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.

Context

This dataset contains 2022-2023 football player stats per 90 minutes. Only players of Premier League, Ligue 1, Bundesliga, Serie A and La Liga are listed.

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

Content

+2500 rows and 124 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
• Sw : Passes that travel more than 40 yards of the width of the pitch
• PasCrs : Crosses
• TI : Throw-Ins taken
• CK : Corner kicks
• CkIn : Inswinging corner kicks
• CkOut : Outswinging corner kicks
• CkStr : Straight corner kicks
• PasCmp : Passes completed
• PasOff : Offsides
• 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
• Blocks : Number of times blocking the ball by standing in its path
• BlkSh : Number of times blocking a shot 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.
• ToAtt : Number of attempts to take on defenders while dribbling
• ToSuc : Number of defenders taken on successfully, by dribbling past them
• ToSuc% : Percentage of take-ons Completed Successfully
• ToTkl : Number of times tackled by a defender during a take-on attempt
• ToTkl% : Percentage of time tackled by a defender during a take-on attempt
• 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
• Rec : Number of times 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 Sky Sports.

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3781 Global import shipment records of Soccer Tables with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

3128 Global import shipment records of Football Soccer with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

歐洲足球資料庫 背景:歐洲足球 內容:歐洲足球分析

Positioning data of 20 female football players collected during the first 5 matchdays of the regular league during the season 2023/2024. They correspond to the third Spanish female category, considered semi-professional.

.SOCCER Whois Database, discover comprehensive ownership details, registration dates, and more for .SOCCER TLD with Whois Data Center.

Este dataset está compuesto por dos secciones, una primera página donde se muestran unas estadísticas básicas de cada jugador, hasta la jornada 29 de todos los equipos de LaLiga Santander. Mientras que el segundo CSV muestra las estadísticas de cada jugador en los diferentes partidos disputados en la presente edición de LaLiga.

This dataset contains a set of statistics regarding the spanish first division teams.

Each field value has been computed as the mean for the last 30 games played, for the following statistics:

Possession

Passes

Tackles

Corners

Shots - Total

Shots - On target

Shots - Off target

Shots - Blocked

Shots - Outside Box

Shots - Inside Box

Fouls

Offsides

Yellow Card

Red Card

Penalties

Data has been obtained from https://playerstats.football

During the 2022 FIFA Men's World Cup in Qatar, the Australian team, the 'Socceroos', played a total of four matches. During those matches, midfielder Aaron Mooy topped the team rank for the number of passes with 200 passes, while Craig Goodwin made the most crosses with 19.

This is our dataset, with the 34,000 images and their manual annotations.

Dataset

This dataset was created by Oluwaseunfunmi Omotunde

Released under CC0: Public Domain

Contents

France E-Commerce Transactions: AOV: Sports: Soccer data was reported at 174.850 USD in 01 Mar 2025. This records a decrease from the previous number of 176.507 USD for 28 Feb 2025. France E-Commerce Transactions: AOV: Sports: Soccer data is updated daily, averaging 136.656 USD from Dec 2018 (Median) to 01 Mar 2025, with 2254 observations. The data reached an all-time high of 261.918 USD in 02 Feb 2023 and a record low of 45.890 USD in 05 Jun 2019. France E-Commerce Transactions: AOV: Sports: Soccer data remains active status in CEIC and is reported by Grips Intelligence Inc.. The data is categorized under Global Database’s France – Table FR.GI.EC: E-Commerce Transactions: by Category.

92 Global import shipment records of Soccer Tables with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

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.

Uruguay E-Commerce Transactions: AOV: Sports: Soccer data was reported at 167.903 USD in 13 Dec 2023. This records a decrease from the previous number of 176.107 USD for 11 Dec 2023. Uruguay E-Commerce Transactions: AOV: Sports: Soccer data is updated daily, averaging 140.421 USD from Jan 2019 (Median) to 13 Dec 2023, with 277 observations. The data reached an all-time high of 1,246.154 USD in 22 Feb 2019 and a record low of 26.235 USD in 02 Nov 2021. Uruguay E-Commerce Transactions: AOV: Sports: Soccer data remains active status in CEIC and is reported by Grips Intelligence Inc.. The data is categorized under Global Database’s Uruguay – Table UY.GI.EC: E-Commerce Transactions: by Category.

SDCC Soccer Pitches within SDCC County. Polygon data identifying location, type, area and number included.

This game of soccer is a book. It was written by Bobby Charlton and published by Cassell in 1967.

This dataset can be used to replicate the empirical results presented in the paper. In sequential competitions, the order in which teams take turns may have an impact on performance and the outcome. Previous studies with penalty shootouts have shown mixed evidence of a possible advantage for the first shooting team. This has led to some debate on whether a change in the rules of the game is needed. This work contributes to the debate by collecting an extensive dataset of shootouts which corroborates an advantage for the first shooter, albeit with a smaller effect than what has been documented in previous research. To evaluate the impact of alternative ordering of shots, we model shootouts as a probability network, calibrate it using the data from the traditional ordering, and use the model to conduct counterfactual analysis. Our results show that alternating the team that shoots first in each round would reduce the impact of ordering. These results were in part developed as an alternative to field studies to support IFAB's consideration of changing the shooting order.

Ecuador E-Commerce Transactions: Volume: Sports: Soccer data was reported at 1.000 Unit in 13 Aug 2024. This stayed constant from the previous number of 1.000 Unit for 08 Aug 2024. Ecuador E-Commerce Transactions: Volume: Sports: Soccer data is updated daily, averaging 1.000 Unit from Jan 2019 (Median) to 13 Aug 2024, with 794 observations. The data reached an all-time high of 12.000 Unit in 23 Jul 2019 and a record low of 1.000 Unit in 13 Aug 2024. Ecuador E-Commerce Transactions: Volume: Sports: Soccer data remains active status in CEIC and is reported by Grips Intelligence Inc.. The data is categorized under Global Database’s Ecuador – Table EC.GI.EC: E-Commerce Transactions: by Category.