  Many popular sports involve matches between two teams or players where each
team have the possibility of scoring points throughout the match. While the
overall match winner and result is interesting, it conveys little information
about the underlying scoring trends throughout the match. Modeling approaches
that accommodate a finer granularity of the score difference throughout the
match is needed to evaluate in-game strategies, discuss scoring streaks, teams
strengths, and other aspects of the game.
  We propose a latent Gaussian process to model the score difference between
two teams and introduce the Trend Direction Index as an easily interpretable
probabilistic measure of the current trend in the match as well as a measure of
post-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 2019-2020
National Basketball Association (NBA) season. We show how the trends can be
interpreted in individual games and how the excitement score can be used to
cluster teams according to how exciting they are to watch.
