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

This dataset was created by SeaLeopard

Contents

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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Context

This dataset contains 2021-2022 regular season NBA player stats per game. Note that there are duplicate player names resulted from team changes.

• 2021-2022 NBA Player Stats
• 2022-2023 NBA Player Stats

Content

+500 rows and 30 columns. Columns' description are listed below.

• Rk : Rank
• Player : Player's name
• Pos : Position
• Age : Player's age
• Tm : Team
• G : Games played
• GS : Games started
• MP : Minutes played per game
• FG : Field goals per game
• FGA : Field goal attempts per game
• FG% : Field goal percentage
• 3P : 3-point field goals per game
• 3PA : 3-point field goal attempts per game
• 3P% : 3-point field goal percentage
• 2P : 2-point field goals per game
• 2PA : 2-point field goal attempts per game
• 2P% : 2-point field goal percentage
• eFG% : Effective field goal percentage
• FT : Free throws per game
• FTA : Free throw attempts per game
• FT% : Free throw percentage
• ORB : Offensive rebounds per game
• DRB : Defensive rebounds per game
• TRB : Total rebounds per game
• AST : Assists per game
• STL : Steals per game
• BLK : Blocks per game
• TOV : Turnovers per game
• PF : Personal fouls per game
• PTS : Points per game

Acknowledgements

Data from Basketball Reference. Image from NBA.

If you're reading this, please upvote.

Data was collected from 753 participants on athlete mental health as the United Kingdom was emerging from a COVID-19 lockdown with a group of non-athletes used as a comparison. Data was collected on participants athletic identity, resilience, wellbeing, depression, anxiety and loneliness using a cross-sectional online survey design using the following measures:The Athletic Identity Measurement Scale (Brewer et al., 1993)The Brief Resilience Scale (Smith et al., 2008)The Mental Health Continuum Short Form (Keyes, 2005)The Hospital Anxiety and Depression Scale (Zigmond and Snaith, 1983)The Short Loneliness Scale (Gierveld and Tilburg, 2006)

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 ---

ObjectiveTo explore the intervention effect of mindfulness training on athletes’ performance using meta-analysis method.MethodsA total of 11 articles and 23 effect sizes were included through retrieval of Chinese and English databases, with a total sample size of 582.ResultMindfulness training improves the level of mindfulness [SMD =1.08, 95%CI (0.30, 1.86), p 

Location of Porirua City sports grounds

This is a Paralympic Games dataset that describes medals and athletes for Tokyo 2020. The data was created from Tokyo Paralympics.

All medals and more than 4,500 athletes (with some personal data: date and place of birth, height, etc.) of the Paralympic Games you can find here. Apart from it coaches and technical officials are present.

Please, click on the ticker to the right top of the dataset to cast an upvote. It will help be on the top.

Data: 1. medals_total.csv - dataset contains all medals grouped by country as here. 2. medals.csv - dataset includes general information on all athletes who won a medal. 3. athletes.csv - dataset includes some personal information of all athletes. 4. coaches.csv - dataset includes some personal information of all coaches. 5. technical_officials - dataset includes some personal information of all technical officials.

Related Datasets

• Olympic Games, 1986-2020
• Beijing 2022 Olympics
• Tokyo 2020 Olympics
• Tokyo 2020 Horses
• Olympic Games Hosts

Data Visualization

Tokyo 2020 Paralympics

Dataset History

2021-09-05 - dataset is updated. Contains full information. 2021-08-30 - dataset is updated. Contains information for the first 6 days of competitions. 2021-08-27 - dataset is created. Contains information for the first 3 days of competitions.

Q&A

If you have some questions please start a discussion.

Dataset

This dataset was created by Yash Bansal

Contents

Dataset

This dataset was created by Michael Chen

Contents

The data presented here was extracted from a larger dataset collected through a collaboration between the Embedded Systems Laboratory (ESL) of the Swiss Federal Institute of Technology in Lausanne (EPFL), Switzerland and the Institute of Sports Sciences of the University of Lausanne (ISSUL). In this dataset, we report the extracted segments used for an analysis of R peak detection algorithms during high intensity exercise.

