Thoughts on the bracket selection, part 1

I followed the "bracketology" debate more closely than usual this year, for two reasons: (1) until recently, the Badgers were a bubble team (at best), and (2) I used the power of T-Rank to produce an objective bracket prediction (T-Ranketology).

I was satisfied with T-Ranketology's results. It missed three at-larges: Syracuse, Vanderbilt, and Tulsa (instead of Saint Mary's, St. Bonventure, and South Carolina). This was about average for the brackets over at bracketmatrix.com, and if you look at the final "consensus" bracket, T-Ranketology would have had 67 of 68 (with only South Carolina not being a consensus tourney team). Seeding predictions were pretty good, too. All in all, not bad for an algorithm.

There's a lot of hootin' and hollerin' about the committee's selections, and I agree with much of it. But I wanted to take a look at the six teams T-Ranketology was wrong on, plus one other, to see maybe what the committee was thinking and if the algorithm could be improved to reflect that thinking or lack thereof.

Syracuse

Syracuse's overall resume was not great, but the one big trump card they had was a road win at Duke, which is worth like a million resume points. Add a couple other decent (early) wins, and they're left ranked No. 32 in T-Ranketology's "resume" ranking, which is an attempt to simulate the committee's fixation with "top 50" wins, etc. They were also 50th in T-Rank's "wins against bubble" rating, so right on the bubble there. But they were very poor in basic RPI, and had tanked coming down the stretch, leading to a bad elo ranking (which usually pretty well approximates current "sentiments" about how good a team is). All in all, this left Syracuse the sixth team out in T-Ranketology.

But I'm not going to worry about fiddling with T-Ranketology to get Syracuse into the field, because they were clearly a special case.

@bkbtNUmbers It certainly should be. But I interpret committee chairman's remarks re Boeheim as "they're in."

— Bart Torvik (@totally_t_bomb) March 9, 2016

The committee chairman had been saying for weeks that Syracuse was going to get special consideration because Syracuse played like shit when Jim Boeheim was serving his 9-game suspension in the middle of the season. Basketball people can look at that argument and see the absurdity on its face -- Syracuse lost five games without Boeheim, and even if you think he is super-god-coach, they almost certainly lose 4 if not all 5 of those games with him. But the committee is not composed of basketball people, so alas. In any event, the handwriting had been on the wall: barring a monumental collapse (which almost happened) Syracuse was going to be in the tournament. If I had been manually fiddling with the T-Ranketolgy bracket, I'd have put Syracuse in for sure.

Vanderbilt

Vanderbilt was the eighth team out in the final T-Ranketology, mainly because it was not in the top 50 of any of the four resume-based metrics the algorithm considered. On the other hand, unlike Syracuse, it was not terrible in any of those metrics, finishing top-70 in all of them.

Clearly Vanderbilt got in because of its efficiency rating. Vandy finished the season ranked 27th in the Kenpom ratings, and top-30 teams always get in nowadays. I think this is why they're slotted in the play-in round against another Kenpom darling, Wichita St.: the committee put two good teams with bad resumes into the ring against each other and said, "If you're so good, prove it."

When I first started T-Ranketology it was loosely modeled after the Easy Bubble Solver, which just averages RPI and Kenpom ranking. Of course, I used T-Rank instead of Kenpom rating, but it was pretty much the same idea. Eventually I took out the efficiency rating component because for good or bad I think it just doesn't play a very big role in the selection or even seeding process, at least not systematically. But I think there's good evidence that it comes into play in edge cases, and I think it's pretty clear that's what got Vandy in. I may have to work in some kind of "top 30" efficiency rating bonus to account for this.

(By the way, I think Syracuse was also helped by a decent Kenpom rating, though I don't think it wouldn't have been enough without The Boeheim Excuse.)

Tulsa

Tulsa was the real shocker, what John Gasaway calls the committee's annual "grenade." But it wasn't a huge shocker to T-Ranketology, which had Tulsa just the fifth team out -- ahead of both Syracuse and Vanderbilt! Indeed, before its loss to Memphis in the AAC tourney on Friday, T-Ranketology had Tulsa the last team in the field.

Why? Like Syracuse, Tulsa scored well in the "resume" score that approximates top 50 wins, etc. This is by far the stupidest possible measure you could come up with, but I'm pleased to say that I think I've done a pretty good job of modeling this particular madness. Tulsa's inclusion in the field of 68 shows that I probably need to weigh it even a little more.

But I think there's another lesson for Tulsa's selection. The committee first gets together early in championship week to get ahead start on the process. As a result, by Friday (when Tulsa got stomped by lowly Memphis) the committee has already made some provisional decisions about which teams it thinks are good enough. I'm pretty sure that Tulsa's resume had already been found deserving by Friday. Here's the way the human mind works: once it decides something, that decision sets. It's like a boulder in a divot; you need a big shove to get it moving again. When new information comes in, we don't start at square one and reevaluate the decision with a blank slate. We say: is this new information enough of a big deal to make me go through this whole process of deciding again? Unsurprisingly, the answer is usually no. This fundamental quality of human psychology (call it laziness if you wish) got Tulsa into the tourney.

(This, by the way, is why college-football-playoff-style in-season tourney rankings are a terrible, terrible idea.)

Well, this has gotten tl;dr so I'm going to stop now. I'll try to post later today with my profound insights into the "snubs."

Is Kenpom biased in favor of significantly lower-ranked teams? (Yes.)

***
NOTE: Kenpom changed his rating system significantly for the 2017 season (after I did this study) so the results don't necessarily apply to his current system—indeed, there's reason to think the changes he made alleviate the issues discussed below.
***


One of the big problems in college basketball—particularly with regards to NCAA tourney selection—is giving proper weight to road games. It is difficult to intuitively grasp how much harder it is to win on the road against a lesser team than it is to win at home against a better team.

This question came to a head when Greg Shaheen was asked about Wichita State’s big home win over Utah, and specifically what he thought an equivalent road win would be. He answered somewhat absurdly: “Utah.”

Seth Burn set out to answer the question using Kenpom numbers, and what he found was that Wichita State playing No. 25 ranked Utah at home is equivalent to them playing No. 109 rated New Mexico on the road. What this means is that the Kenpom system gives them the same chance (about 73%) of winning in each game.

This is kind of shocking, and my own investigation confirms that it is a correct statement about the Kenpom system. But that doesn’t mean it is necessarily a true fact about the universe. Because Kenpom could be wrong about these things. Importantly, it could be biased in favor of lower-rated home teams.

Indeed, it has been my anecdotal observation that this in fact the case: Kenpom seems to have a systematic bias in favor of significantly lower rated teams when they play higher rated teams, particularly at home. So I set out to test this hypothesis.

First, let’s look at all games that Kenpom would predict the home team to have a margin of victory of +/- one point (favored by 1 or a 1-point underdog). By definition, these will be games where the home team is lower rated than the road team, because home-court advantage is built in. Here are the results:

Games where projected MOV is +/- 1 (Kenpom)

Total games: 372

Home team avg expected MOV: 0.01

Home team actual MOV: -1.81

Home team expected win %: .500

Home team actual win %: .438

What we see in these 372 games that Kenpom would expect to be pretty much pick ‘ems is that the better team (the road team) actually won 56.2% of the time, and had an average margin of victory of +1.8 expectations.

Now, this could just be a quirk of this season—maybe the home teams are just underperforming in close games. But when I run the same test using the T-Rank algorithm, the bias pretty much disappears:

Games where projected MOV is +/- 1 (T-Rank)

Total games: 345

Home team avg expected MOV: 0.06

Home team actual MOV: -0.21

Home team expected win %: .502

Home team actual win %: ..487

This still shows a slight bias toward the (better) road team, but it is much less, and looks much more like random variance.

Next I wanted to test my impression that Kenpom particularly breaks down when the spread between the teams is larger (e.g., games like the nearly 100-spot spread between Wichita St. and New Mexico St.). So I looked at games where the home team was rated more than 50 spots lower than the road team. Using Kenpom projections, here are the results:

Games where home team is more than 50 spots lower than road team (Kenpom)

Total games: 1227

Home team avg expected MOV: -4.34

Home team actual MOV: -6.65

Home team expected win %: .345

Home team actual win %: .271

In this rather large set of games, the home team now actually performs 2.3 points worse, on average, than Kenpom’s system would project, and wins only 80% as often as projected. Compare this to the same experiment with T-Rank:

Games where home team is more than 50 spots lower than road team (T-Rank)

Total games: 1150

Home team avg expected MOV: -6.05

Home team actual MOV: -6.80

Home team expected win %: .311

Home team actual win %: .272

Again, we still see a slight bias in favor of the home team, but a much lower one than with the Kenpom system.

