Introduction | Goal-based models in a nutshell | Scoring rates and team abilities | Distributions for the goals | Distributions for the goal difference | Prior distributions | Static and dynamic abilities | Inference | Installation and setup | Data | Static models | Maximum likelihood | Bayesian estimation with stan_foot | Changing the default priors | Alternative algorithms | Dynamic models | Weekly dynamics within a season | Seasonal dynamics and the evolution variance | Historical strengths: the Bradley-Terry-Davidson model | The model | Fitting the model with btd_foot | Visualization tools | Using the strengths as a covariate in stan_foot | Model checking | Predictions | Posterior out-of-sample probabilities | Home win probabilities | League table reconstruction | Model comparison | Predictive performance with compare_foot | Information criteria with loo | References

Example: bivariate Gaussian data | Example: fishery data | References

Pivotal algorithms: how they works, and why | piv_KMeans: k-means clustering via pivotal units | References