Predicting basketball winners through simulation
The terms "Moneyball" and "sabermetrics" are increasingly
being used in pop culture. In fact, there was a 2011 movie on the topic. These terms refer to the relatively new, evidence-based,
statistical approach used in baseball management. Can this approach be
applied to the game of basketball? The short answer is: it's much
trickier. Baseball involves clear, discrete intervals of play
surrounding one interaction (the pitcher interacting with the batter).
Basketball consists of many players interacting simultaneously with
possessions of variable length
In my spare time, a friend and I attempted to create a Monte Carlo simulation of a professional basketball game. The procedure involved pulling the most recent player statistics off of various websites and simulating a match between two teams 1000 times.
The output consisted of the distributions of 1000 final scores for each team, and looked something like this...
Unfortunately, through backtesting on historical games from the season we only achieved a low win-accuracy metric of 58% 🤷♂️. It was a valuable process though, and looking back and taking a closer look at other methods folks have taken, there are probably better ways to encode our domain knowledge.
© Roni Kobrosly 2022