Overall, Kenpom has a very good record of prediction and projection, so if there is a systematic bias in these mismatches, we would expect it to be counterbalanced by good results between more evenly matched teams. And that is in fact the case:

Games where home team and road teams ranked within 50 spots of each other (Kenpom)

Total games: 1804

Home team avg expected MOV: 4.1

Home team actual MOV: 3.58

Home team expected win %: .645

Home team actual win %: .652

(For the record, T-Rank performs similarly in these games, but not quite as good.)

In this set of games, the Kenpom algorithm is extremely well calibrated. My speculation is that the shift from Kenpom 1.0 to Kenpom 2.0 made the algorithm more accurate in these (more common and more important) games, at the expense of some loss of calibration in more mismatched games. This seems worth it, and you can see why it would improve the overall performance of the system.

But I think it’s important to consider this evidence that Kenpom’s projections are systematically based in favor of significantly lower ranked teams when doing this calculation about home/road equivalencies, because the results using Kenpom are not only unintuitive, but in all likelihood just plain wrong.

For example, using T-Rank the equivalent road game to Utah at home for Wichita State is a hypothetical team between No. 83 Temple and No. 84 Hawaii. Utah is ranked 25th in both systems, so this is a great comparison: Kenpom says the road equivalent is No. 109 New Mexico St., and T-Rank says it’s No. 83 Temple or No. 84 Hawaii. Since T-Rank gets better results in these kinds of games, and its results are more in line with most of our intuitions, I’m sticking with T-Rank.

***As to why these difference between T-Rank and Kenpom exist, I believe it has to do with the different "spread" the systems create, and the different "exponents" we use to calculate the Pythagorean Expectation (Kenpom uses 11.5, T-Rank uses 10.25). The different spread is caused mainly by the fact that Kenpom much more aggressively caps margin of victory and the effect of blowouts in mismatches than T-Rank does. My hypothesis is that by mostly ignoring those mismatched games, his system ends up being less accurate in mismatched games, but the upside is that it may be more accurate in games between teams of similar quality. 

2016 Big Ten Tourney predictions

It's time for the annual Bart versus Adam Big Ten Tournament Smackdown Challenge.™

I won last year, which is what counts. Unsure of previous years.

Here goes.

Wed:
Nebraska over Rutgers
Illinois over Minnesota

Thurs:
Badgers over Nebraska
Iowa over Illinois
Northwestern over Michigan
Ohio State over Illinois

Friday:
Indiana over Northwestern
MSU over Ohio State
Badgers over Maryland
Purdue over Iowa

Saturday:
Purdue over Indiana
MSU over Badgers

Sunday:
MSU over Purdue

5 Biggest Dunks in Badger History

If you haven't seen the highlight from the Iowa game you have probably been living under a rock. Yes, that one is on the list. The rest are some of my favorites. Feel free to add others if you deem them worthy.

Iverson, from UW vs Iowa
www.youtube.com/watch?v=whk7-F6qe8c


The others are from my memory, but you may be able to find video if you search youtube long enough.

Alando Tucker dunks off a pass from Devin Harris in the closing seconds vs Michigan State in 2002-2003 season. OK so the dunk itself wasn't anything special. Just a breakaway dunk with no defense around, but it's what Izzo said after the game that made this dunk huge. This was the "I'll remember that" game. More than annoying Izzo, this game announced that the Badgers were the real deal and started a string of victories against MSU and Izzo. Bo owned Izzo in these days.

Alando again, but this time vs North Carolina in the elite 8 game vs the Sean May lead Tarheels. This game was tight throughout and the narrative that UW didn't have athletes was even stronger in these days than today. After a timeout, Bo Ryan goes to a rare called play, alley oop dunk to Tucker. UW doesn't win the game, but UW shows it can run with the biggest and best.

I don't remember the year of this one, the team they were playing or if they won the game (I'm pretty sure they won the game), but if you are old enough you will know what I'm talking about. Before Iverson and Tucker, UW had another great dunker back when UW was just becoming a decent program. The ball goes up and it is a UW miss. The ball comes off perhaps a bit harder than Mike Finley expected. Nevertheless he grabs it with one hand and hammers it down, landing with feet spread to a shocked and rocking student section. At the time this was by leaps and bounds the best dunk by a badger ever. It still holds up as a great one over time.

Finally an alley oop that wasn't technically a dunk, but was still spectacular. It was January of 1990 and UW was playing hated rival Minnesota. There was 1 second left in a tied 75-75 ball game and the Badgers had the ball. Kurt Portman was the inbounder and threw the ball up high to give his team a chance. The best dunker of the Steve Yoder era, Patrick Tompkins, catches the ball and puts it in. I found this quote from after that game that summed up Patrick Tompkins game: "When I got the pass I was going to dunk it, but sometimes I have a tendency to dunk too hard and I didn't want to miss".

Am I in love with Greg Gard?

My love of Bo Ryan is well known. It was the reason this blog was started. Over the past few weeks I have been questioning if I am becoming a polygamist with my basketball coaches. After Maryland, I'm falling for this guy. This lead me to think about Bo, and what lead me to fall in love the first time. 

Bo's kids played entertaining basketball. They played hard on defense. They played efficient offense that forced the ball into the low post as much as possible (first through the swing and later through more iso). They scrapped for every loose ball. The players got better every year. These are all things I like about basketball, but most of all, they won. That's what makes a great coach. It's not so much how they win, but that they win. 

If UW had hired a coach like Shaka Smart instead of Bo, and he had the same success with fast athletic recruits, I doubt I would have cared about any of the other reasons I liked Bo's teams. Winning is the biggest thing in sports (with the caveat that they have to be as clean as is needed to satisfy the NCAA, whatever clean means anymore). Bo figured out how to win better than any coach in Big Ten History, and he did it at a school that had never seen sustained success at that level. 

So do I love Greg Gard? We'll see. His players are getting better and better as the season goes on. They play hard on defense and are routinely forcing the ball into the post with success. They have scrappers getting on the floor for loose balls. They have become fun to watch, but the proof is in the winning. Beating #2 on the road certainly helps his cause, but will he keep it up? My impression after 13 games is that he will. 

He is certainly giving me hope that UW can sustain the success that Bo had here. Maybe not to the level that Bo did, but if he even gets close he will be one of the greatest coaches of all time. I remember when Barry retired and I thought that UW had probably hit the high mark they could reach. Then Bielema went to 3 consecutive Rose Bowls. Maybe Gard will take UW to new levels too. Here's hoping for the start of a love story for the ages.....

T-Ranketology

***Revamp in 2018!

As you'll see below, the original method of T-Ranketology was to take five ranks—T-Rank, Elo, WAB, RPI, and "Resume" rank—and given them various weights to come up with a score (with a couple other tweaks like a good record bonus and a bad record penalty).

Recently I wanted to come up with a way to use the T-Ranketology score to give an estimate of a team's chances for receiving an at-large. In looking into this, it occurred to me that I could use the five inputs to do a "logistic regression," and use the resulting model to provide the chance I was looking for, using the actual ranks themselves (rather than my own weighted score) as the input.

I did this, and the results were pleasing. So pleasing that I've now decided to abandon the old "weighted score" method and just use the model from the logistic regression all the way down. The results are pretty similar—after all, the inputs are the same—but I can do a bit more with the new system, and feel a bit more confident in the results. For example, on Teamcast I can now provide an estimate of how much a win or loss affects a team's chances, in percentage terms, of making the tournament.

So there you have it, that's the T-Ranketology update for this year.

***Another update:

See this post for some changes made in 2017.

***Update:

See this post for an attempt to retrofit T-Ranketology to fit last year's results. Next year's algorithm will be adjusted accordingly.

***Update on  2/17/16:

I've revamped the T-Ranketology formula a little bit, as I think the original version was more wishful thinking than predictive. Here are the changes:

1) I removed the current T-Rank as a factor. I put it in originally on the theory that this would be similar to the "Easy Bubble Solver" method which uses Kenpom ranking and RPI ranking to project likely tournament teams. But T-Rank is already a factor in that it is used to project the Elo and RPI ratings that make up the other two factors. Obviously, no one is looking at T-Rank (or Kenpom ranking, really) to determine who gets into the tournament.

