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Preview: Kansas at Texas Print E-mail
Feb 8, 2010

Kansas at Texas (Austin, TX)

  Kansas Texas

Performance Indicators

   
Record (conference)
22-1 (8-0) 19-4 (5-3)
AP Rank 1
NR
Pomeroy Rating
(Updated daily)
1
11
Consensus Ranking
(Updated periodically - avg several computer ratings and polls)
1
10
Best opponents defeated this season
(Ranking from Pomeroy)

@ Kansas St. (#9) W 81-79 OT

vs Michigan St. (#20) W 79-68
RPI
(Used for schedule strength, not team strength. Current and forecast RPI from RPIForecast .)

Current: 1

Forecast: 1

Current: 21

Forecast: 13

Projected NCAA Tournament Seed
(Latest consensus from The Bracket Matrix)
 #1 Seed
 # Seed

Prediction Models

   
Vegas Oddsmakers Win by 2.0
Est. Projection: 78-76
 
Prediction Tracker
(Average of several power ratings)
 Win by 2.53  
Sagarin Power Ratings (Predictor)  Win by 0.43  
Pomeroy
(Efficiency and Tempo-Adjusted)

 Win 78-75
60% chance of victory

 
 TeamRankings.com

(Use home rating for home team, road for road.  If fewer than 3 games played at corresponding locations, use "predictive" ratings for both teams but use specific home advantage rather than standard average homecourt advantage for all NCAA teams.)

Win by 7.1  
Last 7 Games (Venue Adjusted)
(Uses team performance and consistency over last 5 home/non-home games, whichever pertains to this game + 2 other most recent games regardless of location.  This takes offense and defense into account separately to reflect how consistent teams have played on each end of the court)
 Est. Projection: 88-77
71% chance of victory
 
Similar Opponent
(Examines offensive performance against similar defensive teams and defensive performance against similar offensive teams.  Threshold for similarity set at efficiencies of 5 pts/100 possessions above or below that of opponent in this game.  If necessary, threshold adjusted to find a minimum of three games to analyze for offense and defense.)
 Est. Projection: 85-77
65% chance of victory
 
Weighted Average of All Models Above
(40% Vegas + 10% Pred Trckr + 10% SagP + 10% Pom + 10% TmRnk + 10% SimOpp + 10% Last 7)
 Win by 4.0
Est. Projection: 80-76
 

 

 

PSAN-Related Player Ratings - Cumulative This Season

(PSAN-O = Offensive impact, PSAN-D = Defensive impact.  For PSAN-D, lower ratings are better.  PSAN70 ratings are just PSAN expressed as "per 70 possessions" to reflect efficiency.  The difference between ePSAN and PSAN is that "e" is enhanced and weighs recent games more - used for Kansas only.)

Kansas

 

Impact Ratings

 

PLAYER ePSAN-O ePSAN-D ePSAN-Comp
Cole Aldrich 65.88 -81.68 147.56
Marcus Morris 76.09 -23.52 99.61
Sherron Collins 53.64 -16.85 70.49
Xavier Henry 16.30 -40.42 56.72
Brady Morningstar 35.00 -17.92 52.92
Markieff Morris 26.90 -25.95 52.85
Tyrel Reed 30.47 -16.51 46.98
Tyshawn Taylor 18.89 -9.79 28.68
C.J. Henry* 11.42 -0.70 12.12
Elijah Johnson* 8.33 -3.40 11.74
Jeff Withey* 3.62 -8.02 11.64
Thomas Robinson -5.05 -11.82 6.78
Conner Teahan* -0.96 -3.43 2.48
Chase Buford* -0.14 -0.40 0.26
Jordan Juenemann* 0.17 0.37 -0.21

 

 Efficiency Ratings

 

PLAYER ePSAN70-O ePSAN70-D ePSAN70-Comp
Jeff Withey* 3.42 -7.59 11.01
Cole Aldrich 4.25 -5.27 9.52
C.J. Henry* 7.00 -0.43 7.43
Marcus Morris 5.30 -1.64 6.93
Markieff Morris 3.03 -2.93 5.96
Tyrel Reed 3.52 -1.91 5.42
Brady Morningstar 3.39 -1.74 5.13
Elijah Johnson* 2.97 -1.21 4.18
Sherron Collins 2.83 -0.89 3.72
Xavier Henry 1.07 -2.65 3.72
Tyshawn Taylor 1.48 -0.77 2.25
Conner Teahan* -0.68 -2.42 1.74
Thomas Robinson -1.08 -2.52 1.44
Chase Buford* -0.26 -0.76 0.50
Jordan Juenemann* 0.55 1.24 -0.69

 

* Rating not based on enough data.