Protocol of the experiments
The protocol of the experiment was the following.

• 22 subjects performing a cardio-pulmonary maximal exercise test on a cycle ergometer, using a gas mask. A single-lead electrocardiogram (ECG) was measured using the BIOPAC system.
• An initial 3 min of rest were recorded.
• After this baseline, the subjects started cycling at a power of 60W or 90W depending on their fitness level.
• Then, the power of the cycle ergometer was increased by 30W every 3 min till exhaustion (in terms of maximum oxygen uptake or VO2max).
• Finally, physiology experts assessed the so-called ventilatory thresholds and the VO2max based on the pulmonary data (volume of oxygen and CO2).

Description of the extracted dataset

The characteristics of the dataset are the following:

• We report only 20 out of 22 subjects that were used for the analysis, because for two subjects the signals were too corrupted or not complete. Specifically, subjects 5 and 12 were discarded.
• The ECG signal was sampled at 500 Hz and then downsampled at 250 Hz. The original ECG signal were measured at maximum 10 mV. Then, they were scaled down by a factor of 1000, hence the data is represented in uV.
• For each subject, 5 segments of 20 s were extracted from the ECG recordings and chosen based on different phases of the maximal exercise test (i.e., before and after the so-called second ventilatory threshold or VT2, before and in the middle of VO2max, and during the recovery after exhaustion) to represent different intensities of physical activity.

seg1 --> [VT2-50,VT2-30]
seg2 --> [VT2+60,VT2+80]
seg3 --> [VO2max-50,VO2max-30]
seg4 --> [VO2max-10,VO2max+10]
seg5 --> [VO2max+60,VO2max+80]

• The R peak locations were manually annotated in all segments and reviewed by a physician of the Lausanne University Hospital, CHUV. Only segment 5 of subject 9 could not be annotated since there was a problem with the input signal. So, the total number of segments extracted were 20 * 5 - 1 = 99.

Format of the extracted dataset

The dataset is divided in two main folders:

• The folder `ecg_segments/` contains the ECG signals saved in two formats, `.csv` and `.mat`. This folder includes both raw (`ecg_raw`) and processed (`ecg`) signals. The processing consists of a morphological filtering and a relative energy non filtering method to enhance the R peaks. The `.csv` files contain only the signal, while the `.mat` files include the signal, the time vector within the maximal stress test, the sampling frequency and the unit of the signal amplitude (uV, as we mentioned before).
• The folder `manual_annotations/` contains the sample indices of the annotated R peaks in `.csv` format. The annotation was done on the processed signals.

Objectifs : Diffuser aux internautes la liste des organismes sur le territoire de la Ville de Longueuil.Territoire concerné : Ville de LongueuilFréquence de la mise à jour : QuotidienneFréquence d'extraction : HebdomadairePropriétés des fichiers : NomDescriptionTypeORGANISMENom de l'organismeTexteMISSIONMission de l'organismeTexteADRESSEAdresseTexteCOURRIELAdresse courrielTexteSITE_INTERNETSite internetTexteTELEPHONENuméro de téléphoneTexteFAXNuméro de faxTexte

A DataSet of Supply Chains used by the company DataCo Global was used for the analysis. Dataset of Supply Chain , which allows the use of Machine Learning Algorithms and R Software. Areas of important registered activities : Provisioning , Production , Sales , Commercial Distribution.It also allows the correlation of Structured Data with Unstructured Data for knowledge generation.

Type Data : Structured Data : DataCoSupplyChainDataset.csv Unstructured Data : tokenized_access_logs.csv (Clickstream)

Types of Products : Clothing , Sports , and Electronic Supplies

Additionally it is attached in another file called DescriptionDataCoSupplyChain.csv, the description of each of the variables of the DataCoSupplyChainDatasetc.csv.

