OL Experience as Predictor for Success

Submitted by Gulo Gulo Luscus on

After the past two games, much discussion has centered around a rapid transition from guarded optimism to total panic in regards to our offense.  The relative merits of our interior lineman, in particular, have been debated widely in platitudes as well as UFR minutiae.  While Miller is facing a bit of a talent and size deficiency, we all return to the inexperience of these (and other young) Wolverines as a large factor in our offensive struggles.  Though not speaking exclusively on the OL, ST3 hammers the matter of "youth" home in his most recent Inside the Box Score.

It’s widely accepted that an experienced line correlates with a successful offense.  I didn’t expect to have to dig deep into an MGoSearch to find some statistical evidence accompanied by glorious charts, but the hunt turned up empty other than a 2009 Unverified Voracity linking to a WSJ article confirming the strong correlation.  This particular evaluation used combined OL starts as a metric, determining that “offensive-line experience is one of the telltale predictors of success in college football.”  I sought out to see how this correlation might look for Michigan and its immediate cohorts: the Big Ten member teams, Notre Dame, and next year’s new kids Rutgers/Maryland.  I’ve dubbed this the B1G+.

So how would a lurking, stat-friendly but non-mathletic blog poster make some evaluations?  Without data on career starts, I used eligibility year (per rivals depth charts as of 9/26/13) as a metric for experience of an offensive line.  True freshman are a 1, redshirt seniors a 5.  Herein lies an obvious limitation: "age” and “experience” can be quite different in matters of football.

Given that I’m interested in the effect of a young OL, my metric for success was an offense's yards per play; see Ron Utah's recent diary for another breakdown of how our offense stacks up based on yards per play.  The WSJ study used AP poll result to measure success; see LSAClassOf2000 question the legitimacy of this measure.  I included data on team RPI to give some sense of overall team strength.

Scientists: I got a B.A. in Psych from LSA and something called “arts and ideas” from the Residential College, so forgive me for my sins.   If I understand your process, I'm testing the hypothesis that offensive lines with a greater average age will produce more yards per play.  If I understand your caveats, it’s unlikely that my data set is a large enough sample to draw significant conclusions.  But I've got a nifty heat map:

(Green = 1+ standard deviation above average, Orange = within 1 standard deviation either way, Red = 1+ standard deviation below average)

 

The hypothesis would suggest we see a lot more green on the top half of the map (other than SOS, which is mostly for reference).  Of teams with older than average B1G+ OLs, Ohio State fits the hypothesis best with Wisconsin a close second.  To be fair, 3.8 years into eligibility per OL in Madison is probably closer to 4+ anywhere else.  What do they put in the cheese up there?

MSU and Purdue are extreme outliers against expectation.  Michigan St. may be explained by the effect of the "age does not equal experience" limitation.  If I recall correctly, they have shuffled guys to the line from other positions out of necessity.  Purdue... I don't know anything about the makeup of that line, but to be fair their SOS is tops in the B1G+.  Note that Michigan is the epitome of an average team across the entire row, including SOS.

On the bottom half of the map, there are several overperforming young offensive lines.  Maryland is cranking out more yards per play than anyone but Wisconsin despite having the youngest OL in the sample.  Indiana is having no problem moving the ball against a schedule more difficult than MIchigan.  Same for Illinois, though the rush numbers are right on the fringe of going "red," leaving them an average overall offense.  Notre Dame's rushing attack is a minor anomaly.  How about a scatter plot?

 

 

At a glance, the hypothesis is bogus through four weeks of B1G+ action.  That's clearly a negative correlation, both across and within quadrants.  On the other hand, the trend line looks about right if you throw out Purdue, MSU and Maryland.  Michigan is to the B1G+ as David is to man, but Minnesota will be out to prove they are the more perfectly mediocre offense from the most perfectly mediocre conference next weekend.

The tone of the blog after UConn has shifted towards acceptance of our averageness rather than extreme panic or outdated optimism.  If nothing else, these cute visuals may lend credence to that MGoStageOfGrieving.  Sure, we're not that "young," but we're not that bad either, independently or relative to age/competition.

Comments

UMgradMSUdad

September 28th, 2013 at 12:43 AM ^

It will be interesting to see how this plays out as the season progresses.

I have seen OLines with inexperienced players that looked over matched in the first several games of the year work much better as the year progresses and they gain experience working together. Of course, sometimes they remain a liability all season, too. 

Mgoscottie

September 28th, 2013 at 7:55 AM ^

Hypotheses are bad science as they tend to introduce a bias. I don't see how that would affect this particular study without you selecting data or choosing misleading factors for success/failure, but I would love to see schools stop teaching hypothesis as a good thing so we don't end up with a bunch of millikan's oil drop failure repeats.

aiglick

September 28th, 2013 at 10:54 AM ^

What's the alternative to hypotheses though? As humans we are subject to biases based on our experiences and genetic makeup. Doesn't it make sense to come up with an idea and an experiment that is as unbiased as possible that either proves or disproves the original statement. Even if the experiment proves the statement to be true you have to accept that the results may change over time and no theory is beyond question or criticism. New information can always come out. I do agree that if you just try to find information to prove your hypothesis it is useless since many times you can find information that supports the original idea if you look hard enough. Also thanks to the diarist this is really interesting.

