We will create a customized sports dataset tailored to your specific requirements. Data points may include player statistics, team rankings, game scores, player contracts, and other relevant metrics.

Utilize our sports datasets for a variety of applications to boost strategic planning and performance analysis. Analyzing these datasets can help organizations understand player performance and market trends within the sports industry, allowing for more precise team management and marketing strategies. You can choose to access the complete dataset or a customized subset based on your business needs.

Popular use cases include: enhancing player performance analysis, refining team strategies, and optimizing fan engagement efforts.

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.

Sports Betting Predictive Analysis Dataset

This comprehensive synthetic dataset contains 1,369 rows and 10 columns specifically designed for predictive modeling in sports betting analytics. The dataset provides a rich foundation for machine learning applications in the sports betting domain, featuring realistic match data across multiple sports with comprehensive betting odds, team information, and outcome predictions.

Dataset Overview Table

Key Applications

Machine Learning Use Cases

• Outcome Prediction Models: Train classification algorithms to predict match winners
• Odds Analysis: Analyze betting market efficiency and identify value bets
• Feature Engineering: Create derived features for advanced predictive models
• Model Validation: Compare predicted vs actual outcomes for performance metrics
• Risk Assessment: Evaluate betting strategy performance and risk management

Data Science Applications

• Exploratory Data Analysis: Understand patterns in sports betting markets
• Statistical Modeling: Build probabilistic models for outcome prediction
• Time Series Analysis: Analyze temporal trends in betting odds and outcomes
• Comparative Analysis: Study performance differences across sports and teams
• Visualization Projects: Create interactive dashboards for betting analytics

Research Applications

• Academic Research: Study sports betting market dynamics
• Algorithm Development: Test new machine learning approaches
• Benchmarking: Compare different predictive modeling techniques
• Educational Projects: Learn data science concepts with realistic data
• Portfolio Development: Demonstrate skills in sports analytics domain

Data Characteristics

Realistic Market Simulation

• Betting odds within industry-standard ranges (1.2 - 5.0)
• Sport-specific logic (draws only applicable for Football and Hockey)
• Strategic null value placement to simulate real-world data gaps
• Temporal consistency across 2-year historical period
• Unique match identifiers for easy reference and tracking

Comprehensive Coverage

• Multi-Sport Analysis: Five major sports for diverse modeling scenarios
• Balanced Distribution: Even representation across all sports categories
• Team Diversity: Unique city-based team names preventing data leakage
• Outcome Variety: Includes wins, losses, and draws where applicable
• Prediction Comparison: Both model predictions and actual results included

Sports Analysis

## Overview

Sports Analysis is a dataset for object detection tasks - it contains Sports annotations for 3,985 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 [Public Domain license](https://creativecommons.org/licenses/Public Domain).

NBA Players Performance

Players Performance & Statistics

By [source]

About this dataset

This dataset contains comprehensive performance data of National Basketball Association (NBA) players during the 2019-20 season. It includes all the crucial performance metrics crucial to assess a player’s quality of play. Here, you can compare players across teams, positions and categories and gain deeper insight into their overall performance. This dataset includes useful statistics such as GP (Games Played), Player name, Position, Assists Turnovers Ratio, Blocks per Game, Fouls per Minutes Played, Rebounds per Game and more. Dive in to this detailed overview of NBA player performance and take your understanding of athletes within the organization to another level!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides an in-depth look into the performance of NBA Players throughout the 2019-20 season, allowing an informed analysis of various important statistics. There are a number of ways to use this dataset to both observe and compare players, teams and positions.

• By looking at the data you can get an idea of how players are performing across all metrics. The “Points Per Game” metric is particularly useful as it allows quick comparison between different players and teams on their offensive ability. Additionally, exploratory analysis can be conducted by looking at metrics like rebounds or assists per game which allows one to make interesting observations within the game itself such as ball movement being a significant factor for team success.

• This dataset also enables further comparison between players from different positions on particular metrics that might be position orientated or generic across all positions such as points per game (ppg). This includes adjusting for positional skill sets; For example guard’s field goal attempts might include more three point shots because it would benefit them more than larger forwards or centres who rely more heavily on in close shot attempts due to their size advantage over their opponents.

