Abstract: A method and system of dynamically pricing tickets for an event is disclosed herein. A computing system retrieves historical pricing information for a given team. The historical pricing information includes ticket sale information for a plurality of events. The computing system generates a predictive model using a machine learning model. The computing system receives a set of tickets for an upcoming event. The upcoming event is between the given team and an opponent. The computing system generates, via the predictive model, an event score and a spring value for the upcoming event based on historical ticket sale data for the given team, team-specific information, and opponent-specific information. The computing system constructs a price for each ticket in the set of tickets based on parameters of each ticket, the event score, and the spring value.

Abstract: A method and system of dynamically pricing tickets for an event is disclosed herein. A computing system retrieves historical pricing information for a given team. The computing system generates a predictive model using a machine learning model. The computing system generates the predictive model by generating a plurality of input data sets based on historical pricing information and learning, by the machine learning model, a price for each ticket based at least on the team-specific information, opponent-specific information, and historical price data. Each input data set includes team-specific information, opponent-specific information, and historical ticket sale data. The computing system receives a set of tickets for an upcoming event. The upcoming event is between the given team and an opponent. The computing system generates, via the predictive model, a price for each ticket in the set of tickets based on parameters of each ticket, the team-specific information, and opponent-specific information.