A simulation model for football championships

In this paper we discuss a simulation/probability model that identifies the team that is most likely to win a tournament. The model can also be used to answer other questions like ‘which team had a lucky draw?’ or ‘what is the probability that two teams meet at some moment in the tournament?’. Input to the simulation/probability model are scoring intensities, that are estimated as a weighted average of goals scored. The model has been used in practice to write articles for the popular press, and seems to perform well.

Comparing league formats with respect to match importance in Belgian football

Recently, most clubs in the highest Belgian football division have become convinced that the format of their league should be changed. Moreover, the TV station that broadcasts the league is pleading for a more attractive competition. However, the clubs have not been able to agree on a new league format, mainly because they have conflicting interests. In this paper, we compare the current league format, and three other formats that have been considered by the Royal Belgian Football Association. We simulate the course of each of these league formats, based on historical match results. We assume that the attractiveness of a format is determined by the importance of its games; our importance measure for a game is based on the number of teams for which this game can be decisive to reach a given goal. Furthermore, we provide an overview of how each league format aligns with the expectations and interests of each type of club

Statistics of football dynamics

We investigate the dynamics of football matches. Our goal is to characterize statistically the temporal sequence of ball movements in this collective sport game, searching for traits of complex behavior. Data were collected over a variety of matches in South American, European and World championships throughout 2005 and 2006. We show that the statistics of ball touches presents power-law tails and can be described by $q$-gamma distributions. To explain such behavior we propose a model that provides information on the characteristics of football dynamics. Furthermore, we discuss the statistics of duration of out-of-play intervals, not directly related to the previous scenario.Comment: 7 page

Co-opetition models for governing professional football

In recent years, models for co-creating value in a business-to-business context have often been examined with the aim of studying the strategies implemented by and among organisations for competitive and co-operative purposes. The traditional concepts of competition and co-operation between businesses have now evolved, both in terms of the sector in which the businesses operate and in terms of the type of goods they produce. Many researchers have, in recent times, investigated the determinants that can influence the way in which the model of co-opetition can be applied to the football world. Research interest lies in the particular features of what makes a good football. In this paper, the aim is to conduct an analysis of the rules governing the “football system”, while also looking at the determinants of the demand function within football entertainment. This entails applying to football match management the co-opetition model, a recognised model that combines competition and co-operation with the view of creating and distributing value. It can, therefore, be said that, for a spectator, watching sport is an experience of high suspense, and this suspense, in turn, depends upon the degree of uncertainty in the outcome. It follows that the rules ensuring that both these elements can be satisfied are a fertile ground for co-operation between clubs, as it is in the interest of all stakeholders to offer increasingly more attractive football, in comparison with other competing products. Our end purpose is to understand how co-opetition can be achieved within professional football

Integration of Forecasting, Scheduling, Machine Learning, and Efficiency Improvement Methods into the Sport Management Industry

Sport management is a complicated and economically impactful industry and involves many crucial decisions: such as which players to retain or release, how many concession vendors to add, how many fans to expect, what teams to schedule, and many others are made each offseason and changed frequently. The task of making such decisions effectively is difficult, but the process can be made easier using methods of industrial and systems engineering (ISE). Integrating methods such as forecasting, scheduling, machine learning, and efficiency improvement from ISE can be revolutionary in helping sports organizations and franchises be consistently successful. Research shows areas including player evaluation, analytics, fan attendance, stadium design, accurate scheduling, play prediction, player development, prevention of cheating, and others can be improved when ISE methods are used to target inefficient or wasteful areas

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