I'VE HAD JUST ABOUT ENOUGH OF YOU SONNY
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Poor, poor Bump. Well, not really. After all, he subjected himself to becoming a Sparty, so he knew what he was getting himself into. But, on the other hand, I'd like to think Bump was a Spartan his whole life, even before he was turned down by Michigan.
Tomorrow brings more Charlie.
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going 12-0 is a often a recipe for this, but especially this year
With the pre-bowl season officially under wraps for 2012, it’s time for my annual review of teams whose record most greatly deviated from what it “should" have been.
To (attempt and fail to) avoid confusion, here is how I define Luck for this exercise.
What I Am Measuring
Luck can mean a lot of things but for this, I am comparing a team’s actual wins this year versus taking their opponent adjusted performance and re-simulating the season with the exact same schedule. Two teams who play a tightly contested game are roughly the same on that Saturday. Over a long horizon these wins and losses tend to even out but over a 12 game season there will always be teams whose final records don’t quite match how they played throughout the year.
What I Am Not Measuring
I am not looking at any preseason expectations. I am not looking at how each team did versus the recruits on their team. Those two would look at over-achieving teams of 2012 more than lucky. I am not going back to individual games or plays to look at if one or two games would have been different. I am also not looking at injuries on personnel changes throughout the year.
Think of this exercise as a sort of Pythagorean Wins for College Football. A lucky season is a great one to have for a fan, because no matter what the expected value is, the end result is all that matters in looking back. But like Pythagorean Wins, “Luck” is a great starting point for looking ahead. There are a lot of different ways to get to the same record. Last year Texas A&M had the most unlucky season in the country and was nearly 4 games below their performance. Kevin Sumlin did a great job this year and having the Heisman Trophy winner certainly helped, but Sumlin’s team was in a much better position than their prior year’s record would have indicated.
Teams with great records are rarely unlucky and vice versa. The formula is [Actual Wins] – [Simulated Wins]. If you win most all of your actual games there is very little room for your simulated wins to be higher. It’s more a factor of math than destiny.
Coach Hoke’s alma mater was 2012’s luckiest team. Ball State was simulated to win 6.4 games this year but pulled out a 9-3 record. Beyond that, three of the four teams following Ball State are of high interest to Wolverine fans.
|Team||Actual Wins||Simulated Wins|
Michigan’s two biggest rivals and bowl opponent all crack the top 5. As noted above, Ohio St and Notre Dame were easy candidates for this list with perfect seasons, but their perfect seasons were the luckiest undefeated seasons in the seven years I have been measuring the luck factor, and by a considerable margin.
Michigan ended the season slightly lucky with 8 wins versus an expected 7.6 based on their total season performance.
Of the teams that finished the year with 2 or fewer losses, Florida State is the only team to finished at least 0.5 games unlucky, thanks to their upset to NC State and an otherwise weak ACC schedule. Their loss to the Wolfpack was the 7th most unlikely outcome of the season based on the simulation but the most likely outcome based on Seminole history. Of the Top 10 biggest upsets looking back, five happened in Week 1 and all by road teams (Youngstown over Pitt, McNeese St over Middle Tennessee, Tennessee-Martin over Memphis, Ohio over Penn St and Iowa over Northern Illinois). Only three of the top 10 happened after the second week of the season with
UMass topping Western Michigan and Florida Atlantic over Western Kentucky joined the NC St upset. The Ohio-Penn St game was an interesting one because people acted like it was at the beginning of the season even though it really wasn’t at the time. By the end of the season Ohio had tailspinned and Penn St turned out to be a much better team.
The unlucky list features some of the same teams from the biggest upsets above
|Team||Actual Wins||Simulated Wins|
Michigan State was a few spots down, as they finished nearly 2 games below their simulated totals, falling on the wrong side a few too many 16-13 totals.
Is This Luck Repeatable?
