Description This dataset contains stats for 10 football players from Europe's top leagues. You will use this data to solve the following problem statement.

Case Study Manchester United football club wants to know which player they should sign for the Striker position from the list provided. You need to perform a comparative Analysis between players and suggest two players whom they should sign.

Additional Note 1. One of the players should be less than 25 years of age 2. One of the players should have preferably played in the English premier league

Column name & description 1. Player Name: Name of the player 2. Age: The age of the player 3. Current Club: Name of the club that the player currently plays for 4. Opponent: Name of the team that the player played against 5. Competition: Name of the competition. 6. Date: Date of the match played 7. Position: Playing position of the player 8. Mins: Minutes played 9. Goals: Total goals 10. Assists: Total assists 11. Yel: Yellow card 12. Red: Red card 13. Shots: Total shots 14. PS%: Pass success percentage 15. AerialsWon: Aerial duels won 16. Rating: Rating per match

• This dataset belongs to @dataanalystduo. Unauthorized use or distribution of this project prohibited @dataanalystduo
• Dataset has been downloaded from the internet using multiple sources. All the credit for the dataset goes to the original creator of the data

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.

Context

This dataset contains the average players by month for the current top 100 games. It was scraped off https://steamcharts.com/top and converted into this easier to analyze format.

https://i.imgur.com/PLS0HB3.gif" alt="Example Video from Deploy Tab">

Here are a few use cases for this project:

1. Sports Analytics: The Soccer Players computer vision model can be used to analyze player performance during games by tracking player and ball positions, individual player actions, and goal-scoring events, allowing coaches and trainers to make data-driven decisions for improving performance and strategies.

2. Automated Highlight Reels: The model can be used to automatically curate soccer match highlights by identifying crucial moments such as goals, outstanding player performances, and referee decisions. This can streamline the video editing process for broadcasting and streaming companies.

3. Virtual Assistant for Soccer Enthusiasts: The Soccer Players model can be integrated into a mobile application, allowing users to take pictures or upload images from soccer matches and receive instant information about the teams (USA, NED), player roles (goalie, outfield player, referee), and other relevant classes such as ball and goal locations, enhancing their understanding and engagement with the sport.

4. Real-Time Augmented Reality (AR) Applications: The model can be used to create AR experiences for soccer fans attending live matches, providing pop-up information about players (such as player stats, team affiliations, etc.) and game events (goals, referee decisions) when viewing the live match through an AR device or smartphone.

5. Training and Scouting Tools: Soccer scouts and trainers can use the Soccer Players model to evaluate potential recruits or assess the performance of their own players during practice sessions. By rapidly identifying key actions (goals, saves, tackles) and providing context for each play, the model can help scouts and trainers make informed decisions faster.

Fortnite Player Performance

Understanding Player Performance with Granular Data

By Kristian Reynolds [source]

About this dataset

This dataset contains 88 end-game Fortnite statistics, giving a comprehensive look at player performance over the course of 80 games. Discover the time of day, date, mental state and more that contribute to winning strategies! Measure success across eliminations, assists, revives, accuracy percentage, hits scored and head shots landed. Explore distance traveled and materials gathered or used to gauge efficiency while playing. Examine damage taken versus damage dealt to other players and structures alike. Use this data to reveal peak performance trends in Fortnite gameplay

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset is a great resource for analyzing and tracking the performance of Fortnite players. It contains 88 end game stats that provide insights into player performance, such as eliminations, assists and revives. This dataset can help you gain a better understanding of your own performance or another player’s overall effectiveness in the game.

• Analyzing Performance: This dataset can be used to analyze your own or other players’ overall performance in Fortnite across multiple games by looking at statistics like eliminations, assists, revives and head shots (by looking at comparisons between different games).
• Tracking Performance: The dataset also has valuable data that enables you to track any changes in performance over time since it includes data on when the games were played (Date) as well as when they ended (Time of Day). This can be used to measure progress or stagnation in your play over time by comparing different stats like accuracy and distance traveled per game.
• Improving Performance: By combining this data with other information about gear and character builds, one can use this information to look for patterns between successful playstyles across multiple matches or build an optimal loadout for their particular playstyle preferences or intentions see what works best their intended approach

Research Ideas

• Using this dataset to develop player performance indicators that can be used to compare players across games. The indicators can measure each player's ability in terms of eliminations, assists, headshots accuracy and other data points.
• Establishing correlations between the mental state and performance level of a player by analyzing how their stats vary before and after playing under different mental states.
• Analyzing the relationship between overall game performance (such as placement) and specific statistics (such as materials gathered or damage taken). This could provide useful insights into what aspects of gameplay are more important for high-level play in Fortnite

