League Of Legends

## Overview

League Of Legends is a dataset for object detection tasks - it contains GameAssets annotations for 612 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 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.

The League of Legends Champions 2024 dataset provides comprehensive information about each champion in the renowned online multiplayer game, League of Legends, as of 2024. This dataset is tailored for players, analysts, and developers interested in understanding champion characteristics, their roles in gameplay, and overall mechanics.

Key Features

• Name: The official name of the champion as recognized in the game.
• Nick Name: The colloquial or commonly used name for the champion, often derived from their lore or gameplay style.
• Classes: Classification indicating the champion’s primary traits, such as Fighter, Mage, Marksman, Tank, and Support.
• Release Date: The date when the champion was first introduced to the game, providing context on their availability and relevance in the evolving meta. Last Changed: The last version update of the champion, indicating when they received significant balance changes or adjustments.
• Blue Essence: The in-game currency required to unlock the champion, indicating their accessibility for players.
• RP (Riot Points): The premium currency price for purchasing the champion, representing the monetary value for players who prefer buying over earning.
• Difficulty: A rating that assesses how challenging the champion is to play, helping players determine suitability based on their skill level.
• Role: The designated gameplay role of the champion, such as Top, Jungle, Mid, ADC (Attack Damage Carry), or Support, influencing team composition and strategy.
• Range Type: Specifies whether the champion is melee or ranged, affecting their playstyle and engagement strategies in matches.
• Resource Type: Indicates the type of resource used by the champion to cast abilities, such as Health, Mana, or Energy, influencing their gameplay mechanics and sustainability.
• Base HP: The initial health points of the champion at level one, impacting their survivability in the early game.
• Base Mana: The initial mana points of the champion at level one, determining their capacity to cast spells early in the game.

Purpose and Applications

This dataset serves as a valuable resource for players and analysts seeking to:

• Gain insights into champion characteristics and strategies.
• Analyze champion performance and balance within the game.
• Understand the evolving meta and make informed decisions on champion selection.

This dataset contains up-to-date information on the characteristics and attributes (e.g., roles, stats) of every champion in League of Legends as of Season 15, Split 1. It was scraped from the League of Legends Wiki Champion Data Module on January 20th, 2025. Click the remote source link to see the script used to scrape the data. Note that the dataset provides stats for multiple game modes (e.g., swift, aram).

League Of Legends Detection

## Overview

League Of Legends Detection is a dataset for object detection tasks - it contains Champion annotations for 4,468 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 contains detailed player end-game statistics (e.g., number of kills) and in-game events (e.g., player kills) from all professional League of Legends matches held between September 15, 2019, and September 15, 2024. It encompasses a total of 37,388 matches across 392 tournaments, featuring 4,927 unique players. Matches come from all regions and tiers of play. Initially released as part of the research paper PandaSkill: Player Performance and Skill Rating in Esports – Application to League of Legends, the dataset supports a wide range of applications, including performance analysis, esports analytics, and skill rating modeling.

League of Legends is a free online battle arena game. The objective of this game, in almost all game modes, is to destroy the enemy Nexus. The game is not only popular among players, but also among eSports viewers. League of Legends events on the video streaming service Twitch were watched by an average of 144,000 viewers in January 2025 after peaking at 280,000 average concurrent viewers in October 2021, when the animated series Arcane, which is based on the game, was released on Netflix.

First released in 2009, League of Legends (LoL) is an online multiplayer battle arena game which generated revenue of 1.75 billion U.S. dollars in 2020. This figure represents recovery from the dip to 1.4 billion U.S. dollars in annual revenue generated in 2018.

Still going strong after 10 years League of Legends has built a loyal and large fanbase in the ten years since its release. The game reached a landmark 100 million monthly active users in 2016, an impressive increase from the 15 million users it had in 2011. As with many online games, players can gain experience points through playing the game and completing certain missions. This ranking system is then used to match players of similar skill levels against each other in online games. The League of Legends ranking system has nine levels to indicate the skill level of the gamer. Amongst North American players, the silver skill level was the most common, whilst only a small handful of gamers were able to reach the top levels.

