Breen Derek, Norris Michelle, Healy Robin, Anderson Ross
Int J Sports Physiol Perform. 2018 Mar 1;13(3):332-338. doi: 10.1123/ijspp.2016-0730. Epub 2018 Mar 9.
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.
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.
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%).
High-performing masters athletes use more-controlled pacing strategies than their lower-ranked counterparts during a competitive marathon, independent of age and gender.
配速策略是长跑项目整体成绩的关键。目前,尚无文献研究各跑步水平的成年运动员在竞争性马拉松比赛中所采用的配速策略。因此,本研究旨在按性别、年龄和成绩水平对成年运动员的配速策略进行研究。
数据取自2015年纽约市马拉松赛的31762名成年运动员(20019名男性和11743名女性)。通过将总完赛时间与当前世界纪录进行比较,确定了七个成绩分类(PC)组别,适用于不同年龄和性别的运动员。数据按年龄、性别和成绩水平进行分类。计算了最初40公里的平均5公里速度,并确定了最快和最慢的5公里分段速度,并表示为比平均速度快或慢的百分比。然后分析以最快和最慢分段百分比的绝对值之和计算的配速范围。
年龄、性别和成绩水平存在显著的主效应(P <.001),成绩水平是最具决定性的因素。PC1组的运动员配速范围最低(14.19% ± 6.66%),随着运动员成绩水平的降低,配速范围呈线性增加(PC2 - PC7,17.52% ± 9.14%至36.42% ± 18.32%)。发现性别×成绩存在显著的交互效应(P <.001),女性的配速范围较小(-3.81%)。
在竞争性马拉松比赛中,高水平成年运动员比低水平运动员采用更可控的配速策略,且不受年龄和性别的影响。