A random point process model for the score in sport matches

Abstract

A sequence of goals scored during sport match is modelled as a realization of two dependent random point processes. It is assumed that the scoring intensity of each team has several components depending on time or on factors describing the teams and other conditions of the match. This dependence is modelled with the aid of a semi-parametric multiplicative regression model of intensity. A method of model evaluation is presented and demonstrated on a real data set. Prediction obtained from the model via the Monte Carlo simulation is compared with real results.

Joint Modelling of Goals and Bookings in Association Football

“…Most notably they found that when the home team have a narrow lead the home and away team scoring rates decreased and increased respectively. In a similar bivariate Poisson process framework for goals scored, Volf () considered a semiparametric model formulation consisting of a non‐parametric baseline intensity and regressors reflecting rival teams' defensive strength and the state of the match. The model was used to analyse quarter‐finalists data from the 2006 World Cup; model‐based Monte Carlo predictions were then compared with the actual results.…”

Strategy selection and outcome prediction in sport using dynamic learning for stochastic processes

“…Other extensions include the familiar compound and mixed Poisson processes, and the hybrid intensity models of Percy et al (2010). Perhaps due to its widespread popularity, football leads the way in published applications of point processes in sporting contexts; see Volf (2009) for an excellent description. Again, though, discretetime models dominate over point processes in practice because their implementation is generally considered to be easier.…”

A Bayesian In-Play Prediction Model for Association Football Outcomes

“…In attempting to align themselves with practical circumstances, Zou et al [21] proposed a discrete-time and finite-state Markov chain model that is grounded within the Poisson processes, where no more than one goal in a minute had happened, except during time intervals (44,45] and (89,90] in consideration of injury time, and a recursive algorithm was derived to accurately calculate the outcome probability. Volf [22] and Titman [23] studied the effects of other events such as cards in match. Volf [22] considered a semi-parametric model that contains a non-parametric baseline intensity, with the regression component reflecting the actual match state and defensive strength of rival teams.…”