Over the last seven days, the Texas Longhorns have only played one game. They played pretty well against Temple, particularly in the second half. It is good to escape exam week with a win, as funny things can happen this time of year. Tonight, Texas faces their toughest opponent of the season in the North Carolina Tarheels. I have already pretty exhaustively previewed this game, so I won't have much to say about it here.
Texas has now played about one month of basketball. It has been a fun month, as we have already witnessed a lot of evolution and growth in the Longhorns. The season started off with the J'Covan Brown show. But now, there are a number of different players on this team who can step up and win a game for Texas on a given night. This group of freshman that Rick Barnes has brought in is a lot of fun to watch. It may not be his most highly regarded recruiting class, but it is a very deep and good group of players. Nearly all of the freshman are making significant contributions already in only their first month of college ball.
This week, I review the game against Temple, introduce a simple little calculation I do to blend scoring efficiency and scoring volume, and compare the AP poll results with the Simple Rating System and the kenpom.com ratings.
The Week In Review
Background information on the statistics is posted here and here.
TEXAS vs TEMPLE
|
CATEGORY |
TEXAS |
TEMPLE |
DIFFERENCE |
|
FGA |
56 |
51 |
5 |
|
FTA |
35 |
20 |
15 |
|
FGA + 0.475 |
72.6 |
60.5 |
12.1 |
|
Off Rebs |
17 |
3 |
14 |
|
TOs |
15 |
11 |
4 |
|
ORB - TO |
2 |
-8 |
10 |
|
TS% |
0.530 |
0.537 |
-0.007 |
|
ORB% |
46% |
9% |
|
|
TO% |
21% |
16% |
|
|
Points/100 |
109 |
95 |
|
After a rough start, the Longhorns cruised to an easy victory on the strength of superior rebounding. True shooting percentage was about the same for both teams. Texas took 12 extra shots, and won by 12 points. I have been detailing Texas' struggles on the defensive glass in this space all season. Over the last four games, Texas has done pretty nice work on the glass, and against Temple they absolutely dominated, pulling down 46% of the available offensive rebounds and 91% of the available defensive rebounds. Alexis Wangmene and Jonathan Holmes both were in some foul trouble, so Clint Chapman and Jaylen Bond played a significant fraction of the game. Chapman had a defensive rebounding percentage of 39%. Bond had a defensive rebounding percentage of 21%. Sheldon McClellan also chipped in, with a defensive rebounding percentage of 26%. Bond and Chapman also did well on the offensive boards, with offensive rebounding percentages of 20% and 16%, respectively.
I will talk a little more about the Temple game in the next section.
Points above median
I do a little calculation after every game to help quickly identify which players are doing the most damage with their scoring. It helps me to identify which players are giving their team both efficiency (as measured by true shooting percentage) and volume (as measured by the number of shots they take). The theory behind this calculation is simple. Let's say a player takes a ton of shots, but misses a bunch of them. Players like this will often rack up a decent point total, but will use a lot of shots to do it. These players provide scoring volume (something a team needs), but not much efficiency. There are other players that will take very few shots, but do so with great efficiency. These players help a team, but the impact of their low volume scoring can only be so great. In a good scoring game, a player will combine both volume and efficiency.
So how do we account for this? In principle, we can do this by taking the number of points a player has scored, and subtracting what a "typical" player might score with the same number of shots. Not too complex, but the problem here is in how we select a typical player. If you are a fan of advanced baseball statistics, then you are probably familiar with the concept of the "replacement player." Statistics like wins above replacement attempt to measure the contribution of a player against what a kind of crappy fringe player might do. This is all well and good, but I don't know that I want to expend a lot of effort determining what a replacement player looks like in NCAA basketball. The whole replacement player theory doesn't really apply in college sports; teams have vastly different levels of bench talent. A replacement player for North Carolina is probably better than most of the starters in the Southland conference. And there isn't exactly a large pool of replacement players sitting in AAA or on waivers.
So instead of that, I have somewhat arbitrarily selected a simple way to come up with a baseline for my little scoring calculation. I use as a baseline the approximate median level of points a team in the NCAA scores on shots from the floor. The NCAA median for team effective field goal percentage is generally about 0.48. So the points above median (PAM) calculation I do for each player after each game is
PAM = points - 2 x 0.48 x (FGA + 0.475 x FTA)
It is a simple thing to calculate. I won't claim it is the be all and end all of basketball statistics (it is far, far from that), but it is easy and simple to use. In fact, you can basically calculate it to a very good approximation in your head by simply looking at the box score. PAM is approximately
points - (FGA +FTA/2)
We can use PAM to highlight the players whose scoring did the most to raise a team's total over this baseline median level. In the game against Temple, Jaylen Bond led all Texas players with a PAM of 5.6. Myck Kabongo had a PAM of 2.0, J'Covan Brown had a PAM of 1.9, and Jonathan Holmes had a PAM of 1.4. For Temple, only Khalif Wyatt hurt Texas significantly. Wyatt's PAM was 5.6. Rahlir Hollis-Jefferson had a PAM of 2.2.
