It's been a long time, I shouldn't have left you / Without a strong rhyme to step to / Think of how many weak shows you slept through / Time's up, I'm sorry I kept you
-Eric B. & Rakim
feat. Cptn. Comeback, I Know You Got Soul
I’ve been doing these for a while now and have written many diaries on the subject of quarterback play data. Its hard to summarize everything I consider but one of the comments to last year’s version of this diary prompted this response from me which I will repeat here because it covers the situation thoroughly and reasonably efficiently:
The goal of this exercise is to categorize players into expected tiers based on what I think history has shown given what I think I know about the player today. I don't want to bag these guys, I just want to size up their skill and their context. Indeed, I'm generally expecting guys to be better than they were last year because that what I think happens when a player gets more experience and development time.
Known Unknowns: Future events
Unknown Unknowns: I don't know
Predicting worse than 130 is just mean and that's not a goal of mine. A performance at or below level requires an unskilled player or a lot of bad things to stack on each other. Either way I don't expect the guy to be a significant threat. I simply reference those things in the thesis about my expectation. Likewise predicting over 145 takes a really good situation. Precision beyond this is just asking to be wrong.
2015 Post Mortem
|Nate Sudfeld, Indiana||Thesis: ...Kevin Wilson's offense can put up numbers … schemes might be more pass oriented. Schedule looks favorable.
Post Mortem: Discounted supporting talent too much
|135 - 140||151.0||ok|
|Jake Rudock, Michigan||Thesis: His presence ensures that we will have a competent QB at the helm … tough B1G East schedule…
Post Mortem: Late season surge was nuts. Also, Harbaugh.
|C.J Beathard, Iowa||Thesis: A good schedule should help Beathard put up decent numbers.
Post Mortem: hit
|JT Barrett, OSU||Thesis: For me, JT Barrett should be the guy for the Buckeyes this year.
Post Mortem: Should have started from jump street. Regardless, I put the number at a point where he needed to be a monster for me to be right and missed. Sue me.
|Connor Cook, MSU||Thesis: Connor is conundrum to me.
Post Mortem: hit
|Dude, Rutgers||Thesis: "Rutgers' new starter has good skill position players around but the OL needs to rebuild a bit."
Post Mortem: hit
|Tommy Armstrong Jr., Nebraska||Thesis: Massive losses around him … regime change … tough to see Tommy doing much
Post Mortem: hit
|Joel Stave, Wisconsin||Thesis: Feels like I'm falling into a trap again … drop off and regime change give me pause but I'm going to go ahead and just push play.
Post Mortem: Failed to heed instincts.
|135 - 140||125.7||off|
|Christian Hackenberg, PSU||Thesis: I expect significant mean reversion here ... he has everyone around him coming back--for better or for worse. I'm doubling down on Hack …
Post Mortem: Just plain wrong, here.
|Mitch Leidner, Minnesota||Thesis: Didn’t see it happening in 2015.
Post Mortem: hit
|Austin Appleby, Purdue||Thesis: tried to be nice but expected him to struggle at best.
Post Mortem: hit
|Wes Lunt, Illinois||Thesis: The OL needs to fill some holes … schedule is fairly easy
Post Mortem: Support problem plus impromptu regime change.
|Dude, NW||Thesis: "… that schedule is a nightmare and dude might struggle …"
Post Mortem: hit
|Dude, Maryland||Thesis: schedule looks QB friendly
Post Mortem: Oddly over valued schedule though facing B1G East.
Out of the 14 QBs in the league I got 7 right, 5 wrong, Rudock was pretty close to the allowed tolerance, and Sudfled exceeded a pretty optimistic outlook. I’ll take it.
Michigan’s 2016 OOC Opponents
|Ikaika Woolsey, Hawaii||Woolsey wasn’t very good last year and Hawaii is in year 1 of a regime change. ON the plus side their OL returns 4 starters and their skill position players come back intact. Still, regime change and is Hawaii. I hope the guy loves football.
|Justin Holman, UCF||Basically, ditto from Hawaii but at least they have Scott Frost. It looks like the whole offense will be coming back but they have a lot of things to learn. When they do, Frost will leave. If they don’t, Frost will leave.
|Sefo Liufau, Colorado||Liufau is legit. He added improved decision making to good accuracy last year and ended up with Single Factor Ratings for both categories approaching 140. Alas he plays for the Buffaloes who have had a tough time gaining traction under MacIntyre. They have experience coming back but Mac's running out of time. I think I'll make the Buffs one of my side pieces this year.
Projection: 135 - 140
Perry Hills, Maryland, SR, 3 Stars
|2015 Rating: 96.9||CMP%||YPA||TD%||INT%|
|Single Factor Rating||106.5||95.0||118.8||102.2|
Hills got the nod but this has to be pretty tenuous. Durkin’s choices were INT machine1, INT machine 2, or a NOOB (ie. INT machine 3). What’s there to say here? MD’s QB were God awful last year. They should have run more than they did especially when the QBs are tossing INTs like candy at a parade. WTF. The new OC should be able to figure out that throwing the ball isn't the most awesome idea with this team but coaches can be stubborn mofos sometimes. Maryland’s OL is pretty green, and there’s regime change, so this could be a tough deal to watch.
Bart Houston, Wisconsin, SR, 3 Stars
Regime change sucks. I think Joel Stave is a guy that had a lot of potential but got wrapped up in the churn of regime change and could never get back to the form he showed in inaugural season behind center. With his exit, Wisconsin unwraps a new QB, a red shirt senior with little playing experience. Breaking in a new guy is takes time, so it helps that Wisconsin has a lot of support players around Houston with plenty of experience returning at both the skill positions and the OL. If it were me, I’d go with the other guy so I didn’t have to break in another QB next year but what do I know, I’m just a nerd.
David Blough, Purdue, SO, 3 Stars
|2015 Rating: 108.6||CMP%||YPA||TD%||INT%|
|Single Factor Rating||126.8||91.9||110.7||130.0|
If you’ve kept up with these diaries over the years you’ll remember that I largely attribute completion percentage and interception rates to the QB’s skill and YPA and TD rate to support and scheme. Bearing that in mind, Blough’s freshman campaign wasn’t that bad a starting point. The team around him was not very good though and there’s just no way one player can make up for those many problems. Looking forward, Purdue’s OL has some upperclassmen but isn't very deep. Markell Jones is a decent back, so if Purdue can find some pass catchers on the outside, their offense could be OK.
Clayton Thorson, Northwestern, SO, 4 Stars
|2015 Rating: 95.9||CMP%||YPA||TD%||INT%|
|Single Factor Rating||108.8||88.4||102.5||128.0|
True freshman, OL decimated by injuries, tough schedule. And they won 10 games anyway. Give it up for Pat FItzgerald y’all. The OL now returns a bunch of upperclassmen with starting experience plowing the way for RB Justin Jackson. The receiving corps needs to find some guys so a run first offense is probably the way to go here. Baby steps…
Trace McSorley, Penn State, SO, 3 Stars
It appears I may have set my expectations for Christian Hackenberg juuust a bit high but these things happen from time to time. This year Penn State will be breaking in a new QB as well as unpacking a new scheme (spread, no huddle, sometime with tempo) which I don’t think is a terrible idea; might as well get all the growing pains in the passing game over with at the same time. Besides, Saquon Barkley is a beast at running back so the coaches can protect their passing game with their running game. McSorley will have experience receivers to throw to and the OL is also bringing back a lot of starting experience for the first time in a while. Both of these things help stabilize the situation. McSorley should be fine.
Mitch Leidner, Minnesota, SR, 2 Stars
|2015 Rating: 121.2||CMP%||YPA||TD%||INT%|
|Single Factor Rating||131.5||112.7||110.9||130.1|
I’m not ready to go full McShay—‘cause you never go full McShay—but, the fine grain numbers do seem to support an upward sloping trend in Leidner’s play from 2014 to 2015. Furthermore Bill Connolly shows an inflection point during last season during his preview of the Golden Gophers over at SB Nation. Now, his overall passer rating stayed flat year over year but the whole point of this exercise is to look past the top line numbers and try to figure out where a dude stands on his own and where he needs some help. Leidner’s completion percentage and interception rates both improved last season; it was the scheme/support components (YPA, TD rate) that pulled his rating down. That said, 130 isn’t setting the world on fire but what can you expect from a guy who’s as much a fullback as he is a quarterback. This year Minnesota brings back a lot of production at the skill positions but lost a lot on the OL though they do return 3 players with starting experience there. There’s a but of regime change too (new OC) but it doesn’t sound like the offensive philosophy will be drastically different. If the line holds up, I can see Leidner being a candidate for this year’s Stanzi leap.