2) In place of T-Rank, I've added a "good wins / bad losses" analysis. Teams get 10 points for top 50 wins, 3 points for other top 100 wins and lose 3 points for sub-100 losses, and 6 points for other sub-200 losses. (I may fiddle with these values some -- let me know if you have an idea what they should be instead.) Ideally I would use projected RPI as the source of the rankings, but I'm using current T-Rank because it's much easier for me. [Edit: after further review, it's not hard to use projected RPI, so I've fixed this.] I did this because "good wins" is clearly a big factor in how the people who actually decide this stuff go about deciding it.

The result is that I get a projected bracket that looks a lot more like the consensus. Valpo is no longer projected a 7-seed. Gonzaga no longer seems safely in. Princeton and Yale no longer look like serious bubble teams. Providence is no longer in serious danger of dropping out (although they're still much lower than in other brackets because of T-Rank's expectation that they'll lose some games coming home).

In my opinion, the former T-Ranketology produced a better bracket, but it wasn't as realistic.

*******


As promised, I've been playing around quite a bit with various T-Rank projects this year. Lately, I've added a couple of fun new rankings, including the Q-Rank (based on performance in Tournament Quality Tests), the H-Rank (ratings in last 10 games only) and the E-Rank, a slightly modified "Elo" rating system. I've also put together a program that uses T-Rank to forecast what teams' RPI will be at the end of the regular season.

My marketing people think I'm diluting the T-Rank brand with all these other rankings, but I have to give the people what they want, even if they don't know that it's what they want, yet.

Speaking of which, the latest: T-Ranketology. This is my NCAA bracket projections system. The inputs, in equal measure:

1) Current T-Rank
2) Projected RPI Rank at the end of the regular season
3) Projected Elo Rank at the end of the regular season

Both RPI and Elo are "resume" ranks, and T-Rank is a measure of team strength. The key to understand here is that the RPI and Elo inputs are projections -- that is, I run simulations of the rest of the season to get an average final RPI and Elo. (The T-Rank, however, is treated as a static determinant of team quality.)

So T-Ranketology is not a prediction based on "if the season ended today." It's a projection based on how the season will likely play out, if the current T-Rank is a correct measure of team strength.

One thing to make clear: my projection of the rest of the season does not include conference tournaments. I ain't got time for that.

Finally, I use current T-Rank to pick the autobids. That is, the highest team in the T-Rank is gifted the autobid, on the assumption (not always correct, given seeding effects) that the best team is most likely to win the conference tournament.

As always, this is all in good fun. If you see anything goofy, let me know.

Wisconsin Sports Person of the Year -- 2015

I'm pleased to announce the winner of the 2015 Bart Torvik Wisconsin Sports Person of the Year Award.

It is  ...
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Does Northwestern have a chance?

Northwestern is off to a promising start, sitting at 10-1, with its lone loss a reasonably competitive game against North Carolina on a neutral court. They finish out the non-conference season with home games against Sacred Heart and Loyola Maryland, so they have a good chance of heading into the Big Ten season at 12-1. They sit at 55th in the T-Rank, 51st in Kenpom, and have been climbing in both.

The bad news is that their schedule has been really weak. They have just one win against a T-Rank top-100 team, a road win over Virginia Tech in the B1G/ACC challenge. That's a decent win, but not one that is going to knock the committee's socks off. Virginia Tech will probably be battling to finish top 10 in the ACC and projects to finish 5-13.

They also won their other true road game, at DePaul (T-Rank #188) in overtime. That's not a good win by any means, but it's the kind of game that even good teams can lose. It was important for Northwestern to avoid that bad loss.

Their only other remotely respectable non-con wins were a home game against Columbia (T-Rank #128, which they also won in overtime, and a close neutral-court win over Missouri (T-Rank #172). Again, nothing to write home about.

Because of this weak schedule, Northwestern is only 74th in the RPI. Worse, RPIForecast.com projects them to finish the regular season at 81st, and that with a projected conference record of 10-8. Once again, the reason for this is that Northwestern is blessed/cursed with a soft Big Ten schedule -- based on T-Rank, only Michigan St. has an easier slate (and that's because the Spartans don't have to play themselves).

The non-conference part of the schedule is by design: Chris Collins purposely set up the schedule so Northwestern could bank enough non-conference wins to give themselves a good chance of getting into the post-season. He's basically admitted as much:

"To me," coach Chris Collins told the Tribune, "this was the best (scheduling) philosophy for this team." 

Some background: Collins' first team in 2013-14 doomed itself with a nonconference slate featuring Stanford, Illinois State, Missouri, UCLA, N.C. State and DePaul. Even after pulling off road victories against Indiana, Wisconsin and Minnesota, the Wildcats finished 14-19. No postseason. 

They went 9-4 last season in nonconference play and finished 15-17 — about three victories short of an NIT berth. 

The NIT is not the goal, of course, but it would be a step forward. And Collins wants his players to have a winning season, at minimum. 

"We're still a young program, in Year 3," he said. "We're in a great place with all the stuff behind the scenes: culture, attitude, practice habits, strength and conditioning. The next step is you learn how to win. A place like here is not a quick fix. It's development."

So this year was essentially "NIT or bust" for the Wildcats. That's a perfectly rational and defensible strategy for this program.

What Collins likely did not anticipate, however, was that the Big Ten schedule would have so many wins there for the taking. At least two perennial powers (Wisconsin and Ohio State) look very beatable this year, and several other middling teams (Minnesota, Illinois, Nebraska) look downright bad. That's why the computers (T-Rank, Sagarin, Kenpom) think Northwestern will probably win 9 or 10 B1G games, even though they're ranked around 50th in all of them.

This is not last year's Penn State, which lucked its way to a 12-1 record facing a slew of terrible teams. So despite the gaudy record, Penn State was 100th in the T-Rank and projected to win just six Big Ten games.

For the most part, Northwestern has looked like a solid high-major in its games against bad teams -- blowing out its sub-200 competition. (A seven-point win over Fairfield is the exception there.) And it has no bad losses. Thus, the respectable T-Rank / Kenpom / Sagarin ratings.

So, does Northwestern have a chance to make its first NCAA tournament? Yes. Like the Badgers, they'll have a good chance if they get at least 11 Big Ten wins. T-Rank gives them about a 16% chance of going 11-7 (or better) in the regular season, and a 17% of going 10-8. If they go 10-8, they'll certainly have to avoid a bad loss in the B1G tourney's first round. Let's say they've got only a ~50% chance of winning that game, leaving about 9% chance to get to 11 total B1G wins in that scenario.

Add it up, and it's about a 25% chance of getting to 11 wins. That seems about right. It's too bad, because with a decent non-conference schedule they would probably be a lock with 10 wins, which would get them close to 50/50.

It will make for an intriguing season for Northwestern fans, though. They're in it -- they just need a little bit of luck. Their first six B1G game include winnable road games at Nebraska and Minnesota, winnable home games against Ohio State, Wisconsin, and Penn St., and a big win opportunity at home versus Maryland. Probably need four wins to stay viable. Stay tuned!

Miss you already Bo

Last night I got home from the game in time to watch the press conference. It was a disappointment. I had bought into the idea that Bo would stay at UW forever and somehow a half robot Bo would still be coaching after I passed away. I don't want to think about UW without Bo.

I went to bed eventually. My girlfriend said I woke her up because I was talking in my sleep. I was yelling "WOW, awesome" and "OH Yeah, just like that!". On a normal day I would assume that meant some kind of sex dream, but last night I bet I was dreaming about the Final 4 win against Kentucky.

Thoughts on Badgers' schedule and tourney hopes

After two more home losses to Milwaukee and Marquette, the Badgers are now 6-5. They've fallen to 60th in the T-Rank and 42nd in Kenpom (where they are still somewhat buoyed by their implausibly high preseason rating of 9th in the country).

T-Rank currently projects the Badgers to finish the season 15-16 and go 7-11 in the Big Ten.

Kenpom currently projects 17-14 and 9-9.

I'm hopeful that the Kenpom projection is more realistic. Although UW is now playing with a very tight rotation the features four juniors, redshirt freshman Happ, and spot duty from three freshman (Thomas, Iverson, and occasionally Illikainen), it's still reasonable to expect that the four freshman in the rotation could significantly improve as the season goes on. I do think we've seen the worst of this team, and I hope we haven't seen the best of it.

Even so, the Badgers will face a tough test in the Big Ten schedule, as almost all their double-plays are against the top teams in the conference: Michigan St., Maryland, Purdue, Indiana, and Illinois. This is why T-Rank projects them to have the second-toughest conference schedule.

