metadata
dataset_info:
features:
- name: date
dtype: date32
- name: competitor_1
dtype: string
- name: competitor_2
dtype: string
- name: outcome
dtype: float64
- name: match_id
dtype: string
- name: page
dtype: string
splits:
- name: league_of_legends
num_bytes: 26169433
num_examples: 151191
- name: counterstrike
num_bytes: 32225593
num_examples: 228435
- name: rocket_league
num_bytes: 28789234
num_examples: 187560
- name: starcraft1
num_bytes: 14403669
num_examples: 118147
- name: starcraft2
num_bytes: 64108013
num_examples: 462943
- name: smash_melee
num_bytes: 48143609
num_examples: 421280
- name: smash_ultimate
num_bytes: 33192412
num_examples: 287595
- name: dota2
num_bytes: 10683912
num_examples: 82660
- name: overwatch
num_bytes: 5930878
num_examples: 41428
- name: valorant
num_bytes: 11710786
num_examples: 84373
- name: warcraft3
num_bytes: 16724122
num_examples: 141327
- name: rainbow_six
num_bytes: 11846712
num_examples: 82565
- name: halo
num_bytes: 2547519
num_examples: 17048
- name: call_of_duty
num_bytes: 3451092
num_examples: 23488
- name: tetris
num_bytes: 1049271
num_examples: 8272
- name: street_fighter
num_bytes: 20594773
num_examples: 125955
- name: tekken
num_bytes: 12591253
num_examples: 80059
- name: king_of_fighters
num_bytes: 3276683
num_examples: 20308
- name: guilty_gear
num_bytes: 4455586
num_examples: 28310
- name: ea_sports_fc
num_bytes: 5737025
num_examples: 43323
download_size: 40451016
dataset_size: 357631575
configs:
- config_name: default
data_files:
- split: league_of_legends
path: data/league_of_legends-*
- split: counterstrike
path: data/counterstrike-*
- split: rocket_league
path: data/rocket_league-*
- split: starcraft1
path: data/starcraft1-*
- split: starcraft2
path: data/starcraft2-*
- split: smash_melee
path: data/smash_melee-*
- split: smash_ultimate
path: data/smash_ultimate-*
- split: dota2
path: data/dota2-*
- split: overwatch
path: data/overwatch-*
- split: valorant
path: data/valorant-*
- split: warcraft3
path: data/warcraft3-*
- split: rainbow_six
path: data/rainbow_six-*
- split: halo
path: data/halo-*
- split: call_of_duty
path: data/call_of_duty-*
- split: tetris
path: data/tetris-*
- split: street_fighter
path: data/street_fighter-*
- split: tekken
path: data/tekken-*
- split: king_of_fighters
path: data/king_of_fighters-*
- split: guilty_gear
path: data/guilty_gear-*
- split: ea_sports_fc
path: data/ea_sports_fc-*
TESTING
EsportsBench: A Collection of Datasets for Benchmarking Rating Systems in Esports
EsportsBench is a collection of 20 esports competition datasets. Each row of each dataset represents a match played between either two players or two teams in a professional video game tournament. The goal of the datasets is to provide a resource for comparison and development of rating systems used to predict the results of esports matches based on past results. Date is complete up to 2024-03-31.
Recommended Usage
The recommended data split is to use the most recent year of data as the test set, and all data prior to that as train. There have been two releases so far:
- 1.0 includes data up to 2024-03-31. Train: beginning to 2023-03-31, Test: 2023-04-01 to 2024-03-31
- 2.0 includes data up to 2024-06-30. Train: beginning to 2023-06-30, Test: 2023-07-01 to 2024-06-30
import polars as pl
import datasets
esports = datasets.load_dataset('EsportsBench/EsportsBench', revision='1.0')
lol = esports['league_of_legends'].to_polars()
teams = pl.concat([lol['competitor_1'], lol['competitor_2']]).unique()
lol_train = lol.filter(pl.col('date') <= '2023-03-31')
lol_test = lol.filter((pl.col('date') >'2023-03-31') & (pl.col('date') <= '2024-03-31'))
print(f'train rows: {len(lol_train)}')
print(f'test rows: {len(lol_test)}')
print(f'num teams: {len(teams)}')
# train rows: 104737
# test rows: 17806
# num teams: 12829
The granulularity of the date column is at the day level and rows on the same date are not guaranteed to be ordered so when experimenting, it's best to make predictions for all matches on a given day before incorporating any of them into ratings or models.
# example prediction and update loop
rating_periods = lol.group_by('date', maintain_order=True)
for date, matches in rating_periods:
print(f'Date: {date}')
print(f'Matches: {len(matches)}')
# probs = model.predict(matches)
# model.update(matches)
# Date: 2011-03-14
# Matches: 3
# ...
# Date: 2024-03-31
# Matches: 47
Data Sources
- The StarCraft II data is from Aligulac
- The League of Legends data is from Leaguepedia under a CC BY-SA 3.0
- The data for all other games is from Liquipedia under a CC BY-SA 3.0