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 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 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 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: 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: 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 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 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 plurality of game files corresponding to a plurality of games across a plurality of seasons. The computing system generates a prediction model configured to generate a possession value for an event. The computing system receives a target event, in real-time or near real-time, from a tracking system monitoring a target game. The computing system generates target features for the target event based on target event data associated with the target event. The computing system generates, via the prediction model, a target possession value for the target event based on the target event data and the target features. The target possession value represents a likelihood that a team with possession will score within a following x-seconds after the target event.

Abstract: A computing system receives a request to project a performance of a first player from a current team on a destination team. The computing system generates, based on the request, player-position features corresponding to the first player. The computing system generates team features corresponding to the first player. The computing system generates rating features for the first player. The computing system generates, via a prediction model, a player box score prediction based on the player-position features, the team features, and the rating features. The player box score prediction includes a plurality of per game metrics of the first player on the destination team.

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 system and method for generating a role summary associated with one or more players are disclosed herein. A computing system retrieves event information for a plurality of teams for a plurality of events. The computing system generates a spatial output that describes each player. The computing system identifies a playing style associated with each team. The computing system identifies a subset of paths a player or team takes between two zones. The computing system identifies each player's involvement in a team's process. The computing system generates a score corresponding to a value of a player's involvement in a given play based on the event information. The computing system generates a score associated with each player's passing ability based on the event information. The computing system determines a shot style of each player based on the event information. The computing system identifies a role associated with each player.