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.

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.

This dataset contains up-to-date information on the characteristics (e.g., roles, stats) of every champion in League of Legends as of 2025, Season 2, patch 25.09. It was scraped from the League of Legends Wiki Champion Data Module on May 5th, 2025.

Note that the dataset provides stats for all game modes (e.g., SwiftPlay, ARAM).

Click the remote source link to see the script used to scrape the data.

Related datasets: - 25.S1.1 League of Legends Champion Data (2025) - 25.S1.3 League of Legends Champion Data (2025) - 25.S1.4 League of Legends Champion Data (2025) - 25.05 League of Legends Champion Data (2025)

The source of the header image is Fruit Basket on Unsplash.

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 2022-06-12. 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 2166 unique matches. The matches are further cleaned only to include games that last more than 16 minutes (n=2078), which are stored in matches.json.

Data set for investigating team compositions in Legend of Legends. See the readme file for file and variable descriptions.

Dataset

This dataset was created by Danilo Donato

Contents

It contains the following files:

GPTilt: 10K League of Legends Challenger Matches

This dataset is part of the GPTilt open-source initiative, aimed at democratizing access to high-quality LoL data for research and analysis, fostering public exploration, and advancing the community's understanding of League of Legends through data science and AI. It provides detailed data from high-elo matches. By using this dataset, users accept full responsibility for any consequences arising from its use. GPTilt assumes no… See the full description on the dataset page: https://huggingface.co/datasets/gptilt/lol-basic-matches-challenger-10k.

A repository with sensor data collected from 10 players in 22 matches in League of Legends. The sensor data collected include:

• Hand/head/chair movements.
• Heart rate.
• Muscle activity.
• Gaze movement on the monitor.
• Galvanic skin response(GSR).
• Electroencephalography (EEG).
• Mouse and keyboard activity.
• Facial skin temperature.
• Environmental data.

The data were collected for one team of 5 people simultaneously. We also provide in-game logs and meta information for each match.

Please find more details and analysis in the paper.

@article{smerdov2020collection, title={Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset}, author={Smerdov, Anton and Zhou, Bo and Lukowicz, Paul and Somov, Andrey}, journal={arXiv preprint arXiv:2011.00958}, year={2020} }

Acknowlegement

Foto von Florian Olivo auf Unsplash

I have created this dataset for people interested in League of Legends who want to approach the game from a more analytical side.

Most of the data was acquired from Games of Legends (https://gol.gg/tournament/tournament-stats/LEC%20Spring%20Season%202024/) and also from official account of the League of Legends EMEA Championship (https://www.youtube.com/c/LEC)

Dataset Contents:

• Player: Name of the player.
• Role: Role of the player (e.g., TOP, JUNGLE, MID, ADC, SUPPORT)
• Team: Name of the player's team
• Opponent Team: Name of the opposing team
• Opponent Player: Name of the opposing player
• Date: Date of the match
• Week: Week of the tournament
• Day: Specific day of the tournament
• Patch: Version of the game patch during the match
• Stage: Stage of the tournament
• No Game: Game number in the series
• all Games: Total number of games in the series
• Format: Format of the match (e.g., Best of 1, Best of 3)
• Game of day: Number of the game that day
• Side: Side of the map the team started on (Blue/Red)
• Time: Duration of the match

Team Performance Metrics:

• Kills Team: Total kills by the team
• Turrets Team: Total turrets destroyed by the team
• Dragon Team: Total dragons killed by the team
• Baron Team: Total barons killed by the team

Player Performance Metrics:

• Level: Final level of the player
• Kills: Number of kills by the player
• Deaths: Number of deaths of the player
• Assists: Number of assists by the player
• KDA: Kill/Death/Assist ratio
• CS: Creep Score (minions killed)
• CS in Team's Jungle: Creep Score in the team's jungle
• CS in Enemy Jungle: Creep Score in the enemy's jungle
• CSM: Creep Score per Minute
• Golds: Total gold earned
• GPM: Gold Per Minute
• GOLD%: Percentage of team's total gold earned by the player

Vision and Warding:

• Vision Score: Total vision score
• Wards placed: Number of wards placed
• Wards destroyed: Number of wards destroyed
• Control Wards Purchased: Number of control wards purchased
• Detector Wards Placed: Number of detector wards placed
• VSPM: Vision Score Per Minute
• WPM: Wards Placed per Minute
• VWPM: Vision Wards Placed per Minute
• WCPM: Wards Cleared per Minute
• VS%: Vision Score percentage

Damage Metrics:

