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predictions for Milwaukee game

  • asteroid
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4 years 3 months ago #23990 by asteroid
In recent seasons, Kansas has kept its strength of schedule on the
high side by scheduling opponents who are likely to win their
mid-major conference.  That strategy has mostly led to games from
teams from either the middle tier or top tier of Division I.
Unfortunately, Milwaukee doesn't qualify as such an opponent.  To
make matters worse, Milwaukee's strength of schedule is near the
bottom of Division I, though that will improve some by virtue of
playing Kansas.  So, there seems to be little to gain from playing
Milwaukee, other than a W.

The most pessimistic prediction is for a 22 point win by Kansas
(Sagarin's eigenvector analysis).  The most optimistic prediction
is for a 46 point win by Kansas (Real Time).  The average is 28.8
points in favor of Kansas.  That's a bit more than the predicted
margin for Duke over Stephen F. Austin, which worked out badly for
Duke, as you probably know.  But don't think that we've already had
the low-probably event in college basketball this season, so the
rest of us are safe from such a major upset.

It's a busy day for me, so I'm going to cut this short.

Greenfield Stats Comparison
===========================
Offensive Stats     Milw    KU       Defensive Stats     Milw    KU
Points/Game         71.0    80.0     Opp Points/Game     70.1    64.0
Avg Score Margin    +0.9   +16.0     Opp Effective FG %  47.7    44.0
Assists/Game        10.7    16.1     Off Rebounds/Gm     11.3     8.6
Total Rebounds/Gm   38.7    38.6     Def Rebounds/Gm     24.6    27.9
Effective FG %      45.4    56.9     Blocks/Game          3.0     4.3
Off Rebound %       29.5    28.0     Steals/Game          5.3     9.4
FTA/FGA            0.373   0.390     Personal Fouls/Gm   22.7    16.7
Turnover %          16.9    17.9

Common Opponents
================
There are no common opponents, though this game will create a common
opponent for our next game.

Players to Watch
================
most minutes       Darius Roy (guard)
most points        Te'Jon Lucas (guard)
most rebounds      Amir Allen (forward)
most assists       Te'Jon Lucas (guard)
most steals        Te'Jon Lucas (guard)
most blocks        Amir Allen (forward)
most turnovers     Te'Jon Lucas (guard)
most fouls         Amir Allen (forward)

                                                           7-1           5-4
                       Margin   Predicted   Win Prb      Kansas       Milwaukee
Predictor              points     Score     percent   Rating   SOS   Rating   SOS 
====================   ======   =========   =======   ============   ============
Sagarin Overall        +27.88   82   54       96       #  3   # 10    #247   #326 
Sagarin Predictor      +27.37   81   54       99.9     #  3   # 10    #239   #326 
Sagarin Golden Mean    +28.18   82   54                #  3   # 10    #253   #326 
Sagarin Recent Games   +28.10   82   54                #  4   # 10    #246   #326 
Sagarin Eigenvector    +22.24   79   56       94  
Massey                 +34.00   88   54       99       #  6   #  8    #274   #341
Pomeroy                +24.92   79   54                #  5   # 12    #237   #335
Greenfield             +26.00   82   56                #  2   #  2    #243   #333
Dunkel                 +29.50   87   57                # 10           #271                           
Vegas (via Dunkel)     +26.00   82   56                                          
Dolphin Predictive     +24.87   81   57       98.4     #  3   #  1    #244   #199
Real Time              +46.00   89   43       99.8     #  1   #  1    #226   #292 
Seven Overtimes        +24.00   83   59       94       #  3   # 10    #180   #332
DPPI                   +28.80   79   51       99.9     #  4   #       #230   #   
ESPN BPI               +25.00                 97.7     #  9   # 34    #284   #350
Whitlock                                               #      #       #      #   
Colley Matrix          +37.37                          #  4   #  2    #222   #272
NCAA NET                                               #              #   
common opponents                                                  
====================   ======   =========   =======   ============   ============ 
average                +28.8    82.6 54.2

Here is Kansas' season, for which the projected record is now 25-6, despite the
fact that there are no projected losses.  Toughest game would be the road game
with Baylor.

SITE   OPPONENT                         SCORE    PREDIC    ERROR    PROB.
----   -----------------------------   -------   ------    -----    -----
NEUT   #  1 Duke                        66  68    -2.55    +0.55
HOME   # 71 NC Greensboro               74  62   +14.89    -2.89
HOME   #256 Monmouth-NJ                112  57   +28.34   +26.66
HOME   # 69 East Tennessee State(ETS    75  63   +14.73    -2.73
Div2        Chaminade                   93  63
NEUT   # 42 BYU                         71  56    +8.90    +6.10
NEUT   # 30 Dayton                      90  84    +6.83    -0.83
HOME   # 36 Colorado                    72  58   +11.40    +2.60
HOME   #239 Milwaukee                            +27.37             0.999
HOME   #219 Kansas City(UMKC)                    +26.08             0.993
AWAY   # 20 Villanova                             +2.26             0.584
AWAY   # 53 Stanford                              +6.86             0.739
HOME   # 43 Oklahoma                             +12.19             0.873
AWAY   # 32 Iowa State                            +3.79             0.638
HOME   # 63 TCU                                  +14.41             0.911
AWAY   #  9 Baylor                                +0.33             0.512
HOME   # 47 Texas                                +12.90             0.886
AWAY   # 29 West Virginia                         +3.57             0.631
HOME   # 32 Iowa State                           +10.29             0.832
HOME   # 18 Tennessee                             +8.53             0.787
AWAY   # 47 Texas                                 +6.40             0.725
HOME   # 22 Texas Tech                            +8.91             0.798
AWAY   # 93 Kansas State                         +10.67             0.841
HOME   # 38 Oklahoma State                       +11.46             0.858
AWAY   # 63 TCU                                   +7.91             0.770
HOME   # 29 West Virginia                        +10.07             0.827
AWAY   # 22 Texas Tech                            +2.41             0.589
HOME   # 93 Kansas State                         +17.17             0.946
AWAY   # 38 Oklahoma State                        +4.96             0.679
AWAY   # 43 Oklahoma                              +5.69             0.703
HOME   #  9 Baylor                                +6.83             0.738

