%0 Journal Article
	%A Mark Harmon and  Abdolghani Ebrahimi and  Patrick Lucey and  Diego Klabjan
	%D 2021
	%J International Journal of Sport and Health Sciences
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 179, 2021
	%T Predicting Shot Making in Basketball Learnt from Adversarial Multiagent Trajectories
	%U https://publications.waset.org/pdf/10012320
	%V 179
	%X In this paper, we predict the likelihood of a player making a shot in basketball from multiagent trajectories. To approach this problem, we present a convolutional neural network (CNN) approach where we initially represent the multiagent behavior as an image. To encode the adversarial nature of basketball, we use a multichannel image which we then feed into a CNN. Additionally, to capture the temporal aspect of the trajectories we use “fading.” We find that this approach is superior to a traditional FFN model. By using gradient ascent, we were able to discover what the CNN filters look for during training. Last, we find that a combined FFN+CNN is the best performing network with an error rate of 39%.
	%P 973 - 983