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.

Dataset consists of the data produced by nine cyclists. Data were directly exported from their Strava or Garmin Connect accounts. Data format of sport s activities could be written in GPX or TCX form, which are basically the XML formats adapted to specific purposes. From each dataset, many following information can be obtained: GPS location, elevation, duration, distance, average and maximal heart rate, while some workouts include also data obtained from power meters.

Dataset accompanying the Synthetic Daisies post "Are the Worst Performers the Best Predictors?" and the technical paper (on viXra) "From Worst to Most Variable? Only the worst performers may be the most informative".

Analysis of ‘Summer Sports Experience’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/23cda0e6-055d-4d90-8e67-4f73e8c4e612 on 13 February 2022.

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

Activity and attendance records from the "Summer Sports Experience" program, which provides sports instruction to children ages 8 to 14.

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

Here are a few use cases for this project:

1. Sports Analytics: Use the "Basketball Players" model to automatically track players' movements, ball possession, and referee decisions during live games or post-game analysis. This data can be used by coaches, analysts, and teams to inform and improve strategies, tactics, and player performance.

2. Real-time Game Commentary: Integrate the model into sports broadcasting platforms, providing real-time updates and statistics to commentators, allowing them to focus on in-depth analysis and storytelling while the model handles identification and stat-tracking.

3. Automated Sports Highlights: Utilize the model to automatically create highlights from basketball games by identifying key moments, such as successful shots, blocks, and referee decisions. This can streamline post-production process for sports media outlets and social media channels.

4. Training and Skill Development: Leverage the "Basketball Players" model to create feedback tools for players, identifying areas of improvement in team dynamics and individual technique during practice sessions or games.

5. Fan Experience: Employ the model in smartphone apps or AR devices, providing fans with real-time information on their favorite teams and players during live games, enhancing their overall experience and engagement.

A collection of sport activity datasets for data analysis and data mining 2017a

Dataset

This dataset was created by Sagidur Rahman

Contents

Here are a few use cases for this project:

1. Sports Analytics: Use the "soccer data" model to automatically classify and track players' actions during a soccer match, helping teams and coaches analyze player performance, decision-making, and ball possession patterns.

2. Soccer Training Applications: Incorporate the model into a soccer training app or system that provides real-time feedback to players, assisting them in improving their ball-handling skills, positioning, and decision-making on the field.

3. Interactive Sports Broadcasting: Enhance the viewer experience during live broadcasts or replays of soccer matches by automatically identifying which player has the ball, enabling new interactive features such as instant player statistics or alerts for key events.

4. Augmented Reality Sports Experiences: Implement the model into an AR app that allows users to watch live or recorded soccer games with an overlay that highlights player positions and their current ball possession status, making it easier for viewers to follow and understand the game's progression.

5. Automated Soccer Highlights Generation: Utilize the "soccer data" model to automatically identify and extract key moments in soccer matches (such as goals, saves, or exciting plays) based on player and ball possession patterns, making it more efficient to create highlight reels or videos for fans to enjoy.

Analysis of ‘College Basketball Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/andrewsundberg/college-basketball-dataset on 28 January 2022.

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

Content

Data from the 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, and 2021 Division I college basketball seasons.

cbb.csv has seasons 2013-2019 combined

The 2020 season's data set is kept separate from the other seasons, because there was no postseason due to the Coronavirus.

The 2021 data is from 3/15/2021 and will be updated and added to cbb.csv after the tournament

Variables

RK (Only in cbb20): The ranking of the team at the end of the regular season according to barttorvik

TEAM: The Division I college basketball school

CONF: The Athletic Conference in which the school participates in (A10 = Atlantic 10, ACC = Atlantic Coast Conference, AE = America East, Amer = American, ASun = ASUN, B10 = Big Ten, B12 = Big 12, BE = Big East, BSky = Big Sky, BSth = Big South, BW = Big West, CAA = Colonial Athletic Association, CUSA = Conference USA, Horz = Horizon League, Ivy = Ivy League, MAAC = Metro Atlantic Athletic Conference, MAC = Mid-American Conference, MEAC = Mid-Eastern Athletic Conference, MVC = Missouri Valley Conference, MWC = Mountain West, NEC = Northeast Conference, OVC = Ohio Valley Conference, P12 = Pac-12, Pat = Patriot League, SB = Sun Belt, SC = Southern Conference, SEC = South Eastern Conference, Slnd = Southland Conference, Sum = Summit League, SWAC = Southwestern Athletic Conference, WAC = Western Athletic Conference, WCC = West Coast Conference)

G: Number of games played

W: Number of games won

ADJOE: Adjusted Offensive Efficiency (An estimate of the offensive efficiency (points scored per 100 possessions) a team would have against the average Division I defense)

ADJDE: Adjusted Defensive Efficiency (An estimate of the defensive efficiency (points allowed per 100 possessions) a team would have against the average Division I offense)

BARTHAG: Power Rating (Chance of beating an average Division I team)

EFG_O: Effective Field Goal Percentage Shot

EFG_D: Effective Field Goal Percentage Allowed

TOR: Turnover Percentage Allowed (Turnover Rate)

TORD: Turnover Percentage Committed (Steal Rate)

ORB: Offensive Rebound Rate

DRB: Offensive Rebound Rate Allowed

FTR : Free Throw Rate (How often the given team shoots Free Throws)

FTRD: Free Throw Rate Allowed

2P_O: Two-Point Shooting Percentage

2P_D: Two-Point Shooting Percentage Allowed

3P_O: Three-Point Shooting Percentage

3P_D: Three-Point Shooting Percentage Allowed

ADJ_T: Adjusted Tempo (An estimate of the tempo (possessions per 40 minutes) a team would have against the team that wants to play at an average Division I tempo)