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Fortnite Statistics.csv | Column name | Description | |:-------------------------|:--------------------------------------------------------------| | Date | Date of the game. (Date) | | Time of Day | Time of day the game was played. (Time) | | Placed | Player's placement in the game. (Integer) | | Mental State | Player's mental state during the game. (String) | | Eliminations | Number of eliminations the player achieved. (Integer) | | Assists | Number of assists the player achieved. (Integer) | | Revives | Number of revives the player achieved. (Integer) | | Accuracy | Player's accuracy in the game. (Float) | | Hits ...

Player Ball Detection Data

## Overview

Player Ball Detection Data is a dataset for object detection tasks - it contains Players annotations for 238 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).

Football Player

## Overview

Football Player is a dataset for instance segmentation tasks - it contains Ball Player annotations for 663 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).

This dataset captures granular player-level performance data from recent IPL seasons, curated for analysts, fantasy strategists, and competitive programmers. Inspired by the need for reproducible, contest-compliant data formats, it supports modeling, visualization, and simulation workflows. It can be used to practice and learn Data Visualization, Data Analysis and Model Building and prediction by using Feature Extraction and other techniques.

Discover the most active players across all Tibia worlds. View top players by online time and search for specific player statistics.

2019

The return-to-sport (RTS) process is multifaceted and complex, as multiple variables may interact and influence the time to RTS. These variables include intrinsic factors related the player, such as anthropometrics and playing position, or extrinsic factors, such as competitive pressure. Providing an individualised estimation of time to return to play is often challenging, and clinical decision support tools are not common in sports medicine. This study uses epidemiological data to demonstrate a Bayesian Network (BN). We applied a BN that integrated clinical, non-clinical factors, and expert knowledge to classify time day to RTS and injury severity (minimal, mild, moderate and severe) for individual players. Retrospective injury data of 3374 player seasons and 6143 time-loss injuries from seven seasons of the professional German football league (Bundesliga, 2014/2015 through 2020/2021) were collected from public databases and media resources. A total of twelve variables from three categories (player’s characteristics and anthropometrics, match information and injury information) were included. The response variables were 1) days to RTS (1–3, 4–7, 8–14, 15–28, 29–60, > 60, and 2) injury severity (minimal, mild, moderate, and severe). The sensitivity of the model for days to RTS was 0.24–0.97, while for severity categories it was 0.73–1.00. The user’s accuracy of the model for days to RTS was 0.52–0.83, while for severity categories, it was 0.67–1.00. The BN can help to integrate different data types to model the probability of an outcome, such as days to return to sport. In our study, the BN may support coaches and players in 1) predicting days to RTS given an injury, 2) team planning via assessment of scenarios based on players’ characteristics and injury risk, and 3) understanding the relationships between injury risk factors and RTS. This study demonstrates the how a Bayesian network may aid clinical decision making for RTS.

Discover the most active players on Rasteibra world. View top players by online time and search for specific player statistics on Rasteibra.

This dataset provides granular records of how players unlock achievements across various games, capturing player progression, session context, and monetization events. It is designed to help game designers analyze engagement patterns, optimize achievement systems, and correlate player actions with monetization opportunities for improved game design and revenue strategies.

Detailed online statistics for player Jack Naber from world Celebra. View daily activity and session history.

About Dataset – TL;DR

Comprehensive football (soccer) data lake from Transfermarkt, clean and structured for analysis and machine learning.

• 93,000+ players worldwide
• 2,200+ clubs across all major leagues
• 5.7M+ total records across 10 categories
• 902,000+ market valuations
• 1.9M+ player performance stats
• 1.2M+ player transfer histories
• 144,000+ injuries & 93,000+ national team appearances
• 1.3M+ teammate relationships

Everything in raw CSV format – perfect for EDA, ML, and advanced football analytics.

⚽ The Most Comprehensive Transfermarkt Football Dataset

A complete football data lake covering players, teams, transfers, performances, market values, injuries, and national team stats. Perfect for analysts, data scientists, researchers, and enthusiasts.

