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---
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: 24834581
num_examples: 143559
- name: counterstrike
num_bytes: 30785421
num_examples: 217878
- name: rocket_league
num_bytes: 27167034
num_examples: 177058
- name: starcraft1
num_bytes: 13557336
num_examples: 111129
- name: starcraft2
num_bytes: 63194003
num_examples: 456195
- name: smash_melee
num_bytes: 47158421
num_examples: 412698
- name: smash_ultimate
num_bytes: 32757924
num_examples: 283774
- name: dota2
num_bytes: 10202069
num_examples: 78820
- name: overwatch
num_bytes: 5584276
num_examples: 39004
- name: valorant
num_bytes: 11194479
num_examples: 80620
- name: warcraft3
num_bytes: 17154529
num_examples: 144918
- name: rainbow_six
num_bytes: 11738237
num_examples: 81826
- name: halo
num_bytes: 2482918
num_examples: 16602
- name: call_of_duty
num_bytes: 3333120
num_examples: 22640
- name: tetris
num_bytes: 952594
num_examples: 7455
- name: street_fighter
num_bytes: 17988467
num_examples: 110399
- name: tekken
num_bytes: 11842941
num_examples: 74683
- name: king_of_fighters
num_bytes: 3170138
num_examples: 19645
- name: guilty_gear
num_bytes: 4125098
num_examples: 26110
- name: ea_sports_fc
num_bytes: 5208329
num_examples: 39570
download_size: 58858156
dataset_size: 344431915
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
```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/)
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