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 identifies player tracking data and event data corresponding to a match. The match includes a first team and a second team. The player tracking data includes coordinate positions of each player during the event. The event data defines events that occur during the match. The computing system divides the player tracking data into a plurality of segments based on the event information. For each segment of the plurality of segments, the computing system learns a first formation associated with a respective team in possession. For each segment of the plurality of segments, the computing system learns a second formation associated with a respective team not in possession. The computing system maps each first formation to a first class of known formation clusters. The computing system maps each second formation to a second class of known formation clusters.

Abstract: A computing system identifies player tracking data and event data corresponding to a match. The match includes a first team and a second team. The player tracking data includes coordinate positions of each player during the event. The event data defines events that occur during the match. The computing system divides the player tracking data into a plurality of segments based on the event information. For each segment of the plurality of segments, the computing system learns a first formation associated with a respective team in possession. For each segment of the plurality of segments, the computing system learns a second formation associated with a respective team not in possession. The computing system maps each first formation to a first class of known formation clusters. The computing system maps each second formation to a second class of known formation clusters.

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: 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: Disclosed techniques relate to improving predictions of machine learning models. In an example, a method involves receiving, from a machine learning model, predictions associated with a class of scenarios. The method includes identifying, in response to receiving the predictions, a subset of the class of scenarios that are beyond a threshold tolerance of accuracy. The method includes based on identifying the subset of the class of scenarios a training data set that includes emphasized event data from a plurality of historical sporting events. The method includes generating an updated machine learning model and deploying the updated machine learning model.

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 identifies player tracking data and event data corresponding to a match. The match includes a first team and a second team. The player tracking data includes coordinate positions of each player during the event. The event data defines events that occur during the match. The computing system divides the player tracking data into a plurality of segments based on the event information. For each segment of the plurality of segments, the computing system learns a first formation associated with a respective team in possession. For each segment of the plurality of segments, the computing system learns a second formation associated with a respective team not in possession. The computing system maps each first formation to a first class of known formation clusters. The computing system maps each second formation to a second class of known formation clusters.

Abstract: A computing system retrieves tracking data from a data store. The computing system converts the tracking data into a plurality of graph-based representations. The prediction engine learns to model defensive behavior based on the plurality of graph-based representations. The computing system generates a trained prediction engine based on the learnings. The computing system receives target tracking data for a target event. The target tracking data includes a plurality of target frames. The computing system converts the target tracking data to a plurality of target graph-based representations. The computing system models, via the trained graph neural network, defensive behavior of a team in the target event based on plurality of graph-based representations.