But their single-plays are looking better. In the single-play games, you want to play the tougher teams at home, and the easier teams on the road. Overall, I think the Badgers got a bit lucky on the locations of their single play games:

Rutgers, home: would prefer this on the road. Minus.
Northwestern, away: tough to tell, but for now this looks like a preferred home game. Minus.
Penn St., away: a very winnable road game. Plus.
Ohio St., home: a game they should win, but would be tough on the road. Plus.
Nebraska, home: Nebraska looks tough at home, bad on the road. Plus.
Iowa, away: Minus
Michigan, home: Plus.
Minnesota, away: given how terrible MN has looked: Plus.

So, +2 overall. Only the Minnesota and Northwestern games are really questionable calls, so maybe call them both pushes and they're still +2.

In any event, only one of those games would be a shocking win (at Iowa). Anything less than 5-3 would be disappointing, and 6-2 seems doable.

In a normal year, you'd look at that and say the Badgers can count on splitting the double plays, so 10 or 11 wins and -- given that the Badgers will have a top-10ish RPI SOS -- an at-large berth are likely. This year I'm not so sure. This year home wins over Purdue, Michigan St., or Maryland seem like steals. So it's going to be interesting.

How interesting? I think it's likely that the Badgers' season will come down to their first game in the Big Ten tournament. Let's take a mildly optimistic but still reasonable scenario and run it through the RPI Wizard at rpiforecast.com:

Wins: Rutgers, @NW, @PSU, IU, @IL, OSU, Neb., IL, Mich., @MN
Losses: Purdue x 2, Michigan St. x 2, Maryland x 2, @IU, @Iowa
Total: 10-8
BTT: Beat Northwestern, lose to Michigan St.
Result: RPI 41, SOS 5

That scenario would put the Badgers right on the bubble. On the downside, they'd have no really good wins in conference -- only IU and Michigan would probably be tournament teams, possibly Northwestern (though NW probably has to beat Wisconsin to be in contention). And they'd be carrying the baggage of at least one (Western), probably two (Milwaukee), and maybe three (Marquette) "bad losses." Still, a > .500 conference record, a decent RPI, an elite SOS, and two decent non-con wins would probably be enough to at least get the Badgers into the field of 68 (if not the field of 64) -- though they'd probably be in better position if they traded the win at Northwestern for a more "marquee" win over Michigan St., Purdue, or Maryland.

Now run those same numbers, but with a BTT loss to Northwestern:

Result: RPI 55, SOS 9

That RPI would probably be insurmountable, unfortunately.

Of course, the chances of either of these particular scenarios playing out exactly this way is vanishingly small. It's more likely the Badgers will lose a couple here, gain a couple there. But since this is probably worst resume the Badgers could have with 10 wins, it's worth considering. And even in alternate 10-win scenarios, that first BTT game is likely to be a must-win or close to it.

One way or another, I think the Badgers will have to scratch out 11 wins against Big Ten teams to get into the tournament. I think they can do it. I think they will do it. But there's really no margin for error anymore.

Introducing FUN

Later today at T-Rank I'll be adding a team stat I call FUN, which stands for

Fortune Unexplained by Numbers

or

Failure Unexplained by Numbers

This is an equivalent stat to what Ken Pomeroy calls "luck." Here's how it is calculated: T-Rank goes back in time and calculates an expected winning percentage for each team schedule so far, based on their T-Rank profile. Then it compares it to their actual winning percentage. The difference (expected minus actual) is their FUN.

I'm avoiding the word "luck" here for a reason. If I call it luck, that implies that the T-Rank is a rather absolute reflection of a team's quality, and that any deviation from T-Rank's expected winning percentage is simply random variance, or luck.

Undoubtedly, that is a big part of the story. But it's probably not the whole story. 

Let's look at a team like Maryland, which last year was very "lucky" in that it's actual record was much better than it's Kenpom / T-Rank expected record. The reason for this difference is pretty easy to suss out: Maryland went 12-1 in games decided by 6 or fewer points. That's kind of ridiculous. Most of the time, teams will go about .500 in close games -- even very good teams.

But maybe there's something about Maryland that makes them particularly good in close games. Whatever this something might be, there's no chance Kenpom or T-Rank will reflect it. It could be random variance, just plain luck. But it could be something else.

And with Maryland, there's actually a pretty obvious hypothesis about what that something might be: an all-American point guard (Melo Trimble) who can get to the line at will, and hits 90% of his free throws. This is something that is particularly useful in close games. And it turns out that there's good evidence that Trimble does indeed get a disproportionate share of his free throws in crunch time of close games:

Trimble shot 11 of his 15 FTAs in 2nd half of Terps' win vs UConn. In 11 career games w/ 10+ attempts 72% of his FTAs have come in 2nd half.

— John Gasaway (@JohnGasaway) December 9, 2015

So this is the kind of thing that might be something other than random variance that causes a team to outperform T-Rank expectations. 

The other reason I'm calling it FUN is that variation from reasonable expectations is one of the things that makes college basketball so interesting and, well, fun. After all, college basketball is where the unexpected becomes ordinary. Or so I've been told.

[T-Rank says: FUN is luck, nothing more.]

The last 5, and the next 5...

You may recall that I was pretty worried about the five-game stretch that just ended with the Badgers' thrilling overtime win at Syracuse. My conclusion was pretty pessimistic:

Long story short, 2-3 over the next five games would probably be the best we can reasonably hope for, with 1-4 sitting out there as a disturbingly plausible scenario.

Even in hindsight, I don't think that was unduly alarmist. The Badgers' two losses -- to Georgetown and Oklahoma -- were not all that closely contested. Both were double-digit losses that the Badgers had no chance to win in the second half.

The Badgers' two big wins -- over VCU and Syracuse -- on the other hand, were extremely tight, touch-and-go affairs that went down to the wire. It took a great drive by Bronson to beat VCU at the buzzer, and of course it took an extra period for the Badgers to beat Syracuse.

In other words, even knowing what I know now, if we played those five games again I would expect the Badgers to go 2-3, or maybe even 1-4. So: hooray!

Luckily, we don't have to play those five games again.** Instead, the Badgers now start a seven-game home-stand, and it starts with these five games:

As you can see, T-Rank has the Badgers significantly favored to win each of the five, with the Temple and Marquette games being the only serious stumbling blocks. If you add up the win percentages, it comes to a projected record of 4.1 - 0.9, so there's certainly a legitimate likelihood of dropping one. But this will be by far the softest five-game stretch the Badgers will have for the rest of the season, and I'm looking for them to make some hay and solidify their tournament resumé.

Prediction: 5-0

That will set up a potentially monster matchup against Purdue on December 29th in the Big Ten opener. Purdue has been absolutely dominant so far this year, and they have a real chance of coming into that game undefeated -- ranked in the top 5 or even No. 1. We'll see how things go, but I'm looking to that game as a coming out party for the Baby Badgers.

**Looking back at the Badgers' non-conference schedules since Bo's first year, I can't find a five-game stretch that is even remotely as difficult as the last 5, which included four games against likely tournament teams, all of them away from the Kohl Center. Going 2-2 in those four games was a tremendous achievement for such a young team.

Maryland

Maryland is a very good basketball team. They are deserving of their lofty preseason ranking, and have a chance to win the Big Ten and NCAA tournament. I can already hear my co-blogger's blood beginning to boil as he is a Maryland hater, so I'll explain a bit.

Preseason ranking are stupid. They exist only to make money for websites and newspapers/magazines because they know college basketball starved fans will read anything after months of no basketball. With that in mind, Maryland is as deserving of a ridiculous preseason rank as any team. Enough with that rant.

In college basketball there are often one or two great teams a year that stand out as better than the others. Last year we were fortunate to be able to watch four: UK, Duke, UW, and AZ. This year I believe there are none. Time will tell if a team grows into that, but having seen several very good teams play so far I just don't see it. This is the kind of year where a 12-seed like George Mason can make the final four, and a team like 7-seeded UConn can win it all. With that in mind, Maryland is capable of winning the Big Ten and NCAA championship.

On to Maryland. They are like Indiana if IU had size and didn't have Tom Crean. They score the ball well and in different ways, but they aren't as good as they could be defensively. If the defense comes around as the season goes on, and they stay healthy, this team can contend.

