I enjoyed John Horgan's piece on Bayes Theorem for Scientific American. Bayes Theorem and Bayesian reasoning are highly applicable when thinking about forecasting and prediction markets; indeed, one prediction market built a Bayes Net into its platform. In this post I'll explain what Bayesian reasoning is, why it matters to prediction markets, and give a concrete (but semi-fictitious) example of how it's applied.
I've been trying to pick NFL game winners. I'm not using any complex analytical model; rather, I'm making decisions the way most sports bettors do--I watch some games, read the news, and use my judgment. I make each of my picks on SportsCast, which allows me to track my performance, interact with other forecasters, and track the performance of the prediction market--that is, the collective performance of all the forecasters on SportsCast.
Joining a prediction market can be confusing and anxiety-inducing. It's easy to be overwhelmed by all the questions, to not understand the forecasting interface, or to have trouble forming opinions to base forecasts on. All of this is pretty natural--as a now-experienced forecaster, I can remember these feelings the first time I joined a prediction market. In this post I'll address a few specific emotional barriers that make it difficult to start forecasting.
One use of prediction markets I've been really excited about is forecasting individual players' performances in major sports. These predictions are incredibly useful when playing fantasy sports--both daily fantasy and season-long leagues--and the forecasts that currently exist tend to be, in my experience, pretty mediocre. Prediction markets present an opportunity for the wisdom of the crowds to intervene, and will likely lead to more accurate forecasts.