Objectifs : Diffuser aux internautes la liste des lieux où des événements se tiennent.Territoire concerné : Ville de LongueuilFréquence de la mise à jour : QuotidienneFréquence d'extraction : HebdomadairePropriétés des fichiers : NomDescriptionTypeNOMNom du lieuTexteADRESSEAdresseTexteARRONDISSEMENTArrondissementTexteDISTRICTDistrict électoralTexteTELEPHONENuméro de téléphoneTexte

Based on the extended Theory of Planned Behavior (TPB) model, this paper reveals the influencing mechanism of planned behavior and the moderated mediation effect of social interactivity in Chinese sports tourism in the post-pandemic era. To provide a reference basis for improving people’s intentions regarding sports tourism and promoting the construction of sports tourism in China. This paper takes Chinese sports tourists as the research object, and 1422 questionnaires were used for data analysis. Comparisons were made through a structural equation model (SEM), and the direct/indirect effects of the hypotheses were tested. The moderated mediation effect of social interactivity was tested using the PROCESS macro model 14.

Dataset

This dataset was created by Jason Menaguale

Contents

The information contains the number of major discounts applied monthly to users of sports centres as a result of the application of the general and specific provisions for the application of public prices for the provision of services on their premises. It gives us an approximate idea of the number of users who benefit from these reductions over the established general rate. Data are provided by the following criteria: Discounts by age: reflect the number of discounts on the purchase of subscriptions, classes and medical examinations. They do not reflect discounts related to the sale of tickets and purchase of bonds as these are non-nominative securities. Discounts large family: reflect the total number of monthly discounts for all services. In the entries there is no information about sex as they are non-nominative. Discounts for inclusion (medical-sports and social): reflect the number of monthly discounts on classes for individuals included in any of these programs. Discounts for people with disabilities: reflect the number of monthly discounts on classes and medical examinations. The number of accesses to free-use services (pool and bodybuilding) is not reflected as they are free of charge. Discounts Job Demand Card: reflect the number of monthly discounts for free use of the pool. As it is not necessary to register the identification of the person in the database, some of them do not have gender information. Discounts Madrid Mayor card: reflect the number of discounts on the purchase of subscriptions, classes and medical examinations for cardholders under 65 years of age. They do not reflect discounts related to the sale of tickets and purchase of bonds as these are non-nominative securities. NOTICE: As of October 2022, this information is made available in the following formats: CSV, JSON and XML”. In this same portal, there are different datasets of sports information .

BBC News Topic Dataset

Dataset on BBC News Topic Classification consisting of 2,225 articles published on the BBC News website corresponding during 2004-2005. Each article is labeled under one of 5 categories: business, entertainment, politics, sport or tech. Original source for this dataset:

Derek Greene, Pádraig Cunningham, “Practical Solutions to the Problem of Diagonal Dominance in Kernel Document Clustering,” in Proc. 23rd International Conference on Machine learning… See the full description on the dataset page: https://huggingface.co/datasets/SetFit/bbc-news.

We have collected data for six participants aged from 18 to 55 years, three males and three females. The study followed ethical protocols as per ethics requirements (UNE HE19-239). We have measured the Centre of Mass (COM) for each participant walking and running on the treadmill as follows:* X-axis in the direction of gait progression with positive pointing forward.* Y-axis in the medial-lateral direction with positive pointing to the right.* Z-axis in the vertical direction with positive pointing upward.The markers we used were the Left and Right PSIS and ASIS, then we computed the average of all four. The data were collected for different velocities, at 100 frames per second, over 10 seconds, for each velocity, using an 8 camera, Qualisys Motion capture system with the COM reconstructed using a pelvic marker set within Visual3D.une_gait_participant_info.csv provides metadata about each of the anonymised participants.une_gait.csv provides the participants' time and position information for each of the measured speeds.