A Machine Learning Model That Predicts Who Wins Survivor
A Model for Winning Survivor
I built a logistic regression model to predict who gets voted out next and who wins Survivor, using data from the survivoR GitHub repo. The win model picks the eventual winner about twice as often as chance, and it flagged Jonathan as a contender before I took him seriously. It also showed why Cirie's age capped her win probability, even as her elimination odds spiked after a big move.
I don’t know whether Cirie’s low P(Win) is a result of the model missing Cirie’s idiosyncratic strengths, or a result of it capturing something real that fans would rather ignore. Probably some of both.
- jihadjihad
> I loved using it while watching Season 50, and it identified several interesting threads early on.
The ideas in TFA are interesting, but it’s important to keep in mind that when we watch Survivor, we’re not watching something that happened in real time and then was simply compiled together. Survivor is edited after the fact, once the winners and all the arcs of contestants are known.
And don’t get me wrong! I love watching the show even knowing that in my mind. But a model built on the edited material is necessarily biased due to the format of the show itself.
- quickthrowman
The model in reality is “Hope the producers like you enough to write you into the show as the winner.” Survivor is hyperreality, not an actual competition.
- NitpickLawyer
> By the finale, Kalshi’s “Survivor” market had reached a volume of $32.7 million
I don't get this. How are people "betting" on something that is "known information" for other people? What's the point? I get betting on stuff that no one can know, but who's taking "the other side" on these bets? Why would you put money into something knowing that there are people who already have the absolute answer?