Gameboy

September 28th, 2013 at 11:25 AM ^

That is not how science  works.

You can have as much bias as you want in your hypothesis, because unless the data and facts back it up, it amounts to nothing. Even if you supply biased data to support it, unless everyone else who recreate your study agrees with you, it won't go anywhere either.

That is why science, in the end, is devoid of biases.

Profwoot

September 28th, 2013 at 1:57 PM ^

Not sure what this could possibly mean. Without a hypothesis, how do you know what data to collect and analyze? Unless you just think choosing a hypothesis should be replaced with choosing a question, in which case you haven't actually changed anything. It's just as likely as not that a chosen hypothesis is being tested because the researchers think it's wrong, and choosing only a question and not "taking a side" just confuses the issue without providing any advantage -- the researchers can still have a bias about which answer they think is correct, but now the results become much harder to interpret, since there isn't a hypothesis with delineated predictions against which to compare the results of the study.

grumbler

September 29th, 2013 at 11:38 AM ^

I sure hope that yours was the only school to teach that hypotheses are "bad science."  Hypotheses are good things, because they give scientists something to disprove.  The scientific method requires that the process of investigating a hypothesis be designed to show the hypothesis is wrong - mostly because it isn't possible to show that it is right.

This particular example isn't an example of science, though.  This is an example of a "hypothesis" as the common English term (which means a guess that doesn't contradict the known data), and an attempt to see what measures of success conform to the "hypothesis."  Bad science, to be sure, but interesting and relevant reading.  This isn't a science journal.

MGlobules

September 29th, 2013 at 7:10 PM ^

of your tree. Feynman and Hawkins beg to differ. Don't go muttering this in the faculty or GA lounge in any science department on this campus. You'll be laughed all the way to East Lansing.

Lots of people can crunch numbers; thinking's a little harder. 

True Blue in CO

September 28th, 2013 at 8:37 AM ^

Updating this every 3 or 4 games of the season will be interesting.  Both the chart and the scatter plot give an interesting view to this subject and helps all of wanting to know how the O-Line is progressing through the season.

aiglick

September 28th, 2013 at 10:58 AM ^

As much criticism as we heap on the line this shows they are outperforming what we should expect given our SOS. It will be interesting to see if Wisco can continue to run the ball given their competition is about to step up. Of course I'm not sold on OSU being a defensive power and think the greater driver of their team is the offense.

teldar

September 28th, 2013 at 12:14 PM ^

this does not take into account the difference between a spread and a prostyle offense. I would like to see something about inexperienced vs experienced lines over several years at the same school or in the same program. 

Like Oregon changes head coaches, but the system lives on. Ditto with Stanford. 

But i think the sample size would be insanely small and unreliable. 

 

Profwoot

September 28th, 2013 at 1:59 PM ^

As you point out, this sample is way too small for these results to be meaningful. Talent disparity is more than enough to explain any variance in the analysis, regardless of experience level.

ca_prophet

September 28th, 2013 at 5:23 PM ^

Either on the OLine or elsewhere on offense. A young line looks better if they're all five stars with a senior All-American QB and Heisman RB, and poor if they're three stars protecting your backup QB. One way to look at that problem is to look at NFL drafted lineman. These guys are the very best - how many college games did they play? Is it different for Day one or Day two? In general, I would expect that these guys have solid health records (otherwise you move on to the next guy unless he's really exceptional despite an injury), and so would only redshirt if they're not ready. And sure enough, most of them have a redshirt year and solid health histories. Another way to do this is to track college starts against stars for each position. Someone did this on the board within the last ... year? ... Anyway, they found that on average, RBs have the most college starts per star and OL have the least. The inference I draw is that you'd rather start experienced lineman and you should expect problems if you can't. Now, we do have talent, at least, and Glasgow's progress is hopeful as far as coaching 'em up goes. After turnovers, our problem is that Miller hasn't gotten the job done. For us to hit the upside we'd all like, we need Good Devin, and one of the following combinations: - Miller to step up his game substantially. - Glasgow to snap the ball consistently, make the line calls, and maintain his production going from the easiest spot on the line to the hardest, plus Bryant being healthy and competent. One of those has a lot more conditions in it. Put that way, I can see why the coaches have held off making a change, especially since if you do and it fails, you might wreck Miller's confidence completely. The coaches are certainly better equipped to judge if Glasgow at center would do any better - we might just trade one set of problems for another (and the second set might be more costly - yakaty sax snaps anyone?) ...