Machine-Learned Attendance Prediction for Stadium Ticket Distribution

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing ticket distribution systems fail to optimize stadium seating utilization and value determination for vacated seats during events, leading to underutilization of available space and difficulty in valuing tickets fairly.

Innovation Solution

A ticket exchange server that uses machine-learned models to predict attendance and seat vacancies, dynamically distributing tickets and adjusting values based on real-time statistics and historical data to maximize seating utilization and ticket value.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If traditional ticket distribution systems are used, then ticket sales are simplified, but stadium seating utilization is underoptimized and vacant seats cannot be dynamically reallocated

Engineering Contradiction:

Improvestadium seating utilizationVSAvoidticket distribution system complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system dynamically adjusts ticket availability and seat assignments based on real-time attendance data and predictive analytics. Seats transition between locked and unlocked states, and ticket values are continuously adjusted based on predicted attendance and game conditions, enabling optimal seating utilization throughout the event.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system automatically monitors attendance, predicts seat vacancies, and reallocates tickets without manual intervention. The machine learning model continuously analyzes data and makes autonomous decisions about ticket distribution and pricing, reducing the need for human operators while maximizing seating utilization.

Inventive Principle:

Principle #25Self-service

2Productivity

If tickets are distributed based on static capacity limits, then overbooking is prevented, but potential attendees are turned away when actual attendance is lower than predicted

Engineering Contradiction:

Improveattendance maximizationVSAvoidticket value accuracy

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system implements continuous feedback loops where actual attendance data is fed back into the machine learning model to refine future predictions. Real-time monitoring of seat occupancy and attendance patterns allows the system to adjust ticket availability and pricing dynamically, maximizing attendance while maintaining accurate value assessment.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system changes key parameters such as ticket availability, pricing, and seat assignment based on evolving conditions including predicted attendance, actual turnout, and game dynamics. This allows flexible adjustment of distribution strategies to maximize attendance without compromising ticket value integrity.

Inventive Principle:

Principle #35Parameter changes

3Productivity

If seats are locked to original ticket holders, then ticket holder rights are protected, but vacant seats cannot be utilized by other attendees

Engineering Contradiction:

Improveseat utilization rateVSAvoidseat access flexibility

Core Design Contradiction:

ProductivityVSEase of operation

Solution Approach 1:

The system performs preliminary actions by predicting which seats are likely to become vacant before the event concludes. By identifying probable vacancies in advance, the system can prepare alternative ticket assignments and notify waiting attendees, enabling smooth transitions without disrupting current seat occupants.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system acts as an intermediary between original ticket holders and potential replacement attendees. It monitors seat usage, identifies vacancies, and facilitates transfers through automated ticket reassignment, balancing the rights of original holders with the needs of utilization optimization.

Inventive Principle:

Principle #24Intermediary (Mediator)

4Productivity

If machine learning models predict attendance accurately, then ticket distribution can be optimized, but data processing complexity and computational requirements increase

Engineering Contradiction:

Improveticket distribution efficiencyVSAvoidsystem computational complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system segments the stadium into zones or sections and processes attendance data at multiple levels - from individual seat sensors to section aggregates to overall stadium totals. This hierarchical approach reduces computational complexity by processing data in manageable segments rather than analyzing every individual data point simultaneously.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS20250217832A1Machine-learned attendance prediction for ticket distribution

Publication Date: 2025.07.03 UPTIX INC

AI summary

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.