From expert opinion to data driven selection of sports equipment: Boot selection in alpine ski racers.

To compete successfully in alpine ski racing, not only physical, mental, and skiing technique preparation are important, but also wisely chosen and properly tuned skiing equipment. While equipment related safety aspects are mostly ensured by official rules (e.g., competition rules provided by the International Skiing Federation), the performance optimization and customization of equipment is mostly reliant on coaches and experts in the skiing industry. Therefore, knowledge often is dependent on individual person's experience and not publicly available. This aspect can also hinder companies or teams to spread knowledge within their own team. However, there is the necessity to select the optimal equipment for each athlete individually within a range of available equipment variations. To improve the process of ski boot selection for racers within a whole company, the presented research had the goal to provide a decision model by extracting and objectifying knowledge from few experts within the company. Based on a Delphi-type expert elicitation approach, together with the company's boot experts a data collection sheet was designed, including the relevant parameter to decide which ski boot is appropriate for an individual athlete. Thereafter, data analysis of 198 datasets included classification methods based on random forest models to create boot choice recommendation models. Results revealed well predictable recommendation for boot size (accuracy 77%) and acceptable accuracy (57%) for prediction of boot model. With existing limitations of extracting subjective and individual expertise, this approach helps to objectify personalized expertise and distribute knowledge within relevant interest groups.