• This dataset also allows for simple visualisation of player performance with respect to each other; For example one can view points scored against assists ratio when comparing multiple point guards etc., providing further insight into individual performances on certain metrics which otherwise could not be analysed quickly with traditional methods like statistical analysis only within similarly situated groups (e.g.: same position). Furthermore this data set could aid further research in emerging areas such as targeted marketing analytics where identify potential customers based off publically available data regarding factors like ppg et cetera which may highly affect team success orotemode profitability dynamicsincreasedancefficiencyoftheirownopponentteams etcet

Research Ideas

• Develop an AI-powered recommendation system that can suggest optimal players to fill out a team based on their performances in the past season.
• Examine trends in player performance across teams and positions, allowing coaches and scouts to make informed decisions when evaluating talent.
• Create a web or mobile app that can compare the performances of multiple players, allowing users to explore different performance metrics head-to-head

Acknowledgements

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

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: assists-turnovers.csv | Column name | Description | |:--------------|:----------------------------------| | GP | Number of games played. (Integer) | | Player | Player name. (String) | | Position | Player position. (String) |

File: blocks.csv | Column name | Description | |:--------------|:----------------------------------| | GP | Number of games played. (Integer) | | Player | Player name. (String) | | Position | Player position. (String) |

File: fouls-minutes.csv | Column name | Description | |:--------------|:----------------------...

Basketball Player Analysis - 2023/2024 Season

Introduction

This dataset provides a comprehensive overview of basketball players' performance during the 2023/2024 season. The following analysis highlights intriguing insights into individual statistics and players' impact on the games.

Data Used

• Source: Basketball Reference
• Key Variables:
  • Player Name
  • Points per Game
  • Assists
  • Rebounds
  • Other relevant statistics

Key Insights

1. Points per Game:

   • Average points of top players.
   • Distribution graph of scoring.

2. Assists and Rebounds:

   • Relationship between assists and rebounds.
   • Emphasis on versatile players.

3. Efficiency:

   • Shooting efficiency analysis.
   • Players with the best performance in crucial moments.

Code

Link to the code snippet on my GitHub: etl_nba_data

Feel free to explore the detailed code for extracting insights from the dataset.

Enjoy the read!

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".

Here are a few use cases for this project:

1. Sports Analytics and Performance Tracking: Use Vision stat v2.1 to analyze player performances, movements, and interactions in real-time or in post-game analysis, providing valuable insights for coaches to improve team strategies and individual player development.

2. Automated Game Highlights and Summaries: Vision stat v2.1 can quickly identify key moments in a game (goals, corners, saves, referee decisions) to automatically create game highlights or summaries, saving time for sports media and content creators.

3. Virtual and Augmented Reality Applications: Incorporate Vision stat v2.1 into VR and AR experiences to overlay real-time information about players, team positions, and game events onto live or recorded footage, enhancing the viewing experience for fans.

4. Smart Stadium Solutions: Integrate Vision stat v2.1 into the security and monitoring systems of sports venues to improve crowd management, detect unauthorized individuals on the field, and ensure a safe and enjoyable experience for attendees.

5. Betting and Fantasy Sports: Use the advanced statistics and live game data generated by Vision stat v2.1 to enhance betting platforms and fantasy sports apps, providing users a more comprehensive understanding for making informed decisions.

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.

A collection of sport activity datasets for data analysis and data mining 2016b

📂 About This Dataset This dataset combines detailed player performance statistics from WhoScored with team and player meta-data from Transfermarkt. It covers over 1,500 players from top European leagues and includes metrics such as:

Expected Goals (xG) & xG per 90

Tackles, Interceptions, Key Passes, Assists

Pass Accuracy, Crosses, Long Balls

Total Minutes Played & Formations

Player Age, Height, Positioning

🧩 Use Cases Player Rating Prediction

Team Formation Impact Analysis

Identifying Underrated Players via xG vs. Goals

Clustering Players by Style or Efficiency

Fantasy Football Recommendations

🏗️ Data Sources WhoScored.com: Player match stats, tactical analysis.

Transfermarkt.com: Player bio, team formations.

📊 Features Snapshot 32 Columns

Over 20 numerical performance metrics

Cleaned, ready-to-analyze format

Small number of missing values (mostly in passing stats)

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.

Here are a few use cases for this project:

1. Sports Analysis Tool: The "sports ball" computer vision model could be used in a variety of sports analysis tools. These tools could automatically track the ball during a game, assessing player strategies, speed, and overall game dynamics.

2. Game Highights Creation: The model could be used to automate the creation of game highlights. By recognizing when and how a sports ball is used in action, it could automatically identify the key moments of a game.

