In a recent forum post I put out a request for diary ideas and Brian requested a deeper look at special teams. So here goes.
As any good Michigan fan knows, punting from the opponents 35 yard line is excruciating to watch. But how painful is it?
As you can see above, net punting holds pretty steady in the 37-38 yard range all the way till around the 40 yard line. At that point it drops a couple yards into 35 yards per punt range. It’s when you get to midfield that the averages really start to tank. Over the next 15-20 yards of the field, the average drops from 35 yards per punt all the way down to 20. This is somewhat obvious as the field shrinks the longer punts turn into touchbacks where they would have been 50 yarders on the other side of the field.
But lets look at it another way. On average, where can you expect to start your defensive drive based on where you are punting from.
It’s a pretty linear relationship all the way to midfield and then it stops. On your side of the field, one more yard on offense takes one away from the opposition if you have to punt. You get to the other side of the field and that relationship disappears. At the 50, the other team will, on average, start their next drive at the 16. If you drive all way inside the opponent’s 35 and decide your punter is still the best call, the 16 becomes the 12. The 15-20 yards of offense only translate into about 4 yards of worse field position for the other team. A punt from the 37 should realistically only be expected to cover a net of 25.
All of this is accounted for in my special teams rankings. Punters evaluations on each punt are measured on gross distance, net distance and then compared to punts from that spot on the field. A punt from your own 20 yards that nets 35 is below average but a punt from the opponents 40 that has a net of 30 is above average.
Last year, Georgia led the nation in my measurements in gross punting, it was worth 8.2 points above average on the season. Zoltan came in 7th (1st in the Big 10) at 4.3 points above average.
Punt returners and coverage teams are evaluated in the same manner. A 50 yard punt that isn’t a touchback or out of bounds (but including fair catches and downed punts) will average 6 yards per return but a 30 yard punt should only expect a 2 yard return. Again, teams are only evaluated against the situation they are given, a 4 yard return on a 50 yard punt is a positive play for the coverage team and a negative one for the return team. A 4 yard return on a 30 yard punt is a negative for the coverage team and a positive for the return team. I don’t currently have a way to split the value between the punter and the coverage, so the coverage is a joint metric.
LSU led the nation last year, gaining 9.7 points more than average on their punt coverage. Michigan came in 28th, 3.7 point above average.
What ultimately matters is the total punting rating, the combination of the punt and the cover. Michigan’s combined value of 8.0 points above average was 3rd nationally behind only Oklahoma and Missouri.
Michigan was somewhat unique in that even when adjusting the coverage rating for distance of punt, there is a negative correlation between punt coverage and gross punting. Possibly meaning that the punters getting the most distance on their punts are doing so by kicking more returnable punts than their peers who aren’t kicking as far as consistently.
Michigan did not fare so well on the returning end of the punt game. Michigan averaged a mere 2.35 yards of return per punt (excluding touchbacks and out of bounds) and was 3.9 points below average on the season. LSU again led the nation with 19.3 points above average for their return team.
Kick - Offs
I approached the kick off in the same manner as the punt, with the obvious exception that almost all kick offs are from a fixed spot on the field.
Unlike punting, kick offs have a correlation between good kick offs and good coverage, even when adjusting coverage for kick length. Good kick off specialists provide a more coverable kick than when weaker kickers get a kick of the same distance.
Michigan had another strong showing out of their kick off teams. Ranking 16th nationally at 6.3 points above average. The coverage wasn’t as good, 1.6 paa and 49th nationally. The 7.9 paa was 25th overall in a category that was dominated by Nebraska. Nebraska’s kickoff team was worth 27.9 paa on the season. 50 of their 74 kickoffs either went for touchbacks or were stopped inside of the 20 yardline.
The Wolverine kick return team was a respectable 36th overall, 2.6 paa. Cincinnati dominated the country at 19.7 paa.
