The 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.…