Health has already been a problem for this team's backcourt as they lost Dion Wiley before the season started. Wiley got good experience as a freshman and was penciled in as the starting 2-guard before blowing out his knee. He will be replaced by Duke transfer Rasheed Sulaiman. Sulaiman is a very good all-around guard. He shoots the 3, he can create his own shot, and he distributes well. He will do the ball handling when Trimble is out of the game. Trimble should be all Big Ten. He has not shot the 3 well this year, but after shooting 41% last year this is probably just a blip. Dez Wells ate up some of the ball handling last year but Trimble should be the man all the time this year. Jaylen Brantley is a backup point but he looks shaky, so I think he only gets minutes when Sulaiman is there to help.

Maryland has 2 wings in Layman and Nickens. Layman will be allowed to play the 3 this year more than the 4 he was forced to play last year. I expect this to benefit Maryland much as moving Dekker from the 4 to 3 benefited UW last year. He is a good enough shooter to stretch defenses at the 3 while giving Maryland elite size at the position. Nickens will play some 2 and some 3. He is mostly a spot up shooter, but again gives them good size at 6'7" for the position.

Maryland has 3 guys that can play center. That size will give them a chance to compete with the best teams in the country. The 3 are all pretty similar. None are very versatile or stretch a defense. They are all bangers, at least so far in this young season. Dodd and Cekovsky look pretty much the same as last year, but perhaps more confident in what they do. Diamond Stone is a better post scorer then the others, but still just a post scorer. Stone has the pedigree to be great, but will he be this year? Probably not. Robert Carter is more versatile than the other 3 and slightly smaller, although many teams play a guy his size at center. He can play inside and out and can drive as well as post.

What does this all add up to? A team that looks good on paper. They haven't blown anyone away with their play so far but they keep on winning. When a team has this much size and talent they usually win a lot. I don't see any reason to think this team will be different. I'm not ready to decide if they are better than MSU, or even Purdue, but I think they have a chance to be the best.

VCU: Huge win

We know the Badgers are going to be a work in progress, and I think we can agree that Bo is likely to make something wonderful out of these guys. But, in the meantime, they face a brutal schedule in both the noncon and the conference, and can't afford to lose many games against equal or lesser competition if they want to feel comfy on selection sunday.

So after VCU hit a prayer of a 3-pointer at the buzzer to take a 6-point lead into the locker room at half-time today,  I was feeling a bit gloomy. I'm about as non-Chicken Little as can be when it comes to Bo Ryan coached teams, but facts are facts. An 0-2 showing against probable bubble teams Georgetown and VCU doesn't exactly scream tourney team, and the loss to Western is very likely going to be an anchor the Badgers will be dragging around all season. So I went so far as to tweet out:

Not to be alarmist, but if the Badgers don't play well in the second half, Kohl Center is probably hosting an NIT game in March.

— Bart Torvik (@totally_t_bomb) November 22, 2015

The Badgers proceeded to start the second half in a nightmare: two turnovers and two missed free throws by Nigel on their first three possessions. But after that, they played really well. They scored 32 points on their next 15 possessions to turn a six-point deficit into a six-point lead.  Of course, VCU wasn't done. They scored on four straight possessions to get back in the game and then some.

Then: Bronson.

You know I love Bronson. I think he's one of the best players we've ever had, and I think he's good enough to get this team to do something special (in relative terms) this year. Today was a great day for him.

But it was also a great day for his counterpart in the backcourt. Zak Showalter stuffed ye olde statsheet with 10 points, 6 assists, 6 boards, 3 steals, and 2 blocks. The kid can play the game.

Now the Badgers just need to steal one in Norman or Syracuse and win out the month of December and I can rest easy.

UW Nu

A decent announcer just said college football in November is crazy, or something to that effect. He was right.

The loss UW just took sucked. I sat in the freezing ass cold to watch this team turn the ball over 5 times and still deserve to win the game but lose.

I have racked my brain for a better way to officiate a game. Even people with the benefit of replay, overturn a correct call on the field, and get it wrong.

I don't think this can be fixed. It just sucks.

Indiana

I'm going to try to get up some posts about team personnel, but I just don't have as much time as  I would like so these will be somewhat abbreviated. I'll start with Indiana.

Indiana lost some guys but returns a lot of familiar faces. Yogi is around for one last year and he has sophomores Blackmon and Johnson to run the show with him. All 3 can run the point but Yogi will have the ball when it's crunch time. Blackmon still shoots like a stud and could move on to the NBA after this year. Johnson has moved from the starting lineup to the bench (more on this later). IU also returns Zeisloft for spot up shooting and Williams who still can't shoot but can still get in the lane and jump out of the gym.

There are 4 new faces in the frontcourt. Anunoby and Morgan are athletic freshman but probably wouldn't be in the rotation if IU was in a close game. They may play more as the season goes on if they develop, or if IU wants to play bigger which it appears they do. IU played very small last year with 3-4 guards in the lineup most of the time. They added transfer Max Bielfeldt from MI and have inserted him in the starting lineup. Max was not that good a player at MI, so it's a bit surprising he is starting at IU, especially at the expense of Johnson who is a young developing talent. Moving Max into the starting lineup helps Troy Williams, who now gets to play the 3 instead of matching up against much bigger guys at the 4. Max can shoot so IU can still space the floor with shooters with him out there. Colin Hartman comes off the bench and gives IU another forward who can hit spot up shots.

Freshman Thomas Bryant is very impressive. He has great size for a kid at 6'10" and 245. He uses his size very well. He has an old man at the Y game, and I mean that as a compliment. At 18 he already has multiple post moves. He looked just as comfortable spinning into a drop step as he did turning on a guy and scooping with his left hand. He has intensity and leadership qualities on the court already. He is not a superior athlete, but he's no slouch. He will be a very fun player to watch for as long as he sticks around.

IU will score. They will probably score even better than they did last year which was pretty good. The question for IU is can they play enough defense to go from a good team to an elite team. Hence the switch to Bielfeldt at the 4. It's hard to play good defense without size, and IU just hasn't had it lately. When they had Zeller and Oladipo they still weren't great defensively, so Max is not going to make this team into Virginia, but size helps.

I like this IU team, so I just wonder how Crean will fuck it up. They certainly have a good mix of talent to contend for the Big Ten Championship, but they won't. They have an easy conference schedule which they should ride into a top 4 finish. They are Indiana, so people will get overexcited about them and they probably end up with a record and ranking that looks better than they are.

Introducing G-Score

Here's the way T-Rank works, basically:

• For every game, T-Rank calculates an adjusted offensive and defensive efficiency for each team. 
• Then, it averages those numbers to come up with overall offensive and defensive efficiency for each team. 
• Then it uses those numbers to calculate a Pythagorean expectancy, which I jokingly call the Barthag. 

That's the T-Rank.

But if you go back and look at the first step, you can see it's actually possible to calculate a Barthag for each team for each individual game, since adjusted offensive and defensive efficiencies are calculated game-by-game.

Because I can, I did. I'll call that game-by-game rating the G-Score. It goes from 0 (bad) to 100 (perfect). The average is 50. It will appear on the schedule portion of each team's T-Page. For example:

That number -- e.g., the 95 for the Siena game (let's focus on the positive) -- is the team's individual game Barthag multiplied by 100. So Wisconsin's performance against Siena is what you'd expect from a team with a Barthag of .9500. (Like Kentucky!) On the other hand, the performance against Western is what you'd expect from a team with a Barthag of .2300. Like Howard. :(

At this early stage of the season, the preseason ratings are still heavily influencing the T-Rank. A nice thing about G-Score, then, is it indicates how the teams are playing if you leave that influence partially** aside. For example, the Badgers' average G-Score is 58, so their overall performance is akin to #139 ranked UC Santa Barbara. A loss to Western Illinois will do that.

Some other nice things about G-Score is that it will allow us to see trends in a team's performance over time, and also to get an idea about how consistent a team is. Once more data is in, we could even calculate fancy standard deviations and such.

G-Scores are not set in stone. They will fluctuate as we learn more information about the teams, and as teams improve or get worse. So, for example, if it turns out that Western Illinois is actually the class of the Summit league, that loss will start to look better and the G-Score will rise.

Finally, a caveat. Individual basketball games are small-number events. Crazy stuff happens over a single 40 minute period, and we shouldn't make too much of any single game, or any single G-Score. For example, Wisconsin's G-Score of 56 against North Dakota is a bit deceptive since the scrubs lost by 11 in their two minutes of play. If the scrubs had maintained the 25-point lead they inherited, the G-Score would probably be more like 85 or 90, which is a pretty big difference. So that's something to keep in mind.

*Specifically, this would leave the preseason influence aside only for the team whose G-Scores you're looking at, while assuming the T-Rank is correct for all their opponents.

The next five games...

