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February 7, 2012

The Consistency of LaMarcus Aldridge

Filed under: Uncategorized — Kevin Pelton @ 3:57 pm

LaMarcus Aldridge did something unusual in last night’s overtime loss to the Oklahoma City Thunder: he scored 39 points. The Portland Trail Blazers forward, who is likely to be chosen an All-Star for the first time when reserves are announced on Thursday, ranks fifth in the league in scoring at 23.7 points per game. Yet Aldridge has scored 30-plus points just three times this season; Monday was the first time all year he’s topped the 35-point mark. He has fewer 30-point games than Chris Bosh, who averages 19.7 points per game.

The difference between the two players is consistency. Aldridge has scored at least 20 points in 19 of his 25 games (as compared to 11 such games for Bosh) and has yet to score fewer than 13 points in any game. Aldridge’s splits this season are essentially identical. Per Basketball-Reference.com, he averages 23.7 points at home and 23.8 on the road. He averages 23.6 points in wins and 23.8 in losses. On no day of the week has he averaged more than 25.3 points or fewer than 20.8.

It’s no surprise, then, that when Tom Ziller of SBNation.com looked at consistency among the NBA’s leading scorers last week, Aldridge ranked third, trailing superstars Kevin Durant and LeBron James. What Ziller calls “volatility” has been termed “shake” in past research on the issue by Benjamin Golliver of Blazersedge.com–a blog featuring Blazers news and analysis. Both are user-friendlier ways of referencing the statistical concept of coefficient of variation, which improves upon the familiar standard deviation by allowing us to better compare scorers with different averages. (That is, a point of deviation per night is much more meaningful for a player who averages five points per game than one who averages 25.)

Before last night’s game, Golliver put together shake ratings for Aldridge and retired Portland star Brandon Roy throughout their careers. Here’s what those look like graphically:

Consistency of Blazers stars LaMarcus Aldridge and Brandon Roy

In the past, Golliver has noted that Aldridge wasn’t given enough credit for his consistency. His numbers tracked closely to Roy’s between 2007-08, when he broke into the starting lineup full time, and 2009-10. Last season, a hobbled Roy was physically unable to produce consistent efforts, forcing Aldridge into the role of go-to scorer. Really, last year’s mark for Aldridge doesn’t do his performance justice. In this as in most everything else, Aldridge had two different seasons–a relatively disappointing start followed by an All-NBA finish. From Dec. 15 onward, Aldridge’s shake was 23.1, as compared to 28.1 over the full campaign. This year, Aldridge’s shake has dropped again to 18.1, making him far more consistent as a scorer than Roy ever was.

(Note that these numbers differ slightly from Ziller’s because Golliver is using absolute difference, rather than squaring the differences as standard deviation does.)

Ziller left open the question of how valuable consistency is among leading scorers. My answer, as it always is with consistency, is “it depends.” Dean Oliver has done phenomenal research on the meaning of consistency in basketball, and has concluded that it’s positive for good teams, but negative for bad teams. Think about it this way: a player like Monta Ellis (one of the least consistent 20-point scorers, per Ziller) is liable to win the Warriors some games with his hot outings. When he struggles, well, Golden State was probably going to lose anyway. This is a legitimate reason to believe that Ellis, and players like him, are more valuable on bad teams.

Put Ellis on the Heat, by contrast, and his bad nights might mean shooting Miami into a loss. When he goes off, well, the Heat probably would have won anyway. So a consistent scorer like James is much more useful to Miami, leaving aside their varying levels of efficiency.

The Blazers are a good team that has in practice been wildly inconsistent, alternating blowout wins and close losses. But that has been an issue with role players and can’t be blamed on Aldridge, who is giving Portland an All-Star effort every night.

2012 APBRmetric All-Stars

Filed under: Uncategorized — Neil Paine @ 9:44 am

This is a series I always traditionally did over at Basketball-Reference, but I’m bringing it over to Prospectus for 2012. The concept is simple: if voters only looked at various advanced stats, which players would make the All-Star teams?

