Porovnať metódy

	Pomerový ukazovateľ akútnej a chronickej záťaže	Banister TRIMP
Odbor	Športové vedy	Športové vedy
Rodina	Hypothesis test	Hypothesis test
Rok vzniku	2016	1975
Tvorca	Tim Gabbett	Eric Banister
Typ	workload monitoring	mathematical modeling
Pôvodný zdroj	Gabbett, T. J. (2016). The training-injury prevention paradox: should athletes be training smarter and harder? British Journal of Sports Medicine, 50(5), 273-280.	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.
Ďalšie názvy	ACWR, workload ratio, training load balance	TRIMP, training impulse, fitness-fatigue model
Príbuzné	3	3
Zhrnutie	The acute-chronic workload ratio (ACWR) is the ratio of acute training load (typically the past 1 week) to chronic training load (typically the rolling 4-week average). Formalized by Tim Gabbett (2016), ACWR is a widely adopted metric for predicting injury and illness risk in sports. The logic is straightforward: rapid increases in training load—when acute load spikes far above what the athlete has adapted to—exceed tissue tolerance and increase injury risk. Conversely, maintaining ACWR within optimal ranges (typically 0.8-1.3) is associated with better performance and lower injury incidence. ACWR monitoring is now standard in elite sports for load management.	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 Although superseded by newer frameworks, the Banister model remains influential and intuitive.