Field goal kickers have never really had a good stat with which to measure them by. So much depends on where you are kicking from. Leigh Tiffin from Alabama garnered All-American honors despite missing 4 extra points and making 24 of his 30 field goals from inside 40 yards. Meanwhile in the same conference, Blair Walsh from Georgia makes a nation leading 12 field goals of 40 yards or longer versus only one miss from the same distance and is perfect on extra points and doesn’t even sniff All-American. Walsh’s performance gave Georgia 21.6 paa where Tiffin providing a respectable but not that close 7.4 paa. So how do you evaluate kickers. The easiest way would be to put up a chart.
A nice straight line, right? Look closely at the attempts and something changes around the 30 yard line. With the 30 as a general benchmark, coaches become more and more reluctant to trot the kicker out from that distance or beyond. With this selection bias, the true field goal percentage of field goals from 47 yards and longer is almost certainly overstated. By only getting attempts from the better kickers, the percentage is artificially high.
So now it’s time for a new chart, right?
Using the assumed misses from coaches foregoing the field goal, the true field goal percent drops. The straight line out to the 25-30 yard line goes south fast as the distance is stretched.
Last year Michigan’s kicking game came in at 44th with 1.6 paa on the season.
In attempting to determine how much coaches were passing up field goals in “no man’s land” I did also produce one more interesting but not necessarily special teams chart. The 4th down decision chart.
Between the 3 and 25 yard lines its a consistent trend, 80-85% field goal attempt 15-20 % go for the touchdown. It rises to 60/40 at the 2 and flips to 20/80 at the 1. The going for it actually peaks between 30 and 35 as more coaches don’t really know what to do so they just go for it.
Final Thoughts and Notes
There are a couple of things not included in this analysis. Exception plays such as blocked punts and kicks and their returns, fumbled returns (not that those ever happen) and the like are all excluded. These play obviously have huge impacts on the game in which they occur but they are so rare and have little or no impact on other plays of that type that they are excluded . The very best teams in the country may block 4-5 kicks in a season and for all but a few teams, these plays have virtually no net effect.
In general, for any one special team unit, the difference between average and the best and worst is about 2 touchdowns in either direction over the course of the season. Being the best at special teams is worth about a half game a season versus the average team and a full game a season versus the worst team. If there is one unit to excel at, the opportunity is on the kickoff team where last year there was a 53 point differential between the best (Nebraska) and the worst (West Viriginia).
[Ed: meant to bump this sooner but there was a lot of stuff yesterday.]
After the disastrous offensive performance of 2008, the 2009 Wolverine offense really had nowhere to go but up. Using my offensive ratings, the 2008 Michigan offense was 7.4 points per game below average, 107th out of 120 FBS teams. 2009 brought another year in the system and real quarterbacks and huge improvements. While far from consistently excellent, Michigan moved up to a modest 1.2 points per game above average, 50th nationally. No one outside of the eternal optimists like Fred Jackson could see another 57 place ranking improvement, but what has happened to teams that have shown big offensive improvements in year in the following year.
Presently my database has the 2007-2009 years completed, just enough for a 3 year case study. From 2007 to 2008 there were 28 teams that improved offensively by at least 5 points per game. I broke those team into three categories, teams that saw a second major (+5) increase in the third year, teams that saw a major (-5) regression back in the third year and teams that were in the middle and didn’t necessarily continue gaining, but didn’t fall back much either.
*Only BCS teams shown
With 14 of the 28 teams in this group, half of the teams that show big gains can expect a return to the mean the next season. In fact, these teams were worse offensively in 2009 than they were in 2007, let alone the beacon season of 2008. The average team in this group was 2.5 points per game worse in 2009 than they were in 2007 before they peaked.
The closest thing to a consistent thread is the quarterback possession as five of the eight, Oklahoma, Baylor, USC, Arizona and Utah, spent most or all of the season with a new quarterback.
In general, the regressers look like a group that is just regressing to the mean and that replacing a quarterback is damaging when your success has not been sustained for longer than a single season.
With the exception of Alabama, these teams were pretty average in returning starts and had no major position group gaps to fill. Alabama had a new quarterback and was 97th in returning offensive starts nationally, the ability to sustain the offensive success is likely attributable to the influx of talent Saban brought into Alabama since he arrived.
