Analytics in Action: Picking Manuel

I was reading ESPN and caught this quote from Marrone's interview on Sirius XM Radio regarding EJ Manuel:

""We started talking about the quarterbacks, and we went back and researched all the teams that played in the Northeast in the bad weather and all the success they had with quarterbacks and the common traits that they had," Marrone told Sirius XM. "They were big and had big hands and were able to throw the ball in tough weather, and that's what we were looking at."

This is the first direct evidence I've seen of the Bills using analytics to guide their selections (in spite of a recent Russ Brandon comment I saw somewhere that suggested they're not using it yet).

The analysis they did sounds like the following to me:

1.) Identify quarterbacks who played in all games where the starting game conditions were under 32 degrees, greater than 15 mph winds, and precipitation, over the past 10-20 years.

2.) Run a series of regression analyses to determine which QB physical variables correlate most strongly with high QB ratings - including things like height, weight, handsize, speed, 3-cone, etc.

3.) Tune the analysis in a variety of ways

  • reflect the intensity of a variable - cold, colder, coldest; windy, windier, windiest; with or without snow; light, medium or heavy
  • invent an algorithm to judge success other than QB rating
  • adjust for strength of that QB's offense, or the opposition's defense

What Marrone and team seem to have found is that big QBs with big hands did the best in tough weather.

One of the great challenges in analytics though is that it doesn't prove cause-and-effect, it just says the most common variables that correlate with successful bad weather quarterbacks are (in this case) being big and having big hands. Also, an analyst who's being driven to a conclusion (either because of personal bias or the boss is looking for an outcome) can "tune" the algorithm by changing the weights to really achieve a variety of outcomes. So if the analyst (or their boss) were pre-disposed towards Ryan Nassib, there may be a way to reach that outcome by tweaking a few numbers.

Bottom line is that I believe analytics, properly applied, can increase the odds that we have the right guy - in this case, that we have a QB with the highest likelihood to perform well in bad weather games. But they definitely don't guarantee it, and must be considered with other qualitative and quantitative factors, as EJ Manuel may be an outlier.

I know there are a few quants about - thoughts on this and any other evidence anyone has seen of analytics in action with the Bills?

Just another great fan opinion shared on the pages of

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