Texas

  

Impact Ratings

 

PLAYER PSAN-O PSAN-D PSAN-Comp
Damion James 54.08 -72.23 126.30
Dexter Pittman 59.63 -31.79 91.42
Gary Johnson 32.02 -20.02 52.04
Avery Bradley 26.03 -19.06 45.09
Dogus Balbay 12.91 -26.88 39.78
Jordan Hamilton 16.07 -19.80 35.86
Alexis Wangmene* 9.41 -6.53 15.93
Justin Mason 4.84 -10.09 14.92
Clint Chapman* 6.11 -1.56 7.67
Jai Lucas 9.29 3.69 5.60
J'Covan Brown -10.09 -13.71 3.62
Andrew Dick* 1.81 -0.65 2.46
Shawn Williams* 1.26 -0.78 2.04
Matt Hill* -1.72 -3.51 1.79
Dean Melchionni* -2.30 0.00 -2.30

 

 

 

Efficiency Ratings

 

PLAYER PSAN70-O PSAN70-D PSAN70-Comp
Andrew Dick* 7.59 -2.72 10.31
Dexter Pittman 4.97 -2.65 7.62
Damion James 3.01 -4.02 7.03
Clint Chapman* 3.97 -1.01 4.98
Gary Johnson 2.51 -1.57 4.07
Jordan Hamilton 1.48 -1.83 3.31
Alexis Wangmene* 1.90 -1.32 3.23
Dogus Balbay 0.94 -1.95 2.89
Avery Bradley 1.54 -1.13 2.67
Shawn Williams* 1.22 -0.75 1.97
Justin Mason 0.46 -0.95 1.41
Jai Lucas 2.17 0.86 1.31
Matt Hill* -0.94 -1.91 0.98
J'Covan Brown -0.79 -1.08 0.28
Dean Melchionni* -10.83 0.00 -10.83

 

* Rating not based on enough data.




Player Analysis:

(largely in context of ratings above)

 

Contrary to what some might think, it looks as though Dexter Pittman is the primary offensive guy for Texas, while Damion James is the main man on defense.  Pittman shoots ridiculously well (68 eFG%), but James rebounds like a machine on the defensive glass (26 OREB%).  The balance is generally better for Kansas though.  Only Cole Aldrich has separated himself from the pack, leaving four players with efficiencies of at least +5, not to mention Sherron Collins who is the heart and soul of the team and feared by many as one of the best players in the nation.  Collins simply hasn't had the efficiency throughout the whole game to get higher ratings here.  Texas has two guys who have separated from the rest in Pittman and James.  After that, they have players rated lower than corresponding ranks from KU. Still, having two top guys helps diversify the risk of a bad game.  If Aldrich doesn't do any of the things that make him so effective, KU may well be in trouble.

 

 

Last 7 Game Projection

 

  Kansas Texas
Expected Score 88.5 77.2
Win 71.4% 28.6%
Win by 3 or less 4.9% 5.0%
Win by 10 or more 53.0% 14.7%

 

Down to the Wire?

 Margin of game was less than 1 point in 3.5% of simulated games from "Last 7 Game Analysis."  Many of these would be "overtime" games.

 

(Methodology of Last 7 Game Analysis: Here, we look at the last seven venue-appropriate games to see how the teams are performing.  If the game takes place on the road for Kansas, for example, the analysis looks at the five most recent non-home games for Kansas.  In addition, the two most recent games, regardless of venue, are added.  That way, we get a picture of the how the team is performing of late.  This season, the analysis also splits offense and defense.  Teams often are more or less consistent on one side of the court than the other.  This analysis will reflect that.  Based on the strength of the opposing offense and defense they've played over the last seven games, each team's offense and defense is evaluated based on strength and consistency.  Those numbers are then plugged into a simulation of 8,000 games.  The results are what you see in the table.)

 

Similar Opponent Projection

 

  Kansas Texas
Expected Score 85.0 77.2
Win 64.6% 35.4%
Win by 3 or less 5.5% 5.3%
Win by 10 or more 45.9% 19.8%

 

Down to the Wire?

 Margin of game was less than 1 point in 3.3% of simulated games from "Last 7 Game Analysis."  Many of these would be "overtime" games.