*Michigan 2007 results omitted (-1.1)
With a relatively new coach and a total offensive system overhaul, Georgia Tech is clearly the most similar situation to Michigan and their path is one that Michigan would be thrilled to follow. Tech went from –1.1 ppg in 2007 to 7.6 in 2008 to 14.5 and my top rated offense in the country in 2009. Even though Johnson and Rodriguez were hired the same year, the Michigan offense is about 2 years behind Georgia Tech. Georgia Tech went from average to very good to best in the country. Michigan went from average, to very bad and back to average. Even with the offset timeline, Michigan seems comparable to Georgia Tech’s situation and therefore a second year of offensive gain seems very possible under this comparison.
All five of these teams either returned 20+ starts at the quarterback position (except GT who had the same quarterback from the start of the system), although Stanford’s returning quarterback was replaced. The other major similarity between these schools in neither of the last two years did they have stratospheric gains, there is less flukiness to these teams success.
When looking at the progression from very bad to roughly average, there are four BCS level schools who showed that same progression. Three of those (TCU, Notre Dame and Pittsburgh went on to see big gains in year 3 as well, and NC St still saw modest improvement. Teams fitting this profile for a potential second year of strong offensive progress in 2010 along with Michigan include Kentucky, UConn, Wake Forest and Mississippi St.
Although teams that show a big jump like Michigan last year are more likely to fall back than continue the progress, the recruiting profile, experience at quarterback (even if the returner loses his job), progressions comps and system change all point to Michigan as being a good candidate to at least sustain and probably show more improvement next year. Every 3 point gain is worth about one additional win on the season and based on this look I would say that from the offense alone, a 3 point gain seems likely and a 6 point gain entirely possible.
Inspired by a tweet from Chris Brown a.k.a smartfootball on this interesting but largely meaningless article from Rivals I decided to put together a post on offensive balance. This is going in place of my normal Monday post and I should be back in a week or so with Brian’s requested special teams primer.
Game Theory and Play Calling
Game theory suggests that teams will adjust their choices so that the average value of each choice (run or pass) will be equal. We also know that not all coaches are rational decision makers and there are likely very few who understand what game theory is. That is where nerds like myself come it to explain on blogs and help them understand.
The thinking goes like this: a team is really good at running the ball and really bad at passing the ball, but they are perfectly “balanced,” half their plays are rushes and half are passes. Since there is more value on a running play than a passing play, it doesn’t make sense to be calling so many pass plays, so the first adjustment happens and this team starts calling more rushes to take advantage of their more efficient running game. At some point, the defense responds to the new strategy and begins to stack against the run which of course makes success in the passing game easier. If the offense is playing optimal strategy, their final mix will be one that garners the same value for each play, regardless of whether it is a run or a pass, even if the distribution of plays is not 50/50.
This is how every team in the country fared on a per down basis in both rush and pass. The diagonal line represents balanced results on a per play basis. Teams on the top right are balanced and successful, teams on the bottom left are balanced and unsuccessful. Teams to the left of the line (Northwestern, Iowa, Michigan St, Notre Dame, Penn St, Wisconsin, Indiana and Minnesota) should pass the ball more to become more optimal where teams below the line (Michigan, Ohio St and Illinois) have the opportunity to run the ball more to become more optimal. Purdue sits right at the intersection of all lines, balanced and mediocre. Most of the teams in the Big 10 where within reach of balance with the notable exceptions of Michigan St and Notre Dame, two teams that couldn’t get their rushing outputs to match their passing success. Time for another chart.
In this chart you can see how balance in calls does not necessarily equal balance in output. In fact, some of the least balanced play callers, on both ends of the spectrum no less, produced the most balanced results in output. Teams who rushed over 80% of the time like Georgia Tech, Navy and Army got almost the same value from passes as they did form rushes. On the other end, pass happy squads like Texas Tech, Kansas and Houston saw similar production on a per play basis from their running games as they did from their passing games. These teams are still running or passing teams, but their play calling balance has found an equilibrium where they are maximizing their total points by finding balance, even if they are calling a lot more of one type of play than another.
You can see that Michigan St and Notre Dame are both outliers in their respective deviations in success between run and pass, despite being towards the middle (especially MSU) when it comes to run pass selection.
