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Exploring the dynamics of sports records evolution through the gembris prediction model and network relevance analysis

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  • Lu Tang
  • Mingliang Yang

Abstract

Background: Sports records hold valuable insights into human physiological limits. However, presently, there is a lack of integration and evolutionary patterns in the recorded information across various sports. Methods: We selected sports records from 1992 to 2018, covering 24 events in men’s track, field, and swimming. The Gembris prediction model calculated performance randomness, and Pearson correlation analysis assessed network relevance between projects. Quantitative study of model parameters revealed the impact of various world records’ change range, predicted value, and network correlation on evolutionary patterns. Results: 1) The evolution range indicates that swimming events generally have a larger annual world record variation than track and field events; 2) Gembris’s predictions show that sprint, marathon, and swimming records outperform their predicted values annually; 3) Network relevance analysis reveals highly significant correlations between all swimming events and sprints, as well as significant correlations between marathon and all swimming events. Conclusion: Sports record evolution is closely linked not only to specific sports technology but also to energy expenditure. Strengthening basic physical training is recommended to enhance sports performance.

Suggested Citation

  • Lu Tang & Mingliang Yang, 2024. "Exploring the dynamics of sports records evolution through the gembris prediction model and network relevance analysis," PLOS ONE, Public Library of Science, vol. 19(9), pages 1-10, September.
    • Handle: RePEc:plo:pone00:0307796
    DOI: 10.1371/journal.pone.0307796
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    1. Daniel Gembris & John G. Taylor & Dieter Suter, 2007. "Evolution of Athletic Records: Statistical Effects versus Real Improvements," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(5), pages 529-545.
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