@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}, }