In the realms of professional and semi-professional sports across various major leagues, the role of a scouter, scout, or talent scout is pivotal. This individual attends games to analyze and collect comprehensive data about the events unfolding during the match. Traditionally, scouters rely on scouting software where they input match data, which then generates extensive statistics, conclusions, and recommendations. These insights are subsequently utilized by sports clubs for purposes such as player recruitment and devising strategies against specific rivals. This research introduces a novel line of inquiry, harnessing AI/ML (Artificial Intelligence/Machine Learning) techniques, predominantly convolutional neural networks, to automate the collection of data from physical sports events. This includes tracking the movement of the ball, the positioning of players, the arm used for hitting the ball, and more. The objective is to advance current sports scouting systems, which predominantly depend on manual data collection, towards a more automated, accurate, and efficient methodology. Utilizing nine tennis match videos from YouTube covering three different court surfaces (hard court, grass court, and clay court), this study has achieved high effectiveness in detecting players (99.3%), their body joints (100%), the tennis ball (94.13%), the segments of the tennis court ( ∼ 100%), and identifying the type of stroke (forehand or backhand) (100%). Moreover, it successfully extracts detailed statistics, such as the number of points played, stroke effectiveness, and efficiency, all categorized by specific court areas, culminating in the creation of a heat map. This advancement not only streamlines the scouting process but also offers richer, data-driven insights into player performance and game dynamics.
Moreno et al. (Wed,) studied this question.
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