Thursday, January 24

(Late) Maryland Wrap


$`Four Factors`
       OPP      NAME ORTG perEFG perORB  FTR TORATE
1 Maryland     Total 0.87   0.39   0.33 0.28   0.17
2 Maryland oppTotals 0.73   0.41   0.29 0.26   0.30



$`Last Game`
           POSS ORTG  USG perFTM perFGM perFGM.3 perORB perDRB perTS  FTR perEFG ARATE TORATE
Bullock      17 1.41 0.29   0.86   0.47     0.57   0.05   0.11  0.66 0.47   0.60  0.00   0.06
McAdoo       18 1.06 0.30   0.43   0.50     0.00   0.13   0.21  0.50 0.44   0.50  0.05   0.22
Hubert        0  NaN 0.00    NaN   0.00      NaN   0.06   0.08  0.00 0.00   0.00  0.00    NaN
Strickland    9 0.67 0.19   0.50   0.33      NaN   0.03   0.09  0.39 0.67   0.33  0.13   0.22
Paige         7 0.29 0.12    NaN   0.14     0.00   0.00   0.11  0.14 0.00   0.14  0.33   0.00
James         4 1.00 0.20    NaN   0.40      NaN   0.08   0.00  0.40 0.00   0.40  0.00   0.00
Hairston      9 0.33 0.20    NaN   0.12     0.20   0.07   0.10  0.19 0.00   0.19  0.21   0.33
Johnson       5 0.40 0.35    NaN   0.17      NaN   0.11   0.14  0.17 0.00   0.17  0.00   0.00
McDonald     NA   NA   NA     NA     NA       NA     NA     NA    NA   NA     NA    NA     NA
Tokoto        1 2.00 0.06    NaN   1.00      NaN   0.10   0.12  1.00 0.00   1.00  0.39   1.00
Davis        NA   NA   NA     NA     NA       NA     NA     NA    NA   NA     NA    NA     NA
Simmons       1 0.00 0.08    NaN    NaN      NaN   0.00   0.00   NaN  NaN    NaN  0.00   1.00
Total        71 0.87 1.00   0.61   0.35     0.29   0.33   0.71  0.43 0.28   0.39  0.61   0.17
oppTotals    71 0.73 1.00   0.64   0.40     0.08   0.29   0.69  0.44 0.26   0.41  0.24   0.30

$`Season Totals`
         NAME POSS ORTG  USG perFTM perFGM perFGM.3 perORB perDRB perTS  FTR perEFG ARATE TORATE
1     Bullock  165 1.37 0.20   0.86   0.48     0.48   0.08   0.13  0.63 0.17   0.60  0.14   0.12
2      McAdoo  260 0.96 0.28   0.60   0.46     0.00   0.09   0.18  0.49 0.40   0.46  0.06   0.20
3      Hubert   21 1.10 0.06   0.23   0.53      NaN   0.07   0.10  0.47 0.68   0.53  0.01   0.48
4  Strickland  153 0.89 0.18   0.59   0.45     0.21   0.02   0.08  0.50 0.45   0.46  0.20   0.16
5       Paige  167 0.66 0.21   0.80   0.33     0.30   0.01   0.08  0.43 0.12   0.40  0.22   0.24
6       James   44 1.11 0.12   0.56   0.49      NaN   0.10   0.18  0.51 0.39   0.49  0.03   0.34
7    Hairston  170 1.11 0.28   0.81   0.39     0.33   0.09   0.13  0.53 0.27   0.48  0.10   0.12
8     Johnson  108 1.24 0.26   0.46   0.59      NaN   0.09   0.23  0.59 0.12   0.59  0.04   0.13
9    McDonald  111 1.06 0.24   0.69   0.40     0.43   0.05   0.07  0.54 0.16   0.52  0.11   0.12
10     Tokoto   54 1.17 0.16   0.36   0.53     0.14   0.11   0.14  0.52 0.43   0.54  0.12   0.24
11      Davis    6 1.33 0.05   1.00   1.00     1.00   0.00   0.03  1.39 1.00   1.50  0.42   0.50
12    Simmons   10 2.60 0.07   1.00   0.56      NaN   0.18   0.15  0.63 0.33   0.56  0.07   0.30
13      Total 1255 1.07 1.00   0.64   0.45     0.37   0.39   0.68  0.52 0.27   0.50  0.60   0.19
14   oppTotal 1255 0.93 1.00   0.66   0.40     0.35   0.30   0.62  0.49 0.28   0.46  0.50   0.21

Sunday, January 13

FSU Wrap-up

    There is really only one main takeaway from Saturday's game in terms of team advanced stats. Let me reiterate how the Four Factors work (in case they've slipped your mind). Dean Oliver decided that it would be best to distill any game into 4 measures of a team's production: Shooting, Rebounding, Free Throws and Turnovers. They carry importance in that order and are measured in a tempo-free way in the following graph I show all of you after each UNC game:
     Now, something should stick out about that graph. You may proceed to the raw numbers below to confirm, but you will find that yes, UNC lost three of the four factors including shooting, the most important. However, the Heels did such a phenomenal job on the glass that they won the game. The things that go into an offense can be widely varied, but the important thing to learn from Oliver's factors are that many things can alter the number of points a team gets. Even though UNC turned the ball over more, took fewer free throws, and missed a higher percentage of their shots, they got enough second (and third) chances and prevented FSU from doing the same which created 10 extra shots for UNC*. So, if you take one thing away from the beautiful pentagon graph above, note the giant edge in perORB and understand that is the reason UNC won Saturday.

           OPP      NAME ORTG perEFG perORB  FTR TORATE
1 FloridaState     Total 1.22   0.55   0.58 0.27   0.22
2 FloridaState oppTotals 1.14   0.59   0.21 0.34   0.16


    Now, to be honest I'm not great at spotting overall trends, but I've got to assume that two things went into that advantage, game planning and a renewed effort on the boards and I can only hope this trend continues. This iteration of North Carolina basketball doesn't have a formidable big man to create a rebounding edge, but does maintain a size advantage at positions 1-4 and should be able to rebound well. This is potentially vital going forward, as offensive rebound is one of the more simple remedies for poor shooting. Shooting will always win games, but if a team is regularly able to corral its own misses it has something of a security blanket against bad shooting nights.

    On to the individual offensive performances. Inconsistency continues to be the only rule here, as each player continues to find new directions on my game graph. Hairston ventures out to territory unseen since McNeese State, while McAdoo and Bullock took fewer shots and saw their efficiency improve. Brice Johnson had an uncharacteristically poor game, though two of his turnovers were on entry passes "over the top" of a defender that was fronting him in the post. That hasn't been a common avenue for delivering the basketball to him, hopefully he can improve his pass catching from that angle. Paige had a high usage game, but wasn't impressive and Dexter returned back from purgatory with a lackluster game. Desmond Hubert continues to improve, putting the ball in the basket when asked on one end while playing successful defense on the other.
    With so many ho-hum performances, how did the Heels nab victory from the jaws of defeat? The answer, in a few words, is Jackson Simmons. He's missing on the graph above, because he wrecks the curve. Below is the same graph with his inclusion, Simmons used 1 possession and scored 8 points. He took and made 3 shots and 2 free throws with 1 turnover and 4 offensive rebounds. While on the floor he rebounded 33% of UNC's missed shots. I'm not sure if his role will continue to be this large, but he certainly has played like the Brice Johnson-Joel James combo many have been yearning for. His offensive production was both timely and significant, while he was always in the right place on defense and seldom got beat for points. Florida State doesn't boast the most menacing front court in the league, but they're not small by any means.
    One last note, in Tallahassee last year the Seminoles took 27 three-point field goals and made 12, Saturday they took 22 and made only one fewer, 11. I'm not directly comparing the production, but given that the 'Noles were nearly as hot as the team that knocked off last year's #3 squad the fact that this year's Tar Heels were able to come away with a win is indicative of the moxie I hope these Heels continue to show. If they do we'll be in for a fun ACC season.

