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 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.

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

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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.

Getting started

A loading example for the dataset can be found under https://www.kaggle.com/ezalos/loading-lol-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 for a combined 134 million hours in October 2024.

This data set has 2 other notebooks, One for collecting data: https://www.kaggle.com/code/makspl/collecting-data-script Another for analysing and modelling: https://www.kaggle.com/code/makspl/eda-modelling?scriptVersionId=161968252

Data collected used Riot Games public APi, I created a function which made API calls and formated player statistics into a dictionary which became a row in a csv file.
Around 2200 unique players, with around 300 players from each rank.

ABOUT DATA SET - summonerName - player username - summonerLevel - Experience accumilated across multiple lol games (different to in game champion level(think of this as total time spent playing games)) - rank - Leader board system which seperates players into different brackets, indicator of skill - wins - games won out of 25 recent - losses - games lost out of 25 recent games - winRate - number of wins divided by total games played (25) - kills - average kills aquired over past 25 games - deaths - avg deaths aquired over past 25 games - assists - avg number of people this player helped to kill - prefLane - most played lane out of the 25 (ADC and SUPPORT play together in the bottom lane) - campsKilled - jungle minions killed - minionsKilled - lane minions killed - goldEarned - avg of gold accumilated in each game - turretTakedowns - avg number of towers the player has destroyed (not total towers destroyed in a game) - visionScore - avg point system revolving around revealing hidden areas of map and destroying enemy vision wards - dragonKills - avg number of dragons the player has killed (killing dragons lends the team extra buffs such as more damage or health) - longestTimeSpentLiving - time in seconds - totalDamageDealt - avg of total damage dealt to enemy players - totalDamageTaken - avg of total damage taken from enemy players - gameDuration - avg time spent playing a single match in minutes - gameStart - avg time in hours where the player will start playing games, (eg 15.06 == 15:04)

This statistic illustrates the number of League of Legends (LoL) monthly active users worldwide from 2011 to 2016. In 2016, LoL had 100 million MAU, up from 90 million in 2015. Being one of the most prominent eSports games, in 2015 LoL championship finals attracted 36 million viewers worldwide. What is more, with the rise of gaming video content popularity, video games enthusiasts can now watch other players try their chances in the game, from the comfort of their own home. And so, League of Legends was one of the top games on Twitch, based on the number of hours viewed in 2016. It comes a no surprise that LoL held the highest share of the MOBA games revenues that year.

Context

This dataset represents, at the time of writing and to the best of the author's knowledge, the largest and most comprehensive dataset of League of Legends ranked matches from NA. There are approximately 10 000 matches in this data set, with each match containing over 700 individual items, ranging from champion/spell choice, team stats, to individual player performance.

This dataset is the result of my personal project, and I wanted a comprehensive dataset of ranked matches from NA. Each match is pulled from players who are approximately Gold-ranked.

Acknowledgments

This dataset would not have been possible without the help of the RiotWatcher wrapper library (https://riot-watcher.readthedocs.io/en/latest/), which is primarily responsible for calling the Riot Games API.

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).

As of January 2024, South Korea was the leading country in the League of Legends (LoL) eSports space. The country has won a total of almost 36 million U.S. dollars in prize money.

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.

amitrajitbh1/lol dataset hosted on Hugging Face and contributed by the HF Datasets community

League of Legends is a free online battle arena game in which the objective in almost all game modes is to destroy the enemy base, called the Nexus. The game is not only popular among players, but also among eSports viewers. The cumulative prize pool for League of Legends eSports competitions stood at 14.55 million U.S. dollars in 2018 and is expected to reach 23.42 million U.S. dollars by 2023.

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.

Dataset

This dataset was created by James Jung

Contents

LEAGUE OF LEGENDS SEXY GIRLS is a company.

This dataset contains the statistic data related to each League of Legends champion's profile of the op.gg webpage. It contains data related to every champion within the last month of ranked season 11. It has been created only for educational purposes with a web scraper based on scrapy.

The statistic presents the share of League of Legends fans in the United States in 2018, by age group. According to the estimates, teenagers aged 13 to 17 accounted for 12 percent of U.S. League of Legends fans.

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.

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 121 thousand viewers in July 2024 after peaking at 280 thousand average concurrent viewers in October 2021, when the animated series Arcane, which is based on the game, was released on Netflix.