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 970 thousand app downloads in December 2023.

Introduction

• This data is collection of League of Legends Item, champion data
• version 2020

Data information

CHAMPION

• version : lol version
• name : champion name
• key : champion_id(other data foreign key)
• title : champion title
• blurb : champion explain
• etc

ITEM

• item_id : item_id(other data foreign key)
• name : item name
• upper_item
• explain : item explain
• buy_price : item price
• sell_price : item sell price

I hope this will be great!!

GitHub repository

Click Here

Why?

I am writing articles on League of Legends and Machine Learning. You can find the full repository where this information is stored here.

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 data set is the High elo Ranked Games data for a TFT game called TeamFights Tactics.

It is a game in which 8 participants fight against each other by forming various combinations suitable for their strategy, and it is composed of individual exhibitions.

The data was collected only from High elo Ranked Games, and we built three ranking games, Rolltoches' Top Rank Challenger, Grandmaster, and Master.

A similar game is Auto Chess, which combines approximately 50 champions to create their own combination. There are 10 series synergies and 13 job synergies, and you can create various combinations.

The final level is 9 levels, and there is a limit to what you can put on the board for each level. You can raise 1 champion per level, and when you reach the final 9 level, you can raise 9 champions on the board. (May vary slightly depending on champion skill and combination characteristics)

Content

There are a total of 8 variables, and each description is described in the data set column description below.

Most of the columns are explained intuitively, but we'll cover the two columns in more detail.

First, "Combination" Column

• This column is in json format and shows what combination synergy each participant in the game has.
• json data {key: value} key and value are as follows.
  • key: Combination
  • value: the number of combinations

Second, "Champion" Columns

• This column shows which champions each of the eight players set (which is synergistic). This is of course also in json format.
• json data {key: value} key and value are as follows.
  • key : Champion name
  • value : Item, Stars - Item : Champion's items - Stars : Enhance Champion n (Min 1 Star, Max 3 Star) - 1 Star : 1-star champion, - 2 Star : 1-star champion three - 3 Star : 2-star champion three

Acknowledgements

• gameduration : Time taken per game
• level : Level of each participant in the game
• Ranked : In-game ranking

Inspiration

• Rank 1 ~ Rank 8 Pattern
• Top Rank EDA

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.

Game stats for all matches in the League of Legends Worlds 2021 Play-in Groups

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 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

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.

Getting started

A loading example for the dataset can be found under https://www.kaggle.com/ezalos/loading-lol-dataset

Dataset

This dataset was created by Danilo Donato

Contents

It contains the following files:

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.

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.

This dataset is coming from Oracle Elixir (https://oracleselixir.com/) and gather the games information of every single official competitive game of League of Legends in 2024.

Analysis of ‘League of Legends Diamond Ranked Games (10 min)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/bobbyscience/league-of-legends-diamond-ranked-games-10-min on 21 November 2021.

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

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.

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

Dataset

This dataset was created by James Jung

Contents

Dataset

This dataset was created by Rei Cripto

Contents

Includes data and code for both analysis and data mining.

Github link with a .README file https://github.com/Vutya/LoL_player_segmenation

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.