Speaker
Description
At the 1980 Winter Olympics, the men’s 15 km cross-country skiing race was decided by one hundredth of a second. Rather than debating historical causes, we treat this as a modeling problem: could a physically plausible effect—such as a small change in aerodynamic drag—have been large enough to matter?
In this talk, we demonstrate how Bayesian forward simulation in R can be used to reason about extreme performance margins under limited data. Using publicly available split times and a realistic proxy for course profile, we construct a physics-informed probabilistic model of aerodynamic drag. Key uncertain quantities—frontal area, drag coefficient, and power output—are represented as prior distributions. We then propagate uncertainty through the model to obtain a posterior distribution for time loss attributable to drag.
The resulting visualization makes a subtle point clear: margins commonly described as “negligible” may lie well within the distribution of physically plausible outcomes. The emphasis is not on rewriting history, but on illustrating how R can be used to formalize counterfactual reasoning, propagate uncertainty, and communicate probabilistic thinking in an intuitive way.
The talk focuses on reproducible simulation, transparent assumptions, and effective visualization strategies for Bayesian reasoning in applied contexts.
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ChatGPT/ to help find data and Lake Placid's skiing track profiles
| Keywords: Please list up to 5 keywords to help us find the right session for your contribution. | Bayesian modeling, simulation, uncertainty propagation, reproducible research, sports analytics |
|---|---|
| Virtual Option | This submission is for onsite presentation primarily, but I would also like it to be considered for pre-recorded virtual presentation if I don't get an onsite slot |
| Video Recording | Video sharing is fine |
| The author(s) agree(s) to take responsibility and be accountable for the contents of the submission and is/are authorized to present it. | Confirm |