• vision: Rating for vision.
• penalties: Rating for penalties.
• composure: Rating for composure.
• marking: Rating for marking.
• standing_tackle: Rating for standing tackle.
• sliding_tackle: Rating for sliding tackle.

Use Case:

This dataset is ideal for data analysis, predictive modeling, and machine learning projects. It can be used for:

• Player performance analysis and comparison.
• Market value assessment and wage prediction.
• Team composition and strategy planning.
• Machine learning models to predict future player potential and career trajectories.

Note:

Please ensure to adhere to the terms of service of SoFIFA.com and relevant data protection laws when using this dataset. The dataset is intended for educational and research purposes only and should not be used for commercial gains without proper authorization.

Total attendance at National Football League (NFL) games reached about ***** million fans across the regular season in 2024. This represented a slight decrease over the previous year's figure of approximately **** million spectators. Attendance at NFL games Over the last few years the total attendance at regular season games of the National Football League has consistently been at more than ** million per season. The NFL is composed of 32 teams and each team plays a minimum of 8 home games during the regular season for a total of 256 games per season. The average attendance at National Football League games was at around ****** in the 2023 season. Dallas Cowboys drew record crowds In 2023, the Dallas Cowboys drew the most spectators to their home games with a total attendance of more than *** thousand. The Cowboys also had the highest average attendance that season with around ****** people attending each home game. The average price for a ticket to an NFL game was at ****** U.S. dollars in 2023. On average, tickets to Las Vegas Raiders games were the most expensive (****** U.S. dollars), while tickets for Arizona Cardinals games were the least expensive, with an average price of ***** U.S. dollars.

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...

Analysis of NFL team offensive and defensive Expected Points Added (EPA) per play performance

In 2023, the 32 teams of the National Football League (NFL) generated a total revenue of approximately **** billion U.S. dollars. This shows an increase of nearly *** billion U.S. dollars over the previous year. How does the NFL compare to other major sports leagues? The NFL is the most profitable professional sports league in the United States. Between 2001 and 2019, the total revenue of all 32 NFL teams steadily increased, reaching ** billion U.S. dollars in 2019. This figure dropped to approximately **** billion U.S. dollars in 2020, due to the impact of the coronavirus (COVID-19) pandemic, however, shot back up in 2021 to exceed pre-pandemic levels. In comparison, the revenue generated by Major League Baseball (MLB) teams amounted to around ***** billion U.S. dollars in 2023, while the revenue of the National Basketball Association (NBA) was ***** billion U.S. dollars in the 2022/23 season. NFL revenue streams In the list of most watched TV programs in the U.S., football games dominate the top spots. Duly, the NFL has a diverse array of lucrative revenue streams, such as sponsorships, media partnerships (both broadcasting and digital), ticket sales and concessions. As of February 2024, media deals between the NFL and networks in the United States alone accounted for over ** billion U.S. dollars annually. Meanwhile, NFL league and team sponsorships provided nearly *** billion U.S. dollars in revenue in 2022. Which team generates the most income? In 2023, the five-time Super Bowl champion Dallas Cowboys topped the rankings of NFL teams with the highest revenues. That year, the Cowboys were the only team to generate more than *** billion U.S. dollars in revenue. The Las Vegas Raiders ranked second with approximately *** million U.S. dollars in revenue. Meanwhile, the team with the most Super Bowl titles of the last 20 years - the New England Patriots - sat in fourth place with *** million U.S. dollars in revenue.

Comprehensive dataset of college football teams ranked by first round nfl draft picks. Includes historical data, statistics, and performance metrics for NCAA Division I FBS teams.

IFC, private nonguaranteed (NFL, current US$) in Brazil was reported at 1106842805 USD in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Brazil - IFC, private nonguaranteed (NFL, current US$) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

PPG, bonds (NFL, current US$) in Guinea was reported at 0 USD in 2018, according to the World Bank collection of development indicators, compiled from officially recognized sources. Guinea - PPG, bonds (NFL, current US$) - actual values, historical data, forecasts and projections were sourced from the World Bank on August of 2025.

AZ: External Debt: NFL: Net Flows: Long-Term data was reported at 981.636 USD mn in 2018. This records a decrease from the previous number of 1.102 USD bn for 2017. AZ: External Debt: NFL: Net Flows: Long-Term data is updated yearly, averaging 279.992 USD mn from Dec 1994 (Median) to 2018, with 25 observations. The data reached an all-time high of 2.337 USD bn in 2010 and a record low of -174.042 USD mn in 2013. AZ: External Debt: NFL: Net Flows: Long-Term data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Azerbaijan – Table AZ.World Bank.WDI: External Debt: Net Flows and Net Transfers. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars.; ; World Bank, International Debt Statistics.; Sum;

Comprehensive dataset of college football teams ranked by all-americans. Includes historical data, statistics, and performance metrics for NCAA Division I FBS teams.

Net financial flows, others (NFL, current US$) in Djibouti was reported at 144926171 USD in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Djibouti - Net financial flows, others (NFL, current US$) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

PPG, bonds (NFL, current US$) in China was reported at --6032715000 USD in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. China - PPG, bonds (NFL, current US$) - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

Comprehensive dataset of college football teams ranked by bowl games. Includes historical data, statistics, and performance metrics for NCAA Division I FBS teams.