Abstract: A ticket exchange server is configured to determine a number of tickets to distribute for an event. The ticket exchange server accesses, for a stadium, training data describing attendance at historical events, historical opponents of a sports team, and a historical win/loss record of the sports team. The ticket exchange server trains a machine-learned model configured to predict an attendance for a future event at the stadium based on an opponent of the sports team at the future event and a current or predicted win/loss record of the sports team. The ticket exchange server selects an event for the sports team against an opponent and determines a predicted attendance using the machine-learned model. The ticket exchange server identifies a number of tickets greater than a capacity of the stadium to make available based on the predicted attendance and distributes the number of tickets to prospective attendees.

Abstract: A ticket exchange server is configured to distribute tickets to an event based on predicted attendance. The ticket exchange server accesses a set of training data describing statistics of and attendance during a historical event for a plurality of historical events. Using the training data, the ticket exchange server trains a machine-learned model, which is configured to predict a likelihood of a seat at a stadium being vacant during an event based on real-time statistics. During an event at the stadium, the ticket exchange server detects a vacant seat associated with a first ticket of a first user. The ticket exchange server determines a value of a second ticket for the vacant seat at least in part by applying the machine-learned model to real-time statistics of the event and distributes the second ticket to a second user.