京公网安备11010802038645号京ICP备20022436号-3京EDI证京B2-20220639区块链京网信备 1101082276223860002X号以下为卖家选择提供的数据验证报告：
数据描述
Victory prediction from League of Legend champion selection data
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
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
| Set | Proportion | size |
|---|---|---|
| Training | 90% | 75970 |
| Validation | 5% | 4239 |
| Test | 5% | 4231 |
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.
| Column name | Use das input | Path from Match-V5 | type | description |
|---|---|---|---|---|
gameId | No | info/gameId | str | unique value for each match |
matchId | No | metadata/matchId | str | gameId prefixed with the players region |
gameVersion | No | info/gameVersion | str | game version, the first two parts can be used to determine the patch |
gameDuration | No | info/gameDuration | int | game duration in seconds |
teamVictory | No | info/teams[t]/win | int | Team victory, either 100 for blue, or 200 for red |
team_100_gold | No | info/participants[]/goldEarned | int | Total gold earned by blue team |
team_200_gold | No | info/participants[]/goldEarned | int | Total gold earned by red team |
Player_id | Yes | info/participants/participantId | int | Player id ranging from 1 to 10 included |
Player_{Player_id}_team | Yes | info/participants/teamId | int | Player team, either 100 for blue team, or 200 for red team |
Player_{Player_id}_ban | Yes | info/teams[t]/bans[i]/championId | int | Player champion banned |
Player_{Player_id}_pick | Yes | info/participants[i]/championId | int | Player champion picked |
Player_{Player_id}_ban_turn | Yes | info/teams[t]/bans[i]/pickTurn | int | Player pick order |
Player_{Player_id}_victory | No | info/teams[t]/win | int | Either 1 for victory or 0 for defeat |
Player_{Player_id}_role | No | info/participants[i]/role | str | Role declared by the player before match. Possible values: DUO, DUO_CARRY, DUO_SUPPORT, NONE, and SOLO |
Player_{Player_id}_position | No | info/participants[i]/teamPosition | str | Role deduced after match from every players position. Possible values: TOP, MIDDLE, JUNGLE, BOTTOM, UTILITY, APEX, and NONE |
Player_{Player_id}_time_game | No | info/gameDuration | int | Game duration in seconds |
Player_{Player_id}_gold | No | info/participants[i]/goldEarned | int | Total gold earned |
Player_{Player_id}_xp | No | info/participants[i]/champExperience | int | Total XP accumulated |
Player_{Player_id}_dmg_dealt | No | info/participants[i]/totalDamageDealtToChampions | int | Total damages dealt to other champions |
Player_{Player_id}_dmg_taken | No | info/participants[i]/totalDamageTaken | int | Total damages received |
Player_{Player_id}_time_ccing | No | info/participants[i]/timeCCingOthers | int | Total time of crowd control inflicted to other champs |
Getting started
A loading example for the dataset can be found under https://www.kaggle.com/ezalos/loading-lol-dataset
🔮 LoL : predicting victory before the game starts
20.13MB
申请报告