Almost certainly not. The scatter plot of current year versus prior year luck:
There are a lot of teams in each of those quadrants, each season is its own animal. Notre Dame’s was nearly 2 games above simulated this year but was –5.5 over the last three. Those who remember Northwestern as the team continually defying expectations. The Wildcats continued this year and are one of only two teams (Rice) who have had above average luck for all seven years. With Wake Forest right behind them I began started to draft a “smart schools are more lucky” section until I looked at the rest of the all-time top 10 and saw Middle Tennessee, Kentucky, Auburn and Ball State all on the list.
When you look at the spread of lucky years by
Count of teams by number of lucky seasons from 2006-2012
The twin peaks could mean there is a lucky and unlucky group, each normally distributed. It could also just a be bump in the data or it could be part of the fact that wins by program is somewhat consistent and luck is slanted if you are at one end of the spectrum. My biggest conclusion is that most of it is truly luck but that there is the possibility that teams like Northwestern or coaches like Les Miles have a true ability to consistently win more than they should but also that statistically, teams like that are bound to turn up even if its truly random.
“KEEPING THE DRIVE ALIVE”
A slow time on the board is a good time to post some summary information on how successful the Big Ten was as a conference in converting third and fourth downs and to discuss the relative success of individual teams as well.
Overall, the Big Ten lined up for third down 2,139 times and converted 856 of those downs for a conference success rate of 40.02%. The best team was actually Michigan, converting 51.3% of its third downs, followed by Northwestern and Penn State at 45.2% and 43.08% respectively. The worst teams in the conference include, in no particular order, Wisconsin, Illinois and Iowa, all of them sitting in the 34% to 36% range.
The conference went for it on 4thdown 205 times this season and managed to get a fresh set of down on 111 of these occasions, making for an overall success rate of 54.15% for the Big Ten. Penn State, Purdue and Wisconsin were the most successful teams in this regard, converting well over half of their 4thdown attempts. Iowa, Minnesota and Illinois were the least successful teams on 4thdown, with Iowa attempting to convert twenty times and only making it on seven attempts.
The average Big Ten team attempted 178 conversions on third down and was successful 71 times. On fourth down, the average team went for it 17 times and converted 9 of them. The median values were similar actually, indicating 178 attempts to 75 successes on third down, and 15 attempts to 7 successes on fourth down.
How good was the Big Ten then in this regard? It might be worth mentioning that, if the conference were in fact a team, it would have bested Southern Methodist for a solid 62ndplace in the rankings among Division I programs (point of trivia – the worst individual team at converting third down was Boston College at 28%). On fourth down, the “conference as a team” fared somewhat better, as in this scenario, we would be in 53rdplace, slightly better than Air Force (by about 0.1%).
Below are links to the tables for third and fourth down conversions for the season as well as relative success among individual conference members.
When it comes to looking at conference statistics sometimes...
having already equalled their 2011-2012 win total at 2-8…and…well…they aren’t good at basketball.
6-4 freshman shooting guard Jordan Reed paces the bearcats with 18 points per game. He also paces them with 10 rebounds, shooting 46% from the field. Truth be told, stop him and you’ve beaten Binghamton.
6ft senior point guard Jimmy Gray leads the team in assists with 4/game, and is second in scoring at 10 points per game, shooting 33% from the field and 27% from deep(8 attempts/game).
6-1 junior guard Rayner Moquete chips in with 7 points shooting 36% from the field and 34% from deep(4 attempts/game).
6-1 junior guard KJ Brown contributes 5 points on 30% shooting, 21% from deep(2 attempts/game).
6-7 senior forward Taylor Johnston leads the frontcourt scoring an efficient 7 points/game and pulls down 4 boards shooting 54% from the field and a scorching 54% from deep(3 attempts/game).
6-8 junior forward Ronald Brown is also a major contributor in the post, adding 8 points and 5 boards, shooting 50% from the field.
6-8 junior forward Alex Ogundadegbe( Oh gun Dad! Egg be! )
(M R Piders M R. C D E D B D I’s.) will drop 4 points and 3 rebounds on you in a heartbeat…or in a game, shooting 45%, but whatevs.
That’s basically the rotation.