           POSS ORTG  USG perFTM perFGM perFGM.3 perORB perDRB perTS  FTR perEFG ARATE TORATE
Bullock       7 1.71 0.13    NaN   0.45     0.33   0.18   0.12  0.55 0.00   0.55  0.16   0.14
McAdoo       13 1.08 0.24   0.57   0.45      NaN   0.10   0.21  0.50 0.64   0.45  0.08   0.15
Hubert        2 2.00 0.11    NaN   1.00      NaN   0.00   0.12  1.00 0.00   1.00  0.00   0.00
Strickland    5 0.80 0.13   0.67   0.33     0.00   0.00   0.17  0.46 1.00   0.33  0.11   0.20
Paige        15 0.53 0.34   1.00   0.22     0.50   0.00   0.15  0.40 0.22   0.33  0.20   0.33
James         1 0.00 0.16    NaN   0.00      NaN   0.00   0.33  0.00 0.00   0.00  0.00   0.00
Hairston     15 1.53 0.34   0.50   0.64     0.44   0.04   0.15  0.77 0.14   0.79  0.05   0.07
Johnson       7 0.57 0.40    NaN   0.40      NaN   0.00   0.12  0.40 0.00   0.40  0.00   0.29
McDonald     NA   NA   NA     NA     NA       NA     NA     NA    NA   NA     NA    NA     NA
Tokoto        0  NaN 0.00    NaN   0.00      NaN   0.14   0.00  0.00 0.00   0.00  0.34    NaN
Davis        NA   NA   NA     NA     NA       NA     NA     NA    NA   NA     NA    NA     NA
Simmons       1 8.00 0.04   1.00   1.00      NaN   0.33   0.00  1.03 0.67   1.00  0.09   1.00
Total        63 1.22 1.00   0.69   0.48     0.40   0.58   0.79  0.57 0.27   0.55  0.55   0.22
oppTotals    63 1.14 1.00   0.76   0.48     0.50   0.21   0.39  0.63 0.34   0.59  0.71   0.16

    As promised, a few thoughts on the Tar Heel's performances to open the ACC season. My general feeling is that UNC has played two games well and one game poorly and unfortunately for them if you rearrange the performances in most other ways they'd be 2-1. That said, though the overall performances haven't been great (and certainly not at the team's ceiling) the players appear to be buying in more than they were earlier in the year. I can't quantify effort with any one stat, but I'm prepared to provide rebounding as a proxy. Below I've plotted UNC's rebounding performance against all of their KenPom top-100 opponents. As you can see, earlier in the year the Heels were getting out-rebounded by most of their top-flight competition. In recent games those numbers have improved. Hopefully this is a sign of things to come, as a rebounding edge would certainly improve the defense and make the offense more robust.
$`Season Totals`
         NAME POSS ORTG  USG perFTM perFGM perFGM.3 perORB perDRB perTS  FTR perEFG ARATE TORATE
1     Bullock  148 1.36 0.19   0.86   0.48     0.47   0.08   0.13  0.63 0.14   0.61  0.15   0.13
2      McAdoo  242 0.95 0.28   0.62   0.46      NaN   0.09   0.18  0.49 0.40   0.46  0.06   0.19
3      Hubert   21 1.10 0.06   0.23   0.56      NaN   0.07   0.10  0.48 0.72   0.56  0.01   0.48
4  Strickland  144 0.90 0.18   0.60   0.45     0.21   0.02   0.07  0.51 0.44   0.47  0.20   0.15
5       Paige  160 0.68 0.22   0.80   0.34     0.32   0.01   0.08  0.44 0.13   0.42  0.21   0.25
6       James   40 1.12 0.11   0.56   0.50      NaN   0.10   0.18  0.52 0.44   0.50  0.03   0.38
7    Hairston  161 1.15 0.29   0.81   0.40     0.34   0.09   0.13  0.55 0.29   0.50  0.10   0.11
8     Johnson  103 1.28 0.26   0.46   0.62      NaN   0.09   0.23  0.61 0.13   0.62  0.04   0.14
9    McDonald  111 1.06 0.24   0.69   0.40     0.43   0.05   0.07  0.54 0.16   0.52  0.11   0.12
10     Tokoto   53 1.15 0.17   0.36   0.52     0.14   0.11   0.14  0.51 0.44   0.53  0.11   0.23
11      Davis    6 1.33 0.05   1.00   1.00     1.00   0.00   0.03  1.39 1.00   1.50  0.42   0.50
12    Simmons    9 2.89 0.07   1.00   0.56      NaN   0.19   0.16  0.63 0.33   0.56  0.07   0.22
13      Total 1184 1.08 1.00   0.64   0.45     0.37   0.39   0.68  0.53 0.27   0.51  0.60   0.19
14   oppTotal 1184 0.94 1.00   0.66   0.40     0.36   0.30   0.62  0.49 0.28   0.46  0.52   0.21


*An aside here, for those of you interested Dean Smith's book "Multiple Offense and Defense" explains his game charting methods. He would have counted these rebounds as extra possessions which they essentially are, but it throws off a lot of tempo-free stat counting if you do the accounting that way.

Friday, January 11

Miami Stats

 $`Four Factors`

    OPP      NAME ORTG perEFG perORB  FTR TORATE
1 Miami     Total 0.94   0.45   0.32 0.13   0.17
2 Miami oppTotals 1.08   0.56   0.27 0.25   0.19



$`Last Game`
           POSS ORTG  USG perFTM perFGM perFGM.3 perORB perDRB perTS  FTR perEFG ARATE TORATE
Bullock      18 0.61 0.34    NaN   0.25     0.38   0.06   0.15  0.34 0.00   0.34  0.25   0.22
McAdoo       15 0.93 0.30   0.80   0.36      NaN   0.07   0.17  0.43 0.36   0.36  0.00   0.07
Hubert        0 -Inf 0.00   0.50   0.00      NaN   0.18   0.00  0.27 2.00   0.00  0.00    NaN
Strickland    1 0.00 0.02    NaN   0.00      NaN   0.00   0.00  0.00 0.00   0.00  0.00   0.00
Paige        10 1.00 0.19    NaN   0.57     0.67   0.00   0.20  0.71 0.00   0.71  0.25   0.30
James         1 2.00 0.16    NaN   1.00      NaN   0.00   0.33  1.00 0.00   1.00  0.00   0.00
Hairston      8 0.62 0.20    NaN   0.29     0.25   0.04   0.10  0.36 0.00   0.36  0.13   0.25
Johnson       7 1.43 0.25   0.00   0.62      NaN   0.12   0.07  0.59 0.12   0.62  0.09   0.14
McDonald     NA   NA   NA     NA     NA       NA     NA     NA    NA   NA     NA    NA     NA
Tokoto        3 1.33 0.16    NaN   0.50      NaN   0.09   0.22  0.50 0.00   0.50  0.14   0.00
Davis        NA   NA   NA     NA     NA       NA     NA     NA    NA   NA     NA    NA     NA
Simmons       1 2.00 0.21    NaN   1.00      NaN   0.00   0.50  1.00 0.00   1.00  0.56   0.00
Total        63 0.94 1.00   0.62   0.40     0.40   0.32   0.70  0.46 0.13   0.45  0.62   0.17
oppTotals    63 1.08 1.00   0.69   0.47     0.35   0.27   0.70  0.58 0.25   0.56  0.40   0.19

