Tuesday, October 18, 2016

UW Iowa

4-2 after UW covered again. UW is a 3.5 point favorite on the road and the over/under is a measly 42.

I have mostly rode the Badgers this year and they are 5-1 vs the spread. Both of Iowa's losses this year came at home, and they haven't faced a defense like Bucky yet this year. UW almost got the offense uncorked last week when they had over 300 yards in the first half vs. OSU #2 ranked defense.

I'm going to keep riding the Badgers and give the points, but I'm very uncomfortable with this pick. Weird things seem to happen when UW plays Iowa or Northwestern, and I can see this game as a 7-6 final or a 38-28 final.

Monday, October 17, 2016

Michigan State's struggles are good for Wisconsin

After losing just four regular-season games over the past three seasons, Michigan State has now lost four games in a row, starting with Wisconsin's win in East Lansing last month. The Spartans still have to play Michigan, Ohio State, and Penn State, so things are looking bleak.

I have to admit I have a bit of a soft spot for the Spartan football team, because their program is similar to Wisconsin's in a lot of ways. Not flashy, usually underrated, etc. Obviously familiarity breeds contempt, so I still hate their guts. But it's a respectful hate.

The reality, however, is that Michigan State's struggles are probably a good thing for Wisconsin, even if it means that Michigan—a truly evil program—benefits by reestablishing unquestioned dominance in the state of Michigan. Wisconsin and MSU compete for recruits in a way that Wisconsin and Michigan do not. One can dream, but it's unlikely that Wisconsin will ever really challenge Michigan in recruiting.

So the worst situation is a strong MSU program that regularly humiliates its in-state rival, thus amassing the cache to beat out Wisconsin for the 4* recruits they compete for, while Michigan still uses its institutional advantages to still come in and cherry pick 5* guys out of Wisconsin's natural recruiting territory. That's basically been the situation for the last five years or so.

A better situation is that Michigan definitively supplants State, and State becomes a perennial also-ran behind Michigan and Ohio State in the East. Meanwhile, Wisconsin can lay claim to being the superior program in the West, and pitch the opportunity to regularly play for championships. That way they can dominate MSU in regional recruiting, and have at least a chance at warding off the heavy-hitters on Wisconsin's home recruiting turf.

So, upon reflection, I will revel without remorse in Sparty's downfall.

Tuesday, October 11, 2016


Back on the happy side with UW covering against michigan to go to 3-2.
OSU is a 10 point favorite and the o/u is 44. This matchup looks a lot like last week. 2 very good defenses, and probably a low scoring affair. There's a chance OSU is really, really good and totally blows UW out, but I think the home field helps keep this close.
I'm taking UW and the points.

Sunday, October 9, 2016

Will a Badger Sophomore Redshirt?

Usually when a player takes a redshirt, it is because they are a freshman and need time to build strength, develop a skill, or are just buried on the depth chart and are biding their time until they will have more opportunities. The Badgers have used the redshirt rule liberally over the years, as they have often had a core of upperclassmen waiting to eat up most of the minutes. Brian Butch redshirted despite being the most heralded Badger recruit in years.

Most of the time when a redshirt occurs after the freshman season it is due to a medical redshirt when a player gets hurt early in the season. However the Badgers have used the redshirt for non-freshman for the same reasons as above. Most notably this occurred with Duje Dukan, which allowed him to develop enough to be a contributor on the best Badger team of all time. It also occurred with current Badger Jordan Hill, who played as a freshman, redshirted as a sophomore, then played last year as redshirt sophomore.

The Badgers had several freshman last season that at least contemplated a redshirt, but none did (although Pritzl was later granted a medical redshirt after reinjuring his foot in the non-conference season). The Badgers had to deal with the loss of Van Vilet due to academic reasons, Pritzl due to medical, and Dearing due to transfer shortly after the season started. This left them with just 9 scholarship players last year, including no seniors and 4 freshman. With so few bodies those freshman had chances to play, and while some could have used the year to develop, none turned down the opportunity to play.

This year could be different. The Badger had a lot of questions last year beyond Hayes, Happ, and Koenig. Both Brown and Showalter were question marks going in, and if they had not improved so much could have been surpassed by a younger player. Both are now firmly entrenched as starters and should get major minutes. The bench will need to be sorted out, but I don't see how there are enough minutes to go around for all the big men, barring injuries.

