Modeling Performances and Competitive Balance in Road Cycling Competitions

Cabaud, Benjamin; Scelles, Nicolas; Avril, François; Morrow, Stephen

doi:10.1007/978-3-031-11258-4_11

Cited by 1 publication

(2 citation statements)

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“…The case of cycling races is somewhat unique. It is widely accepted that cycling races are won by individual riders, but the role of the team is of crucial importance [ 6 , 7 ]. Quoting Cabaud et al [ 7 ]:…”

Section: Introductionmentioning

confidence: 99%

“…However, such a measure does not appear to be currently available. Physiological performance is often linked to competitive balance as well [ 7 ]. For additional perspectives, readers may refer to economic-related papers, such as those focusing on the Tour de France [ 15 , 16 ] (During the writing of this paper, an interesting example of indirect imbalance occurred in the 2023 Giro when organizers provided helicopters to teams for transporting a few riders down from the Gran Sasso finish line after stage 7, but at a cost that only a few teams could afford [ 17 ]).…”

Section: Introductionmentioning

confidence: 99%

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Shannon Entropy and Herfindahl-Hirschman Index as Team’s Performance and Competitive Balance Indicators in Cyclist Multi-Stage Races

Ausloos

2023

Entropy

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It seems that one cannot find many papers relating entropy to sport competitions. Thus, in this paper, I use (i) the Shannon intrinsic entropy (S) as an indicator of “teams sporting value” (or “competition performance”) and (ii) the Herfindahl-Hirschman index (HHi) as a “teams competitive balance” indicator, in the case of (professional) cyclist multi-stage races. The 2022 Tour de France and 2023 Tour of Oman are used for numerical illustrations and discussion. The numerical values are obtained from classical and and new ranking indices which measure the teams “final time”, on one hand, and “final place”, on the other hand, based on the “best three” riders in each stage, but also the corresponding times and places throughout the race, for these finishing riders. The analysis data demonstrate that the constraint, “only the finishing riders count”, makes much sense for obtaining a more objective measure of “team value” and team performance”, at the end of a multi-stage race. A graphical analysis allows us to distinguish various team levels, each exhibiting a Feller-Pareto distribution, thereby indicating self-organized processes. In so doing, one hopefully better relates objective scientific measures to sport team competitions. Moreover, this analysis proposes some paths to elaborate on forecasting through standard probability concepts.

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Section: Introductionmentioning

confidence: 99%