Abstract: Embodiments disclosed herein generally relate to a system and method for updating a set of strength-based ratings for a sporting event using market information. The present embodiments provide a data-driven approach leveraging market information to derive an updated team strength prior to an upcoming event. For example, a prediction model can internally determine an initial team strength measurement. The prediction model can also obtain futures market information and update the initial team strength measurement using the futures market information.

Abstract: A method of generating predictions for teams and players for each team associated with a sporting event, the method including: receiving one or more top-down predictions for the sporting event; providing the top-down predictions as one or more top-down feature vectors to a computing system; receiving, by the computing system, a second set of feature vectors comprising data for one or more players associated with one or more respective teams and data for one or more teams associated with a sporting event; inputting the one or more top-down feature vectors and second set of feature vectors into a transformer-based neural network; and generating, using the transformer-based neural network, one or more predictions for the sporting event based on the one or more top-down feature vectors and the second set of feature vectors.

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 method is disclosed herein. A computing system receives a data feed for an event. The data feed includes real-time player and team information. The computing system generates a feature representation of the data feed. The computing system generates micro predictions for the event. The micro predictions for the event are associated with predictions at a match level. The computing system generates macro predictions for each team involved in the event. The macro predictions are associated with predictions across one or more seasons. The computing system generates an output. The output includes the micro predictions and the macro predictions.

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.