Let’s take a look at their last 5 games. Binghamton lost to Bryant 78-56. They were outrebounded by 9 and outshot by 12%. Monmouth beat them 77-65 by hitting on 10% more of their attempts. Mt. St. Mary’s squeaked out a 71-70 win when Binghamton lost the turnover battle by 12(yeah, Binghamton turns over the ball a lot…not good at basketball remains the general theme). Binghamton lost to Pennsylvania 65-54 despite grabbing 10 more boards than the quakers. Penn went 10-25 from deep(40%).
Binghamton beat Marywood of the Colonial States Athletic Conference 76-51, outrebounding them by 20 and outshooting the pacers by 14%.
So what does this mean for Michigan? Well...Binghamton is going to turn over the ball...a lot. Michigan is going to outrebound Binghamton...but not by a ton. Michigan is going to outshoot Binghamton by about 15%.
I've got Michigan 95-55. This should not be a challenge.
This week's rankings include Ohio State passing Illinois and a good deal of movement in the bottom half of the big board. Changes since the last rankings:
12-2-12: Indiana picks up T.J. Simmons.
12-4-12: Illinois picks up Kyle Kragen.
12-5-12: Minnesota picks up Nate Andrews.
12-6-12: Danny Mattingly decommits from Notre Dame. Indiana picks up Marcus Oliver.
12-7-12: Ohio State picks up Gareon Conley. Nebraska picks up Antoine Miles.
12-8-12: Michigan State picks up Michael Geiger.
12-9-12: Notre Dame picks up Greg Bryant. Ohio State picks up Donovan Munger. Iowa picks up Damond Powell. Penn State picks up Zayd Issah, Anthony Smith, and Jonathan Walton.
|Big Ten+ Recruiting Class Rankings|
|Rank||School||# Commits||Rivals Avg||Scout Avg||24/7 Avg||ESPN Avg||Avg Avg^||POINTS*|
^The average of the average rankings of the four recruiting services (the previous four columns). The figure is calculated based on the raw numbers and then rounded, so the numbers above may not average out exactly.
*The product of number of Commits and Average Average
NOTE: Unranked recruits are counted as two-star players.
On to the full data after the jump.
An idea has been nagging me for the last few weeks that goes like this: to say a team's goal is to win the game is needlessly over-specific. Any rational team’s goal is to have the lead all game. So every second you don’t have the lead is a failure to some degree. Not only that, but a measurable failure.
With this in mind, I was surprised that none of the computerized rankings sound like they take lead time into account. Sagarin, Massey, Colley, Wolfe and Harris don’t mention it on their sites. This fed my curiosity of whether it’s any good as a metric. For the record, I didn't seek out to prove anything. Most of all, I just wanted to take a look at the season through a different lens. With that said, onto the…
I started with the 2012 per-drive data from cfbstats.com (H/T to mgousesr TSS for pointing me there), then calculated lead times in each game. Then I weighted those leads against the strength of the team the lead was against. I used my own results from the first calculation for the team strength metric, so that my results were not skewed in the slightest by anyone else’s formula. Then I weighted those results one more time for good measure, so opponents’ opponents are weighed in. The only factor considered is amount of time teams had the lead in games.
The Norm 1 (or normalized 1 time) ratings rank teams based on the amount of time they had a lead this season, nothing else. Norm 2 weights lead times against the Norm 1 rating of the opponent. Norm 3 weights lead times against the Norm 2 rating of the opponent.
The list, in three parts:
Top teams in graph form:
Some important notes with the data and/or formula
- No 2-pt conversions or missed extra points are accounted for because the data I used doesn’t mention them. All touchdowns are assumed to be 7 points.
- After calculating the running score in games, some of the outcomes of games were...off. Just a little bit. This is probably because of the last bullet.
- Tie scores are ignored. I think it might be worth it to value them somehow, but I didn’t have time.
- Because of the last caveat, a constantly tied slugfest is worth less than a back and forth game. This should only affect the kinds of teams that get into these kinds of games, i.e. the middling ones, but it still bothers me.