In the backcourt things are more simple. Koenig and Showy will start, and since Pritzl and Hill have both used their redshirt already, they have to play. Throw in that Gard likes to play some 3 guard lineups and there should be enough minutes to go around. If Trice is good enough he could take minutes from either backup, or could redshirt. Since this post isn't about freshman redshirts I'll move on the the frontcourt.

Hayes, Happ, and Brown will all start and should all play a lot of minutes. Hayes played 36 minutes per game last year and it's hard to see that going down much. Of the 3, Vitto played the fewest minutes per game at 25, so it's hard to see how there will be enough minutes for the rest. That includes Iverson, Thomas, Illikainen, and Van Vilet. You could also throw true freshman Aleem Ford into the mix here, but I'm assuming he will not be ready to be a major contributor this year. Let's look at each of the 4.

Van Vilet- Seems very unlikely to redshirt. Having not seen him play or practice it is hard to say how good he is, but rumors indicate he can play and will be ready to this year. How much is hard to say, but I can't see him redshirting after sitting out all of last year due to academics, and spending 2 full seasons not playing.

Iverson- Seems very unlikely to redshirt. Having seen him play all last year he looks like a star waiting to explode. He led the freshman other than Happ in games played, and minutes played. He will be buried this year behind Hayes, but he should get opportunities because of his defense. He can guard 3 or 4 positions, and if he has developed any kind of jump shot he could play a lot.

Thomas- Possible, but not likely. Thomas was not your typical freshman at UW last year. He came in with the grown man body that most kids need 2-3 years to develop. He also has a decent jump shot for a man that big, and does not lack the confidence to let it go. He played a lot early when Bo was still coaching, but got fewer minutes once Gard took over and played more smaller lineups. He does need to work on post moves of which he showed none last year, but he could have worked on that this summer. Since physical development is not needed, a redshirt seems unnecessary. This only happens if the other 3 kids have moved so far in front of him on the depth chart that no minutes are likely.

Illikainen- Possible. Illikainen was the closest to redshirting last year, but with so little depth last year he ended up playing in 33 games and getting almost 10 minutes per game. He looked good earlier in the year when he was shooting the ball with confidence, but struggled with that shot as the season went along. He showed pluck in defending the post against bigger players, but you could see he was outmuscled sometimes. He has had all summer to bulk up, so maybe he won't need the time to get stronger. I think it's possible that he ends up as the best player in the 2015 class 4 years from now, but right now I think he's the most likely to redshirt.

Saturday, October 8, 2016

MI next year

While Bucky didn't win in the matchup with MI this year, I had fun in my first trip Ann Arbor for a game. After the game the MI fans were quite nice. I stayed in the downtown area, so maybe if I would have wandered over more toward campus it would have been different. I heard a lot of: that's the best defense we will face all year, and go beat OSU.

I could not help myself from starting to think about next year's matchup. Earlier this year I blogged about how crazy young this Badger team was and how they bring back so much talent next year.

That got me thinking about MI and the game next year, and who MI would be bringing back. Not much is the answer. 8 of 11 starters on defense are seniors, and 9 of 11 starters on offense. It gets worse, as 13 of the 22 in the 2 deep are seniors on both offense and defense. That is a lot of seniors to lose. By the way, that doesn't include losing underclassmen to the pros, of which MI should have at least one- Peppers, the best player on the team.

I can already hear the MI folks saying that MI doesn't rebuild, we reload. In the 2016 draft, OSU had 5 first round picks, and 12 players drafted overall. They currently sit at #2 in the country and look like a playoff contender one year later. While MI has upgraded their recruiting, they are not OSU. MI will take a hit from losing all those players.

WI is better than MI. In regards to the matchup next year I quote the immortal Bart Scott.
"Can't Wait"

Top 10 vs. Top 10

When Bucky played #8 MSU this season both teams were ranked in the top 10 (Bucky was 10 in coaches, 11 in AP). Big games when both teams are ranked in the top 10 are rare during the regular season. Even very good teams with very good schedules often only get one of these games a year, maybe 2. 

