Diaries

Jack Becker had two tallies, including a great re-direct at the top of the crease [James Coller]

OFFENSE

	Corsi	House	Possession %
First Period	18	8	49%
Second Period	20	8	48%
Third Period	16	7	53%
Overtime	n/a	n/a	n/a
TOTAL	54	23	49%

Analysis:  Going back a couple of years, Michigan has not outplayed Penn State at even strength. They pounded the Nitany Lions on the scoreboard two years ago, but that was mostly due to special teams. That was not the case tonight. They played a top-five Corsi team to a draw and generated better offensive chances with more consistency. I was not as high on Peyton Jones coming into this game, but he played very well and definitely kept his team in the game until Michigan finally blew it open late. Michigan pressured the Lion defense and got into the house area all night. They also moved the puck from side to side very well and could have scored more much earlier if not for a very nice game from Peyton Jones. Lastly: three even strength non-DMC-line goals. Ye-uh!

[After THE JUMP: the defense holds and special teams take a step in the right direction] 

Quick note: For those unfamiliar with the FSI, it is a weekly survey asking fans to rate their feelings about each game and the season so far on a 0-100 scale. To catch up check out my blog here: http://mgoblog.com/diaries/onefootin

Who has it better than us? Well, according to my calculations, more than half of the Big Ten has it better right now. And I’m going to bet you won’t like who’s on top.

Let’s take this in two parts.

The Outback Bowl

First, there was that bowl game. As Figure 1 makes clear, this game felt bad. In fact, at a satisfaction level of 17.6 on our 0-100 scale, it felt worse than every regular season game except the Michigan State game.

This isn’t too surprising. It was bad enough to lose when favored by 7 points against an uninspired-looking South Carolina team that had just fired its offensive coordinator. It got worse when Michigan, leading 19-3, managed to fumble at the 5. It bottomed out when it turned out that was just the beginning of the second half Errorpalooza. Watching Michigan self-immolate while the Gamecocks scored 23 unanswered points was deeply aggravating, to put it mildly.

Figure 1: Outback Bowl Game Satisfaction.

(On a scale of 0 to 100, where 0 is the worst you ever felt after a game and 100 is the best you ever felt after a game, where would you rate your feelings about the Outback Bowl?)

X-axis is game satisfaction and Y-axis is # of respondents

Adding insult to injury, the loss to the Cocks took most of the remaining mojo from the fan base regarding the season as a whole. Season satisfaction clocked in at 24.9 – its lowest point of the season. 8-5 doesn’t feel good, as it turns out.

Figure 2: Season Satisfaction after the Outback Bowl.
(On a scale of 0 to 100, where 0 means the season went horribly and 100 means the season went perfectly, how do you feel Michigan's season went?)

X-axis is season satisfaction and Y-axis is # of respondents

Calculating B1G Fans’ Season Satisfaction

Okay, now for part two. Michigan’s season was unsatisfying but perhaps – out of a morbid sense of curiosity – you are wondering how Michigan fan satisfaction stacks up against other fan bases around the league.

Modeling Satisfaction from Our Data

Since I did not survey non-Michigan fans directly I used a regression analysis of our Michigan fan data to come up with a formula for calculating satisfaction for other fan bases. This approach comes with clear limitations. First, since we only have one season of Michigan data we don’t even have a perfect model of how Michigan fans will react to all situations. Just to take a couple of examples, we have no data on how fans respond to an unexpected victory over a ranked opponent, nor any idea how season satisfaction would look during a season where Michigan outperformed overall expectations. For that reason, our regression model is certainly far from perfect.

Second, even if our model were perfect for Michigan fans, it is very likely that other fan bases would react somewhat differently to the same situations. Given historical circumstances (spoiler alert!), Purdue’s fan base is likely to be happier with a 7-6 record on the season than Michigan’s is with 8-5. And though all teams have rivalries, we probably shouldn’t assume that all fans feel the same about them. I am pretty convinced, for example, that Sparty and Buckeye fans get more satisfaction from beating Michigan than the other way around.

With these caveats in mind, I still think we can provide a pretty reasonable estimate of B1G fan base satisfaction based on how Michigan fans responded during the season. For Michigan fans, based on 2605 responses over 13 games, the basic equation for game satisfaction is: 49.63 + (1.03 x Margin of Victory/Defeat) + (0.28 x Margin vs. Vegas) – (20.8 x Surprise Loss).

