Who this is for
- Analysts and data scientists who can write R and want to actually build Bayesian models in Stan, hands-on.
Not for you if
- You want a point-and-click stats tool
- You've never written R
- You want frequentist-only methods
- You want theory without building models
What you leave with
- Bayesian models coded and fit in Stan, from priors to posteriors
- GLMs and hierarchical models on real sports data
- The judgment to check, compare, and trust your models
- A complete worked case study — soccer expected goals
The full curriculum
20 sections · 80 lessons. Every section is listed with its lesson count; click any section to see its lessons.
01Introducing Bayesian Analysis for Sports2 lessons
1.1Introduction
1.2Course Topics
02Exploring Uncertainty & Variation3 lessons
2.1Uncertainty & Variation
2.2Example — 100 Meter Olympic Sprint
2.3Visualizing the Example Data
03Introducing Probability, Random Variables & Distributions15 lessons
3.1Probability, Random Variables & Distributions
3.2Random Variables
3.3Discrete Bernoulli & Binomial Distributions
3.4Poisson Distribution
3.5Counts Approach Normal Distribution
3.6Continuous Distributions & the Uniform
3.7Continuous Uniform Distribution
3.8Beta Distribution
3.9Normal Distribution
3.10Summary Statistics
3.11Joint Distributions
3.12Marginal Distributions
3.13Conditional Distributions
3.14Independence between Variables
3.15Getting to Bayes Rule
04Priors, Likelihoods, and Posteriors4 lessons
4.1Priors, Likelihoods, and Posteriors
4.2Likelihoods
4.3Normalizing Constant
4.4Conjugate Priors
05Simulating Distributions in R3 lessons
5.1Simulating Distributions in R Intro
5.2Transforming Random Numbers to Distributions
5.3Discrete Distributions
Career payoffQuantifying uncertainty in Stan is on nearly every serious quant and sports-analytics job description.
06Random Variable Code Objects1 lesson
6.1Representing Distributions with a Random Variable Code Object
07Simulations and Models in Stan4 lessons
7.1Simulations and Models in Stan Intro
7.2Stan Documentation
7.3Simple Stan Example, Simulating Values
7.4Second Example, Beta Binomial
08Posterior Simulation, Example with Grid Approximation1 lesson
8.1Grid Approximation Example
09Approximate Posteriors with MH and HMC2 lessons
9.1Approximate posteriors with MH and HMC Intro
9.2Hamiltonian Monte Carlo
10A Language for Describing Models1 lesson
10.1A Language for Describing Models Intro
Career payoffGLMs and hierarchical models are interview staples — here you build them yourself.
11Simple Normal Regression5 lessons
11.1Simple Normal Regression Intro
11.2Coding a Normal Regression Model
11.3Compiling & Fitting the Model
11.4Checking HMC Diagnostics
11.5Reviewing the Model Parameters
12cmdstanr Model Object, Helper Functions, Model Evaluation5 lessons
12.1cmdstanr Model Object, Helper Functions, Model Evaluation Intro
12.2Posterior Predictive Checks, Three (3) Approaches
12.3First and a Half Approach
12.4Third Approach
12.5Model Comparison, ELPD, and LOOCV
13Extending Normal Regression3 lessons
13.1Extending Normal Regression Intro
13.2Categorical Predictors
13.3Multiple Predictors
14Generalized Linear Models, A Conceptual Introduction3 lessons
14.1Generalized Linear Models Intro
14.2Logit Link Function
14.3Log Link Function
15GLMs, Modeling Integer or Count Outcomes9 lessons
15.1GLMs
15.2Binomially-Distributed Count Outcomes
15.3Example Model 1 in Basketball
15.4Example Model 2 in Basketball
15.5Example Model 3 in Basketball
15.6Poisson-Distributed Count Outcomes
15.7Example Two Using Soccer Data
15.8Example Three Using Soccer Data
15.9Example Four Using Soccer Data
Career payoffThe soccer expected-goals case study is a portfolio piece you can walk an interviewer through.
16More GLMs, Modeling Categorical Outcomes4 lessons
16.1More GLMs: Modeling Categorical Outcomes
16.2First Categorical Model
16.3Second Categorical Model
16.4Third Categorical Model
17Hierarchical Models, an Introduction3 lessons
17.1Hierarchical Models Intro
17.2Parameters Sharing Information
17.3Diagnostics and Reparameterization
18Workflow Recap1 lesson
18.1Workflow Recap
19Case Study: Soccer and Expected Goals10 lessons
19.1Case Study Intro
19.2Visually Exploring the Pitch Data
19.3Modeling Goals as Bernoulli
19.4Expanding the Model
19.5Adding Angle Between the Goal Posts
19.6Adding Predictor for Body Part
19.7Modeling Correlation Between Predictors
19.8Adding Hierarchical Information to the Model
19.9Reparameterizing the Model
19.10Using the Model Estimates for Decision Making
20Next Steps1 lesson
20.1Next Steps
What's included
- Lifetime accessEvery module and lesson, yours to keep and revisit, forever.
- Posit cloud workspaceA provisioned enterprise IDE and compute. Nothing to install.
- Project files and codeEvery notebook, dataset, and finished build, to keep and adapt.
- All future updatesNew lessons and refreshes as the tools move, at no extra cost.
- Self-pacedStart today, go at your own pace, no cohort to wait for.
- Free with MembershipOr get this course and the full catalog with Membership.
Your instructor
Scott Spencer, PhD
Applied Analytics Professor, Columbia University | Consultant, Nottingham Forest
Known for Consultant, Nottingham Forest
Scott teaches Bayesian statistics at Columbia University and has trained analysts across professional sports, pharma, and tech. His approach emphasizes building intuition through real data before touching any formula, then immediately translating that intuition into working Stan code. His teaching method is distinctive: every concept is grounded in sports examples (Olympic sprints, basketball free throws, soccer expected goals) before being generalized to the broader statistical framework. Students leave with both deep understanding and production-ready skills. He believes Bayesian modeling is a superpower for anyone working with data, giving a principled framework to combine domain knowledge with evidence, with Stan as the engine that makes it practical.
Questions
Is this included with Membership?
Yes. AthlyticZ Membership ($2,999/yr) includes this and the entire course catalog, plus the live Masterclass. If you plan to take more than a couple of courses, Membership is the better math.
Is it self-paced?
Yes. Start today and go at your own pace, with lifetime access to every lesson and all future updates.
Do I need to install anything?
No. You work on the provisioned Posit platform in the browser, on the same enterprise tools professional teams use.