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Motivated by the goal of evaluating real-time forecasts of home team winprobabilities in the National Basketball Association, we develop new tools formeasuring the quality of continuously updated probabilistic forecasts. Thisincludes introducing calibration surface plots, and simple graphical summariesof them, to evaluate at a glance whether a given continuously updatedprobability forecasting method is well-calibrated, as well as developingstatistical tests and graphical tools to evaluate the skill, or relativeperformance, of two competing continuously updated forecasting methods. Thesetools are studied by means of a Monte Carlo simulation study of simulatedbasketball games, and demonstrated in an application to evaluate thecontinuously updated forecasts published by the United States-basedmultinational sports network ESPN on its principle webpage {\tt espn.com}. Thisapplication lends statistical evidence that the forecasts published there arewell-calibrated, and exhibit improved skill over several na\"ive models, but donot demonstrate significantly improved skill over simple logistic regressionmodels based solely on a measurement of each teams' relative strength, and theevolving score difference throughout the game.

Evaluating real-time probabilistic forecasts with application to National Basketball Association outcome prediction