🗺 Entity-Relationship Overview

Here’s the high-level schema to help you understand the dataset structure:

https://i.imgur.com/WXLIx3L.png" alt="Transfermarkt Dataset ER Diagram">

📊 Key Coverage

• Players: 93,000+ professional players
• Teams: 2,200+ clubs, 7,700+ club relationships
• Data Volume: 5.7M+ total records
• Global Scope: Major leagues and competitions worldwide

🗂 Data Structure

Organized into 10 well-structured CSV categories:

Player Data (7 categories)

• Player Profiles
• Performances (matches, goals, assists, cards, minutes)
• Market Values (historical valuations)
• Transfer Histories
• Injury Records
• National Team Performances
• Teammate Networks

Team Data (3 categories)

• Team Details (club info)
• Competitions & Seasons
• Parent/Child Team Relations

🔗 What’s Inside?

• 902K+ market value records to track valuation trends
• 1.1M+ transfer histories with fees and movement
• 1.9M+ performance stats across seasons and competitions
• 144K+ injury records with days and matches missed
• 93K+ national team appearances
• 1.3M+ teammate relationships for chemistry analysis

💡 Why This Dataset?

Most football datasets are pre-processed and restrictive. This one is raw, rich, and flexible:

• Build custom KPIs and models
• Perform deep exploratory analysis (EDA)
• Train machine learning and prediction pipelines
• Combine with other football data sources

🚀 Example Use Cases

• Predictive Modeling – Player ratings, transfer value forecasts, injury risk
• Data Visualization & Dashboards – Club comparisons, performance analytics
• Scouting & Recruitment – Discover undervalued talent
• Network Analysis – Teammate relationships and synergy

🖥 Technical Details

• Format: CSV files, UTF-8 encoded
• Easy to Use: Ready for Python (pandas, numpy), R, SQL, BI tools
• Scalable: 5.7M+ rows for big-data analysis

💡 Working on a Cool Project?

I’m always excited to collaborate on innovative football data projects. If you’ve got an idea, let’s make it happen together!

📬 Contact Me

• GitHub: @salimt
• Issues: Feel free to use GitHub Issues if you’ve got dataset-specific questions.

⭐ Support & Visibility

If this dataset helps you:
- Upvote on Kaggle
- Star the GitHub repo
- Share with others in the football analytics community

Tags

football analytics soccer dataset transfermarkt sports analytics machine learning football research player statistics

🔥 Analyze football like never before. Your next AI or analytics project starts here.

Detailed online statistics for player Kaiohsan from world Ourobra. View daily activity and session history.

This is a compilation of data collected from the official VNL website (link can be found here).

The data on Volleyball World was too separated and unusable, with them categorizing data by Attackers, Blockers, Setters, etc. This makes the data inflexible and hard to use for statistical purposes. I manually copy and pasted the data into an Excel sheet, where I used some functions to clean and organize the data. Some columns found on the official website (like efficiency or success rate) were dropped to keep the dataset simple and generalizable.

Please see column descriptions below: - Name: Name of Player - Team: First three letters of the team they represent - Attack Points: Points scored off spikes and tips - Attack Errors: Points lost on spikes or tips - Attack Attempts: Includes Attack Points, Attack Errors, and spikes/tips that did not lead to points for either team - Block Points: Points scored off of blocks - Block Errors: Points lost from blocks - Rebounds: Blocks that did not lead to points for either team - Serve Points: Services aces directly led to a point - Serve Errors: Points lost directly from serves - Serve Attempts: Serves that did not directly lead to points for either team - Successful Sets: Sets that led to a successful attack - Set Errors: Points lost directly from a set - Set Attempts: Sets that did not directly lead to a point for either team - Spike Digs: Number of tips or spikes that a player dug - Dig Errors: An attempt to dig a tip or spike that lost the defending team a point - Successful Receives: A near-perfect or perfect receive, resulting in an easy-to-set ball for the setter - Receive Errors: An attempt at a serve receive that lost the defending team a point - Receive Attempts: A receive of a serve that got the ball up in a non-ideal spot

Discover the most active players on Dracobra world. View top players by online time and search for specific player statistics on Dracobra.

These files contain data about football players in Europe's most popular leagues between 2017-2024. There are a wide variety of attributes, the names of teams and players have been properly formatted. If you are unsure what a column is, please visit fbref.com and find the relevant section for a better description of each column.

I used this data to build a Euro 2024 Match predictor (Spoiler: Germany and France Final) using Random Forest Regressor, a machine learning algorithm and I would greatly appreciate any feedback on the project. Project link: https://github.com/GurpreetSDeol/Euro-2024-Match-Predictor-/tree/main

Detailed online statistics for player Ryooxz from world Honbra. View daily activity and session history.