Using Analytics to Project Carson Wentz and Jared Goff
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Recently RotoViz debutante Chris Hatcher wrote an interesting piece about quarterback ball velocity and how that translates into one metric of passing success at the NFL level. I decided to take his work a step further and used logistic regression to model QB success. There are multiple ways to define QB success. One might be with fantasy stats. Another might be raw totals. The metric Chris used was whether or not a QB threw for an AYA of 7.0 or higher for at least one season in his career, while also starting 8+ games in said season. I will also use this as my criteria for success. I like this criteria for a few reasons:
- The data was easy to get1
- It leaves out rushing statistics, so we’re focusing only on passing
- It incorporates touchdowns and interceptions along with yards
- It strongly correlates with NFL win percent
- It doesn’t matter how long a QB plays, if they reach the threshold, they usually do so early in their careers. This is important because you might expect the criteria to bias against players with only one or two seasons under their belt. But both Marcus Mariota and Jameis Winston met the threshold in their first year. I tested number of seasons in the NFL against probability of success, and found there was no correlation (p=0.8). This held true even when controlling for other variables, such as draft position.
- It was right there in Chris’ article…yeah, I took a shortcut (back)