Texas Longhorns Basketball: Inside the Numbers, Week 14

by Reggieball on Feb 15, 2012 5:00 AM CST

Is it possible that the Longhorns are turning the corner? The Texas basketball team has been playing really well for the last few weeks, but a difficult schedule and some bad breaks have prevented them from turning high quality play into wins. But that wasn't a problem this week. Texas played very well against both Kansas State and Oklahoma. Kansas State is a tough team, and while Oklahoma has been struggling lately, you should never take a road win for granted.

Why are things looking up? I think that we are starting to see the effects of this team gaining experience against tough competition. While the losses to Kansas, Missouri, and Baylor were tough, playing against a higher level of competition seemed to really help this team. Texas is now playing defense at a much higher level than they were at the start of the conference season, and the offense is better than it is given credit for. In fact, Texas has the #18 ranked offense in the country, based on the kenpom.com rankings. While Texas' offense has looked inconsistent at times, much of this is due to the high quality of defenses that this team has faced. Additionally, Texas really excels at getting offensive rebounds (#11 ranked team by kenpom.com) and getting to the free throw line (#15 ranked team). And unlike some recent Texas teams, this year's squad hits their free throws.

In this edition of Inside the Numbers, I will recap the week's games and talk about some troubling home vs. road splits on free throw attempts in Big 12 games.

The Week In Review

Background information on the statistics is posted here and here.

TEXAS vs KANSAS STATE

CATEGORY	TEXAS	K-STATE	DIFFERENCE
FGA	41	58	-17
FTA	48	12	36
FGA + 0.475 x FTA	63.8	63.7	0.1
Off Rebs	13	12	1
TOs	16	16	0
ORB - TO	-3	-4	1
TS%	0.588	0.502	0.085
ORB%	39%	35%
TO%	24%	24%
Points/100 poss	112	95

This was a great win for Texas. Both teams took the same number of shots (where shots refers to FGA+ 0.475xFTA), and Texas' advantage in true shooting percentage led to a win. Texas shot a staggering number of free throws in this game, with 48 attempts from the line. Kansas State only shot 12 free throws. I think it is instructive to look back at the previous matchup between these two teams, when Kansas State took 39 free throws and Texas took 21 free throws. There were a lot of fouls in both of these games.

So were Texas' 48 free throws simply the result of Big 12 home cooking from the officials? This was probably a factor, but Texas was also far more aggressive at attacking the basket than was Kansas State. In addition to shooting 1.17 free throws for every field goal attempt, Texas also took 34% of their field goal attempts at the rim. Kansas State shot only 0.21 free throws for every field goal attempt and only 17% of their field goal attempts were taken at the rim. Simply put, Texas was getting to the basket (and getting fouled) and Kansas State was not.

The Texas defense turned Kansas State into a jump shooting team. 47% of their field goal attempts were two point jump shots. Kansas State also lives on offensive rebounds, averaging just under 42% for offensive rebounding percentage on the season. Texas held their own on the defensive glass (usually a big weakness for the Longhorns), holding Kansas State to a 35% offensive rebounding percentage.

Alexis Wangmene had the game of his career. He was fantastic on offense, with 6.6 Points Above Median (PAM). He also did a great job on the defensive glass, pulling down 35% of the available defensive rebounds. I have picked on Wangmene's rebounding a lot, so it is only fair to point out just how good and important it was against Kansas State. Wangmene's work on the defensive glass was the key in neutralizing Kansas State's greatest strength, offensive rebounding. Wangmene came to play.

J'Covan Brown and Myck Kabongo both had good shooting games. Brown had a PAM of 5.0, and Kabongo had a PAM of 3.6. Unfortunately, both of these guys struggled protecting the ball a bit. Kabongo turned the ball over in 34% of the possessions that he "used." Brown's turnover rate was 18%, which actually isn't bad, but is well below his standard. On the season, Brown's turnover rate is a fantastic 11.3%. There weren't very many assists in this game, but you don't get assists on free throws.

Shane Southwell and Will Spradling did the most damage for Kansas State, with PAM totals of 3.2 and 2.4, respectively. In the previous Texas / Kansas State game, Rodney McGruder went off with a PAM of 9.5. In this game, he was held to a PAM of 1.4.

TEXAS vs OKLAHOMA

CATEGORY	TEXAS	OU	DIFFERENCE
FGA	52	58	-6
FTA	29	8	21
FGA + 0.475 x FTA	65.8	61.8	4
Off Rebs	14	13	1
TOs	11	15	-4
ORB - TO	3	-2	5
TS%	0.533	0.469	0.063
ORB%	41%	36%
TO%	18%	24%
Points/100 poss	110	91

There was a lot to like about this win for Texas. Texas had more shots than Oklahoma and was more efficient with their shots, which is generally a recipe for an easy win. Although they were ice cold from the floor, overall the Texas offense was OK. Texas had a true shooting percentage of 0.533, a decent turnover rate, and a high offensive rebounding rate. Texas made up for poor shooting from the field by going 24 of 29 from the free throw line. 29 free throws is a lot of shots from the line. And hitting 83% on that many free throws is excellent.

