Efficiency Preview: Big 12 Tournament

The Big 12 tourney starts in a couple days. Here's the schedule if you haven't already seen it. Now let's get right to the numbers. First, here's how all the teams have been playing over the last 10 games:

Last 10 Games Adjusted Efficiency Averages
Team Seed Tempo Rnk Offense Rnk Defense Rnk Pythag Rnk
Kansas 1 69.8 3 126.6 3 80.7 1 0.9944 1
TexasA&M 2 65.1 7 126.8 2 90.3 2 0.9803 2
Texas 3 67.1 5 128.9 1 94 5 0.9742 3
KansasSt 4 64.6 8 119.1 4 97.8 7 0.9059 4
TexasTech 5 64.3 9 113.5 7 99.7 9 0.8163 8
Missouri 6 69.8 2 117.4 6 98.6 8 0.8811 6
OklahomaSt 7 65.8 6 108.9 9 101.9 10 0.6834 10
IowaSt 8 62.7 10 97.8 12 93 4 0.642 11
Oklahoma 9 61.4 11 109.5 8 90.9 3 0.8947 5
Nebraska 10 61 12 104.4 11 94.6 6 0.7552 9
Baylor 11 67.2 4 118.6 5 103.4 12 0.8291 7
Colorado 12 71.1 1 107.1 10 102.7 11 0.6195 12

stats glossary

Hey, Colorado's 1st in something? What the... oh, tempo. OK. Looking at the Pythagorean rating, the top 3 teams here are no surprise. What might surprise you is that if I made this same chart for ALL of Division I NCAA basketball, those three would be ranked 1st, 5th, and 8th. Texas has officially joined the party. Even more surprising is that all three are top 5 in offense. I haven't actually run the numbers on ALL teams, so the ranks in this next table are where they rate among the 78 that I have looked at. Any team with an outside shot at a 12-seed or better in the big dance was included, along with all Big 12 teams.

Last 10 Games Rank Among All Teams Within Shouting Distance of Getting an At-Large Bid to the NCAA Tournament
Team Tempo Offense Defense Pythag
Kansas 7 5 2 1
TexasAM 36 3 20 5
Texas 18 1 41 8

I'd be shocked if somebody outside that group cuts down the nets in Oklahoma City. That said, if it happens, it's bound to be a team out of the KU side of the bracket. Any dark horse from the other side will have to take down KU, A&M, and Texas. Sorry, but nobody's winning a tournament by beating all three top-10 rated Big 12 powers in consecutive games. That's impossible.

I'm talking a lot about the top seeds here because I'm not going to have time to do individual game previews of the later rounds. Don't worry, we'll get to everyone else right after this page break.

#8 Iowa State vs. #9 Oklahoma



  • Previous meeting: Oklahoma 51 @ Iowa St 58
  • Pomeroy prediction: Oklahoma 62 Iowa St 52
  • Last 10 prediction: Oklahoma 58 Iowa St 50
  • Trendline prediction: Oklahoma 56 Iowa St 50

Since these two teams last met, Oklahoma hasn't won a game. Slumping? Not particularly, actually, just a really tough stretch (vs Texas A&M, @ Missouri, vs Texas, vs Kansas, @ Kansas St). I think this game will be a relief for Oklahoma, and they should take advantage of the weaker competition.

#5 Texas Tech vs. #12 Colorado



  • Previous meeting: Colorado 74 @ Texas Tech 95
  • Pomeroy prediction: Texas Tech 82 Colorado 68
  • Last 10 prediction: Texas Tech 78 Colorado 72
  • Trendline prediction: Texas Tech 78 Colorado 74

Colorado started out the year disgustingly bad, but they've improved recently to level of "below average" (for a major conference). Maybe that's true, and maybe Texas Tech is maddeningly inconsistent this year. The recent play says this is gonna be a close game. Still, I don't see a Bob Knight-coached team bowing out in the first round of a conference tourney to the worst team in the conference. (Cue someone leaving a comment informing me that this has happened in the recent past.). I say the General shows up and hunts him some Bison. With a chair.