Tyler O'Connor, Michigan State, SR, 3 Stars
I’m of the opinion the Kirk Cousins is the best QB Mark Dantonio has had at Michigan State but Connor Cook enjoyed the best teams (specifically the defenses) Dantonio’s had. Cook also had way better receivers than Cousins had. This is not to say that Cook was bad, just that the support systems around him were peaking as he came into the job. He did his job well by avoiding mistakes and getting the ball to the likes of Tony Lippet and Aaron Burbridge. Having NFL Offensive Tackles is also nice. So the question is clear: is this year’s MSU offensive squad capable of supporting a new starter at QB?
The OL gets the benefit of the doubt from me. They have two returning starters and a bunch of others guys with game time experience due to injuries to players ahead of them on last year’s depth chart. They also have plenty of upperclassmen to plug in. The unit might not be an asset per se but I find it very unlikely that it will be a liability. They’ll be fine.
Which means that the running game should also be fine against normal teams, which means the staff should be able to take the pressure off O’Connor unless he or the situation warrants otherwise. His season will be schedule dependent. I don’t see O’Connor having a better first year that Connor Cook did.
Wes Lunt, Illinois, SR, 4 Stars
|2015 Rating: 111.5||CMP%||YPA||TD%||INT%|
|Single Factor Rating||122.7||98.0||106.7||139.1|
On paper Lunt should be a decent QB. His Interception rate is really good and his completion percentage is better than you’d expect given the passer rating. Illinois was a daggum tire fire all year and that’s a tough position to be successful in. RB Ke'Shawn Vaughn has the potential to be pretty good but Illinois’ OL is kind of thin even though they have 3 experienced upper classmen returning. Regime Change is typically not a good thing but compared to last season’s drama this is relative stability. I’m giving Wes the benefit of the doubt because doing so never ever bites me in the ass.
Chris Laviano, Rutgers, JR, 3 Stars
|2015 Rating: 131.8||CMP%||YPA||TD%||INT%|
|Single Factor Rating||135.3||124.0||124.9||122.7|
Last year was a good starting point for Laviano who showed decent accuracy (CMP%) though he had the typical poor decision making (INT%) of a first year starter. Losing Leonte Carroo would be tough on for any QB but Rutgers brings back some good options at WR and most of their RB carries. Their OL should be reasonable too. Their OC is an Urban Meyer, Tom Herman guy so it’ll be interesting to see how Laviano’s skillset gets folded into that mix. I can see him taking a good step toward good here but think he’s still a year out from being a major threat. Famous last words.
Tommy Armstrong Jr., Nebraska, SR, 3 stars
2015 Rating: 128.6
Single Factor Rating
How in the hell does Tommy Armstrong have any eligibility left? Anyway, Armstrong’s passer rating regressed a bit in 2015 from where it was in 2014 but this is another situation where the top level number is a bit deceiving. Tommy’s completion percentage has steadily improved since his first year at starter and his interception rate has at least stabilized. Now, he still has a long way to go in those areas but the point is that he is in fact getting incrementally better. So, the slide in passer rating can most likely be attributed to the overhaul to the offensive scheme the Huskers underwent when Bo Pelini got the boot.
A year later, that transition should be mostly complete. Moreover, the receiving corps returns intact and they have talented players in the backfield. The problem will be that they have to rebuild their OL. Tommy’s athleticism will be an asset in that scenario and I can see him. After doing this for so long I’ve seen many players I thought were maxed out go into eff it mode their senior seasons and put together a season that defies their own gravity. Ricky Stanzi, Tommy Reese, Gary Nova, Nathan Scheelhasse, Kain Colter. Hell, you could the case that Jake Rudock is another one.
Richard Lagow, Indiana, JR, 3 Stars
…if you want wins then bring the ruckus ‘cause Indiana’s offense aint nothin’ to [mess] with /wutang clan.
Uh, yeah. They have great receivers returning and running backs with potential. They lose some great talent off the OL so that could be somewhat problematic. I boil all that down to thinking that Kevin Wilson will throw the ball even more than he usually does. So, Lagow could put up some big numbers. #chaosteam
C.J. Beathard, Iowa, SR, 3 Stars
|2015 Rating: 139.5||CMP%||YPA||TD%||INT%|
|Single Factor Rating||137.2||131.2||120.8||138.3|
Without the benefit of hindsight or Harbaugh, rolling with Beathard was probably a good move for Ferentz to make. Beathard turned in a legitimately good performance last year. But the support stuff around him needs to improve for him to break through to great. Even though they bring quite a few people back, it’s hard for me to see Beathard jump up to Drew Tate level. I think he’ll be good but I cant bring myself to fade the odds too hard here.
JT Barrett, Ohio State, JR 4 Stars
2015 Rating: 139.2
Single Factor Rating
JT Barrett’s 2015 season is a very good example of how a player’ performance can be derailed if just one of the prism factors gets jacked up—in this case I think it was scheme. Passer rating can be thought of a signal comprised of component signals. If all of those component signals peak together, the QB has a monster season. In most cases however, come of the components decline as others rise and their effects offset each other. In still other cases, the signals decline together and their effects compound into a nightmare (see: Michigan 2008). The charts at left are cartoons I drew to illustrate this theory but I bet Connolly’s database could be analyzed to synthesize real versions if these signals.
Anyway, After OSU took home the 2014 title, then OC Tom Herman cashed in and went to run his own show in Houston. I thought his departure would be absorbed because Urban Meyer is an aiight offensive guy himself. However, the numbers show some things here. While JT’s interception rate could improve, the only contributing factor that is way off kilter is his YPA; that’s a scheme thing. Then the drama with the QB battle made matters worse by limiting both players ability to learn whatever wrinkles they needed to learn in transitioning to the new OC however minor those were. Also, the lack of top shelf weaponry on the outside made the whole thing sketchy. Then they try to pass, not run, to beat MSU in bad weather. I mean, ha ha and all that, but this whole shebang smacks of a case where the coaches got to cute for their own good.
This year the scheme issues should recede and Barrett should be expected to remain a dangerous passer. However, OSU’s roster turned over like whoa and they now find themselves in a reload situation. The little experience OSU returns on offense is concentrated in the interior of the OL which is probably the best place to have it considering Meyer’s offense. No doubt the Buckeyes have plenty of talent, but even great recruits have a break in period and player development is never instantaneous.
Dude, Michigan, Chosen and Crafted by Jim M.F. Harbaugh
Last year, I spilled coffee over the whole deal by getting the spelling of Rudock’s name wrong and also getting his flippin’ visage wrong. Way to ruin any semblance of your own fake credibility, yo. While those mistakes were embarrassing, they were also honest. Ultimatelty, all I know is data. I start with that, sprankle in some logic and creativity, then hope real hard. Sometimes it turns to gold. This shit is alchemy, man. And I love it.
Jim Harbaugh loves his shit too. Except with him, its science. Maybe not science the way Newton or Darwin knew it but his results are too consistent. Too predictable.
Check this chart out:
That is the average Passer Rating by Year since 1969. I mean, I cant explain it but I’m not about to question it either. I don’t need to understand and I don’t need to agree. I just need to believe. The data knows things I cannot. Maybe if something changes I can nerd it out, but the formula was created in the early ‘70’s and ain't a damned thing changed. I bet Harbaugh could explain it. He’d lose me after 20 minutes but I’d still hang out just because.
Let me show you something else:
Those are all the QB seasons any Michigan fan has seen since 1980; that’s about when they started changing the rules. That’s when it became the passing game we know now. On an era adjusted basis, Harbaugh has two of the top five seasons under center and that’s before you start discounting for smaller sample sizes (below 200 attempts) and precision errors. Harbaugh is top 10 in average era adjusted passer rating for QBs with at least two seasons above 200 attempts since 1980; out of all of them.
Harbaugh also knows things we cannot. Then he knows competition, he knows quaterbacking, and he knows Meeechigan. What the hell is there on a football field on a Saturday?
Projection: Over 9000
[Ed-S: Bumped from diaries]
Who needs a football fix? Every year I like to size up the relevant QB competition to Michigan in an effort to convince myself that we’ll go undefeated. Please see the following links for the ghosts of seasons past as well as an overview of the general thought processes behind these projections (2010, 2013, 2014). I was unable to post diaries for 2011 and 2012. This exercise is pretty hit and miss but its fun so let’s get to it.
I don’t actually expect anyone to click through to all those links so here’s a summary of the foundational ideas I’ve developed over the years:
1. As far as I’m concerned, the feel good year end rating for a QB is about 140. It’s a tough standard but that’s what the subjective good looks like. Great starts setting in above 145.
