Purpose: To analyze 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 groups 1 to 4, and speeds maintained in the race were 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 35 km. Significant differences (P < .01) in the percentage of PB speed maintained were observed between groups 1 and 4 and groups 2 and 4 in all segments after 20 km and groups 3 and 4 from 20 to 25 km and 30 to 35 km.Conclusions: Group 1 athletes achieved better 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.

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, rating of perceived exertion (RPE), positive and negative affect, and iEMG activity of the active musculature were recorded every 0.5 km, confidence in achieving preexercise goals was assessed every 5 km, and blood lactate and pH were measured postexercise. 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. Rating of perceived exertion 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.

Purpose: Previous literature has presented pacing data of groups of competition finalists. The aim of this study was to analyze the pacing patterns displayed by medalists and nonmedalists in international competitive 400-m swimming and 1500-m running finals.Methods: Split times were collected from 48 swimming finalists (four 100-m laps) and 60 running finalists (4 laps) in international competitions from 2004 to 2012. Using a cross-sectional design, lap speeds were normalized to whole-race speed and compared to identify variations of pace between groups of medalists and nonmedalists. Lap-speed variations relative to the gold medalist were compared for the whole field.Results: In 400-m swimming the medalist group demonstrated greater variation in speed than the nonmedalist group, being relatively faster in the final lap (P < .001; moderate effect) and slower in laps 1 (P = .03; moderate effect) and 2 (P > .001; moderate effect). There were also greater variations of pace in the 1500-m running medalist group than in the nonmedalist group, with a relatively faster final lap (P = .03; moderate effect) and slower second lap (P = .01; small effect). Swimming gold medalists were relatively faster than all other finalists in lap 4 (P = .04), and running gold medalists were relatively faster than the 5th- to 12th-placed athletes in the final lap (P = .02).Conclusions: Athletes who win medals in 1500-m running and 400-m swimming competitions show different pacing patterns than nonmedalists. End-spurtspeed increases are greater with medalists, who demonstrate a slower relative speed in the early part of races but a faster speed during the final part of races than nonmedalists.

N 20 novice cyclists (experiment 1) and 32 experienced ultramarathon 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 ultramarathon. Split times and perceived exertion were recorded at seven checkpoints. 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 kilometer was faster among lower risk perceivers compared with higher risk perceivers (t(18) = 2.0, P = 0.03) and faster among higher risk takers compared with lower risk takers (t(18) = 2.2, P = 0.02). Actual pace was slower than predicted pace during the first kilometer 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 risk perceivers compared with 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 suggest that the performance template lacks accuracy, perhaps indicating greater reliance on momentary pacing decisions rather than preplanned strategy....

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.