Utilizing the Maximum Workload Range for Practice Periodization Commentary
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Keywords
Sports, Workloads, Overtraining, Undertraining, Training Load
Abstract
The maximum workload range (max range) is a concept suggested by Sanders et al.1 regarding a method used to prescribe adequate practice workloads based off wearable technology data. The max range is calculated as follows from game data:
Max Range = (Mean Total Distance + 1 St. Dev.) to (Maximum Total Distance)
While the example provided utilizes total distance, the max range can be applied to key performance indicators such as high-speed distance, training load, jumps, etc. that are tracked throughout the competitive season in team sports. The max range concept was developed from research that found 12-17% of the time, football athletes, depending on position, accumulated game workloads outside their position’s mean + 1SD. Anecdotally, many coaches and practitioners use simple game averages as a control for ideal practice volumes. Based on previous research, using the game average as control training threshold may result in some high performing athletes being under-conditioned. It is reasonable to suggest that potential compound effects may occur throughout an entire season if athletes are not engaging in rigorous training loads that mimic game-like volumes and intensities.
Figure 1. Theoretical football periodization structure for a defensive back using the max range for high intensity training days.
References
2. Sanders GJ, Boos B, Rhodes J, Kollock RO, Peacock CA. Competition-Based Heart Rate, Training Load, and Time Played Above 85% Peak Heart Rate in NCAA Division I Women's Basketball. J Strength Cond Res. 2021;35(4):1095-1102.
3. Sanders GJ, Roll B, Peacock CA. Maximum Distance and High-Speed Distance Demands by Position in NCAA Division I Collegiate Football Games. J Strength Cond Res. 2017;31(10):2728-2733.
4. Sanders GJ, Boos B, Shipley F, Scheadler CM, Peacock CA. An Accelerometer-Based Training Load Analysis to Assess Volleyball Performance. J Exerc Nutri. 2018;1(1).
5. Sanders GJ, Boos B, Rhodes J, Kollock RO, Peacock CA, Scheadler CM. Factors associated with minimal changes in countermovement jump performance throughout a competitive division I collegiate basketball season. J Sports Sci. 2019;37(19):2236-2242.
6. Stojanovic E, Stojiljkovic N, Scanlan AT, Dalbo VJ, Berkelmans DM, Milanovic Z. The Activity Demands and Physiological Responses Encountered During Basketball Match-Play: A Systematic Review. Sports Med. 2018;48(1):111-135.
7. Ben Abdelkrim N, Castagna C, El Fazaa S, El Ati J. The effect of players' standard and tactical strategy on game demands in men's basketball. J Strength Cond Res. 2010;24(10):2652-2662.
8. Ben Abdelkrim N, El Fazaa S, El Ati J. Time-motion analysis and physiological data of elite under-19-year-old basketball players during competition. Br J Sports Med. 2007;41(2):69-75.
9. Berkelmans DM, Dalbo VJ, Kean CO, et al. Heart Rate Monitoring in Basketball: Applications, Player Responses, and Practical Recommendations. J Strength Cond Res. 2018;32(8):2383-2399.
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