Abstract: A computing system identifies a target player. The computing system generates rookie priors for the target player based on characteristics of the target player. The computing system generates time series data points for the player based on at least one of the rookie priors and historical statistics of the target player. The computing system projects a game position of the target player based on the historical statistics of the target player. The computing system projects next game projections for the target player based on at least one of the rookie priors, the time series data points, the game position, and the historical statistics of the target player. The computing system generates a contribution of the target player to a team's production based on the next game projections.

Abstract: A system and method of generating trackable frames from a broadcast video feed are provided herein. A computing system retrieves a broadcast video feed for a sporting event. The broadcast video feed includes a plurality of video frames. The computing system generates a set of frames for classification using a principal component analysis model. The set of frames are a subset of the plurality of video frames. The computing system partitions each frame of the set of frames into a plurality of clusters. The computing system classifies each frame of the plurality of frames as trackable or untrackable. Trackable frames capture a unified view of the sporting event. The computing system compares each cluster to a predetermined threshold to determine whether each cluster comprises at least a threshold number of trackable frames. The computing system classifies each cluster that includes at least the threshold number of trackable frames as trackable.

Abstract: Customized highlight shows for sporting events, entertainment events, and/or the like, having a narrative component, are generated and presented. The events can be sporting events, entertainment events, and/or the like. For example, in the context of sporting events, a determination is made as to what types of sports, teams, leagues, players, plays, and/or the like are of interest to the user. A customized highlight show is then generated and presented, containing those specific portions of the sporting events that are likely to be of interest, arranged in a manner that is likely to be entertaining and interesting to the user and that presents a cohesive narrative.

Abstract: A system and method of re-identifying players in a broadcast video feed are provided herein. A computing system retrieves a broadcast video feed for a sporting event. The broadcast video feed includes a plurality of video frames. The computing system generates a plurality of tracks based on the plurality of video frames. Each track includes a plurality of image patches associated with at least one player. Each image patch of the plurality of image patches is a subset of the corresponding frame of the plurality of video frames. For each track, the computing system generates a gallery of image patches. A jersey number of each player is visible in each image patch of the gallery. The computing system matches, via a convolutional autoencoder, tracks across galleries. The computing system measures, via a neural network, a similarity score for each matched track and associates two tracks based on the measured similarity.

Abstract: A method of generating a player prediction is disclosed herein. A computing system retrieves data from a data store. The computing system generates a predictive model using an artificial neural network. The artificial neural network generates one or more personalized embeddings that include player-specific information based on historical performance. The computing system selects, from the data, one or more features related to each shot attempt captured in the data. The artificial neural network learns an outcome of each shot attempt based at least on the one or more personalized embeddings and the one or more features related to each shot attempt.

Abstract: A computing system retrieves player tracking data for a plurality of players across a plurality of events. The player tracking data includes coordinates of player positions during each event. The computing system initializes the player tracking data based on an average position of each player in the plurality of events. The computing system learns an optimal formation of player positions based on the player tracking data using a Gaussian mixture model. The computing system aligns the optimal formation of player positions to a global template by identifying a distance between each distribution in the optimal formation and each distribution in the global template to generate a learned formation template. The computing system assigns a role to each player in the learned template.

Abstract: A computing system receives a request to generate one or more narrative frameworks for a worksheet. The worksheet is hosted by a third party server. The computing system interfaces with the third party server to retrieve data associated with the worksheet. The computing system infers attributes of the worksheet from the retrieved data. The attributes correspond to a type of data represented in the worksheet. The computing system generates a narrative template for the one or more narrative frameworks. The computing system generates one or more data variables for the one or more narrative frameworks based on the generated narrative template. The computing system constructs the one or more narrative frameworks by selectively retrieving data from the worksheet for each data variable of the one or more data variables in the narrative framework.

Abstract: A system and method of re-identifying players in a broadcast video feed are provided herein. A computing system retrieves a broadcast video feed for a sporting event. The broadcast video feed includes a plurality of video frames. The computing system generates a plurality of tracks based on the plurality of video frames. Each track includes a plurality of image patches associated with at least one player. Each image patch of the plurality of image patches is a subset of the corresponding frame of the plurality of video frames. For each track, the computing system generates a gallery of image patches. A jersey number of each player is visible in each image patch of the gallery. The computing system matches, via a convolutional autoencoder, tracks across galleries. The computing system measures, via a neural network, a similarity score for each matched track and associates two tracks based on the measured similarity.

Abstract: A system and method for predicting multi-agent locations is disclosed herein. A computing system retrieves tracking data from a data store. The computing system generates a predictive model using a conditional variational autoencoder. The conditional variational autoencoder learns one or more paths a subset of agents of the plurality of agents are likely to take. The computing system receives tracking data from a tracking system positioned remotely in a venue hosting a candidate sporting event. The computing system identifies one or more candidate agents for which to predict locations. The computing system infers, via the predictive model, one or more locations of the one or more candidate agents. The computing system generates a graphical representation of the one or more locations of the one or more candidate agents.

Abstract: A system and method of re-identifying players in a broadcast video feed are provided herein. A computing system retrieves a broadcast video feed for a sporting event. The broadcast video feed includes a plurality of video frames. The computing system generates a plurality of tracks based on the plurality of video frames. Each track includes a plurality of image patches associated with at least one player. Each image patch of the plurality of image patches is a subset of the corresponding frame of the plurality of video frames. For each track, the computing system generates a gallery of image patches. A jersey number of each player is visible in each image patch of the gallery. The computing system matches, via a convolutional autoencoder, tracks across galleries. The computing system measures, via a neural network, a similarity score for each matched track and associates two tracks based on the measured similarity.

