  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) index 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 demonstrates 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 to distinguish various team levels, with in each a
Feller-Pareto distribution, thereby pointing to self-organized processes. In so
doing, one hopefully better relates objective scientific measures to sport team
competitions, and, besides, even proposes some paths to elaborate on
forecasting through standard probability concepts.