League of Legends tournaments Due to its entertaining and fast-pace gameplay, League of Legends is one of the most popular eSports, with almost 15 million U.S. dollars up for grabs in LoL tournaments worldwide in 2018. The pinnacle of the League of Legends competitive scene is the World Championship, which became one of the most watched eSports events in history in 2018 as almost 100 million unique viewers tuned in. The Chinese eSports team, Invictus Gaming, emerged victorious in 2018, thereby pocketing a whopping 2.4 million U.S. dollars in prize money.

League Of Legends Icon Detection

## Overview

League Of Legends Icon Detection is a dataset for object detection tasks - it contains League Icons annotations for 358 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).

League of Legends is a multiplayer online battle arena game which is popular with gamers and eSports viewers alike. During a survey, 42 percent of consumers stated that they both played the game and watched professional eSport events of it being played online.

This dataset contains start & end timestamps for League of Legends games played via the Duowan plugin. We randomly sampled 100k user_ids and gathered all games associated with those users.

League of Legends (LOL) is the most popular game on PC, drawing 8 million concurrent players. A common activity of gamers, besides playing games, is to watch other players presenting tips and tricks. Streaming platforms allow some players to show gameplays and live games. Twitch.tv is the world´s leading live streaming platform.

Considering that hate speech is a ubiquitous problem in online gaming, we collected 985,766 comments from five videos of the top 10 LOL streamers in Twitch.tv platform.

The dataset is freely available in a single file, ensembling all videos/players; and divided by players as well.

These comments are a rich data source for opinion mining, sentiment analysis, topic modeling, and hate speech detection (including sexism and racism).

League of Legends KR High Elo 5v5 Match Data

Related project link: GitHub

The dataset is retrieved using the Riot API. For documentation of the API please visit the website.

The dataset contains information about all League of Legends KR server challengers (n=300) as of 2022-05-23. The account information is stored in accounts.json, whereas the information about the challenger league is in kr_challenger_league.json.

Match data was retrieved from the 5 most recent 5v5 ranked solo matches for each challenger account. There are in total 811 unique matches, and the information is stored in matches.json. The matches are further cleaned only to include games that last more than 16 minutes (n=787), which are stored in matches_cleaned.json.

Analysis of ‘League of Legends Stats’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/vivovinco/league-of-legends-champion-stats on 13 February 2022.

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

Context

This dataset contains champion stats of ranked League of Legends games. The dataset will be updated for every patch.

Content

+100 rows and 11 columns. Columns' description are listed below.

• Name : Name of the champion
• Class : Fighter, Assassin, Mage, Marksman, Support or Tank
• Role : Top, Mid, ADC, Support or Jungle
• Tier : God, S, A, B, C or D
• Score : Overall score of the champion
• Trend : Trend of the score
• Win % : Win rate of the champion
• Role % : Role rate played with the champion
• Pick % : Pick rate of the champion
• Ban % : Ban rate of the champion
• KDA : (Kill+Death)/Assist ratio of the champion

Acknowledgements

Data from METAsrc. Image from League of Legends Wiki.

If you're reading this, please upvote.

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

Dataset DescriptionThis dataset is designed for analyzing and predicting comeback victories in Multiplayer Online Battle Arena (MOBA) games. It is derived from match data where an objective bounty mechanism was active, providing features that highlight differences between teams with and without the bounty advantage. The dataset is ideal for machine learning tasks, such as binary classification and feature importance analysis, and it enables researchers and analysts to explore factors influencing comeback scenarios in competitive gaming.Dataset Contents: The dataset includes the following files:match_data.csvContains match-level features derived from differences between teams with and without the objective bounty advantage. Each row represents a single match, labeled with whether a comeback victory occurred.

