Going Inside the Inner Game: Predicting the Emotions of Professional Tennis Players from Match Broadcasts

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Abstract: The mental side of the game has been one of the most elusive aspects of performance analysis in tennis. We present a framework for predicting seven emotional states relevant to sport (‘anxiety’, ‘anger’, ‘annoyance’, ‘dejection’, ‘elation’, ‘focus’, and ‘fired up’) from the observed facial expressions of players in match broadcasts. Our methodology applies pre-trained models to extract two feature sets: predicted emotions in the Facial Action Coding System and 17 facial action units. Multiple prediction approaches were trained and tested using these features and a labeled dataset of 1,700 facial images of professional male and female tennis players extracted from 505 match videos. We applied the prediction models to establish emotional profiles for the ‘Big 4’ (Roger Federer, Rafael Nadal, Andy Murray, and Novak Djokovic) at the 2017 Australian Open. Rafael Nadal exhibited the most ‘anxiety’ of the four players (32%, 95% CI 29 to 35%), while Roger Federer was the only player whose predominant state was ‘neutral’ (24%, 95% CI 21 to 27%). When the predicted emotions were associated with point outcomes, we found that all of the Big 4 except for Roger Federer showed significant emotional reactions to the outcomes of points. Further, several emotional sates of Rafael Nadal and Novak Djokovic were significantly predictive of their chances of winning the next point. Our framework for extracting emotional data from single-camera video in professional tennis shows the feasibility of bringing the quantitative study of the inner game into sports performance analysis.

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