3. Sports Equipment Inventory Management: The model can be utilized for inventory management in sports stores by automatically identifying different types of sports balls in storage.

4. Real-time Match Statistics: The model can be used in real-time applications, providing statistics on ball possession, passes, shots and goals during live sports broadcasts.

5. Sports-themed Video Games: The model could be used to design smarter, more realistic sports-themed video games. This could allow for dynamic play and more interactive gaming experiences.

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.

Comprehensive dataset of 2 Sports in South Carolina, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

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

Here are a few use cases for this project:

1. Sports analysis and statistics: Use the "Film Finder" model to identify and track famous athletes, referees, and other relevant persons in sports events. This can be combined with other AI models to gather insights about player performance, referee decisions, or real-time match data, which can support coaches, analysts, and commentators in their work.

2. Sports content curation: Media companies and content creators can utilize the model to automatically tag, categorize, and generate keywords for videos, photos, and news articles related to various sports events. This will aid in content discovery, search, and recommendations across various platforms and social media channels.

3. Fan engagement and interaction: Sports teams and athletes often interact with their fans and followers on social media platforms. The "Film Finder" computer vision model could be used by these entities to detect their teammate or athlete's images and initiate custom content or campaigns that target interaction with the specific athlete's fanbase.

4. Fitness and sports training applications: The model can be integrated into fitness apps or sport-specific coaching software to monitor and analyze users' progress, compare their technique with those of professional athletes, and automatically provide personalized feedback, tips, or recommendations.

5. Broadcast and streaming platforms enhancement: The "Film Finder" model can be incorporated into sportscasting and streaming services. It will enable automated player identification, referee decision analysis or overlays, and real-time statistics generated during live events. This results in an improved and engaging sports-watching experience for viewers.

We will create a customized NBA dataset tailored to your specific requirements. Data points may include player statistics, team rankings, game scores, player contracts, and other relevant metrics.

Utilize our NBA datasets for a variety of applications to boost strategic planning and performance analysis. Analyzing these datasets can help organizations understand player performance and market trends within the basketball industry, allowing for more precise team management and marketing strategies. You can choose to access the complete dataset or a customized subset based on your business needs.

Popular use cases include: enhancing player performance analysis, refining team strategies, and optimizing fan engagement efforts.

Analysis of ‘Moneyball’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/wduckett/moneyball-mlb-stats-19622012 on 30 September 2021.

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

Context

In the early 2000s, Billy Beane and Paul DePodesta worked for the Oakland Athletics. While there, they literally changed the game of baseball. They didn't do it using a bat or glove, and they certainly didn't do it by throwing money at the issue; in fact, money was the issue. They didn't have enough of it, but they were still expected to keep up with teams that had much deeper pockets. This is where Statistics came riding down the hillside on a white horse to save the day. This data set contains some of the information that was available to Beane and DePodesta in the early 2000s, and it can be used to better understand their methods.

Content

This data set contains a set of variables that Beane and DePodesta focused heavily on. They determined that stats like on-base percentage (OBP) and slugging percentage (SLG) were very important when it came to scoring runs, however they were largely undervalued by most scouts at the time. This translated to a gold mine for Beane and DePodesta. Since these players weren't being looked at by other teams, they could recruit these players on a small budget. The variables are as follows:

• Team
• League
• Year
• Runs Scored (RS)
• Runs Allowed (RA)
• Wins (W)
• On-Base Percentage (OBP)
• Slugging Percentage (SLG)
• Batting Average (BA)
• Playoffs (binary)
• RankSeason
• RankPlayoffs
• Games Played (G)
• Opponent On-Base Percentage (OOBP)
• Opponent Slugging Percentage (OSLG)

Acknowledgements

This data set is referenced in The Analytics Edge course on EdX during the lecture regarding the story of Moneyball. The data itself is gathered from baseball-reference.com. Sports-reference.com is one of the most comprehensive sports statistics resource available, and I highly recommend checking it out.

Inspiration

It is such an important skill in today's world to be able to see the "truth" in a data set. That is what DePodesta was able to do with this data, and it unsettled the entire system of baseball recruitment. Beane and DePodesta defined their season goal as making it to playoffs. With that in mind, consider these questions:

• How does a team make the playoffs?
• How does a team win more games?
• How does a team score more runs?

They are all simple questions with simple answers, but now it is time to use the data to find the "truth" hidden in the numbers.

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

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