RotoViz 101: Which NFL Team Stats Are Predictive (And Which Aren’t)?

In this series of posts I’ll discuss which of the various box score and advanced stats forecasters should pay attention to when projecting teams and players into the upcoming season. Player projections begin with opportunity. The overall league offensive environment is important, and it’s one that Ben Gretch has looked at already this offseason. To get a sense of what opportunity will be available, either via targets or touches, you typically forecast each team’s total play count, along with a run/pass percentage split. This is actually fairly difficult to do accurately. It’s even harder if you use the wrong metrics. Since perhaps the biggest error an analyst can make is to confuse a descriptive stat (“here’s what happened”) with a predictive stat (“here’s what may happen in the future”), I compiled a list of various offensive and defensive stats and tested how well they predict themselves year-over-year. To test the year-over-year predictiveness I used r-squared. It can be thought of as a measure of the stability, or stickiness, of a particular stat or metric. Stable metrics are always better for forecasting. They allow us to tether our evaluations and opinions to firm analytical ground. Conversely it is extremely valuable to know which stats and metrics are unstable, or subject to huge variance year-over-year. As analysts we can discount these and try to account for the unknown they represent in our models. With all that out of the way, here are the year-over-year r-squared values for 29 defensive and 32 offensive stats and metrics with (bonus!) completely arbitrary color-coding.

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By Josh Hermsmeyer | @friscojosh | Archive