PURPOSE: Pacing strategies are key to overall performance outcome in distance-running events. Presently, no literature has examined pacing strategies used by masters athletes of all running levels during a competitive marathon. Therefore, this study aimed to examine masters athletes' pacing strategies, categorized by gender, age, and performance level. METHODS: Data were retrieved from the 2015 TSC New York City Marathon for 31,762 masters athletes (20,019 men and 11,743 women). Seven performance-classification (PC) groupings were identified via comparison of overall completion time compared with current world records, appropriate to age and gender. Data were categorized via, age, gender, and performance level. Mean 5-km speed for the initial 40 km was calculated, and the fastest and slowest 5-km-split speeds were identified and expressed as a percentage faster or slower than mean speed. Pace range, calculated as the absolute sum of the fastest and slowest split percentages, was then analyzed. RESULTS: Significant main effects were identified for age, gender, and performance level (P < .001), with performance level the most determining factor. Athletes in PC1 displayed the lowest pace range (14.19% ± 6.66%), and as the performance levels of athletes decreased, pace range increased linearly (PC2-PC7, 17.52% ± 9.14% to 36.42% ± 18.32%). A significant interaction effect was found for gender × performance (P < .001), with women showing a smaller pace range (-3.81%). CONCLUSIONS: High-performing masters athletes use more-controlled pacing strategies than their lower-ranked counterparts during a competitive marathon, independent of age and gender.
PURPOSE: Pacing strategies are key to overall performance outcome in distance-running events. Presently, no literature has examined pacing strategies used by masters athletes of all running levels during a competitive marathon. Therefore, this study aimed to examine masters athletes' pacing strategies, categorized by gender, age, and performance level. METHODS: Data were retrieved from the 2015 TSC New York City Marathon for 31,762 masters athletes (20,019 men and 11,743 women). Seven performance-classification (PC) groupings were identified via comparison of overall completion time compared with current world records, appropriate to age and gender. Data were categorized via, age, gender, and performance level. Mean 5-km speed for the initial 40 km was calculated, and the fastest and slowest 5-km-split speeds were identified and expressed as a percentage faster or slower than mean speed. Pace range, calculated as the absolute sum of the fastest and slowest split percentages, was then analyzed. RESULTS: Significant main effects were identified for age, gender, and performance level (P < .001), with performance level the most determining factor. Athletes in PC1 displayed the lowest pace range (14.19% ± 6.66%), and as the performance levels of athletes decreased, pace range increased linearly (PC2-PC7, 17.52% ± 9.14% to 36.42% ± 18.32%). A significant interaction effect was found for gender × performance (P < .001), with women showing a smaller pace range (-3.81%). CONCLUSIONS: High-performing masters athletes use more-controlled pacing strategies than their lower-ranked counterparts during a competitive marathon, independent of age and gender.
Authors: Katja Weiss; David Valero; Elias Villiger; Volker Scheer; Mabliny Thuany; Ivan Cuk; Thomas Rosemann; Beat Knechtle Journal: Front Physiol Date: 2022-06-14 Impact factor: 4.755
Authors: Pantelis Theodoros Nikolaidis; Ivan Cuk; Thomas Rosemann; Beat Knechtle Journal: Int J Environ Res Public Health Date: 2019-05-20 Impact factor: 3.390
Authors: Daniel Suter; Caio Victor Sousa; Lee Hill; Volker Scheer; Pantelis Theo Nikolaidis; Beat Knechtle Journal: Int J Environ Res Public Health Date: 2020-09-27 Impact factor: 3.390