#7 Oklahoma St. vs. #10 Nebraska



  • Previous meeting: Oklahoma St 73 @ Nebraska 85
  • Pomeroy prediction: Oklahoma St 68 Nebraska 64
  • Last 10 prediction: Nebraska 62 Oklahoma St 62 ... tossup
  • Trendline prediction: Oklahoma St 69 Nebraska 68

Couldn't get enough of Monday's OSU-Nebraska game? You're in luck - they get to play again 3 days later. The Sooners are 16-3 in games played in the state of Oklahoma, and the last game they played in OKC was a 2-OT win over Pitt. I know all the predictions say this will come down to the wire, and they're probably right. But I'm looking for the crowd to get behind O State at the end and help them pull out the win.

#6 Missouri vs. #11 Baylor



  • Previous meeting: Baylor 71 @ Missouri 78
  • Pomeroy prediction: Missouri 82 Baylor 75
  • Last 10 prediction: Missouri 84 Baylor 80
  • Trendline prediction: Baylor 91 Missouri 85

Here's the first round match up for those of you who enjoy watching the NBA All-Star game. No, not the "star-filled and skillful" part. The "fast pace and no defense" part. Here's my angle. Neither team defends the 3-pt line well. On offense, Baylor shoots a lot of them (3's) but makes them at an average rate, while Missouri shoots them well but doesn't shoot that many of them. I think they'll both shoot quite a few in this game, and whoever shoots them better walks away the winner. But I have no idea what I'm talking about.

And The Other Graphs

(Those Lazy Teams That Don't Play Thursday)





I feel bad, I haven't really said anything about Kansas State. There, now I have.

Pomeroy Big 12 Tournament Percentages

  2nd Round Semis    Finals    Champs  
Kansas 100.0% 86.1% 77.8% 50.5%
Texas A&M 100.0% 89.6% 68.1% 35.3%
Texas 100.0% 75.7% 24.8% 8.0%
Oklahoma 87.4% 13.5% 8.6% 2.4%
Kansas St. 100.0% 58.8% 8.5% 1.7%
Texas Tech 89.7% 40.3% 5.0% 0.9%
Missouri 74.6% 21.3% 3.8% 0.7%
Oklahoma St. 69.5% 8.6% 2.8% 0.5%
Iowa St 12.6% 0.3% 0.1% 0.0%
Colorado 10.3% 0.9% 0.0% 0.0%
Nebraska 30.5% 1.8% 0.3% 0.0%
Baylor 25.4% 3.0% 0.2% 0.0%


These were done, as always, with data from www.kenpom.com.  Each column is the team's chance of advancing to that round.  They take into account the fact that these games are being played at the Ford Center in Oklahoma City- all Ok. St.'s games are considered "semi-home".

A couple specifics of interest- Kansas beats Texas A&M 57.5% of the time, and beats Texas 77.9% of the time.  They beat Colorado 98.9% of the time by an average of 29.2 points (although that may or may not be of interest).

Efficiency Preview: Texas at Kansas

Click here for the preview that actually gives you some ideas about how this game's going to be played. Read on for the one with pretty graphs and hand waving. Format is the same as last time, so you can skip the next paragraph unless you need a refresher. (And for reference, here is the original post that kind of explains what I'm doing). After the break, for both teams I've included a graph that charts the offensive and defensive ratings for each game of the season. Keep in mind that for the defensive rating, lower is better. For both offense and defense, I've included a trendline showing roughly how each unit has progressed over the year. The dotted line shows the national average efficiency. I've also included the average ratings for their last ten games, to give a snapshot of how the team is playing right now. To give these numbers some context, I show where this would rank in the full-season stats, and what team's full-season rating is the closest.


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Offense 126.4 1 Georgetown
Defense 95.0 69 Fordham
Pythag .9640 13 Michigan St

I was going to lead off with a comment about how great the Texas offense played against Texas A&M, and that it was the best any offense had done against the Aggies since _______. But _______ turns out to be Baylor, just 4 days earlier, so apparently the A&M D wasn't quite at top form heading into the game. Still, look at that graph. 7 of the past 9 games Texas has had a rating over 125 (better than the year-long efficiency of the #1-rated Hoyas). And they've put some great numbers up against very good defenses. They've played 8 games against Pomeroy's top 30 defenses, and reached 1.09 PPP in 5 of them:

Opponent Adj Def Eff* Texas Game Diff
Michigan St. 85.8 95.5 9.7
Louisiana St. 90.1 98.7 8.6
Arkansas 90.5 120.4 29.9
Oklahoma 88.1 124.9 36.8
Villanova 87.4 95.4 8
Texas A&M 84.0 109.2 25.2
Oklahoma 85.3 112.5 27.2
Texas A&M 86.8 118.5 31.7
AVERAGE 87.7 109.4 22.1
Kansas 79.0 ??? ???