Context

League of Legends is a MOBA (multiplayer online battle arena) where 2 teams (blue and red) face off. There are 3 lanes, a jungle, and 5 roles. The goal is to take down the enemy Nexus to win the game.

Content

This dataset contains the first 10min. stats of approx. 10k ranked games (SOLO QUEUE) from a high ELO (DIAMOND I to MASTER). Players have roughly the same level.

Each game is unique. The gameId can help you to fetch more attributes from the Riot API.

There are 19 features per team (38 in total) collected after 10min in-game. This includes kills, deaths, gold, experience, level... It's up to you to do some feature engineering to get more insights.

The column blueWins is the target value (the value we are trying to predict). A value of 1 means the blue team has won. 0 otherwise.

So far I know, there is no missing value.

Glossary

• Warding totem: An item that a player can put on the map to reveal the nearby area. Very useful for map/objectives control.
• Minions: NPC that belong to both teams. They give gold when killed by players.
• Jungle minions: NPC that belong to NO TEAM. They give gold and buffs when killed by players.
• Elite monsters: Monsters with high hp/damage that give a massive bonus (gold/XP/stats) when killed by a team.
• Dragons: Elite monster which gives team bonus when killed. The 4th dragon killed by a team gives a massive stats bonus. The 5th dragon (Elder Dragon) offers a huge advantage to the team.
• Herald: Elite monster which gives stats bonus when killed by the player. It helps to push a lane and destroys structures.
• Towers: Structures you have to destroy to reach the enemy Nexus. They give gold.
• Level: Champion level. Start at 1. Max is 18.

Acknowledgements

Thanks, Rito Gaming.

Gold Bar League Of Legends

## Overview

Gold Bar League Of Legends is a dataset for object detection tasks - it contains Gold Bar annotations for 673 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).

League of Legends is a MOBA (Multiplayer Online Battle Arena) video game in which a player controls a single character in a team who competes against another team of players. Fabian Diepstraten aka Febiven earned a little over 111 thousand U.S. dollars in professional gaming tournaments up until December 2024, making him the highest earner from LoL competitions in the Netherlands.

League of Legends MatchID dataset V2.0

As people who like data analysis , but young enough to still like gaming, we thought that League of legends would be a great game to analyze. Due to competitive play some statistics and predictions were quite welcome. There are of course a lot of websites that offer that by them selves, but we think that League community needed an open dataset to work with, as there was none that offered some real volume of data. There came the idea for a bigger dataset which would offer other people to drive their projects without the struggle of long lasting process of parsing matches with Riot API (which has a limit of 500 calls per 10 minutes...so yea)

This is NOT the finished project, but more like a post along the way. The dataset only consists of one column and its basically useless by it self. The file consists of 223 715 match IDs of ranked games . Each column represents the MatchId of a single match played in League, which can be than accessed with Riot API The purpose is only to allow others like us, to continue the research with Riot API with some pre gathered data and save them some precious time that way.

The final dataset "League of Legends MatchesDataset V1.0" we will be posting, consists of 100 000 matches in JSON which will be directly suitable for data analysis.

Link to the dataset: WIP

We are also open sourcing the data gathering program (written in python)

GitHub link: GitHub program

This project has been posted by me (Lan Vukušič) as data scientist but the main credit goes to lead programmer Matej Urbas who is responsible for the data gathering in this project and without whom the project would not exist.

We are happy to give the dataset out for free, to let the comunity use that dataset. We would love to see what people are going to create. We know that we are "rookies" in that field but would still like to contribute to evergrowing field of data science. So if there is really anything that should be changed in upcoming updates please feel free to message us and tell us your thoughts.

Contacts : leaguedataset@gmail.com

Best regards

League of Legends MatchID dataset V1.0 and League of Legends MatchID dataset V2.0 aren't endorsed by Riot Games and doesn't reflect the views or opinions of Riot Games or anyone officially involved in producing or managing League of Legends. League of Legends and Riot Games are trademarks or registered trademarks of Riot Games, Inc. League of Legends © Riot Games, Inc.