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 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: 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: 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 computing system receives pre-match data for an upcoming match between a first player and a second player. The computing system generates, using one or more prediction models, one or more pre-match predictions based on the pre-match data. The computing system receives in-match data for the match currently in progress. The computing system generates, using the one or more prediction models, one or more live match predictions based on the in-match data.

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: Disclosed techniques relate to using one or more of soccer match statistics, textual insights, predictions (e.g., team and player at the match level, and team at the season level), graphics, video overlays, and player and ball tracking data. Tracking data may be generated using an in-venue feed or a broadcast feed. The tracking data may be supplemented with event data which may be provided by an operator or an automated system based on the events related to a given sport within a venue or via a broadcast feed. The tracking data and/or event data may be used to generate insights such as match statistics, textual insights, predictions, graphics, video overlays, and or the like. Accordingly, the tracking data and insights generated in accordance with the subject matter disclosed herein may be specific to a given sporting event and/or the sport associated with the sporting event.

Abstract: Disclosed techniques relate to using one or more of racing event statistics, textual insights, predictions (e.g., team and player at the event level, and team at the season level), graphics, video overlays, and player and ball tracking data. Tracking data may be generated using an in-venue feed or a broadcast feed. The tracking data may be supplemented with event data which may be provided by an operator or an automated system based on the events related to a given sport within a venue or via a broadcast feed. The tracking data and/or event data may be used to generate insights such as event statistics, textual insights, predictions, graphics, video overlays, and or the like. Accordingly, the tracking data and insights generated in accordance with the subject matter disclosed herein may be specific to a given sporting event and/or the sport associated with the sporting event.

Abstract: Disclosed techniques relate to using one or more of football match statistics, textual insights, predictions (e.g., team and player at the match level, and team at the season level), graphics, video overlays, and player and ball tracking data. Tracking data may be generated using an in-venue feed or a broadcast feed. The tracking data may be supplemented with event data which may be provided by an operator or an automated system based on the events related to a given sport within a venue or via a broadcast feed. The tracking data and/or event data may be used to generate insights such as match statistics, textual insights, predictions, graphics, video overlays, and or the like. Accordingly, the tracking data and insights generated in accordance with the subject matter disclosed herein may be specific to a given sporting event and/or the sport associated with the sporting event.

Abstract: Disclosed techniques relate to using one or more of baseball match statistics, textual insights, predictions (e.g., team and player at the match level, and team at the season level), graphics, video overlays, and player and ball tracking data. Tracking data may be generated using an in-venue feed or a broadcast feed. The tracking data may be supplemented with event data which may be provided by an operator or an automated system based on the events related to a given sport within a venue or via a broadcast feed. The tracking data and/or event data may be used to generate insights such as match statistics, textual insights, predictions, graphics, video overlays, and or the like. Accordingly, the tracking data and insights generated in accordance with the subject matter disclosed herein may be specific to a given sporting event and/or the sport associated with the sporting event.

Abstract: Disclosed techniques relate to using one or more of tennis match statistics, textual insights, predictions (e.g., team and player at the match level, and team at the season level), graphics, video overlays, and player and ball tracking data. Tracking data may be generated using an in-venue feed or a broadcast feed. The tracking data may be supplemented with event data which may be provided by an operator or an automated system based on the events related to a given sport within a venue or via a broadcast feed. The tracking data and/or event data may be used to generate insights such as match statistics, textual insights, predictions, graphics, video overlays, and or the like. Accordingly, the tracking data and insights generated in accordance with the subject matter disclosed herein may be specific to a given sporting event and/or the sport associated with the sporting event.

Abstract: Disclosed techniques relate to using one or more of basketball match statistics, textual insights, predictions (e.g., team and player at the match level, and team at the season level), graphics, video overlays, and player and ball tracking data. Tracking data may be generated using an in-venue feed or a broadcast feed. The tracking data may be supplemented with event data which may be provided by an operator or an automated system based on the events related to a given sport within a venue or via a broadcast feed. The tracking data and/or event data may be used to generate insights such as match statistics, textual insights, predictions, graphics, video overlays, and or the like. Accordingly, the tracking data and insights generated in accordance with the subject matter disclosed herein may be specific to a given sporting event and/or the sport associated with the sporting event.

Abstract: Disclosed techniques relate to using one or more of cricket match statistics, textual insights, predictions (e.g., team and player at the event level, and team at the season level), graphics, video overlays, and player and ball tracking data. Tracking data may be generated using an in-venue feed or a broadcast feed. The tracking data may be supplemented with event data which may be provided by an operator or an automated system based on the events related to a given sport within a venue or via a broadcast feed. The tracking data and/or event data may be used to generate insights such as match statistics, textual insights, predictions, graphics, video overlays, and or the like. Accordingly, the tracking data and insights generated in accordance with the subject matter disclosed herein may be specific to a given sporting event and/or the sport associated with the sporting event.

Abstract: Disclosed techniques relate to using one or more of golf match statistics, textual insights, predictions (e.g., team and player at the event level, and team at the season level), graphics, video overlays, and player and ball tracking data. Tracking data may be generated using an in-venue feed or a broadcast feed. The tracking data may be supplemented with event data which may be provided by an operator or an automated system based on the events related to a given sport within a venue or via a broadcast feed. The tracking data and/or event data may be used to generate insights such as match statistics, textual insights, predictions, graphics, video overlays, and or the like. Accordingly, the tracking data and insights generated in accordance with the subject matter disclosed herein may be specific to a given sporting event and/or the sport associated with the sporting event.

Abstract: Disclosed techniques relate to using one or more of rugby match statistics, textual insights, predictions (e.g., team and player at the match level, and team at the season level), graphics, video overlays, and player and ball tracking data. Tracking data may be generated using an in-venue feed or a broadcast feed. The tracking data may be supplemented with event data which may be provided by an operator or an automated system based on the events related to a given sport within a venue or via a broadcast feed. The tracking data and/or event data may be used to generate insights such as match statistics, textual insights, predictions, graphics, video overlays, and or the like. Accordingly, the tracking data and insights generated in accordance with the subject matter disclosed herein may be specific to a given sporting event and/or the sport associated with the sporting event.

Abstract: A method for generating multi-modal response to a query using a generative machine learning model, the method including: receiving, from a client device, a query data object related to a sporting event; providing the query data object and a first prompt to a machine learning system; receiving, from the machine learning system, a function, from a set of functions, associated with the query data object; receiving, from the machine learning system, an output format; providing a data source mapped to the function, the query data object, and a second prompt to the machine learning system, receiving, from the machine learning system, a response to the query data object, wherein the response is formatted based on the output format; and outputting the response to one or more users.

Abstract: A computing system receives pre-match data for an upcoming match between a first player and a second player. The computing system generates, using one or more prediction models, one or more pre-match predictions based on the pre-match data. The computing system receives in-match data for the match currently in progress. The computing system generates, using the one or more prediction models, one or more live match predictions based on the in-match data.

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 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.