Tracking Career Performance of Successful Triathletes

• Published in: Medicine and science in sports and exercise 2014-06, Vol.46 (6), p.1227-1234
• Main Authors: MALCATA, RITA M, HOPKINS, WILL G, PEARSON, SIMON N
• Format: Article
• Language: English
• Subjects: Adult; Age Factors; Athletic Performance - physiology; Bicycling - physiology; Biological and medical sciences; Competitive Behavior - physiology; Environment; Female; Fundamental and applied biological sciences. Psychology; Human physiology applied to population studies and life conditions. Human ecophysiology; Humans; Linear Models; Male; Medical sciences; Reference Values; Running - physiology; Space life sciences; Swimming - physiology; Vertebrates: body movement. Posture. Locomotion. Flight. Swimming. Physical exercise. Rest. Sports
• ISSN: 0195-9131; 1530-0315
• DOI: 10.1249/MSS.0000000000000221

PURPOSETracking athletes’ performances over time is important but problematic for sports with large environmental effects. Here we have developed career performance trajectories for elite triathletes, investigating changes in swim, cycle, run stages, and total performance times while accounting for environmental and other external factors. METHODSPerformance times of 337 female and 427 male triathletes competing in 419 international races between 2000 and 2012 were obtained from triathlon.org. Athletes were categorized according to any top 16 placing at World Championships or Olympics between 2008 and 2012. A mixed linear model accounting for race distance (sprint and Olympic), level of competition, calendar-year trend, athlete’s category, and clustering of times within athletes and races was used to derive athletes’ individual quadratic performance trajectories. These trajectories provided estimates of age of peak performance and predictions for the 2012 London Olympic Games. RESULTSBy markedly reducing the scatter of individual race times, the model produced well-fitting trajectories suitable for comparison of triathletes. Trajectories for top 16 triathletes showed different patterns for race stages and differed more among women than among men, but ages of peak total performance were similar for men and women (28 ± 3 yr, mean ± SD). Correlations between observed and predicted placings at Olympics were slightly higher than those provided by placings in races before the Olympics. CONCLUSIONSAthletes’ trajectories will help identify talented athletes and their weakest and strongest stages. The wider range of trajectories among women should be taken into account when setting talent identification criteria. Trajectories offer a small advantage over usual race placings for predicting men’s performance. Further refinements, such as accounting for individual responses to race conditions, may improve utility of performance trajectories.