So the Badgers have recovered from the fluke loss to Western on opening night. They dominated Siena, raising hopes that maybe the opener was a true aberration. Then they muddled through North Dakota, not quite eviscerating them like they usually do such terrible teams, but still building up a 25 point lead before the scrubs came in.

Now we enter a crucial stretch:

Georgetown (neutral)
VCU or Duke (neutral)
[cupcake]
at Oklahoma
at Syracuse

That's four games very losable games against tournament-quality competition, all away from home.

Georgetown is 0-2, having suffered perhaps the only loss worse than the Badgers' loss to Western, an opening day home shocker to Bradford ... er, Radford. Then they blew a late lead at Maryland tonight. So they seem like they're probably okay, good enough to beat the Badgers on a neutral court for sure. Are they really going to start 0-3?

VCU is 2-0, having beat up PVA&M and Radford, but we don't know much about them. They're Shaka-less, but they've got that Shaka-protege. Typically, a pressing team is the best possible matchup for the Badgers. And since we've got Brondad at the point, that's probably still true. But with jittery freshmen and Vitto Brown littering the lineup, I'm not so so sure this year.

Duke lost to Kentucky tonight. Can't make too much of those early losses on neutral courts, but they are young and figure to be relatively beatable early in the year. Still, are they going to lose two games so early?

So: 0-2 in NYC seems very possible, probably 33% chance. (Going with my gut here, not consulting T-Rank or box scores (for the moment)). Maybe a 10% chance of two wins, and you do the math for the split.

After the cupcake, it's at Oklahoma and at Syracuse. Oklahoma has a good-looking squad led by a bright shining star in Buddy Hield. They played their first game tonight, and beat Memphis on the road. Hard to imagine the Badgers winning that game. If they do, it would be probably the most shocking non-con win since Bo stole one in Austin in 2007.

At Syracuse won't be much easier, obviously. Syracuse has beaten Lehigh and St. Bonaventure -- middling teams -- at home, by middling margins. So they're still a bit of a mystery. But winning at the Carrier Dome seems an unlikely proposition.

Long story short, 2-3 over the next five games would probably be the best we can reasonably hope for, with 1-4 sitting out there as a disturbingly plausible scenario.

Buckle up.

What's the difference

It is amazing what can happen in basketball when you just make shots. Sienna and Western IL played UW this week and the difference in the game came down to what every boy and girl spends 99% of their basketball time doing, shooting the basketball. 

I rewatched the DVRed games (while either quite drunk or while riding an exercise bike so the numbers were being kept in my head and may be a bit off) and my unofficial count had Western making 11-19 jump shots including 8-12 from 3. A lot of that damage was done in the first half, but they continued making enough shots in the 2nd to keep Bucky off balance.

Sienna came out hot scoring 16 points in the first 8 minutes by mostly attacking the rim. They hit just one 3 point jumper in that stretch. They then scored 17 points in the next 16 minutes which included just one more jumper hit at the 16:29 mark of the 2nd half. By that time UW was up by over 20 and the game was over. 

Bucky has had 3 good offensive halves. In the first half against Western and the 2 halves against Sienna they scored 49, 43, and 44. They scored 23 in the second half against Western, and shot just 25%. Those types of halves will lose you a game even against a bad team. 

I have told my coblogger that UW is going to lose some games they shouldn't this year, and they are going to win some games they shouldn't this year. I wasn't expecting it to come so soon, but I guess that just makes my point. When you have this many freshman, defense and shooting will come and go in streaks. 

Some of you may call this an oversimplification. I would ask you to look at last years box score of the Rutgers game. Sometimes shooting can make all the difference in the world. 

New T-Rank features

In case you haven't noticed, I've made some major improvements in the T-Rank for this season:

1) A specific page for every team, with predictions, results, and team stats (more to come).

2) A page for each conference with similar stuff, and more to come.

3) Player stats, found at the bottom of each team page. Eventually I'll put up some overall and conference stat pages as well.

Basically, I'm now able to do a lot of the stuff that Ken Pomeroy does at his site. T-Rank is sort of becoming Kenpom Lite. But if you're reading this, you should definitely pay the $20 and get yourself a Kenpom subscription.

I've got a lot of other ideas for improvements that I think I can implement, given the time and motivation. Now that I've broken away from Excel and figured out how to get player boxscore data in a usable form, there's not much of theoretical limit on the kinds of analysis I can run.  (There are a lot of practical limits, though, of course.)

If you've got any ideas for something you'd like to see, let me know.

Going to be a wild one.

I've started looking over rosters and who is coming back from previous years in anticipation of the coming season. I'll wait until I get to see the teams play before passing judgment, but here are a few things that I'll be looking for/interested in.

If T-Rank is reasonable, and I don't think it's probably too far off, it's going to be a wild and crazy year. No Big Ten team in the top 8 of T-Rank, but 8 teams between 9-35. Throw in Minnesota, NW and IL who may not be NCAA tourney teams, but should all be competitive, and this should be a season chock full of great games.

Last year was a coming out season for freshman guards, especially point guards. These freshman all got major PT last year:

D'Angelo Russell (NBA now), Bryant McIntosh, Melo Trimble, Nate Mason, Robert Johnson, James Blackmon, Lourawls Nairn, Shep Garner and Kam Williams.

Those are just some of the the Freshman, and there are other young guards like Koenig, Walton Jr, and Zach Irvin who also played big roles. Which of these young guards takes their game to the next level will be interesting.

Last season the Big Ten lacked talented big men outside of Kaminsky. This was probably part of what allowed all those young guards to break out and play so well so soon. What last season was for guards, this season will be for big men. The following players will all have an opportunity to make an immediate impact as freshman:

Diamond Stone- Maryland
Caleb Swanigan- Purdue
Thomas Bryant- Indiana
Deyonta Davis- MSU
Daniel Giddens- OSU
Aaron Falzon- NW
Mike Watkins- PSU
Ethan Happ- UW

Some of these guys won't be ready in year 1, but some of them will. Most of these big men are also on teams with very high expectations. There are going to be some fans that want them to be one and doners and carry their already very good teams to the greatest heights. Doesn't seem out of the realm of possibility that one or two of these guys turns out to be great right away. We'll see.

No surprises yet

I saw a couple beat writers post that the Badgers White team at practice was made up of Hayes, Happ, Brown, Koenig and Showalter. Red team was Hill, Thomas, Illikainen, Van Vilet, and Iverson.

Rumor is that Showy has worked on his jump shot. He has never had much of one before and I have never been much of a Showy fan for that reason. If he can just be an OK shooter (maybe 30-33% from 3) and continue his effort on D, and fearlessness going to the rim, he could be OK. My hope was that Pritzl would be good enough to play right away at the 2 to give the Badgers more shooters, but that obviously is not the case. Maybe if he recovers from injury he could still be that guy by the end of the year, but who knows.

I hope they start Nigel at the 3, although I'm sure he will get plenty of minutes at the 4-5 too. I have not heard a mention of Dearing making any plays in practices, so it's not much of a surprise he is not in the top 10. We'll see if he looks any better against UW river falls. It will be fun to see the team get at it again this year.

Can't wait for Wednesday.

Houston

I know some people will remember this unfortunate post from long ago, but I am not a Stave hater. I don't think Houston should take over the starting job, I am just very pleased with what I saw on Saturday from Houston. 

Here's what I like the best about what I saw. Probably not what you are thinking. 

Play-by-Play Summary (1st quarter) Wisconsin vs Illinois (Oct 24, 2015 at Champaign, Ill.)

WISCONSIN drive start at 09:52.

1-10 WIS 29 Ogunbowale, D. rush for 2 yards to the WIS31 (Barton,Taylor), PENALTY WIS illegal block (Deiter, Michael) 15 yards to the WIS16. 1-23 WIS 16 1st and 23. 1-23 WIS 16 Stave, Joel pass incomplete to Ogunbowale, D.. 2-23 WIS 16 Stave, Joel sacked for loss of 4 yards to the WIS12 (Phillips,C.). Wisconsin 2 (QB) injured on the last play. 3-27 WIS 12 Houston, Bart sacked for loss of 3 yards to the WIS9 (Phillips,C.). 4-30 WIS 09 Meyer, Drew punt 37 yards to the WIS46, Bentley, V. return 9 yards to the WIS37

To summarize: Their starting quarterback got hit on every drop he took including the one he got hurt on. Their running back got stuffed both attempts he got. They are facing a 3rd and 27 from their own 12 yard line. Bart Houston (who has never thrown a pass in conference play and has never been in a meaningful game since he was in high school 4 years ago) is under center. Paul Chryst dials up a pass play. 

