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Many popular sports involve matches between two teams or players where eachteam have the possibility of scoring points throughout the match. While theoverall match winner and result is interesting, it conveys little informationabout the underlying scoring trends throughout the match. Modeling approachesthat accommodate a finer granularity of the score difference throughout thematch is needed to evaluate in-game strategies, discuss scoring streaks, teamsstrengths, and other aspects of the game.  We propose a latent Gaussian process to model the score difference betweentwo teams and introduce the Trend Direction Index as an easily interpretableprobabilistic measure of the current trend in the match as well as a measure ofpost-game trend evaluation. In addition we propose the Excitement Trend Index -the expected number of monotonicity changes in the running score difference -as a measure of overall game excitement.  Our proposed methodology is applied to all 1143 matches from the 2019-2020National Basketball Association (NBA) season. We show how the trends can beinterpreted in individual games and how the excitement score can be used tocluster teams according to how exciting they are to watch.

Having a Ball: evaluating scoring streaks and game excitement using in-match trend estimation