$`Season Totals`
         NAME POSS ORTG  USG perFTM perFGM perFGM.3 perORB perDRB perTS  FTR perEFG ARATE TORATE
1     Bullock  141 1.35 0.20   0.86   0.48     0.48   0.07   0.13  0.63 0.15   0.61  0.15   0.13
2      McAdoo  228 0.95 0.28   0.62   0.46      NaN   0.09   0.18  0.49 0.38   0.46  0.06   0.20
3      Hubert   19 1.00 0.06   0.23   0.50      NaN   0.08   0.10  0.44 0.81   0.50  0.02   0.53
4  Strickland  139 0.91 0.19   0.59   0.46     0.22   0.02   0.07  0.51 0.42   0.48  0.20   0.15
5       Paige  145 0.70 0.21   0.77   0.36     0.31   0.01   0.08  0.45 0.12   0.43  0.21   0.24
6       James   39 1.15 0.11   0.56   0.51      NaN   0.10   0.18  0.54 0.46   0.51  0.03   0.38
7    Hairston  146 1.11 0.28   0.83   0.38     0.32   0.09   0.13  0.53 0.30   0.47  0.10   0.12
8     Johnson   96 1.33 0.25   0.46   0.63      NaN   0.09   0.23  0.62 0.13   0.63  0.04   0.12
9    McDonald  111 1.06 0.24   0.69   0.40     0.43   0.05   0.07  0.54 0.16   0.52  0.11   0.12
10     Tokoto   53 1.15 0.17   0.36   0.53     0.14   0.11   0.14  0.52 0.45   0.54  0.10   0.23
11      Davis    6 1.33 0.05   1.00   1.00     1.00   0.00   0.03  1.39 1.00   1.50  0.42   0.50
12    Simmons    8 2.25 0.07   1.00   0.47      NaN   0.17   0.20  0.54 0.27   0.47  0.07   0.12
13      Total 1122 1.07 1.00   0.64   0.45     0.37   0.38   0.68  0.53 0.27   0.50  0.61   0.19
14   oppTotal 1122 0.93 1.00   0.65   0.39     0.35   0.30   0.63  0.49 0.27   0.46  0.51   0.21


at FSU prediction
> prediction(106.4,93.7,70.5,107.4,92.7,74.5)
$HomePts
[1] 80.33294

$AwayPts
[1] 77.49864

$Pace
[1] 78.04235

$HomeWinP
[1] 0.6018243

Monday, January 7

UVA Wrap Up


I would normally write a few paragraphs, but sometimes pictures really are worth a thousand words. I've explained this style of graph several times, but I don't think I need to even introduce it to know that it both confirms everything anyone watching the game saw as well as the main reason the Heels didn't "steal anyone's brownies" last night:

I watched the game with Cason (as much as one can from two and a half hours away). We took a number of things away from this game, mainly that the defensive team that showed up against UNLV apparently isn't here to stay. Maybe UNLV has more of an individual style of offense, and when you have a talented set of players you can defend an offense of individuals. Last night UVA showed (much as ECU did in December) that UNC has a ways to go in the team defense/communication area.

From a more broad point of view, it doesn't really do significant damage to this team to lose this game. If UVA plays the remainder of the season to their current KenPom rating they'll be an NCAA tournament team. Assuming the ACC has 5 or 6 tournament teams I would only predict UNC wins half of those road games if they're lucky. What stuck in my craw more during last night's game was the fact that it was one UNC could have won, even playing some poor basketball in spots. Reggie Bullock played so well that it was 51-50 late in the second half, but the same team defense issues and poor offensive decisions appeared and prevented the Tar Heels from winning their ACC opener. I am willing to bet that (some time in late February) everyone is going to wish UNC had taken advantage of this opportunity and won a winnable game, largely because if this team doesn't improve its consistency it will take every win they can find to make the NCAA tournament.

$`Four Factors`
  OPP      NAME ORTG perEFG perORB  FTR TORATE
1 UVA     Total 0.83   0.42   0.22 0.33   0.19
2 UVA oppTotals 0.97   0.53   0.16 0.37   0.17

$`Last Game`
           POSS ORTG  USG perFTM perFGM perFGM.3 perORB perDRB perTS  FTR perEFG ARATE TORATE
Bullock       9 2.44 0.18   1.00   0.78     0.67   0.07   0.12  1.02 0.44   1.00  0.07   0.00
McAdoo       15 0.67 0.32   0.50   0.44      NaN   0.00   0.30  0.46 0.44   0.44  0.00   0.27
Hubert        3 1.00 0.10   0.50   0.50      NaN   0.00   0.13  0.52 1.00   0.50  0.00   0.00
Strickland    7 0.57 0.16    NaN   0.29      NaN   0.00   0.00  0.29 0.00   0.29  0.23   0.00
Paige         9 0.22 0.20    NaN   0.14     0.00   0.00   0.14  0.14 0.00   0.14  0.15   0.22
James         4 0.00 0.28   0.00   0.00      NaN   0.00   0.57  0.00 2.00   0.00  0.00   0.50
Hairston     10 0.80 0.29   0.33   0.33     0.20   0.05   0.24  0.39 0.33   0.39  0.19   0.10
Johnson       2 1.00 0.13    NaN   0.50      NaN   0.11   0.12  0.50 0.00   0.50  0.21   0.50
McDonald      5 0.00 0.21    NaN   0.00     0.00   0.00   0.00  0.00 0.00   0.00  0.00   0.40
Tokoto        2 0.50 0.18   0.50   0.00      NaN   0.17   0.00  0.17 1.00   0.00  0.00   0.00
Davis        NA   NA   NA     NA     NA       NA     NA     NA    NA   NA     NA    NA     NA
Simmons      NA   NA   NA     NA     NA       NA     NA     NA    NA   NA     NA    NA     NA
Total        63 0.83 1.00   0.53   0.37     0.29   0.22   0.87  0.44 0.33   0.42  0.47   0.19
oppTotals    63 0.97 1.00   0.50   0.45     0.57   0.16   0.75  0.54 0.37   0.53  0.64   0.17

