I have been hard at work developing the course content for Bayesian Sports Betting. At this point I have around 75% of the course built. Normally I’m not the type to work 3-4 months ahead, but I have really enjoyed developing this content and have found myself “in the zone” coming up with analogies and real-world sports betting examples to teach this stuff. It’s been a fun challenge taking material that ranges from “101 level” undergraduate to PhD level and making it interesting and accessible to practitioners with nothing more than a high school math background. “Accessible” to me means no calculus, no Greek letters, no nitpicky technicalities, no unnecessary jargon or formalism, etc. 

 

I am now able to share the lesson plan in a little more detail than before: (All subject to change)

 

Lesson #1: What Is Small Data?

Lesson #2: Priors, Posteriors and the Credibility Scale

Lesson #3: The Ballast Model

Lesson #4: Introduction to Probability Distributions

Lesson #5: Fitting Parameters, part 1 – mean matching, market imputation

Lesson #6: Fitting Parameters, part 2 – Maximum Likelihood, regression and Generalized Linear Models

Lesson #7: Bayes Theorem, Prior and Posterior Probabilities

Lesson #8: Parameter Variance, Epistemic Uncertainty and Mixed Models

Lesson #9: Conjugate Priors

Lesson #10: Fitting Bayesian Models Using Maximium Posterior Likelihood

Lesson #11: Models for Power Rankings

Lesson #12: Combining Bayesian and Regression Models, “The Conjugate GLM”

 

EXCITING ANNOUNCEMENT #1: On Tuesday November 16 from 8:30-10:00 PM Eastern Time, I will be presenting Lesson #1 “What Is Small Data?” for free and open to the public, followed by a 30 minute Q&A where you can ask me anything about Bayesian Sports Betting, analytics.bet, my articles or anything else. Think of it as a free sample! Click here to register and mark your calendar, hope to see you there!

Enrolled students will receive a recording of Lesson #1, and live classes for enrolled students will begin on Tuesday January 4th from 8:30-9:30PM Eastern with Lesson #2. Enrolled students will still receive 12 live classes – my lessons #2 through #12, plus one bonus guest lecture…

 

EXCITING ANNOUNCEMENT #2: Bayesian Sports Betting will feature a guest lecture by Rufus Peabody. Rufus is widely regarded as one of the world’s top professional bettors.  He is a co-founder of Massey-Peabody Analytics and of Unabated.com, and is co-host of the Bet the Process podcast. Previously he was ESPN’s predictive analytics expert and a statistical analyst for Las Vegas Sports Consultants. He has a B.A. in Economics from Yale University where he wrote his senior thesis on inefficiencies in the MLB betting markets. He has been a panelist at the MIT Sloan Sports Analytics Conference and a speaker at numerous other global outlets and has been featured in numerous national publications including USA Today, the Washington Post, Sports Illustrated, ESPN, Sporting News, and the Wall Street Journal, which has also published his NFL and college football ratings for over a decade.

Hope to see you soon! – Matt