Scaled Beta Models and Feature Dilution for Dynamic Ticket Pricing

• URL: http://arxiv.org/abs/2507.23767v1
• Date: Thu, 31 Jul 2025 17:55:07 GMT
• Title: Scaled Beta Models and Feature Dilution for Dynamic Ticket Pricing
• Authors: Jonathan R. Landers,
• Abstract summary: A novel approach is presented for identifying distinct signatures of performing acts in the secondary ticket resale market.<n>Using a newly curated, time series dataset from the SeatGeek API, we model ticket pricing distributions as scaled Beta distributions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A novel approach is presented for identifying distinct signatures of performing acts in the secondary ticket resale market by analyzing dynamic pricing distributions. Using a newly curated, time series dataset from the SeatGeek API, we model ticket pricing distributions as scaled Beta distributions. This enables accurate parameter estimation from incomplete statistical data using a hybrid of quantile matching and the method of moments. Incorporating the estimated $\alpha$ and $\beta$ parameters into Random Forest classifiers significantly improves pairwise artist classification accuracy, demonstrating the unique economic signatures in event pricing data. Additionally, we provide theoretical and empirical evidence that incorporating zero-variance (constant-value) features into Random Forest models acts as an implicit regularizer, enhancing feature variety and robustness. This regularization promotes deeper, more varied trees in the ensemble, improving the bias-variance tradeoff and mitigating overfitting to dominant features. These findings are validated on both the new ticket pricing dataset and the standard UCI ML handwritten digits dataset.

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