Abstract: A method including receiving video data including a plurality of video frames captured during a sporting occasion, wherein each frame of the plurality of video frames includes data corresponding to one or more agents. The method including receiving event data associated with the video data. The method including processing the plurality of video frames and the event data to generate imputed tracking data, wherein the imputed tracking data includes positional data and movement data for each of the one or more agents. The method including determining one or more metrics based on the imputed tracking data, wherein the one or more metrics includes at least one of a pass option, a pressure, a line detection, and a marking. The method including determining an output based on the one or more metrics for the one or more agents.

Abstract: A system interconnects multiple client devices over a network. A local group of the client devices is located at a live event and a remote group is located remote from the live event. Each client device of the local group is a potential source of a live stream which, when rendered by another client device, causes display of a vantage point of the live event. The system dynamically updates a subscription list to include an indication of any client device of the local group that is actively live streaming and remove an indication of any client device of the local group that terminated live streaming. The system can enable selective access to any live streams of any client device of the updated subscription list and disable access to any live streams of any client device removed from the updated subscription list.

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 computer implemented method for tracking one or more individuals during a sporting event, the method including: receiving, as an input, broadcast tracking data of a sporting event and labeled event data of the sporting event; performing multi-object tracking of one or more agents of the received broadcast tracking data to determine one or more vectors; inputting the labeled event data and one or more vectors into a diffusion model; and determining, using the diffusion model, one or more trajectory sequences for the one or more agents; and determining, an output, based on the one or more trajectory sequences for the one or more agents.

Abstract: A boundary of a highlight of audiovisual content depicting an event is identified. The audiovisual content may be a broadcast, such as a television broadcast of a sporting event. The highlight may be a segment of the audiovisual content deemed to be of particular interest. Audio data for the audiovisual content is stored, and the audio data is automatically analyzed to detect one or more audio events indicative of one or more occurrences to be included in the highlight. Each audio event may be a brief, high-energy audio burst such as the sound made by a tennis serve. A time index within the audiovisual content, before or after the audio event, may be designated as the boundary, which may be the beginning or end of the highlight.

Abstract: A method including receiving a first data set. The method including comparing the first data set to one or more template plays. The method including determining a representative play in response to comparing the first data set to the one or more template plays. The method including receiving at least one player performance data set. The method including determining a threat score associated with the representative play based on the at least one player performance data set for each of a plurality of players associated with the first data set. The method including combining the at least one player performance data set and the threat score associated with the representative play. The method including generating a sports play graphic in response to combining the at least one player performance data set and the threat score associated with the representative play. The method including outputting the sports play graphic.

Abstract: A boundary of a highlight of audiovisual content depicting an event is identified. The audiovisual content may be a broadcast, such as a television broadcast of a sporting event. The highlight may be a segment of the audiovisual content deemed to be of particular interest. Audio data for the audiovisual content is stored, and the audio data is automatically analyzed to detect one or more audio events indicative of one or more occurrences to be included in the highlight. Each audio event may be a brief, high-energy audio burst such as the sound made by a tennis serve. A time index within the audiovisual content, before or after the audio event, may be designated as the boundary, which may be the beginning or end of the highlight.

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: Systems and methods are disclosed for data object testing. One or more processors may receive a video data object. The one or more processors may receive one or more video specifications from a database. The one or more processors may generate an on-demand cloud testing instance having a subset of video analysis tools from a plurality of video analysis tools and computational resources determined based on the video data object and the one or more video specifications. The one or more processors may perform, in the cloud testing instance, a compliance test on the video data object based on the one or more video specifications. generate a compliance output based on the compliance test, the compliance output including instructions or parameters for automatic video editing of the video data object. The one or more processors may provide the compliance output to a downstream component.

Abstract: A computing system receives historical match data associated with a plurality of tennis players. The computing system generates player rankings. The player rankings include a player ranking for each tennis player of the plurality of tennis players based on the historical match data. The computing system receives information associated with a tennis tournament. The information includes a subset of tennis players in the tournament and a seeding of each tennis player in the subset of tennis players. The computing system generates initial predictions based on the information associated with the tournament and the player rankings. The computing system identifies a trigger event that causes an update to the initial predictions. Responsive to identifying the trigger event, the computing system generates an updated predictions based on in-match data. The in-match data includes a change to a score in a match of the tournament. The computing system outputs the updated predictions.

