I_PREDICT_SPORT
About
I_PREDICT_SPORT publishes daily ATP match probabilities from a purpose-built prediction engine: chronological Elo-family ratings (surface-specific, margin-of-victory aware, uncertainty-scaled), an exact point-by-point serve/return match model, and gradient-boosted ensembles — trained on tour-level results and enriched with Challenger and lower-tour data that most public models never see.
On strictly time-ordered validation the model holds roughly 66–67% winner accuracy with a Brier score near 0.21 — approaching the practical ceiling for pre-match tennis prediction from public data. Every published probability is logged and audited against settled outcomes, and the confidence labels on the board are recalibrated from that live track record, not from backtests alone.
The person behind it
Alex Houck is a machine learning and AI engineer who builds prediction systems where being wrong has a price: sports outcome modeling, medical-device AI, surgical robotics, clinical outcome prediction, financial markets, and applied data science. His work spans the full arc from raw data pipelines to validated, deployed models with live accountability for their accuracy.
Alex has consulted for Fortune 500 companies and early-stage startups alike, and is open to consulting engagements — particularly where rigorous prediction, calibration, and honest evaluation matter more than hype.