Margin of Victory/Defeat, clearly, is just measured by how many points more/less Michigan scored than its opponent. This captures both whether a game is a victory or defeat as well as its intensity. Margin vs. Vegas is how many points more/less Michigan scored than its opponent relative to the Vegas line. This captures general fan expectations about how the game went, which as we have discussed in past weeks is a critical component of how people feel about the outcome of a game. Surprise Loss is a variable I threw in because it was clear that unexpected losses – i.e. where Michigan was favored to win by Vegas – hurt more than usual.

In English, the model assumes satisfaction is about 50 points on our 100-point scale and then slides things up or down based on whether Michigan won or lost, by how much, and by how much relative to expectations. An additional point of margin in a victory adds about one point to fan satisfaction (vice versa for a loss). For every touchdown by which Michigan beats the Vegas spread you can add another 2 points of satisfaction, while a surprise loss sucks about 21 points of satisfaction from the average fan.

According to the magic of statistics this formula explains 70% of the variation in individual game satisfaction ratings. In the land of predicting individual opinions, 70% is pretty darn good, especially since all we have is data about the games and we don’t have any information on the respondents (Imagine, for example, trying to predict presidential popularity from economic conditions but without any information on respondents’ political affiliations). 

Table 1 below illustrates how well the formula does predicting the typical fan’s satisfaction compared to the average satisfaction measured by the survey for each game. Though the predicted satisfaction misses big in a couple cases, overall it tends to come pretty close, with an average absolute difference of less than six points across all 13 games. After a few more seasons worth of data the predictions should get better.

Table One. Real vs. Predicted Michigan Fan Game Satisfaction

Game	Actual Sat	Predicted Sat	Actual - Predicted
Florida	80.9	74.5	6.4
Cincinnati	59.9	65.3	-5.4
Air Force	62.9	61.2	1.7
Purdue	76.5	71.3	5.2
Michigan State	17.5	14.9	2.6
Indiana	51.6	56.5	-4.9
Penn State	23.9	6.1	17.8
Rutgers	73.9	69.5	4.4
Minnesota	78.5	78.6	-0.1
Maryland	73.5	81	-7.5
Wisconsin	28.8	30.7	-1.9
Ohio State	27.7	39	-11.3
Outback Bowl	17.6	11.5	6.1
		Average diff	5.8

 

 

 

 

 

 

 

 

 

 

 

 

The formula for season satisfaction is pretty similar. If you’ve been reading the diary this season you know that the average fan’s sense of the season is heavily tied to the game they just watched. As a result, assessments of the season varied a lot more on a weekly basis than they probably should have based strictly on the amount of new data coming in each week. The other significant variable in the season satisfaction formula, unsurprisingly, is the number of cumulative losses. Nothing says satisfaction like winning; nothing destroys it more than losing.

As a result, our season satisfaction formula after the 2017-18 season looks like this: 29.84 + (.62 x Game Satisfaction) – (3.388 x # Cumulative Losses). This model explains 73% of the variation in individual season satisfaction assessments over the 13 games of the season. Again, not too shabby. Table Two provides the summary.

Table 2 Real vs. Predicted Michigan Fan Season Satisfaction

Game	Actual Sat	Predicted Sat	Actual - Predicted
Florida	85	80	5
Cincinnati	77.2	67	10.2
Air Force	72.7	68.8	3.9
Purdue	76.7	77.3	-0.6
Michigan State	40.5	37.3	3.2
Indiana	53.7	58.5	-4.8
Penn State	33.7	37.9	-4.2
Rutgers	62.9	68.9	-6
Minnesota	69.1	71.7	-2.6
Maryland	69.9	68.6	1.3
Wisconsin	36.3	37.5	-1.2
Ohio State	36.8	33.5	3.3
Outback Bowl	24.9	23.8	1.1
		Average diff	3.6

 

 

 

 

 

 

 

 

 

 

 

 

Who Has It Better Than Us? Season Satisfaction across the Big Ten

If you’re still with me, Table 3 brings home the sad fact: Michigan’s implosion in the Outback Bowl, combined with its five losses on the season, put Michigan fan satisfaction below all seven B1G teams that won their bowl games and even below Indiana, which lost to its rival Purdue to end its season.

