“How to Get an Open Shot”: Analyzing Team Movement in Basketball using Tracking Data


Patrick Lucey (Disney Research Pittsburgh)
Alina Bialkowski (Queensland University of Technology)
Peter Carr (Disney Research Pittsburgh)
Yisong Yue (Disney Research Pittsburgh)
Iain Matthews (Disney Research Pittsburgh)

MIT Sloan Sports Analytics Conference 2014

April 30, 2014

How to Get an Open Shot-Analyzing Team Movement in Basketball using Tracking Data-Image

Figure 2. (a) Harden drives to the basket and passes to Douglas in the corner. (b) Harden does the same thing but passes to Anderson who has changed positions with Douglas – a computer will see these plays differently as it does not know they have changed positions. (c) We use the role-labeling convention so the relative positions are maintained.

In this paper, we use ball and player tracking data from STATS SportsVU from the 2012-2013 NBA season to analyze offensive and defensive formations of teams. We move beyond current analysis that uses only play-by-play event-driven statistics (i.e., rebounds, shots) and look at the spatiotemporal changes in a team’s formation. A major concern, which also gives a clue to unlocking this problem, is that of permutations caused by the constant movement and interchanging of positions by players. In this paper, we use a method that represents a team via “role” which is immune to the problem of permutations. We demonstrate the utility of our approach by analyzing all the plays that resulted in a 3-point shot attempt in the 2012-2013 NBA season. We analyzed close to 20,000 shots and found that when a player is “open” the shooting percentage is around 40%, compared to a “pressured” shot which is close to 32%. There is nothing groundbreaking behind this finding (i.e., putting more defensive pressure on the shooter reduces shooting percentages) but finding how teams get shooters open is. Using our method, we show that the amount of defensive role-swaps are predictive of getting an open-shot and this measure can be used to measure the defensive effectiveness of a team. Additionally, our role representation allows for large-scale retrieval of plays by using the tracking data as the input query rather than a text label – this “video Google” approach allows for quick and accurate play retrieval.

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