The results show that our approach makes the complex analytical processes needed for the identification of tactical behavior directly accessible to domain experts for the first time, demonstrating our support of coaches in preparation for future encounters. We evaluated our approach with several experts from European first league soccer clubs. ![]() Appropriate simplified result visualization supports in-depth analyses to explore team behavior, such as formation detection, movement analysis, and what-if analysis. Despite such seemingly imprecise query input, our approach is highly usable, supports quick user exploration, and retrieval of relevant results via query relaxation. Users place and move digital magnets on a virtual tactic-board, and these interactions get translated to spatio-temporal queries, used to retrieve relevant situations from massive team movement data. Our approach is walk-up usable by all domain stakeholders, and at the same time, can leverage advanced data retrieval and analysis techniques: a virtual magnetic tactic-board. We bridge the gap between these approaches by contributing a light-weight, simplified interaction and visualization system, which we conceptualized in an iterative design study with the coaching team of a European first league soccer team. Existing approaches in this domain typically rely on manual video analysis and formation discussion using whiteboards or expert systems that rely on state-of-the-art video and trajectory visualization techniques and advanced user interaction. Which allows real-time pass assessment during games on commodity hardware asĬoaches and analysts prepare for upcoming matches by identifying common patterns in the positioning and movement of the competing teams in specific situations. The proposed model is not computationally demanding Show that our model captures domain expert evaluations of a number of example We performed a user study with domain experts, and the results We have implemented our model in a soccer dataĪnalytics software. Our model captures the perception of domain experts with a careful combination As a solution, in this paper, we present aĭescriptive model to quantify the effectiveness of passes in soccer to differentiateīetween key passes and regular passes with not much contribution to the play of a Key passes that start or improve an attack. Ordinary passes, usually in their own pitch to a close teammate, from those who make However, the number of passes metricĬonsiders each pass the same, and cannot differentiate players who are making Key players using network modeling theory. Usually define the weight of each edge, and these weights are employed to identify the ![]() ![]() Players as nodes, and passes between them as the edges. More specifically, each game is usually modeled as a network with WithĬollaborating and competing 22 players on the field, soccer is often considered as aĬomplex system. ![]() The emerging data explosion in sports field has created new opportunities to practiceĭata science and analytics for deeper and larger scale analysis of games.
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