2. I treat player skill as a ratcheting riding-a-bike type thing. You don’t just forget what you once “knew” When performance recedes, its because of other factors outside of skill. Therefore, performance must be parsed in order to not over assign skill to a given performance.
3. I strive for an accuracy of +/- 4 points in passer rating. I.e. 131 vs. 135 and 135 vs. 139 are acceptable but 131 vs. 139 is a miss. Below 130 is bad and if I put you in that category and you score at or below 130, I claim a hit.
4. Barring injury, if I pick the wrong starter the a priori assessment carries over to the replacement. More on this later.
5. I use Bill Connolly's RB Rating system to guide my commentary regarding specific players. I had developed a version but, the data streams I needed dried up and his method accounts for most of the factors I did so its good enough for me.
[Hit the Jump for a post-mortem of 2014 QBs]
I figure I should get this in now before the annual Brian Cook killer content tsunami hits next week (so stoked). This is an exercise if have done publically twice now (2010, 2013) and its turning out to be a worthwhile thing to do. I think last years results are about as good as can be expected and suspect that it will be difficult to match them again. That’s not going to stop me from trying though.
Here are a few process/assessment notes to help you judge where I’m coming from on these. As with all fortune tellers I try to give myself as much wiggle room as I think I need. I think I can generally get the tier correct even if the number is off somewhat.
- Getting within 4 points of the actual value is good enough for me to call it a bingo. In essence I'm claiming an error of +/- 4 rating points. That’s kinds of a wide berth but I think seeking for more accuracy than that is a fool’s errand.
- In cases were I see the potential for high variation, I post a range (sometimes narrow, sometimes wide). This is partially to help me list the guys in the order I think they'll land and partially to maximize my opportunity to be right. If the actual result goes through the window I claim it as “on the money”, otherwise I use the closest goal post to conduct the assessment. This is about identifying potential and likely threats.
- I try to be as positive as I can about these assessments. It is my nature to be optimistic and look for the ways good outcomes might manifest. If the stated range is below or contains 130, that's my polite way of saying that I think the player might post a poor performance. I'm not going to predict a dude is going to suck because that's a shitty thing to do. If the predicted range is below/contains 130 and the player end up below that, I count it as a bingo: I thought he would struggle and he did.
- If the player listed gets beat out for the starting gig, the assessment transfers to the new player. I try to figure out either who I think should be the starter or who will be. If I get that wrong then so be it and bad on me for not teasing it out. Plus, it should avoidable by waiting to post closer to the season.
I try not to pop my collar this hard but I'm very pleased with the results from last year. The chart below is a brief tabulated review of what was said and what played out.
|"…there’s regime change in West Lafayette and the Boilermakers only have 5 starters returning on offense. ...will do well to post a 125."||125||116.1|
|"It wouldn’t be a shock if he jumped up to the low 130 range but that would be a neat trick….125-135"||125||119|
|"I think Sokol can do 125."||125||126.5|
|"I’m thinking freshman Chad Henne and Braylon Edwards here. "||130||134|
|"MSU’s offense improves to the basic level: meaning Maxwell (or alternate) posts a 130-ish passer rating (125-134)"||130||135.5|
|"I’ll wager that it takes a year for [the new offense] to hum and look for Stave to slide a little to the 140 range"||140||138.1|
|"This will be his fourth year as starter and at this point he has leveled off at the seasoned veteran level for a passer."||140||140|
|"I expect Nate to return to his 130 form."||130||140.7|
|"It’s possible either [Coffman or Sudfeld] will be the guy this coming season but I’m going to assume Coffman’s experience gives him the nod. 130 – 140"||140||142|
|paraphrasing: potentially a monster but will most likely fall from the monster category to the really friggin good category.||145||146|
|"I view Kain’s rating as stable and unfortunately can’t see him doing more than a 130 … I think he’s better than that but the numbers don't lie."||130||148.3|
|"The YPA and TD% are where the magic will happen for OSU...If those numbers improve, then Braxton will keep folks up at night. I suspect they will. 145 - 160"||160||158.1|
Six of the twelve ratings predictions were very close to the actual value. I would never had guessed it could be this high. Either this stuff is more predictable than I ever would have imagined or The KNOWLEDGE has taken over my computer. Of the 12 Big Ten QB’s assessed only two 2 broke out of the expected tiers: Nathan Scheelhasse and Kain Colter.
Scheelhasse gets a tip of the cap for defying the numbers and pulling off the Stanzi Leap even though he had to overcome scheme and support issues. Colter’s case was a flat out miss. I stated that I felt like he was better than I could justify with the data; that was wrong he had previously performed at the monster level in 2011 but his roll expanded in 2012 and I assumed it would continue to expand and therefore continue to reduce the oops-pow-surpriseness of using him as a changeup. It didn't and the change-up nature of his role along with his skill set allowed him to be a part time monster.
But, Tommy Rees was my pièce de résistance:
Looking forward, maybe Tommy finally says [eff] it and let’s it rip a bit in his last go around. To me that looks like Tommy Reese 2011 with fewer Interceptions. That means 135 –140, probably. … Otherwise, he is what he is: 130.
|Year||RAT||ATT||CMP%||YPA||TD||INT||TD %||INT %|
If lower completion percentage and higher YPA don't constitute “eff it, let it rip”…well, I disagree.
/dirt off my shoulder
/appeal for authority
One more thing, this year I’m trying to account for schedule strength both in retrospect as well as looking forward. Retrospect is easy, I’m just looking at Football Outsider’s 2013 Passing Defense S&P+ and looking at how many defenses were easy (bottom 30), hard (top 30), and in between. Very arbitrary, but its better than nothing.
Looking forward I have taken 2013 final rankings and looked at the number of returning starters as well as returning defensive production (percentage of tackles returning) to get an idea of where I think teams are likely to end up. I also bake in mean regression in the sense that if you’re #1, you’re not likely to be that again even if you remain good and if you’re terrible regression should pull you up. It’s all kinda vague and this diary is already super long so here’s the chart I put together to help me figure out which of the B1G schedules I expect to be QB friendly or not-so-friendly. This chart is forward looking:
Kam Bryant, Appalachian State
|2013 Rating: 151.1||CMP%||YPA||TD%||INT%|
|Single Factor Rating||183.6||148.2||116.6||221.9|
Eh boy…Kam Bryant was kind of good last year. And, he actually improved his completion percentage from the previous year. Sure, sure, FCS, but you still have to make the ball go where you want it to. They had a lot of returning players last year and I can’t figure out why they lost so many games. My guess is bad defense and the fact that they we in the first year of a coaching transition. This year they once again have a lot of experience returning on offense including all 5 offensive lineman with 126 career starts among them. So, like, good QB, veteran team, um, uh…eh boy. Its good that we like our defense this year.
Projection: too many unknowns
Everett Golson, Notre Dame
|2013 Rating: 131||CMP%||YPA||TD%||INT%|
|Single Factor Rating||129.8||136.8||111.0||185.9|
Obviously, having Golson return is good but he wasn’t that good of a passer in 2012. Remember, Notre Dame’s defense was stellar that season and Goslon could bail himself out with his legs. I think the passer rating factors prove it: low accuracy, meh YPA, low TD rate, awesome INT rate. The TD and INT rates are what they are because Golson would simply pull the ball down and run rather than force the ball into a bad spot. Smart.
However, I wouldn't say he’s a a scary runner either judging by his rushing stats from that season. Sure, he can do some things but we’re not talking about Johnny Football here. He’s two off-seasons removed from that performance and I expect his skill level to be much improved. Nothing to do but work on technique. Yeah man, he should be pretty good.
The scandal type substance going on down in South Bend damages the defensive roster for the most part. Otherwise, ND has some to replace 2 starters on the offensive line and new primary receivers. Notre Dame has recruited very well under Kelly so I don't expect them to have a problem finding the answers.
Andrew Hendrix, Miami (OH)
Hendrix was an ESPN four-star prospect in the class of 2010 and was simply stuck behind Everett Golson and Tommy Rees the whole time. Realistically, last year would have been his first real chance to start and though Rees wasn’t a stellar QB he was a solid one. Chuck Martin, Miami's new head coach, the offensive coordinator and quarterbacks coach at Notre Dame the last two seasons and actually worked for an eventually replaced Brian Kelly at Grand Valley. This is a pretty good situation, in that regard. Unfortunately, Miami has a new head coach for a reason, they stunk the last 3 years and were particularly bad last year. Their offensive line is all upper classmen but have very little starting experience between them.All told, I think Miami can have a decent offense this year and Hendrix should do well.
Projection: 135 – 140.
Travis Wilson, Utah
|2013 Rating: 129.6||CMP%||YPA||TD%||INT%|
|Single Factor Rating||118.1||139.6||149.5||50.8|
Wilson played and started in nine games last year, leading Utah to a 4-2 start, including an upset win over Stanford. But then he hurt his throwing hand and his season ended after suffering a concussion against Arizona State. Wilson also played in 12 games in 2012 so, this will be his third year as a starter. Utah offensive line will be young on the right side and but returns 3 players who are now upper classmen. Their leading receiver from last year (Dres Anderson) is back as is their leading rusher (Bubba Poole) but Poole doesn't look like a dynamic runner to me.Wilson should be decent.
Projected B1G Rankings
Danny Etling, Purdue
|2013 Rating: 116.1||CMP%||YPA||TD%||INT%|
|Single Factor Rating||116.8||112.6||110.7||147.4|
True freshman Etling generally played to his rating during his first year as a starter and with poor support around him and a new coaching staff. On top of that Purdue's schedule was light on the cupcakes—probably because they didn't play themselves (zing!)—yet they played the normal amount of good and manageable teams. So the deck was stacked way against Etling last year and that is also reflected in his rating. That said, his INT% was very good which bodes well for his decision making.
Etling will naturally improve as a second year player and the Boilermakers return experienced skill position players. Unfortunately, they need to break in new starters at 3 locations on the O Line so that's bad for Danny. Also, Akeem Hunt does not look to be a very dynamic runner according to my little RB Rating thing. But, I expect the passing defenses Purdue will be facing to be generally favorable*.
Projection: 125 – 130
Gary Nova, Rutgers
|2013 Rating: 124.7||CMP%||YPA||TD%||INT%|
|Single Factor Rating||110.9||128.2||139||93.75|
I understand why that Gary Nova anti-hype video exists now… The team/scheme stuff looks OK and RB Paul James looks legit though he missed some time last year with a broken tibia. Also, the schedule Rutgers tilted against last year ended up being pretty soft from a QB's perspective so there's really no excuse - Nova straight up performed poorly last year. Its on Nova and his coaches to improve the efficiency of the passing game.
Unfortunately, Nova is probably maxed out in terms of improvement. Dude is a senior who was a returning starter that had played in 18 games and started 13 going into the 2013 season. If he was going to make a leap, it should have showed up by now. Their offensive line returns plenty of experience, James will tote the rock like a boss, the offense returns 9 starters, and the defense returns a lot of production. Unfortunately, I think Nova is what he's going to be: a mediocre QB. For the record, I said similar things about Ricky Stanzi going into 2011 and he threw an egg at my face.
Mitch Leidner, Minnesota
|2013 Rating: 131.9||CMP%||YPA||TD%||INT%|
|Single Factor Rating||113.8||144.1||112.0||217.6|
Mitch no longer has to worry about competing for the starting spot after Phillip Nelson transferred in the offseason. This should allow him to focus on learning the offense and improving his game. Unfortunately there’s not a whole lot to base a projection on other than his recruiting profile and Kill’s track record for developing QBs. The offensive line for the gophers returns a lot of experience and RBs Rodrick Williams Jr. and David Cobb should both be able to contribute to Mitch’s progression. Between the line and the backs, Leidner should find enough time to be okey but his skills are grossly lacking at this point in time.
J.T. Barret, Ohio State
File Not Found, Man. When I find myself in a desert of data I turn to Proxy analysis. I did this to great affect in 2010 when trying to figure out what might be possible out of Denard that year. The thing is, there was *some* data to work with there. We knew he wasn’t a very good passer but that it sounded like he had tangibly improved to the point of being a viable QB. Here, we’ve got nothing. Well, not *nothing*…
We know the style of QB he is (Dual Threat), the he was a well regarded recruit (top 100-ish, 4 star), and that he’ll be playing in a very good offensive system (Urban Meyer). The proxies that I think are reasonable comps are listed in the table below.
The bigger problem here is that Ohio State’s offense just got gutted. With the loss of Braxton Miller they only return 4 starters and have an offensive line that has the same issues as Michigan’s does. I don’t doubt that there’s talent available but getting good at this game requires experience and there’s only one way to do that: play. Their two leading rushers (Miller, Hyde) are gone and though Ezekiel Elliot and Bri’onte Dunn are talented, they’re inexperienced…and so is everybody else! The run game can’t cover for the pass game and the pass game can’t cover for the run game.
In terms of schedule, Ohio State will have to deal with Virginia Tech, Michigan State, and Michigan all of which I project to be very good defenses and they’re light on cupcakes. I think this is the second toughest schedule in the conference behind Maryland.
That’s a bad overall mix, y’all. We are dealing with Ohio State so maybe things come together, but those are headwinds...that’s a daggum hurricane. I’m expecting JT to be in the lower end of his proxy range.
Projection: 130 - 135
Trevor Siemian, Northwestern
|2013 Rating: 126.4||CMP%||YPA||TD%||INT%|
|Single Factor Rating||133.9||129.9||110.0||126.5|
Trevor was the primary QB for his second year last season and his performance was bad though he showed significant improvement over 2012. The only factor truly lagging his rating was TD rate which probably had something to do with Kain Colter’s skill set. The other three factors are right around where you'd expect them to be for a QB with a rating of 126. The offensive line did give up a lot of sacks last year between he and Kain Colter and the loss of Venric Mark as a backfield weapon certainly hurt, but Trayvon Green did just fine as a primary back so the run game must have been OK. I will say that NW's schedule was light on cupcakes last year as they played only 3 teams I would consider to have weak pass defenses where typical B1G schedule features about 5 of those not including FCS teams. So that's a tough draw that might help explain some of the performance problems.
This year Siemian returns for his 3rd year as a primary starter with an offensive line that has a ton of experience on it. Although Venric Mark has moved on, Treyvon Green is a capable back. I think NW's B1G West schedule will be QB friendly and Trevor should put in his best performance yet.
Tommy Armstrong Jr., Nebraska
|2013 Rating: 124.3||CMP%||YPA||TD%||INT%|
|Single Factor Rating||99.85||133.1||151||63.8|
Armstrong split time with Ron Kellogg filling in when Taylor Martinez was injured last year. Both Taylor and Kellogg are gone now so Tommy is the man. In regards to his performance, he struggled pretty hard with his his completion percentage and interception rate which are both kind of hideous. His run-to-pass ratio is pretty high but he doesn't look like an ultra dynamic runner either judging by his Rusher Rating. That doesn't sound very ... intimidating. His YPA was solid and his TD% was elite so if he can improve his accuracy and the support/scheme stuff holds, he could do some damage. He was a first year starter that split time last year whereas he's the man now so he could definitely show rapid year-over-year improvement.
We know from Denard Robinson how quickly a player can develop into a devastating weapon in the right system and situation. For Denard the right system was worth 20 or points in passer rating. Tommy is currently under the Mendoza line so gravity is pulling him up and Nebraska's schedule looks workable from a pass defense perspective so, I can see him easily improving his passer rating by 10-15 points or so; 20 points is not out of the question. The problem is that, although Armstrong has good RBs behind him in Ameer Abdullah and Imani Cross, Nebraska needs to replace a lot of experience on the offensive Line. He'll be better, but he's got a ways to go before he's Taylor F. Martinez. I think he can get there, just not sure if he get there this year or next. High variance here.
Jake Rudock, Iowa
|2013 Rating: 126.5||CMP%||YPA||TD%||INT%|
|Single Factor Rating||130.5||123.6||129.5||111.2|
Ruddock's first year as a starter was...OK relatively speaking. INTs are what done it. His YPA is also pretty weak. The schedule he faced wasn’t particularly difficult either. So, lack of experience really is the number one thing standing out to me. In regards to the support he had, the Gain% by the running backs looks fine and the sacks were low so it looks like the offensive line did their job. Unfortunately, the wrath that AIRBHG hath wrought has left the Iowan Dilithium stores in dire straights and the Hawkeye running attack was a plodding, cloud-of-dust type of game. There is one guy though: Jordan Canzeri. He didn't get a lot of play last year but he looks legit by the numbers. If I were a Hawkeye fan I'd want to see Canzeri’s role expand in a big way.
Getting back to Ruddock, a year of experience and the switch to the B1G West should bode well for him. The OOC slate is QB-licious and, the way I see it, the top tier B1G pass defenses are in the B1G East. Iowa returns a decent amount of experience on the offensive line and Canzeri is at least available, whether or not he's the guy remains to be seen. With Iowa's defense needing to reload a bit, this could be a breakout year for Ruddock.
Christian Hackenberg, Penn State
|2013 Rating: 134||CMP%||YPA||TD%||INT%|
|Single Factor Rating||130.36||136.3||128.2||151.1|
That was a solid true freshman campaign out of Hack last year: *nice* INT rate with all other factors being where they should have been. He was missing a dynamic running threat but throwing to Allen Robinson is a nice outlet to have. Here again it looks to me like the offensive line did their job just fine in terms of Gain% and Sacks so his biggest hurdle was probably straight up experience. He has that now.
Unfortunately, what he doesn't have any more is Allen Robinson, Bill O'Brien, and an experienced OL. Learning a new system isn't easy no matter how talented you are. Then sprinkle in the schedule: Penn State will have to deal with Michigan State and Michigan in the B1G East. And, oh yeah, UCF's pass D wasn't so bad either last year and they're returning a lot of experience and production. On the plus side there are also some pretty soft pass defenses on there, too.
If I'm Hack, I want Akeel Lynch to by my main backfield weapon as he's the most dynamic runner Penn State has as far as I can tell from my shuper shweet command shenter. Regardless, there are very
shtiff stiff headwinds blowing in Happy Valley. I'm looking for Hack to improve completion percentage and maintain his touchdown and interception rate but expect to see his YPA go down. Net result: flat passer rating. That said, there’s no way I can drop him any further on this list in good conscience.
Wes Lunt, Illinois
|2013 Rating: 137.3||CMP%||YPA||TD%||INT%|
|Single Factor Rating||143.0||154.3||121.4||79.2|
Wes Lunt got hosed. He won the starting gig at OKST as a true freshman in 2012 but got injured and his the job so he transferred. He posted a rating of 137 on 131 attempts which is pretty good even when you discount the lesser competition he faced. The TD Rate and INT rate weren't up to par, but that's typical of a first year starter. Having to sit out 2013 after transferring, he's had the opportunity to absorb Bill Cubits offense from the sideline which should help him get on plane faster once he sees the field. Illinois returns 4 of its OL and a pretty good RB in Josh Ferguson, but they need to replace their best receiving option. The schedule difficulty for 2014 is fine from a QB's perspective.
Connor Cook, Michigan State
|2013 Rating: 135.5||CMP%||YPA||TD%||INT%|
|Single Factor Rating||129.3||130.7||137.0||202.1|
Connor Cook is getting a lot of love this off season and why not, my man has a Rose Bowl ring. Forever and ever. But the defense got him that ring; all he had to do is not screw things up. That's my take on the situation. I will say that Cook's INT rate is outstanding but given that his completion percentage was just okay I think that's more a product of a conservative offensive game plan than the residue of well honed skill. There’s just no way to reasonably expect him to be able to repeat that INT rate. And, since he didn't have to play against his own team, the schedule he faced was easier than most of his interleague peers. Then there's Jeremy Langford who is a solid back to hand off to, so... solid initial season but that's all as far as I'm concerned. His rating is probably inflated due to the INT rate.
Looking ahead, he'll have more experience, Langford, probably an expanded playbook, still doesn't have to play vs. MSU, and a pretty normal OL situation so: he should be able to post some nice numbers but I’m not seeing anything better than early Kirk Cousins just yet. That'll do pretty nicely if you ask me.
Joel Stave, Wisconsin
|2013 Rating: 138.1||CMP%||YPA||TD%||INT%|
|Single Factor Rating||143.3||134.0||146.8||108.9|
In my write up about Stave last year I indicated that while I liked Stave, I thought his stellar passer rating from 2012 might slide back due to issues with switching offenses in the Bielema departure. It looks like that is exactly what happened. His completion percentage and TD rate both improved but his YPA and INT rate plummeted. The drop in YPA might be attributable to the scheme change but the INT rate is that of a guy who tried to force things to happen.
This year the Badgers return 4 on the OL and Melvin Gordon is definitely the next great Badger running back (where do they find these guys?). The issue he'll have to overcome is the loss of all of his primary pass catchers most notably Jared Abbrederis. The schedule is pretty QB friendly as LSU in week 1 presents the only formidable defense they should see all year. This is another situation where he could get better and not change his rating.
Nate Sudfeld, Indiana
|2013 Rating: 142||CMP%||YPA||TD%||INT%|
|Single Factor Rating||136.1||142.1||146.5||138.3|
Sudfled slayed two former incumbents (Roberson, Coffman) to claim to the starting role early last year. That's kind of a big deal as those guys weren't scrubs and Kevin Wilson knows quarterbacks; dude has game. It was his first season with extended starting experience and he put up really good numbers. The skill factors (CMP%, INT%) were slightly low relative to his rating but they were still good. They system/support numbers (YPA, TD%) were great which is exactly what I’d expect from a Kevin Wilson offense targeting Cody Latimer and Kofi Hughes with good QB play. The schedule last year was appropriately challenging as well. Very good performance.
Heading into this year Sudfled returns with a great track record, more experience, all 5 of his offensive linemen, and a dangerous RB in Tevin Coleman. He does have to deal with both Michigan State and Michigan but going up a against tough competition didn't phase this guy last year. I think WR Shane Wynn can step in just fine for Kofi Hughes but the loss of Latimer will hurt the vertical game. I think they find enough answers to stay dangerous.
C.J. Brown, Maryland
|2013 Rating: 135.9||CMP%||YPA||TD%||INT%|
|Single Factor Rating||130.1||144.4||121.8||154.7|
I don’t think I like this guy. Not because he's bad but because he might be pretty dang good. I'm thinking about a Kain Colter type of guy that doesn't get taken out of the game for a less dynamic player. In 2011 he started 5 games and was set to be the incumbent in 2012 but he had to take medical redshirt in that year due to a torn ACL he suffered in a non-contact drill during fall camp. Last year was CJ's first full season as a starter and his completion percentage was OK, but his YPA and INT rate were very good; a low TD rate is what held his rating down. Plus, he was a dynamic runner out of the backfield in his first season after ACL surgery. So, like, no thanks. Send this guy back. Oh yeah, he did all that against a tough schedule...Florida State, Virginia Tech, Virginia, Clemson. Two elite pass Ds and to good ones. Now I REALLY don't like him. Nope. No me gusta. Not even un poquito.
It keeps getting worse. Remember Juice Williams? The OC at Illinois calling plays for him is currently in the same capacity at Maryland calling plays for Brown. He's been there 3 years so the system should be well established and the Terp OL is in normal shape. The offense returns 8 starters. Now I really hate him. /Doc Holliday #tombstone
His only issue is the schedule. He has to face 3 probably good pass defenses in Michigan State, Michigan, and Syracuse and no real Illinois-level cupcakes. Don’t sleep on this guy.
Projection: 140 - 150
Devin Gardner, Michigan
|2013 Rating: 146.1||CMP%||YPA||TD%||INT%|
|Single Factor Rating||136.3||156.7||140.9||122.6|
I’m about to officially become the self-proclaimed Devin Gardner hype man. In fact, I’m going to feed the machine some cash money for a 98 jersey. That’s a good friggin’ jersey. Look at that thing….
News Flash: I ab-so-lu-te-ly L-O-V-E this guy. I would have taken him as the most talented QB in the league even before Braxton Miller sustained his unfortunate injury. That's not a slight to Braxton—dude has game—but that's how much potential I see in Devin. I’ve had him on Monster Watch since last year but blah, blah, offensive line, yadda, yadda, borges, blah. Yeah man, there is *one* known bad, and we cant see how it’ll become a known good. So what? There’s a lot to like about out situation, man:
I'm assessing the schedule as unfriendly to QBs not because I expect many tough games but I don't see as many vulnerable defenses for him to feast on as a typical B1G schedule. That said, there is only one defense that should pose a problem—Michigan State. Whatever I’ll grant them the ability to simply fill the loss of a first round defensive back (Dennard) and a multi-generationally-died-in-the-wool-spartan-baller (Bullough). Big deal, those grow on trees. What? Denicos, Isaiah, Tyler, and Micah are gone too? Psh, ‘Duzzi’s got pockets full of guys better than that. Be warned whereas the practice squad’s just crawling with replacements better than those guys, just you wait. You don't have to believe me this is an acurate statement.
….Otherwise I think it will be a very manageable schedule to say the least.
Devin’s backfield (Green, Smith) frankly haven’t had the opportunity to show their talent because they weren't capable of displacing Fitzgerald Toussaint as starter last year and blah, blah, offensive line, yadda, yadda, blah. But we’ve been worse off going into the season in the very recent past. Even those guys, the offensive line, they’re talent laden (no reason to believe otherwise yet) though still incubating. Do you really want to be around when a god damn baby alien breaks through its shell? Do you? I do, but only because they’re on my side, hoss.
And the weapons, the weapons! The guys Devin will be throwing to are either obnoxious already (Funchess), have shown us real dynamism in the open field (Norfleet), or have observable talent backed up by that sweet, delicious, gloriously unconfirmable-yet-undeniable off-season hype (Darboh, Canteen). Sheeit, I’ll bet on the come no dizzo, all day er’day, son. Scurred money don't make none, holmes. And Chesson, my man, just blowing punk asses up like a neo Lamar “Guns-Don't-Kill-People-I-Do” Woodley.
Yeah, I said it. And it’s too late for take-backs. Lamar. M---a. F---in’. Woodley.
I can see it now
~~~~~~~~~~~~~~~~~~~~~~diddly do, diddly do, diddly do~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Second and a long two, deep in the fourth quarter at the Spartan thirty-five. Michigan’s down four. The Refs peel away a two tons of flesh and bone and sinew to reveal the ball then quickly scramble to spot it. Several players from both teams sub in and out but most of them are running to the spot jaw-jacking like boys do when they’re about to throw down.
[Is Darboh limping!?! You gotta be f---ing kidding me!]
On his way to the spot, Devin looks towards the sideline and reads the play call, then stops dead in his tracks and swags a little as he rubs his hands together out of anticipation.
[Huh? Oh snap…its about to go down for real].
As he gets to the line of scrimmage, Devin barks out a call to the linemen and does some Bruce Lee ninja nunchaku hand motions for the receivers. The ball is on the right hash and the team settles into Pistol Trips TE with Norfleet, Chesson and Funchess on the line, at the boundry.
[…the f--- is that?]
The line settles for a moment, then Devin motions the TE to the left side of the formation. “Hutt!” The back shows play action before flaring to the left flat ad Norfleet drops into a into the right flat.
Safeties and LBs close down hard on ‘Fleet and the offensive line pushes the defensive tackles and ends play side. Fleet takes a hard jab step forward then drops his left foot towards the feild.
[huh? oh sh---]
Before Dantonio can say ass, Fleet hurls a cross-field pass to Devin who has set up behind a convoy running up the hash.
Glide, glide, touchdown. WHAT!?!
[Can you say trickeration, m---f---er? Tanscontinental Railroad in the m---a f---in’ house! CHOO-CHOO, BABY! CHOO-CHOOOOOOO!!!!1!!1111]
Vodka, tequila, [other stuff]… make some babies. Do it again.
~~~~~~~~~~~~~~~~~~~~~~diddly do, diddly do, diddly do~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Ahem… I’m very interested in seeing how this season plays out. Go Blue.
Projection: M---er F---in’ MONSTER (or 145, whichever actually happens).
[Author note: This thing is long and pretty technical. That said, I think there will be sufficient payoff and value for you the reader. Still, be ye warned.]
Have you ever wished there were a convenient way to rate rushers the same way we rate passers? Sure, passer rating has its weaknesses—all mathematical formulas do—but despite it's issues, I've come to appreciate passer rating as a very useful framework to evaluate a player/team when it comes to passing the ball. In the same way that finding a corner piece to a jigsaw puzzle helps you figure out it's entire quadrant, once you have an idea of what to expect from the passing game you can leap to other touchstones to determine what to expect from the running game. A rusher rating would be just the sort of touchstone needed to really start messing around for those of us who are so inclined. This diary lays out what I think should work for these purposes.
To recap some of my previous work: passer rating combines four important factors—completion percentage, yards per attempt, interception rate, and touchdown rate—and blends them into one number. For rushing stats, important information for coming up with an analogous metric has been hard to come by until cfbstats.com came along. Tons of fascinating and useful data, for free. God bless the internet.
To come up with the rating, I looked only at positions that would be considered normal rushers (QB, RB, TB, FB, HB, SB, WR) that have an average YPC greater than zero. If you can’t meet those criterion, then you cant represent a normal rusher, thus sayeth the me. Other positions register rushing attempts but allowing the odd rush by a punter to color your view of what normal looks like would be dumb. See the chart below for more information. Also, if a guy averages negative YPC, uh, find something else to do, kthx. Other than that, no other filter was applied but some math wonk tricks were and I’ll talk about those as we go.
Completion Percentage → Gain Percentage : Parsed play by play is necessary to generate a replacement for completion percentage. I opted to go for Gain Percentage: the percentage of attempts that resulted in more than zero yards. I figured the basic goal of a pass is to complete it (brilliant insight, I know) and the basic goal on a rush attempt is to gain positive yardage so…any gain of more than zero yards is mission accomplished. This parameter is as much about team skill as it is about player skill but the same can be said for Completion Percentage.
Interception Rate → Fumble Rate: The direct analogue would of course be fumbles lost per attempt but that’s not the right way to do it IMO. The luck factor that influences whether or not the team actually loses possession has nothing to do with the fact that bringing possession into question is a terrible idea. So, all fumbles whether lost or not are counted in the calculation.
There is also a bit of mathematical wonkiness deployed as well. Mike Hart is famous—at least around here—for his deftness at protecting the rock. It was awesome: 991 carries, 5 loose balls, 3 losses of possession. That was an aiight career, but these guys were kinda, sorta, maybe, better (!) at protecting the rock:
|Jacquizz Rodgers||Oregon State||789||1|
OK, so the wonkiness…a lot of people who register meaningful rushing attempts do so at a pretty low level of opportunity. Even stud RBs often split carries with other backs: Eddie Lacy siphoned off carries from Mark Ingram before becoming the man, and T.J Yeldon did the same to Eddie Lacie. So in order for fumbles to make sense for players that get meaningful carries in low doses, we need to consider the question: at which point does a low fumble rate cross the threshold from wait-and-see to holy-crap-check-that-dude-for-stickem?
What we have here is a chart comparing the observed percentage (red dots) and the mathematical probability (blue line) that a player will have at least 1 fumble versus the number of carries he has registered. The red dots are binned in increments of 1 so the sample sizes out past 150 are pretty thin but if bigger bins are used, you’d see a scatter of points that more closely follow the mathematical fit, because… math. The blue line was derived using logistic regression.
The weirdness at zero for the mathematical expectation might be concerning as it suggests that there’s a 20% chance you’ve fumbled despite not having a single carry to your credit. However, that is just an artifact of the data. It is possible to fumble on your one and only carry as actual observations show. What the math does, though, is it considers the sample size of the observations and then finds the best fit possible to the overall dataset. There are ways of dealing with that issue, but…I rather talk about football. Also, KISS. This is good enough for my intended purpose.
Anyway, the point of doing all that is it allows me to apply what I’ll call the Phantom Protocol. Basically, I take that curve, subtract it from 1, and add the resulting value to the player’s fumble total. As the number of carries increases, the effect of the phantom fumble recedes thus leveling the playing field and letting us evaluate players with low sample size as best we can. The result of this bit of data manipulation is that a guy with no fumbles in 16 carries is assigned an average fumble rate and by the time 100 carries are registered, the penalty is not perceivable. Below 16 carries, the assigned penalty is pretty stiff but this trick levels the playing field to let us look at guys with few carries and not just dismiss them with the low sample size red card. Sure, 16 carries is still a low sample but at least the rating self corrects for the fact that fumbles take time to manifest.
Most importantly though, the protocol adequately acknowledges players with low fumble rates even though they have a lot of carries. It’s easier to have a 1% fumble rate after 100 carries than it is to have the same rate after 789 carries. That said, after a while the fumble rates should be allowed to speak for themselves. Quizz Rodgers and Mike Hart need their proper allocation of DAP; nothing more, nothing less. I think the ghost protocol concept accomplishes exactly that.
Touchdown Rate: This one is also directly analogous but here again I’ve deployed the ghost protocol to credit guys with low sample the expectation of an eventual TD. TDs come about much more freely than fumbles do with goal line attempts and the like so this credit vanishes very quickly. But fair is fair: the protocol giveth and it taketh away.
Those are the components directly analogous to the ones used in passer rating and these would be enough to go about the business at hand. However, whereas a passer’s job is to get the ball into the hands of a play maker, players that are given the ball whether by pass of handoff are called upon to be the playmaker. Certainly the scheme, play call RPS, and execution of the supporting cast all have major influence on the results of a play but the ball carrier can do things that elevate the call from good to great. I wanted to be all formal-like and call this the Impact Run Rate but this [stuff] is s’posed to be fun, man. Hence—
Another Dimension: the Dilithium Quotient
The 20 yard threshold is usually referenced as registering a play as a big play. That would certainly qualify as a big play by any standard but that threshold seems to have been established somewhat arbitrarily in my opinion. On average, a generic runner on a generic team in a generic game gains about 4 yards per attempt with a standard deviation of about 7.5. Its called the standard deviation for a reason as a huge swath of observations (about 2/3rds) occur within 1 SD of the mean, or between –3 and +11 (remember: discrete data). The other 1/3 of observations get split evenly with 1/6 below -3 yards and 1/6 above 12. I’ve used objective criterion, you know, math, to define Impact Runs as those that register 12 yards or more. To register one of these the player’s entire team has to execute the play correctly, then the carrier he has to do something special (i.e. juke a dude, break a tackle, be fast). This is the real life manifestation of the Madden Circle Button and its informative. It’s the difference between Barry Sanders and Emmitt Smith.
Denard Robinson was great at this but it might be surprising to hear that he wasn’t the best. Percy Harvin in the spread option was ridiculous in this category. Percy had touched the ball a lot when he was a Gator and 27% of the time, he darted for an impact run. By Contrast, Denard’s DQ% was ‘only’ about 15%. Could you imagine Denard breaking loose almost twice as often? Of course, the scheme, the team’s execution of the scheme, and the player’s deployment within the scheme has a lot to do with this number. Florida circa Percy Harvin was galaxies away from Michigan circa Denard Robinson. Percy Harvin was the 3rd rushing option in Florida’s spread and shred, Denard Robinson was options 1-10. Also, being the QB in the spread-option means you are concern #1 for defenses: the cornerstone. That was triply the case when facing Michigan with Denard in the captain’s chair. Harvin was usually one-on-one with a guy 10 times slower than he was who was also probably pooping his pants.
Denard’s DQ% was pretty stable around 15% (scheme be damned) but his utility rate (723 career carries) was second to none save minor conference QBs. His closest proxy Pat White (684 career carries) broke loose at a 19% clip in RichRod’s Scheme. However, the Big EEEast sans Miami and Virginia Tech wasn’t quite the Big TEEEN. Denard went up against stout defenses way more often than Pat White did and did so without the benefit of Steve Slaton or Noel Devine and the benefit of a revolutionary offensive scheme. When Pat White lost RichRod is DQ% dropped to under 12%, Denard didn’t bat an eye. Everyone *knew* they had to stop Denard and only him on *every play* and they still had their hands full trying to actually do it. The fact that Michigan could never position itself for him to win the Heisman trophy will always be one of my sports fan laments. For ever and ever and ever. He better get a Legends Jersey or I’m qui’in’. I don't care if that’s silly. You’re silly. Where’s my bourbon?
Blending It All Together
Passer Rating was developed such that an average QB would end up with a rating of 100 according to the data set that was used to develop it, which was gathered two maybe three football eras ago when linemen couldn’t really block and scholarship limits weren’t so much. I’m not sure how they went about the process of pinning the rating to average==100 and I don’t have the data to try an replicate the results…so, I kinda, sorta, you know, pulled something outta my [hat]. That is to say: I did what I think is correct or at least valid. I normalized each parameter by it’s par value, summed them together, then forced resulting rating to equal 100. Ultimately the 100 thing is completely arbitrary, but negative numbers are weird, I guess. All said, a rating of 100 means the player was a solid runner but not special, below that you wonder if he should be running at all.
Where in the World is
Carmen San Diego Mario Mendoza
Now that we have a calibrated formula its time to get down to business, application. I calibrated the rating so that 100 was a normal guy, but to figuring out what par should be is a little more complicated. I mentioned earlier that if you cant get to a rating of 100 I don't think you should be a primary running option and I also think we should only look at primary running options to establish our benchmark. But being a primary running option means different things depending on where you’re lining up.
When trying to crack a nut like this I often find that the data itself will help you figure out where to chop it. In the chart below I have plotted Average Rating vs. Amount of Carries. Obviously, the better runner you are, the more carries you should see but runners that are REALLY good are few and far between…this chart shows that dichotomy very nicely. I like to look for population gaps and/or inflection points in a performance curve. Those usually a good places to drop an anchor as far as I’m concerned. When they are near each other it’s a dead giveaway. Based on the data itself I’m using 115 for RBs, 70 for QBs, and 120 for WR as performance benchmarks.
So, this is all well and good but the real test is whether or not things make sense. Here the values for the B1G in 2013:
|Team Name||Player Name||RB Rat||Attempt||Yds/ATT||TD%||FMB%||Gain%||Dillitium%|
This generally looks pretty reasonable to me in terms of an overall ranking as well as a relative ranking. The players/team you’d expect to be at the top and bottom of the list are where they are supposed to be. If anything I’d criticize the Mendoza line at 115 given how we all feel about Michigan’s running game last year. Maybe 115 is just the threshold of suicide and 130 or better is what we fans really want from our teams. But, even this jibes with what I think.
As with passer rating, this rating depends on player skill, surrounding support, and offensive scheme. Toussaint’s YPC and Gain%—components heavily influenced by surrounding support (i.e. the O-Line)—are way under par. So is his Dilitium % which is a skill/talent/speed thing but the dude had a bum knee and he’s not that far off of par there. Makes sense. So, he hit the Mendoza line even though he had bad support in front of him, sorta like Gardner. These numbers make sense to me.
Re: Smith Vs. Green
I mentioned in my last diary that it was interesting to hear grumblings about De'Veon Smith being ahead/competitive with Derrick Green because I think the numbers bear this out. Check this out:
|Player Name||Att||TD||Fum||Gain %||Yds/ATT||TD%||Fum%||DIL%||RB Rat|
These guys played with the same support and in the same system so the differentiators on display here are essentially Skill and Opportunity. Neither Green nor Smith actually registered a fumble but the Ghost Protocol affect Smith’s rating more because he has far fewer carries. Indeed, Smith’s rating is also bolstered by a phantom touchdown, but this effect dissipates faster because TDs occur more frequently. So the math is screwing Smith over here a bit. Meanwhile, Smith’s Gain % and YPC (hitting the right hole at the right time) and DIL% (juking, speed, whatever) were the highest on the team last season. Yep, Small samples yadda yadda. Just sayin’.
Anyway, that's a lot of words and I hope this was worth the read. Of course, I will be referring to this information in future diaries. Thanks for reading and let please provide and criticisms or comments you might have in, uh, the comments section.
I can't see where you’re comin' from / but I know just what you’re runnin' from / And what matters ain't the who's baddest / but the ones who stop you fallin' from your ladder.
For a little over four years now I’ve had a summer time hobby of trying to predict plausible performance levels from various QBs for the upcoming football season. I have tried to root these projections as deeply into the bedrock of reality as is possible for a figment of one’s imagination and at this point there is a codex of sorts in the diary archives describing my methods. It’s fun to go back and see what worked and learn from what didn’t. There’s something there, man.
For Devin Gardner 2013 I laid out two stat lines hinging on two sets of assumptions—a reasonable/prudent set, and a ‘sexy’ set. The reasonable prediction: Gardner would complete 225 of 360 passes for 2900 yards, 23 TDs, and 10 INTs. In reality he went 208 of 345 for 2960 yards, 21 TDs, and 11 INTs. There’s a HEAVY dose a good fortune involved there but, hot damn, that’s pretty good. The assumptions here were basically looking at only QB stats and nothing else Devin had shown enough in his 5 QB starts during the 2012 season to perform at the “seasoned veteran QB” level which I think of as an incumbent with 2 years of experience in tow. That's a brutal benchmark, IMO but that's what I measure guys up against. That's what we want them to be.
Anyway, the sexy set of assumptions were:
- Devin has elite talent. I believe this one held. More on that later.
- The O-line would be fine despite the possibility of being “a touch weaker than last year (2012).” Eh boy…
- The offensive scheme would be well tailored to Gardner’s skill set and that of the support around him. This was sometimes true but not consistently often enough for Borges to keep his job.
Ok, so the necessary assumptions for DG to be the second coming of Vince Young vanished into the ether. But those last two assumptions about the support and scheme are really kind of baked into the reasonable prediction too. For my money, the fact that DG put up the numbers he was able to in spite of the glaring flaws of the team is a testament to just how good he can be if the conditions are reasonable.
The fact that there are so many straight-faced questions being asked about Devin Gardner’s incumbency status is ludicrous. Sure, numbers don’t tell the whole story but they tell a good part of it. DG went from being one of the darlings of the 2013 Manning Passing Academy to needing to prove his talent simply because he couldn't compensate for all of the flaws around him last season. He did as well as could reasonably be expected without adjusting for other very real headwinds.
[After THE JUMP: Gardner under the microscope.]
I don’t practice Santeria. I ain’t got no crystal ball. I had a million dollars but I … I “spent” it all.
In an obscure part of Jim Mora's famous playoffs(?!?) presser, he gave the sports world the skinny on turnovers: "I don't care who you play--whether it be a high school team, a junior college team, a college team, much less an NFL team --when you turn the ball over 5 times...you ain't gon' beat anybody I just talked about. Anybody.” We all understand this via basic football intuition (ahem) but, stick around if you care to see if we can stick a number on that intuition.
Plenty of previous work on the subject has been done by many folks including myself. Football Study Hall recently conducted a study in similar fashion to how I’ve done it in the Blue Moon stuff and estimated the effect of per game turnover margin on season win percentage. FSH’s look lines up with the BMM, both suggesting that the gain on Season Win Percentage for per game Net TOM is about 100 basis points. The effect on overall record is useful but when watching a singular football game we’re not thinking about the whole season; we’re only thinking about the next few hours or so. How do the turnovers within a game affect the outcome of that specific game? To answer the question we’ll have to use math skills that go beyond grouping, counting, and arithmetic.
Soulja Boy Huey Lewis MC Hammer. Wha?
To answer the question at hand you need special math. In this situation you need to estimate probabilities because the outcome of a single football game is categorical (specifically binary) rather than discrete as in the case of full season wins. Herm Edwards gets it: “This is what’s great about sports …you play to win the game. [/Pitch Perfect Cumong, Man Glare]. Hellooo? You play, to win, The Game.” The point of sports is to beat Ohio State. Herm gets it. /Michigan orthodoxy
The special math is called Logistic Regression. It’s still a kind of linear regression but that regression is run through what is known as a link function to deal with the binary nature of the thing being modeled. This is done in all kinds of technical fields but for sports, um, investors this is a particularly nifty trick to have stashed next to your rabbit’s foot. The data for the model comes from NCAA.org as always. Sorry, no coefficients this time but I’ll show you a—
Here’s a useful way to think about this chart: suppose we were to play a Sunday morning game where I told you a team’s Final Turnover Margin and you had to tell me if they won the game or not—what would the payout odds need to look like for you to break even? This chart is the first step in answering that question.
Several features on this chart stand out to lend intuitive validity to the model. First there is neutral win probability at neutral TOM. Second, negative TOM hurts your odds, positive TOM helps them. Third, there are diminishing returns. By the time you get to +/- 3 in final TOM, the next turn over for/against you doesn’t affect win probability that much.
*DO NOT MISS THAT LINK. Grab a drink because its MC Hammer’s 15 minute (yezzir!) extended length 2 Legit 2 Quit video. The word epic gets tossed around a lot these days but it’s the only appropriate word to use here. BiSB, you’d dig it the most. It’s like a mockumentary / old school kung fu movie / ridiculous dance video. The hairdos, man. And the cameos: Marky Mark, EAZY E(!!!), Queen Latifah, Milli Vanilli, James M--F--in Brown in full regalia with full on wizard abilities, Hammer's Wang, Jose Canseco, Isaiah Thomas, Kirby Pucket, Jerry Rice, Ricky Henderson, Deion Sanders, Andre Rison, Roger Clemens, Roger Craig, Ronnie Lott, , and Jerry Glanville. And that’s not all of them. Epic, man. Epic.
What’s Wrong, McFly?
Here’s the rub though, actually there are two rubs. First, that curve represents a generic team facing a generic opponent and neither of these things actually exist. I’ve used this example before but its worth a reprise: the generic US household has something like 2.4 children in it, but show me a household with 2.4 kids in it and I’ll show you a crime scene. Real football games are played by real football teams and they’re not all created equal. That curve shifts and bends according to the strengths of the teams in the contest. For reference, the math says “Nick Saban’s Alabama” can survive a –3 TOM against the nameless faceless generic team before it’s a coin flip situation. Let that sink in for a minute. Personally, I think that might be an underestimate.
So what’s the second rub? It’s related to the first one, actually. Here’s where our man Marty McFly comes in. I broke a major rule of predictive analytics to create this chart, I gave the model knowledge of the future. That’s a no-no for models that are supposed to be predictive because, duh. Don't give me that look, I told you I was a sinner last time. Deal with it. In addition to Final TOM and Game Outcome, I fed the model an end of season strength rating as well.
That disclosure may spawn some skeptics and I welcome thoughtful discourse, but allow me to explain myself before you tar and feather me. I think we’re OK to do this for the specific goals at hand. Remember, the goal here isn’t to create a predictive model, it is to estimate as closely as possible the impact of Final Turnover Margin on Win probability. On the chart shown previously, you can’t make an evenly matched game a toss-up unless you know for certain that the teams are evenly matched, right? The final strength ratings serve as a discount mechanism to let the computer know “look man, we’re talking about Oregon vs. Colorado here…the Buffs are going to need a lot of help to have ANY shot.”
Here’s another and more specific example from the past but closer to home: Michigan vs. Toledo 2008. Going into the game, Michigan was a 17 point favorite. “This is Michigan vs. Toledo, fergodsakes.” Um, no, Biff, it was Michigan **2008** vs. Toldeo. If you had read the almanac you would know that Michigan 2008 couldn’t lay points on anybody. Why the hell did you risk your existence in space-time if you weren’t even going to read the damn thing?
(I can’t do Jim and Herm and not do Denny. Its the rules).
So, now that we know what that chart is and what it cannot be, what does it tell us? Well, it says that turnovers are kind of a big deal, bro. How big a deal? The first extra possession is worth 16% in Win Probability. Basically, you’d need 2:1 odds in our little game to bet against the team with +1 TOM at the end of their game . In fact, in the generic case, its a simple equation: y = 2^x.
Sans The KNOWLEDGE, we would significantly under estimate the required odds by an increasing amount with each step away from neutral. Yes, I did the math the right way too, don’t worry about it, it’s irrelevant.
News Flash: we lack The KNOWLEDGE at several junctures. First the curve needs to be adjusted according to the true strength of both teams. You wont know how good each team actually is until they are done with their schedule—and maybe not even then—so, you’ll always have an error in your estimation for one or both teams. That error is lethal over the long run.
Second, and this is a biggie, you can’t consistently predict Final TOM. Both teams are in active competition to cause and avoid turnovers. Sure, if there’s a significant mismatch between the two teams, then you might be able to get a good guess in. But then, the end effect of turnovers go down as the rating gap increases so…well, let it suffice to say that there’s an error which is convoluted within an error.
Taking Destiny by the Bit
[Author note: this bit requires further discussion, please share your thoughts.]
Before I wrap this up, I need to talk about one more thing. TOM is one of those things, man. It’s out of your control. Try as they may, the defense can not expect to to get turnovers. They can try to provide the conditions necessary for turnovers to occur but they cannot make them happen. If a QB makes good decisions, no interceptions. If the ball carriers are Mike Harty, no fumble opportunities. Even if they aren't Mike Harty, you *might* be able to force fumble opportunities but you can’t guarantee a fumble recovery. You can try as hard as you can and still come up empty.
The offense however…seems like the offense can expect to not ever turn the ball over. Don't throw a pick, don’t drop a live ball, out scrap a guy for a loose live ball if you do lose your mind and drop it. You have agency in those things even if your opponent is trying as hard as they can.
So, screw TOM. Put the onus on the offense to not turn the ball over and then see what happens…Let me show you another—
I think this chart is astounding. Basically, it says that a generic team can cough the ball up twice to a par competitor and not hurt it’s win probability in any significant way. Eliminate turnovers completely (again, generic on generic) and you can lay 3:1. Cough it up once and lay 3:2 (ish). Actually, what this really says is that the typical team gives up two turnovers in a game against an equally matched opponent.
Interceptions are the Worst
This is bogue to QBs but the data don't lie:
Again this curve shifts and flexes depending on several factors but that’s the generic shape right there.If you’re up against a par opponent, your QB is “allowed” 1 mistake before he puts the team in a bad spot. Generic-vs-generic, the team that throws no INTs, wins 75% of the time. Which team will do that? What if they both do that?
Absent from this analysis is the timing of the turnover which is of course critical to its specific effect on the outcome of a game (Anthony Thomas fumble v. Northwestern). If that’s what you’re interested in, The Mathlete is your man.
I write this often because its important to remember: football is not a math test. Your game thesis could be dead to rights down to the weather forecast and you’ll still feel the break, feel the break, feeeel the break (/Santeria) very often. Often the decision comes down to believing in things you don't understand and/or can’t necessarily prove—not guilty and innocent are different things. Failure to reject the null hypothesis is not rejection of the alternate hypothesis. The rooting interest often defies logic and reasoning but that's what makes it so damn entertaining to have.
Welcome back to football.