This data does not include games for any team versus non D1 opponents (Baby Seal U). Only plays when the lead is 2 TDs or less or if the game is still in the first half are counted. Points per play uses my expected points model and is adjusted based on opponents played. Interceptions are included in this analysis but fumbles, fumble returns and interception returns are not. Including fumbles pushes the balance to passing as fumbles are more likely to occur on running plays than pass plays. Most of that difference is negated if you include returns as interceptions are much more likely to be returned than fumbles and the total value of interception returns is nearly equal to the difference between fumbles on running plays versus passing plays. The net of it all is that excluding fumbles and returns does not materially affect any of the data above.
Since we are blowing the whole thing up, why don’t we look at what a truly consolidated power structure in which all the big boys stay at the table, invite a few of the little guys that have been chirping the loudest and push everyone else out of the picture. The result is 5 conferences, 15 teams each and a 75 team SugerMegaUltraDivision1BowlPlayoffChampionshipDivision.
My rules, 15 teams per conference, Big East disappears, no members poached from within the 5 remaining conferences. 65 teams from the Big Six Conferences, Notre Dame and 9 other teams survive.
The conferences would each be split into 3 divisions, 5 teams each. Four inter-division games are obvious and from there we have two options, option one is 3 cross division games per division and 2 non conference games and option 2 is 2 cross division games per division and 4 non conference games. Personally, I like the first option because if you mix any match-ups for rivalries you still play all the teams every other year as opposed to once every four years. I think if you are consolidating like this, non-conference games are less of a necessity.
Since I have already blatantly stolen some of the ideas from UMFootballCrazy’s Big 16, why stop now. Besides the 3 division format, I also liked the 4 team conference playoff at the end. 3 Division winners plus a wildcard face off in a four team conference championship. The 5 conference winners plus 3 wild cards could then face off in an 8 team playoff for the national championship.
10 11 12 14 15
|Ohio St||Notre Dame||Iowa|
New teams: ND, Pitt, Rutgers and Syracuse
2009 projected playoffs: MSU @ OSU, PSU @ Iowa
Michigan could preserve rivalries with Ohio St and Minnesota and under the 3 game scenario, rotate among the other 4 in each division every other year.
New teams: Memphis, Cincinnati, Louisville
2009 projected playoffs: LSU @ Florida, Cincinnati @ Alabama
Big 12 Bible Belt Conference
|Texas A&M||Oklahoma St||Missouri|
|Texas Tech||Colorado||Iowa St|
New teams: TCU, Colorado St, Houston
2009 projected playoffs: Nebraska @ Texas, TCU @ Oklahoma St
|Virginia||NC St||Georgia Tech|
|Virginia Tech||Wake Forest||Florida St|
|Conn||West Virginia||South Florida|
New teams: UConn, West Virginia, South Florida
2009 projected playoffs: WVU @ Georgia Tech, Clemson @Virginia Tech
|Washington St||USC||Arizona St|
New teams: Boise St, San Diego St or Fresno St, Utah, BYU, UNLV
2009 projected playoffs: Arizona @ Boise, BYU @ Stanford
Conference champions are then seeded 1-5 with the three at large selections going 6-8. Seeding are adjusted so that conference opponents can’t meet until the championship game. To make this work #7 TCU is switched with #8 Iowa.
Personally I like some of the ideas brought in from international soccer better than this, but that radical of a change isn’t likely to happen anytime soon or ever for that matter. National championship participants would probably be playing 17 games in a season which does seem like a bit of a stretch. Basketball scheduling would be an 18 game conference schedule with 4 opponents (probably division) getting a home and home and the other 10 teams getting one match-up each during the regular season. Conference tournaments could either be 1st round bye for 1st place team or Big East style bracket where 1-9 get a first round bye and then 1-4 get a second round bye. I didn’t have much trouble making up the conferences or the divisions, but a couple teams could probably move around. West Virginia could fit better in the Big 10 and switch with Syracuse or Rutgers. As far as teams in or out of these conferences, the hardest calls where which California team to take, San Diego St or Fresno St and which teams to add to the former Big 12, could potentially bring in Tulsa instead of Houston or Colorado St.