$`Season Totals`
         NAME POSS ORTG  USG perFTM perFGM perFGM.3 perORB perDRB perTS  FTR perEFG ARATE TORATE
1     Bullock  123 1.46 0.18   0.86   0.51     0.49   0.07   0.13  0.67 0.17   0.64  0.15   0.11
2      McAdoo  213 0.95 0.28   0.61   0.46      NaN   0.09   0.18  0.50 0.38   0.46  0.06   0.21
3      Hubert   19 0.95 0.06   0.18   0.53      NaN   0.07   0.11  0.45 0.73   0.53  0.02   0.53
4  Strickland  138 0.91 0.20   0.59   0.46     0.22   0.02   0.07  0.51 0.42   0.48  0.22   0.15
5       Paige  135 0.67 0.21   0.77   0.34     0.28   0.01   0.07  0.43 0.13   0.40  0.21   0.24
6       James   38 1.13 0.11   0.56   0.50      NaN   0.10   0.17  0.52 0.47   0.50  0.03   0.39
7    Hairston  138 1.14 0.29   0.83   0.38     0.33   0.10   0.13  0.53 0.32   0.48  0.10   0.11
8     Johnson   89 1.33 0.25   0.50   0.63      NaN   0.09   0.24  0.63 0.13   0.63  0.03   0.12
9    McDonald  111 1.06 0.23   0.69   0.40     0.43   0.05   0.06  0.54 0.16   0.52  0.11   0.12
10     Tokoto   50 1.14 0.18   0.36   0.53     0.14   0.11   0.14  0.52 0.49   0.54  0.09   0.24
11      Davis    6 1.33 0.05   1.00   1.00     1.00   0.00   0.03  1.39 1.00   1.50  0.41   0.50
12    Simmons    7 2.29 0.07   1.00   0.43      NaN   0.18   0.19  0.51 0.29   0.43  0.05   0.14
13      Total 1059 1.08 1.00   0.64   0.45     0.37   0.39   0.68  0.53 0.28   0.51  0.61   0.19
14   oppTotal 1059 0.92 1.00   0.65   0.39     0.35   0.30   0.62  0.48 0.28   0.45  0.51   0.21

Current Prediction (Math-based, sorry Bethy)
UNC 70, Miami 69
Win Prob 53%, Pace 70

Saturday, January 5

Easy-B's Questionable D?


I read this article today and decided to investigate some of its claims:

In the piece they argue that Brice Johnson is a defensive liability who shouldn't get playing time in conference until he proves himself. They also cite his 8.9 PPG scoring as coming largely on dunks (ok, this is an overstatement, but they're not as in love with his offensive production as I am, which is an extremely high bar to set).

Ok, first let's look over his shots by location:(http://www.scacchoops.com/tt_player_page.asp?hplayer=697&tab=3) Johnson has taken 27 dunks/layups and 44 'other' types of shots (other meaning likely not from transition). To me he isn't getting all his offense from dunks (some of which he is creating with offensive rebounds* and not out of transition) and he is an extremely valuable offensive player, something most of you have already grown tired of hearing me say. The article above seems to indicate that a lot of his offense comes on fast breaks and any player on the court would be capable of collecting those points. This backs up the fact that KenPom's numbers have him at the 2nd highest Offensive Efficiency on the team (1.33 to Bullock's 1.38).

Furthermore, advanced stats show that Brice is the best defensive rebounder on the team, collecting 25% of opponent misses (second is McAdoo at 18%). Many people forget, but this is a valuable defensive skill and can serve as a "pro" to balance out the "cons" of his slender frame and poor post defense.

Basically, I just wanted to take a moment to point out how advanced stats can help to prove or disprove common wisdom and what we see as we watch basketball. Brice Johnson isn't a defensive force, but he rebounds the ball very well and his offense is more valuable than might be evident at first glance. I do think any McAdoo-Johnson pairing should be used sparingly when playing a team with a strong offensive post presence and I'd be floored to see Johnson as the only 5-man on the floor. That said, I don't think Johnson should expect his time to shrink as the team enters the ACC portion of the schedule. In fact given how this team is under performing past UNC squads on offense and Roy's inner desire to shirk the 3 ball, Easy-B should see more P.T.

*Johnson has 16 offensive rebounds on the year, if a third of these were immediate layup/dunk put backs the numbers go to 22 fast break dunks and 49 others

Friday, January 4

UVA odds & UNLV post-game

Primarily, I'd like to laud the team (as most have already done) for the victory against UNLV. They played cohesively and for the first time all season looked consistently above average on defense all game. On the offensive end, McAdoo (among others) got to the free throw line, and was able to shake off another shaky start. From a less statistical viewpoint, it was impressive to see the team not give up and let a game slip away that was very tenuous in the second half. The ability of 19 and 20 year old kids to maintain focus and play through losing the lead in a tight game is something Tar Heel fans frequently take for granted. Games like this one are what really give me enjoyment out of the "rebuilding years", the ones that you can almost see everyone taking a step forward and growing into a better team.

This game had nearly the opposite trend that I complimented against McNeese St. In that game Strickland and Paige got out of the way, and had elevated efficiency ratings by using fewer possessions. This game was just as positive an outcome in nearly the opposite direction. Both players took more shots than average while improving their rating. I wouldn't have predicted that to be successful, at least in Paige's case but it worked and the more these two perform at this level the better the offense as a whole will look.
Similarly the trips to the foul line improved markedly. As you can see from the pentagon graph, UNC was on a similar level with UNLV in 3 of the 4 factors (perEFG, perORB, TORate) but the Free Throw Rate was much higher. The large advantage there and to a lesser extent the small one in TORate led to the efficiency advantage that won the game.


$`Four Factors`
   OPP     NAME ORTG perEFG perORB  FTR TORATE
1 UNLV    Total 1.10   0.54   0.33 0.60   0.17
2 UNLV oppTotal 1.01   0.53   0.38 0.36   0.24


The Heels also did an excellent job forcing turnovers and running the secondary break. This is crucial as it gives the offense the best set of looks (one on an initial break and another in the secondary) possible and yields much better offensive performance than playing against a set half-court.


Speaking of the set half-court, up next is UVA. Tony Bennett's Virginia squad is notoriously slow, playing the 342nd slowest pace in the country. Fast breaks will be few and far between, something that could make things appear quite ugly. KenPom's prediction makes this the slowest game the Heels have played this season (LBSU at 70 holds the current low mark). Looking to that game for clues, offensive rebounding will be big. When the offensive isn't performing well, rebounding can help UNC puts points on the board. As always 3pt shooting is important, but especially so this weekend with UVA's "pack-line" defense. If Bullock can play and he and the other shooters are hot, it could give the offense a little more room to operate. Similarly with the pack-line in operation, the long jumpers of McAdoo and Johnson could prove pivotal. That said, UVA isn't exactly torching the nets themselves, ranking 80th in offense (OE 105.2). With the defense coming off a strong performance (the kind I doubted the existence of after the Texas game) this could be another opportunity for a strong showing.

