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In team-based invasion sports such as soccer and basketball, analytics isimportant for teams to understand their performance and for audiences tounderstand matches better. The present work focuses on performing visualanalytics to evaluate the value of any kind of event occurring in a sportsmatch with a continuous parameter space. Here, the continuous parameter spaceinvolves the time, location, score, and other parameters. Because thespatiotemporal data used in such analytics is a low-level representation andhas a very large size, however, traditional analytics may need to discretizethe continuous parameter space (e.g., subdivide the playing area) or use alocal feature to limit the analysis to specific events (e.g., only shots).These approaches make evaluation impossible for any kind of event with acontinuous parameter space. To solve this problem, we consider a whole match asa Markov chain of significant events, so that event values can be estimatedwith a continuous parameter space by solving the Markov chain with a machinelearning model. The significant events are first extracted by considering thetime-varying distribution of players to represent the whole match. Then, theextracted events are redefined as different states with the continuousparameter space and built as a Markov chain so that a Markov reward process canbe applied. Finally, the Markov reward process is solved by a customizedfitted-value iteration algorithm so that the event values with the continuousparameter space can be predicted by a regression model. As a result, the eventvalues can be visually inspected over the whole playing field under arbitrarygiven conditions. Experimental results with real soccer data show theeffectiveness of the proposed system.

Visual analytics for team-based invasion sports with significant events and Markov reward process