Comparar métodos

	Análisis de Tiempo-Movimiento con GPS	TRIMP de Banister
Campo	Ciencias del deporte	Ciencias del deporte
Familia	Hypothesis test	Hypothesis test
Año de origen	2010	1975
Autor original	Osgnach & Di Prampero	Eric Banister
Tipo	GPS tracking	mathematical modeling
Fuente seminal	Gregory, P., & Drust, B. (2007). Physical demands of rugby union: quantification of accelerations and movements patterns in play. Journal of Strength and Conditioning Research, 21(2), 309-314.	Banister, E. W., Calvert, T. W., Savage, M. V., & Bach, T. (1975). A systems model of training responses and its relationship to muscular strength. Transactions of the ASME, 97(3), 177-183.
Alias	GPS analysis, movement tracking, workload quantification, physical demands	TRIMP, training impulse, fitness-fatigue model
Relacionados	4	3
Resumen	Time-motion analysis with GPS and micro-sensor technology quantifies the movement patterns, workload, and physical demands during training or match play in team sports. Pioneered by Osgnach and colleagues (2010), modern GPS units track athletes' positions in real-time, calculating distance covered, velocity profiles, and acceleration/deceleration frequencies. Combined with heart rate and other sensor data, GPS analysis provides comprehensive workload quantification enabling coaching staff to monitor player fatigue, balance training intensity, and prevent injury. GPS is now standard in elite soccer, rugby, Australian Rules football, and other intermittent sports.	The Training Impulse (TRIMP) model, developed by Eric Banister and colleagues (1975), quantifies the physiological stimulus of a training session by combining duration and intensity. The Banister fitness-fatigue model proposes that training effects on performance follow two opposing dynamics: fitness (beneficial) accumulates with time constant tau_f (~42 days) and fatigue (temporary decrement) accumulates faster but decays quickly (tau_d ~5-10 days). By tracking TRIMP and modeling these two processes, coaches can predict performance trajectories and optimize training load. Although superseded by newer frameworks, the Banister model remains influential and intuitive.