Why Humble Farmers May in Fact Grow Bigger Potatoes: A Call for Street-Smart Decision-Making in Sport

Sports Medicine - Open

Leading Article

Anne Hecksteden¹  Thomas Hauser²  Guangze Zhang³  Tim Meyer³  Niklas Keller⁴ 

[1] Chair of Sports Medicine, Institute of Sport Science, Universität Innsbruck, Innsbruck, Austria;Institute of Physiology, Medical University Innsbruck, Innsbruck, Austria;German Football Association, Medicine and Science, Frankfurt, Germany;Faculty of Applied Sport Sciences & Personality, Business and Law School, Berlin, Germany;Institute of Sports and Preventive Medicine, Saarland University, Saarbrücken, Germany;Simply Rational, The Decision Institute, Berlin, Germany;Institute of Psychology and Ergonomics, Technical University Berlin, Berlin, Germany;Harding Centre for Risk Literacy, Faculty of Health Science, University of Potsdam, Potsdam, Germany;

关键词: Decision making;    Heuristic;    Crowd intelligence;    Machine learning;    Bayesian updating;    Evidence;    Forecasting;   

DOI  :  10.1186/s40798-023-00641-0

 received in 2023-02-15, accepted in 2023-09-26,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundThe main task of applied sport science is to inform decision-making in sports practice, that is, enabling practitioners to compare the expectable outcomes of different options (e.g. training programs).Main BodyThe “evidence” provided may range from group averages to multivariable prediction models. By contrast, many decisions are still largely based on the subjective, experience-based judgement of athletes and coaches. While for the research scientist this may seem “unscientific” and even “irrational”, it is important to realize the different perspectives: science values novelty, universal validity, methodological rigor, and contributions towards long-term advancement. Practitioners are judged by the performance outcomes of contemporary, specific athletes. This makes out-of-sample predictive accuracy and robustness decisive requirements for useful decision support. At this point, researchers must concede that under the framework conditions of sport (small samples, multifactorial outcomes etc.) near certainty is unattainable, even with cutting-edge methods that might theoretically enable near-perfect accuracy. Rather, the sport ecosystem favors simpler rules, learning by experience, human judgement, and integration across different sources of knowledge. In other words, the focus of practitioners on experience and human judgement, complemented—but not superseded—by scientific evidence is probably street-smart after all. A major downside of this human-driven approach is the lack of science-grade evaluation and transparency. However, methods are available to merge the assets of data- and human-driven strategies and mitigate biases.Short ConclusionThis work presents the challenges of learning, forecasting and decision-making in sport as well as specific opportunities for turning the prevailing “evidence vs. eminence” contrast into a synergy.

【 授权许可】

CC BY   
© Springer Nature Switzerland AG 2023

【 图 表 】

Fig. 3

12951_2015_155_Article_IEq34.gif

12951_2015_155_Article_IEq32.gif