---
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: 22572695
    num_examples: 130729
  - name: counterstrike
    num_bytes: 28458863
    num_examples: 200829
  - name: rocket_league
    num_bytes: 24124091
    num_examples: 158119
  - name: starcraft1
    num_bytes: 12392196
    num_examples: 101643
  - name: starcraft2
    num_bytes: 61102139
    num_examples: 440810
  - name: smash_melee
    num_bytes: 45460545
    num_examples: 397744
  - name: smash_ultimate
    num_bytes: 31790101
    num_examples: 275316
  - name: dota2
    num_bytes: 9521977
    num_examples: 73449
  - name: overwatch
    num_bytes: 5101991
    num_examples: 35633
  - name: valorant
    num_bytes: 9693145
    num_examples: 69769
  - name: warcraft3
    num_bytes: 15986386
    num_examples: 135082
  - name: rainbow_six
    num_bytes: 9785813
    num_examples: 68370
  - name: halo
    num_bytes: 2338800
    num_examples: 15640
  - name: call_of_duty
    num_bytes: 2825298
    num_examples: 19202
  - name: tetris
    num_bytes: 812520
    num_examples: 6353
  - name: street_fighter
    num_bytes: 13354154
    num_examples: 82199
  - name: tekken
    num_bytes: 9937932
    num_examples: 63099
  - name: king_of_fighters
    num_bytes: 2906404
    num_examples: 18010
  - name: guilty_gear
    num_bytes: 3484594
    num_examples: 22105
  - name: fifa
    num_bytes: 3943137
    num_examples: 31342
  download_size: 53959001
  dataset_size: 315592781
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

```python
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.

```python
# 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](http://aligulac.com/)
* The League of Legends data is from [Leaguepedia](https://lol.fandom.com/) under a [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/)
* The data for all other games is from [Liquipedia](https://liquipedia.net/) under a [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/)