elo-grad 0.1.0.dev1

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EloGrad

Extended Elo model implementation.

EloGrad leverages the framing of the Elo rating system as logistic regression with stochastic gradient descent (see this blog for a nice walkthrough) to offer a collection of extensions to the rating system. All models are scikit-learn compatible.

Installation

You can install elo-grad with:

uv add git+https://github.com/cookepm86/elo-grad

Quick Start

Minimal Example

from elo_grad import LogisticRegression, SGDOptimizer

model = LogisticRegression(beta=200, default_init_rating=1200, init_ratings=None)
sgd = SGDOptimizer(k_factor=20)

# Check initial weights (NOTE: time is None)
print("Initial weights:")
print(model.ratings["Tom"], model.ratings["Jerry"])

# Update after Tom beats Jerry at time t=1
sgd.update_model(model, y=1, entity_1="Tom", entity_2="Jerry", t=1)

# Check new weights
print("\nNew weights:")
print(model.ratings["Tom"], model.ratings["Jerry"])

Output:

Initial weights:
(None, 1200) (None, 1200)

New weights:
(1, 1210.0) (1, 1190.0)

References

1. Elo rating system: https://en.wikipedia.org/wiki/Elo_rating_system
2. Elo rating system as logistic regression with stochastic gradient descent: https://stmorse.github.io/journal/Elo.html