As in-person events begin to restart over summer, many of us will return to enjoying spectator sports, particularly football as the delayed UEFA Euro 2020 finally kicks-off. As a field dependent on performance and analytics, the intersections between statistics and sport are wide. Through a series of livestreamed talks, we welcome two speakers to discuss statistical approaches in football, including assessment, evaluation and prediction of team performances.
The
Royal Statistical Society’s Merseyside Local Group are pleased to announce our next online event "Statistics and Football".
As in-person events begin to restart over summer, many of us will return to enjoying spectator sports, particularly football as the delayed UEFA Euro 2020 finally kicks-off. As a field dependent on performance and analytics, the intersections between statistics and sport are wide. Through a series of livestreamed talks, we welcome two speakers to discuss statistical approaches in football, including assessment, evaluation and prediction of team performances.
The event will take place on Wednesday 2nd June from 16.00-17.00 and will be broadcasted live via the RSS Merseyside Group’s official YouTube channel.
Prof. David Firth (University of Warwick): "Schedule-adjusted league tables during the football season."
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 Premier League in recent seasons.
Dr Rob Mastrodomenico (Global Sports Statistics): "An introduction to modeling soccer."
The increased data in sports has provided statisticians with the tools to build models of sporting events. This talk will look to show how data from soccer matches can be used to create models with the ability to predict upcoming matches. Starting from a very simple approach we will show how a modified poisson approach is able to characterise the dynamics of the beautiful game. Following from the model definition we fit it on data from England and show how the output can be used in predicting games from the English Premier League.