In December, I put up a post on the top Heisman candidates and my thoughts on them. With the emergence of Ndamukong Suh and locally with Brandon Graham, I wondered the best way to evaluate defensive players strictly from the stat sheet. Defense is made is more for the UFR, not for stat comparisons. The problem is, with over 800 games played in the FBS every year, it would take an army to break down the film for all the players in all the games. My stats based approach has the advantage of being able to quickly look at every game played last year.
There is no way to evaluate from the play by play who is responsible for a bad play on defense, but you can get a decent idea of who is responsible for a good one. Sure someone else could have opened up the hole, taken on extra blockers or forced a cutback, but over the course of a season, if you you made a lot key tackles, chances are you did a lot of work on those plays.
I took all of the plays from the season and immediately cut out all the plays from the second half where one team led by more than 2 TDs, no garbage time stat padding (same goes for any games against Baby Seal U opponents, always excluded from all my work). I then reduced the list of plays to ones that put the offense in a worse position than were they started the play. This doesn’t just mean TFL plays. A 2nd and 8 is worse than a 1st and 10 for an offense, so a tackle on a 2 yard gain on first down counts. Any third down stop counts, and often these are the biggest plays a defender can make. Turnovers are obviously the holy grail, stop the offense, create field position for your offense. The players are measured by two metrics, number of plays and magnitude of plays. A defensive tackle might make a lot of plays but most of them for relatively small values. A cornerback probably doesn’t get the chance to make many plays on a down by down basis, but each interception is huge, and has a very high value.
I then compared each players production versus what the average player at his position accomplishes to get a sort of VOAP, Value Over Average Player. Unfortunately, I couldn’t get good enough roster data to split all positions, so everyone is either DL, LB or DB, not perfect but better than nothing. I assumed that the average team would split the majority of the playing time between 6 DL, 4 LB and 5 DB. From the best I can tell, there isn’t much variation between the safeties and the corners, but DE’s get a bit of a benefit being compared to DT’s and DT’s get a slight hit compared to DE’s.
Top National Players
All player data is available here. It is easier to work with if you download into Excel since G Docs doesn’t like pages with a lot of rows. There are some slight changes to the numbers from my Heisman post as I reloaded the 2009 data and tweaked the expected value formulas.
I don’t have all the historical data but I would be shocked if a defensive tackle has ever had a better season than Ndamukong Suh did in 2009. He made 52 plays more and was nearly 5 TDs better than the average d-lineman and that includes defensive ends. No player in the country had a VOAP within a touchdown of him.
The closest, none other than Michigan’s Brandon Graham. Graham produced 27 extra plays and over 27 points of value more than the average defensive lineman. This made him the top value adding defensive end in the country and second to Suh overall.
In fact, the Big 10 had four of the top 6 defensive lineman in the country. O’Brien Schofield, Ryan Kerrigan and Adrian Clayborn all managed at least 23 points per page above replacement.
Luke Kuechly of Boston College led all linebackers with 57 plays and 25 points above average. Kuechly produced a ridiculous 87 negative plays on the season, 10 more than any other player at any position and 17 more than anyone else from a BCS a conference. Navorro Bowman, Nate Triplett and Brian Smith all cracked the bottom half of the top 10 linebackers at 17-18 points above average each.
Defensive backs were highly unproductive in the Big 10 relative to other conferences. Tyler Sash of Iowa came in at +12, 15th nationally and Donovan Warren was second best in the conference, but his +9 barely cracked the Top 40 nationally. Walter McFadden of Auburn was the top producing defensive back nationally, providing a +22 for the season.
Reviewing All-American Teams
I was curious to see how the national All-American team selections would compare with this metric. For positions like defensive end, linebacker and safety I would hope quite well because these positions are very output oriented and most of the value is in making plays. For defensive tackles and corner backs, I wasn’t as confident. A good defensive tackle will often add value by making plays for others. A good cover corner will often see the action go away from him and might not get many opportunities.
There were five players that were selected as defensive ends on the All-American teams, four of them stack up very well in my ratings, one does not. Brandon Graham was rated 1st and was +27. Derrick Morgan of Georgia Tech was 7th and +18. Von Miller of Texas A&M and Jason Pierre-Paul were 12th and 13th at about +15.5 for the season.
