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

This dataset contains 2021-2022 football player stats per 90 minutes. Only players of Premier League, Ligue 1, Bundesliga, Serie A and La Liga are listed.

• 2022-2023 Football Player Stats
• 2021-2022 Football Team Stats
• 2022-2023 Football Team Stats

Content

+2500 rows and 143 columns. Columns' description are listed below.

• Rk : Rank
• Player : Player's name
• Nation : Player's nation
• Pos : Position
• Squad : Squad’s name
• Comp : League that squat occupies
• Age : Player's age
• Born : Year of birth
• MP : Matches played
• Starts : Matches started
• Min : Minutes played
• 90s : Minutes played divided by 90
• Goals : Goals scored or allowed
• Shots : Shots total (Does not include penalty kicks)
• SoT : Shots on target (Does not include penalty kicks)
• SoT% : Shots on target percentage (Does not include penalty kicks)
• G/Sh : Goals per shot
• G/SoT : Goals per shot on target (Does not include penalty kicks)
• ShoDist : Average distance, in yards, from goal of all shots taken (Does not include penalty kicks)
• ShoFK : Shots from free kicks
• ShoPK : Penalty kicks made
• PKatt : Penalty kicks attempted
• PasTotCmp : Passes completed
• PasTotAtt : Passes attempted
• PasTotCmp% : Pass completion percentage
• PasTotDist : Total distance, in yards, that completed passes have traveled in any direction
• PasTotPrgDist : Total distance, in yards, that completed passes have traveled towards the opponent's goal
• PasShoCmp : Passes completed (Passes between 5 and 15 yards)
• PasShoAtt : Passes attempted (Passes between 5 and 15 yards)
• PasShoCmp% : Pass completion percentage (Passes between 5 and 15 yards)
• PasMedCmp : Passes completed (Passes between 15 and 30 yards)
• PasMedAtt : Passes attempted (Passes between 15 and 30 yards)
• PasMedCmp% : Pass completion percentage (Passes between 15 and 30 yards)
• PasLonCmp : Passes completed (Passes longer than 30 yards)
• PasLonAtt : Passes attempted (Passes longer than 30 yards)
• PasLonCmp% : Pass completion percentage (Passes longer than 30 yards)
• Assists : Assists
• PasAss : Passes that directly lead to a shot (assisted shots)
• Pas3rd : Completed passes that enter the 1/3 of the pitch closest to the goal
• PPA : Completed passes into the 18-yard box
• CrsPA : Completed crosses into the 18-yard box
• PasProg : Completed passes that move the ball towards the opponent's goal at least 10 yards from its furthest point in the last six passes, or any completed pass into the penalty area
• PasAtt : Passes attempted
• PasLive : Live-ball passes
• PasDead : Dead-ball passes
• PasFK : Passes attempted from free kicks
• TB : Completed pass sent between back defenders into open space
• PasPress : Passes made while under pressure from opponent
• Sw : Passes that travel more than 40 yards of the width of the pitch
• PasCrs : Crosses
• CK : Corner kicks
• CkIn : Inswinging corner kicks
• CkOut : Outswinging corner kicks
• CkStr : Straight corner kicks
• PasGround : Ground passes
• PasLow : Passes that leave the ground, but stay below shoulder-level
• PasHigh : Passes that are above shoulder-level at the peak height
• PaswLeft : Passes attempted using left foot
• PaswRight : Passes attempted using right foot
• PaswHead : Passes attempted using head
• TI : Throw-Ins taken
• PaswOther : Passes attempted using body parts other than the player's head or feet
• PasCmp : Passes completed
• PasOff : Offsides
• PasOut : Out of bounds
• PasInt : Intercepted
• PasBlocks : Blocked by the opponent who was standing it the path
• SCA : Shot-creating actions
• ScaPassLive : Completed live-ball passes that lead to a shot attempt
• ScaPassDead : Completed dead-ball passes that lead to a shot attempt
• ScaDrib : Successful dribbles that lead to a shot attempt
• ScaSh : Shots that lead to another shot attempt
• ScaFld : Fouls drawn that lead to a shot attempt
• ScaDef : Defensive actions that lead to a shot attempt
• GCA : Goal-creating actions
• GcaPassLive : Completed live-ball passes that lead to a goal
• GcaPassDead : Completed dead-ball passes that lead to a goal
• GcaDrib : Successful dribbles that lead to a goal
• GcaSh : Shots that lead to another goal-scoring shot
• GcaFld : Fouls drawn that lead to a goal
• GcaDef : Defensive actions that lead to a goal
• Tkl : Number of players tackled
• TklWon : Tackles in which the tackler's team won possession of the ball
• TklDef3rd : Tackles in defensive 1/3
• TklMid3rd : Tackles in middle 1/3
• TklAtt3rd : Tackles in attacking 1/3
• TklDri : Number of dribblers tackled
• TklDriAtt : Number of times dribbled past plus number of tackles
• TklDri% : Percentage of dribblers tackled
• TklDriPast : Number of times dribbled past by an opposing player
• Press : Number of times applying pressure to opposing player who is receiving, carrying or releasing the ball
• PresSucc : Number of times the squad gained possession withing five seconds of applying pressure
• Press% : Percentage of time the squad gained possession withing five seconds of applying pressure
• PresDef3rd : Number of times applying pressure to opposing player who is receiving, carrying or releasing the ball, in the defensive 1/3
• PresMid3rd : Number of times applying pressure to opposing player who is receiving, carrying or releasing the ball, in the middle 1/3
• PresAtt3rd : Number of times applying pressure to opposing player who is receiving, carrying or releasing the ball, in the attacking 1/3
• Blocks : Number of times blocking the ball by standing in its path
• BlkSh : Number of times blocking a shot by standing in its path
• BlkShSv : Number of times blocking a shot that was on target, by standing in its path
• BlkPass : Number of times blocking a pass by standing in its path
• Int : Interceptions
• Tkl+Int : Number of players tackled plus number of interceptions
• Clr : Clearances
• Err : Mistakes leading to an opponent's shot
• Touches : Number of times a player touched the ball. Note: Receiving a pass, then dribbling, then sending a pass counts as one touch
• TouDefPen : Touches in defensive penalty area
• TouDef3rd : Touches in defensive 1/3
• TouMid3rd : Touches in middle 1/3
• TouAtt3rd : Touches in attacking 1/3
• TouAttPen : Touches in attacking penalty area
• TouLive : Live-ball touches. Does not include corner kicks, free kicks, throw-ins, kick-offs, goal kicks or penalty kicks.
• DriSucc : Dribbles completed successfully
• DriAtt : Dribbles attempted
• DriSucc% : Percentage of dribbles completed successfully
• DriPast : Number of players dribbled past
• DriMegs : Number of times a player dribbled the ball through an opposing player's legs
• Carries : Number of times the player controlled the ball with their feet
• CarTotDist : Total distance, in yards, a player moved the ball while controlling it with their feet, in any direction
• CarPrgDist : Total distance, in yards, a player moved the ball while controlling it with their feet towards the opponent's goal
• CarProg : Carries that move the ball towards the opponent's goal at least 5 yards, or any carry into the penalty area
• Car3rd : Carries that enter the 1/3 of the pitch closest to the goal
• CPA : Carries into the 18-yard box
• CarMis : Number of times a player failed when attempting to gain control of a ball
• CarDis : Number of times a player loses control of the ball after being tackled by an opposing player
• RecTarg : Number of times a player was the target of an attempted pass
• Rec : Number of times a player successfully received a pass
• Rec% : Percentage of time a player successfully received a pass
• RecProg : Completed passes that move the ball towards the opponent's goal at least 10 yards from its furthest point in the last six passes, or any completed pass into the penalty area
• CrdY : Yellow cards
• CrdR : Red cards
• 2CrdY : Second yellow card
• Fls : Fouls committed
• Fld : Fouls drawn
• Off : Offsides
• Crs : Crosses
• TklW : Tackles in which the tackler's team won possession of the ball
• PKwon : Penalty kicks won
• PKcon : Penalty kicks conceded
• OG : Own goals
• Recov : Number of loose balls recovered
• AerWon : Aerials won
• AerLost : Aerials lost
• AerWon% : Percentage of aerials won

