On December 31st, 2016, the Houston Rockets hosted the New York Knicks for a regular season NBA game in Texas' Toyota Center. Although the Rockets started the game as favorites having a 26-9 record at that point in the season, New York (16-17) was able to put on a good performance during practically all of the first half of the game.
The amount of data available nowadays in the sports field is hard to comprehend using classic analytic methods. This calls for the development of systems such as the prototype discussed here, which makes it possible to manipulate chunks of data to then portray them in visual ways, easing their understanding. Based on basketball, this tool helps users in reaching conclusions regarding performances during individual matches. This enables them to gather knowledge about play sequences, the events occurring at different moments, and the style of play teams employ based on player chemistry.
Sports data are usually stored in databases and made avail- able as plain tables that, in the best of the cases, allow the users to sort them by a given column. Although this technique is the most used to show the present state of a competition or its final outcome, it does not provide all of the information an analyst may require. Hence, this work aims to provide a new method of visualizing full seasons of base- ball by means of a heatmap that holds every game of the season in it.