WASET
	@article{(Open Science Index):https://publications.waset.org/pdf/10012320,
	  title     = {Predicting Shot Making in Basketball Learnt from Adversarial Multiagent Trajectories},
	  author    = {Mark Harmon and  Abdolghani Ebrahimi and  Patrick Lucey and  Diego Klabjan},
	  country	= {},
	  institution	= {},
	  abstract     = {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%.},
	    journal   = {International Journal of Sport and Health Sciences},
	  volume    = {15},
	  number    = {11},
	  year      = {2021},
	  pages     = {973 - 983},
	  ee        = {https://publications.waset.org/pdf/10012320},
	  url   	= {https://publications.waset.org/vol/179},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 179, 2021},
	}