Peppers at 10, which seems low.
(Click the image to view full size)
Yeah, I know there's still the bowl game, but... Basketball. Let's do this.
Tomorrow we'll catch up with Baby Bo.
THE BLOCKHAMS™ runs (typically) every Tuesday here at MGoBlog,
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check out Friday Roughs, a spontaneous low-end comic based on trending
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So, Now that we’re simply awaiting the start of the bowl season to cap off an interesting year in Big Ten, and indeed, college football, I thought I might present to the board for comment something I had been considering doing for a while – “The Big Ten Scorecard”.
It’s not by any means scientific, and I don’t pretend to be an expert at these things, but what I tried to do here is take summary metrics and compare them to what the conference game averages would have been. Including the BTCG, there were 146 games played (allow that two teams played 13 games, of course), so the same size is sufficient, in my view, to present what an average Big Ten game stat line would look like.
Bearing that in mind, there are several tables – passing offense and defense, rushing offense and defense, scoring offense and defense, a summary for offensive and defense metrics and how many were met, and an overall “score” for the team.
Glad you asked. I kept it fairly simple for this first pass at the idea. You will see in the tables many boxes shaded in red with numbers in red as well. I went with the mean for each statistic as the target, so essentially, what we’re discussing is the team’s performance against the Big Ten mean on 31 measures.
So, for the most part, on offense, if a team was below the mean on a certain measure, the box is shaded because it indicates a performance which was generally subpar compared to the rest of the Big Ten. The sole exception would be interceptions, in which case being below the mean is preferred obviously.
On defense, on the other hand, generally numbers below the mean would be preferred; the sole exception again (for purposes of this experiment) would be interceptions, as more indicates an opportunistic defense.
There are some confounding factors, of course, such as teams facing pass-heavy or run-heavy opponents, but the human performance aspect of football allows teams proficient in stopping such attacks to meet other targets.
In other words, in my totally contrived system, there are 31 possible points, and if you “exceed the target” (perform well against the mean), you get one point for that measure. I have even included handy icons to graphically illustrate which teams are making the grade compared to their conference compatriots, if you will, in the summary tables.
Basically, the final score is the percentage of measures against which you exceeded the conference average. No team obviously scored 100%. Indeed, no one even hit 80%, so there is a bit of a curve involved as well. One other thing that some will undoubtedly notice – sometimes, a team which came in at what appears to be average is still in a shaded box. I rounded the numbers for purposes of simplicity in the tables, but what it means more often than not is that the unrounded figure is still slightly below the actual mean.
TL;DR – Michigan, Nebraska, Ohio State, Penn State and Wisconsin all exceed the Big Ten mean at least most of the time in most areas. Michigan State and Northwestern would be teams that do this only slightly more than half the time. Iowa, Minnesota, Indiana and Purdue struggle, needless to say. Illinois...well, at least this season gets “Firestone Smoldering Rubber Award”.
Again, this is my first stab at such a thing, and I welcome comments and suggestions. For as long as I am here, I would like to make it a yearly thing with perhaps even midpoint reports.
THE TABLES (offense first, then defense, in each case):
PASSING – OFFENSE AND DEFENSE
RUSHING – OFFENSE AND DEFENSE
SCORING – OFFENSE AND DEFENSE
OVERALL OFFENSE AND DEFENSE
I had some incredible years playing at Michigan. And that was because of my teammates, the staff, the fans, the tradition. I'm so thankful for my Michigan experience and wanted to share a new venture I'm working on with the Michigan faithful. It's called Merit.
Ok, so I saw a request for these on the board, so I figured I'd just throw them out there. I'll work on some additional wallpapers to add some variety, but this is a busy season for work and I'll most likely not have too much free time to kill. That being said, here's the first installment of basketball wallpapers, both Desktop (16:9) and Mobile formats. Hope this helps sate the current wallpaper hunger pains until we get some more on the board.
Mobile (iPhone, etc.)
Our good friend TomVH passed this along and I thought it was worth posting before the holidays—Mike Barwis is raising money for the Athletic Angels Foundation, which will provide food, clothing, and toys to needy families in the Detroit area. If you're interested in donating, the information is below. Checks shoud be sent to:
Athletic Angels Foundation
44191 Plymouth Oaks Blvd., Suite 600
Plymouth, MI 48170
Restating What You Already Knew: For the second time this year, turnovers were the primary cause for losing the game (ND was the other). The Turnover Margin of –2 for the game resulted in a net of 8.92 expected points benefitting ohio. God dammit! Michigan ends the regular season with a dismal TOM of –8 (ranked #101).
Michigan Football: Michigan had 20 pass attempts and 27 rushing attempts for a 58% run play percentage. For the year, M has a 61% run play percentage overall (ranked #18). In 2011 M ranked #11 at 65% run play %.
Robinson recovered the fumbled punt to add his name to the list of takeaways for defensive players. Clark recovered the other ohio fumble while Avery and Ryan both forced fumbles. There are now 17 different M players that have either forced a fumble, recovered a fumble, or intercepted a pass.
For giveaways, Michigan plummeted to #27 in fumbles, #33 in fumbles lost, and remains at #124 in interceptions thrown %.
For takeaways, M improved to #50 in forced fumbles, #55 in fumbles recovered, #83 in takeaway fumble recovery %, and fell to #90 in interceptions.
The folks at Football Outsiders – FEI are also doing weekly "Revisionist Box Scores" that strips out TOs, Special Teams, and Field Position. FEI calculates the value generated by each drive and then lost on the drive up until the turnover, as if the drive had concluded at that spot on the field. Thru Week #12, FEI has 16% of games where TOs were significant.
(See the Section on Gory Details below for how the adjustment for Expected Points (EP) is calculated.)
National Rankings: All rankings include games between two FBS teams ONLY and are from TeamRankings except for forced fumbles which is from CFBStats. The four columns with *** show the best correlation to offense and defense (per Advanced NFL stats).
The Gory Details
Expected Point (EP) Analysis: Basically, the probability of scoring depends on the line of scrimmage for the offense. Therefore, the impact of a TO also depends on the yard line where the TO is lost and the yard line where the TO is gained. Each turnover may result in an immediate lost opportunity for the team committing the TO and a potential gain in field position by the opponent. Both of these components can vary dramatically based upon the down when the TO occurred, the yards the TO is returned, and whether the TO was a fumble or an interception.
Here are the details for the game.
The analysis is a bit tricky because: (A) the TO may directly result in lost EP for the offense but (B) only modifies the EP for the team gaining the TO because the team gaining the TO would have gotten another possession even without the TO (due to a punt, KO after a TD, KO after a field goal, etc.). The Net EP Gain must take into account the potential EP gain without the TO. The EP gain without the turnover is based on where the field position would have been for the next possession if the TO had not occurred.
The expected point calculations are based on data from Brian Fremeau at BCFToys (he also posts at Football Outsiders). Fremeau's data reflects all offensive possessions played in 2007-2010 FBS vs. FBS games. I "smoothed" the actual data.