UVA Prediction:
UVA 68.2, UNC 63.6
67.937 Poss
Home Win % 0.69169


Along with the following breakdown of individual players:
$`Last Game`
           POSS ORTG  USG perFTM perFGM perFGM.3 perORB perDRB perTS  FTR perEFG ARATE
Bullock      NA   NA   NA     NA     NA       NA     NA     NA    NA   NA     NA    NA
McAdoo       15 0.87 0.25   0.60   0.38      NaN   0.11   0.19  0.43 0.38   0.38  0.13
Hubert        3 1.00 0.08   0.50   1.00      NaN   0.00   0.17  0.80 2.00   1.00  0.00
Strickland   14 1.14 0.24   0.60   0.62     0.00   0.00   0.10  0.65 1.25   0.62  0.09
Paige        14 0.86 0.23   0.75   0.33     0.00   0.04   0.10  0.48 0.89   0.33  0.17
James         3 0.00 0.56    NaN   0.00      NaN   0.00   0.00  0.00 0.00   0.00  0.00
Hairston     10 1.50 0.17   0.75   0.50     0.40   0.08   0.07  0.64 0.40   0.60  0.04
Johnson       8 1.50 0.30   0.00   0.75      NaN   0.25   0.07  0.68 0.25   0.75  0.00
McDonald      4 1.25 0.12    NaN   0.50     0.50   0.00   0.06  0.62 0.00   0.62  0.00
Tokoto        2 1.50 0.10   0.50   1.00      NaN   0.11   0.10  0.80 2.00   1.00  0.26
Davis        NA   NA   NA     NA     NA       NA     NA     NA    NA   NA     NA    NA
Simmons      NA   NA   NA     NA     NA       NA     NA     NA    NA   NA     NA    NA
Total        72 1.10 1.00   0.61   0.51     0.30   0.33   0.59  0.57 0.60   0.54  0.43
oppTotal     72 1.01 1.00   0.52   0.46     0.47   0.38   0.73  0.53 0.36   0.53  0.56


And the season's stats in total:

$`Season Totals`
         NAME POSS ORTG  USG perFTM perFGM perFGM.3 perORB perDRB perTS  FTR perEFG ARATE
1     Bullock  114 1.38 0.19   0.82   0.49     0.48   0.07   0.13  0.64 0.15   0.62  0.15
2      McAdoo  198 0.97 0.28   0.62   0.46      NaN   0.10   0.18  0.50 0.38   0.46  0.06
3      Hubert   16 0.94 0.06   0.11   0.54      NaN   0.08   0.11  0.44 0.69   0.54  0.02
4  Strickland  131 0.93 0.20   0.59   0.47     0.22   0.02   0.08  0.52 0.45   0.49  0.22
5       Paige  126 0.71 0.21   0.77   0.35     0.30   0.01   0.07  0.45 0.14   0.42  0.21
6       James   34 1.26 0.10   0.64   0.52      NaN   0.10   0.16  0.55 0.42   0.52  0.04
7    Hairston  128 1.16 0.30   0.87   0.38     0.34   0.10   0.12  0.54 0.32   0.48  0.09
8     Johnson   87 1.33 0.26   0.50   0.63      NaN   0.09   0.25  0.63 0.14   0.63  0.03
9    McDonald  106 1.11 0.24   0.69   0.41     0.45   0.05   0.07  0.56 0.16   0.54  0.11
10     Tokoto   48 1.17 0.17   0.35   0.56     0.14   0.11   0.14  0.54 0.47   0.57  0.10
11      Davis    6 1.33 0.05   1.00   1.00     1.00   0.00   0.03  1.39 1.00   1.50  0.40
12    Simmons    7 2.29 0.07   1.00   0.43      NaN   0.18   0.19  0.51 0.29   0.43  0.05
13      Total  996 1.09 1.00   0.64   0.46     0.37   0.40   0.66  0.54 0.28   0.51  0.61
14   oppTotal  996 0.91 1.00   0.66   0.39     0.34   0.31   0.61  0.48 0.27   0.45  0.51

Saturday, December 29

UNLV odds & McNeese St stats


First the important stuff, odds for today's game:

UNC 81.11508, UNLV 78.49534
Pace 80.86226
HomeWinP 0.5932798
Check out that pace! Could be a real barn burner, which I feel would only help Carolina. More possessions against a poorly set up defense can only be a good thing. It'll also be interesting to see who draws Bennett on the defensive end. McAdoo might be the most similar player but he typically reserves his effort for offense. Could it be a task for Bullock, who is a hair shorter but is frequently lauded for his defense? I don't know if Joel James is quick enough or experienced enough, time will certainly tell.
Now the results from McNeese St (my apologies, the laptop didn't make it home to Charlotte) arguably the Tar Heels' best performance of the season (at least measuring as compared to KenPom predictions). It also came in a home game, hopefully the shooters continue to see a big basket today. Most notably, and I can't emphasize this enough, the Carolina offense is more inextricably linked to Field Goal shooting than ever this year. The performance against McNeese State did nothing to dissuade me of that notion. Yes, all teams are beholden to their shooting percentage but with more 3 point attempts and less ability to rebound and play defense this Tar Heel team is particularly at the mercy of their shooters (much to Roy's chagrin "If you want to give me something, that means that you think it’s to your advantage. I want to take what I want.")
One of the most encouraging signatures from the graph is the location of the point guards. Both Strickland and Paige used fewer possessions and had higher ratings than their season averages. I didn't watch this one, but it would indicate to me that they didn't turn the ball over, or take too many shots, instead relying on McAdoo and thewing players to generate offense. Assuming I'm correct this would indicate that the offense was operating well with fewer broken plays and forced shots by those two players. To use a sports metaphor, imagine a QB like Cam Newton able to find many different receivers and less reliant on scrambling out of trouble.
Jackson Simmons and Joel James both broke the possession meter. Each had more offensive rebounds than shots attempted and thus created possessions for the Heels and had "negative" possession used numbers.Also, PJ Hairston's name is really really big, which can happen when a blowout limits his playing time. He used 39 percent of the team's possessions while on the floor, a comfortable first place over McAdoo at 29%. 
$`Four Factors`
        OPP      NAME ORTG perEFG perORB  FTR TORATE
1 McNeeseSt     Total 1.28   0.56   0.51 0.30   0.18
2 McNeeseSt oppTotals 0.83   0.38   0.31 0.26   0.13

$`Last Game`
           POSS  ORTG   USG perFTM perFGM perFGM.3 perORB perDRB perTS  FTR perEFG ARATE
Bullock      11  1.55  0.24    NaN   0.60     0.62   0.04   0.13  0.85 0.00   0.85  0.19
McAdoo       13  0.92  0.29   0.33   0.45      NaN   0.08   0.16  0.44 0.55   0.45  0.14
Hubert        3  0.67  0.16    NaN   1.00      NaN   0.18   0.23  1.00 0.00   1.00  0.00
Strickland    8  1.00  0.16    NaN   0.67     0.00   0.00   0.09  0.67 0.00   0.67  0.26
Paige         6  1.00  0.13    NaN   0.33     1.00   0.00   0.00  0.50 0.00   0.50  0.41
James         0  -Inf  0.00   1.00   1.00      NaN   0.18   0.23  1.06 2.00   1.00  0.00
Hairston     12  1.67  0.39   0.88   0.45     0.38   0.24   0.19  0.69 0.73   0.59  0.14
Johnson       7  1.43  0.26    NaN   0.50      NaN   0.20   0.22  0.50 0.00   0.50  0.00
McDonald     10  1.10  0.25   0.67   0.33     0.43   0.00   0.04  0.53 0.33   0.50  0.16
Tokoto        3  1.00  0.11   0.33   0.50      NaN   0.07   0.11  0.45 1.50   0.50  0.16
Davis         0   NaN  0.00    NaN    NaN      NaN   0.00   0.00   NaN  NaN    NaN  0.00
Simmons      -1 -4.00 -0.09    NaN   0.67      NaN   0.67   0.12  0.67 0.00   0.67  0.00
Total        76  1.28  1.00   0.64   0.47     0.46   0.51   0.67  0.58 0.30   0.56  0.83
oppTotals    76  0.83  1.00   0.42   0.36     0.18   0.31   0.49  0.39 0.26   0.38  0.46