Table 3 End of Season Fan Satisfaction in the B1G

Team	Season Sat	Record (Ranking)	Final Game (Game Sat)
MSU	70.2	10-3 (15)	Beat #18 WSU 45-17 (81.5)
OSU	65.9	12-2 (5)	Beat #8 USC 24-7 (69.1)
Wisconsin	63	13-1 (7)	Beat #10 Miami 34-24 (61)
PSU	59	11-2 (8)	Beat #11 UW 35-28 (58)
Purdue	56.1	7-6	Beat Arizona 38-35 (75.2)
Northwestern	50.1	10-3	Beat Kentucky 24-23 (49.1)
Iowa	49	8-5	Beat Boston College 27-20 (58)
Indiana	31.4	5-7	Lost to Purdue 31-24 (40.7)
Michigan	24.9	8-5	Lost to South Carolina 24-17 (17.6)
Minnesota	14.9	5-7	Lost to Wisconsin 31-0 (14.2)
Rutgers	9.5	4-8	Lost to MSU 40-7 (10.9)
Nebraska	2.74	4-8	Lost to Iowa 56-14 (0)
Maryland	2.74	4-8	Lost to Penn State 66-3 (0)
Illinois	1.2	2-10	Lost to Northwestern 42-7 (8.4)

There is plenty to quibble with about these satisfaction predictions. Looking at the final game satisfaction figures, for example, it seems to my eye that they are probably too low for teams that won a bowl game. For most fans, winning a bowl game is likely more satisfying than winning a regular season game for any given margin of victory and performance against the Vegas spread. And in particular I think the model clearly undervalues the impact of beating a highly ranked opponent in a bowl game, even in these cases where the B1G team was favored. As a result of this, those teams’ final season satisfaction ratings should probably be higher than they are predicted here.

The reason the model misses on this is simple: so far we have no Michigan bowl victories and zero victories over ranked opponents in our satisfaction database. Until we do we’re stuck guessing at how much those things affect the predictions. Likewise, since we only have one season’s worth of data we can’t model the effects of teams significantly outperforming (or underperforming) season expectations. Going 7-6 is worse than 8-5, but Boilermaker fans are looking at their 7 wins through a very different lens than Michigan fans are viewing 8 wins. Similarly, OSU is close to the top, but how satisfied can the Bucks really be at this point with a two-loss season? And what about Wisconsin? Was that a great season or was that like winning a silver medal and wishing you’d won the damn gold?

Looking at the results from 30,000 feet, however, they make sense. Thanks to the fact that game satisfaction is a big driver of how fans rate the season, the seven teams that won their bowl games generated higher season satisfaction scores than Michigan. It’s important to remember here that this is an analysis of fan satisfaction – the fact that the satisfaction rankings don’t mirror objective measures of season quality (i.e. win/loss records) is pretty much the whole point. Fans are emotional, irrational, and short-term thinking animals. We have the S&P to tell us how good teams are. We have the satisfaction index to have fans tell us how they feel about the teams.

For our grand finale, in case you want to compare Michigan’s roller coaster of satisfaction with others on a week-by-week basis, I leave you with the season trends for each of the B1G teams.

Michigan State (10-3)

Ohio State (12-2)

Wisconsin (13-1)

Penn State (11-2)

Purdue (7-6)

Northwestern (10-3)

Iowa (8-5)

Indiana (5-7)

Michigan (8-5)

Minnesota (5-7)

Rutgers (4-8)

Nebraska (4-8)

Maryland (4-8)

Illinois (2-10)

Alright, last night was about helping people. Tonight…let’s see what we can get Ace (and maybe Brian –if there’s a revolt!) to do! More things on the table! Hair cuts, dye jobs, ink, piercings, limb severances…ok, maybe I’ve taken this too far. Just give, okay?
https://www.crowdrise.com/o/en/campaign/in-honor-of-the-anbenders-lets-find-a-cure-for-cfs/

OFFENSE

	Corsi	House	Possession %
First Period	14	7	45%
Second Period	13	5	56%
Third Period	11	3	61%
Overtime	n/a	n/a	n/a
TOTAL	38	15	52%

Analysis: This was not an offensive juggernaut by any means, but this is not what the game lent itself to for Michigan. They scored super early, a nice wrister by Brendan Warren– again. Michigan then got a PP tally a few minutes later. While they did create a few chances, Michigan was mostly content to control play and suffocate this game away…which they seemed to do starting in the mid 2nd period. Aside from trading PP goals in the 2nd, Michigan enjoyed a lot possession and generally put the puck in safe places. In a series that generally requires goalz to win, this one did not, and Michigan played it well.