In the moment while Houston was getting sacked, I said to myself, what are you thinking. Why not run a draw, let Mcvoy run a wildcat play, or do anything else but throw it and let your backup QB have the chance to make a huge mistake. 

Turns out Chryst had a ton of confidence in his backup QB. Also turns out maybe he had pretty good reason to, after Houston lead UW to a road win in conference play. 

I like that. I like that Chryst has confidence in his guys. Chryst may never be an electric personality or fun to watch in a press conference, but his players will feed off of that type of confidence more than any bluster or bravado he might lack. 

I hope Stave is OK and we don't get to watch Houston play again until next year. If we do I am not as scared that there is a total void behind Stave. It gives me comfort to know there is a guy who can start at QB next year so we don't have to force an underclassman QB on to a stage he isn't ready for. 

UW is in good hands at QB, and Chryst is making me a believer that UW is in good hands with it's coach too. 

T-Rank Team Pages: T-Pages

So I've been learning a little "programming." That's what we called it back when I used to know something about computers. Talking early 90s here.

Nowadays they call it coding. Whatevs.

Anyhow, I've added a feature to the T-Rank, which is a page for each team — just click on the team names and you'll get what I'm calling a T-Page™. It will update automatically (more or less) as I update the T-Rank throughout the season.

I plan to add features to the T-Pages in the coming ... years.

Here's the Badgers' T-Page.

You're welcome.

Bucks at Kohl

It appears I have been neglecting this blog so I will put something up today. No metrics here. Just good old fashioned human observation.

I went to the Bucks preseason game at the Kohl Center tonight. A few observations.

1) It is amazing how small the Kohl Center court looks with NBA players of massive size and length. We had lower section seats about 20 rows up and the court looks small with those giants out there. The windows they have to fit passes into with all the length is tiny. It gives you an appreciation for the difference between the college and NBA game to watch it on a court I am so familiar with.

2) It was fun to watch the crowd boo every time Tyus Jones touched the ball (he was 1-4 with a TO and only 1 assist).

3) It was fun to watch Marcus Landry get some time as a Milwaukee pro on the old home court. (he was 2-3 for 5 points, 3 rebounds, 2 blocks). He only has an outside chance of making the roster, but it's always good to see Badgers get their chance in the pros.

4) It was very sweet to hear the crowd chant 38 and 1, every time Karl Anthony Towns touched the ball.

I may have to watch the UW vs. Kentucky game this weekend that has been sitting on my DVR. Only about 2 weeks until the first game. Can't wait.

T-Rank: Mid-Major contenders

Mid-Major Madness is out with a poll of the Top 15 mid-majors (as they define "mid-major") teams for the upcoming season.

For what it's worth, here's the T-Rank's Top 15 mid-majors:

Differences:

BYU. The mid-major experts are much higher on BYU than the T-Rank, ranking them 3d of the MMs while T-Rank has BYU a bubble team at best. BYU is always a tough team to rank by algorithm, given that they have guys coming back from missions and stuff like that. But they lose 50% of their minutes from last year's squad, including star Tyler Haws. And 9 of their 15 players are newcomers to the program. Should be interesting. Here's a nice article on their team.

Rhode Island. The experts are high on the Rams, voting them the No. 5 mid-major. Rhode Island is coming off a good year, in which they finished 56th in the Kenpom and 60th in the T-Rank. They also finished 3rd in a resurgent A-10. Dan Hurley hopes to continue his turnaround project (they've moved up from 193 to 103 to 56 in the Kenpom in his three years). T-Rank recognizes the momentum, but it's hard to algorithm-out those sub-100 years. I think the T-Rank is too probably too low.

George Washington. T-Rank has them 8th, while the experts put the 18th (third in the "also receiving votes" category.) GW returns three of last year's starters, a solid core, but suffer a blow with the transfer of Kethan Savage (headed to Butler). On the other hand they bring in Tyler Cavanagh from Wake Forest.

VCU. T-Rank doesn't know that Shaka Smart isn't at VCU anymore, which probably explains why it has them significantly higher rated than the experts.

Old Dominion. This is another team on the rise under a new coach, and the experts put them 10th among mid-majors. Jeff Jones inherited a team that had finished 267th in the Kenpom the previous year. In his two years at the helm, the Monarchs have risen to 170 and 56. This gives them the third best "momentum" score in all the land. Even with that, they come in at 100th in the T-Rank. Probably too low.

Stuff happens

The Badgers utterly dominated Iowa in the second half of yesterday's game, out-gaining them 189 to 68. Iowa never even sniffed a scoring chance.

Yet the Badgers managed to win the half just 3-0, and lost the game 10-6. Stuff happens.

Overall, the Badgers out-gained Iowa 320-221. Since 2000, when the Badgers have out-gained an opponent by 99 yards or more, they are 77-8 (including yesterday's loss).

Even looking at the halo around a 99-yard advantage: when the Badgers have out-gained an opponent by 49-149 yards since 2000 they are 38-9 (including yesterday's loss).

The point is, today's game was a freak out. Stuff happens.

Obviously, Wisconsin is not a great football team this year. But they aren't some sad sack bunch of shit bags either. They had a bad, unlucky day, and when mediocre teams have bad, unlucky days they lose to decent opponents.

But I'm seeing otherwise smart people actually call for Tanner McEvoy to be inserted at Quarterback.

Honestly, this makes me despair. How can democracy be a viable form of government when there are reasonable people calling for Tanner McEvoy to be inserted at Quarterback?

In defense of the BPI, etc.

In the past couple of years, ESPN has come out with some proprietary "advanced metrics." These include the "Total QBR" for rating QB play in football, the FPI, or "football power index," for rating football teams, and the BPI, or basketball power index, for rating college basketball teams.

A lot of the folks I engage with on Twitter like to lampoon these metrics, mainly on the notion that they are "black boxes"—that is, we don't know how they're calculated, so we can't really evaluate them. Thus, they are entitled to no respect.

I certainly agree with the notion that power ratings should tell us how they work, at least in general terms. But both the FPI and the BPI are actually pretty open, or at least can be figured out rather easily.  And it's not like other rating systems, such as Ken Pomeroy's ratings, are completely open about how their calculations are made. As with the BPI, Kenpom tells us what he considers in general terms, but the exact weights and calculations are his special formula.

Looking specifically at BPI, it is very clear that it is a Kenpom-style per-possession-efficiency rating system with basically one twist: it adjusts the weight of individual games based on whether one or both of the teams was at less than full strength. So, for example, when Rutgers beat Wisconsin with Frank Kaminsky sitting on the bench, they didn't get "full credit" for the win. Instead, the BPI only counted that game as .832 of a game because of Frank's injury. This is all open and published.

ESPN doesn't explain exactly how they came up with that number of .832 to account for Frank sitting out. But that's fine. Kenpom doesn't explain exactly how he discounts blowout mismatches, or exactly how much more weight he gives to recent games.

In the end, BPI and Kenpom (and T-Rank) come up with very similar results. For example, here's the top 20 of last year's BPI, with their T-Rank and Kenpom rank also listed:

BPI	TeamName	KenPom	Trank
1	Kentucky	1	1
2	Wisconsin	3	3
3	Arizona	2	2
4	Virginia	5	4
5	Villanova	6	6
6	Duke	4	5
7	Gonzaga	7	8
8	Kansas	14	12
9	Notre Dame	9	11
10	Utah	8	7
11	North Carolina	11	9
12	Wichita St.	10	16
13	Oklahoma	13	10
14	Northern Iowa	12	22
15	Louisville	16	17
16	Iowa St.	18	18
17	Ohio St.	21	14
18	Michigan St.	15	15
19	Baylor	17	13
20	Butler	20	19

A lot of agreement there, and this holds true for most teams.

The fun thing about a power rating like BPI (or T-Rank) is that it allows one to give probabilities of various events happening, and estimate points spreads for hypothetical or far-off matchups. I think it's cool that T-Rank Pure can currently give you a prediction of about 5,000 upcoming college basketball games, and ESPN can do the same thing with BPI. Given that we basically know what the BPI is, except for minor details, I think the lampooning is unwarranted.

T-Rank 2016 Preview: Nuts and Bolts

Update for 2017-18:

Been making some pretty big improvements—well, changes—to the preseason projections. Maybe I'll do a separate post on it at some point, but here's the gist:

I've completely revamped the offensive projections, moving away from the prior "team based" model to an almost completely "player based" model. The old model, explained in detail below, was to look at past team performance, then adjust up or down based on the characteristics of the returning players. Now the model starts with projected player performance, and builds a team projection from that. One fun aspect of this is that I'm actually projecting core offensive player stats—offensive rating, usage, and minutes—and you can go look at those on the team pages at the T-Rank site.