• Total damage to Champion: Total damage dealt to champions
• Physical Damage: Total physical damage dealt
• Magic Damage: Total magic damage dealt
• True Damage: Total true damage dealt
• DPM: Damage Per Minute
• DMG%: Percentage of team’s total damage dealt by the player

Combat Metrics:

• K+A Per Minute: Kills and Assists per Minute
• KP%: Kill Participation percentage
• Solo kills: Number of solo kills
• Double kills: Number of double kills
• Triple kills: Number of triple kills
• Quadra kills: Number of quadra kills
• Penta kills: Number of pentakills

Early Game Metrics:

• GD@15: Gold Difference at 15 minutes
• CSD@15: Creep Score Difference at 15 minutes
• XPD@15: Experience Difference at 15 minutes
• LVLD@15: Level Difference at 15 minutes

Objective Control:

• Objectives Stolen: Number of objectives stolen
• Damage dealt to turrets: Total damage dealt to turrets
• Damage dealt to buildings: Total damage dealt to buildings

Healing and Mitigation:

• Total heal: Total healing done
• Total Heals On Teammates: Total healing done on teammates
• Damage self mitigated: Total damage self-mitigated
• Total Damage Shielded On Teammates: Total damage shielded on teammates

Crowd Control Metrics:

• Time ccing others: Time spent crowd controlling others
• Total Time CC Dealt: Total crowd control time dealt

Survival and Economy:

• Total damage taken: Total damage taken
• Total Time Spent Dead: Total time spent dead
• Consumables purchased: Number of consumables purchased
• Items Purchased: Number of items purchased
• Shutdown bounty collected: Total shutdown bounty collected
• Shutdown bounty lost: Total shutdown bounty lost

Data set for investigating and measuring illicit bot prevalence in North American and Western Europe League of Legends PvP matches associated with

C. S. Lee and I. Ramler, "Rise of the bots: Bot prevalence and its impact on match outcomes in league of Legends," 2015 International Workshop on Network and Systems Support for Games (NetGames), Zagreb, 2015, pp. 1-6. doi: 10.1109/NetGames.2015.7382992

Description of Variables:

level: level of summoner

matchId: de-identified identification number for match

winner: flag indicating whether or not the team won

kill: number of kills

death: number of deaths

assist: number of kills

timeCreated: match creation time (UTC-05 for N. Amer, UTC-00 for EUW)

duration: match duration (seconds)

matchType: match type

numOfRunes: number of runes

numOfMasteries: number of masteries

isBot: Flag indicating whether or not the player is a bot

First released in 2009, League of Legends (LoL) is an online multiplayer battle arena game which generated revenue of **** billion U.S. dollars in 2020. This figure represents recovery from the dip to *** 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 ** 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 **** 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 tournamentsDue to its entertaining and fast-pace gameplay, League of Legends is one of the most popular eSports, with almost ** 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 *** million U.S. dollars in prize money.

Released in October 2020, League of Legends: Wild Rift is a mobile MOBA (multiplayer online battle arena) gaming title which was developed and published by Riot Games. The title is a mobile version of the MOBA genre classic League of Legends and generated approximately 1.25 million app downloads in December 2024.

League of Legends Wild Rift kısaltılmış LoL WR veya kısaca Wild Rift Android iOS ve belirtilmemiş konsollar için Riot Ga

GPTilt: League of Legends Challenger Matches' Snapshots At 15 Minutes

This dataset is part of the GPTilt open-source initiative, aimed at democratizing access to high-quality LoL data for research and analysis, fostering public exploration, and advancing the community's understanding of League of Legends through data science and AI. It provides detailed data from high-elo matches. By using this dataset, users accept full responsibility for any consequences arising from its use.… See the full description on the dataset page: https://huggingface.co/datasets/gptilt/lol-ultimate-snapshot-challenger-15min.

Dataset

This dataset was created by Isaac Min

Released under MIT

Contents

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).

Analysis of ‘League of Legends Worlds 2021 Play-In Group Stats’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/braydenrogowski/league-of-legends-worlds-2021-playin-group-stats on 28 January 2022.

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

As a massive League of Legends fan for 10+ years, I realized that there weren't any datasets that helped us stay updated with Worlds 2021, thus this dataset was born!

All data was acquired from lolesports.com which shows all in-depth statistics available for each match that others can use to find correlations between in-game statistics and wins.

I would love to see this data used to answer how vision (ward interactions) and gold distribution (how a team's gold is divided among it's positions) correlate with win percentage.

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

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 ---

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 109,000 viewers in April 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.