For example:
Alabama, despite probably being in the top 10 all year, will likely only have 1 top 10 regular season opponent all year- Texas A&M.
MI and OSU both will play top 10 UW, and will play each other so they could both have 2. 
Louisville got Clemson and FSU in the top 10, but with Houston's loss today they will likely finish with 2 top 10 matchups. 
Washington's whooping of Stanford will likely be their only Top 10 matchup of the year. 
Clemson's defeat of Louisville will likely be their only matchup this season.

It's hard to stay in the top 10 all year, especially when you are playing other top 10 teams. Bucky has had 2 of these games already with the MSU game and against #4 MI when UW was #8. With Houston and Tennessee losing today, UW should return to the top 10 prior to the #2 OSU matchup giving UW it's 3rd such matchup of the year. If UW were to pull off an upset over OSU, and beat Iowa on the road, (also no small feat) they could then have a 4th top 10 matchup if Nebraska were to defeat Indiana and Purdue in the next 2 weeks (no large feat). Mind you this does not include UW's win over #5 LSU because Bucky was not ranked at the time. 

At worst, UW will have 3 Top Ten Matchups this season. What a great fucking season.

While it seems unlikely, UW could also have a Top 10 opponent in a Big Ten Championship game, and another in a Bowl game, or maybe 2 in the playoff. 7 Top 10 matchups for UW is theoretically possible in one season. 

Sunday, October 2, 2016

How much do unbalanced conference schedules matter? Not much.

One of the many downsides of the trend towards mega-conferences is the death of the round-robin conference schedule in basketball. The resulting unbalanced schedules raise the distinct possibility that regular season conference championships will be decided by quirks of scheduling fate rather than talent and coaching tantrums directed at referees as God intended.

The Investigation

But how much difference do unbalanced schedules actually make? To find out, I looked at every season since 2008-09 and used T-Rank to calculate each team's:

1) Expected wins against the conference schedule it actually played;
2) Expected win percentage against a true round robin, and used this to calculate expected wins based on actual number of conference games;
3) Expected chance of winning a share of the title against actual schedule;
4) Expected chance of winning a share of the title against round-robin schedule.

The differences between 1 & 2, and 3 & 4 show the effect of an unbalanced schedule, though in slightly different ways. So I looked at both.

First, the conclusion

Overall, the data is pretty clear that unbalanced schedules are rarely extreme enough to be decisive. By far the dominant force in college basketball is variance, and that's why we love it. All the data is available here.

An Extreme Example: Wisconsin 2016

Let's look at the results for Wisconsin last year as an example.

Expected wins against actual schedule: 10
Expected wins against balanced schedule: 10.8
Chance of winning title against actual schedule: 1.8%
Chance of winning title against balanced schedule: 4%

Obviously, the Badgers got a bad draw last year. Indeed, the -.8 wins is the second most difficult schedule in the entire database. But it's hard to say that even this extreme schedule had much of an effect on anything. The Badgers finished a full 3 games behind Indiana. Even accounting for Indiana's favorable schedule (+.34 expected wins) the Badgers still finished 1.86 adjusted games back:

Team Wins EW Diff Adj. Wins Actual EW Blnced EW Actual Ch% Blnced Ch% Ch% Diff  Adj GB
Indiana 15 0.34 14.66 13.4 13 29 22.4 6.6 0
Wisconsin 12 -0.8 12.8 10 10.8 1.8 4 -2.2 1.86
Michigan St. 13 0.22 12.78 14.5 14.3 61.9 56.6 5.3 1.88
Iowa 12 -0.59 12.59 11.3 11.9 6.3 10.8 -4.5 2.07
Maryland 12 -0.37 12.37 11.5 11.9 6.8 11.8 -5 2.29
Purdue 12 -0.2 12.2 13 13.2 23.3 25.7 -2.4 2.46
Ohio St. 11 0.23 10.77 8.3 8.1 0.1 0.1 0 3.89

Perhaps more importantly, the Badgers were an extreme long-shot to win the title under a balanced schedule (just 4%) and while the unbalanced schedule cut those long odds by more than half, it's still hard to complain too much about that.