Acknowledgements

Data from Football Reference. Image from UEFA Champions League.

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Football Analysis Project

## Overview

Football Analysis Project is a dataset for object detection tasks - it contains Ball annotations for 1,126 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Our Market Analysis dataset uncovers consumer movement patterns across brands and categories, helping you define your true trade area and optimize location strategy.

Using foot traffic data tied to specific POIs, this GDPR-compliant, non-PII dataset highlights where your visitors also shop — enabling smarter site selection, lease renegotiation, and competitive market analysis.

Key data points include: - Cross-visitation trends by brand/category - Consumer reach and trade area definition - Weekly, monthly, and quarterly aggregations - Cleaned, normalized, and updated data - Non-PII and fully GDPR-compliant

Focused on the U.S. market, this dataset is ideal for retailers, landlords, and consultants looking to map behavior, refine market coverage, and drive informed decisions.

Football Analysis

## Overview

Football Analysis is a dataset for object detection tasks - it contains Player Ball Goalkeeper Referee annotations for 7,352 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Dataset

This dataset was created by Chukwuebuka Obi

Released under MIT

Contents

Dataset

This dataset was created by Sravan Bunny

Contents

Football Players Ontology and Dataset is a comprehensive resource built upon data retrieved from Transfermarkt, a leading platform for football player information. This ontology and accompanying dataset offer structured insights into football players' attributes, including position, personal information, current club, career history, to name a few. By leveraging Transfermarkt's rich repository of player data, this publication provides researchers and practitioners with a standardized framework for analyzing and categorizing football players, enabling advanced research in player profiling, talent identification, and performance analysis. Explore the ontology structure, dataset contents, and potential applications to unlock valuable insights into the world of football. The included files are:

1. players-transfermarkt.ttl, which is the dataset
2. players.ttl, which is the ontology
3. players.shexc, which is the modelling of the entities using the Shape Expressions language

Here are a few use cases for this project:

1. Player Performance Analysis: Use the "Football Player Tracker" to analyze individual player performances during football games. This could include tracking their movements, analyzing their tactical decisions, or assessing the overall efficiency of the team's formations and strategies.

