Proceedings of Mathsport international 2017 conference

Proceedings of MathSport International 2017 Conference, held in the Botanical Garden of the University of Padua, June 26-28, 2017. MathSport International organizes biennial conferences dedicated to all topics where mathematics and sport meet. Topics include: performance measures, optimization of sports performance, statistics and probability models, mathematical and physical models in sports, competitive strategies, statistics and probability match outcome models, optimal tournament design and scheduling, decision support systems, analysis of rules and adjudication, econometrics in sport, analysis of sporting technologies, financial valuation in sport, e-sports (gaming), betting and sports

Actions Speak Louder Than Goals: Valuing Player Actions in Soccer

Assessing the impact of the individual actions performed by soccer players during games is a crucial aspect of the player recruitment process. Unfortunately, most traditional metrics fall short in addressing this task as they either focus on rare actions like shots and goals alone or fail to account for the context in which the actions occurred. This paper introduces (1) a new language for describing individual player actions on the pitch and (2) a framework for valuing any type of player action based on its impact on the game outcome while accounting for the context in which the action happened. By aggregating soccer players' action values, their total offensive and defensive contributions to their team can be quantified. We show how our approach considers relevant contextual information that traditional player evaluation metrics ignore and present a number of use cases related to scouting and playing style characterization in the 2016/2017 and 2017/2018 seasons in Europe's top competitions.Comment: Significant update of the paper. The same core idea, but with a clearer methodology, applied on a different data set, and more extensive experiments. 9 pages + 2 pages appendix. To be published at SIGKDD 201

On Elo based prediction models for the FIFA Worldcup 2018

We propose an approach for the analysis and prediction of a football championship. It is based on Poisson regression models that include the Elo points of the teams as covariates and incorporates differences of team-specific effects. These models for the prediction of the FIFA World Cup 2018 are fitted on all football games on neutral ground of the participating teams since 2010. Based on the model estimates for single matches Monte-Carlo simulations are used to estimate probabilities for reaching the different stages in the FIFA World Cup 2018 for all teams. We propose two score functions for ordinal random variables that serve together with the rank probability score for the validation of our models with the results of the FIFA World Cups 2010 and 2014. All models favor Germany as the new FIFA World Champion. All possible courses of the tournament and their probabilities are visualized using a single Sankey diagram.Comment: 22 pages, 7 figure

Forced distribution rating systems and team collaboration

This study provides three real-effort experiments on how a forced distribution rating system (FDRS) influences team collaboration. In the first and the second experiment, we examine the performance implications of an FDRS in a card sequencing task (1) when working alone and (2) when working in a team. In the third experiment, we test how an FDRS affects knowledge sharing within teams. Our findings show that an FDRS increases the speed of completing the card sequencing task when working alone and decreases the speed of completing the card sequencing task when working in a team. Beyond that, we find that an FDRS also significantly decreases knowledge sharing within teams. As the FDRS was perceived as unfair in collaborative settings but not when working alone, we provide evidence on the role of perceived justice concerning the effects of an FDRS and shed light on the psychological and economic consequences of introducing an FDRS in environments where team collaboration is essential for success. © 2021 The Author(s

Comparing Predictive Models For English Premier League Games

Data science has become an important aspect of modern day society. While the term was first coined in 1960 by Peter Naur, over the past decade, it has been applied to many different fields, one of which is sports. Over the past years, many ranking methods and rating systems have been developed for different sports; the Massey Ranking method, the Elo-rating system, and the Pomeroy ranking method are just a few examples of such models. However, there has been a lack of research in the area of accurate predictive modeling in soccer. The goal of this thesis is to compare and contrast a set of predictive models for determining the outcome of English Premier League (EPL) games

Predicting match outcomes in association football using team ratings and player ratings

The main goal of this article is to compare the performance of team ratings and individual player ratings when trying to forecast match outcomes in association football. The well-known Elo rating system is used to calculate team ratings, whereas a variant of plus-minus ratings is used to rate individual players. For prediction purposes, two covariates are introduced. The first represents the pre-match difference in Elo ratings of the two teams competing, while the second is the average difference in individual ratings for the players in the starting line-ups of the two teams. Two different statistical models are used to generate forecasts. The first type is an ordered logit regression (OLR) model that directly outputs probabilities for each of the three possible match outcomes, namely home win, draw and away win. The second type is based on competing risk modelling and involves the estimation of scoring rates for the two competing teams. These scoring rates are used to derive match outcome probabilities using discrete event simulation. Both types of models can be used to generate pre-game forecasts, whereas the competing risk models can also be used for in-game predictions. Computational experiments indicate that there is no statistical difference in the prediction quality for pre-game forecasts between the OLR models and the competing risk models. It is also found that team ratings and player ratings perform about equally well when predicting match outcomes. However, forecasts made when using both team ratings and player ratings as covariates are significantly better than those based on only one of the ratings. Keywords: Elo rating, competing risk, ordered logit regression, plus-minus rating, survival analysis.acceptedVersio