Predicting Shot Making in Basketball Learnt from Adversarial Multiagent Trajectories

Neural networks have been successfully implemented in a plethora of prediction tasks ranging from speech interpretation to facial recognition. Because of groundbreaking work in optimization techniques (such as batch normalization, Ioffe and Szegedy (2015)) and model architecture (convolutional, deep belief, and LSTM networks), it is now tractable to use deep neural networks to effectively learn a better feature representation compared to handcrafted methods. 1 One area where such methods have not been utilized is the space of adversarial multiagent systems (for example, multiple independent players in competition), specifically when the multiagent behavior comes in the form of trajectories. There are two reasons for this: i) procuring large volumes of data where deep methods are effective is difficult to obtain, and ii) forming an initial representation of the raw trajectories so that deep neural networks are effective is challenging. In this paper, we explore the effectiveness of deep neural networks on a large volume of basketball tracking data, which contains the x, y locations of multiple agents (players) in an adversarial domain (game). To thoroughly explore this problem, we focus on the following task: "given the trajectories of the players and ball in the previous five seconds, can we accurately predict the likelihood that a player with position/role X will make the shot?"