Here is Milwaukee's season to-date:

SITE   OPPONENT                         SCORE    PREDIC    ERROR    PROB.
----   -----------------------------   -------   ------    -----    -----
Div3        Concordia                   72  62
HOME   #257 Western Michigan           110 115    +4.26    -9.26
Div3        Wisconsin Lutheran         103  53
HOME   #219 Kansas City(UMKC)           61  52    +1.96    +7.04
HOME   #266 North Dakota                79  70    +4.72    +4.28
NEUT   #221 Rice                        69  75    -1.12    -4.88
NEUT   #322 Morgan State                62  57    +6.29    -1.29
NEUT   #254 George Washington           63  66    +0.90    -3.90
AWAY   #141 Drake                       53  56    -9.03    +6.03
AWAY   #  3 Kansas                               -27.37             0.001
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4 years 3 months ago #23991 by NotOstertag
I wonder how the computers factor in the walk-ons participation in the game. If we're up big, the practice squad might get significant minutes, which would no doubt screw up the algorithms.

This is one of those games where I look for specific points of emphasis since we're likely better than them in every position.

Some stuff to watch:
1.) taking care of the ball. The team has been "ok" in regard to turnovers, but they're not the finely tuned machine we've seen at other times. No dumb passes, no throwing the ball out of bounds on breaks, let's hope they clean things up.

2.) Rebounding. If I'm Self, I'd challenge our guys to keep them from getting ANY rebounds at all for as long as possible. The only thing stopping our bigs from getting rebounds should be the long variety off. We should own 100% of balls that come off within 5' of the basket.

3.) Silvio, the stage has been set for you. Dok is doing his thing, expectations for McCormack are lower. Silvio is the big that's not quite living up to his potential. There's nothing stopping him today. Time for the big man to break through and gain some confidence.

Those are my 3. As a bonus, I'd like to see Dok shoot 60% or better from the free throw line. There should be zero pressure on him to make them tonight and supposedly he hits above that mark at practice.

"When I was a freshman, I remember Coach Naismith telling us how important it was to play good defense." - Mitch Lightfoot
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4 years 3 months ago #23992 by CorpusJayhawk
NotO, I am probably going to give you way more than you want to know in response to your question about computers and how they account for walk-on's playing and so forth. It is absolutely accounted for in my DPPI. It determines the non-linear relationship of scoring margin versus difference in raw rating. For instance, the closer 2 teams are in raw rating, the closer the game is likely to be and the less likely an aberrant lineup will contribute significant minutes. The farther apart 2 teams are in raw rating (a linear function) the more likely the game will be a blowout and an aberrant lineup will play for significant minutes, thus producing a final margin that deviates from the linear raw rating. This is one of several adjustments that is accounted for. It is interesting to look at this adjustment because some teams are far more likely to play subs earlier and longer than others, that is why this adjustment has to be a function unique to every team but normalized into an acceptable range since the data can sometimes be skewed with only limited data points (games).

Don't worry about the mules, just load the wagon!!
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4 years 3 months ago #24002 by NotOstertag
Impressive, and I think last night demonstrates how strange things can get. We were up 25 at the half. If that pace had been maintained, we'd have won 104-54. As we all know, the 2nd half score was 43-41, so things didn't stay at the same pace.

Not surprisingly, KU came out a little flat and coasted for a while. Then in the last 5 minutes, KU scored 17 points to Milwaukee's 10. Much of that time, KU had Braun and De Sousa in, where had it been a closer game, I don't think they'd have gotten as much playing time (Braun averages 9 minutes and he played 15 last night. Silvio played about 2 minutes more than his average).

Nevertheless, it's interesting that you can account for this, but it's also the reason why if I was going to gamble on sports, I'd avoid betting any kind of spread on basketball. Last night was a blowout to be sure, and that was predictable, but the difference between last night's 27 point win vs. a potential 15 point win vs. a 40 point win just don't seem that far apart in the realm of possibility.

"When I was a freshman, I remember Coach Naismith telling us how important it was to play good defense." - Mitch Lightfoot

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4 years 3 months ago #24004 by CorpusJayhawk
NotO said, "but the difference between last night's 27 point win vs. a potential 15 point win vs. a 40 point win just don't seem that far apart in the realm of possibility."

You are precisely correct. In college basketball (at least with my algorithm) the standard deviation of outcome (thus far this season through about 1500 games) is 6.2 points either side of the mean. So a 29 point favorite could just as easily be a 23 point win or a 35 point win. Well not just as easily but it is hard to get any more accurate in any given game than 6 points in any given game That standard deviation is highly skewed by the big outliers. For KU, they have averaged coming within 3.5 points of projection if you take away the Monmouth game. With the Monmouth game it is 5.9. It is hard to explain games like Utah vs. Miss Valley St.. Utah was a giant 44 point favorite but won by 94. That is a 50 point deviation. Games like that skew the curve. Or another game we discussed on the board. Mizzou was a 27 point favorite against Charleston Southern but loses by 8. That is a 35 point deviation. 53% of games are played within that 6.2 point standard deviation. So almost 50% of the games will be outside that range. The whole goal of an algorithm is to get that standard deviation down the most. I guess my definition of the best algorithm is the one that has the lowest standard deviation in predictive accuracy.

Don't worry about the mules, just load the wagon!!
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