The outlier was TCU’s Jerry Hughes who came in 60th at +6 and was actually selected to the most All-American teams as any d-lineman. The large is probably due to the fact that 5 of his 11 sacks (10th nationally) came during garbage time.
Top non-selections were O’Brien Schofield, Wisconsin (2nd, +27), Jeremy Beal, Oklahoma (3rd, +25), Ryan Kerrigan, Purdue (4th, +25) and Adrian Clayborn, Iowa (5th, +23)
Again five players were picked to All-American teams as defensive tackles. Three had elite level production and 2 did not. Not to say that they were undeserving as discussed earlier, defensive tackles value can be difficult to attribute.
Suh was an obvious selection, and both Brian Price of UCLA (3rd, +20) and Gerald McCoy of Oklahoma (7th, +9) were well-deserved. The two potentially questionable selections, Mount Cody had the reputation and the highlight but did not have the direct productio, 79th nationally and 1.4 points below average. The other low production selection was Penn State’s Jared Odrick who just made the top 50 and was only 1.7 points above average.
Other top non-selections were Nate Collins, Virignia (2nd, +21), Lamarr Houston (4th, +16), Jared Crick, Nebraska (5th, +15) and Corey Peters, Kentucky (6th, +14).
There wasn’t much consensus among the All-American teams on the linebacker position. A total of 10 different selections were made. It appears the selections are more weighted on quantity of plays instead of quality of plays. This makes sense because most linebackers don’t make a lot of big stat sheet plays like interceptions or sacks and so the good old tackle stat is the most used.
When looking at the top values for linebackers, Luke Kuechly at #1 is the only player from the top 14 to receive any All-American honors. When you look at the play quantity, Kuechly and 3 others are in the top 8. The three are Rennie Curran from Georgia (+13), Pat Angerer, Iowa (+8) and Greg Jones from Michigan State (+12). Consensus pick Rolando McClain of Alabama is in the top 50 in both quantity and quality, and played for the top defense in the country.
There was one player who seemed to make the team purely on reputation and team success alone, because his production was dramatically less than the rest of the group. In defense of the selections, I won’t even name this individual for he might be the scariest man alive that played for the 2008 Florida National Championship game. He only produced 2 more plays and was just below average (-.1) in point production versus average player, and still received recognition from three different groups.
Of the five cornerbacks named to an All-American team in 2009, four landed in the Top 11 of my rankings. Joe Haden, Florida (3rd, +18), Javier Arenas (4th, +15), Alterruan Verner, UCLA (8th, +13) and Patrick Peterson from LSU (11th, +11) all produced very highly. The only exception was Perrish Cox from Oklahoma State who still managed to make the top 40 with a +6 for the season.
Walter McFadden from Auburn and Brandon Brinkley from Houston were the top two rated cornerback and both produced over +20 for the season.
It took seven selections to cover all of the picks for safeties. Five of the seven fit nicely at the top, including the top 3. DeAndre McDaniel, Clemson (1st, +20), Earl Thomas, Texas (2nd, +17), Rahim Moore, UCLA (3rd, +17), Tyler Sash, Iowa (7th, +12) and Eric Berry, Tennessee (11th, +10).
The two outliers were Kurt Coleman from Ohio State (44th, +1) and another apparent reputation selection, Taylor Mays, USC (77th, –3).
|Player||Position||Group||Plays||Value||Adj Plays||Adj Value|
|Ryan Van Bergen||DE||DL||23||16.1||5||6.5|
Michigan’s top two producers, Brandon Graham and Donovan Warren were covered above. After those two, only two players managed to be above +3 on the season. Ryan Van Bergen was +6.5 on the season on the defensive line and should have the potential for a big season this year. Stevie Brown was next at +6 (if you count him as a safety, +4 as a LB).
Jonas Mouton, Mike Martin, Obi Ezeh, Jordan Kovacs and Craig Roh all sit around the average mark for their positions. The most glaring point for me is that Michigan’s top linebacker, Mouton, barely makes the top 150 linebackers nationally in production. If Michigan’s defense is going to turn things around there is going to have be some new playmakers step up and there has to be more production from the linebackers.
