Julien Guyon – Risk of Collusion in FIFA World Cup and a New Tournament Design

Friday, December 4th, 2020, 6pm EST
Julien Guyon (Bloomberg, NYU, Columbia)
(1) Risk of Collusion: Will Groups of Three Ruin the FIFA World Cup?
(2) “Choose Your Opponent”, a New Tournament Design

Please find a recording of the presentation below.

In 2026, the FIFA World Cup will for the first time gather 48 men’s national teams. It will consist of a group stage made of 16 groups of three, with the best two teams in each group advancing to the knockout stage. Using groups of three raises several fairness issues, including risk of match fixing and schedule imbalance. In this article we examine the risk of collusion. The two teams who play the last game in the group know exactly what results will let them advance to the knockout stage. Suspicion of match fixing occurs when a result qualifies both of them at the expense of the third team of the group, and can seriously tarnish the tournament. We quantify how often this is expected to happen and explain how to build the match schedule so as to minimize the risk of collusion. We also quantify how the risk of collusion depends on competitive balance. Moreover, we show that forbidding draws during the group stage (a rule considered by FIFA) does not eliminate the risk of match fixing, and that surprisingly when draws are forbidden the 3-2-1-0 point system does not do a better job at decreasing the risk of collusion than the 3-0 point system. Finally we describe alternate formats for a 48 team World Cup that would eliminate or strongly decrease the risk of collusion. Then, we present a new knockout format for sports tournaments, that we call “choose your opponent”, where the teams that have performed best during a preliminary group stage can choose their opponents during the subsequent knockout stage.

Ioannis Ntzoufras – A Unified Bayesian Model for Volleyball Data

Tuesday, November 24th, 2020, 11am EST
Ioannis Ntzoufras (Athens University of Economics and Business)
Bayesian Quest for Finding a Unified Model for Predicting Volleyball Games

See a recording of the presentation below.

Volleyball is a team sport with unique and specific characteristics. We introduce a new two level-hierarchical Bayesian model which accounts for these volleyball specific characteristics. In the first level, we model the set outcome with a simple logistic regression model. Conditionally on the winner of the set, in the second level, we use a truncated negative binomial distribution for the points earned by the loosing team. An additional Poisson distributed inflation component is introduced to model the extra points played in the case that the two teams have point difference less than two points. The number of points of the winner within each set is deterministically specified by the winner of the set and the points of the inflation component. The team specific abilities and the home effect are used as covariates on all layers of the model (set, point, and extra inflated points). The implementation of the proposed model on the Italian Superlega 2017/2018 data shows an exceptional reproducibility of the final league table and a satisfactory predictive ability.

Queen’s Geometric Sport Analysis Group – Geometrically Modeling Soccer and Basketball Games

Friday, November 20th, 2020, 6pm EST
Dan Forestall, Emily Hunter, Sara Stephens & Maia Gibbon (Queen’s Geometric Sport Analysis Group)
Geometrically Modeling Soccer and Basketball Games

To watch the presentation, see below.

With a focus on understanding the impact of player position, and how it evolves over time, we look at several 3-dimensional models of soccer & basketball games. This work builds off of that of Pleurer, Spearman, and others.

David Perdomo Meza – Stylistic Representation of Team Playing Style Using Latent Dirichlet Allocation

Monday, November 16th, 2020, 4pm EST
David Perdomo Meza (Twenty3 Sport)
Email: dperdomomeza@gmail.com
Twitter: @dperdomomeza1
Title: Tactical Insight through Stylistic Representation of Team Playing Style Using Latent Dirichlet Allocation

Check out the recording below:

We’ll showcase the application of Latent Dirichlet Allocation as a topic modelling technique on football statistics to obtain a mixture model representation of “team playing style” – and go through its application to evaluating tactical choices in team head to heads in the English Championship.