Abstract: A ticket exchange server is configured to monitor seat usage in a stadium during an event. The ticket exchange server provides a first ticket for a seat to a first client device in response to a request from a first user of the first client device. The ticket exchange server unlocks the seat in response to detecting a presence of the first user at the seat. The ticket exchange server receives an indication from the first user surrendering the seat prior to completion of the event and locks the seat. The ticket exchange server provides a second ticket for the seat for a remainder of the event to a second client device of a second user in response to a request for the seat from the second client device. The ticket exchange server unlocks the seat in response to detecting a presence of the second user at the seat.

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 monitor seat usage in a stadium during an event. The ticket exchange server provides a first ticket for a seat to a first client device in response to a request from a first user of the first client device. The ticket exchange server unlocks the seat in response to detecting a presence of the first user at the seat. The ticket exchange server receives an indication from the first user surrendering the seat prior to completion of the event and locks the seat. The ticket exchange server provides a second ticket for the seat for a remainder of the event to a second client device of a second user in response to a request for the seat from the second client device. The ticket exchange server unlocks the seat in response to detecting a presence of the second user at the seat.

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.

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.