Previously, I described a model for taste preference appropriate for some experiments in cocktail design I conducted years ago. I noted that this model was so elegant and simple, it must have been discovered previously, and have a rich theory around it. In the two weeks since then, I discovered a new paper on arxiv about inference on ranks from comparisons. They review a model much like the one I outlined, calling it the Bradley-Terry-Luce model. (Hey, look, there is indeed a package on CRAN for this with a vignette!)
The paper by Shah and Wainright outlines a very simple method for estimating the top \(k\) of \(n\) participants when the contests include exactly two participants each. If I am reading it correctly, you take the average number of observed wins for each contestant, then grab the top \(k\). They prove that this algorithm is optimal under certain conditions. This seems to me like an ideal outcome for a research result: the algorithm is dead simple, and people have likely been using it for years, while the proof is somewhat intricate. Unfortunately, it does not seem straightforward to generalize the algorithm to the case where there are covariates, or 'features' about the various contestants, nor necessarily to the case of multiple contestants in a given contest. The Bradley-Terry model, on the other hand, is readily adaptable to these modifications.