Art of Sports Betting Analytics

Develop probabilistic intuition and modeling expertise to design and evaluate profitable strategies.

Part 1: Small Data

Hidden generators, process variance, parameter variance, ballast methods, credibility, model assumptions. 

Rolling enrollment. Begin today.

Part 2: Bayesian Methods

Bayes theorem, conditional probability, hyperparameters, conjugate priors, mixed models, case studies. 

Rolling enrollment. Begin today.

Part 1 Small Data

Hidden generators, process variance, parameter variance.
Review of the hidden generator, Bayesian inference, prediction updating, skill vs. performance, framework of outcome as unobservable plus measurables plus noise, сase study on basketball in-game totals, priors, market information, market efficiency, modeling objectives, distribution of points by quarter, simplicity vs. accuracy tradeoff, extrapolation, credibility of new information, relationship with correlation, the fundamental question of inference, credibility theory.
Review of the credibility scale, examples of high and low credibility in sports, credibility and outcome biases, posteriors, the ballast model, the judgment balance prompt, automatic credibility adjustment, model fitting, data-driven selection, NBA historical example, numerical optimization, objective function, squared vs. absolute errors, Solver, model error, overfitting, backtesting considerations, outliers, sensitivity tests, sensitivity analysis.
Model evaluation, positive expected value, advantage betting, definitions, event space, probability, summary statistics, indicator variables, information compression, distribution functions, Normal curve, discrete vs. continuous distributions, mass functions, cumulative functions, distribution examples: uniform, binomial, Poisson, Normal, discrete Normal, Gamma, Beta.
Review of distributions: domains, parameters, shapes, fitting parameters, matching averages, standard deviations, imputation from market odds, golf example, solving for parameter values, joint probabilities and joint distributions, empirical distributions, strength of courses, NHL and NBA examples, American to Decimal odds, best fit optimization.
Review of fitting parameters, maximum likelihood estimation, NHL example, vanishing floating point computations, logarithms and exponentials, log-likelihood, mass and density functions, unconstrained maximizing with Solver, relationship with regression, generalized linear models, transformations, NHL Poisson example, model error, black swans, antifragility, potential errors such as line shading, early foul trouble, overtime, and hot hand expectation effects.
Conditional probability, the law of total probability, Bayes theorem, conditional vs. unconditional likelihood, priors, posteriors, correlated parlays, prior distributions and posterior distributions, sharpness detection example, NFL quarterback passing ability example, expected points added per dropback, sums of normally distributed variables, this domework will be discussed at the beginning of the next course: The Art of Sports Betting Analytics Course 2: Bayesian Methods.

Part 2 Bayesian Methods

Review of domework assigned at the end of the Art of Sports Betting Analytics Course 1: Small Data, review of law of total probability, prior and posterior probability, conditional probability, Bayes theorem, NFL season win totals case study, epistemology, process or aleatory uncertainty, parameter or epistemic uncertainty, Bayesian inference, the hidden generator, overdispersion,Binomial distribution, latent variables, mixed models, noise vs. signal, hyperparameters, prior and posterior distributions, Bayesian updating.
Review of latent variables and mixed models, Posterior distribution issues, conjugate priors, prior and posterior hyperparameters, Normal-Normal mixture, model learning speed, Beta prior example, assumptions for distribution calibration, relationship between ballast model and mixed model, Binomial-Beta mixed model and Beta-Binomial distribution, Poisson-Gamma mixed model and the Negative Binomial distribution, Gamma-Gamma mixed model and the Compound Gamma distribution.
Maximum likelihood with discrete and continuous distribution functions, NFL quarterback ratings case study with latent variables and hyperparameters, NCAA basketball case study with Binomial-Beta mixed model, properly weighting prior year win percentages, model issues to consider: away-home outcome dependance, strength of schedule, margin of victory, recency.

Rufus Peabody is widely regarded as one of the world’s top professional bettors. He is a co-founder of Massey-Peabody Analytics and of, and is co-host of the Bet the Process podcast

Pairwise comparison models, Bradley-Terry, double Beta distribution, NFL Bayesian power rankings, model refinements.

Review of generalized linear models, introduction of conjugate GLMs, applications to NFL and other sports, dealing with dynamic covariates.


Matt Buchalter is the owner of Plus EV Sports Analytics, which offers consulting and education to bettors of all skill levels. Matt holds a Bachelor of Mathematics degree from the University of Waterloo, where he was awarded the Samuel Eckler Medal for highest academic standing in his graduating class. Trained as an actuary, he has spent his spare time applying quantitative methods to sports betting for the past 12 years. He has had success in the Canadian sports lotteries (which Wikipedia wrongly claims are unbeatable), North American horse racing and various prop betting markets in major American sports. He has been a guest on several leading podcasts in the betting space, and he has written articles for Pinnacle Sports and more recently for his own blog where his articles have been recommended by some of the world’s leading professional bettors. Matt has achieved worldwide acclaim in the field of sports betting analytics with specialized expertise in Bayesian analysis, applied probability, optimal bet sizing, betting market dynamics and evaluation of betting results. His teaching style blends academic theory and real-world practice while keeping the atmosphere casual and fun.


Twitter: @PlusEVAnalytics

Guest Lecturer

Rufus Peabody is widely regarded as one of the world’s top professional bettors.  He is a co-founder of Massey-Peabody Analytics and of, 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.


Twitter: @RufusPeabody


Aaron Green
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The course was awesome!! Harry and Philip did a great job of laying the foundations while simultaneously providing value to more experienced bettors with advanced topics and guests. My motivation for taking the course came down to my interest in forecasting and markets in general. I think the lessons here are broadly applicable and will help me in my explorations on where to focus my time and energy on my next big project. I think regardless of whether or not I get into betting more seriously, my investment in your course is worth it...
Tom Quinn
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I have really enjoyed the course. The biggest negative I can think of is that I wish each class was 4 hours, which is definitely a compliment. Overall, great class. I went into the class thinking I needed to refresh / enhance my quant skills to continue to improve. My primary takeaway is that I need to improve my data management and efficiency more than anything.
Michelangelo Whitson
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Enjoyed the course - given me a lot of food for thought and opened my eyes to some different approaches to things too. I like the dynamic between Harry and Philip presenting the course, and if it weren't for the late night start/finish for me in the UK I might contribute more but it's fast-paced enough that there's plenty to digest too.
Caveman Sam
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The class exceeded my expectations! 10/10 would recommend to others. Outside of arbing, my knowledge on sports betting, prior to this class, was minimal. For the novice, this class is exactly what I was looking for. I come from other advantage play worlds so I signed up looking to add more tools to the toolkit. One aspect about the class I really enjoy is the level of detail you guys dive into by taking the time to prove concepts. A lot of “ah ha” moments. Overall the course has been invaluable. Thanks for putting this together!!

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