New inequality indicators for team ranking in multi-stage female professional cyclist races

Cycling competition is highly interesting since the team ranking is based on the best performance of some subset of team members. The paper develops new inequality indicators, a methodology to construct them, and numerical illustrations allowing to provide operative arguments in their favor. The numerical illustrations subsequently deal with hierarchical ranking indicators of (female) cyclist teams, competing in multi-stage races. For the illustration, the 2023 editions of the most famous long races for females are considered: 34th Giro d’Italia Donne, 2nd Tour de France Femmes, 9th Vuelta Femenina. Several classical ranking indicators are recalled and adapted to the study cases. The most usual indicator, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:msub> <mml:mi>T</mml:mi> <mml:mi>L</mml:mi> </mml:msub> </mml:math> , is based on the riders arriving time for the various stages, i.e., according to Union Cycliste Internationale (UCI) standard rules. One also uses another indicator, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:msub> <mml:mi>A</mml:mi> <mml:mi>L</mml:mi> </mml:msub> </mml:math> , which requires that the riders finish the race, whence each stage, in order to define the race best team. Another contribution of the paper derives from specific developments of these indicators, thereby leading to new measures: the “leadership gap” based on <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:msub> <mml:mi>A</mml:mi> <mml:mi>L</mml:mi> </mml:msub> <mml:mo>−</mml:mo> <mml:msub> <mml:mi>T</mml:mi> <mml:mi>L</mml:mi> </mml:msub> </mml:math> , and the “competition temperature”, based on entropy. It is argued that the numerical values point to differences in team strategy based on rider skill levels. The ranking of contributions to indicators allow to observe the “crucial core” made of the most competitive teams.

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Publication Details

Journal
International Journal of Sports Science & Coaching
Published
2025-08-12
DOI
https://doi.org/10.1177/17479541251365797
Primary Topic
Sports Analytics and Performance
Type
article
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article

New inequality indicators for team ranking in multi-stage female professional cyclist races

Marcel Ausloos
International Journal of Sports Science & Coaching
Sports Analytics and Performance
article

New inequality indicators for team ranking in multi-stage female professional cyclist races

Marcel Ausloos
article en

Abstract

Cycling competition is highly interesting since the team ranking is based on the best performance of some subset of team members. The paper develops new inequality indicators, a methodology to construct them, and numerical illustrations allowing to provide operative arguments in their favor. The numerical illustrations subsequently deal with hierarchical ranking indicators of (female) cyclist teams, competing in multi-stage races. For the illustration, the 2023 editions of the most famous long races for females are considered: 34th Giro d’Italia Donne, 2nd Tour de France Femmes, 9th Vuelta Femenina. Several classical ranking indicators are recalled and adapted to the study cases. The most usual indicator, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:msub> <mml:mi>T</mml:mi> <mml:mi>L</mml:mi> </mml:msub> </mml:math> , is based on the riders arriving time for the various stages, i.e., according to Union Cycliste Internationale (UCI) standard rules. One also uses another indicator, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:msub> <mml:mi>A</mml:mi> <mml:mi>L</mml:mi> </mml:msub> </mml:math> , which requires that the riders finish the race, whence each stage, in order to define the race best team. Another contribution of the paper derives from specific developments of these indicators, thereby leading to new measures: the “leadership gap” based on <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:msub> <mml:mi>A</mml:mi> <mml:mi>L</mml:mi> </mml:msub> <mml:mo>−</mml:mo> <mml:msub> <mml:mi>T</mml:mi> <mml:mi>L</mml:mi> </mml:msub> </mml:math> , and the “competition temperature”, based on entropy. It is argued that the numerical values point to differences in team strategy based on rider skill levels. The ranking of contributions to indicators allow to observe the “crucial core” made of the most competitive teams.

International Journal of Sports Science & CoachingVol. 21(1)
University of Leicester (GB), Babeș-Bolyai University (RO), Bucharest University of Economic Studies (RO)
Openalex Percentile: Top 72%
Sports Analytics and Performance
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