$`Season Totals`
         NAME POSS ORTG  USG perFTM perFGM perFGM.3 perORB perDRB perTS  FTR perEFG ARATE
1     Bullock  114 1.38 0.19   0.82   0.49     0.48   0.07   0.13  0.64 0.15   0.62  0.15
2      McAdoo  183 0.98 0.28   0.62   0.47      NaN   0.10   0.17  0.50 0.38   0.47  0.06
3      Hubert   13 0.92 0.06   0.00   0.50      NaN   0.09   0.10  0.40 0.58   0.50  0.02
4  Strickland  117 0.91 0.20   0.59   0.46     0.24   0.02   0.08  0.51 0.38   0.48  0.23
5       Paige  112 0.69 0.21   0.80   0.36     0.32   0.01   0.06  0.45 0.06   0.43  0.22
6       James   31 1.39 0.09   0.64   0.53      NaN   0.10   0.16  0.56 0.44   0.53  0.04
7    Hairston  118 1.14 0.32   0.88   0.37     0.33   0.11   0.13  0.54 0.31   0.47  0.10
8     Johnson   79 1.32 0.25   0.60   0.62      NaN   0.08   0.26  0.62 0.13   0.62  0.03
9    McDonald  102 1.11 0.24   0.69   0.41     0.45   0.05   0.07  0.55 0.17   0.54  0.12
10     Tokoto   47 1.13 0.18   0.33   0.55     0.14   0.11   0.15  0.53 0.43   0.56  0.08
11      Davis    6 1.33 0.05   1.00   1.00     1.00   0.00   0.03  1.39 1.00   1.50  0.40
12    Simmons    7 2.29 0.07   1.00   0.43      NaN   0.18   0.18  0.51 0.29   0.43  0.05
13      Total  924 1.09 1.00   0.65   0.45     0.37   0.40   0.67  0.53 0.26   0.51  0.63
14   oppTotal  924 0.90 1.00   0.68   0.38     0.33   0.31   0.61  0.47 0.26   0.44  0.50

Thursday, December 20

UNC @ Texas, Post Game Wrap


Well, no one can say KenPom didn't warn us. On one end the game was largely what I expected it to be given Texas' ratings. The Heels' offense has been hot and cold this year, centering largely around the 3 point shot without which it seems to flounder. And last night flounder it did, greatly. There was the all too familiar 4-6 point hump (similar to Butler, couldn't quite get close enough to lead), and if a few more 3's drop (team 3FG% was 16, normally it is 36) Carolina would have briefly seized the lead. As an exercise, just check out the game graph, every UNC player except Brice Johnson under-performed their season offense. Texas is a good defensive team, and those numbers are the evidence to back up Bullock no having open looks at 3 and McAdoo getting bullied out of the post.
The other end of the game was much more troubling. Texas isn't a good offensive team. Much like a teacher who knows which material is going to be on the test, I'm going to repeat that in case you missed it, TEXAS ISN'T A GOOD OFFENSIVE TEAM (98.7 adjusted, 174th after last night). Upcoming on the schedule is UNLV (45th), UVA (104th), Miami (25th), FSU (96th) and Maryland (72nd). These are all teams better than Texas, a bad offensive team that UNC refused to guard at times. To me this is as alarming a problem as the offense, defense is based on communication and effort and should "gel" a lot faster than the offense.
Now, if you've made it through the two doomsday paragraphs above I have some positive notes. In general these problems seem fixable. I wasn't super impressed with Texas' defense, they were good but the Heels gave the ball away multiple times, you remember the ball bouncing off McAdoo's hands, James' hands, through Bullock's leg, off Tokoto's hands out of bounds. Also, the issues overall are more of consistency than ability (which frequently makes it all the more frustrating). Take the 11 minutes spanning halftime, where UNC used a 30-15 run and closed a 39-20 deficit to 54-50, and did it only using 2 3-pointers (of 3 made in the game :-/).
Consistency problems have always plagued Roy Williams' teams, this isn't something that should come as a surprise to no one. Even in 2009 the team had ups and downs (Loss to Boston College anyone?), but no one notices when you're 15 to 20 points better than your opponents. When you're basically even to start with and you under-perform you lose basketball games. I don't think expecting 30-15 runs for 40 minutes is realistic, but somewhere in that 10 minute span exists the Carolina team that the media, coaches and fans went into the season expecting.
-Ryan
Cason, here's some +/- stats for you, that 30-15 run consisted of 10m51s here is the breakdown:
Bullock 10:51
McAdoo 9:01
Strickland 9:01
Paige 7:30
Tokoto 6:53
Hairston 5:19
McDonald 3:46
James 3:15
The name that jumps out to me is Tokoto, the others are all in proportion to their minutes played across the whole game, while Tokoto's minutes are elevated. Roy made the choice to start Tokoto in the second half and it payed off as the run continued.

$`Four Factors`
    OPP      NAME ORTG perEFG perORB  FTR TORATE
1 Texas     Total 0.84   0.34   0.39 0.48   0.22
2 Texas oppTotals 1.06   0.45   0.38 0.35   0.16

$`Last Game`
           POSS ORTG  USG perFTM perFGM perFGM.3 perORB perDRB perTS  FTR perEFG ARATE
Bullock      14 1.29 0.23   0.83   0.35     0.25   0.16   0.20  0.46 0.35   0.38  0.13
McAdoo       18 0.78 0.28   0.60   0.36      NaN   0.07   0.16  0.45 0.91   0.36  0.12
Hubert        1 0.00 0.06    NaN   0.00      NaN   0.10   0.00  0.00 0.00   0.00  0.00
Strickland   11 0.82 0.18   0.75   0.43     0.00   0.00   0.06  0.51 0.57   0.43  0.06
Paige        12 0.58 0.21   1.00   0.25     0.17   0.00   0.00  0.39 0.25   0.31  0.34
James         3 0.00 0.21    NaN    NaN      NaN   0.11   0.00   NaN  NaN    NaN  0.00
Hairston     12 0.75 0.27   1.00   0.17     0.14   0.14   0.08  0.33 0.33   0.21  0.09
Johnson       3 1.33 0.25    NaN   0.67      NaN   0.00   0.29  0.67 0.00   0.67  0.00
McDonald      6 0.33 0.17    NaN   0.14     0.00   0.04   0.10  0.14 0.00   0.14  0.00
Tokoto        4 1.00 0.11   0.33   1.00      NaN   0.04   0.23  0.55 6.00   1.00  0.00
Davis        NA   NA   NA     NA     NA       NA     NA     NA    NA   NA     NA    NA
Simmons      NA   NA   NA     NA     NA       NA     NA     NA    NA   NA     NA    NA
Total        80 0.84 1.00   0.69   0.31     0.16   0.39   0.57  0.41 0.48   0.34  0.52
oppTotals    80 1.06 1.00   0.73   0.41     0.35   0.38   0.55  0.50 0.35   0.45  0.40
$`Season Totals`
         NAME POSS ORTG  USG perFTM perFGM perFGM.3 perORB perDRB perTS  FTR perEFG ARATE
1     Bullock  103 1.36 0.18   0.82   0.48     0.45   0.07   0.13  0.62 0.16   0.59  0.15
2      McAdoo  170 0.99 0.28   0.65   0.47      NaN   0.10   0.18  0.51 0.37   0.47  0.05
3      Hubert   10 1.00 0.05   0.00   0.45      NaN   0.08   0.09  0.36 0.64   0.45  0.02
4  Strickland  109 0.90 0.20   0.59   0.45     0.25   0.03   0.08  0.50 0.41   0.47  0.22
5       Paige  106 0.67 0.22   0.80   0.36     0.28   0.01   0.07  0.44 0.06   0.43  0.20
6       James   31 1.26 0.10   0.58   0.52      NaN   0.10   0.16  0.54 0.39   0.52  0.04
7    Hairston  106 1.08 0.31   0.88   0.36     0.33   0.10   0.13  0.52 0.26   0.46  0.10
8     Johnson   72 1.31 0.25   0.60   0.64      NaN   0.07   0.27  0.64 0.14   0.64  0.03
9    McDonald   91 1.12 0.24   0.69   0.42     0.46   0.06   0.07  0.56 0.15   0.54  0.12
10     Tokoto   43 1.16 0.19   0.33   0.55     0.14   0.11   0.15  0.54 0.38   0.56  0.07
11      Davis    6 1.33 0.05   1.00   1.00     1.00   0.00   0.03  1.39 1.00   1.50  0.41
12    Simmons    8 1.50 0.08   1.00   0.36      NaN   0.12   0.19  0.47 0.36   0.36  0.05
13      Total  848 1.07 1.00   0.65   0.45     0.36   0.39   0.67  0.53 0.25   0.51  0.61
14   oppTotal  848 0.91 1.00   0.70   0.38     0.34   0.31   0.62  0.48 0.26   0.45  0.51

