  Motivated by the goal of evaluating real-time forecasts of home team win
probabilities in the National Basketball Association, we develop new tools for
measuring the quality of continuously updated probabilistic forecasts. This
includes introducing calibration surface plots, and simple graphical summaries
of them, to evaluate at a glance whether a given continuously updated
probability forecasting method is well-calibrated, as well as developing
statistical tests and graphical tools to evaluate the skill, or relative
performance, of two competing continuously updated forecasting methods. These
tools are studied by means of a Monte Carlo simulation study of simulated
basketball games, and demonstrated in an application to evaluate the
continuously updated forecasts published by the United States-based
multinational sports network ESPN on its principle webpage {\tt espn.com}. This
application lends statistical evidence that the forecasts published there are
well-calibrated, and exhibit improved skill over several na\"ive models, but do
not demonstrate significantly improved skill over simple logistic regression
models based solely on a measurement of each teams' relative strength, and the
evolving score difference throughout the game.
