General Info

This is a collection of over 50,000 ranked EUW games from the game League of Legends, as well as json files containing a way to convert between champion and summoner spell IDs and their names. For each game, there are fields for:

• Game ID
• Creation Time (in Epoch format)
• Game Duration (in seconds)
• Season ID
• Winner (1 = team1, 2 = team2)
• First Baron, dragon, tower, blood, inhibitor and Rift Herald (1 = team1, 2 = team2, 0 = none)
• Champions and summoner spells for each team (Stored as Riot's champion and summoner spell IDs)
• The number of tower, inhibitor, Baron, dragon and Rift Herald kills each team has
• The 5 bans of each team (Again, champion IDs are used)

This dataset was collected using the Riot Games API, which makes it easy to lookup and collect information on a users ranked history and collect their games. However finding a list of usernames is the hard part, in this case I am using a list of usernames scraped from 3rd party LoL sites.

Possible Uses

There is a vast amount of data in just a single LoL game. This dataset takes the most relevant information and makes it available easily for use in things such as attempting to predict the outcome of a LoL game, analysing which in-game events are most likely to lead to victory, understanding how big of an effect bans of a specific champion have, and more.

This dataset has all the results of the Premier League 17/18 season, with much additional data, e.g.:

• Half-/fulltime result, home/away shots, - shots on target, - corners, - fouls
• Odds from different sources

The dataset is collected from http://www.football-data.co.uk/

Here is a textfile from their website, which describes the data fields/column names: http://www.football-data.co.uk/notes.txt (text file key to the data files and data source acknowledgements)

This dataset consists in 22 JSON files representing a season of the Spanish Football League ("La Liga").

The dataset represents several hierarchically related elements, however, only the Match, Event and Player elements contain relevant information for analysis. The rest of the elements simply serve to keep the data structured, by seasons and matchdays. The dataset collects information from several seasons between the years 2000 and 2022. The attributes of each of the elements that make up the dataset are described below:

Season: JSON documents represent a season, their root contains the following information:

competition: Name by which the competition is known

country: Country where the competition is held

season_id: Identifier of the season, example: Season 2021/22

season_url: Relative URL of the season's web page

rounds: List of Round elements, the days into which the championship is divided

Rounds: (or matchdays) Collection of matches:

number: Name of the matchday, e.g.: Matchday 1.

matches: List of Match elements, matches that are played on the same day/s of the championship.

Match: contains relevant match information.

id: Match identifier used at BeSoccer.com

status: Code representing the status of the match: Played (1), Not Played (0)

home_team: Name of the home team

away_team: Name of the away team

result: List of two integers representing the match score

date_time: Date and time at which the match started

referee: First and last name of the referee of the match

href: URL relative to the match page

home_tactic: Tactical arrangement of the home team, e.g.: 4-3-3

home_lineup: List of players in the starting lineup of the home team

home_bench: List of the home team's substitute players

away_tactic: Tactical arrangement of the away team, e.g. 4-3-3

away_lineup: List of players in the home team's starting lineup

away_bench: List of substitute players of the away team

Event: contains information that defines each of the relevant actions that occur during a soccer match. Events can be described by the following attributes:

player: Player identifier. Relative URL

team: Team of the player who participates in the event

minute: Minute of the match in which the event occurs

type: Event type (Enumeration)

Players: Player information:

name: First name

fullname: Player's full name

dob: Date of birth

country: Nationality

position: Position the player usually occupies: GOA (GoalKeeper), DF (Defender), MID (Midfielder), STR (Striker)

foot: Dominant Foot: Right-footed, Left-footed, Two-footed, Unknown

weight: Weight of player in kilograms

height: Player height in centimeters

elo: Measurement of the player's skills on a scale of 1 to 100

potential: Estimate of the maximum ELO that a player can reach on a scale of 1 to 100.

href: Relative URL of the player's record

Premier League

## Overview

Premier League is a dataset for object detection tasks - it contains Premier League annotations for 400 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).

Ethiopian Premier League Analysis Project, based on live steamed footage.

League Players

## Overview

League Players is a dataset for object detection tasks - it contains Vladi annotations for 606 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 provides comprehensive Premier League statistics covering:

• 9 full seasons (2016/2017 to 2024/2025)
• Weekly club performance tables (GW1-38 for each season)
• Detailed club statistics (goals, xG, shots, touches, etc.)
• Complete player profiles (2024/2025 season)
• Player performance metrics (goals, assists, xG, xA, defensive stats)
• Home/away performance breakdowns

Data Sources: Official Premier League website (premierleague.com) Collection Method: Python Selenium web scraping scripts Potential Use Cases:

• Performance trend analysis across seasons
• Player valuation models
• Team strength comparisons
• Predictive modeling for match outcomes
• Fantasy Premier League optimization

At the beginning of the ********* season, the English Premier League introduced Video Assistant Referees (commonly known as VAR) to all matches. This statistic shows the results of a representative survey of the British Public in relation to the English Premier League, presenting the opinions, of British adults who watch the Premier League very or fairly frequently, on the Influence of VAR on the enjoyment of English Premier League football matches in 2020 by NRS social grade.

The NRS social grades are a system of demographic classification used in the United Kingdom. The grades are grouped here into ABC1 and C2DE; these are taken to equate to middle class and working class, respectively.