- To add to the last point, I therefore believe the very best and very worst teams are ranked the most accurately
- Overtime is ignored
- Even with team weightings, you are rewarded slightly more for leading the whole game against #19 Utah State than for leading for half of the game against #1 Alabama.
- You are rewarded more for giving away a game where you led all the way than for being on the other side of that.
- Injuries that affect today’s team are not factored into yesterday’s results.
- A strategy to wear other teams out may arguably be lead-agnostic early in the game. However, Oregon and Alabama are the kings of this strategy—in radically opposite ways no less—and they are the top two teams rated. So there’s that.
But anyway, onto…
Well, the results are unique, that's for sure. But they're not exactly out of left field, either. And some of them are downright acceptable.
- Michigan: I have to admit, part of the reason I did this was to prove that Michigan is better than their record. This may still be true, but not according to my formula. Why would this be? It's simple, really. I've given them a lot of credit for playing top teams, but they rarely led in these games. Deep down, what's the difference between losing all game and never showing up? In regards to the Alabama game I can say not much. Furthermore, their most dominant performances came against the worst opponents on their schedule. That shouldn't be a surprise, but if it’s true, neither should the fact that they are properly rated. I am disappoint.
- Oklahoma State: A 7-5 team that was competitive in every loss but one is my #6 team. I wonder if their fans and MSU’s fans have a support group, and if so, where would they find a couch.
- Ole Miss: I barely noticed this team this year. I wonder how their fans feel about their season. They were 6-6 but they may be in for a bounce next year if nobody leaves.
- Utah State: Holy crap did they ever have an under the radar season. But they do drop from #4 to #19 once you factor strength of schedule. Let’s not play these guys, you guys.
Not Surprise Bullets
- Notre Dame is not the best team but they are good. They look better when the strength of opponent is factored in (#4 vs #8).
- Ohio State is not an elite team. That's probably partly why Michigan played them so close. Like Notre Dame, the strength of opponents they led against does bump them up quite a bit (from #26 to #13).
- Texas A&M beating Alabama is somewhat less of a surprise—they’re my #3 team.
- Florida is overrated, said everyone ever until they beat Florida State. But guess who else is overrated? Florida State (their line happens to be one of the most interesting ones, though).
- Michigan State is...marginally better than Michigan? Well, no one would be surprised if you had claimed this in August.
- Stanford beat Oregon, had a tougher schedule, and won the Pac 12. So why do a lot of people just assume that Oregon is the better team? These results might explain why. Oregon was actually a lot more dominant all season, all else being equal. I mean if you don’t count all the stuff that counts.
- Proving my assumptions about Notre Dame and Ohio State almost offsets the disappointment in not proving my assumptions about Michigan.
- The championship game should probably be Oregon-Alabama, just like a lot of people assumed for most of the season. Go BCS.
- In a 4-team playoff, Notre Dame and their undefeated record would deserve a shot. As would Texas A&M, owners of the best win by any team all season.
- These results would be considerably more controversial if Georgia had defeated Alabama, or Michigan had eked out a 2011-esque win against Notre Dame. But none of this happened and maybe there’s a lesson in that.
- I do think that completely removing wins and losses from the equation takes a little of the fun out of it. And it leads to teams with 6 wins being rated higher than BCS juggernauts…like Northern Illinois. But on the other hand, I don’t see why this metric couldn’t be used in unison with a few others in determining how dominant of a season a team had.
- Vegas, which you may know is in the business of predicting games, would no doubt give less than ten points to Bama against Oregon, the current line against Notre Dame. Hey, if Vegas agrees with my relatively simple formula more than the one the big boys use, maybe my poll is better.**
Phew, sorry for the long post. If anyone’s interested, I would consider running this against previous seasons, and hopefully writing a lot less. I would also consider tweaking the formula if the improvements are obvious and consistently better.
* I can’t think of a good name for this. “Lead metric”?
** for the record, Vegas does disagree with some of my rankings. For example, in the bowl games Vegas favors Miss State over Northwestern and Stanford over Wisconsin. Could be because the Big Ten sucked and I didn’t weight the data properly. Also, I already warned you about middling teams. Ctrl-F it.