[After THE JUMP: a defensive juggernaut in wait?]

Note: Sorry this is so late – work and the holidays conspired against me this year.

Sigh. Another regular season ends with a disappointing loss that could have been a win. Buoyed by a great game plan, the Wolverines jumped out to lead, made me break my promise not to have any hope whatsoever, and then the football gods took that hope away and crushed my heart. Again. Yeah, Harbaugh has things pointed in the right direction and the future is bright. But I live in the present and in the present I feel like shit (Edit: this goes double after the Outback Bowl – see part 2).

And so, evidently, do most of you. As I will explain below in just a bit, game satisfaction “without trolls” checked in at 27.7. This was almost identical to the Wisconsin game (28.8). This surprised me some given it was another loss to our biggest rival, though the Wolverines certainly played a better game than most people expected. A less optimistic take, on the other hand, might be that the Michigan fan base has become a bit numb from losing so often to the Buckeyes and that low expectations led to less anger and upset than is sometimes the case.

Figure 1. OSU Game Satisfaction

Season satisfaction (without trolls) also held more or less steady from last week at 36.8. In scientific terms this means the season was…not good. As I discussed last week, even if your rational self knew with great certainty that an 8-4 record was the most likely result of this season, you still felt like shit on Saturday. It turns out that expecting 8-4 and *experiencing” 8-4 are two totally different things. Sure the season probably would have felt worse had we expected to go undefeated, but losing is losing and no one likes it.

Figure 2. Season Satisfaction after OSU

Thus the regular season ended with satisfaction on a decided down note after the "Peters Resurgence."

 

Figure 3 Season Trends

Themes, Thoughts, Trends

Here Come the Trolls

The trolls found our survey. It’s the Internet so I knew it was bound to happen, but still. This is why we can’t have nice things. Of the 227 responses I logged for the OSU survey, I estimate that somewhere between 15 and 33 of them were our enemies – you probably know them as “jive turkeys.”

How do I know they were trolls? Well, if you rated both your game and season satisfaction as 100, as 15 people did, then I’m pretty sure you’re a Buckeye (or possibly a Schadenfreude Sparty) taking the survey for the lulz. Another 5 people rated their game satisfaction as 100 but with a strange variety of other season satisfactions. And another 13 people rated their game satisfaction as somewhere between 80 and 99.

Now, I’m sorry, but an actual living and breathing Michigan fan does not give this game an 80. Did you? If you are a real Michigan fan and you did, please let me know in the comments. Otherwise I have to assume you were high or live in Ohio, or likely both.

That said, after a long conversation with my scientifically inclined son, I realized that in the name of science we couldn’t just delete data, even Buckeye data. So in the interest of transparency and truth and the like, here is your satisfaction sensitivity analysis, under various troll identification parameters.

As you can see, there are enough trolls to make a difference in the results.

Table 1. Who’s Trolling?

Troll ID Rule	Game Sat	Season Sat	# Clean Responses	# Trolls
Assume no trolls	37.7	42.2	227	0
Game & Season Sat = 100	33.3	38.1	212	15
Game Sat = 100	31.6	38	207	20
Game Sat = 80+	27.7	36.8	194	33

 

 

 

 

 

Another way to find the trolls is to use a simple scatterplot. As you can see, there is an obvious central cluster and then there are some obvious outliers near the maximums on each axis. These are probably your trolls. It’s even more obvious something’s fishy when you compare this data to the data from Michigan’s wins (which were unlikely to result in opposing fans filling out our survey). In those cases there just aren’t any fans adopting the 0/0 position – so I’m pretty confident we can rule out anyone who answered 100 on both counts.

