Abstract: A virtual system includes a memory for storing instructions and processors configured to execute operations. The operations include receiving a request from a user in a closed-loop system for an online transaction, wherein the transaction includes transferring a fund from a closed-loop account to a virtual account. The operations include generating the virtual account and a virtual identifier associated with the virtual account, wherein the virtual account is further associated with a primary identifier. The operations include blocking the primary identifier for the transaction to prevent the primary identifier's use for the fund transfer. The operations include processing the transaction by transferring the fund using the virtual identifier, wherein the expense using the virtual identifier is lower than using the primary identifier.

Abstract: A method for generating video clips of a sporting occasion by implementing a machine learning model, the method including: receiving a video feed of a sporting occasion; receiving a plurality of event data objects related to the sporting occasion, each of the event data objects indicating an action in the sporting occasion and including corresponding metadata and timestamps; determining, based on the plurality of event data objects, that a trigger event occurred, the trigger event being a predefined or dynamically determined action in the sporting occasion; determining, using a machine learning model, a qualifier associated with the trigger event; and generating a video clip of the trigger event from the video of the sporting occasion, the video clip being generated based on the determined qualifier.

Abstract: A system and method of generating a player tracking prediction are described herein. A computing system retrieves a broadcast video feed for a sporting event. The computing system segments the broadcast video feed into a unified view. The computing system generates a plurality of data sets based on the plurality of trackable frames. The computing system calibrates a camera associated with each trackable frame based on the body pose information. The computing system generates a plurality of sets of short tracklets based on the plurality of trackable frames and the body pose information. The computing system connects each set of short tracklets by generating a motion field vector for each player in the plurality of trackable frames. The computing system predicts a future motion of a player based on the player's motion field vector using a neural network.

Abstract: A method of generating an outcome for a sporting event is disclosed herein. A computing system retrieves tracking data from a data store. The computing system generates a predictive model using a deep neural network. The one or more neural networks of the deep neural network generates one or more embeddings comprising team-specific information and agent-specific information based on the tracking data. The computing system selects, from the tracking data, one or more features related to a current context of the sporting event. The computing system learns, by the deep neural network, one or more likely outcomes of one or more sporting events. The computing system receives a pre-match lineup for the sporting event. The computing system generates, via the predictive model, a likely outcome of the sporting event based on historical information of each agent for the home team, each agent for the away team, and team-specific features.

Abstract: A computing system receives an image for processing. The image includes at least a player of interest. The computing system executes an image processing workflow for automatically processing the image in accordance with standards set by an organization associated with the computing system. The executing includes removing, via a background module, background pixels from the image. The executing further includes, after removing the background pixels, resizing, via a resizing module, the image to a set size defined by the organization. The executing further includes, after resizing the image, renaming, via a renaming module, the image to a name format defined by the organization. After executing the image processing workflow, the computing system saves the image to a database for retrieval. The removing, the resizing, and the renaming are performed without user intervention.

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 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 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 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.