The few examples where it made a difference

Now for the fun stuff: Looking for examples where an unbalanced schedule actually made a difference. First, I looked for teams that didn't win the title and finished less than .5 "adjusted games back." Here they are:

Team Year Conf Wins EW Diff Adj. Wins Actual EW Blnced EW  Adj GB
Coastal Carolina 2014 BSth 11 -0.22 11.22 10 10.3 0.43
VCU 2012 CAA 15 0 15 14.3 14.3 0.47
Dayton 2009 A10 11 -0.46 11.46 9.7 10.2 0.48

So just three teams over 7 seasons finished less than .5 adjusted games back, and even these three barely cleared the .5 threshold.

VCU is an interesting one because their schedule actually did not disadvantage them compared to a balanced one. The problem was that that the Colonial champs that year, Drexel, had a very favorable schedule, worth +.53 wins. Most importantly, Drexel only had to play VCU once, and that game was at Drexel. This is when an unbalanced schedule can be really unfortunate: when there is a clear top two that play only once, the team that gets the home game has a big advantage.

The other way I looked at this was to calculate likelihood of winning a championship. I think this is less good than looking at the "adjusted wins" because it's entirely hypothetical. For example, Wisconsin vastly outperformed expectations in conference play last year -- winning 12 games when T-Rank would have expected a team of their quality to win only 10 on overage. As it happened, Indiana also won two more games than expected. But if they'd won 13, as expected, it would have been fair to say that their easier schedule and Wisconsin's harder schedule combined to rob UW of a championship. But if you just look at the simulated difference in Championship expectations (4% vs. 1.8%) for Wisconsin, that doesn't show up.

That said, here are the five teams whose championship odds were negatively affected by 10% or more:

Team Conf Year Wins Actual Ch% Blnced Ch% Ch% Diff GB
Mount St. Mary's NEC 2010 12 37.1 53.9 -16.8 3
VCU CAA 2010 11 28 43.5 -15.5 4
VCU CAA 2012 15 48.6 60 -11.4 1
Syracuse BE 2013 11 12.9 24.3 -11.4 3
Butler A10 2013 11 15.1 26 -10.9 2

VCU's 2012 team shows up again, as does its 2010 team. But the 2010 team illustrates the downside of this purely hypothetical analysis: that team actually finished in a tie for 5th pace, a full four games behind Old Dominion. Similarly, each of the other teams (other than 2012 VCU) finished at least 2 games out of first. So although all these teams definitely got screwed by their schedules, they didn't perform well enough to really feel sorry for them.


So the overarching conclusion is that most of the time unbalanced schedules are not that big a deal. But a couple caveats:

1) The conclusion that unbalanced schedules don't really affect conference championships doesn't mean that there aren't other effects. Clearly, a tough schedule can easily cost a team one win, and it's not that unusual that one win is the difference between making the NCAA tournament and sitting at home. Pertinently, the 2010 VCU team noted above has a good claim to losing one win based on a bad schedule. That team ended up losing in OT to Old Dominion the Colonial Tournament Championship game, and didn't make the tourney -- despite being No. 51 in Kenpom. It was the next year that VCU snuck into the tournament, as a First Four participant, despite a significantly worse profile, but rode Almighty Variance to the Final Four.

2) Although most schedules are balanced enough most of the time, this is only true in the end. It's still very important to look at who's played whom at a given point in the season. For example, Indiana got a lot of flack about its Big Ten schedule last year, even though in the end it was just marginally favorable. But the real issue was that their schedule was extremely unbalanced temporally, with a very soft 7-game stretch to start. That was a legitimate thing to point out at the time, even though IU continued to surprise even when things got tougher, and cruised to the title in the end.

3) I haven't looked into this systematically, but the trend does seem pretty clear toward more extreme results recently. This is not a surprise, as the rise of the super-conference is relatively recent, and is still in progress. So although we haven't definitely seen it yet, we likely will see an unbalanced schedule decide a major-conference championship soon enough.

Friday, September 30, 2016

Kenpom esoterica

As you may be aware, I've spent the college basketball offseason upgrading and backfilling the T-Rank website. I've now got player stats back to 2009-10 and team stats back to 2008-09.