WAB: Wins Above Bubble (The bubble refers to the cut off between making the NCAA March Madness Tournament and not making it)

POSTSEASON: Round where the given team was eliminated or where their season ended (R68 = First Four, R64 = Round of 64, R32 = Round of 32, S16 = Sweet Sixteen, E8 = Elite Eight, F4 = Final Four, 2ND = Runner-up, Champion = Winner of the NCAA March Madness Tournament for that given year)

SEED: Seed in the NCAA March Madness Tournament

YEAR: Season

Acknowledgements

This data was scraped from from http://barttorvik.com/trank.php#. I cleaned the data set and added the POSTSEASON, SEED, and YEAR columns

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

This statistic contains data on sports facilities by type and agency for the years 2018-2019.

3 x 3 women series data

This repository contains the dataset for our study "A Large-Scale Empirical Study of Android Sports Apps in the Google Play Store" and this will help to replicate our study, also the replication package to direct you to help replicate it for your dataset too.

Note: The dataset given are protected with password, and the password is available in our published paper

Base de données Data ES, équipements sportifs et lieux de pratiques mis à jour quotidiennement.

PMData Dataset

  About Dataset

Paper: https://dl.acm.org/doi/10.1145/3339825.3394926 In this dataset, we present the PMData dataset that aims to combine traditional lifelogging with sports activity logging. Such a dataset enables the development of several interesting analysis applications, e.g., where additional sports data can be used to predict and analyze everyday developments like a person's weight and sleep patterns, and where traditional lifelog data can be… See the full description on the dataset page: https://huggingface.co/datasets/aai530-group6/pmdata.

Abstract copyright UK Data Service and data collection copyright owner.

The Active Lives Children and Young People Survey, which was established in September 2017, provides a world-leading approach to gathering data on how children engage with sport and physical activity. This school-based survey is the first and largest established physical activity survey with children and young people in England. It gives anyone working with children aged 5-16 key insight to help understand children's attitudes and behaviours around sport and physical activity. The results will shape and influence local decision-making as well as inform government policy on the PE and Sport Premium, Childhood Obesity Plan and other cross-departmental programmes. More general information about the study can be found on the Sport England Active Lives Survey webpage and the Active Lives Online website, including reports and data tables.

The Active Lives Children and Young People Survey, 2017-2018 commenced during school academic year 2017 / 2018. It ran from autumn term 2017 to summer term 2018 and excludes school holidays. The survey identifies how participation varies across different activities and sports, by regions of England, between school types and terms, and between different demographic groups in the population. The survey measures levels of activity (active, fairly active and less active), attitudes towards sport and physical activity, swimming capability, the proportion of children and young people that volunteer in sport, sports spectating, and wellbeing measures such as happiness and life satisfaction. The questionnaire was designed to enable analysis of the findings by a broad range of variables, such as gender, family affluence and school year.

The following datasets are available:

1) Main dataset includes responses from children and young people from school years 3 to 11, as well as responses from parents of children in years 1-2. The parents of children in years 1-2 provide behavioural answers about their child's activity levels, they do not provide attitudinal information. Using this main dataset, full analyses can be carried out into sports and physical activity participation, levels of activity, volunteering (years 5 to 11), etc. Weighting is required when using this dataset (wt_gross / wt_set1.csplan).

2) Year 1-2 pupil dataset includes responses from children in school years 1-2 directly, providing their attitudinal responses (e.g. whether they like playing sport and find it easy). Analysis can be carried out into feelings towards swimming, enjoyment for being active, happiness etc. Weighting is required when using this dataset (wt_gross / wt_set1.csplan).

3) Teacher dataset includes responses from the teachers at schools selected for the survey. Analysis can be carried out into school facilities available, length of PE lessons, whether swimming lessons are offered, etc. Weighting was formerly not available, however, as Sport England have started to publish the Teacher data, from December 2023 we decide to apply weighting to the data. The Teacher dataset now includes weighting by applying the ‘wt_teacher’ weighting variable.

For further information about the variables available for analysis, and the relevant school years asked survey questions, please see the supporting documentation. Please read the documentation before using the datasets.

Latest edition information

For the second edition (January 2024), the Teacher dataset now includes a weighting variable (‘wt_teacher’). Previously, weighting was not available for these data.

Main Topics:

Topics covered in the Active Lives Children and Young People Survey include:

• Sport and physical activity participation
• Well-being
• Health

Analysis of ‘Sports resources of the Basque Country’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/https-opendata-euskadi-eus-catalogo-recursos-deportivos-de-euskadi- on 17 January 2022.

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

Information on the sports resources of the Basque Country: sports activities, organisers of sporting events, rental of sports equipment, Basque mountain bike centres, sports facilities, etc.

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

Data description: This dataset presents sEMG signals acquired while performing 30 different sports and recreational activities. Eleven healthy subjects were involved in this data collection. The sEMG signals were collected from 9 electrodes placed at different sites on both the upper limbs. The following fig: 1 and table shows the sensor locations on the hand during these activities. The activities included 6 cricketing actions, 4 basketball passes, 3 tennis shots, 4 ball throwing, 4 badminton shots, 3 weight training and finally 6 recreational activities such as throwing frisbee and tennikoit ring. Each activity has three phases: 1) Rest 2) action and 3) release. The duration of each phase depends on the type of activity. These details are shown in table II. The main purpose of the sports data (EMAHA-DB3) is to study the role of muscle activations in various movement sequences.

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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