Abstract: A system and method for generating a play prediction for a team is disclosed herein. A computing system retrieves trajectory data for a plurality of plays from a data store. The computing system generates a predictive model using a variational autoencoder and a neural network by generating one or more input data sets, learning, by the variational autoencoder, to generate a plurality of variants for each play of the plurality of plays, and learning, by the neural network, a team style corresponding to each play of the plurality of plays. The computing system receives trajectory data corresponding to a target play. The predictive model generates a likelihood of a target team executing the target play by determining a number of target variants that correspond to a target team identity of the target team.

Abstract: Techniques are provided for receiving and decoding a sequence of video frames at a computing device, and analyzing a current video frame N to determine whether to skip or render the current video frame N for display by the computing device. The analyzing includes generating color histograms of the current video frame N and one or more previous video frames, determining a difference value representing a difference between the current video frame N and a previous video frame N?K, where K>0, the difference value being based upon the generated color histograms, in response to the difference value not exceeding a threshold value, rendering the current video frame N or a recently rendered video frame N?K using the current video frame, and in response to the difference value exceeding the threshold value, skipping the current video frame N from being rendered.