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EsportsBenchTest

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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: 22790280
      num_examples: 131949
    - name: counterstrike
      num_bytes: 28489348
      num_examples: 201043
    - name: rocket_league
      num_bytes: 23932780
      num_examples: 156934
    - name: starcraft1
      num_bytes: 12584609
      num_examples: 103117
    - name: starcraft2
      num_bytes: 61519133
      num_examples: 443823
    - name: smash_melee
      num_bytes: 45519895
      num_examples: 398219
    - name: smash_ultimate
      num_bytes: 31829863
      num_examples: 275617
    - name: dota2
      num_bytes: 9521977
      num_examples: 73449
    - name: overwatch
      num_bytes: 4875572
      num_examples: 34081
    - name: valorant
      num_bytes: 9695065
      num_examples: 69783
    - name: warcraft3
      num_bytes: 15988152
      num_examples: 135094
    - name: rainbow_six
      num_bytes: 10033549
      num_examples: 70208
    - name: halo
      num_bytes: 2338800
      num_examples: 15640
    - name: call_of_duty
      num_bytes: 2834747
      num_examples: 19267
    - name: tetris
      num_bytes: 834532
      num_examples: 6502
    - name: street_fighter
      num_bytes: 14087802
      num_examples: 86872
    - name: tekken
      num_bytes: 10218577
      num_examples: 64892
    - name: king_of_fighters
      num_bytes: 2918517
      num_examples: 18085
    - name: guilty_gear
      num_bytes: 3578596
      num_examples: 22714
    - name: fifa
      num_bytes: 3999325
      num_examples: 31772
  download_size: 54326379
  dataset_size: 317591119
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: fifa
        path: data/fifa-*

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