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The amount of data and consequently machine learning (ML) approaches are increasing at a fast pace in sports sciences, opening many new possibilities but on the other hand, also challenges. Generally limited data together with attractiveness and accessibility of ML methods without proper knowledge lead to faulty models and results with improper interpretations. Therefore, it is critical that researchers are aware of the risks related to the use of ML and that there are clear standards and robust procedures for how to perform and report ML studies. Answering the urgent need, the first aim of this thesis is to provide guidelines on how to properly perform and report (predictive) ML studies in the field of sports science. The second aim is to assess whether predictive modeling methods can be used for producing more individual information, compared to traditional statistics, namely in sports injury prediction and talent identification.

This article-style dissertation consists of four published articles. Articles I, II, and III utilize predictive modeling methods for sports injury prediction or talent identification and especially highlight the proper use of methods and data. Article IV utilizes unsupervised machine learning to discover kinematic running patterns among healthy and injured runners.

As main results of this thesis, the predictive power of multiple contemporary sports science datasets and ML approaches is assessed, and their potential for individual response discussed. Moreover, guidelines for utilizing predictive modeling are described and a framework for robust and generalizable results is introduced. Results from Article IV further confirm the need for individual approaches and provide useful information for future prediction studies. Through the included articles, advances are achieved for ACL injury prediction, recognizing predictive knee and ankle injury risk factors, utilizing ML for talent identification in s occer as well as discovering novel and useful information and patterns from running injury data. Important information about potentially best data types and variables for sports injury prediction and talent identification is produced. The approaches developed and used in this research can be utilized similarly in many other tasks and domains as well.

M.Sc. Susanne Jauhiainen defends her doctoral dissertation in Computational Science "Potential of Predictive Modeling Methods for Individual Response: Applications and Prediction Protocols for Sports Sciences" on 18 August 2023 at 12 noon. Opponents are Professor Olavi Airaksinen (University of Eastern Finland) and Docent Tuomo Kauranne (Arbonaut Ltd.). Custos is Docent Sami Äyrämö (Ä¢¹½Ö±²¥). The doctoral dissertation is held in Finnish.

The audience can follow the dissertation in lecture hall Agora Auditorium 3, or online.

Link to the online event: