Grantland, Betting Dork and 3 football predictions

I got into some NFL football this week.

First, I agreed to appear on Betting Dork, the podcast of Gill Alexander. During the NFL season, he invites a guest to appear with his regular round table that talks NFL games. Gill is a friend and all around great guy; dork might be the last word I would use to describe him. You can listen to the podcast each week here.

Second, an opportunity at Grantland came up. They made an excellent video on Kevin Kelley, the high school football coach in Arkansas that always goes for it on 4th down and always onside kicks. They wanted a blog post to accompany the video, so they asked me this: if not punting is one revolution in football analytics, what’s the next big revolution?

My first thought was that NFL teams should stop running the ball.

While this might seem crazy, numbers back up the argument. Including negative yards from sacks, NFL teams throw for 6.10 yards per pass attempt. On the ground, they only gain 4.17 yards per rush.

Moreover, over the last 10 NFL seasons, there is no correlation between rush efficiency, measured by yards per rush on offense, and winning. I found this lack of correlation shocking. The NFL is truly a quarterback’s league. Winning teams can throw the ball downfield while preventing their opposition from doing the same.

The article left a lot of room for further analysis, as people noted in the comments. Pass efficiency might decline with a higher percentage of passes. (Note that I do not think this is a given, especially with good play calling.) There’s also a higher risk for turnovers on pass plays. Hopefully, Grantland will let me follow up on these thoughts later.

You can read the article here. Be sure to watch the awesome video at the bottom on Kevin Kelley’s Pulaski Bruins.

I do think passing matters most in the NFL, especially if you want to predict the future. Yards per pass attempt correlates with winning even more than yards per play, the key stat I look at in college football.

This analysis is based on my NFL yards per pass attempt adjusted for strength of schedule. I’ll make all these numbers available soon.

Of course, I couldn’t resist talking about a college game at the end.

Kansas City at Denver

Kansas City has been one of the luckiest NFL teams this season. They have played a soft schedule and have benefitted from turnovers. The Chiefs needed 2 defensive touchdowns to beat Buffalo 23-13 in their last game.

So I was shocked when my numbers came down on the side of the Chiefs. The line has held steady at Denver at 8, while yards per pass attempt predicts Denver by 5. What gives?

I think people understand the problems with Kansas City. ESPN ran a piece on how the Chiefs were the most troubled 9-0 team in the history of the NFL. And I think that’s right.

However, people might be missing how bad Denver’s defense is. They are 28th in my pass defense rankings, which is just terrible for a Super Bowl contender. They have been a bit better the last 3 games since Von Miller has returned.

Overall, Denver gets its edge in this game from Peyton Manning and it’s top ranked pass offense against Kansas City’s 6th ranked pass defense. Denver has better than even odds to win.

However, don’t be surprised to see the Chiefs go to 10-0, especially if they can generate a pass rush against Manning and get some more turnover luck.

Minnesota at Seattle

Seattle is a legit Super Bowl contender. Minnesota is a poor team that features Christian Ponder at QB. However, a line that favors Seattle by 12 seems like too much. Yards per pass attempt predicts a 8.6 point win for Seattle. Remember, this prediction includes the throwing performance of both Christian Ponder and Josh Freeman.

Moreover, the run game could play a role in this game. Minnesota has RB Adrian Peterson, one of the most explosive players in the game. Their rush attack, ranked 5th by raw yards per rush, faces a Seattle defense ranked 21st in rush defense. While I don’t recommend building a team around a RB like Peterson, his presence can certainly affect this game in favor of Minnesota.

Georgia at Auburn

This game plays a surprising role in the national championship race. A Georgia win (with an Alabama win over Mississippi State) locks up the SEC West for Alabama. Auburn would have 2 conferences losses, and it wouldn’t matter if they beat Alabama in 2 weeks.

However, if Auburn wins, then their game with Alabama decides the SEC West. Then an Auburn win puts Alabama out of the title picture… like I predicted in Grantland a month ago.

Can Auburn win? My team rankings predict a 6 point win for Auburn. However, these rankings can be heavily impacted by turnovers, and Georgia has 7 more give aways than take aways this season. Had they performed better in this department, Georgia probably beats Missouri in their key SEC East battle.

Yards per plays predicts a Georgia win by 2 based on the strength of their offense. Despite a rash of injuries to key skill players, QB Aaron Murray has led the Bulldogs to 6th in my offensive rankings by yards per play. The line favors Auburn by 3.5, so expect a tight game that could come down to a last second field goal.

Thanks for reading.

Comments

  1. Really enjoying the site and the articles. For fun I’m trying to power rank a fantasy basketball league that I’m in. I’ve used basic mean category rankings, I used Z scores for each participants category, and I used % differential from the mean (essential a Z score not taking into account std dev). I’m trying to relearn my college stats (I think I’m going to use Kahn academy for that) but in general do you have any suggestions for stats that I could use to better understand the value of our fantasy basketball teams within my league? any articles, links, or whatever would be greatly appreciate.

    my goal is to start by creating a basic power rank based on total league stats. Then I’d like to use past data to be able to follow week to week trends in an attempt to take into account teams that will/are improved by returning injured players or real life NBA trades. Lastly I would hope that eventually I could use this information to create a basic predictive model for future head to head matchups.

    FWIW I’m trying to do this all within XL, although I suppose I should learn PosgreSQL at some point and maybe this would help me down that path.

    Totally understand if you don’t have the time or inclination to respond to this post, but would be very appreciative for any thoughts.

    best

    Jim

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