Prediction of elite athletes’ performance by analysis of peak-performance age and age-related performance progression

Abstract

The aim of this study was to analyse age-related performance progression and peak-performance age (PPA) in elite track and field athletes and to use a model to predict peak performance. Best performances of world-class athletes from ages 14 to 15 y up to and including the last Olympic year (n = 798), all-time top lists (n = 444), and world record-holders (n = 43) were considered in all 22 disciplines for men and 21 disciplines for women. A discipline/sex-specified model was used by applying dynamic panel data methods to analyze the performance trends. Profile analysis showed that PPA of all-time top list throwers was higher than middle-distance runners (P < 0.001), distance runners (P < 0.05), and jumpers (P < 0.05) in men and higher (P < 0.05) than middle-distance runners in women. Olympic year top list athletes showed that PPA of women throwers was higher than sprinters (P < 0.001) and middle-distance runners (P < 0.05), and PPA of women distance runners was higher (P < 0.05) than sprinters. In both all-time (P < 0.05) and Olympic year (P < 0.05) top lists, the PPA of men race walkers was higher than middle-distance runners. Performance over the preceding 1–2 years (in all disciplines), height (in Long Jump Men; Long Jump Women; Triple Jump Men) and weight (in Discus Throwing Men) indices, respectively, are important (P < 0.05) for predicting future records with different coefficients in different disciplines. The models provide a useful tool for coaches to predict peak performance records and PPA of their athletes which may be of benefit with goal-setting and evaluation of performance progression at different ages in track and field athletics.

GRAPHICAL ABSTRACT

Keywords:

• Conditioning
• goal-setting
• track & field
• sex differences
• prediction of records

Acknowledgements

We thank Farideh Ghasemi, Neda ahmadpour, Samaneh Khaleghi and Fatemeh Khatami (University of Zanjan) for helping us in providing data. AG and MK: research concept and study design, literature review, data collection, data analysis and interpretation; AG: writing of the manuscript; OK: statistical analyses, writing of the manuscript; RE: literature review and reviewing and editing a draft of the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).