To pick teams, I used the official positional designations from the 2012 ballot; each team must have 4 guards, 4 forwards, and 2 centers, with room for 2 wildcards from any position to fill out the roster. The metrics of choice are: Win Shares, Adjusted Plus/Minus (in the form of Jeremias Engelmann’s RAPM), Statistical Plus/Minus, Player Efficiency Rating, and Alternate Win Score+ (AWS, the best linear-weights metric, adjusted for team & position and scaled where 100 = average). Unless otherwise noted, numbers are current through February 5, 2012. To qualify, players needed at least 512 minutes played.

Win Shares per 48 Minutes

***Eastern Conference***   ***Western Conference***
-------------------------  -------------------------
Guards                     Guards
-------------------------  -------------------------
Derrick Rose, CHI    .252  Chris Paul, LAC      .255
Louis Williams, PHI  .207  Kobe Bryant, LAL     .187
Paul George, IND     .183  Steve Nash, PHO      .169
Jodie Meeks, PHI     .179  Kyle Lowry, HOU      .167
-------------------------  -------------------------
Forwards                   Forwards
-------------------------  -------------------------
LeBron James, MIA    .320  James Harden, OKC    .254
Ryan Anderson, ORL   .254  Kevin Love, MIN      .249
Paul Pierce, BOS     .201  L. Aldridge, POR     .229
Thaddeus Young, PHI  .195  Paul Millsap, UTA    .225
Chris Bosh, MIA      .185  Kevin Durant, OKC    .222
Carlos Boozer, CHI   .185  Nicolas Batum, POR   .206
-------------------------  -------------------------
Centers                    Centers
-------------------------  -------------------------
Tyson Chandler, NYK  .248  Andrew Bynum, LAL    .184
Dwight Howard, ORL   .194  Marc Gasol, MEM      .181
-------------------------  -------------------------

Adjusted Plus/Minus (thru 2/2/12)

***Eastern Conference***   ***Western Conference***
-------------------------  -------------------------
Guards                     Guards
-------------------------  -------------------------
Jrue Holiday, PHI    +3.2  Chris Paul, LAC      +5.7
Joe Johnson, ATL     +2.7  Steve Nash, PHO      +4.9
Landry Fields, NYK   +2.7  Andre Miller, DEN    +4.2
Derrick Rose, CHI    +2.6  Mike Conley, MEM     +4.1
                           Kyle Lowry, HOU      +3.9
-------------------------  -------------------------
Forwards                   Forwards
-------------------------  -------------------------
Luol Deng, CHI       +4.9  Dirk Nowitzki, DAL   +6.0
LeBron James, MIA    +4.8  Paul Millsap, UTA    +4.6
Kevin Garnett, BOS   +4.3  Gerald Wallace, POR  +4.2
Thaddeus Young, PHI  +4.2  L. Aldridge, POR     +4.0
Ryan Anderson, ORL   +4.1  Kevin Durant, OKC    +3.7
Amir Johnson, TOR    +3.8
-------------------------  -------------------------
Centers                    Centers
-------------------------  -------------------------
Dwight Howard, ORL   +3.2  Nene, DEN            +3.4
Roy Hibbert, IND     +1.4  Marc Gasol, MEM      +2.9
-------------------------  -------------------------

