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.

Original study

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
Click here for abstract

Background & Objective

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.

What They Did

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.

What They Found

Internal and external TL differed between positions. Specifically:

Wide backs spent 4-10-min more in the SZupper zone, and more time at %HRhigh during the pre-season period compared to other positions.

Furthermore, wide backs had lower TQR scores than centre- and wide-midfielders during the Pre2 phase.

Across the In1 phase, centre backs had lower TQR scores compared to midfielders, which was consistent with less time spent in SZlower and more time spent in %HRhigh relative to other positions.

However, TD covered between positions was similar across the season, even reducing ~33% between In2 and In3 for all positions.

Only during the In3 phase was there a discrepancy in RPE load between positions, with centre backs being greater, and recording less time in %HRlow zone during that phase of the season, as well.

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Practical Takeaways

It is critical coaches respect and understand the demands that each position will experience during the competitive season. With this understanding, coaches can appropriately and adequately develop and prepare players during the off- and pre-season periods. Coaches should gradually bring up training loads to the necessary volume, intensity, and frequency that will be experienced in-season, so these known workloads are not as stressful or damaging as they would otherwise be.

Position-specific demands are also specific to the team and a coach’s given style of play. Research gives us a general idea (see HERE), but collecting data GPS across a season or two provides specific and relative understanding for a playing position’s workload in a match (e.g. backs being required to be more active, experiencing higher intensity efforts, or centre midfielders operating steadily at %HR-low to -mid in a defensive and reactive role).

When collecting HR values, coaches should not look at data with the goal that a higher HR is better. Heart rate is a relative measure of intensity, but at the same time a more favourable heart rate response (lower value) to a given effort would signify a more efficient cardiovascular system and a more robust (‘fit’) athlete. Coaches can monitor and assess improvements (increased fitness) like this by comparing an early pre-season session to a similar (volume and intensity) early in-season session, with the hopes of %HR being lower.

Data metrics should rarely be used in isolation, but rather, coaches should be pairing measured variables to find relationships. For example, when comparing RPE with HR, TD, or time in SZupper, if subjective RPE Load is elevated, we would hope we could identify another objective measure that has also increased in accordance. If not, then coaches should either watch that athlete closely in the coming days (e.g. checking for an improved TQR score before the next training session) or inquire directly about how things are going outside of playing (e.g. stress, sleep, nutrition) without being overly intrusive.

Collecting individualised, subjective measures (RPE) after training is vital insight that allows coaches to better understand TL. Research has shown that coaches are poor predictors of identifying an athlete’s perception of effort (see HERE). Coaches should take time to collect this information unbiased and use it with a supportive mindset.

Coaches need to respect that RPE is a measure of TL, reflecting an athlete’s internal intensity, and is not necessarily a mark of readiness (performance potential). Simply, a high or low RPE is not predictive of a good or bad performance (see HERE), but coaches should appreciate that more recovery (time) may be necessary following higher reports of intensity.

Alternatively, TQR quantifies an athlete’s perceived level of readiness, which represents a psychophysiological state that must be appreciated, especially when planning to expose athletes to high-intensity efforts (sprinting and change of direction at full speed). If reports of recovery are low (poor) at the start of practice, coaches may consider modifying training (reducing intensity or volume). This can help build trust between the coach and athlete, not to bail them out of complaining but to optimally prepare them for competition. If TQR reports are habitually low and training is constantly being modified, then there needs to be follow up and problem solving between coach and athlete.

Cody Roberts’ Comments

“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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