Texas' struggles from the field had a lot to do with missing jump shots. Texas only hit 28% of their two point jump shots (their season average is a 35% rate of hitting two point jump shots) and 26% of their three point jump shots (they normally hit about 33% from three point range). Clint Chapman really struggled offensively, missing all three of his attempts at the rim, and going 1 for 6 on two point jump shots. Chapman ended up with a PAM of -4.5. Ouch.

The Texas offense was a team effort. The most efficient scorers were Myck Kabongo (PAM=3.5), Sheldon McClellan (PAM=3.0), J'Covan Brown (PAM=2.8), and Jonathan Holmes (PAM=2.3). All four of these guys got to the line and made good use of their time there.

Defensively, this was another solid effort by the Longhorns. For Oklahoma, only Cameron Clark was particularly effective as a scorer, with a PAM of 3.4. Oklahoma generally struggles to get to the basket, attempting only 25% of their shots at the rim on the season. In this game, Texas completely shut off opportunities at the basket, as Oklahoma went 2 for 6 on shots at the rim (only 10% of their total field goal attempts were at the rim). 64% of the Sooner field goal attempts were two point jump shots. OU knocked down 46% of these two point jump shots, which kept the game closer than it otherwise would have been. Their season average is to hit 36% of two point jump shots -- 46% is a very high field goal percentage for two point jump shots.

Free throw attempts and home court advantage

I have wanted to write about this issue for a while, but I wanted to wait until Texas had won a home game with a big free throw shooting advantage. I didn't want this to look like sour grapes.

The free throw shooting disparities between home and road teams in the Big 12 are troubling. The series of games played between Texas and Kansas State serve as an interesting example. The game at Kansas State saw the Wildcats attempt 18 more free throws than Texas attempted. In Austin, the Longhorns attempted 36 more free throws than Kansas State.

While these two games may be an extreme example, there is a systematic effect that is easy to see. In the table below, I have compiled the ratio of free throw attempts to field goal attempts for each Big 12 team in home and road games during conference play. Only Texas Tech has gotten to the line more frequently during road games. Kansas and Oklahoma State only have seen modest increases in their free throw shooting rates at home. For all other teams, the differences are pretty large. On average, road teams attempt 0.33 free throws for every field goal, while home teams attempt 0.42 free throws for every field goal.

Team	road FTA/FGA	Home FTA/FGA
Texas	0.39	0.54
K-State	0.35	0.52
Iowa State	0.28	0.50
Missouri	0.35	0.41
Kansas	0.32	0.35
Baylor	0.30	0.40
Ok. St.	0.39	0.42
Oklahoma	0.25	0.39
A&M	0.27	0.35
Texas Tech	0.44	0.36
League total	0.33	0.42

This is a pretty large difference. Using a formula I have previously developed, I estimate that increasing your free throw rate from 0.33 to 0.42 should raise a typical team's points per 100 possessions total by about 1 or 2 points per 100 possessions. Now we should consider that the road team shows a similar reduction in offense, compared with when they play at home. If a typical game has around 67 possessions, then we estimate that the home team will enjoy roughly a 2 to 3 point advantage based off of extra free throws. This is a non-trivial effect when we consider that 20% of Big 12 conference games this season have been decided by 3 points or less.

This probably isn't all on the officials. But I am not naive enough to believe that it doesn't have anything to do with them.

0 recs | 11 comments

Comments

Fast work - you must have done the OU part overnight

As to the free throw disparity, I’d also like to see the numbers for favorite vs underdog. Big 12 refs may not be homers as much as toads who know they get less flack when the favorite wins.

Caradoc - February 15, 2012 reply

It doesn't take me that long to compile the numbers after the game

Tuesday night games are the worst, because they often times end at or shortly after 11 PM Eastern time, and I want the post ready first thing Wednesday morning. Last night’s game had an earlier start, as it wasn’t part of an ESPN double header.

Reggieball - February 15, 2012 reply

Big Improvement over last year

I’m I the only one blown away about the improvement in free throw shooting over last year? Tristan Thompson couldn’t buy a free throw. Opponents used it against us successfully more than once.

Rosey Bowl - February 15, 2012 reply

Last year's free throw problem...

was 100% due to Tristan Thompson. Tristan was 127 of 261 (49%) from the line. The rest of the team was 465 of 644 (72%) from the line. Tristan took 29% of the free throws for last year’s team. This was because teams knew that they could afford to foul him, and his style of play (getting the ball inside or off of rebounds) lends itself to drawing fouls.

This season in the NBA, Tristan is still hitting only 45% of his free throws.

Reggieball - February 15, 2012 reply

Wangmene played like a man against KState.

The offense might be an aberration, but there’s no reason he can’t do everything else like that every night. It was a force of will.

