Talent ID in 100M Sprinting: How the World’s Best Sprinters Solve the Problem Based on Mathematical Models and Multivariate Statistics

Tim Heazlewood

Research output: Chapter in Book/Report/Conference proceedingConference Paper published in Proceedingspeer-review

Abstract

Talent identification is now common practice in Australian sport to identify future Olympians and Paralympians. Mathematical models and multivariate statistical approaches to predicting performance in sport are frequently recommended, however rarely implemented, to identify those factor that are associated withhigh performance athletes. Australia had difficulty finding talent to complete in the 100m at the recent 2008 Beijing Olympics and at the 2009 World Athletics Championships in Berlin. This research is focussed on how the best 100m sprinters, both male and female, actually run the race and to derive models for high performancesprinting. The research analysed data (IAAF, 2009) from the recent 2009 World Athletics Championships in Berlin. The data set consisted of 23 male performances from the 100m final and semi-finals and 36 female performances from the 100m final and the fastest athletes in the 100m sprint rounds. The data analysedconsisted of reaction time to the gun, race segment time for 20m, 40m, 60m, 80m and 100m and conversion of race segments times to average velocity per 20m. Comparisons were conducted for male and female sprinters in terms of multivariate factor analysis of times per segment to assess if the race segments represented distinct race constructs or were representing the underlying factor of sprint ability, nonlinear regression (curve estimation) of average velocity with distance, and linear regression analysis in terms of predicting 100m time from race segments times. Individual (male=.844-.994, 88.9% common variance; female=.906-.993, 92.1% common variance) and pooled (0.985-0.999, 98.7% common variance) factor loadings indicated that sprintingability is common construct across all race segments. The best fit nonlinear regression mathematical functions for the relationship of average velocity per distance segment were cubic functions for both males (R=.987,R2=.975) and females (R=.989, R2=.977), where the transition from positive to negative acceleration occurring at 58-59m for both genders. The regression analysis using pooled data indicated that 60m time was an excellent predictor of 100m time (R=0.994, R2=0.982, p<0.001) indicating both males and females solve the problem of sprinting the 100m in almost an identical manner in terms of mathematical and statistical models.
Original languageEnglish
Title of host publicationProceedings of the tenth Australasian Conference on Mathematics and Computers in Sport
EditorsAnthony Bedford, Matthew Ovens
PublisherMathSport (ANZIAM)
Pages189-196
Number of pages8
ISBN (Print)9780957862357
Publication statusPublished - Jul 2010
EventAustralasian Conference on Mathematics and Computers in Sport - Darwin, Australia
Duration: 5 Jul 20107 Jul 2010
Conference number: 10
https://www.anziam.org.au/Mathsport

Conference

ConferenceAustralasian Conference on Mathematics and Computers in Sport
Abbreviated title10MCS
CountryAustralia
CityDarwin
Period5/07/107/07/10
Internet address

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