Context/Background: International-level swimmers periodize their training to qualify for major championships, then improve further at these events. However, the effects of various factors that could affect performance progressions have not been described systematically. Purpose: To quantify the pattern of change in performance between season best qualifying time and the major championships of the year and to assess the influence of time between performance peaks, ranking at the major events, stroke, event distance, sex, age, and country. Methods: A total of 7832 official competition times recorded at 4 FINA World Championships and 2 Olympic Games between 2011 and 2017 were compared with each swimmer’s season best time prior to the major event of the year. Percentage change in performance was related with the time elapsed between season best and major competition, race event, sex, age, and country using linear mixed modeling. Results: Faster performance (−0.79% [0.67%]; mean [SD]) at the major competition of the year occurred in 38% of all observations vs 62% no change or regression (1.10% [0.88%]). The timing between performance peaks (<34 to >130 d) had little effect on performance progressions (P = .83). Only medal winners (−0.87% [0.91%]), finalists (−0.16% [0.97%]), and US swimmers (−0.44% [1.08%]) progressed between competitions. Stroke, event distance, sex, and age had trivial impact on performance progression. Conclusions: Performance progressions at Olympic Games and World Championships were not determined by timing between performance peaks. Performance progression at a major competition appears necessary to win a medal or make the final, independent of race event, sex, and age.
Mujika is with the Dept of Physiology, Faculty of Medicine and Nursing, University of the Basque Country, Leioa, Basque Country, and the Exercise Science Laboratory, School of Kinesiology, Faculty of Medicine, Universidad Finis Terrae, Santiago, Chile. Villanueva is with Real Federación Española de Natación, Madrid, Spain. Welvaert and Pyne are with the Research Inst for Sport and Exercise, University of Canberra, Canberra, ACT, Australia. Welvaert is also with Applied Technology and Innovation, Australian Inst of Sport, Canberra, ACT, Australia.
IssurinVB. New horizons for the methodology and physiology of training periodization. Sports Med. 2010;40:189–206. PubMed ID: 20199119 doi:10.2165/11319770-000000000-0000010.2165/11319770-000000000-0000020199119)| false
MujikaIHalsonSBurkeLMBalaguéGFarrowD. An integrated, multifactorial approach to periodization for optimal performance in individual and team sports. Int J Sports Physiol Perform. 2018;13:538–561. PubMed ID: 29848161 doi:10.1123/ijspp.2018-0093
MujikaI, HalsonS, BurkeLM, BalaguéG, FarrowD. An integrated, multifactorial approach to periodization for optimal performance in individual and team sports. . 2018;13:538–561. PubMed ID: 29848161 doi:10.1123/ijspp.2018-00932984816110.1123/ijspp.2018-0093)| false
MujikaIPadillaSPyneD. Swimming performance changes during the final 3 weeks of training leading to the Sydney 2000 Olympic Games. Int J Sports Med. 2002;23:582–587. PubMed ID: 12439774 doi:10.1055/s-2002-35526
MujikaI, PadillaS, PyneD. Swimming performance changes during the final 3 weeks of training leading to the Sydney 2000 Olympic Games. . 2002;23:582–587. PubMed ID: 12439774 doi:10.1055/s-2002-355261243977410.1055/s-2002-35526)| false
HellardPScordiaCAvalosMMujikaIPyneDB. Modelling of optimal training load patterns during the 11 weeks preceding major competition in elite swimmers. Appl Physiol Nutr Metab. 2017;42:1106–1117. PubMed ID: 28651061 doi:10.1139/apnm-2017-0180
HellardP, ScordiaC, AvalosM, MujikaI, PyneDB. Modelling of optimal training load patterns during the 11 weeks preceding major competition in elite swimmers. . 2017;42:1106–1117. PubMed ID: 28651061 doi:10.1139/apnm-2017-018010.1139/apnm-2017-018028651061)| false
StewartAM, HopkinsWG. Consistency of swimming performance within and between competitions. . 2000;32:997–1001. PubMed ID: 10795792 doi:10.1097/00005768-200005000-000181079579210.1097/00005768-200005000-00018)| false