Master of Sports Betting

The basics of sports betting: the ethics of sports betting, where to bet, common bet types, angles, edges, and strategies, along with American, Decimal, and Fractional odds and how to convert between them.

Probability and its application in sports betting: break-even probability, vig, overround, implied probability, synthetic vig, synthetic overround, expected value, and edge.

How to evaluate and effectively take advantage of sportsbook promotions and bonuses, including deposit bonuses, free bets, bonus money, risk-free bets, and related opportunities.

The key elements of arbitrage: identifying arbitrage opportunities, sizing bets correctly, working across multi-way outcomes, and practicing execution through examples and exercises.

Essential components of bankroll management: accurate record-keeping, betting within your bankroll, avoiding overbetting, optimal bet sizing, the Kelly criterion, and the practical judgment needed to apply them.

How to determine the value of points: middling for spreads and totals, empirical data analysis, half-middling, and practical considerations when deciding whether a middle is worth pursuing.

A review of key concepts and how they connect to the bundled suite of tools, including odds conversion, promos and bonuses, line shopping, middling, and future tools for continued education. Participants receive permanent access to these resources.

Understanding sports betting markets through reverse engineering of market lines, alternative lines, and systematic market analysis; an NBA alternative-lines case study; empirical distributions, statistical assumptions, random variables, expected value, edge, variance, Gaussian methods, exploratory data analysis, and an introduction to NHL historical odds work in R.

Different approaches to profitable sports betting, including top-down, bottom-up, and hybrid workflows; inter-book mispricings, line grinding, market coherence, steam chasing, and methods of evaluation such as naive backtesting, coherent backtesting, shrinkage, closing line value, z-scores, goodness-of-fit testing, and practical caveats.

Regression, residuals, market resistance, market support, parimutuel versus fixed odds, fractional odds, and the definition and empirical justification of closing line value, with real-data demonstrations showing how CLV and expected value interact in practice.

Closing line value for spreads and totals, derivation and use of CLV calculations, alternative-line approaches, partial markets, teasers, historical empirical and Gaussian approaches, and the theory and practical execution of middling across NBA, NFL, and NHL markets.

Record keeping, additive versus multiplicative dynamics, growth-rate maximization, loss aversion, prospect theory, overbetting, the Kelly criterion, the components of an edge, and the relationship between arbitrage, Kelly sizing, and real-time hedging.

A guest session with "JM," a renowned sports bettor and market maker leading a dynamic licensed Maltese syndicate focused on U.S. sports trading, offering perspective on how advanced betting ideas operate in live professional environments.

A review of the top-down approach before introducing bottom-up and hybrid modeling, including data acquisition and quality control, file management, variables, distributions, logistic regression, deep learning, model life cycle, backtests, and the transition from exploratory work to structured model development.

Best practices and common pitfalls in model evaluation, including coherence checks, market checks, residual analysis, flat versus Kelly betting, in-sample and out-of-sample testing, variable development, weighting schemes, and the practical differences between backtesting and live betting.

Shrinkage from both theoretical and applied perspectives: calibration, model refinement, loss functions, regression shrinkage, evaluation metrics, variable selection, model assumptions, and how shrinkage helps refine probabilities, totals, and live-betting execution.

A deeper study of optimal betting and Kelly sizing, including fractional Kelly, multiple-outcome Kelly, parlays, correlated outcomes, growth-rate comparisons, expected value under different correlation structures, and practical sizing considerations in sportsbook and parimutuel settings.

Modeling beyond Gaussian assumptions, including binary outcomes, logistic regression, shrinkage for probabilities and moneylines, player and team relative-ability models, binomial and Poisson frameworks, generalized linear models, hybrid modeling, and robustness checks.

Four in-depth case studies showing real applications of course concepts in profitable betting, with examples drawn from NFL, NBA, NHL, and MLB markets and covering hybrid modeling, uncertainty pricing, distributional assumptions, and reverse engineering of model parameters.

A guest lecture by Dr. William T. Ziemba, Alumni Professor (Emeritus) of Financial Modeling and Stochastic Optimization at the Sauder School of Business, University of British Columbia, and the author of several of the most influential books on gambling and betting theory.

Review of the hidden generator, Bayesian inference, prediction updating, skill versus performance, outcome as unobservable plus measurables plus noise, a basketball in-game totals case study, priors, market information, market efficiency, modeling objectives, distribution of points by quarter, simplicity versus accuracy tradeoff, extrapolation, credibility of new information, correlation, and the fundamental question of inference.