In at least the last 2 games (all I’ve been able to watch recently), the entire team has been playing with an incredible energy. There’s part of me that thinks this team can make a nice run in the tourney.

bevosbackside - February 15, 2012 reply

that's skrong work

i’m wondering if you got the PAM formula from anywhere in particular?

elcapitan009 - February 15, 2012 reply

Where does PAM come from...

Well, I kind of cooked it up, although I imagine many others have done similar things. In a way it is like the sort of “linear weight” methods used by some of the baseball analysts, and that are discussed in some detail in Dean Oliver’s book, Basketball on Paper, except that it only looks at scoring. (By the way, if you are interested in really understanding the statistical analysis of basketball, Oliver’s book is where you should start.)

With PAM, what I was trying to address was how much does a player help or hurt his team’s true shooting percentage, relative to some baseline level. An NCAA median team gets a true shooting percentage of about 0.48 on shots taken from the floor (also the eFG% for these teams). PAM basically asks how many more points did a player score than this median level, given the number of shots that they took.

Anyway, it is not rocket science.

It is probably most heavily influenced by some of the baseball stuff, where people try to figure out value over replacement or value over average level. I have placed this baseline level at a pretty arbitrary point (0.48 might be a little bit too low, but it seems good enough for me).

Best part about PAM is that you can closely approximate it as

points – FGA – 0.5xFTA

which means that you can judge it pretty closely by just glancing at a box score.

Reggieball - February 15, 2012 reply

IIRC, the book Scorecasting...

…says that the home court advantage is based almost totally on the refs.

BobInHouston - February 15, 2012 reply

Great book...

…here’s an excerpt from a review about Scorecasting that talks about this phenomenon.

It’s not the crowd, it’s not the travel, it’s not the stadiums, it’s not the players or the managers. So what’s left? Well, there are always the referees (or umpires as they are known in most American sports). And that’s who it is – Moskowitz and Wertheim say home advantage is almost entirely down to the officials. Players aren’t put off by the barracking of the home fans, but the umpires are. It makes sense when you think about it – if tens of thousands of semi-hysterical people were scrutinising your performance, you’d want to try to please them if you could, if only subconsciously. The away players have nothing to gain from the home fans – if they do well they’ll get abuse, if they do badly they’ll get mockery. But the officials can make the home crowd happy and then surreptitiously bask in the warm glow. Away players can’t alleviate the pressure of being in a hostile environment. Referees can.

Moskowitz and Wertheim find plenty of evidence to back this up. In football, it turns out that referees consistently award more injury time when home teams are losing, and less when they are winning (on average, four minutes in the first case and two minutes in the second, enough to make a difference in plenty of matches). Home teams get far fewer players sent off, and receive many more free-kicks. Maybe this is down to the fact that the home side simply plays better and the away players are reduced to desperate measures. But Moskowitz and Wertheim find evidence that crowd effects make a real difference. In the German Bundesliga, for instance, where many of the teams used to play in stadiums incorporating running tracks, putting the crowd much further away from the action, the bias referees normally show to the home side was cut in half. In the British, Spanish and Italian leagues, attendance also has a marked effect on the number of red cards shown to the visitors. The bigger the crowd, the more likely the away team are to end up with fewer players on the pitch at the end.

However, the most compelling evidence for referee bias comes from those sports that have introduced technology to check on the decision-making of the officials. In baseball, a system called QuesTec (similar to Hawk-Eye in cricket and tennis) now shows whether a pitch was in the strike zone or not (the area over the home plate between a batter’s armpits and his knees). Moskowitz and Wertheim have looked at a mass of data and discovered that when a pitch is clearly a strike, baseball umpires do not advantage the home hitters. Equally, when a pitch is way outside the strike zone, they call it against the pitcher. But when it’s on the edges, the home team were getting a large percentage of favourable calls. This shows two things. First, given the choice, umpires prefer to please the locals who are breathing down their necks (in many baseball stadiums almost literally). Second, they know what they are doing – they restrict their bias to areas where it won’t be so obvious (in stadiums that have installed QuesTec umpires have started to eliminate their home bias, now that they realise it’s there for all to see). Moskowitz and Wertheim find the same thing in ice hockey and American football, where the introduction of instant replay reviews showed that for close calls, and in tight games, the officials tend to favour the home team by a significant margin (calls against the away side are more likely to be corrected when impartial technology is called in evidence). Tight games are by definition the ones that can turn on one or two key decisions. And it appears that tight games are also the ones in which the officials go out of their way to help the home team. That’s enough for Wertheim and Moskowitz to finger them as almost entirely responsible for the phenomenon of home advantage.

jc25 - February 15, 2012 reply

I haven't read this book yet...

but I do remember coming across this. I might do more investigation on this topic, but I really want to read what is out there first before I reinvent the wheel.

Reggieball - February 15, 2012 reply

They are looking so much better in the last few games.

Before they were so herky jerky and now they just seem to FLOW better.

iamjackburton - February 15, 2012 reply

Burnt Orange Nation