*adjusted for site of game

OK, so you already knew Texas had a good offense. But did you know their defense has been playing a little better recently? Now, "better" is relative - it's not like they've all of a sudden turned into last year's team. But prior to Wednesday, they'd held 5 straight opponents to an eFG% under 50, and forced 5 straight to turn the ball over at an 18% rate (their first and second such streaks this season, respectively). I expect the second streak to continue. Whether the first does will probably determine whether this is a close game or an easy win for Kansas.


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Offense 125.3 1 Georgetown
Defense 80.6 2 Kansas
Pythag .9938 1 N Carolina

Scraping out a narrow victory in Norman on senior night against a strong defensive OU team hasn't altered the graph much. The offense is still trending upwards, though not as strongly as before, and the defense is still trending down at the same rate. Take KU's worst offensive game in their last 11, pair it with their worst defensive game in the last 6, and they still should be expected to beat Texas, though it would be close.

One slightly worrisome fact for KU fans is that the team's offensive performance is significantly correlated to their defensive performance, specifically opponent eFG% and OR% (see the bottom of this page). No other KU opponent this year has ranked as high in both eFG% and OR% as Texas. So not only might the Texas offense be a tough test for the Kansas defense, it might also be a tough test for the Kansas offense.

Just a note - the comparison of the offense to Georgetown (for both these teams) is a bit misleading. Sure, they're playing at a level equivalent to GTown's season stats, but the Hoyas are playing even better than that recently. I'd like to get a database up and running that can automatically calculate last-10 ratings for all the teams. But I'd like a free burrito about now, too.


As Jacob pointed out, this is a matchup of two of the hottest teams in the country. If both teams are at the top of their game, there will be 3 elite units on the floor - the exception being the Texas defense. But if Texas can shoot and rebound well, the lack of transition opportunities will keep the Kansas offense from performing at it's best, and Texas will be able to stay in the game. I'm gonna say this ends up being Texas's "A" game vs. KU's "A-" game. That should be enough for the Hawks to pull out a single digit home win.

  • My subjective pick ... +7 ... KU 81 UT 74
  • Vegas ... +9.5 ... KU 79 UT 69

Efficiency based:

  • Pomeroy... +13 ... KU 79 UT 67
  • Last 10 ... +16 ... KU 86 UT 70
  • Trendlines ... +12 ... KU 81 UT 69
  • Streaks ... +14 ... KU 81 UT 67

Power ratings:

Stats Glossary

Here's a glossary of terms thrown around on Phog Blog: (Estimated) Team Possessions = FGA+(.44*FTA)+TO-OR


Effective Field Goal Perentage (eFG%) = (FGM+(0.5*3PTM))/FGA

Free Throw Rate (FT Rate) = (FTM*100)/FGA

Note: For teams, their opponents' FT Rate is calculated as (FTA*100)/FGA on the assumption that, over time, you have minimal control over how well your opponents shoot free throws.

Turnover Percentage (TO%) = TO/Possessions

Offensive Rebounding Percentage (OR%) = OR/(teamOR+oppDR)

Note: individual offensive rebounding percentage = player's OR/((teamOR+oppDR)*(player's MIN/(teamMIN/5)))


Defensive Rebounding Percentage (DR%) = DR/(teamDR+oppOR)

Note: individual defensive rebounding percentage = player's DR/((teamDR+oppOR)*(player's MIN/(teamMIN/5)))


PPWS (Points Per Weighted Shot) = PTS/(FGA+(.44*FTA)) Pts/100 = Points per 100 individual possessions A/100 = Assists per 100 individual possessions TO/100 = Turnovers per 100 individual possessions S/100 = Steals per 100 individual possessions BS/100 = Blocked Shots per 100 individual possessions

Note: individual possessions = (Min/(teamMin/5))*teamPossessions


Offensive Efficiency (OE) = the number of points a team scores per 100 possessions Defensive Efficiency (DE) = the number of points a team allows per 100 possessions

Efficiency ratings come in several flavors. If one of these words is used to describe an efficiency rating, it means the following:

Raw (OE or DE)= tells you what actually happened, without adjusting for the opponent or location Adjusted = adjusted based on opponent rating and location in order to indicate how many points a team would score or allow per 100 possessions against an exactly average opponent. There are two types of adjustments.

