A dynamic bivariate Poisson model for analysing and forecasting match results in the English Premier League

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A bivariate Weibull count model for forecasting association football scores (2017, background): “First, one can build a model in which the team strengths are assumed to vary stochastically, such as in Crowder et al. (2002), Owen (2011) or Koopman and Lit (2015).”
Ranking soccer teams on basis of their current strength: a comparison of maximum likelihood approaches (2017, methodology): “Such dynamic bivariate Poisson models were applied to analyze English Premier League matches by Crowder et al. (2002) and Koopman and Lit (2015).”
Forecasting football match results in national league competitions using score-driven time series models (2019, unknown): “The partially non-Gaussian state space model (20) has been treated by Koopman and Lit (2014) in the context of modelling football match results with an application to the English Premier League.”
Prediction of major international soccer tournaments based on team-specific regularized Poisson regression: An application to the FIFA World Cup 2014 (2015, background): “In this context we want to mention a very recent publication of Koopman and Lit (2015). They assume a bivariate Poisson distribution for the goals in English Premier League matches, with intensity coefficients that change stochastically over time by modeling the teams’ ability parameters as first or”