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Renormalizing individual performance metrics for cultural heritage management of sports records

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  • Petersen, Alexander M.
  • Penner, Orion

Abstract

Individual performance metrics are commonly used to compare players from different eras. However, such cross-era comparison is often biased due to significant changes in success factors underlying player achievement rates (e.g. performance enhancing drugs and modern training regimens). Such historical comparison is more than fodder for casual discussion among sports fans, as it is also an issue of critical importance to the multi-billion dollar professional sport industry and the institutions (e.g. Hall of Fame) charged with preserving sports history and the legacy of outstanding players and achievements. To address this cultural heritage management issue, we report an objective statistical method for renormalizing career achievement metrics, one that is particularly tailored for common seasonal performance metrics, which are often aggregated into summary career metrics – despite the fact that many player careers span different eras. Remarkably, we find that the method applied to comprehensive Major League Baseball and National Basketball Association player data preserves the overall functional form of the distribution of career achievement, both at the season and career level. As such, subsequent re-ranking of the top-50 all-time records in MLB and the NBA using renormalized metrics indicates reordering at the local rank level, as opposed to bulk reordering by era. This local order refinement signals time-independent mechanisms underlying annual and career achievement in professional sports, meaning that appropriately renormalized achievement metrics can be used to compare players from eras with different season lengths, team strategies, rules – and possibly even different sports.

Suggested Citation

  • Petersen, Alexander M. & Penner, Orion, 2020. "Renormalizing individual performance metrics for cultural heritage management of sports records," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    • Handle: RePEc:eee:chsofr:v:136:y:2020:i:c:s0960077920302216
    DOI: 10.1016/j.chaos.2020.109821
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    References listed on IDEAS

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    1. Filippo Radicchi, 2011. "Who Is the Best Player Ever? A Complex Network Analysis of the History of Professional Tennis," PLOS ONE, Public Library of Science, vol. 6(2), pages 1-7, February.
    2. A. M. Petersen & O. Penner & H. E. Stanley, 2011. "Methods for detrending success metrics to account for inflationary and deflationary factors," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 79(1), pages 67-78, January.
    3. Mukherjee, Satyam, 2012. "Identifying the greatest team and captain—A complex network approach to cricket matches," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(23), pages 6066-6076.
    4. Lu Liu & Yang Wang & Roberta Sinatra & C. Lee Giles & Chaoming Song & Dashun Wang, 2018. "Hot streaks in artistic, cultural, and scientific careers," Nature, Nature, vol. 559(7714), pages 396-399, July.
    5. Filippo Radicchi, 2012. "Universality, Limits and Predictability of Gold-Medal Performances at the Olympic Games," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-8, July.
    6. Schaigorodsky, Ana L. & Perotti, Juan I. & Billoni, Orlando V., 2014. "Memory and long-range correlations in chess games," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 394(C), pages 304-311.
    7. Saavedra, Serguei & Powers, Scott & McCotter, Trent & Porter, Mason A. & Mucha, Peter J., 2010. "Mutually-antagonistic interactions in baseball networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(5), pages 1131-1141.
    8. Henry Greely & Barbara Sahakian & John Harris & Ronald C. Kessler & Michael Gazzaniga & Philip Campbell & Martha J. Farah, 2008. "Towards responsible use of cognitive-enhancing drugs by the healthy," Nature, Nature, vol. 456(7223), pages 702-705, December.
    9. Mukherjee, Satyam, 2014. "Quantifying individual performance in Cricket — A network analysis of batsmen and bowlers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 624-637.
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