Real-Time Performance Prediction in Long-Distance Trail Running: A Practical Model Based on Terrain Difficulty and Pacing Variability

Trail running is a demanding endurance sport where performance prediction models often rely on laboratory testing or pre-race data, limiting their practical application. This study presents a real-time predictive model for marathon and ultra-trail races, based on variables recorded during the race, including uphill/downhill pace-times, terrain difficulty coefficients, and partial rankings. A total of 947 runners from the 'Trail Valle de Tena' event (Spain) were analyzed to develop equations that estimate total race time using only the first third of the race. The model incorporates weighted time (WT_n), pacing variability (WTV_n,n+2), and checkpoint percentile rank (CPR_n), showing strong predictive power (adjusted R² > 0.95) across sexes and race modalities. These variables reflect the runner's ability to both overcome elevation and maintain consistent pacing, offering insights into fatigue management and performance optimization. The model enables coaches and athletes to monitor race progression, adjust strategies in real time, and potentially reduce injury risk through better control of effort intensity. Unlike laboratory-based models, this approach is fully applicable in field conditions and does not require prior testing. Further validation in similar endurance events is recommended to confirm its utility as a practical tool for training and competition planning.