Data-driven lowlight and highlight reel creation based on explainable temporal game models

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Evin Keane
Phillippe Desaulniers
Luke Bornn
Mehrsan Javan


Abstract: Consumption of sports is trending towards fragmented content on mobile devices. To quote a recent McKinsey & Co study, “We aren’t losing fans, we are fighting shorter attention spans.” In contrast to live television, highlights are visual and short: perfect content for social media. We describe a framework for automatic highlight reel extraction based on game event data. The framework can be applied to most team sports given the output of any in-game state valuation model, of which there are many. We adjust the valuation of events based on their impact on the game’s result, a highly relevant factor in terms of fan interest. We demonstrate the usefulness of our approach for extracting both highlights and lowlights, the latter of which has proven difficult with previous approaches. We apply a minimum entropy threshold to avoid monotony in highlight reels. We introduce Event Interest, treating the interest of an event as a continuous function across time rather than a discrete instant. We introduce Cumulative Event Interest, the output of which provides a simple means of extracting game highlights. We demonstrate that this output can be easily modified depending on the type of highlights required by adjusting a limited, intuitive number of parameters of the Cumulative Event Interest function. We provide examples of the output in the form of graphical game summaries and their corresponding highlight videos.