SportsAI.ML

Transparency

Methodology

How SportsAI.ML turns licensed data into a transparent, graded prediction — and how we distinguish fact from forecast.

1

Licensed live data

Validated, normalized and timestamped from provider adapters.

2

Statistical + AI models

Poisson, Elo and boosted trees run alongside multiple AI models.

3

Weighted consensus

Reliability, calibration and specialization weight each model.

4

Value & risk analysis

No-vig probability, estimated edge, EV and risk classification.

5

Immutable record

Prediction is published, timestamped and graded after the event.

Every stage is expandable on each prediction. SportsAI.ML distinguishes model predictions, market-implied probability, calculated probability, confirmed facts and unconfirmed reports. Proprietary chain-of-thought is never exposed.

Data sources

All live factual data is sourced from licensed providers through a provider-neutral adapter layer, then validated, normalized and timestamped. Raw source snapshots are stored immutably and separately from normalized records.

Where credentials are absent, the platform shows a clear "Live provider not configured" state rather than fabricating data. All records shown here are clearly-labeled demonstration data.

  • Optional adapters: Sportradar, SportsDataIO, Stats Perform/Opta, Genius Sports, API-Sports and The Odds API
  • Every factual statement carries a provider timestamp
  • Provider disagreements are reconciled with source-confidence scoring

AI transparency

Each selected model receives an identical, normalized evidence package and must return strict structured JSON. We report model predictions, market-implied probability and our calculated probability separately.

We never expose proprietary chain-of-thought. Language models are never permitted to invent live data such as odds or statistics.

  • Reliability-, recency-, sport- and market-weighted consensus
  • Calibration via isotonic regression or Platt scaling
  • Clear labels for confirmed facts, unconfirmed reports and editorial interpretation

Value & no-vig math

Bookmaker margin is removed before comparing our probabilities to the market. We support multiplicative, additive, power and Shin no-vig methods.

  • Decimal implied probability = 1 / decimal odds
  • Expected value = (prob × profit) − ((1 − prob) × stake)
  • Estimated edge = SportsAI.ML probability − no-vig market probability

Prediction grading rules

Every published prediction is written to an immutable record with a publication timestamp. After the event concludes, predictions are graded automatically against the final result.

We track accuracy, log loss, Brier score, calibration error and closing-line value. Where verified history is insufficient, we display "Insufficient verified sample" rather than a fabricated win rate.