How can coaches effectively monitor multiple training load variables?
As Mike Tyson said, “everyone has a plan, until they get punched in the face.” That quote can easily apply to the management and monitoring of training load.
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By Cody Roberts
10th May 2022 | 5 min read
Ryan, G. A., Snarr, R. L., Eisenman, M. L., & Rossi, S. J. (2020). Seasonal Training Load Quantification and Comparison in College Male Soccer Players. Journal of Strength and Conditioning Rese
Periodising and planning training load (TL) is a great start and important step in the process of setting up a team to be prepared for a competitive season. However, the management and monitoring of said plan is arguably more important, as what looks great on paper does not always play out as expected. As Mike Tyson said, “everyone has a plan, until they get punched in the face.”
When the appropriate variables are collected and managed properly (e.g. modulating training to increase readiness), coaches and athletes succeed. This is done through a combination of measuring both internal and external variables, as well as leaning on both subjective reports (e.g. rating of perceived exertion (RPE) or total quality of recovery (TQR) and objective measures (e.g. total distance (TD) or time spent above a certain speed).
Monitoring multiple TL variables is a constant pursuit that provides coaches with actionable feedback towards each athlete’s response to workload, and it is important to recognise the context, interaction, and specificity of this insight. Therefore, researchers assessed multiple subjective and objective TL measures (e.g. TQR, RPE, heart rate (HR), TD, speed) for a NCAA Division-I men’s soccer, assessing variations based on position across a season.
A team of 21 male soccer players (age 19.4±1.4-yr) were assessed over a 14-wk period during their NCAA Division-I season. Players were separated by position (centre forward, centre midfielder, wide midfielder, centre back, and wide back), excluding goalkeepers.
Only full-team (starters and reserves) practices were monitored using heart rate and global positioning system (GPS) technology.
The 14-wk testing period included pre-season (6-wk) and in-season (8-wk) blocks. The pre-season was divided into two 3-wk phases (Pre1 and Pre2), and the in-season block into four 2-wk phases (In1, In2, In3, In4) in order to provide context on competitions during each phase and relative comparison.
Players reported a TQR score upon arrival to training, and an RPE in the 15-30-min post-practice, as subjective-internal measures. RPE was later multiplied by the session duration to provide an ‘RPE Load’ metric. Time spent >85% predicted HRmax, time between 65-85% HRmax, and time <65% HRmax were analysed (%HRhigh, %HRmid, %HRlow, respectively) to represent an objective-internal load measure. Lastly, running speed was measured as time spent(min) >7.2km*h-1 (SZupper) and ≤7.2km*h-1 (SZlower), as well as total distance covered(m) as the objective-external GPS metrics.
The various metrics were analysed for comparison between playing position, as well as phase of the season.
“The purpose of monitoring is to provide specific feedback relative to the efforts and workloads of an athlete and team. With this insight, coaches can provide training that sufficiently prepares them for competitive demands, prescribing and modifying loads based on recovery needs and readiness level. The most effective way to go about this involves collecting multiple measures to provide context and understanding. Therefore, when aiming to understand an athlete’s psychophysiological state, it is important to collect both subjective (athlete perception) measures, which leans heavily to the psychological component, as well as objective measures, which are direct measurements of an athlete’s physiological output. Likewise, these metrics should provide an accurate representation of the intensity of workload. For example, the upper and lower speed zones identified in this research were arguably limited in identifying high-intensity running, seeing that it was polarised to separate out walking and jogging paces (<7.2-km/h) and anything above being acknowledged as ‘upper speed zones.’ Therefore, critical consideration should be given to identifying truly high-intensity demands (>85%-maximum) for a better understanding of demands and performance.
“By pairing metrics, coaches can support their expectations (e.g. prescribing a high-intensity practice, receiving high RPE reports and noting ample time spent in %HRhigh) and gain understanding (e.g. identifying potential fatigue if RPE and HR is high with speeds and distances decreased). This lends itself to the concept of being ‘data informed’ versus ‘data driven,’ where monitoring data can help to feed expectations and guide training decisions.
“But it is critical to appreciate the complexity of recovery, performance and all the athlete is balancing.”
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