Abstract: A system and method of calibrating a broadcast video feed are disclosed herein. A computing system retrieves a plurality of broadcast video feeds that include a plurality of video frames. The computing system generates a trained neural network, by generating a plurality of training data sets based on the broadcast video feed and learning, by the neural network, to generate a homography matrix for each frame of the plurality of frames. The computing system receives a target broadcast video feed for a target sporting event. The computing system partitions the target broadcast video feed into a plurality of target frames. The computing system generates for each target frame in the plurality of target frames, via the neural network, a target homography matrix. The computing system calibrates the target broadcast video feed by warping each target frame by a respective target homography matrix.

Abstract: Systems and methods for generating trajectories for one or more players during an event include receiving broadcast footage of a sporting event, determining tracking data of one or more players in the sporting event from the broadcast footage, the tracking data including one or more vectors, receiving event data of the sporting event, and inputting the one or more vectors and event data into a multimodal model including an event encoder and a tracking decoder. A linear layer of the multimodal model may be applied to the vectors and event data to tokenize the event data and vectors. A tensor representing a sequence of the event data and tracking data may be determined. Perturbed tracking data of the sporting event and the tensor may be input into a diffusion model. The diffusion model may generate one or more trajectories for the one or more players in the sporting event.

Abstract: A computing system identifies data related to a tennis match between a first player and a second player. The data includes a current match state and a current in-match performance. The computing system generates an input data set that includes the data related to the tennis match. The generating includes modifying the current match state to assume that the first player will win a next point in the tennis match. Based on the input data set, the computing system measures an importance of the next point to the first player winning the tennis match.

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: Disclosed techniques relate to utilizing tracking data for predicting player ratings. In an example, a method for utilizing tracking data to predict a player rating includes receiving broadcast data for a plurality of games in a first league, the plurality of games including a first player, generating tracking data for each of the plurality of games, the tracking data comprising coordinates of player positions and ball positions for each frame of the broadcast data, receiving play-by-play data for each of the plurality of games, the play-by-play data describing events that occur within the plurality of games, merging the tracking data and play-by-play data to generate a set of input features, and predicting, based on the set of input features, a player rating for the first player, the player rating being indicative of a predicted level of performance in a second league.

Abstract: A system interconnects multiple client devices over a network. A local group of the client devices is located at a live event and a remote group is located remote from the live event. Each client device of the local group is a potential source of a live stream which, when rendered by another client device, causes display of a vantage point of the live event. The system dynamically updates a subscription list to include an indication of any client device of the local group that is actively live streaming and remove an indication of any client device of the local group that terminated live streaming. The system can enable selective access to any live streams of any client device of the updated subscription list and disable access to any live streams of any client device removed from the updated subscription list.

Abstract: According to systems and techniques disclosed herein, a method for generating an interactive user interface using artificial intelligence models may include receiving one or more streams of event data (e.g., real-time or non-live event data) comprising a plurality of visual elements (e.g., real-time or non-live visual elements). The method may further include providing the plurality of visual elements to a computer vision artificial intelligence model trained to classify the plurality of visual elements and output object identifiers and a confidence score associated with each of the object identifiers. The method may further include receiving user input from the interactive user interface displayed on a user device. The user input may include a user query associated with a first object identifier of the object identifiers. The method may further include updating the interactive user interface with one or more interactive user elements associated with the first object identifier.

Abstract: Examples disclosed herein may estimate locations of players not visible in a sporting broadcast video. A prediction model may be generated based on a training data set of in-venue tracking data that includes locations of all players at all times and the corresponding broadcast tracking data that may not necessarily contain the locations of all players at all times. The prediction model may be based on an algorithmic logic (e.g., a spline regression) or machine learning model (e.g., k-nearest neighbor, deep neural network). The generated predicted model may be used to estimate the unknown locations of players in broadcast tracking based on the known locations.

Abstract: A boundary of a highlight of audiovisual content depicting an event is identified. The audiovisual content may be a broadcast, such as a television broadcast of a sporting event. The highlight may be a segment of the audiovisual content deemed to be of particular interest. Audio data for the audiovisual content is stored, and the audio data is automatically analyzed to detect one or more audio events indicative of one or more occurrences to be included in the highlight. Each audio event may be a brief, high-energy audio burst such as the sound made by a tennis serve. A time index within the audiovisual content, before or after the audio event, may be designated as the boundary, which may be the beginning or end of the highlight.

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.