Agenda

David Firth, University of Warwick, UK

The seminar will take place on Zoom
https://unive.zoom.us/j/82776377762
Passcode: SanMarco1

Abstract:
In this talk I will show how to construct a better football league table than the official ranking based on accumulated points to date.  The aim of this work is (only) to produce a more informative representation of how teams currently stand, based on their match results to date in the current season; it is emphatically not about prediction.  A more informative league table is one that takes proper account of "schedule strength" differences, i.e., differing numbers of matches played by each team (home and away), and differing average standings of the opponents that each team has faced.

This work extends previous "retrodictive" use of Bradley-Terry models and their generalizations, specifically to handle 3 points for a win, and also to incorporate home/away effects coherently without assuming homogeneity across teams.  Playing records that are 100% or 0%, which can be problematic in standard Bradley-Terry approaches, are incorporated in a simple way without the need for a regularizing penalty on the likelihood. A maximum-entropy argument shows how the method developed here is the mathematically "best" way to account for schedule strength in a football league table.

Illustrations will be from the English Premier League, and the Italian Serie A.

Bio Sketch:
David Firth is Emeritus Professor of Statistics at University of Warwick. Before joining Warwick in 2003 he was Professor of Social Statistics in Oxford and he has held academic positions at Imperial College London, at the University of Texas at Austin, and at the University of Southampton. He works on statistical theory, methods and computation, and applications in many disciplines, especially social-science and biostatistical applications. Current research topics include: inference and computation for generalized nonlinear models (with Heather Turner and Ioannis Kosmidis); inference and computation for complex random-effects models (with Cristiano Varin); models of competition and the analysis of pair-comparison data (with Manuela Cattelan, Cristiano Varin, Heather Turner, David Selby and Ian Hamilton); compositional data analysis (with Fiona Sammut and Zeeshan Ali); penalized likelihood methods, especially for modelling discrete data (with Ioannis Kosmidis).