Review of the credibility scale, examples of high and low credibility in sports, credibility and outcome biases, posteriors, the Ballast model, judgment balance prompt, automatic credibility adjustment, model fitting, data-driven selection, an NBA historical example, numerical optimization, objective functions, squared versus absolute errors, Solver, model error, overfitting, backtesting considerations, outliers, and sensitivity analysis.

Model evaluation, positive expected value, advantage betting, definitions, event space, probability, summary statistics, indicator variables, information compression, distribution functions, the Normal curve, discrete versus continuous distributions, mass and cumulative functions, and examples including uniform, binomial, Poisson, Normal, discrete Normal, Gamma, and Beta distributions.

Review of distributions through domains, parameters, shapes, and fitting methods; matching averages and standard deviations; imputing from market odds; a golf example; solving for parameter values; joint probabilities and distributions; empirical distributions; strength-of-schedule style course comparisons; NHL and NBA examples; American-to-decimal conversion; and best-fit optimization.

Review of fitting parameters, maximum likelihood estimation, an NHL example, vanishing floating-point computations, logarithms and exponentials, log-likelihood, mass and density functions, unconstrained maximization with Solver, the relationship with regression, generalized linear models, transformations, an NHL Poisson example, model error, black swans, antifragility, and practical issues such as line shading, early foul trouble, overtime, and hot-hand expectation effects.

Conditional probability, the law of total probability, Bayes theorem, conditional versus unconditional likelihood, priors, posteriors, correlated parlays, prior and posterior distributions, a sharpness-detection example, an NFL quarterback passing-ability example, expected points added per dropback, sums of normally distributed variables, and the homework bridge into the Bayesian Methods portion of the program.

Review of the homework from Course 1, law of total probability, prior and posterior probability, conditional probability, Bayes theorem, an 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 versus signal, hyperparameters, prior and posterior distributions, and Bayesian updating.

Review of latent variables and mixed models, posterior distribution issues, conjugate priors, prior and posterior hyperparameters, Normal-Normal mixtures, model learning speed, the Beta prior, distribution calibration assumptions, the relationship between the ballast model and mixed models, Binomial-Beta and Beta-Binomial structures, Poisson-Gamma and Negative Binomial models, and Gamma-Gamma and Compound Gamma formulations.

Maximum likelihood with discrete and continuous distributions, an NFL quarterback ratings case study with latent variables and hyperparameters, an NCAA basketball case study with a Binomial-Beta mixed model, properly weighting prior-year win percentages, and practical model issues such as away-home dependence, strength of schedule, margin of victory, and 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 Unabated.com, and is co-host of the Bet the Process podcast.

Pairwise comparison models, Bradley-Terry, double Beta distributions, NFL Bayesian power rankings, and the refinements needed to make ranking systems more stable, interpretable, and useful in real betting analysis.

Review of generalized linear models, an introduction to conjugate GLMs, applications to NFL and other sports, and the challenges of working with dynamic covariates in realistic betting environments.

An NBA data example and an introduction to Python, including installation, terminal basics, Jupyter notebooks, modules, variables, lists, and dictionaries, along with HTTP requests and practical spreadsheet shortcuts such as Flash Fill in Excel.

Parsing CSV, XML, JSON, and HTML sources with tools such as BeautifulSoup and regular expressions, while addressing common data-cleansing issues during ingestion.

File management, cloud versus local storage, files versus databases, Postgres and pgAdmin, database types, schemas, primary keys, indexes, SQL, queries, joins, views, extracting from databases, exporting to CSV, connecting to Excel, SQL libraries for Python, building NBA box-score databases, reparsing, and the challenges of play-by-play database construction.

Populating databases, data visualization, exploratory data analysis, common data-cleaning issues, outlier detection, and indexing across different data sources.

A golf data case study covering scraping, parsing, database loading, management, transformation, and downstream analysis in a practical end-to-end workflow.

Production versus staging versus development environments, GitHub workflows, unit tests, docstrings, comments, and the engineering discipline needed for maintainable betting systems.

Batch processing, caching, storage considerations, naming conventions, and cron-based scheduling for repeatable and scalable sports betting workflows.

JavaScript and password-protected sites, Selenium, and the professional habits needed to automate responsibly while working with brittle or access-controlled data sources.

Building a line-shopping tool and comparison tool as a practical introduction to collection bots that pull, normalize, and compare sportsbook information.

APIs, GET and POST requests, authentication and permissions, tracking systems, big red shutdown buttons, circuit breakers, and the relationship between betting automation and high-frequency equity or crypto trading systems.

An end-to-end case study combining APIs and exchange-trading style execution, showing how engineering discipline and automation come together in real-world betting infrastructure.