THE Economics of Match-Fixing

The phenomenon of match-fixing does constitute a constant element of sport contests. This paper presents a simple formal model in order to explain it. The intuition behind is that an asymmetry in the evaluation of the stake is the key factor leading to match-fixing. In sum, this paper considers a partial equilibrium model of contest where two asymmetric, rational and risk-neutral opponents evaluate differently a contested stake. Differently from common contest models, agents have the option of choosing a second instrument to affect the outcome of the contest. The second instrument is assumed to capture positive investments in ‘contest management’ – namely efforts paving the way for a match-fixing. In particular, it will be demonstrated that, under some conditions, an asymmetry in the evaluation of the stake can lead to a concession from one agent to the other and then to a match-fixing. Eventually the intuitions and results of the model will be applied to make a comparison between the FIFA World Cup and the UEFA Champions League tournaments.Contest; Football; Sport Contest; Contest Management; Match-Fixing; Asymmetry in evaluation; Concession; FIFA; UEFA; CHampions League; World Cup

Graph-Based Sports Rankings

Sports rankings are a widely debated topic among sports fanatics and analysts. Many techniques for systematically generating sports rankings have been explored, ranging from simple win-loss systems to various algorithms. In this report, we discuss the application of graph theory to sports rankings. Using this approach, we were able to outperform existing sports rankings with our new four algorithms. We also reverse-engineered existing rankings to understand the factors that influence them

Institutional Forecasting: The Performance of Thin Virtual Stock Markets

We study the performance of Virtual Stock Markets (VSMs) in an institutional forecasting environment. We compare VSMs to the Combined Judgmental Forecast (CJF) and the Key Informant (KI) approach. We find that VSMs can be effectively applied in an environment with a small number of knowledgeable informants, i.e., in thin markets. Our results show that none of the three approaches differ in forecasting accuracy in a low knowledge-heterogeneity environment. However, where there is high knowledge-heterogeneity, the VSM approach outperforms the CJF approach, which in turn outperforms the KI approach. Hence, our results provide useful insight into when each of the three approaches might be most effectively applied.Forecasting;Electronic Markets;Information Markets;Virtual Stock Markets

Spartan Daily, October 27, 1980

Volume 75, Issue 40https://scholarworks.sjsu.edu/spartandaily/6676/thumbnail.jp

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

Solving Hard Graph Problems with Combinatorial Computing and Optimization

Many problems arising in graph theory are difficult by nature, and finding solutions to large or complex instances of them often require the use of computers. As some such problems are $NP$-hard or lie even higher in the polynomial hierarchy, it is unlikely that efficient, exact algorithms will solve them. Therefore, alternative computational methods are used. Combinatorial computing is a branch of mathematics and computer science concerned with these methods, where algorithms are developed to generate and search through combinatorial structures in order to determine certain properties of them. In this thesis, we explore a number of such techniques, in the hopes of solving specific problem instances of interest. Three separate problems are considered, each of which is attacked with different methods of combinatorial computing and optimization. The first, originally proposed by ErdH{o}s and Hajnal in 1967, asks to find the Folkman number $F_e(3,3;4)$, defined as the smallest order of a $K_4$-free graph that is not the union of two triangle-free graphs. A notoriously difficult problem associated with Ramsey theory, the best known bounds on it prior to this work were $19 leq F_e(3,3;4) leq 941$. We improve the upper bound to $F_e(3,3;4) leq 786$ using a combination of known methods and the Goemans-Williamson semi-definite programming relaxation of MAX-CUT. The second problem of interest is the Ramsey number $R(C_4,K_m)$, which is the smallest $n$ such that any $n$-vertex graph contains a cycle of length four or an independent set of order $m$. With the help of combinatorial algorithms, we determine $R(C_4,K_9)=30$ and $R(C_4,K_{10})=36$ using large-scale computations on the Open Science Grid. Finally, we explore applications of the well-known Lenstra-Lenstra-Lov\u27{a}sz (LLL) algorithm, a polynomial-time algorithm that, when given a basis of a lattice, returns a basis for the same lattice with relatively short vectors. The main result of this work is an application to graph domination, where certain hard instances are solved using this algorithm as a heuristic