For quality control, I checked some of the results against the stats on Kenpom.com. From 2013 forward, they are basically identical. But for 2012 and earlier, there are small but systemic differences. For example, the raw points per game for each game (available on the T-Rank "results" page and the Kenpom "Game Plan" page for each team) are usually off by a point or two per 100 possessions.

Based on the nature of the discrepancies, I deduced the differences were likely caused by differences in calculated possessions. Although "possessions" is pretty much the foundation of tempo-free basketball statistics, it's not an officially kept stat, and has to be calculated from other box score stats. The basic formula that both T-Rank and Kenpom use is:

Field Goal Attempts + Turnovers + (Free Throw Attempts * .475) - Offensive Rebounds

The first thing I wanted to check was whether I had different underlying boxscore data than Kenpom was using. My box scores for those older games are from ESPN, and I spot checked them against official team boxscores to feel confident that they are correct. But I couldn't check what Kenpom was using, because he doesn't publish his box score data for games prior to 2013. I took this as a clue that his box score data for 2012 and earlier is somewhat lacking.

The main stats lacking in old box scores that can affect tempo-free statistics are team rebounds (particularly team offensive rebounds) and team turnovers. Sometimes you'll see box scores that show team totals which are just the sum of player totals, and therefore don't include team rebounds and team turnovers. Most games have a few team rebounds and a couple team turnovers, and if we don't have those stats the calculated possessions will be less accurate.

The ESPN boxscores I used do include team rebounds and team turnovers. Other old boxscores, like those available at basketball reference, do not. This gave me an opportunity to see if I could somehow calculate Kenpom's results using the incomplete boxscores.

And I did! What I figured out is that Kenpom's underlying boxscore data from that era apparently doesn't include team offensive rebounds or team defensive rebounds, but does include total rebounds that include team rebounds. So Kenpom knows how many team rebounds each team got, but doesn't know whether they are offensive or defensive team rebounds.

What he apparently did with this data was to assume half of the team rebounds were offensive and half were defensive. I am pretty much positive this is what he did, because doing this also solves another mystery, which is how Kenpom was calculating his rebounding percentages for these older games.

Just for fun, let's walk though an example picked relatively at random: Ohio State's 62-60 loss to Kentucky in the 2011 NCAA tournament. According to T-Rank, Ohio State's PPP that game was 101.6, and Kentucky's was 105.0, based on 59.05 calculated possessions per team:

Ohio St. 58 22 16 20 36 7 60 59.45 101.6
Kentucky 48 14 7 25 32 11 62 58.65 105.0
Avg: 59.05

Ohio State's offensive rebounding percentage (ORB / (ORB + opponents DRB)) was 39% and Kentucky's was 25.9%.

But if you look at Kenpom, it gives Ohio State a PPP of 99.5 and has Kentucky at 102.8 on 60 possessions. A fairly significant difference! But I can produce those numbers using the incomplete box score available at basketball reference (and at the old version of ESPN, which is secretly still accessible at "proxy.espn.com"). Here are the raw stats you can get there:

Ohio St. 58 22 10 20 36* 7
Kentucky 48 14 7 24 32* 11

I've put asterisks in the total rebound columns because that's not actually the data available on the incomplete boxscores I have found (which actually show 30 and 31, just the sum of the incomplete parts) but I'm assuming that Kenpom must have had access to that total rebound figure that included team rebounds, and I have reason to believe that data used to be available. The next step is to divide those "missing rebounds"—6 for Ohio State and 1 for Kentucky—equally into the offensive and defensive columns, yielding:

Ohio St. 58 22 13 23 36 7 60 62.45 99.5
Kentucky 48 14 7.5 24.5 32 11 62 58.15 102.8

Avg: 60.3

This exactly nails the Kenpom PPP for both teams by adding an extra 1.25 possessions per team, thanks to 2.5 fewer offensive rebounds. It also matches up with the rebounding percentages Kenpom has for this game: Ohio State at 34.7% (13 / (13 + 24.5)) and Kentucky at 24.6% (7.5 / (7.5 + 23). 

This game had an unusually large number of team rebounds, and all but one were offensive. As a result, the Kenpom possession estimation is quite a bit off (over a full possession from that calculated using complete data) and the rebounding percentages are even more skewed.

Moral of the story: always trust T-Rank.