Statistical Plus/Minus

***Eastern Conference***   ***Western Conference***
-------------------------  -------------------------
Guards                     Guards
-------------------------  -------------------------
Derrick Rose, CHI    +6.7  Chris Paul, LAC      +8.7
Paul George, IND     +4.9  Kobe Bryant, LAL     +6.2
B. Jennings, MIL     +4.8  Kyle Lowry, HOU      +6.2
Rajon Rondo, BOS     +4.1  Ricky Rubio, MIN     +5.3
Mario Chalmers, MIA  +4.0  Mike Conley, MEM     +4.1
-------------------------  -------------------------
Forwards                   Forwards
-------------------------  -------------------------
LeBron James, MIA   +10.2  Kevin Durant, OKC    +6.8
Ryan Anderson, ORL   +6.2  Kevin Love, MIN      +6.8
Paul Pierce, BOS     +5.7  Paul Millsap, UTA    +6.4
Andre Iguodala, PHI  +4.9  James Harden, OKC    +5.3
                           L. Aldridge, POR     +4.3
-------------------------  -------------------------
Centers                    Centers
-------------------------  -------------------------
Dwight Howard, ORL   +6.7  Marc Gasol, MEM      +4.0
Tyson Chandler, NYK  +4.3  Andrew Bynum, LAL    +3.3
Greg Monroe, DET     +3.9
-------------------------  -------------------------

Alternate Win Score+

***Eastern Conference***   ***Western Conference***
-------------------------  -------------------------
Guards                     Guards
-------------------------  -------------------------
Derrick Rose, CHI     175  Chris Paul, LAC       205
Ray Allen, BOS        162  Steve Nash, PHO       174
Paul George, IND      155  Kobe Bryant, LAL      172
B. Jennings, MIL      152  Kyle Lowry, HOU       148
-------------------------  -------------------------
Forwards                   Forwards
-------------------------  -------------------------
LeBron James, MIA     241  Kevin Love, MIN       199
Ryan Anderson, ORL    201  Kevin Durant, OKC     195
Paul Pierce, BOS      175  Paul Millsap, UTA     187
Andre Iguodala, PHI   166  James Harden, OKC     174
                           L. Aldridge, POR      168
                           Nicolas Batum, POR    164
-------------------------  -------------------------
Centers                    Centers
-------------------------  -------------------------
Dwight Howard, ORL    178  Andrew Bynum, LAL     164
Tyson Chandler, NYK   176  Marcin Gortat, PHO    160
Greg Monroe, DET      167
Anderson Varejao, CLE 156
-------------------------  -------------------------

Player Efficiency Rating

***Eastern Conference***   ***Western Conference***
-------------------------  -------------------------
Guards                     Guards
-------------------------  -------------------------
Derrick Rose, CHI    25.5  Chris Paul, LAC      25.9
Kyrie Irving, CLE    22.1  Kobe Bryant, LAL     25.7
Louis Williams, PHI  21.3  Steve Nash, PHO      23.1
B. Jennings, MIL     20.4  R. Westbrook, OKC    22.5
-------------------------  -------------------------
Forwards                   Forwards
-------------------------  -------------------------
LeBron James, MIA    32.7  Kevin Durant, OKC    26.8
Ryan Anderson, ORL   22.8  Kevin Love, MIN      25.5
Paul Pierce, BOS     21.6  Paul Millsap, UTA    25.1
Carmelo Anthony, NYK 20.4  L. Aldridge, POR     24.3
Chris Bosh, MIA      20.3  Blake Griffin, LAC   22.7
                           James Harden, OKC    22.2
-------------------------  -------------------------
Centers                    Centers
-------------------------  -------------------------
Dwight Howard, ORL   24.5  Andrew Bynum, LAL    22.2
Greg Monroe, DET     23.5  Al Jefferson, UTA    22.1
Tyson Chandler, NYK  20.3
-------------------------  -------------------------

This year, the consensus (mentioned on at least 3 of the 5 teams) is:

