Conference Agenda

Tennis matches are often characterised by momentum shifts – i.e., changes in match dynamics over time – marked by transitions between phases where either player 1 or player 2 dominates. While dominance is clearly reflected in a player’s point wins, rally lengths provide additional valuable information for modelling momentum; short rallies suggest strong momentum, whereas long rallies and point losses indicate pressure. To effectively model momentum shifts, we hence propose considering both the outcomes of the points and the rally lengths. These sequentially observed outcomes reflect the current dynamics of the match (i.e., the level of pressure a player exerts on their opponent), which we regard as an unobserved state process. Thus, we employ a latent-state approach to investigate these momentum shifts. Specifically, we model the outcomes of server wins and rally lengths jointly using Markov-modulated marked Poisson processes (MMMPPs). This flexible framework allows us to relate the events (server wins or loses the point) and the event times (rally length) to an underlying latent state process, modelled as a continuous-time Markov chain. Its states determine the distribution of the outcomes and can be interpreted as proxies for the players’ momentum. For data from all Grand Slam tournaments from 2016 to 2024, we identify momentum shifts within tennis matches using MMMPPs with two latent states, accounting for player- and match-specific effects such as player rankings and court surfaces.