Case Study
Quantitative Sports Modelling
Golf
NFL
eSports Sims
I build, deploy, and monitor predictive systems in environments where being wrong is immediately punished and costly.
Operating Principles
Every model lives or dies by these constraints.
High-Stakes Environments
I build systems where weak assumptions are exposed quickly and being wrong has an immediate cost. There is no room for untested confidence — every prediction must earn its place through empirical validation.
Active Governance
Models drift and signals disappear. My systems are actively monitored to ensure predictions remain unbiased and empirically validated — not just at launch, but continuously in production.
Full-Stack Ownership
I take full ownership of the data science lifecycle — from ingestion and modelling through to deployment and iteration. No hand-offs to separate teams; one accountable pipeline from raw data to live prediction.
No One-Size-Fits-All
Every environment has unique dynamics. I build bespoke engines tailored to specific constraints, not generic templates. The model architecture follows the problem structure — not the other way around.
Signal Governance & Monitoring
Real-time performance tracking ensures predictions remain unbiased and empirically validated.
Technical Prototype: Illustrative Monitoring Interface
Example screenshot demonstrating the integration of MLflow for real-time performance tracking and signal governance.
What Gets Measured
Every system ships with proper scoring rules and continuous evaluation.
CALIBRATION
Predicted probabilities match observed frequencies. Overconfidence is identified and corrected before it costs anything.
LOG-LOSS & BRIER
Proper scoring rules that punish both inaccuracy and overconfidence — the foundation of honest prediction.
DRIFT DETECTION
Continuous monitoring catches signal degradation early, triggering retraining before performance decays in production.