Figure 4. Scatter Trolls

What I am curious about, though, is what you think the most reasonable cut off point is. Is there any way a Michigan fan gave that a 100 for game satisfaction? Or an 80? Maybe on the notion that the lads did their best and gave the Buckeyes all they could handle, etc., etc.?

The Road Ahead

I was going to point out how there was one more shot at redemption, a chance for at least a moderately optimistic ending on the season.

But since I’m writing this after the Outback Bowl I won’t bother.

Stay tuned for part 2 for results from the Outback Bowl and to see how other B1G fanbases fared this season.

 

Hey, so Ace has been very forthcoming about some super serious stuff he’s been dealing with for a long time. He’s put together something if you’d like to help out people going through similar experiences. You should click here:

https://www.crowdrise.com/o/en/campaign/in-honor-of-the-anbenders-lets-find-a-cure-for-cfs/

OFFENSE

	Corsi	House	Possession %
First Period	9	1	41%
Second Period	12	3	50%
Third Period	14	6	42%
Overtime	n/a	n/a	n/a
TOTAL	35	10	44%

Analysis: After two games of outplaying a better team, Michigan definitely ceded the puck to Minnesota for the majority of this game. Ironic that this is the one of the last three games that they won. Two of their four non-empty netters were mostly luck. Michael Pastujov threw a puck into the slot and it was kicked back at Robson and between his pads. Warren’s first goal went off of his stick heel (after making a very nice break for the slot, though) and just inside the post. To be fair, Dancs picked a corner and Marody hid a nice snipe under the bar, though, for the first and third Michigan goals. Both Pastujovs are starting to look a little more dangerous, and I also thought Sanchez created some in the offensive zone. Now, if he can just not take silly penalties...

[After THE JUMP: insane penalty stats, woooo defense?]

Today's front page post about the future off the basketball team, combined with the general optimism surrounding the program, has inspired me to go back and compare this year's recruiting class to the 2012 one. That 2012 class is easily the best in recent Michigan mens basketball history so the comparison is not in the "how succesful will the 2018 class be compared to the 2012 class" but more just to compare the recruiting rankings and how the recruits fit into Beilein's system.

Before I look at the 2012 and 2018 classes, I wanted to remind everyone of the classes in between. For simplicity's sake, all recruiting info (individual player ratings, stars and class rankings) will be pulled from 247 using their composite rankings.

2013: Overall Class Rank - 14, B1G Class Rank - 3, AVG Rating - .9755

Name	Position	Recruiting Stars	Composite Grade	National Ranking	State Ranking
Zak Irvin	SG	4	.9876	28	1 (IN)
Derrick Walton Jr.	PG	4	.9833	45	2 (MI)
Mark Donnal	PF	4	.9579	86	3 (OH)
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2014: Overall Class Rank - 30, B1G Class Rank - 4, AVG Rating -.8818

Name	Position	Recruiting Stars	Composite Grade	National Ranking	State Ranking
Kam Chatman	SF	4	.9896	27	1 (OR)
DJ Wilson	SF	4	.9212	123	14 (CA)
Ricky Doyle	PF	3	.8675	209	21 (FL)
Aubrey Dawkins	SG	3	.8218	396	8 (NH)
MAAR	SG	3	.8094	434	12 (PA)

2015: Overall Class Rank - 107, B1G Class Rank - 14, AVG Rating -.9430

Name	Position	Recruiting Stars	Composite Grade	National Ranking	State Ranking
Moritz Wagner	PF	4	.9273	119	1 (NY?)
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2016: Overall Class Rank - 31, B1G Class Rank - 6, AVG Rating -.9049

Name	Position	Recruiting Stars	Composite Grade	National Ranking	State Ranking
Zavier Simpson	PG	4	.9748	67	7 (OH)
Jon Teske	C	3	.9047	142	10 (OH)
Austin Davis	C	3	.8803	177	3 (MI)
Ibi Watson	SG	3	.8600	239	15 (OH)
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2017: Overall Class Rank - 43, B1G Class Rank - 6, AVG Rating -.9176

Name	Position	Recruiting Stars	Composite Grade	National Ranking	State Ranking
Jordan Poole	SG	4	.9506	92	7 (IN)
Isaiah Livers	PF	4	.9215	132	2 (MI)
Eli Brooks	PG	3	.8807	202	10 (PA)
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Some notes on these classes:

