Champions League 2023/2024 Dataset

Overview

This dataset provides detailed statistics for the UEFA Champions League 2023/2024 season, focusing on team performance across various metrics. The data is sourced from FBref, a comprehensive platform for football statistics. This single-table dataset includes metrics such as matches played, wins, losses, goals scored, expected goals (xG), and more for each team participating in the Champions League.

Dataset Content

The dataset is structured as a single CSV file with the following headers:

• Rk: Rank of the team based on the stage of the competition reached.
• Country: The country of the club.
• Squad: The name of the club.
• MP: Matches played.
• W: Matches won.
• D: Matches drawn.
• L: Matches lost.
• GF: Goals for - total goals scored by the team.
• GA: Goals against - total goals conceded by the team.
• GD: Goal difference (GF - GA).
• Pts: Total points accumulated by the team
• xG: Expected goals - a metric that estimates the number of goals a team should have scored based on the quality of their chances.
• xGA: Expected goals against - a metric that estimates the number of goals a team should have conceded based on the quality of chances they allowed.
• xGD: Expected goal difference (xG - xGA).
• xGD/90: Expected goal difference per 90 minutes.
• Last 5: Results of the last 5 matches (e.g., WWDWL for 3 wins, 1 draw, and 1 loss).
• Attendance: Average attendance for home matches.
• Top Team Scorer: The name of the top scorer for the team.
• Goalkeeper: The name of the main goalkeeper for the team.

Data Source

The data has been scraped from FBref, a well-known source for football statistics. FBref provides detailed and historical data for various football competitions worldwide, including the UEFA Champions League.

Acknowledgements

• FBref: For providing the comprehensive data used to compile this dataset.
• Kaggle: For hosting and facilitating data science competitions and datasets.

League

## Overview

League is a dataset for object detection tasks - it contains Champions annotations for 9,502 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 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

Rocket League

## Overview

Rocket League is a dataset for object detection tasks - it contains Objects annotations for 341 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.

Incoming and outgoing transfers (sales and loans) for the Saudi Professional League since 2000. Data includes player name & age, clubs involved, fee (million €), transfer window (summer or winter) and transfer type (sale or loan).

Will be updated at the end of each window and after major transfers. Last updated Sep 13, 2023, following the closing of the summer transfer window.

Data scraped from Transfermarkt.

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).

The Premier League is one of the most watched sports leagues in the world, so it comes as no surprise that the media rights deals for the league are worth billions. For the period between 2025 and 2029, domestic media rights for the Premier League were worth approximately 8.44 billion U.S. dollars.

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 in 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.

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

JetRacer Soccer League

## Overview

JetRacer Soccer League is a dataset for object detection tasks - it contains Cars annotations for 2,545 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).

Victory prediction from League of Legend champion selection data

• Victory prediction from League of Legend champion selection data
  • Objectif
  • Dataset description
    • Splits
    • Files
      • Champions
      • Matches
  • Getting started

Objectif

The continuous development of e-sports is generating a daily trail of insightful data in high volume, to the point that justifies the use of exploratory data analysis.

In particular, the multiplayer online battle arena (MOBA) game League of Legends (LoL), organizes one of the most viewed tournaments, attracting over 4 million peak viewers.

The game lets participants choose between more than 161 champions with different characteristics and game play mechanics affecting the dynamics of team composition. Thus, champion selection is of capital importance for pro players.

Multiple works focused on champion selection data in order to predict team victory for DOTA, a MOBA similar to League of Legends, but LoL is still under-researched. And with the regular new patches received, it is difficult to compare predictor performances across time.

To this objective, we are releasing this curated dataset such that others can try their own architectures on victory prediction from champion selection data, thus offering a benchmark dataset for the community.

Dataset description

This dataset has been collected by Devoteam Revolve from Riot Developer API

http://france.devoteam.com/wp-content/uploads/sites/21/2021/05/logo-cartouches-RVB-ROUGE.png" alt="Devoteam logo">

The dataset has a total of 84440 games that are from 2022 at the version 12.12 of the game.

The games are only from the highest ELO players, with ranks of either Master, Grand Master and Challenger. This ranks represents the top 1.2% of all players.

Splits

The dataset comes pre splitted

Files

Dataset organization:

12.12.-splits
├── test
|  ├── df_00000.csv
|  |   ...
|  └── df_xxxxx.csv
|
├── train
|  ├── df_00000.csv
|  |   ...
|  └── df_xxxxx.csv
|
└── val
|  ├── df_00000.csv
|  |   ...
|  └── df_xxxxx.csv
|
└── champion.json

Champions

All champions information can be found under ./12.12.-splits/champion.json

This file allows the conversion from Player_{Player_id}_pick id number to the champion name.

Multiple other information are also freely available such has champion damages, HP, etc ...

Matches

All the matches are collected in the 3 directories:

• ./12.12.-splits/train/
• ./12.12.-splits/val/
• ./12.12.-splits/test/

Each of these directories contain multiple df_xxxxx.csv files detailing up to 100 matches.

The description of each column can be read in the below table.

The column which possess {Player_id} in their name are repeated 10 times, one for each player.

For example, the column name Player_{Player_id}_team can be found in each csv as 10 different columns with names ranging from Player_1_team to Player_10_team.

2019

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 study explored factors that influence actual playing time by comparing the Chinese Super League (CSL) and English Premier League (EPL). Eighteen factors were classified into anthropogenic and non-anthropogenic factors. Fifty CSL matches (season 2019) and 50 EPL matches (season 2019–2020) were analyzed. An independent sample t-test with effect size (Cohen’s d) at a 95% confidence interval was used to evaluate differences in the influencing factors between the CSL and EPL. Two multiple linear regression models regarding the CSL and EPL were conducted to compare the influencing factors’ impact on actual playing time. The results showed that the average actual playing time (p 

Comprehensive statistics for the current Premier League season

Guttman Colored Poles Dataset SPL League

## Overview

Guttman Colored Poles Dataset SPL League is a dataset for instance segmentation tasks - it contains Coloredpoles annotations for 226 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 tracks annual distribution of students across grade levels in League Academy

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).

In 2022/23, clubs in the Premier League spent more on wages than any other league in Europe's Big Five, with wage costs totaling over 4.6 billion euros. By comparison, clubs in La Liga spent a combined total of nearly 2.5 billion euros.

Historical Dataset of League Academy is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (1987-2023),Total Classroom Teachers Trends Over Years (1987-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (1987-2023),Asian Student Percentage Comparison Over Years (1991-2023),Hispanic Student Percentage Comparison Over Years (1996-2023),Black Student Percentage Comparison Over Years (1991-2023),White Student Percentage Comparison Over Years (1991-2023),Two or More Races Student Percentage Comparison Over Years (2013-2023),Diversity Score Comparison Over Years (1991-2023),Free Lunch Eligibility Comparison Over Years (1993-2023),Reduced-Price Lunch Eligibility Comparison Over Years (1999-2023),Reading and Language Arts Proficiency Comparison Over Years (2011-2022),Math Proficiency Comparison Over Years (2011-2022),Science Proficiency Comparison Over Years (2021-2022),Overall School Rank Trends Over Years (2011-2022)