Although the variation between the opinion distribution of different social grades was not substantial the largest difference occurs within the respondents indicating that Premier League matches have become less enjoyable to watch since VAR was introduced. This response was also the most frequent response amongst respondents of both social grades, with ** and ** percent of ABC1 and C2DE respondents respectively.

jlbaker361/league dataset hosted on Hugging Face and contributed by the HF Datasets community

Bobble League

## Overview

Bobble League is a dataset for object detection tasks - it contains Bobble Ball Powerup annotations for 664 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).

Rocket League Stats

## Overview

Rocket League Stats is a dataset for classification tasks - it contains Stats annotations for 613 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).

Comprehensive statistics for the current Premier League season

Indian Premier League Dataset This dataset contains info on all of the IPL(Indian Premier League) cricket matches. Ball-by-Ball level info and scorecard info to be added soon. The dataset was scraped in July-2022.

  Mantainers:

Somya Gautam Kondrolla Dinesh Reddy Keshaw Soni

2019

The English Premier League had more players called up for the 2022 World Cup than any other league, with 136 players initially making their way to the tournament. This represented nearly one in every six players at the tournament. The league with the second-most players was Spain's La Liga, with 83.

This dataset comprises comprehensive information from ranked matches played in the game League of Legends, spanning the time frame between January 12, 2023, and May 18, 2023. The matches cover a wide range of skill levels, specifically from the Iron tier to the Diamond tier.

The dataset is structured based on time intervals, presenting game data at various percentages of elapsed game time, including 20%, 40%, 60%, 80%, and 100%. For each interval, detailed match statistics, player performance metrics, objective control, gold distribution, and other vital in-game information are provided.

This collection of data not only offers insights into how matches evolve and strategies change over different phases of the game but also enables the exploration of player behavior and decision-making as matches progress. Researchers and analysts in the field of esports and game analytics will find this dataset valuable for studying trends, developing predictive models, and gaining a deeper understanding of the dynamics within ranked League of Legends matches across different skill tiers.

This data comprises of the most recent Big 5 European Soccer League Statistics.

It provides useful insights with most of the features pointing to analysis performed on the performance of various players which participated in the league.

1. Rk - Rank
2. Squad - Team Name
3. Country - Name of the Country
4. LgRk - Squad Finish In Competition
5. MP - Matches Played
6. W - Wins
7. D - Draws
8. L - Losses
9. GF - Goals For
10. GA - Goals Against
11. GD - Goal Difference
12. Pts - Points (3 for a Win and 1 for a Draw)
13. Pts/G - Points per Game
14. xG - Expected Goals inclusive of penalty kicks (not inclusive of penalty shootouts until noticed)
15. xGA - Expected Goals Allowed (not inclusive of penalty shootouts until noticed)
16. xGD - Expected Goal Difference (not inclusive of penalty shootouts until noticed)
17. xGD/90 - Expected Goal Difference per 90 minutes (not inclusive of penalty shootouts until noticed)
18. Last 5 - Last 5 matches ordered chronologically from left to right
19. Attendance - Attendance per game during the season, only for home matches
20. Top Team Scorer - Inclusive of only League matches during that season
21. Goalkeeper - Goalkeeper with the most minutes in the league play

In 2023/24, Premier League clubs collectively generated over *** billion euros in revenue - significantly more than any other league in Europe's Big Five. This has been forecast to rise to around *** billion euros by the 2025/26 season. Which clubs have won the most Premier League titles? The Premier League is the highest tier of professional soccer in England. The clubs with the most English league titles are Manchester United and Liverpool, with each lifting the trophy on ** occasions. Liverpool won the league most recently in 2024/25 under Arne Slot. Which player has won the Premier League the most times? Given the Red Devils’ success in the Premier League, it is not surprising that the player who has won the Premier League the most times is a United club legend. Throughout his career, Ryan Giggs won the Premier League ** times. The next highest-ranked player was Paul Scholes, who also played for Manchester United.

1) Data Introduction • The Major League Baseball Dataset contains various game data, including Elo ratings, predictive probabilities, pitcher information, and actual scores for each MLB game from 1871 to the latest season.

2) Data Utilization (1) Major League Baseball Dataset has characteristics that: • This dataset provides detailed game-specific information, including match dates, seasons, home/away teams, Elo ratings, team-by-team odds, starting pitchers, pitcher-by-pitcher adjustment scores, and actual match results. (2) Major League Baseball Dataset can be used to: • Development of game outcome prediction model: It can be utilized to build machine learning models that predict MLB game outcomes by utilizing various variables such as Elo rating, pitcher information, and team-specific predictive probabilities. • Team and Pitcher Performance Analysis: Analysis of Elo changes and pitcher impact by season and game can be used for in-depth performance analysis such as team strategy, pitcher replacement, and season outlook.

Analysis of ‘Premier League’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/zaeemnalla/premier-league on 12 November 2021.

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

Context

Official football data organised and formatted in csv files ready for download is quite hard to come by. Stats providers are hesitant to release their data to anyone and everyone, even if it's for academic purposes. That was my exact dilemma which prompted me to scrape and extract it myself. Now that it's at your disposal, have fun with it.

Content

The data was acquired from the Premier League website and is representative of seasons 2006/2007 to 2017/2018. Visit both sets to get a detailed description of what each entails.

Inspiration

Use it to the best of your ability to predict match outcomes or for a thorough data analysis to uncover some intriguing insights. Be safe and only use this dataset for personal projects. If you'd like to use this type of data for a commercial project, contact Opta to access it through their API instead.

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