By John Considine
It is not unusual to hear those opposed to statistical decision making in sport arguing that there is a greater need for situational analysis. One of the more influential minds of the twentieth century, Karl Popper, made a similar suggestion about the benefits of situational analysis for social sciences like economics. Popper later reviewed his position and admitted a greater role for statistics in economics. There is an interesting paper in the latest issue of the European Economic Review that combines the two approaches in the context of professional road cycling.
Julian Mattes and David Piazola outline a range of scenarios with useful illustrations and associated payoff matrices. An example is presented below. The energy levels of each cyclist is printed above them. There is a 2-unit energy gap to the leader from the black team. It will cost 2 units to attack (A) and close the gap. It will cost 1-unit to mark (M), or slipstream, a cyclist making an attack. In situations 1.a and 1.c the cyclists will have an equal likelihood of victory. Situation 1.b is the well known free rider problem applied to public goods. The equilibrium for the pink and blue riders is [0,0].
Julian Mattes and David Piazola outline a range of scenarios with useful illustrations and associated payoff matrices. An example is presented below. The energy levels of each cyclist is printed above them. There is a 2-unit energy gap to the leader from the black team. It will cost 2 units to attack (A) and close the gap. It will cost 1-unit to mark (M), or slipstream, a cyclist making an attack. In situations 1.a and 1.c the cyclists will have an equal likelihood of victory. Situation 1.b is the well known free rider problem applied to public goods. The equilibrium for the pink and blue riders is [0,0].
These scenarios are used to examine if cyclists compete or cooperate. The combination of the two strategies is known as coopetition.
Mattes and Piazola make a brave effort to test their predicted outcomes with the data. Using data on the time gaps at the finish line, they infer what must have happened during the race. This is not ideal but it is the best that can be achieved with the data. The data set captures 40 years of the three Grand Tours and other races.
An alternative, or complementary, approach is to gather data on what actually happened during each stage of a race. This issue has been examined here previously. Jane O'Sullivan examined coopetition in the 2021 Giro D'Italia.
The specifics of what happens in a given situation can make it difficult to conduct statistical analysis. Consider the situation involving three cyclist in Stage 15 of 2023 La Vuelta. The changing dynamics of the last 10km of the stage are sketched here in a YouTube video. This three person contest is very different to other three person contests. Why? Because it involves Rui Costa. Those who follow proffessional road cycling know that Rui Costa is probably the best illustration of a "free-rider". His approach is descripted by a fellow rider as "looking at other riders to do the work and saving as much energy as possible". This description is included in a brilliant book by Peter Cossins called Full Gas! How the race was won: Tactics from inside the peloton. The book is a superb account of various situational situations in cycling. It would be worth reading it as a companion piece to the more statistical European Economic Review article by Mattes and Piazola.
Mattes and Piazola make a brave effort to test their predicted outcomes with the data. Using data on the time gaps at the finish line, they infer what must have happened during the race. This is not ideal but it is the best that can be achieved with the data. The data set captures 40 years of the three Grand Tours and other races.
An alternative, or complementary, approach is to gather data on what actually happened during each stage of a race. This issue has been examined here previously. Jane O'Sullivan examined coopetition in the 2021 Giro D'Italia.
The specifics of what happens in a given situation can make it difficult to conduct statistical analysis. Consider the situation involving three cyclist in Stage 15 of 2023 La Vuelta. The changing dynamics of the last 10km of the stage are sketched here in a YouTube video. This three person contest is very different to other three person contests. Why? Because it involves Rui Costa. Those who follow proffessional road cycling know that Rui Costa is probably the best illustration of a "free-rider". His approach is descripted by a fellow rider as "looking at other riders to do the work and saving as much energy as possible". This description is included in a brilliant book by Peter Cossins called Full Gas! How the race was won: Tactics from inside the peloton. The book is a superb account of various situational situations in cycling. It would be worth reading it as a companion piece to the more statistical European Economic Review article by Mattes and Piazola.
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