• Position is position listed on their 247 recruiting profile, not the position they necessarily played at Michigan.
• Austin Hatch and Brent Hibbits were both excluded from the tables (Hatch for medical reasons, Hibbits because he was never a scholarship player).
• Hibbits was listed in the same class as Wagner but without any rankings or info. Despite Wagner having a rating of .9273 (and being listed as playing in Berlin, NY), the class has an average rating of .9430, which makes no sense.
• The best recruiting class by far was the 2013 class with Irvin, Walton and Donnal, all of which were 4 stars. It was the 14th best class in the country that year per 247.
• The worst class was the 2014 class, with an average rating of .8818. This fits with what we now know from that class, with DJ and MAAR being the only contributors and MAAR being the only player to stay for 4 years. The fact that the class had 5 players pushed the class to 30th in the nation per 247, which actually puts it second highest of these classes.
• The highest rated recruit of these players was Kam Chatman in 2014 (lol), with a national ranking of 27 and composite grade of .9896. He just barely beat out Zak Irvin who was 28th and had a grade of .9876.
• The lowest rated recruit was MAAR, also part of that 2014 class, with a .8034 composite grade and 434 national ranking.. He beat out fellow 2014 recruit Aubrey Dawkins who had a composite grade of .8218 and ranking of 396.

Now the 2012 and 2018 classes:

2012: Overall Class Rank - 8, B1G Class Rank - 2, AVG Rating -.9373

Name	Position	Recruiting Stars	Composite Grade	National Ranking	State Ranking
GR3	SF	5	.9934	17	1 (IN)
Mitch McGary	PF	4	.9897	28	3 (NH)
Nik Stauskas	SG	4	.9369	110	5 (MA)
Spike Albrecht	PG	3	.8556	221	9 (MA)
Caris Levert	SG	3	.8444	239	5 (OH)

2018: Overall Class Rank - 9, B1G Class Rank - 1, AVG Rating -.9434

Name	Position	Recruiting Stars	Composite Grade	National Ranking	State Ranking
Ignas Brazdeikis	SF	4	.9867	34	1 (ON)
Brandon Johns	PF	4	.9718	69 (nice)	2 (MI)
David Dejulius	PG	3	.9331	132	5 (MI)
Colin Castleton	PF	3	.9330	133	14 (FL)
Adrien Nunez	SG	3	.8924	206	2 (CT)

Some comparisons between the two classes:

• Both classes are top 10 nationally and the 2018 class is currently 1st in the B1G (although as more unsigned top propsects commit, both those rankings will drop).
• The 2018 class has a higher average ranting per player (.9434 vs .9373), which is the second highest of any of these classes behind the 2013 class.
• Both classes consist of 5 players. Unlike the 2014 class which also consisted of 5 players, both the 2012 and 2018 classes consist of 5 player classes where each player fits a unique position on a Beilein team.
  • PG - Albrecht vs Dejulius
  • SG - Levert/Stauskas vs Nunez
  • SF - Levert/Stauskas vs Brazdeikis
  • PF - GR3 vs Johns
  • C - McGary vs Castleton
  • This is not saying the play style of each player matches up directly (ie Castleton is not the same player as McGary, he's a stretch big compared to McGary being a post player), but just where they fit on the floor.
• The 2012 class had a better top of the class (McGary and GR3 were more highly regarded than any of the 2018 guys), but the depth of the 2018 class is much better. Iggy and Johns are the only 4 stars but Dejulius and Castleton are 3 and 4 spots away respectively from being 4 stars, and Nunez is a solid mid tier 3 star compared to Albrecht and Levert being bottom tier 3 stars. This is what pulls the average 2018 rating above the average 2012 rating.

Overall the 2018 class should be a great class and another reason to be excited about the future of Michigan basketball. However this should also serve as a reminder that college athletes are fickel beasts and recruiting rankings pretty much mean nothing once the kids get to campus.

The highest rated recruit for Michigan between 2013 and 2017 (Chatman) was arguably the worst recruit in that time, while the lowest rated guy (MAAR) has been arguably the most consistent player in that time. Even in the 2012 class the lowest rated guy (Levert) has had a better career than either of the top 2 guys in that class. So while expectations for the future should be high, keep in mind that struggles and busts may/will occur and are to be expected.