This paper treats the problem of modeling and forecasting the outcomes of NBA basketball games. First, it is shown how the benchmark model in the literature can be extended to allow for heteroscedasticity and estimation and testing in this framework is treated. Second, time-variation is introduced into the model by introducing a dynamic state space model for team strengths. The in-sample results based on eight seasons of NBA data provide weak evidence for heteroscedasticity, which can lead to notable differences in estimated win probabilities. However, persistent time variation is only found when combining the data of several seasons, but not when looking at individual seasons. The models are used for forecasting a large number of regular season and playoff games and the common finding in the literature that it is difficult to outperform the betting market is confirmed. Nevertheless, a forecast combination of model based forecasts with betting odds can lead to some slight improvements.
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