  • Season (AOE or ADE) = Explained in depth here. Essentially, all the ratings for all the teams are adjusted so that predictions based on the ratings best match the actual results.
  • Single game (AGOE or AGDE) = Takes the opponent ratings, location, and score from a single game and tells you the ratings of a hypothetical team that would have gotten the same result.

Pythagorean Rating (Pyth) = a rating that supposedly tells what a team's winning percentage would be over time against an average schedule. As always, a longer explanation is on Pomeroy's site. The formula is:

AOE^11.5 / (AOE^11.5 + ADE^11.5) ... AGOE and AGDE can be used in place of AOE and ADE


Predictions based on efficiency ratings all use the same formula, listed below. More explanation can be found here. The only difference between the various predictions is in what values are used for the offensive and defensive efficiencies.

[TeamA predicted offensive efficiency] = [TeamA offensive efficiency] x [TeamB defensive efficiency] / [National average efficiency]

Home field advantage is accounted for by multiplying the home offense and the visiting defense by 1.014, and dividing the home defense and the visiting offense by the same.

"Last 10" = uses the average of the two teams' last 10 Adjusted Game Efficieny ratings Trendline = A 2nd-order polynomial trendline is fit to the season-long graph of individual AGOE and AGDE ratings. The trendline's value as of the latest game is used in the equation. Streaks = This tries to take a middle ground between two "streak" predicitons. The last N AGOE and AGDE ratings are averaged, where N is between 5 and 10, with the value of N selected to maximize the home team's predicted margin of victory. Then the reverse is done, but N is selected to maximize the visiting team's margin of victory. The two predictions are then averaged.Recommended further reading for those interested is available from the invaluable Ken Pomeroy (some of these are the same links from above):

If any of the above is unclear or incomplete, please ask about it in the comments and we'll be glad to try and do better.

Efficiency Preview: Kansas at Oklahoma

Welcome visitors from Sports Illustrated. If you like what you see, please add Phog Blog to your favorites and tell your friends. I posted an efficiency laden preview of the Ohio St vs. Wisconsin game over at yocohoops. I'm going to do the same thing here for the KU-OU game, but with less explanation of the numbers, since you PB readers have had a couple posts to get used to them. For reference, here is the original post that explains what I'm doing. There's not going to be a lot of analysis, just numbers and graphs. Sorry about that, but I feel Hoopinion and Chalmersfan do a much better job of that than I do.

After the break, for both teams I've included a graph that charts the offensive and defensive ratings for each game of the season. Keep in mind that for the defensive rating, lower is better. For both offense and defense, I've included a trendline showing roughly how each unit has progressed over the year. Also, the dotted line shows the national average efficiency.

I've also included the average ratings for their last ten games, to give a snapshot of how the team is playing right now. To give these numbers some context, I show where this would rank in the full-season stats, and what team's full-season rating is the closest.


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Offense 126.2 1 Georgetown
Defense 79.1 1 Kansas
Pythag .9952 1 nobody really

Obviously this graph looks really nice. Offense is trending upwards, and 4 of the best 5 games have come in the last 6 games. Defense is trending downwards, and 2 of the best 4 have come in the last 4. Really not much to say here that you wouldn't have figured out from using your eyes or traditional stats. The team has playing at a very high level against inferior competition.


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Offense 111.4 61 Marquette
Defense 92.6 49 Missouri
Pythag .8941 52 Kansas St.

Oklahoma's offense has improved over the season, but leveled off recently. Their bad games are around average, and their good games are 25% above average, instead of their bad games being 10% below average and their good games being 15% above average. Still some inconsistency. But the improvement in their offense is canceled out by their decline in defense. They haven't had a very good defensive game in their last seven. Their last 10 games, they've played like a top 50 team, but not won like a top 50 team, which has been the story all year. According to Pomeroy, they're the unluckiest team in the country. Still, they should at least be better competition than Iowa State.