I thought it might be fun to look at PAM for Texas through the first 11 games. As an added wrinkle, I have also calculated each player's PAM on unassisted shots, using play-by-play data. This I call the uPAM. Obviously, everyone will look much worse than normal if we take out all of the assisted shots from the calculation. By definition, assists are only credited on made baskets. Another flaw with uPAM is that free throws are often created by a great pass, but they aren't scored as being assisted. Still, looking at uPAM tells us a little bit about who is doing a good job of creating their own shots with high efficiency. A high uPAM number tells us that a player is pretty good at creating his own shot, but a low number doesn't necessarily mean the player is "bad." I will use the example of Julien Lewis (below) to hopefully better explain this.
| Player | PAM | uPAM | PAM/100 minutes | uPAM/100 minutes |
| J'Covan Brown | 31.1 | -17.2 | 8.4 | -4.7 |
| Sheldon McClellan | 29.0 | -6.9 | 10.6 | -2.5 |
| Jonathan Holmes | 23.1 | 2.5 | 9.7 | 1.0 |
| Myck Kabongo | 17.0 | 0.6 | 5.4 | 0.2 |
| Jaylen Bond | 15.0 | 3.6 | 8.9 | 2.1 |
| Alexis Wangmene | 14.9 | -4.9 | 5.6 | -1.9 |
| Sterling Gibbs | 9.4 | 2.2 | 8.8 | 2.1 |
| Clint Chapman | 7.3 | -8.3 | 3.5 | -4.0 |
| Julien Lewis | 0.4 | -41.5 | 0.2 | -15.9 |
| Dean Melchionni | -1.9 | -1.9 | -21.3 | -21.3 |
| Andrew Dick | -2.8 | -2.8 | -31.5 | -31.5 |
I wouldn't read too much into J'Covan Brown's low uPAM. He is being asked to do a lot offensively, and sometimes he ends up taking difficult shots when no one else is open. Additionally, as the second leading assist man on the Texas team, he deserves some of the credit for the gap between many of the other player's PAM and uPAM numbers. If there is a point to be made from Brown's low uPAM number it is this -- very few players can score a lot of points efficiently without help from their teammates.
Sheldon McClellan has emerged as the second leading scorer on the Texas team. He also has the second highest PAM on the team. His somewhat negative uPAM number also reflects that he gets a fair bit of help from his teammates when creating efficient offense.
Jonathan Holmes is a very gifted offensive player. His positive uPAM number is helped by the fact that he is hitting more than 50% of his two point jump shots. Two point jump shots tend to not be assisted as frequently as most other types of shots.
Julien Lewis' low uPAM total highlights an interesting quirk of the uPAM calculation. Lewis is mostly a catch and shoot player. An extremely high percentage of his points are assisted. So every time he catches the ball and shoots a three, he either makes it, which does nothing to his uPAM, or misses it, which lowers his uPAM. Because such a high percentage of Lewis' points come on three point shots, his PAM is very volatile from game to game. If I would have run these numbers prior to the Nicholls State game, Lewis would have looked much better.
This is as good a time as any to point out how Sterling Gibbs and Jaylen Bond have been playing lately. Both have solid PAM and uPAM numbers. These two have been getting a few more minutes lately, and have been playing reasonably well. Gibbs has struggled a bit with turnovers, but he can shoot the ball. Shooting runs in his family. Bond is the type of hard working player that I predict will become a fan favorite during his career at Texas. He is sort of like a more talented and athletic version of Sonny Alvarado. The fans loved Sonny because he would do just about anything to get to a rebound. Bond has that same kind of feel. He just has a knack for getting position and finding the ball when it comes off the glass. His offensive rebounding is a big factor in his good uPAM total.
