riix

Open source implementations of online rating systems focusing on efficiency for offline experimentation

When to use riix

This package is designed to accelerate experiments studying and comparing rating systems. In the scenario where you have paired comparison datasets with a known number of competitors and time range, riix exploits that information to achieve fast runtimes. It's not useful in the streaming case where new data with new competitors are coming in. It also only currently supports 1v1 competitions but future support for two team competitions is planned. (more than 2 teams is not)

I have a large dataset of player matches for a game and want to determine which rating system out of Elo, Glicko, TrueSkill etc. gives the best predictive accuracy.

Use riix! 👍

I want to incorporate skill based matchmaking into the game I am creating and want a package to compute ratings for players on the fly.

There are lots of other great python packages for that too! (just not riix)

• openskill.py Multi-way competitions with Weng-Lin rating systems
• trueskill Open source implementation of Microsoft's rating system
• PythonSkills A port of Moserware's Glicko and TrueSkill C# code to python
• skelo Elo and Glicko 2 with the scikit-learn interface
• mmr-python Glicko and Weng-Lin rating systems
• glicko2
  • sublee/glicko2
  • deepy/glicko2
• whole_history_rating Python port of WHR

Example

from riix.models.elo import Elo
from riix.utils import MatchupDataset, split_matchup_dataset, generate_matchup_data
from riix.metrics import binary_metrics_suite

df = generate_matchup_data() # replace with your **polars** dataframe
dataset = MatchupDataset(
    df,
    competitor_cols=['competitor_1', 'competitor_2'],
    outcome_col='outcome',
    datetime_col='date',
    rating_period='1D',
)
train_dataset, test_dataset = split_matchup_dataset(dataset, test_fraction=0.2)
print(f'{len(train_dataset)=}, {len(test_dataset)=}')

>>> loaded dataset with:
>>> 10000 matchups
>>> 100 unique competitors
>>> 10 rating periods of length 1D
>>> len(train_dataset)=8000, len(test_dataset)=2000

model = Elo(competitors=dataset.competitors)
model.fit_dataset(train_dataset)
test_probs = model.fit_dataset(test_dataset, return_pre_match_probs=True)
test_metrics = binary_metrics_suite(probs=test_probs, outcomes=test_dataset.outcomes)
print(test_metrics)

>>> {'accuracy': 0.72975, 'log_loss': 0.5359083106524117, 'brier_score': 0.1793377446861956}

model.print_leaderboard(num_places=5)

>>> competitor      rating
>>> competitor_69   1874.170019
>>> competitor_75   1827.933570
>>> competitor_12   1826.119826
>>> competitor_81   1825.071605
>>> competitor_30   1802.338983

License

This package is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. I've chosen a non-commercial license as overbroad protection to prevent the use of this package in the gambling and odds setting industries. If you would like to use riix for your business in any other area please do not hesitate to reach out and I'll happily grant you an eternal lifetime license. :)

About the name

The name riix represents an attempt to cleverly represent the idea of "R8" (pronounced "rate") alphabetically using the Roman numeral IIX in place of 8. By the time I realized the correct numeral would have been VIII I was already attatched to the name riix so I stuck with it. However on further research it turns out the Romans themselves occasionally used this form as well! Why Romans Sometimes Wrote 8 as VIII, And Sometimes as IIX: A Possible Explanation