Efficiency based:

  • Pomeroy... +6 ... KU 65 OU 59
  • Last 10 ... +18 ... KU 77 OU 59
  • Trendlines ... +24 ... KU 80 OU 56
  • Streaks ... +18 ... KU 76 OU 58

Power ratings:


  • Vegas ... +7 ... KU 68 OU 61
  • My subjective pick ... +12 ... KU 70 OU 58

The efficiency data seems to suggest this won't be as close as all the power ratings and Vegas suggest, but I haven't been tracking these predictions for long, so who knows how accurate it will be. I'm going with a balance between the efficiency and power ratings, and saying it's a 12-point KU win.

Efficiency Snapshots

Recently I posted some game-by-game adjusted efficiency ratings for Kansas, derived from Ken Pomeroy's Game Plan and season efficiency ratings. The Hawks' numbers looked good, but Jeremy asked for some context on how the numbers were changing as the season progressed, and how this compared to other top teams. So I ran the game-by-game numbers for Pomeroy's top 11 teams. (Why top 11? I'll explain Michigan State's case later on.) Just showing you a mess o' single game numbers doesn't do a whole lot of good - there's a lot of game to game variation. To smooth that noise out and get a better idea of a team's general trend, we can look at a moving 10-game snapshot. Graphs after the jump...

About the graphs: Each team's line starts with its 10th game and continues through its most recent (as of Wednesday afternoon, so OSU's stinker vs. Penn St is NOT included). Each point is the average of the 10 previous games. The X-axis is "games ago." I tried to get the line colors to mostly correspond to school colors, but there are sooooo many schools that use blue or red. Anyway, here you go...

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I really like the look of this one. You can see that for the first half of the season, KU's offense wasn't at the level of the other elite teams, but over the last month it's steadily risen.

Georgetown's curve looks similar, only they start higher and finish in uber-elite territory. Interestingly, every single team on here has improved over the course of the season. I'm wondering if that's all selection bias (we're looking at the best teams as of NOW, so obviously the recent ratings will be high), or if it's also partly due to the fact that offense is just more difficult to perfect than defense. So defenses start the year already performing at a high level, and the offenses catch up over the next few months.

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[EDIT: Please note that in this graph, DOWN IS GOOD!]

This looks suspiciously like a jumbled mess. Picking out KU's line, you can see they've bounced around between 80 and 85 the whole year, always maintaining their spot as one of the top teams. For a while North Carolina seemed to be quite a bit better than everyone else, but they've fallen back to only "great." (I'm betting the same thing happens with Georgetown's offense over the next few weeks. [EDIT: I originally made a typo and said GTown's defense. This caused some confusion over on Hoya Talk. My bad.]) You can see that this graph doesn't show the consistent improvement that the offenses do.

[WARNING: If you couldn't care less about Michigan State, skip this paragraph.] I think MSU's path is the most interesting one here, and is the reason they're going to be really surprising some people over the next month. Their defense went from elite, to just better than average, back to elite. I took a look a closer look to see if there were injuries that could explain this, and it turns out they were missing freshman Raymar Morgan for most of that swoon. Judging from his scouting report, that didn't seem like such a huge loss. He's a subpar offensive player, and most of his playing time was taken up by another 6 1/2 foot freshman, Isaiah Dahlman. Problem is, Dahlman's only an inch shorter but 40 pounds lighter. He doesn't rebound, block shots, or steal the ball as well as Morgan, and I'm guessing he's easier to score on. Dahmlan played at least 18 minutes in 8 games this season, mostly while Morgan was out. In those 8 games, Michigan State's adjusted defensive rating was 93.5. In all other games, it's 83.4. I wish I would have noticed this 2 days ago, so I could feel smart for predicting a MSU victory over Wisconsin.

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Since around the first week of January, North Carolina has, from an efficiency standpoint, looked like the team to beat. Wisconsin approached their level for a while, as did Texas A&M, Ohio St, and Florida, but nobody else had managed to crack the 0.99 barrier, while UNC had been staying comfortably above it. Well, congrats to Kansas on joining them up there. This is a nice looking graph for Kansas, showing that they seem to be putting it all together. Only problem is, most of this nice rating has come from beating up on lesser opponents. Not bad opponents, necessarily, but lesser. They let up and gave Acie Law IV the win in Lawrence in their one chance to prove they could play elite ball against an elite opponent. Still, the stats are what they are, and they make Kansas look good.

OK, I've got nothing more to add right this moment. I'll probably be doing some kind of individual team graphs for KU game previews in the future. If anybody has any ideas on ways to slice these numbers, or different graphical displays that you think might be interesting or useful, feel free to mention them.