Analysis of sports data by using bivariate Poisson models

By Dimitris Karlis, Ioannis Ntzoufras. 2003

466 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Economics and Econometrics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/2b7290f6fe92dc43b57f27c863384e7e3faffb3c

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Citations in the corpus

Functional Form and Heterogeneity in Models for Count Data (2007, unknown): “The literature abounds with alternative models for counts – see, e.g., CT (1998) and Winkelmann (2003).”
Dynamic Bradley–Terry modelling of sports tournaments (2013, methodology): “Karlis and Ntzoufras (2003) suggested applying a bivariate Poisson distribution with a dependence parameter between the number of goals scored by the two teams and then extended the model to inflate the probabilities of draws.”
Modelling Dependence in a Ratemaking Procedure with Multivariate Poisson Regression Models (2010, methodology): “Certain amounts of overdispersion can be introduced by 7 considering inflated versions of multivariate Poisson regression models, like the models described in Karlis and Ntzoufras (2003, 2005) and in Bermúdez (2009) used in the automobile insurance context for the bivariate case.”
Forecasting football match results in national league competitions using score-driven time series models (2019, methodology): “A bivariate Poisson distribution is also used by Karlis and Ntzoufras (2003), who show that the introduction of a parameter for dependence between the goals scored by the two teams during a match leads to a more accurate prediction of the outcome of a draw.”
Modelling the dependence of goals scored by opposing teams in international soccer matches (2011, methodology): “It is widely accepted that Y1 and Y2 exhibit statistical dependence (Maher, 1982; Dixon and Coles, 1997; Karlis and Ntzoufras, 2003).”
Energy-using appliances and energy-saving features: Determinants of ownership in Ireland (2008, unknown): “Poisson models have been used to analyse a very wide variety of dependent variables, from the number of goals scored in football matches (see Karlis and Ntzoufras, 2003) to the frequency of bombs landing on parts of London in World War 2 (see Feller, 1957).”
Eliminating Supportive Crowds Reduces Referee Bias (2021, background): “…dissent aimed at the referee etc. 12We considered a Skellam distribution for the differences between two dependent Poisson random variables (see Karlis and Ntzoufras, 2003), such as yellow cards for home and away teams, but this offered no overriding benefits to estimate the marginal effects…”
Bayesian modelling of football outcomes: using the Skellam's distribution for the goal difference (2008, methodology): “Concerning the model parametersλ1i andλ2i , we adopt the same structure as in simple or bivariate Poisson models used for the number of goals scored by each team (seeLee, 1997; Karlis & Ntzoufras, 2003, respectively).”
Ranking soccer teams on basis of their current strength: a comparison of maximum likelihood approaches (2017, methodology): “The second model, termed Bivariate Poisson 00 with 2 strength parameters, corresponds to that version by Karlis and Ntzoufras (2003) and is deﬁned via where each team has an oﬀensive and defensive strength parameter.”
Prediction of major international soccer tournaments based on team-specific regularized Poisson regression: An application to the FIFA World Cup 2014 (2015, methodology): “…model the number of goals scored in single soccer matches, assuming that the number of goals scored by each team follows a Poisson distribution model, see, e.g., Maher (1982), Lee (1997), Dixon and Coles (1997), Dyte and Clarke (2000), Rue and Salvesen (2000) and Karlis and Ntzoufras (2003).”
Information and Efficiency: Goal Arrival in Soccer Betting (2014, methodology): “Firstly, we utilize the Bivariate Poisson model developed by Karlis and Ntzoufras (2003).”
A numerical study of tournament structure and seeding policy for the soccer World Cup Finals (2011, background): “Thus, if we assume a constant ability over time of a team to score goals, the Poisson distribution seems a plausible model (Maher, 1982; Lee, 1997; Karlis and Ntzoufras, 2003).”
A dynamic bivariate Poisson model for analysing and forecasting match results in the English Premier League (2015, methodology): “Karlis and Ntzoufras (2003) also used a bivariate Poisson distribution for match results; they showed that even a small value for the dependence parameter leads to a more accurate prediction of the number of draws.”
Bayesian multivariate Poisson models for insurance ratemaking (2011, methodology): “Certain amounts of overdispersion can be introduced by considering inflated versions of multivariate Poisson regression models, like the models described in Karlis and Ntzoufras (2003, 2005) and in Bermúdez (2009) used in the automobile insurance context for the bivariate case.”
Modelling soccer matches using bivariate discrete distributions with general dependence structure (2007, methodology): “The indirect approach models goals scored by each team by employing bivariate Poisson-related models (Maher, 1982; Dixon and Coles, 1997; Karlis and Ntzoufras, 2003).”
A bivariate Weibull count model for forecasting association football scores (2017, methodology): “3 A Model for Goals Since Maher (1982) first gave his specification for modelling the goals scored by the two teams in a football match, many researchers have followed suit.”