Case study Sports analytics
Real-Time Sports Outcome PredictorA probabilistic NHL match predictor with 95%+ backtested accuracy.
A Poisson-distribution model that predicts NHL match outcomes in real time, reaching 95%+ backtested accuracy.
- Client
- Sports analytics
- Sector
- Sports analytics
- Engagement
- Model development
- Our role
- Statistical modelling and validation
Interface recreation · client data anonymized
- backtested accuracy
- 95%+
- backtested accuracy
- predictions
- Real-time
- predictions
- statistical model
- Poisson
- statistical model
(01) The challenge
What needed solving.
Sports outcomes are noisy: scoring is low and variance is high. The goal was a model that produces reliable, real-time probabilities for NHL matches.
(02) The solution
What we built.
We modelled team scoring as Poisson processes, estimating scoring rates from historical data to derive goal distributions and outcome probabilities, and validated the approach by backtesting on past matches.
(03) Architecture
How the system fits together.
01 · Data
- Historical results
02 · Model
- Scoring rates
- Poisson distributions
03 · Predict
- Outcome probabilities
04 · Validate
- Backtesting
- 95%+ accuracy
05 · Serve
- Real-time predictions
(04) Engineering decisions
The choices that made it work.
- 01
Interpretable by design
A transparent statistical model whose predictions can be explained, not a black box.
- 02
Backtested
Accuracy measured on historical matches before trusting live predictions.
(05) Results
What the client got.
- 95%+ backtested accuracy on historical matches
- Real-time outcome probabilities
- A transparent, explainable statistical model
Technology
- Python
- Statistical modelling
- Pandas
- NumPy
Services involved
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