McNeese St prediction(107.4,90.2,75.7,97.5,104.2,65.3)
$HomePts
[1] 85.78433
$AwayPts
[1] 63.7419
$Pace
[1] 73.45037
$HomeWinP
[1] 0.9681834

Saturday, December 15

ECU @ UNC 121215


$`Four Factors`
  OPP     NAME ORTG perEFG perORB  FTR TORATE
1 ECU    Total 1.16   0.55   0.29 0.36   0.15
2 ECU oppTotal 1.09   0.49   0.24 0.40   0.12

wtf, these shapes are way to similar
































































$`Last Game`
           POSS ORTG  USG perFTM perFGM perFGM.3 perORB perDRB perTS  FTR perEFG ARATE
Bullock       7 2.00 0.13    NaN   0.67     0.67   0.17   0.11  0.78 0.00   0.78  0.18
McAdoo       20 0.95 0.31   0.90   0.36      NaN   0.00   0.21  0.52 0.71   0.36  0.00
Hubert        0  NaN 0.00    NaN    NaN      NaN   0.00   0.00   NaN  NaN    NaN  0.00
Strickland   11 1.09 0.17   0.75   0.67     0.50   0.00   0.12  0.77 0.67   0.75  0.37
Paige         9 0.67 0.18   0.50   0.33     0.50   0.00   0.08  0.44 0.33   0.42  0.19
James         4 0.50 0.25    NaN   0.25      NaN   0.00   0.12  0.25 0.00   0.25  0.00
Hairston     11 1.64 0.28   1.00   0.60     0.50   0.17   0.14  0.77 0.40   0.70  0.00
Johnson       5 1.00 0.17   0.50   0.50      NaN   0.00   0.25  0.51 0.50   0.50  0.00
McDonald     10 0.70 0.23   1.00   0.33     0.00   0.00   0.09  0.37 0.11   0.33  0.05
Tokoto        4 2.50 0.20   0.00   1.00      NaN   0.22   0.30  0.92 0.20   1.00  0.12
Davis         0  NaN 0.00    NaN    NaN      NaN   0.00   0.00   NaN  NaN    NaN  0.00
Simmons       0  NaN  NaN    NaN    NaN      NaN    NaN    NaN   NaN  NaN    NaN   NaN
Total        80 1.16 1.00   0.79   0.51     0.43   0.29   0.71  0.60 0.36   0.55  0.59
oppTotal     80 1.09 1.00   0.78   0.42     0.37   0.24   0.74  0.55 0.40   0.49  0.54

$`Season Totals`
         NAME POSS ORTG  USG perFTM perFGM perFGM.3 perORB perDRB perTS  FTR perEFG ARATE
1     Bullock   89 1.37 0.17   0.82   0.51     0.47   0.06   0.12  0.65 0.12   0.63  0.15
2      McAdoo  152 1.01 0.28   0.67   0.48      NaN   0.10   0.18  0.52 0.32   0.48  0.05
3      Hubert    9 1.11 0.05   0.00   0.50      NaN   0.08   0.10  0.38 0.70   0.50  0.02
4  Strickland   98 0.91 0.20   0.57   0.45     0.27   0.03   0.08  0.50 0.39   0.47  0.24
5       Paige   94 0.68 0.22   0.67   0.37     0.30   0.01   0.08  0.45 0.04   0.44  0.19
6       James   28 1.39 0.09   0.58   0.52      NaN   0.10   0.16  0.54 0.39   0.52  0.04
7    Hairston   94 1.12 0.31   0.86   0.39     0.35   0.09   0.13  0.54 0.25   0.49  0.10
8     Johnson   69 1.30 0.25   0.60   0.64      NaN   0.07   0.27  0.64 0.15   0.64  0.03
9    McDonald   85 1.18 0.25   0.69   0.44     0.47   0.06   0.07  0.59 0.16   0.58  0.12
10     Tokoto   39 1.18 0.20   0.33   0.54     0.14   0.13   0.13  0.54 0.23   0.55  0.09
11      Davis    6 1.33 0.05   1.00   1.00     1.00   0.00   0.03  1.39 1.00   1.50  0.40
12    Simmons    8 1.50 0.09   1.00   0.36      NaN   0.12   0.20  0.47 0.36   0.36  0.05
13      Total  767 1.10 1.00   0.64   0.47     0.38   0.39   0.68  0.54 0.23   0.52  0.61
14   oppTotal  767 0.90 1.00   0.70   0.38     0.34   0.30   0.63  0.48 0.25   0.45  0.52

Tuesday, September 18

Monday, February 27

UVA, Duke, and a short treatise on college basketball...

Ok, this might be a long one, I watched a lot of great basketball yesterday (and currently OSU-Wisconsin) so I'm primed and ready to go for March Madness. I'm going to do my typical recap of the UVA game first, which will likely lead into a larger commentary on college basketball (thanks to some awesome graphs from KenPom). That will then be followed by a Duke preview, because I'm travelling again Thursday-Saturday and won't have time to get one out later this week. Alright, time to follow me down the rabbit hole!

Yesterday the Heels seemed to follow an all too familiar formula when playing on the road. They started out good or even great, got a little lazy and trailed early in the second half, and finally put some solid defensive possessions together for the win. The mild difference was that I'm not 100% sure the "solid defensive possessions" were that and not just some luck. Some will say luck is the residue of design, though I'd disagree and say that UVA missed some fairly open shots. Then again if the Heels designs were to be ahead, that fact likely forced Mike Scott into an ill-advised 3 on one of the Hoos' last possessions. In brief summary, UNC played decently and shot poorly, luckily UVA played a little worse and shot poorer. Looking over the Four Factor numbers the Heels took care of the non-shooting aspects of the game, decent TO numbers, good rebounding, good number of trips to the line. Similarly, the Heels did the same on the defensive end of the floor, kept UVA off the glass and the free throw line (UNC eschews forcing turnovers as a defensive mechanism, if you don't agree with me or are unsure why I have a book recommendation for you). The Heels did all the little things right, but their shots didn't fall. Because they did those other things the Heels left Charlottesville still in control of their own ACC (and NCAA?) destiny.