Checking in on the AP poll
We are now about one month into the college basketball season. I thought it might be fun to take Monday's AP poll results and compare them with the SRS rankings and the kenpom.com rankings. These sorts of exercises can be fun, because the AP poll does a pretty good job of telling us which teams are getting attention for their play. In other words, the AP poll probably measures the national perception of teams pretty well. SRS and kenpom don't measure perception at all. They are both pretty straightforward analytical ranking systems that tend to do a pretty good job of predicting game outcomes. I particularly like SRS, because it is easy to make sense of and it has a fairly long track record of more or less agreeing with NCAA tournament outcomes.
I don't want to make a big deal about slight differences in rankings, because there isn't a particularly meaningful difference between being ranked #1 or #4, or much of a difference between #10 and #15. I am interested in large differences. Just so I am making a fair comparison, I have used the SRS and kenpom results from Monday December 19, the same day that the AP poll issued.
Before I highlight a few team of interest, I want to say that all in all, the AP poll results are similar in many ways to the numerical systems.
AP Top 25 teams that aren't ranked highly in SRS or kenpom. Let's start with these teams. I hate making the "overrated" argument, and I hope you don't take what I have written here as that sort of thing. I like to let things play out, and I am more interested in pointing out differences between the AP poll and these other systems. Connecticut is ranked #8 in the AP poll, but #23 by SRS and #15 by kenpom. The difference between the AP result and the kenpom result isn't particularly large, but SRS puts 22 teams better than UConn. This result makes some sense to me. Connecticut has a nice record, but only has a win against one team in the SRS top 50, in their overtime win against Florida State.
My neighbors probably don't want to hear it, but Xavier (AP #14, SRS #38, kenpom #32) wasn't really ranked all that high in SRS even before they lost to Oral Roberts. Now, that loss was sort of a fluke, given all the players suspended. I was planning on attending the Oral Roberts game with my father, but thankfully hadn't yet bought tickets prior to the fight with UC. We will be catching a game in January, and hopefully Tu Holloway can avoid further suspensions. I really want my dad to see him play in person. Holloway plays all out on both ends of the court. He has really fast hands (he eats crossover dribbles up), and is probably amazing at Hungry Hungry Hippos.
Pittsburgh (AP #15, SRS #48, kenpom #33) just barely makes the top 50 in SRS. Texas fans can thank Jamie Dixon for the scholarship shenanigans that landed Jaylen Bond at Texas. Of course, Khem Birch heading back to Toronto couldn't have been a part of the plan. The AP poll ranks Pittsburgh highly right now because they are 10-1 and play in the Big East.
Mississippi State (AP #18, SRS #41, kenpom #52) makes sense on this list. The AP voters looked at their wins against Texas A&M and Arizona as important ones. The thing is, the numerical rating systems don't have these teams very high. Mississippi State's best win now looks to be the one against West Virginia. They are also challenging Texas for the pre-conference Sunbelt championship. The loss to the Zips is a pretty bad one.
Michigan (AP #20, SRS #47, kenpom #41) has their best win against Memphis, a team that was ranked high early in the season when Michigan played them but has not looked all that good since then (SRS likes them better than I do, at #27). Michigan played sort of close with Duke, and lost to a Virginia team that I think is on the rise (I love how Virginia defends). I don't know what to make of Michigan. We will see how they fare in the rough Big 10.
After Illinois (AP #25, SRS #49, kenpom #48) narrowly defeated Cornell Monday night, Basketball Prospectus writer John Gasaway tweeted, "Illinois is the most overrated team in the country." (See. I didn't call them overrated. Someone else did.) Illinois is probably in for a rude awakening come conference season time; there are currently six Big 10 teams with higher SRS rankings.
Teams that the numerical systems rate much higher than the AP poll. This is a more fun list. The two teams I have picked out are teams that highlight the role that margin of victory plays in the numerical rating systems. And still, they are pretty credible, because they have played pretty close to some really tough teams. At the very top of this list is Wisconsin (AP #13, SRS #1, kenpom #1). Wisconsin has two losses, so I understand why the AP poll doesn't have them higher. But those two losses are fairly close ones to North Carolina and Marquette, two teams that are in the top ten in both of the numerical systems. Wisconsin has absolutely crushed everyone else they have played. I don't know if they are really the best team in the country, but with the margin of victory that they typically get I understand why SRS and kenpom rate them so highly.
Stanford (AP unranked, SRS #13, kenpom #22) is unranked in the AP for some reason. Their lone loss was a fairly close one with Syracuse. Other than that, they have been beating up on a relatively weak schedule. I imagine they will work their way into the AP top 25 in the coming weeks, as they are high on the unranked but receiving votes list.
Merry Christmas
I hope to have a short post up next Wednesday recapping the North Carolina game.