Just a couple of team-based effects remain: (1) I've kept the "momentum" modifier for now, and (2) There is a coaching adjustment for teams who've hired a new coach in the last three years. Both of these are pretty ... stupid, I guess is the word—but I like them.

As with all of my T-Rank stuff, this is not to be taken seriously, and it's far from clear this is a "better" way to do things, given my fundamental incompetence. But I've always been convinced that this was the better way to project offensive performance, and Dan Hanner laid out the road map for this model long ago. This new model is basically my attempt to follow the steps he laid out then. Indeed, the compulsion mainly arose after realizing I now have the data and programming ability to do it.

So that's offense. As for defense, I think a team-based model is actually pretty good. In the blog post linked above, Dan Hanner says as much—though he links performance to coach rather than team. So for now I'm continuing with the exact same model as before, with one significant change: an effect based on the projected effective height (that is, height at the center and power forward positions), which is well-correlated with adjusted defensive efficiency.

Update for 2017:

A couple changes to note:

1) I tweaked the recruiting points to make them even more top heavy, but also discounted them less. What this means is that top 10ish recruits are more important, 10-20 about the same, below that less important.

2) Added some coaching effects. This only comes into play for teams with coaches in their 3rd year or less. A coaching change is now expected to induce a reversion to the overall program mean (based on 2002-present). On top of that, each coach has their own ratings on offense and defense, which are based on their past performance compared to their school's overall program mean. Overall, it's a minor thing, but something fun to throw in the mix.


******Original post:

This will be the second year of the full 351-team preseason T-Rank, third year overall.

It's worth emphasizing that this is a hobby of mine. Other preseason ratings are better: more scientific, more well thought out (i.e., thought out at all), etc. But this one is mine.

I don't think I've ever really explained all the inputs to the preseason T-Rank. There's a good reason for this: they're arbitrary. Not completely arbitrary, but pretty arbitrary. And when you look at each individual component too closely, the whole thing seems kind of stupid.

The whole thing is kind of stupid, I guess.

But it's fun.

At this point, I'll be honest, the main function of the preseason T-Rank is to produce an input into what I call the preseason T-Rank+ (brand manager needed), which is a conglomeration of a few similar preseason ratings to create a Voltron-like super rating. Specifically, I intend to combine the preseason T-Rank with the Kenpom preseason ratings and Dan Hanner's preseason ratings (assuming he does them again) to provide the base ratings for the in-season T-Rank that you all love so much (I'm talking to you, mom). This year I think I will probably give them unequal weights, and T-Rank won't be the most hefty. 

Anyhow, here's the basic formula.

T-Rank starts with a "program rating" of every school. This is based on the efficiency ratings of the last three seasons. Previously I used Kenpom historical efficiency data for this. Starting this year, this is pure in-house mojo, because I have the data. I went back and calculated the T-Rank power ratings for 2013 and 2014, and of course there are the 2015 ratings my mom loved so much in real time.

The idea here is that it doesn't take a rocket scientist to figure out that teams that were good last year, and the year before, and the year before that, are probably going to be pretty good this year. Same thing with perennially crappy teams.

The two other main inputs are: returning minutes and returning players. Returning minutes is the main driver of variance of expected defensive efficiency (according to T-Rank). The philosophical idea here is that defense is a lot about team play, smarts, positioning, etc., and players just get better at it -- particularly if a bunch of players are playing together for while. So higher returning minutes means better expected defensive efficiency, lower returning minutes means worse.

The returning players analysis is for the offensive side, and is a little more "granular." There I look at each returning player's offensive rating and usage rating (which measures how often they actually do something on offense), discount it by percentage of team minutes played, and calculate an expected figure I call "Opts" (offensive points). This figure is then adjusted for class. Specifically, sophomores get a 50% bump, juniors get a 30% bump, and seniors and grad transfers get a 10% bump. [For 2017 I've altered the bumps somewhat, more like 40%, 15%, 10%] This reflects the well-known pattern that college players generally take their biggest leap as sophomores, with lesser improvements thereafter.

I add up the returning Opts for each team and then adjust their "program" Offensive rating based on  whether it is above or below average.

I also make an effort to track every transfer in and out, although I'm sure I miss a bunch. [Edit: for 2017 I've more or less automated this, which is one of the great accomplishments of my life.] Transfers in get their Opts added, but somewhat discounted, and there's a separate calculation for their expected effect on defense. This is all pretty guessy-bessy, to coin a phrase.

Similarly, top 100 recruits are tracked and accounted for. The number one recruit has an effect similar to a returning all-American level player. But the effect tapers out pretty quickly. The idea here is that most of the effect of good recruiting is probably already reflected in the "program rating." At this point, we don't really need to know much about the specifics of Duke's recruiting class to know that they're going to be pretty good. 

Finally, there is a "momentum" algorithm, which rewards (or penalizes) teams that are on the way up (or down). These are teams that significantly over- or under-performed their "program rating" last year, with signs (in terms of returning players) that the trend may continue. This year, for example, Vanderbilt and Utah are programs notably on the rise; on the flip side, Creighton and St. Louis seem to be sinking. 

Put it all together, and -- voila! -- the preseason T-Rank. I'll unveil the output in the coming weeks, in dribs and drabs to maximize drama and advertising revenue. Play Draft Kings Daily Fantasy!

The Case Against Maryland

The case for Maryland as Big Ten favorites is straightforward and compelling. They are coming off a surprising 27-8 (14-4) season; they return stars Melo Trimble and Jake Layman; and they add center Diamond Stone, a very highly touted recruit, and Robert Carter, an impact transfer at forward. They were good last year, and they should be significantly better this year.

This is good analysis, and in my opinion Maryland has to be considered at least a contender in the Big Ten for these reasons, maybe even the frontrunner.

But there’s some overhyping going on with this team. The early previews are comparing this year’s Terrapins to last year’s Wisconsin team, which was more or less unanimously anointed the team to beat before last year began, and which carried the burden of Final Four buzz from the very start.

The comparison of this year’s Maryland team to last year’s Wisconsin team has no basis in fact.

Last year’s Badgers were coming off a 30-win season and a trip to the Final Four. They returned four starters, and 7 of their 8 rotation players. They finished 6th in the 2014 Kenpom ratings after taking Kentucky to the final seconds in the national semifinal.

This year’s Terrapins are coming off a 27-win season that ended in the second round of the NCAA tournament. They lose Dez Wells, an extremely high-usage player whom they counted on to create offense. They also lose two other seniors, Richaud Pack and Evan Smotrycz, who contributed significantly. They also finished just 32nd in the Kenpom ratings last year.

It’s this last fact – Maryland’s per-possession profile – that makes me most skeptical about them. Put simply, they overachieved last year. That doesn’t take anything away from what they achieved: all 27 of their wins counted. But it should make us a little skeptical about extrapolating last year’s success into this year.

Finally, Mark Turgeon has been coaching for a lot of years now and his record just isn’t that great. In 17 years as a head coach, he’s won just one conference title (2006, at Wichita St.). In his eight seasons as a major conference coach, his teams have averaged a 5th place finish. He’s never had a particularly good offensive team (last year’s Terps finished 58th in adjusted offensive efficiency) which makes me doubt he’ll deftly adjust to this coming season’s rule changes.

All told, if you give me an even-money bet on Maryland versus the field for the Big Ten title, I'm reaching for my wallet and betting on the field.

Bo's last season

Bo announced that next year will be his last. It's a very emotional day round these parts. But the prevailing emotion is gratitude. We were so lucky to have him.

But he's not done yet.

Good run

So on UW's run in the NCAA tourney, every team they played against after the first round had a player that was drafted in the NBA draft. They played against 13 players that were drafted, and had 2 for a total of 15. The NBA only drafts 60 players, and 14 of those were foreign players. Of the 15 players, 11 were picked in the first 24 picks, and 8 in the top 13.    

Oregon- Joseph Young #43

North Carolina JP Tokoto #58

Arizona Stanley Johnson #8, Rondae Hollis-Jefferson #23

Kentucky Karl-Anthony Towns #1, Willie Cauley-Stein #6, Trey Lyles #12, Devin Booker #13, Andrew Harrison #44, Dakari Johnson #48

Duke Jahlil Okafor #3, Justice Winslow #10, Tyus Jones #24