Demystifying Bayes’ Theorem [Infographic]

Just how does Nate Silver do it? He’s the “King of Quants” and creator of Pecota, the most accurate baseball player performance forecasting system in the world. The man correctly predicted 49 of the 50 states in the 2008 presidential election and all 50 states in the 2012 contest. He even correctly picked the 2012 and 2013 NCAA champion teams. While the math behind Silver’s predictive system is unknown to the public, it is understood to be based on Bayes.

Bayes’ Theorem is a probability theory to measure the degree of belief that something will happen using conditional probabilities. Bayes’ Theorem was first published in 1763, two years after Thomas Bayes’ death. Let’s take a look at how Bayes’ Theorem would apply to baseball. Start with the history of the event you are predicting. If the Yankees, for example, have played 100 games and won 72 and lost 28, plug in the appropriate numbers in the formula. The more variables taken into consideration, the more accurate the prediction will be. How often did the Yankees win their night games? This is an example of another variable that can be brought into the mix.

To learn more about Bayes’ Theorem and how you can apply it to different real life scenarios, take a look at the infographic below presented by

Demystifying Bayes’ Theorem [Infographic] image bayes2Demystifying Bayes’ Theorem [Infographic]
Source: Predicting Baseball: Demystifying Bayes’ Theorem

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