INTRODUCTION: Pacing can be defined as the competitive strategy in which athletes manipulate their speed to achieve the highest performance. Previous literature demonstrated that pacing strategies vary among distances , events  and performance levels . However, limitations arise from a high variability within the groups and the comparison of absolute (rather than normalised) velocities. Since physiological changes are considered the main afferent driver for regulating pace as described by the anticipatory feedback model , it seems reasonable that the individual pacing strategy is largely influenced by the underlying metabolic profile of the athlete. This study aims to categorise homogeneous pacing clusters and analyse differences in terms of performance and their underlying physiological profiles in trained runners and triathletes.
METHODS: 44 competitive female and male endurance runners and triathletes performed several laboratory tests to determine maximal oxygen uptake (VO2max), lactate threshold, running economy and maximal lactate accumulation rate (VLamax) . Furthermore, the participants performed a 5000-m time trial on an outdoor track and were instructed to achieve the best overall performance and adjust their freely chosen pacing strategy accordingly. All 25 split times (every 200 m) were normalised to the individual mean velocity and implemented into hierarchical cluster analysis by using Ward’s linkage method. Clusters were compared by using one-way ANOVA and Bonferroni-adjusted post-hoc tests or non-parametric equivalents.
RESULTS: Three homogeneous clusters were determined. Cluster A demonstrated a moderate start, negative splits and a fast finish over the last 200 metres. Cluster B and C demonstrated rather similar pacing as shown by their fast start and rather positive splits. However, unlike Cluster C, only Cluster B demonstrated a fast finish similarly to Cluster A. There were no differences in 5000-m performance, anthropometrics and physiological parameters between clusters except for VLamax (p = 0.006) and the ratio between VO2max and VLamax (p = 0.004). Post-hoc tests revealed that the Cluster A athletes had a significantly higher VLamax (d = 1.26, p = 0.005) and lower VO2max/VLamax (d = -0.89, p = 0.004) compared to Cluster C.
CONCLUSION: Since research in middle distance running demonstrated similar pacing strategies in females and males , the distribution of sexes between clusters might not influence these findings. It seems that the ability to produce lactate and concomitantly reduce intracellular pH provides an immediate afferent feedback that influences individual pacing. Thus, athletes with a high VLamax might benefit from a negative pacing strategy since a courageous start might lead to premature pace reduction or failure in these individuals.
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