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I appreciate your comment, but I don’t agree with your statement. This “study” represents realistic data-set that a single club can collect (and I followed with how that can be improved by collecting on the league level) and thus have “ecologic” validity (plus I always restrained of making generalizable claims by using “given this data and model”). Publishing only studies that have a positive outcome is what is central problem of publishing bias, so your recommendation is not only ignorant but also harmful. Besides, this study is completely transparent with both the code and data available to help other professional providing a potential way to analyze this type of data set (hopefully larger). The aim of this post was to openly and transparently share the methodology and reproducible code, rather than ‘conclude’ anything or make gross generalizations. In injury prediction domain there are a bunch of studies that didn’t evaluate the predictive performance but made predictive and bold claims (later falsified). Thus I see this as a transparent methodology sharing and promotion of “pluralism” of models as something positive in our industry.
Thank you for being part of the problem we are dealing in transparent sports science, particularly within the predictive domain. I really welcome reading your study that collected data over 10 years span with 20 clubs, without shared data and code that cannot be reproduced, but that found something (p<0.05).