Was Michigan lucky or unlucky last year? Who were the luckiest teams in the Big 10 last year? What teams were the unluckiest nationally?
To try and answer these questions, I took my team PPG values for the full 2009 season and then “re-played” the regular season schedule to see how the season would play out if the teams played at that consistent level and the fluky plays were eliminated. All first half plays and any in the second half with the game within 2 touchdowns were included. Interceptions are included, fumbles are not. Standard special teams plays are included, punt blocks, on-sides kicks etc. are not.
The results were based on the actual schedule (excluding conference championship games or Bowls) and home-field advantage was worth about 3 points per game, based on the actual results.
So what did I find…
Michigan was a fairly unlucky team last year. Based on how their play was over the course of the season, on an average year, they would have won 6.3 games (most likely 6 with an outside shot at 7). Michigan’s results were about 1.3 wins below the expectation based on their schedule and their average performance over the full year.
On average, they should have won about 5.1 of their 8 home games. In reality, they won 5 of 8. Notre Dame was a game with a 40% win likelihood based on the full season for both teams. Indiana was a 73% chance. The pickup from those two games was offset by failing to pick up victories over long shots Penn St (34%) or Ohio State (13%) but mostly due to failing to defeat Purdue which Michigan should have won about 58% of the time.
Michigan was only favored to win one (Illinois, 56%) of its four road games and on average, would have won 1.2. Michigan failed to pick up any of the wins, falling to Michigan St (29%), Iowa (13%) and Wisconsin (22%).
For the season as a whole, Michigan went 1-2 in relative toss-up games. They didn’t lose any games they should clearly won but didn’t win any they clearly shouldn’t have. Michigan was the 20th most unlucky team in the nation last year.
Very similar to 2009 in terms of luckiness of the results. Michigan finished the year a game and a half unlucky. This team was still not a good team by any stretch, but thanks largely to fumbled punts and 100 yard interception returns, the record indicated a season even worse than it should have been.
The biggest chunk of the unluckiness came against Toledo, where an 81% win probability turned into one ugly loss. Beyond Toledo, Michigan went 2-3 in toss-up (40-60% win odds) games (Wisconsin, Minnesota, Illinois, Michigan St, NW) and 0-2 in longer shot games against ND and Purdue.
Before two data points become an indictment on coaching, the scatter plot of 2008 vs 2009 in terms of “luckiness” does not show any correlation between the two. That doesn’t mean that Rich Rodriquez couldn’t be unlucky or that Pat Fitzgerald should be out buying lottery tickets, but it isn’t saying that with any certainty. Two points may make a line, but not much of a trend.
It is interesting, however, that the Big 10 as a whole exhibited a lot more consistency of luck in 2008-09 than the rest of the nation.
The Big Ten
The luckiest team in the nation resided in the Big 10 this year.
Northwestern did not perform like a team that would win 8 games against their schedule. In fact, it was a stretch for them to be bowl eligible. Iowa and Minnesota both came in with about an extra game with Ohio St, Purdue, Indiana and Illinois all managed a game or so of underachievement. Michigan St and Notre Dame proved to be the unluckiest teams in the greater Big 10.
Top 10 Lucky Teams in 2009
Bottom 10 Unlucky Teams in 2009
Oklahoma’s season turned out to be unlucky in more ways than one. Despite getting only a handful of snaps from returning Heisman Trophy winner Sam Bradford and losing one of the top tight ends in the country before the season, Oklahoma managed to have the breaks go against them in win column as well. Throughout the season, the Sooners played like a 10 win team, but only managed 7. This is what happens when you have four of your five losses by a total of 12 points and your average margin of victory in your six FBS wins is over 31 points.
I think there is a lot of true “luck” that can come into play in these numbers. I do think that with a more substantive history, this will also be a good measure of one of the strengths of coaching. A team/coach that consistently shows overwins, or overlosses, throughout the course of several years would be a good testament to the classic “little things” that is often luck but possibly true for a select group. As I get more years in my database, I plan on returning to this topic and seeing how various coaches stack up on this metric over time.