Fan Satisfaction Index End of Season Bball Survey

Fan Satisfaction Index End of Season Bball Survey

Submitted by OneFootIn on April 2nd, 2018 at 11:42 PM

It might feel a bit too soon to share your feelings, but science doesn't stop for your pain or suffering.

It's time for the last basketball fan satisfaction survey.

How are you feeling about the season with all things said and done?
How are you feeling about next year?

Take the survey here:

Fan Satisfaction Index: Regular Season BBall Survey

Fan Satisfaction Index: Regular Season BBall Survey

Submitted by OneFootIn on February 24th, 2018 at 2:06 PM

Michigan's Men's basketball team ends its regular season with a win at Maryland. Having spent the fall polling fans about their feelings about football, it's time for our first fan satisfaction survey for basketball.

How do you feel about the Maryland game?
How do you feel about Michigan's regular season?
How do you think Michigan will do in the NCAA tournament?

Take the survey here:

For more information:

Introduction the Fan Satisfaction Index

Fan Satisfaction Index: Outback & End of Season Results

Fan Satisfaction Index: Outback & End of Season Results

Submitted by OneFootIn on January 15th, 2018 at 10:50 AM

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:

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)

Fan Satisfaction Index: Ohio State Results

Fan Satisfaction Index: Ohio State Results

Submitted by OneFootIn on January 13th, 2018 at 11:52 AM

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.


Fan Satisfaction Index: Outback Survey

Fan Satisfaction Index: Outback Survey

Submitted by OneFootIn on January 1st, 2018 at 3:41 PM

Fan Satisfaction Index: Ohio State Survey

Fan Satisfaction Index: Ohio State Survey

Submitted by OneFootIn on November 25th, 2017 at 3:44 PM

Fan Satisfaction Index: Wisconsin Results

Fan Satisfaction Index: Wisconsin Results

Submitted by OneFootIn on November 22nd, 2017 at 11:40 AM

That sucked. It started as a hard-hitting Big Ten rock fight with Michigan giving Wisconsin all it could handle. Then it dissolved into another miserable and hard to watch affair as Peters went down with a concussion and the team lost its mojo. On paper Michigan did pretty well; Peters had his most promising outing despite a couple of mistakes and the defense kept Wisconsin’s high-powered tailback in check for most of the game. Heck, with a couple of breaks (thanks replay guys!) the outcome could have been very different.

But things went the way they did and from a fan’s perspective it mostly just sucked. This week’s game satisfaction clocked in at a whopping 28.8, down almost 50 points from the past three weeks, slotting in just ahead of the Penn State debacle.

Figure 1. Wisconsin Game Satisfaction

Unsurprisingly, season satisfaction also took a nosedive, dipping to 36.3 after camping out near 70 the past two weeks. At this point most fans seem to be grappling with the cold hard truth that Michigan is likely to end the season 8-4 and without any quality wins (in fact, without a win over a team with a winning record).

Figure 2. Season Satisfaction after Maryland

This brings up an interesting point about fan psychology. Before the season started a lot of analysts, including our own Ecky Pting, predicted Michigan would go 8-4 this year and would have trouble doing better than 9-3. Michigan was rebuilding on both sides of the ball, had tough games on the road against Penn State and Wisconsin, and beating Ohio State is always a challenge. In theory, then, fans should be relatively sanguine about going 8-4. Most fans, of course, are decidedly not sanguine about it. Many of them are losing their shit. The threads and comments this past week have been a mess.

There are many reasons for this psychosis. The most basic reason is that fans are not rational. Emotions don’t obey the laws of analysis and logic. Just look at Michigan Twitter during a loss if you doubt that statement. Feeling better than warranted after crappy wins and worse than warranted after tough losses on the road to the #5 team in the nation is just what it means to be a fan.

Somewhat more specifically, though, I think fans have problems setting expectations. They look at the fancy stats analysis that provides a rational and compelling case for an 8-4 prediction and then they immediately imagine all the ways in which Michigan could beat the prediction. Speight will be better than last year; the receivers are young but more talented; MSU will suck because they lost all those guys; we play OSU at home this year, etc. Pretty soon the fan is screwed because 10-2, not 8-4, has now become his or her emotional baseline for success. I know this because I am one of these people. I can know in my head that 8-4 is a sign of progress, but my heart will still bleed at the failure to go 10-2 (or better, really).

Themes, Thoughts, Trends

I Am Too Rational!

Okay, fine. Figure 3 provides some evidence that fans aren’t entirely irrational. The correlation between scoring margin and game satisfaction is quite high. We can explain 78% of variation in game satisfaction with just the margin of victory (or loss). In my regular season wrap up column I will use my somewhat more complete model to simulate game and season satisfaction scores for each of the other Big Ten teams – I have already done several of them and the variations are very interesting.

Figure 3 Scoring Margin and Game Satisfaction


The Road Ahead

Well, we’ve reached the end of the line. It’s the last chance for Harbaugh and the guys to pull our season satisfaction numbers out of the toilet. A win sends Michigan fandom into bowl season with confidence and boundless optimism about next year. A loss, especially a crushing loss, well, the less said about that the better.

Go Blue.

Figure 4 Season Trends