Money matters: The impact of prize money on doping behaviour

Highlights

•	Using agent-based simulation, doping behaviour can be quantified realistically.
•	The total amount of prize money has little impact on the doping rate.
•	Prize money function with consistently large gradient leads to highest doping rate.
•	Doping costs affect the prevalence of doping only marginally.
•	Allocating prizes more even, sport event organisers can reduce doping by up to 40%.

Abstract

In professional sports, the amounts disbursed in rank-based prize money distributions decline sharply, and differences in performance are extremely small. This disparity may provide a high incentive for doping. Due to the complexity of doping, obtaining meaningful insights on the influence of prize money distribution and the pecuniary value of prize money on doping behaviour of elite athletes using game theory or other approaches has not been possible. The authors perform a computerised social simulation through agent-based modelling to analyse doping behaviour in competitive sport. The results show that the distribution of prize money in particular has an enormous impact on the prevalence of doping. By contrast, the total amount of prize money is less decisive for doping behaviour. Further, doping costs are observed to have only a marginal effect on doping prevalence, depending on the tested prize money distribution and its amount. The simulation results can be used by sports federations and competition organisers who should distribute the prize money more evenly to all athletes to reduce doping.

Keywords:

• Agent-based modelling
• Doping
• Prize money distribution
• Competitive sport
• Social simulation

Acknowledgements

We have presented earlier drafts of the simulation model at the SMAANZ Conference 2015 in Hobart, 2016 in Auckland and 2018 in Adelaide and are grateful for the valuable feedback from the SMAANZ community. William Rand, László Gulyás and Klaus G. Troitzsch provided valuable advice on the technical side during our Münster workshops and beyond. We would also like to thank Andrea Petróczi, Bernd Strauss, Marcel Goelden and Olivier de Hon for the constructive discussions from the sport psychological perspective. Without these supporters, the simulation model could not have been realized to this extent. However, all shortcomings remain our own.

Notes

1 Bette, Schimank, Wahlig, and Weber (2002) describe doping as a rather rational action, through which athletes react to their current circumstances. Thus, in our model, athletes become more risk seeking over time, which is supposed to reflect the increasing opportunity costs in the course of an athlete’s career. Although young athletes have more opportunities to find employment beyond professional sports, older athletes often have to persist in the system. Ntoumanis et al. (2014) support this assumption by providing empirical evidence in their meta-analysis that the intention to dope increases with age.

2 In order to measure the effect of an alteration of the base calibration for CAR and CEF, a sensitivity analysis was conducted, where the values for both variables were increased by one percent to see the effect on the share of doped athletes. The results of this analysis are presented in chapter 4, Table 4.