r055y

r055y ("rossy") is an open-source Python library for building auditable sports analytics and outcome-prediction systems.

It provides the reusable analytics foundation behind R-LAY: strict data contracts, probability evaluation, calibration summaries, and transparent rating baselines that can be used independently in other Python projects.

Install

pip install r055y

r055y supports Python 3.10 and newer.

What is included

The 1.1 release introduces three focused building blocks:

• Contracts — immutable Pydantic models for SPORT SELECT POOLS cards, game predictions, run artifacts, and reproducibility manifests.
• Evaluation — Brier score, binary log loss, and fixed-width calibration bins for probabilistic forecasts.
• Ratings — a stateful Elo baseline with configurable home advantage, neutral-site support, ties, and between-season mean reversion.

Quick start

Elo ratings

from r055y import EloRatingSystem

ratings = EloRatingSystem()

pregame = ratings.predict("BUF", "MIA")
print(pregame.home_win_probability)

# Updates both teams after the result while preserving the pregame prediction.
ratings.update("BUF", "MIA", home_score=31, away_score=24)

Probability evaluation

from r055y import brier_score, calibration_bins, log_loss

probabilities = [0.72, 0.55, 0.31, 0.84]
outcomes = [1, 0, 0, 1]

print(brier_score(probabilities, outcomes))
print(log_loss(probabilities, outcomes))

for bucket in calibration_bins(probabilities, outcomes, bins=5):
    print(bucket)

Auditable predictions

from r055y import GamePrediction

prediction = GamePrediction(
    game_id="2026_01_MIA_BUF",
    model_name="example-rating-model",
    model_version="1.0.0",
    home_win_probability=0.72,
    predicted_home_margin=4.5,
    reasons=("home-field advantage", "higher pregame rating"),
)

print(prediction.model_dump_json(indent=2))

Contracts reject unknown fields, invalid probabilities, and timezone-naive timestamps so bad inputs fail visibly instead of drifting silently through an analytics pipeline.

Design principles

• Prefer calibrated probabilities over unsupported confidence labels.
• Keep model inputs, versions, outputs, and evaluations traceable.
• Use transparent baselines before adding model complexity.
• Keep provider credentials, scraping logic, operational schedules, private datasets, and website code outside the public library.

Package layout

src/r055y/
  contracts/    Portable analytics input/output schemas
  evaluation/   Probability scoring and reliability summaries
  ratings/      Transparent rating baselines

Development

python -m pip install -e ".[test]"
python -m pytest

r055y is deliberately small today. Additional feature, modelling, simulation, and optimization utilities will be added as their contracts and evaluation requirements become dependable.