A Bayesian hierarchical model for estimating the cost of postponing the cyclo-cross national championships

J. T. Fry, Andrew Hoegh, Scotland Leman and Matthew Montesano

Journal of Applied Statistics, 2018, vol. 45, issue 2, 298-305

Abstract: In early January 2015, the multi-event national cyclo-cross bicycle races were set to take place in Austin, Texas. Cyclo-cross has a rich history in this country, and throughout the world, attracting huge crowds and competitors. Being primarily a winter sport, these athletes often compete in harsh conditions, which include rain, snow, mud, and revel in the excitement that comes with such elements. Unfortunately, the competition was postponed mid-event when a local arborist group protested to the parks department. The issue: there was too much mud, in an event where many spectators and racers alike hope for such conditions. For many competitors, the postponement generated additional expenses, such as flights, hotels, and car rentals. Although people on opposite sides of the debate may greatly disagree, we instead focus on the competitors themselves. We analyze the financial impact of the disagreement using a hierarchical Bayesian mixed model which accounts for heterogeneity within the costs endured by the event's participants.

Date: 2018
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DOI: 10.1080/02664763.2016.1276523

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