PingWin PRO methodology
Why every sport needs its own ML model
Which data changes from sport to sport and why one prediction logic cannot be applied to every discipline.
Data
The model uses match history, player form, ratings, set or period scores, and odds snapshots. The feature set depends on the specific sport and is calculated only from information available before the match starts.
Why the model is separate
Tennis, table tennis and other sports differ in pace, scoring structure, calendar density, markets and line noise. Each sport therefore needs its own data-loading, training and pick-selection settings.
Validation
A new model version must pass validation on held-out data for that sport. Public results help distinguish an attractive backtest from real-market performance.
Ready-to-share content
Telegram — RU
📘 Почему каждому виду спорта нужна своя ML-модель Какие данные меняются от спорта к спорту и почему нельзя переносить одну логику прогнозов на все дисциплины. https://pingwin.pro/analytics/sport-specific-ml
Telegram — EN
📘 PingWin PRO analytics: Why every sport needs its own ML model Which data changes from sport to sport and why one prediction logic cannot be applied to every discipline. https://pingwin.pro/en/analytics/sport-specific-ml
Forum — RU
Почему каждому виду спорта нужна своя ML-модель. Какие данные меняются от спорта к спорту и почему нельзя переносить одну логику прогнозов на все дисциплины. В материале приведены формула, ограничения и примеры на открытых данных PingWin PRO. https://pingwin.pro/analytics/sport-specific-ml
Forum — EN
Why every sport needs its own ML model. Which data changes from sport to sport and why one prediction logic cannot be applied to every discipline. The guide includes formulas, limitations and examples using public PingWin PRO data. https://pingwin.pro/en/analytics/sport-specific-ml
See the methodology applied to real results
Completed predictions are public, while subscribers receive new signals before matches start.