Abstract: A system and method of calibrating moving cameras capturing a sporting event is disclosed herein. A computing system retrieves a broadcast video feed for a sporting event. The broadcast video feed includes a plurality of video frames. The computing system labels, via a neural network, components of a playing surface captured in each video frame. The computing system matches a subset of labeled video frames to a set of templates with various camera perspectives. The computing system fits a playing surface model to the set of labeled video frames that were matched to the set of templates. The computing system identifies camera motion in each video frame using an optical flow model. The computing system generates a homography matrix for each video frame based on the fitted playing surface model and camera motion. The computing system calibrates each camera based on the homography matrix generated for each video frame.

Abstract: A customized highlight sequence depicting multiple events, and based on a user's personal characteristics, interests, and/or preferences, is generated and presented. The events can be sporting events, entertainment events, and/or the like. For example, in the context of sporting events, a determination is made as to what types of sports, teams, leagues, players, plays, and/or the like are of interest to the user. In at least one embodiment, the amount of time available to the user can be obtained, so that the length of the highlight sequence can be tailored to the available time. A customized highlight sequence is then generated and presented, containing those specific portions of the sporting events that are likely to be of interest, arranged in a manner that is likely to be entertaining to the user and comports with the time restrictions.

Abstract: A computing system receives data for a game. The data includes at least one of tracking data or event data. Based on the data for the game, the computing system determines that an event has occurred within the game. Based on the determining, the computing system generates a graphic responsive to the event. The graphic includes insights related to the event. The computing system recommends an image relevant to the event based on metatags associated with the event. The computing system generates a visual element by merging the image and the graphic.

Abstract: A method of generating a dynamic rating for an entity and rearranging an icon associated with the entity on a graphical user interface (GUI) of a computer system. The method includes: receiving a first set of data associated with one or more parameters for the entity; calculating a plurality of ability scores based on the first set of data; generating a first player rating based on each ability score of the plurality of ability scores; dynamically adjusting the plurality of ability scores based on receiving a second set of data associated with the one or more parameters for the entity; updating the first player rating based on the adjusted plurality of ability scores; automatically rearranging the icon to a position above or below a current position of the icon on the GUI based on the updated first player rating.

Abstract: A system and method of re-identifying players in a broadcast video feed are provided herein. A computing system retrieves a broadcast video feed for a sporting event. The broadcast video feed includes a plurality of video frames. The computing system generates a plurality of tracks based on the plurality of video frames. Each track includes a plurality of image patches associated with at least one player. Each image patch of the plurality of image patches is a subset of the corresponding frame of the plurality of video frames. For each track, the computing system generates a gallery of image patches. A jersey number of each player is visible in each image patch of the gallery. The computing system matches, via a convolutional autoencoder, tracks across galleries. The computing system measures, via a neural network, a similarity score for each matched track and associates two tracks based on the measured similarity.

Abstract: A method of generating a multi-modal prediction is disclosed herein. A computing system retrieves event data from a data store. The event data includes information for a plurality of events across a plurality of seasons. Computing system generates a predictive model using a mixture density network, by generating an input vector from the event data learning, by the mixture density network, a plurality of values associated with a next play following each play in the event data. The mixture density network is trained to output the plurality of values near simultaneously. Computing system receives a set of event data directed to an event in a match. The set of event data includes information directed to at least playing surface position and current score. Computing system generates, via the predictive model, a plurality of values associated with a next event following the event based on the set of event data.

Abstract: A method of generating a dynamic rating for an entity and rearranging an icon associated with the entity on a graphical user interface (GUI) of a computer system. The method includes: receiving a first set of data associated with one or more parameters for the entity; calculating a plurality of ability scores based on the first set of data; generating a first rating based on each ability score of the plurality of ability scores; dynamically adjusting the plurality of ability scores based on receiving a second set of data associated with the one or more parameters for the entity; updating the first rating based on the adjusted plurality of ability scores; automatically rearranging the icon to a position above or below a current position of the icon on the GUI based on the updated first rating.

Abstract: A system and method of re-identifying players in a broadcast video feed are provided herein. A computing system retrieves a broadcast video feed for a sporting event. The broadcast video feed includes a plurality of video frames. The computing system generates a plurality of tracks based on the plurality of video frames. Each track includes a plurality of image patches associated with at least one player. Each image patch of the plurality of image patches is a subset of the corresponding frame of the plurality of video frames. For each track, the computing system generates a gallery of image patches. A jersey number of each player is visible in each image patch of the gallery. The computing system matches, via a convolutional autoencoder, tracks across galleries. The computing system measures, via a neural network, a similarity score for each matched track and associates two tracks based on the measured similarity.

Abstract: One or more highlights of a video stream may be identified. The highlights may be segments of a video stream, such as a broadcast of a sporting event, that are of particular interest to one or more users. According to one method, at least a portion of the video stream may be stored. The portion of the video stream may be compared with templates of a template database to identify the one or more highlights. Each highlight may be a subset of the video stream that is deemed likely to match the one or more templates. The highlights, an identifier that identifies each of the highlights within the video stream, and/or metadata pertaining particularly to the one or more highlights may be stored to facilitate playback of the highlights for the users.

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.