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

• Published in: Sports medicine - open Vol. 9; no. 1; pp. 94 - 12
• Main Authors: Hecksteden, Anne, Keller, Niklas, Zhang, Guangze, Meyer, Tim, Hauser, Thomas
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 14.10.2023; Springer Nature B.V; SpringerOpen
• Subjects: Bayesian updating; Crowd intelligence; Decision making; Evidence; Heuristic; Leading; Leading Article; Machine learning; Medicine; Medicine & Public Health; Sports Medicine; Bayesian updating; Decision making; Crowd intelligence; Machine learning; Evidence; Forecasting; Heuristic
• ISSN: 2198-9761; 2199-1170; 2198-9761
• DOI: 10.1186/s40798-023-00641-0

Background The 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 Body The “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 In other words, the focus of practitioners on experience and human judgement, complemented—but not superseded—by scientific evidence is probably street-smart after all.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 Conclusion This 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. Key Points Generally, decision making in sports practice is based on “evidence “ from scientific research or on “eminence” based on experience and subjective human judgment. Under the framework conditions of sport—specifically complexity and sparse data—both come with limitations that lead to considerable uncertainty when making decisions in practice. A broader range of strategies, smart strategy combinations and critical evaluation of performance under real-world conditions contribute to better decisions and better outcomes in the field of sport and exercise.