AI Race Performance Predictor
Enter up to 3 past race results and your training details. The predictor blends multiple data points with recency and distance weighting — far more accurate than a single-race Riegel formula.
FAQ
Why is more than one race result better?
A single race result forces the model to extrapolate from one data point. Multiple results at different distances let the algorithm cross-validate predictions and produce a confidence score reflecting how well your performances agree.
How does training volume affect the prediction?
Runners training under 30 km/week get a +3% time penalty (less endurance base), while those over 60 km/week get a -2% bonus. This reflects real-world aerobic development beyond what race results alone show.
Is this accurate for ultramarathons?
Predictions become less reliable beyond marathon distance because ultra performance depends heavily on nutrition, terrain, and pacing strategy. The confidence score will drop accordingly for ultra distances.
Methodology
Core model is the Riegel formula (T2 = T1 × (D2/D1)^1.06) applied per race result, then blended using recency weighting (more recent races count more) and distance similarity weighting (races closer to your target distance are more predictive). Training volume applies a small adjustment factor.
Want to verify the math?
Explore 170+ reference calculators built by engineer-athlete Thomas Prommer. The technical foundation behind our AI.
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