GoKawiilThe new season of Survivor airs next week. My favorite thing about watching Survivor is arguing about the strategy of the show: who should have won the season? Is it better to play under the radar? Or does making big moves set you up to win? Who is the greatest player of all time: Cirie? Tony? Boston Rob? I thought it’d be fun to build a machine learning model to help answer these questions.
On Survivor a bunch of real-life strangers are stranded somewhere remote. They manage camp life, compete in challenges for “immunity,” hunt for advantages, and vote each other out every three days at “tribal council.” Once it’s down to the final two or three, the players who have been voted out (now called “the jury”) vote for the winner. The winner takes home a million dollars and the title of “Sole Survivor.”
Ultimately it’s a social strategy game.
It’s messy and feels in some way outside the scope of what you can do with a mathematical model. But when we watch and argue about the show, we’re implicitly building mental models of what it takes to win. An ML model is just a way to systematize those theories and put them to the test. And maybe, hopefully, it can give a meaningful preview of who will take the title of “Sole Survivor” (assuming, like me, you’re the type to ignore the betting market leaks). I loved using it while watching Season 50, and it identified several interesting threads early on. I’ll share some of those insights with you here as well.
The model predicts, episode by episode, who’s most likely to win, and who’s most likely to go home next. You can play with the model’s results here.
An overview of the Survivor ML dashboard.
The website loads the most recent season by default, but you can explore previous ones too. The top row shows win and elimination probabilities across a season. Below that is a cumulative plot of who’s been ranked #1 most often, which is a cleaner way to see the real contenders. A “Player Breakdown” section lets you pick a player to understand which features bump their odds up or down. More on all of this below. (And if you want all the nerdy ML details, there’s a notes section at the end.)
Caution: Spoilers for the TV show Survivor ahead!
Building the model
The data comes from the survivoR GitHub repo, which has a ton of nicely structured Survivor data like voting history, challenge results, advantages, demographics, edit metrics, etc.
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