[Schneed10]

Originally Posted by GTripp0012

Well, the exact win total is based on some work I've been doing to convert value to team into a win estimate.

For example, if we consider Jon Kitna to be the standard for a starting QB, with most teams having better options, (I just picked a guy who's got a readily available skill set, and doesn't do too much to hurt his team), we can estimate that Tom Brady was worth about 5 wins more than Kitna-level last year alone. Essentially saying that the Patriots, with the same schedule, same team, but Jon Kitna at QB, would have been an 11-5 team.

The value metrics are readily available at places like footballoutsiders.com, profootballreference.com's blog, KC Joyner's articles on ESPN.com, and Brian Burke's Advanced Football Stats blog.

There's always going to be slight disagreement among the systems about certain players value, but generally these four sources paint me a pretty good picture of just how valuable a player is.

Anyway, I'm most concerned with how valuable a rookies' first 4 years in the league are for a few reasons: 1) it's a good indicator of how good he will be, and 2) that's how long in which his draft position will determine how much he exceeds his value by.

For a second rounder, in his 2nd-4th years in the league, we know that he's likely to be better than a replacement level player, and a key contributor on this team. Everything I've done says that a second round pick who doesn't bust out of the league, but also fails to become a superstar will be worth not quite a win per season on his rookie deal.

So I'm estimating over a large sample when I say the average second rounder is worth 2-3 wins over his rookie deal. About a third of the second round selections will be less than that, and a third will be more than that. But that's the expectation for a second round selection, and expectation is really the only thing we are dealing with here.

I'd be interested to hear what kind of analysis you're doing to strip out the covariances involved with the multiple variables driving team performance. That's the big reason sabermetrics has not caught on in football. With baseball, you can normalize your data set rather easily because in the end, it largely boils down to a pitcher vs hitter matchup, with a few variables like day vs night, score, and situation to adjust for. But in football, the QB's performance depend's upon the line's ability to block the defense, the WRs' ability to get open, the effectiveness of the running game, the score, the quarter, etcetera. It's hard enough to quantify some of these variables, and even harder to mathematically formulate the covariance quotients to effectively tease them out and normalize your data. Have you done anything on this front?