Marathon Pace Control in Masters Athletes

Abstract

High-performing masters athletes use more-controlled pacing strategies than their lower-ranked counterparts during a competitive marathon, independent of age and gender.

A Portrait of Pacing Profile of Cross-Country Skiers in the Vasaloppet 2004–2017

“…Thus, the abovementioned disagreement should be attributed to the differ-ent samples -all finishers versus a performance-matched subsample -analyzed between the present study and that of Carlsson and colleagues [6]. With regards to the relationship between performance level and pacing, identifying a more even pace in the fast performance groups using two methodological approaches (i. e. correlation analysis and comparison among groups) was in agreement with previous findings in XC skiing [6,21] and other endurance sports [5,30]. On the one hand, our findings confirmed this existing knowledge; on the other hand, a novel finding was that the two slowest groups (11-12 h and > 12 h) did not show the least even pacing, as shown in ▶Fig.…”

“…The dependent variable was the race speed, whereas the independent variables were sex, performance groups and splits. To study pacing strategies, we calculated three pace parameters for each finisher [5]: a) positive pace range in the fastest split as 100 × (speed in the fastest splitmean race speed) / mean race speed, e. g. positive range = + 24.8 %; b) negative pace range in the slowest split as 100 × (speed in the slowest split -mean race speed)/mean race speed, e. g. negative range = -18.5 %; and c) total pace range as the difference between positive and negative range, e. g. total pace range = + 24.8 % -(-18.5 %) = 43.3 %. The use of these ranges was chosen instead of other measures of pacing (e. g. coefficient of variation [15]) because it was considered more relevant for sport practice [5].…”

Pacing profiles of master athletes according to their predicted finishing time

“…The purpose of the present study was to evaluate pacing abilities of recreational athletes with the hypothesis that athletes who ran slower than their predicted time would adopt a positive pacing strategy (decrease in speed throughout the event) with higher RPE early in the event. Different studies have tried to understand the pacing profile of faster and slower runners during official events both at the elite level (Hanley, 2013(Hanley, , 2015(Hanley, , 2016Renfree & Gibson, 2013) or at the amateur level (Hubble & Zhao, 2016;Lambert et al, 2004;Renfree et al, 2016;Nikolaidis et al, 2018;Breen et al, 2018). In these studies, slower and faster runners were divided based on their finish time or classification (medallists or non-medallists) and final ranking.…”

“…Green et al (2010) saw that pacing accuracy in performing specific splits is different between recreational and collegiate athletes probably this difference driven also by experience. Recently, Breen et al (2018) analysed pacing of the participants in the New York city marathon and reported that, independently of age, the best performers of each age category demonstrate a more even pacing strategy compared to lower level runners. Similar results were reported in the analysis of longer races (100 km) where the best performers of each age category started with a slower relative speed and finished with a higher relative speed compared to slower runners in the same age category (Renfree et al, 2016).…”

“…It therefore seems that while elite athletes often deviate from an optimal distribution of effort for tactical reasons (Hanley, 2013), master and amateur athletes, who mainly race to achieve a specific target time, should benefit from an even or a negative pacing profile. Two recent studies found that, independently of age, the best performers of each age category demonstrate a more even pacing strategy during the New York City marathon (Breen et al, 2018) or a 100 km (Renfree et al, 2016) compared to lower level runners. Especially for these athletes, pacing accuracy may be a critical component in achieving a desired performance irrespective of ideal effort allocation (Green et al, 2010).…”