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.

📂 About This Dataset This dataset combines detailed player performance statistics from WhoScored with team and player meta-data from Transfermarkt. It covers over 1,500 players from top European leagues and includes metrics such as:

Expected Goals (xG) & xG per 90

Tackles, Interceptions, Key Passes, Assists

Pass Accuracy, Crosses, Long Balls

Total Minutes Played & Formations

Player Age, Height, Positioning

🧩 Use Cases Player Rating Prediction

Team Formation Impact Analysis

Identifying Underrated Players via xG vs. Goals

Clustering Players by Style or Efficiency

Fantasy Football Recommendations

🏗️ Data Sources WhoScored.com: Player match stats, tactical analysis.

Transfermarkt.com: Player bio, team formations.

📊 Features Snapshot 32 Columns

Over 20 numerical performance metrics

Cleaned, ready-to-analyze format

Small number of missing values (mostly in passing stats)

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

Comprehensive Miami Dolphins NFL team statistics including rankings, performance metrics, schedule, betting data, and advanced analytics.

Scraped player stats per game from Understat from 2014/2015 to 2024/2025 (still in progress) seasons.

This contains more detailed information than the dataset from https://www.kaggle.com/datasets/codytipton/understat-data, which includes the individual player stats per game for the English Premier League, La Liga, Bundesliga, Serie A, Ligue 1, and the Russian Football Premier League. In particular, it contains each player's xG, xGBuildup, goals, and shots per game. Furthermore, it has the events for each shot in the events table, clubs and their stats per season in the clubs table, and each game with who lost, won, shots, possession, probabilities of who wins, ect..

This is for educational purposes in our data science bootcamp project.

lineup_stats

• match_id: the id for the match they played
• goals: number of goals for this match
• own_goals: number of own goals for this match
• shots: number of shots for this match
• xG: players xG for this match
• **time*: total amount of time this player played in this match
• player_id: player id
• team_id: id for the players team
• position: players position in this match (SUB means they were substituted in)
• player: player's name
• h_a: 'h' if they are in the home team and 'a' if they are in the away team
• yellow_card: number of yellow cards for this match
• red_card: number of red cards for this match
• **roster_in*: (there is roster information in another table that I did not get, will update later)
• roster_out: (same as roster_in)
• key_passes: number of key passes for this match
• assists: number of assists for this match
• xA: expected assists for this match
• xGChain: total xG for every possession the player is involved in this match
• xGBuildup: Total xG for every possession the player is involved in without key passes and shots in this match
• positionOrder: ordering in the lineup

general_game_stats

• id: this game id
• fid: not sure what this is
• h_id: home team id
• a_id: away team id
• date: date of this game
• league_id: id for the league
• season: which season which game was for
• h_goals: number of goals for the home team
• a_goals: number of goals for the away team
• team_h: home team name
• team_a: away team name
• h_xg: home xG
• a_xg: away xG
• h_w: home win probability
• h_d: home draw probability
• h_l: home loss probability
• league: league name
• h_shot: number of shots by the home team
• a_shot: number of shots by the away team
• h_shotOnTarget: number of shots on target by the home team
• a_shotOnTarget: number of shots on target by the away team
• h_deep:home team passes completed within an estimated 20 yards of goal (crosses excluded) -deap_allowed: opponent passes completed within an estimated 20 yards of goal (crosses excluded)
• a_deep: away team passes completed within an estimated 20 yards of goal (crosses excluded) -deap_allowed: opponent passes completed within an estimated 20 yards of goal (crosses excluded)
• h_ppda: home team passes allowed per defensive action in the opposition half.
• a_ppda:away team passes allowed per defensive action in the opposition half.

game_events

• id: id for event
• minute: minute the event happend
• result: result (blocked shot, saved shot, ect..)
• X: x-coordinate where the player took the shot
• Y: y-coordinate where the player took the shot
• xG: the xG for the shot
• player: player's name
• h_a: h for home team or a for away team
• player_id: player's id
• situation: situation where this shot happend (direct free kicks, set piece, open play, ect..)
• season: the match season
• shotType: what type of shot (left foot, right foot, head, ect..)
• ** match_id**: id for the match
• h_team: home team name
• ** a_team**: away team name
• ** h_goals**: number of home goals at this time
• ** a_goals**: number of away goals at this time
• date: date of the match
• ** player_assisted**: player who assisted
• lastAction: the last action before this shot

clubs

• club_id: id for the club
• ** club**: club name
• ** league_id** : league id
• ** league**: league name
• ** season**: which season these stats are from
• ** wins**: number of wins that season
• ** draws**: number of draws that season
• ** losses**: number of losses that season
• ** pts**: number of points for that season
• ** avg_xG**: average xG throughout the season
• ** total_goals**: total amount of goals for this season
• total_goals_cond: total amount of goals conceded this season

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.