Favorite Number: 10 FTR (Free Throw Rate, FTA/FGA) - This is key to the UNC defense, I'm sure you've heard me say it, but I'll hammer on it again. The height the Heels have, and the method of defense they play, relies on the ability to affect shots without fouling. In games the Heels have posted under 25 FTR (19 total this year), the opponent has only exceeded 1 point per possession once (Jan 29th, hosting GT) and the Heels are 19-0 in those games. Not to get ahead of myself but this will be a huge key to the Duke game.


Least Favorite Number: 35.3 eFG% - I'm not a fan of the number itself, but it is more how the Heels earned this metric that was upsetting. There were a number of poorly selected shots (largely 3's) and possessions where the ball never visited the paint. I don't mind a cold shooting night, but only when it is earned by the opposition through defense and not as a result of lazy or misguided offensive flow.

Of the Four Factors, the one that has the most bearing on efficiency is Field Goal percentage. This isn't a debatable fact, you can play a rotten all around game and be bailed out by good shooting. Alternatively you can have a cold night shooting and lose despite be on point everywhere else. That is what makes college basketball exciting and unpredictable, and what makes March worth experiencing. On Saturday, the Heels produced a poor shooting night of epic proportions, only making 17 field goals. This is arguably the bottom of the Heels' efficiency curve, they won't have many nights worse than this, and a lot of that can be attributed to random shooting noise, especially when a team like Virginia slows the pace down to 60 possessions. Fortunately the Wahoos had a poor shooting night of their own, and had an offensive output similarly on the bottom end of their spectrum. I've graphed (below) the offensive efficiency outputs of UNC and UVA over the course of the year to illustrate my point:

Inline image 1

As you can see, the UNC curve exists at higher efficiency values than the UVA one, meaning that the Heels' worst game is better than the Hoos' worst game. I felt that it would be safe to ignore defense here, since UNC and UVA have similarly skilled defenses. Though one could graph each curve with raw data first, and then account for each team's defense after the fact.* As an example, graphing Duke and UNC's curves would show Duke to the right of UNC, but would be ignoring the fact that UNC's defense is measurably better than the Devils' defense this year (yes, I just wrote that sentence, tongue far from cheek, crazy huh?).

Any college basketball game can be simplified to this degree, the point per possession output of one team graphed against the other, and will be fairly accurate as long as you take home court and defense into account. (You might notice that UVA is bi-modal, which complicates things, I'm assuming most teams have offensive outputs that fit a normal curve). This is a really neat way to think about basketball, and in a way provides insight into March Madness. When you look at a given matchup there are a pair of curves. For any two teams that make the NCAA tournament their curves overlap. The only ones that don't (using historical data) are the #1 and #16 teams, which is truly saying something. Of the 67 games that will be played in March (& April), 63 of them could go either way and the large majority of those will contain curves like the above, where the teams only differ by 5-10 points/100 possessions. The largest swings in the curves happen as a result of field goal percentage, something that can come and go in any given game, making March all the more mad. KenPom has talked a bit about this lately on his blog (see the link above), mainly within the context of three point shooting being a force that serves to increase entropy within the system of college basketball.

Now, are there exceptions to this rule? Of course. The 2009 Tar Heels weren't within 10 points of any of their 6 NCAA opponents. There is the occasional team and bracket breakdown that leads to an overwhelming champion, but this year doesn't appear to have an overwhelming favorite. If the chips fall the right way, Kentucky would be the one team to run the table, but they aren't worlds above the other teams in the bracket and they have won a few close games against a lackluster SEC.

So, since UNC falls into the category of "not overwhelming favorite" their chances will be bettered by a safer path to the Final Four. Depending upon how the remainder of the season plays out, UNC seems to be destined for between 3 and 10 on the NCAA tourney "S-curve" (where 1-4 are 1-seeds, 5-8 are 2-seeds, Lunardi currently guesses UNC is at 6 http://espn.go.com/blog/collegebasketballnation/post/_/id/50096/joe-lunardis-latest-bracketology-update-10). For simplicity I'm assuming a win against Maryland, but depending upon the Duke outcome and ACC tournament performance the Heels could end up as high as the 3rd or 4th #1 seed, but as low as a #3. That said, what matters more than any positioning between 4-7 is the ability to avoid the best teams and the teams that are underseeded. For instance, this year it'd be best to stay out of Kentucky's region, and away from a dangerous Buckeye team that seems destined for a 3-seed. Funny that given any small change in the last Duke-UNC game and the entire storyline of the season would be different (see here for the original publishing of this idea http://www.basketballprospectus.com/article.php?articleid=2087). As it stands, Carolina has finished the season strong and in position to set themselves up for a #1 seed regardless of Austin Rivers' heroics.

Next Saturday the ACC crown will be up for grabs (safe to say, because even if either team loses mid-week, the regular season title will still be in the balance). There are a couple of keys to the game in my mind, one that I've touched on already.

1) Free Throws - Duke cannot get to the line as often as they did in the Dean Dome. This may see like an impossibility, and it very well may be, but the point is that if Duke is only taking shots from inside the arc on half their possessions, UNC can't foul them on and additional half of those. Let Duke take contested 3's, or let Rivers drive, but don't let Duke beat you from behind the arc and the stripe.



2) Rebounding, Offensive - Duke isn't a horrible defensive team, just a bad team relative to Dukes of the past. One of the most stark contrasts in this game is UNC's ability to board its own misses (5th in the country/1st in the ACC) with Duke's inability to prevent it (162nd/9th). Even if the shots aren't dropping, the Heels must trust their own inside game and their ability to get multiple shot attempts in a given possession. Last game Duke had 10 offensive rebounds while UNC had 14, if the Heels are going to withstand a hot shooting night from Duke, they'll need to get 2 and 3 attempts off their own misses.

3) Additional Production - In the first game, UNC's key players played well, but no one else stepped up. Bullock and Hairston had 0-fers from behind the arc and McAdoo had 6 points. This was balanced well by the key scorers pouring in above average nights, but that can't be expected to happen. Bullock, Hairston, or even McAdoo getting hot would go a long way to a Carolina win.

The way that Duke and UNC are designed are in such a complementary fashion that each has the other's number. If you were to create an offense UNC would have difficulty with, it would be one with a potent slasher (Rivers) and several role players capable of hitting three's when the defense gets sloppy. Similarly, Duke has problems defending opponents in the post (they're 2nd in the country in opponent's 3PA/FGA, no one shoots 3's against them) and preventing opponents from getting second looks. UNC played an excellent game offensively on February 8th, but Duke's 14 3's were too much to overcome. I doubt Duke will hit 14 3's again (they hit 14 one other time this year, and took over 30 in one other game), but the Heels will have a difficult time reaching 84 this time around as well.


*There are actually even further assumptions that are being made to simplify the situation. One could even make further assumptions about how defenses and offenses interact. Overall UVA and FSU have very similar defensive efficiencies, but they each arrive at the numbers differently. FSU uses their height and length to pester opponents into turnovers, while UVA doesn't focus on them and is shorter overall. Thus the same defensive metric has grossly different effects on UNC. So, much like physics, college basketball theory works best in a vacuum