20 posts tagged “machine learning”.
See how to turn calibrated ML probabilities into disciplined bets: position sizing, risk controls, and the honest limits of the math.
Step-by-step guide to building a sports prediction API in Python, from live data collection and feature engineering to model calibration and FastAPI...
Our NHL model replayed the 2025 Stanley Cup playoffs on pre-playoff data: 52 of 86 games correct (60.5%), with the Cup Final correctly read as a coin flip.
PSG, Arsenal, Atlético Madrid, and Bayern Munich are the 2025-26 UCL semifinalists. Our production club-soccer model (trained on 3,000+ matches, 75%...
Most 'AI picks' sites are ChatGPT wrappers around sportsbook lines. An honest 2026 guide to telling real ML predictors from narrative generators.
Measure reliability in a defined prediction population, including the traded subset. Checked ECE bin endpoints, held-out calibration methods and dated...
Our MLB model replayed the entire 2025 postseason on pre-playoff data: 28 of 47 correct (59.6%). Full breakdown, including every missed ALCS pick.
Our NFL model replayed the 2025-26 playoffs on pre-playoff data only: 9 of 13 correct (69.2%), including Seattle over New England in Super Bowl LX.
After the 2025 NBA Finals wrapped with OKC beating Indiana in seven games, we ran our NBA win-probability model on all 84 postseason games using only...
Our first college football model backtested at -2.1c/trade over 728 simulated positions. The missing feature, the diagnosis, and the corrected numbers.
Our college basketball model backtested on all 5,345 non-tournament games of 2025-26: 68.2% accuracy, Brier 0.2064, ECE 4.39%, full reliability table.
Our NCAAMB model replayed all 67 games of March Madness 2026 on pre-tournament data: 48 correct (71.6%), 3-for-3 on the Final Four. Every miss listed.
How win probability models actually work — from Elo ratings through logistic regression to XGBoost. Includes calibration, feature engineering, and the...
Inside ZenHodl's NBA prediction engine: XGBoost models, team stats, real-time injury overlays, and isotonic calibration. How our ML system processes...
Train and evaluate a calibrated NBA win probability model in Python using play-by-play game states, walk-forward validation, XGBoost and isotonic regression.
Our NBA bot had 65% accuracy and was losing money — a calibration bug left it confidently wrong. How we found it, and why calibration beats accuracy.
A 6-feature model outperforms a 50-feature model at making money — even with a worse accuracy score. Here's why, and what it means for how you should...
Measure sports-model calibration with Brier score, ECE and held-out calibration examples. Separate reliability from executable-price profitability.
A complete step-by-step tutorial to implement an Elo rating system in Python for sports prediction. Covers the math, K-factor tuning, season resets, and...
How to implement Elo ratings from scratch in Python for sports prediction. Covers home advantage, K-factor tuning, and season resets.