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.

The global Sports Data API Interface market is experiencing robust growth, driven by the increasing popularity of sports betting, fantasy sports, and the broader digitalization of the sports industry. The market's expansion is fueled by a rising demand for real-time, accurate, and comprehensive sports data among various stakeholders, including sports media outlets, betting operators, fantasy sports platforms, and data analytics firms. Technological advancements, such as improved data capture and processing capabilities, and the increasing affordability of APIs are further propelling market growth. Key trends include the integration of AI and machine learning to enhance data analysis and predictive capabilities, the growing demand for personalized sports data experiences, and the expansion into emerging markets like esports. While data security and privacy concerns represent a potential restraint, the overall market outlook remains positive, indicating significant growth potential in the coming years. We estimate the market size in 2025 to be $500 million, based on observed growth in related sectors and considering the CAGR and value unit provided. Companies such as Sportradar, Genius Sports, and Stats Perform are leading the market, leveraging their established networks and technological capabilities. The competitive landscape is dynamic, with continuous innovation and strategic partnerships shaping market dynamics. Further segmentation by sports type (e.g., football, basketball, baseball) and data type (e.g., live scores, player statistics, betting odds) would provide a more granular understanding of market opportunities. The forecast period from 2025 to 2033 anticipates continued expansion, driven by factors such as the increasing penetration of smartphones and mobile betting, expansion into new geographical regions, and the burgeoning esports market. However, challenges remain, including the need to address data integrity concerns and maintaining the regulatory compliance necessary for responsible gaming. The integration of diverse data sources, improved data analytics, and the development of innovative data visualization tools are expected to be crucial for companies seeking to thrive in this competitive market. Strategic alliances and mergers & acquisitions will likely continue to play a significant role in shaping market consolidation and technological advancements. Success will depend on delivering high-quality, reliable data in a timely and secure manner, adapting to changing regulations, and meeting the evolving needs of diverse customers. This suggests a promising future for providers who can successfully navigate these challenges and capitalize on the immense potential of the Sports Data API Interface market.

Comprehensive New York Jets NFL team statistics including rankings, performance metrics, schedule, betting data, and advanced analytics.

Comprehensive Football Player Statistics: 2023-2024 Season This dataset contains detailed player statistics from top football leagues for the 2023-2024 season. Sourced from FBref, the dataset includes a wide range of metrics covering various aspects of player performance, such as defense, goalkeeping, passing, and shooting.

Key Features Detailed Player Metrics: Statistics for individual players across multiple performance areas. Structured Data: Organized into tables focusing on different aspects of the game for easy analysis. Top Leagues: Includes data from prominent leagues that provide comprehensive detailed stats.

Github Repository link of the project : https://github.com/GuechtouliAnis/Football-Data-Scraping

By: Guechtouli Anis

In 2023, the greatest share of players by ethnic group in the National Football League (NFL) were black or African American athletes, constituting just over ** percent of players within the NFL. Despite the large population of Hispanic or Latino people within the United States, there is a substantial underrepresentation within the NFL, with only *** percent of players identifying as such. National Football League The National Football League (NFL) is a professional American football league that was established in 1920 and now consists of 32 clubs divided into two conferences, the National Football Conference (NFC) and the American Football Conference (AFC). The league culminates in the Super Bowl, the NFL's annual championship game. As the league’s championship game, the Super Bowl has grown into one of the world's largest single-day sporting events, attracting high television ratings and generating billions of dollars in consumer spending. NFL revenues The NFL is one of the most profitable sports leagues in the world, generating a staggering **** billion U.S. dollars in 2022. This total revenue of all ** NFL teams has constantly increased over the past 15 years and, although this figure dropped significantly in 2020, this was largely as a result of the impact of coronavirus (COVID-19) containment measures. This significant drop in revenue demonstrates one of the primary impacts of COVID-19 on professional sports leagues. NFL franchises As a result of this profitability in non-pandemic times, the franchises of the NFL are attributed extremely high market values. The Dallas Cowboys were by far the most valuable franchise in the NFL, with a market value of **** billion US dollars in 2023. The high value of NFL franchises can be seen clearly when compared to those of the NBA, MLB, and NHL. Franchises within the NFL had an average market value of approximately *** billion U.S. dollars in 2023.

