Influence of Different Performance Levels on Pacing Strategy During the Women's World Championship Marathon Race.

Purpose To analyse pacing strategies displayed by athletes achieving differing levels of performance during an elite level marathon race. Methods Competitors in the 2009 IAAF Women’s Marathon Championship were split into Groups 1, 2, 3, and 4 comprising the first, second, third, and fourth 25% of finishers respectively. Final, intermediate, and personal best (PB) times of finishers were converted to mean speeds, and relative speed (% of PB speed) was calculated for intermediate segments. Results Mean PB speed decreased from Group 1 to 4 and speed maintained in the race was 98.5 + 1.8%, 97.4 + 3.2%, 95.0 + 3.1% and 92.4 + 4.4% of PB speed for Groups 1-4 respectively. Group 1 was fastest in all segments and differences in speed between groups increased throughout the race. Group 1 ran at lower relative speeds than other groups for the first two 5 km segments, but higher relative speeds after 35km. Significant differences (P<0.01) in the percentage of PB speed maintained were observed between Groups 1 and 4, and 2 and 4 in all segments after 20 km, and Groups 3 and 4 from 20-25 km and 30-35 km. Conclusions Group 1 athletes achieved superior finishing times relative to their PB than athletes in other Groups who selected unsustainable initial speeds resulting in subsequent significant losses of speed. It is suggested that psychological factors specific to a major competitive event influenced decision making by athletes and poor decisions resulted in final performances inferior to those expected based on PB times.

Risk Perception Influences Athletic Pacing Strategy.

PURPOSE: To examine risk-taking and risk-perception associations with perceived exertion, pacing and performance in athletes. METHODS: Two experiments were conducted in which risk-perception was assessed using the domain-specific risk-taking (DOSPERT) scale in 20 novice cyclists (Experiment 1) and 32 experienced ultra-marathon runners (Experiment 2). In Experiment 1, participants predicted their pace and then performed a 5 km maximum effort cycling time-trial on a calibrated KingCycle mounted bicycle. Split-times and perceived exertion were recorded every kilometer. In experiment 2, each participant predicted their split times before running a 100 km ultra-marathon. Split-times and perceived exertion were recorded at 7 check-points. In both experiments, higher and lower risk-perception groups were created using median split of DOSPERT scores. RESULTS: In experiment 1, pace during the first km was faster among lower compared to higher risk-perceivers, t(18)=2.0 P=0.03, and faster among higher compared lower risk-takers, t(18)=2.2 P=0.02. Actual pace was slower than predicted pace during the first km in both the higher risk perceivers, t(9)=-4.2 P=0.001, and lower risk-perceivers, t(9)=-1.8 P=0.049. In experiment 2, pace during the first 36 km was faster among lower compared to higher risk-perceivers, t(16)=2.0 P=0.03. Irrespective of risk-perception group, actual pace was slower than predicted pace during the first 18 km, t(16)=8.9 P<0.001, and from 18 to 36 km, t(16)=4.0 P<0.001. In both experiments there was no difference in performance between higher and lower risk-perception groups. CONCLUSIONS: Initial pace is associated with an individual's perception of risk, with low perceptions of risk being associated with a faster starting pace. Large differences between predicted and actual pace suggests the performance template lacks accuracy, perhaps indicating greater reliance on momentary pacing decisions rather than pre-planned strategy.

The Influence of Performance Level, Age and Gender on Pacing Strategy During a 100-km Ultramarathon

The aim of this study is to analyse the influence of performance level, age and gender on pacing during a 100-km ultramarathon. Results of a 100-km race incorporating the World Masters Championships were used to identify differences in relative speeds in each 10-km segment between participants finishing in the first, second, third and fourth quartiles of overall positions (Groups 1, 2, 3 and 4, respectively). Similar analyses were performed between the top and bottom 50% of finishers in each age category, as well as within male and female categories. Pacing varied between athletes achieving different absolute performance levels. Group 1 ran at significantly lower relative speeds than all other groups in the first three 10-km segments (all P < 0.01), and significantly higher relative speeds than Group 4 in the 6th and 10th (both P < 0.01), and Group 2 in the 8th (P = 0.04). Group 4 displayed significantly higher relative speeds than Group 2 and 3 in the first three segments (all P < 0.01). Overall strategies remained consistent across age categories, although a similar phenomenon was observed within each category whereby ‘top’ competitors displayed lower relative speeds than ‘bottom’ competitors in the early stages, but higher relative speeds in the later stages. Females showed lower relative starting speeds and higher finishing speeds than males. ‘Top’ and ‘bottom’ finishing males displayed differing strategies, but this was not the case within females. Although pacing remained consistent across age categories, it differed with level of performance within each, possibly suggesting strategies are anchored on direct competitors. Strategy differs between genders and differs depending on performance level achieved in males but not females.

Complex Interplay Between Determinants of Pacing and Performance During 20 km Cycle Time Trials

Purpose This study examined the determinants of pacing strategy and performance during self paced maximal exercise. Methods Eight well trained cyclists completed two 20 km time trials. Power output, RPE, positive and negative affect, and iEMG activity of the active musculature were recorded every 0.5km, confidence in achieving pre-exercise goals was assessed every 5 km, and blood lactate and pH were measured post-exercise. Differences in all parameters were assessed between fastest (FAST) and slowest (SLOW) trials performed. Results Mean power output was significantly higher during the initial 90% of FAST, but not the final 10%, and blood lactate concentration was significantly higher and pH significantly lower following FAST. Mean iEMG activity was significantly higher throughout SLOW. RPE was similar throughout both trials, but participants had significantly more positive affect and less negative affect throughout FAST. Participants grew less confident in their ability to achieve their goals throughout SLOW. Conclusions The results suggest that affect may be the primary psychological regulator of pacing strategy and that higher levels of positivity and lower levels of negativity may have been associated with a more aggressive strategy during FAST. Although the exact mechanisms through which affect acts to influence performance are unclear, it may determine the degree of physiological disruption that can be tolerated, or be reflective of peripheral physiological status in relation to the still to be completed exercise task.