# The Relationship Between Training Load and Performance: It’s Complicated

BY Rebecca Johansson

If you train more, you'll get faster, right? As these two studies show, it may not be that simple.

Spend some time at a race expo or start line and it won’t take long until you hear banter about what athletes are doing for training.  There is no doubt that training is a necessary and important variable in athletic performance.  But how much does training contribute to performance?  My Ph.D. data explored this topic in endurance runners, and I found that the issue is far more complex than meets the eye.

There is wide variation in amount of training completed:

One of the studies we did included following a group of 69 runners for six weeks leading up to a 56 km road ultramarathon race.  There was no minimum training requirement to be part of the study.  We plotted runners’ relative race performance (race speed as a percentage of best 10 km race speed in the last year) against the total training load completed in the six weeks (AUC rTSS: area under the curve running training stress score) (figure 1).  There was a positive linear relationship whereby runners with better relative performances completed higher amounts of training.  However, there is a lot of variation around the line.  If you just take the two data points circled in blue in figure 1 — The data point towards the top of the graph represents a runner with a relative race performance of 92 percent and an average of 112 km per week in training.  The data point towards the bottom represents a runner with a relative race performance of 91 percent and an average of 47 km per week in training.  These are two runners who have nearly the exact same relative performance but nearly a 2.5-fold difference in the amount of training they completed.

Figure 1. Relationship between relative race performance and area under the curve (AUC) for running training stress score (rTSS) for the six weeks before race day.

Sy.x = standard error of estimate. n = 69.

In a separate study we followed 19 runners for 11 weeks leading up to a marathon race.  To be part of the study runners had to be training a minimum of 40 km per week and have a ten km race time of 55 minutes or faster.  So, compared to the earlier study discussed, the group was more homogenous.  We plotted the relationship between relative race performance and total training load completed each week (AUC rTSS).  There were no significant correlations between relative performance and training load for any week leading up to the marathon (figure 2).  The confidence interval around the mean is very large each week which indicates there is a lot of variation between runners.  To put it into context, the participant with the best relative performance ran an average of only four and a half hours per week.  The participant with the worst relative performance ran an average of eight hours per week.

Figure 2.  Data are presented as mean ± 95% confidence interval.  Group correlation coefficients between relative performance and normalised weekly area under the curve (AUC) running training stress score (rTSS) (n = 19).

## Considerations for the wide range in training load:

One possible reason for the wide range in training load may be that runners train relative to what they have successfully completed in the past.  Let’s take the best (50 hours of running in 11 weeks) and worst relative performer (85 hours of running in 11 weeks) at the marathon as an example.  It is important to consider the best performer averaged 46 km per week in the six months prior to the study while the worst performer averaged 64 km per week during the same time.  So, it is likely the best performer had lower running duration during the 11 weeks before the race, because he was not accustomed to training as much as other participants.

Another possible reason may be life stressors such as work and family.  Stress does impact training and performance (Bali, 2015), therefore runners with demanding jobs and/or families to care for may be unable to train with as high a volume as an athlete who works less or has no family commitments.  Factors such as genetics, age, nutrition, and psychological state should also be considered (Mann et al., 2014).

## Athletic performance is multi-factorial:

There are other factors besides training load that should be considered in the effects on performance.  These may include stress (Bali, 2015), sleep (Soussi et al., 2008), weather on race day (Tatterson et al., 2000), nutrition on race day (Burke et al., 2007), and/or pacing on race day (Renfree & Gibson, 2013).  If an athlete is having poor performances on workouts or a race, reviewing the training should only be one component in assessing possible reasons for the decline in performance.  It is equally important to consider if there have been changes to the athletes work hours, family commitments, sleep patterns, nutrition, etc.  Only then can you assess what needs to change to improve performances.

### References:

Bali, A. (2015). Psychological factors affecting sports performance. International Journal of Physical Education, Sports and Health, 1(6), 92-95.

Burke, L. M., Millet, G., & Tarnopolsky, M. A. (2007). Nutrition for distance events. Journal of Sports Sciences, 25(S1), S29-S38.

Mann, T. N., Lamberts, R. P., & Lambert, M. I. (2014). High responders and low responders: factors associated with individual variation in response to standardized training. Sports Medicine, 44(8), 1113-1124.

Renfree, A., & Gibson, A. S. C. (2013). Influence of different performance levels on pacing strategy during the Women’s World Championship marathon race. International Journal of Sports Physiology and Performance, 8(3), 279-285.

Souissi, N., Souissi, M., Souissi, H., Chamari, K., Tabka, Z., Dogui, M., & Davenne, D. (2008). Effect of time of day and partial sleep deprivation on short‐term, high‐power output. Chronobiology International, 25(6), 1062-1076.
Tatterson, A. J., Hahn, A. G., Martini, D. T., & Febbraio, M. A. (2000). Effects of heat stress on physiological responses and exercise performance in elite cyclists. Journal of Science and Medicine in Sport, 3(2), 186-193