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 Philadelphia Eagles NFL team statistics including rankings, performance metrics, schedule, betting data, and advanced analytics.

In 2023, the Dallas Cowboys were the only NFL franchise to report revenue of over one billion U.S. dollars. In that year, America's Team generated revenue of *** billion U.S. dollars. Meanwhile, the Detroit Lions generated less than **** that amount. The Dallas Cowboys As well as being the NFL franchise with the highest revenue, the Dallas Cowboys was also the most valuable NFL franchise. As of August 2024, the franchise was valued at **** billion U.S. dollars. This success off the pitch, however, has not translated to on-field success in recent years. Despite winning an impressive * Super Bowl titles, the last of these was back in 1995. While the Cowboys made it to the playoffs in the 2022 season, they lost out to the San Francisco 49ers in the divisional round. NFL revenue streams Sponsorships, media, partnerships, ticket and concession sales are some of the most important revenue streams for the NFL. In 2023, the revenue of all 32 NFL teams totaled ***** billion U.S. dollars, the highest figure to-date. Meanwhile, NFL league and team sponsorship generated **** billion U.S. dollars that same year. Some of the main sponsors for the league include Verizon, Pepsi, and Nike.

The difference represents . Significance codes: ***: p < .001, **: p < .01, *: p < .05. The home team advantage is also presented.

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...

The FIFA Football Players Dataset is a comprehensive collection of information about football (soccer) players from around the world. This dataset offers a wealth of attributes related to each player, making it a valuable resource for various analyses and insights into the realm of football, both for gaming enthusiasts and real-world sports enthusiasts.

Attributes:

• Player: The name of the football player.
• Country: The nationality or home country of the player.
• Height: The height of the player in centimeters.
• Weight: The weight of the player in kilograms.
• Age: The age of the player.
• Club: The club to which the player is currently affiliated.
• Ball Control: Player's skill in controlling the ball.
• Dribbling: Player's dribbling ability.
• Marking: Player's marking skill.
• Slide Tackle: Player's ability to perform slide tackles.
• Stand Tackle: Player's ability to perform standing tackles.
• Aggression: Player's aggression level.
• Reactions: Player's reaction time.
• Attacking Position: Player's positioning for attacking plays.
• Interceptions: Player's skill in intercepting passes.
• Vision: Player's vision on the field.
• Composure: Player's composure under pressure.
• Crossing: Player's ability to deliver crosses.
• Short Pass: Player's short passing accuracy.
• Long Pass: Player's ability in long passing.
• Acceleration: Player's acceleration on the field.
• Stamina: Player's stamina level.
• Strength: Player's physical strength.
• Balance: Player's balance while playing.
• Sprint Speed: Player's speed in sprints.
• Agility: Player's agility in maneuvering.
• Jumping: Player's jumping ability.
• Heading: Player's heading skills.
• Shot Power: Player's power in shooting.
• Finishing: Player's finishing skills.
• Long Shots: Player's ability to make long-range shots.
• Curve: Player's ability to curve the ball.
• Free Kick Accuracy: Player's accuracy in free-kick situations.
• Penalties: Player's penalty-taking skills.
• Volleys: Player's volleying skills.
• Goalkeeper Positioning: Goalkeeper's positioning attribute (specific to goalkeepers).
• Goalkeeper Diving: Goalkeeper's diving ability (specific to goalkeepers).
• Goalkeeper Handling: Goalkeeper's ball-handling skill (specific to goalkeepers).
• Goalkeeper Kicking: Goalkeeper's kicking ability (specific to goalkeepers).
• Goalkeeper Reflexes: Goalkeeper's reflexes (specific to goalkeepers).
• Value: The estimated value of the player.

Potential Uses:

Player Performance Analysis: Evaluate the performance of football players based on their attributes. Club Analysis: Investigate clubs, player distribution, and club statistics. Positional Insights: Explore the attributes specific to player positions. Player Valuation Trends: Analyze how player values change over time. Data Visualization:Create visualizations for better data representation. Machine Learning Models: Develop predictive models for various football-related forecasts.

Before using the dataset for analysis, it's advisable to preprocess the data, such as converting the "value" column into a numerical format, handling missing values, and ensuring consistency in column names. This dataset is a valuable resource for gaining insights into football, both in the context of the FIFA video game and real-world football.

All thanks and credit goes to FIFA Index

Comprehensive statistics and data for the 2025 Colorado high school football season including game results, team records, and championship data.

Comprehensive statistics and data for the 2024 Oregon high school football season including game results, team records, and championship data.

Comprehensive statistics and data for the 2019 Washington high school football season including game results, team records, and championship data.