2. Automated Sports Coverage: Employ this computer vision model for automated, real-time sports-broadcast coverage. It could provide detailed tracking information about players to sports commentators to enhance their analysis during live broadcasts.

3. Learning and Coaching: Coaches can use this model to educate players by visually demonstrating their movements and activities on the field. This could be incredibly beneficial for training sessions, providing a unique method to improve player's understanding of their role and performance.

4. Sports Betting: Sports betting companies could use this model to provide real-time data and analytics to their customers, enhancing their betting experience by supplying in-depth information about player performances and behaviors.

5. Game Strategy Development: Use the data gathered by this computer vision model to assist in the creation or tweaking of a team's game strategies. By understanding which player/classes are performing well in certain roles, the coaching staff can better plan their strategies for future games.

Analysis of ‘FIFA FOOTBALL PLAYERS’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/marianaponce/fifa-football-players on 14 February 2022.

--- No further description of dataset provided by original source ---

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

Dataset

This dataset was created by BasangoudaPatil

Contents

Explore the Player Detection and Tracking in Sports Videos Dataset, designed for training YOLOv8 models. Featuring diverse sports images and detailed annotations, this dataset supports robust development of player detection and tracking models, enhancing sports analytics and AI-driven analysis tools.

Analysis of ‘Football players salaries’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/trolukovich/football-players-salaries on 14 February 2022.

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

Content

This dataset contains salaries of 5.5k footbal players including next columns:

• position
• player
• team
• age
• total_value
• avg_year
• total_guaranteed
• fully_guaranteed
• free agency

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

The description of current load monitoring practices may serve to highlight developmental needs for both the training ground, academia and related industries. While previous studies described these practices in elite men's football, no study has provided an overview of load monitoring practices in elite women's football. Given the clear organizational differences (i.e., professionalization and infrastructure) between men's and women's clubs, making inferences based on men's data is not appropriate. Therefore, this study aims to provide a first overview of the current load monitoring practices in elite women's football. Twenty-two elite European women's football clubs participated in a closed online survey (40% response rate). The survey consisted of 33 questions using multiple choice or Likert scales. The questions covered three topics; type of data collected and collection purpose, analysis methods, and staff member involvement. All 22 clubs collected data related to different load monitoring purposes, with 18 (82%), 21 (95%), and 22 (100%) clubs collecting external load, internal load, and training outcome data, respectively. Most respondents indicated that their club use training models and take into account multiple indicators to analyse and interpret the data. While sports-science staff members were most involved in the monitoring process, coaching, and sports-medicine staff members also contributed to the discussion of the data. Overall, the results of this study show that most elite women's clubs apply load monitoring practices extensively. Despite the organizational challenges compared to men's football, these observations indicate that women's clubs have a vested interest in load monitoring. We hope these findings encourage future developments within women's football.

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 ---

In recent years excessive monetization of football and professionalism among the players has been argued to have affected the quality of the match in different ways. On the one hand, playing football has become a high-income profession and the players are highly motivated; on the other hand, stronger teams have higher incomes and therefore afford better players leading to an even stronger appearance in tournaments that can make the game more imbalanced and hence predictable. To quantify and document this observation, in this work we take a minimalist network science approach to measure the predictability of football over 26 years in major European leagues. We show that over time, the games in major leagues have indeed become more predictable. We provide further support for this observation by showing that inequality between teams has increased and the home-field advantage has been vanishing ubiquitously. We do not include any direct analysis on the effects of monetization on football’s ...

Sample game of anonymised football run data. Showing all of the high speed runs made in that match with associated values at the start of the run, the mean value during the run and the accrued value (calculated as the difference). This is calculated for the in-possession team which may be both teams if possession changes over the course of the run, this is distinguished by the opposition value columns and the own-team value columns.

Analysis of ‘Brazilian Football Championship’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/gabrielmeireles/brazilian-football-championship on 14 February 2022.

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

Results of matches played in the first division of the Brazilian championship since 2013

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

Database for the analysis of the association between post-loss pressure in dynamic defensive transitions and offensive transitions’ development of the UEFA Women´s Champions League 2023/2024.

Dataset associated with the research paper: Guppy, Hyunh, Davids, Varley: Understanding the context in which Australian footballers sprint during match-play

Contains anonymised information on the context in which sprint efforts are performed for an Australian football team.