Adaptive Generalized Logistic Lasso and Its Application to Rankings in Sports

The generalized lasso is a popular model for ranking competitors, as it allows for implicit grouping of estimated abilities. In this work, we present an implementation of an adaptive variant of the generalized lasso penalty for logistic regression using conic programming principles. This approach is flexible, robust, and fast, especially in a high-dimensional setting. The methodology is applied to sports data, with the aim of ranking soccer players based on their contribution to possession sequences.