Integrating machine learning and statistical analysis to examine the effect of gamification on biosignals, performance, and motivation in soccer

Abstract

Motivation, a complex construct that influences behavior, plays a critical role in an athlete’s success and has been extensively researched in sports sciences. However, motivation is still primarily assessed through self-report
motivational questionnaires, resulting in a lack of objective, continuous measurements during athletic performance. Biosignals, increasingly used to assess psychological processes, have gained relevance due to their integration into wearable sensors, allowing non-stationary and unobtrusive data collection. Therefore, we assessed performance data, cardiovascular signals, and
eye-tracking metrics to investigate the psychological and physiological processes under two different conditions designed to induce different motivational states via gamification.

In this study, gamification elements, aligned with self-determination theory, were used to influence soccer players’ motivation in an immersive space, with N = 42 participants completing a passing drill in both Gamified and Non-Gamified scenarios. Features were extracted from session recordings and wearable sensors to assess whether performance or biosignals differed between conditions using a combination of machine learning and conventional statistical analysis. While self-report questionnaires and performance
metrics revealed no significant differences, the machine learning classifiers were able to distinguish between scenarios based on eye-tracking-related features. The best-performing model, a k-nearest neighbor classifier, reached a macro F1-score of 82.75 %. We identified by feature importance methods that blink behavior and pupil dynamics, indicative of visual attention, were the main contributors. This study contributes to a deeper understanding of the value of integrating multimodal data and advanced evaluation methods to uncover implicit processes in applied sports contexts involving complex and heterogeneous data.