 

(Methodology of Similar Opponent Analysis: Here, we look at games against opponents with similar offensive and defensive profiles to the opponent of interest to see how the teams are performing.  For Kansas, we would look at its offensive performances against teams that have a defense that is within 5 pts/100 possessions efficiency above or below that of the opponent in this game.  If this does not result in at least three appropriate comparisons, the threshold will be adjusted until it finds three.  If there are more than three that are within the original threshold, all of those will be used.  The same is done for defense but using offensive profiles of opponents.  The analysis splits offense and defense.  Teams often are more or less consistent on one side of the court than the other.  This analysis will reflect that.  Those numbers are then plugged into a simulation of 8,000 games.  The results are what you see in the table.)

 

Four Factors Regression Analysis

KenPom.com's "Game Plan" feature shows the Four Factors results for every game a team has played.  Below the results, there is a table with correlation coefficients that show how closely related each of the Four Factors is to a team's offensive and defensive efficiency (both for itself and its opponent).  Unfortunately, the correlations are based on raw efficiencies, which of course has much less value because a team's true efficiency is significantly affected by the strength of the opponent.  Below, I have taken each team's Four Factors results and run a multiple regression analysis with the Four Factors as the variables of interest and the adjusted efficiencies as the outcome of interest to see whether a team's eFG%, for example, is related to its true offensive efficiency.

 

Statistical Significance = There is less than a 10% chance that the relationship between this statistic and the team's adjusted offensive or defensive efficiency is due merely to chance.

Four Factors Rank = In ascending order, which of the Four Factors has the lowest chance of being related to efficiency due merely to chance (i.e., best significance).

 

Kansas

 

This team's offensive efficiency (points per 100 poss.) can be estimated by the following equation:

 

1.0*eFG% - 0.71*TO% -0.80*oppTO% + 0.44*oppFTR + 81.05

Standard Error: 9.28

 

This team's defensive efficiency (points allowed per 100 poss.) can be estimated by the following equation:

 

1.04*OppeFG% - 0.60*OppTO% + 0.35*OppOREB% + 0.45*eFG% + 18.01

Standard Error: 5.30
Texas

 

This team's offensive efficiency (points per 100 poss.) can be estimated by the following equation:

 

1.10*eFG% - 1.55*TO% + 0.41*OppeFG% + 65.61

Standard Error: 5.69

 

This team's defensive efficiency (points allowed per 100 poss.) can be estimated by the following equation:

 

1.37*OppeFG% + 26.18

Standard Error: 5.73
 
Four Factors Regression Takeaways

 

eFG%: This will be huge for KU.  Take a look at the equations above.  KU's eFG% is a part of BOTH offense and defensive efficiency estimates for BOTH teams.  Interestingly, the higher KU's eFG%, the higher its opponents' efficiency will likely be as well.  It will also be critical for Texas.  In a separate regression with the use of the MARGIN of each factor as an variable used to predict efficiency, both teams had eFG% as a variable of interest, with the coefficient being much higher for Texas.  That means that KU's own eFG% may be huge, but the difference between KU's eFG% and UT's eFG% may be even more important for Texas.

TO%: Looks like UT's TO% will be very important, as it appears in three of the four equations above.  Strangely, the higher opponents' TO rates are, the worse KU's offensive efficiency is predicted to be, but the higher opponents' FTR is, the better KU's efficiency.  So, KU does better on offense when opponents are taking care of the ball and getting to the line ... maybe KU is not a very good fast-break team.  Obviously, UT's TO% is important to its own offensive efficiency, too, and it's HUGELY so.  The coefficient is about double what KU's is.

OREB%: This only shows up in one equation, and it's KU's defensive efficiency being affected by UT's OREB%.  So, look to see how the Longhorns do on the board.

FTA/FGA: Looks like it only matters to KU's offense how well KU gets to the line.  What Texas' FTA/FGA is doesn't seem relevant anywhere.

 

 

Statistical Strengths and Weaknesses Analysis

(Note: These are based on raw statistics that are unadjusted for strength of opposition.) 

