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 computing system receives a video stream of a game. The computing system generates tracking data corresponding to the video stream using one or more artificial intelligence models. The computing system generates interactive video data by combining the video stream of the game with the tracking data. The computing system causes a media player to render graphics corresponding to the tracking data over the video stream by sending the interactive video data to a client device executing the media player.

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 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 method for generating a probability for a first action of a sporting event by implementing a feature set, the method including: obtaining an initial set of data relating to the first action of a sporting event, the initial set of data including at least a position of a first player on a surface and a position of a target area on the surface; generating, by a machine learning model, an initial projected scoring probability based on the initial set of data; generating a feature set relating to the sporting event; and modifying, by the machine learning model, the initial projected scoring probability to an updated scoring probability using the feature set.

Abstract: A method of identifying a defensive alignment and an offensive alignment in a set-piece is disclosed herein. A computing system receives one or more streams of tracking data. The computing system identifies a set-piece contained in the one or more streams of tracking data. The computing system identifies a defensive alignment of a first team and an offensive alignment of a second team. The computing system extracts, via a convolutional neural network, one or more features corresponding to a type of defensive alignment implemented by the first team by passing the set-piece through the convolutional neural network. The computing system scans the set-piece, via a machine learning algorithm, to identify one or more features indicative of a type of offensive alignment implemented by the second team. The computing system infers the type of defensive alignment implemented by the first team.

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: 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 computing system receives live event data corresponding to a live game. The live event data includes events occurring within the live game. The computing system analyzes the live event data to identify a potential error in the live event data. The computing system generates a ticket corresponding to the potential error flagged in the live event data. The computing system assigns the ticket to a first quality assurance agent to resolve. The computing system receives an indication that the ticket has been reviewed by the first quality assurance agent. the computing system provides the reviewed event data to an end user.

Abstract: A method of identifying a defensive alignment and an offensive alignment in a set-piece is disclosed herein. A computing system receives one or more streams of tracking data. The computing system identifies a set-piece contained in the one or more streams of tracking data. The computing system identifies a defensive alignment of a first team and an offensive alignment of a second team. The computing system extracts, via a convolutional neural network, one or more features corresponding to a type of defensive alignment implemented by the first team by passing the set-piece through the convolutional neural network. The computing system scans the set-piece, via a machine learning algorithm, to identify one or more features indicative of a type of offensive alignment implemented by the second team. The computing system infers the type of defensive alignment implemented by the first team.

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: A method and system of generating agent and actions prediction based on multi-agent tracking data are disclosed herein. A computing system retrieves tracking data from a data store. The computing system generates a trained neural network by generating a plurality of training data sets based on the tracking data by converting each frame of data into a matrix representation of the data contained in the frame and learning, by the neural network, a start frame and end frame of each action contained in the frame and its associated actor. The computing system receives target tracking data associated with an event. The target tracking data includes a plurality of actors and a plurality of actions. The computing system generates, via the trained neural network, a target start frame and a target end frame of each action identified in the tracking data and a corresponding actor.

Abstract: A computing system retrieves ball-by-ball data for a plurality of sporting events. The computing system generates a trained neural network based on ball-by-ball data supplemented with ball-by-ball data with ball-by-ball match context features and personalized embeddings based on a batsman and a bowler for each delivery. The computing system receives a target batsman and a target bowler for a pitch to be delivered in a target event. The computing system identifies target ball-by-ball data for a window of pitches preceding the to be delivered pitch. The computing system retrieves historical ball-by-ball data for each of the target batsman and the target bowler. The computing system generates personalized embeddings for both the target batsman and the target bowler based on the historical ball-by-ball data. The computing system predicts a shot type for the pitch to be delivered based on the target ball-by-ball data and the personalized embeddings.

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: Embodiments disclosed herein generally relate to a system and method for updating a set of strength-based ratings for a sporting event using market information. The present embodiments provide a data-driven approach leveraging market information to derive an updated team strength prior to an upcoming event. For example, a prediction model can internally determine an initial team strength measurement. The prediction model can also obtain futures market information and update the initial team strength measurement using the futures market information.

Abstract: A computing system receives live event data corresponding to a live game. The live event data includes events occurring within the live game. The computing system analyzes the live event data to identify a potential error in the live event data. The computing system generates a ticket corresponding to the potential error flagged in the live event data. The computing system assigns the ticket to a first quality assurance agent to resolve. The computing system receives an indication that the ticket has been reviewed by the first quality assurance agent. the computing system provides the reviewed event data to an end user.

Abstract: Metadata for one or more highlights of a video stream may be extracted from one or more card images embedded in the video stream. The highlights may be segments of the video stream, such as a broadcast of a sporting event, that are of particular interest. According to one method, video frames of the video stream are stored. One or more information cards embedded in a decoded video frame may be detected by analyzing one or more predetermined video frame regions. Image segmentation, edge detection, and/or closed contour identification may then be performed on identified video frame region(s). Further processing may include obtaining a minimum rectangular perimeter area enclosing all remaining segments, which may then be further processed to determine precise boundaries of information card(s). The card image(s) may be analyzed to obtain metadata, which may be stored in association with at least one of the video frames.

Abstract: The present embodiments relate to generating various predictive overlays over a video of a tennis match. Tracking data of a tennis match can be processed to derive insights into the tennis match. For example, the tracking data can indicate a current state of the match and a likelihood of a ball being returned to various regions of the court. This can be used to create a first overlay that can be added over a video of the tennis match. Further, player rankings based on various categories (e.g., technical, tactical, mental, physical) can be generated to provide insights into skills of each player relative to other ranked players. Additional insights, such as a forecast predicting a likelihood of winning an upcoming match or tournament can also be generated. A player card can be generated using such information to provide various aspects of a player skill onto a single output.

Abstract: The present embodiments relate to tracking player movements from a video broadcast and determining a defensive influence score from the tracked movements of the player. The present embodiments can implement one or more models to generate a defensive influence score that quantifies a defensive intensity of a player during the course of a game. The defensive influence score can include a frame-by-frame machine learning prediction that can be used to estimate the defensive pressure a player is having on another player during the course of the game. Additionally, the present embodiments can capture and estimate fitness metrics, such as sprints and efforts around detected plays such as pick-and-rolls and off-ball screens, which can be good proxies for player effort. Further, event detection outputs (both offensive and defensive metrics), can be used as features to estimate fitness metrics for the player (e.g., player load, sprints, jogs, etc.).

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.