 

** Denotes that team with advantage also ranks in Top 50 in that category
Clear Advantage for Kansas No Clear Advantage Clear Advantage for Texas
Texas FT%    
Texas % Poss STL by Opp**    
  Texas eFG%  
  Kansas OREB  
  Texas 3pt FG%  
  Kansas TO rate  
  Texas % own 2FGA's blocked  
  Texas FT Rate  
  Kansas 3pt FG%  
  Kansas FT Rate  
  Texas PTS/Poss  
  Texas 2pt FG%  
  Kansas PTS/Poss  
  Kansas % Poss STL by Opp  
  Kansas eFG%  
  Kansas 2pt FG%  
  Texas OREB  
    Kansas % own 2FGA's blocked**
  Texas TO rate  
    Kansas FT%**

 

************************************************************* 

Kansas will take below avg % of its FG's from 3-pt
Texas will take below avg % of its FG's from 3-pt
Expect uptempo game

 

 

 

Game Projections

(Not a prediction.  Read more details in "FAQ & Terms" section.)

Manual adjustments: Brady Morningstar to play 15 minutes.  His end-of-season stats from last year are used for this projection.

 


Projected Boxscore

 

Kansas 78                            
Texas 76                            
                               
Kansas                              
PLAYER MIN 2FGM 2FGA 3FGM 3FGA FTM FTA PTS OREB DREB TREB AST TO STL BLK
Brady Morningstar 26 1 2 1 2 1 1 6 1 2 3 3 2 1 0
Cole Aldrich 27 4 8 0 0 4 4 12 4 8 12 1 2 1 3
Marcus Morris 26 5 8 0 1 3 5 13 3 4 7 1 1 1 0
Markieff Morris 16 2 4 0 1 1 3 5 2 4 6 1 1 1 1
Sherron Collins 33 3 7 2 5 3 4 15 0 2 2 3 3 1 0
Thomas Robinson 6 1 3 0 0 1 2 3 1 2 3 0 1 0 1
Tyrel Reed 16 0 1 1 3 0 0 3 0 1 1 1 1 1 0
Tyshawn Taylor 23 2 4 1 2 2 3 9 1 2 3 3 2 1 0
Xavier Henry 27 2 6 2 5 2 3 12 1 3 4 2 3 2 1
TOTALS 200 20 43 7 19 17 25 78 13 28 41 15 16 9 6
                               
Texas                              
PLAYER MIN 2FGM 2FGA 3FGM 3FGA FTM FTA PTS OREB DREB TREB AST TO STL BLK
Alexis Wangmene 6 1 2 0 0 1 1 3 1 1 2 0 0 0 1
Avery Bradley 30 3 8 2 4 1 1 13 1 2 3 2 2 1 0
Damion James 32 5 11 1 3 4 7 17 4 7 11 1 3 2 1
Dexter Pittman 22 4 7 0 0 2 4 10 4 3 7 1 2 0 2
Dogus Balbay 25 1 3 0 1 1 2 3 1 2 3 4 2 1 1
Gary Johnson 23 3 6 0 0 2 2 8 2 3 5 1 1 0 0
J'Covan Brown 24 1 4 2 5 2 3 10 1 2 3 2 3 1 0
Jordan Hamilton 19 1 4 2 5 1 2 9 1 2 3 2 1 1 0
Justin Mason 19 1 3 0 1 1 2 3 1 1 2 2 1 1 0
TOTALS 200 20 48 7 19 15 24 76 16 23 39 15 15 7 5

 

 

Projection Summary

 

 
Projection
Comments

 OVERALL RESULTS

 Final Score  KU 78-76  
 Tempo (# poss)
 76 

 FOUR FACTORS ADVANTAGES

 eFG%  KU 49-46%  
 TO Rate (lo better)  UT 21-20%  
 O-Reb% UT 36.4-36.1%
 
 FT Rate KU 40-36%
 
 Four Factors Overall
 Pretty even across the board, with just under a 5-point edge in eFG% being the difference for KU.  
 

PLAYER PROJECTIONS (10+ min played)

 Leading Scorers

 KU - Collins, Marcus Morris

 Opp - James, Bradley

 
 Highest PSAN-Comp
(game impact)

 KU - Aldrich

 Opp - James

 
 Highest PSAN70-Comp
(efficiency)

 KU - Aldrich

 Opp - Pittman

 
 Highest efficiency vs season-to-date

 KU - Taylor, Aldrich

 Opp - Brown, Hamilton

 Looks like KU is really banking on Aldrich this game.  Look to see if he is in foul trouble or playing passively and not getting his shots.
 Lowest efficiency vs season-to-date

 KU - Reed, Collins

 Opp - Balbay, Mason

 Reed to struggle based on limited shooting.  Making one shot would be a big difference here.  Collins is another story.

 

Sports and Numbers Projection

Kansas wins 78-76

(all prediction models included/complete)

 

 

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