***Eastern Conference***   ***Western Conference***
-------------------------  -------------------------
Guards                     Guards
-------------------------  -------------------------
Derrick Rose, CHI          Chris Paul, LAC
B. Jennings, MIL           Kobe Bryant, LAL
Paul George, IND           Kyle Lowry, HOU
                           Steve Nash, PHO
-------------------------  -------------------------
Forwards                   Forwards
-------------------------  -------------------------
LeBron James, MIA          Kevin Durant, OKC
Ryan Anderson, ORL         L. Aldridge, POR
Paul Pierce, BOS           Paul Millsap, UTA
                           James Harden, OKC
                           Kevin Love, MIN
-------------------------  -------------------------
Centers                    Centers
-------------------------  -------------------------
Dwight Howard, ORL         Andrew Bynum, LAL
Tyson Chandler, NYK        Marc Gasol, MEM
Greg Monroe, DET
-------------------------  -------------------------

That fills 20 out of the 24 slots; all that’s left is to pick a guard and a forward from the East, and the final wildcard from each conference.

If you go with players mentioned twice, the East roster is completed with some combination of Chris Bosh and the Philly trio of Andre Iguodala, Lou Williams, and Thaddeus Young (FWIW, I’d go with Williams, Iguodala, & Young). In the West, Mike Conley & Nicolas Batum were each mentioned twice — and even though he’s not starting and Conley is, IMO Batum is having a stronger statistical season. So that means my recommended 2012 APBRmetric All-Star team would be:

***Eastern Conference***   ***Western Conference***
-------------------------  -------------------------
Guards                     Guards
-------------------------  -------------------------
Derrick Rose, CHI          Chris Paul, LAC
B. Jennings, MIL           Kobe Bryant, LAL
Paul George, IND           Kyle Lowry, HOU
Louis Williams, PHI        Steve Nash, PHO
-------------------------  -------------------------
Forwards                   Forwards
-------------------------  -------------------------
LeBron James, MIA          Kevin Durant, OKC
Ryan Anderson, ORL         L. Aldridge, POR
Paul Pierce, BOS           Paul Millsap, UTA
Andre Iguodala, PHI        James Harden, OKC
Thaddeus Young, PHI        Kevin Love, MIN
                           Nicolas Batum, POR
-------------------------  -------------------------
Centers                    Centers
-------------------------  -------------------------
Dwight Howard, ORL         Andrew Bynum, LAL
Tyson Chandler, NYK        Marc Gasol, MEM
Greg Monroe, DET
-------------------------  -------------------------

Email Neil at np@sports-reference.com. Follow him on Twitter at @Neil_Paine.

February 3, 2012

Why my top 25 looks weird

Filed under: Uncategorized — John Gasaway @ 12:33 pm

This season for the first time I’m filling out a top 25, one that, I need hardly add, is of infinitesimally small consequence even in college basketball terms, much less in the real world. My humble little top 25 plays no part in the non-coach version of “real” (AP) rankings, and even at ESPN it forms just one-sixteenth of the evaluative verdict to be found in each week’s Power Rankings. Nevertheless the responsibility of coming up with a top 25 every seven days has directed my thinking toward rewards and predictions.

There’s been a top 20-something (it was originally a top 20) for men’s college basketball since 1948, and over the years it’s become an absolute jewel of a reward. I know because like any fan I’ve waited impatiently for the rankings to come out to see if my team is included and/or has been moved up this week. By common understanding, sanctioned by decades of actual experience, what a No. 1 ranking says is something like: Congratulations! You’ve won enough games against strong competition for you to deserve this No. 1 ranking.

That is what’s so cool about the AP and ESPN/USA Today polls. They engender real interest and even excitement. If you don’t think so, I feel sorry for you because it probably means your team has never reached No. 1. Maybe the No. 1 spot has become old hat for fans of Duke and North Carolina, but for those of us who are stuck being fans of “normal” programs, trust me, it’s an amazing brand of euphoria.

The other thing that’s cool about the polls, of course, is that in this sport we then turn around in March and dispose of these interesting and exciting baubles entirely, so that we can settle things definitively on the court. The system works.

In addition to a top 25 that acts as a reward, I thought it’d be interesting to use the information I have and concoct a top 25 that functions as a set of predictions. What my top 25 is saying is something less congratulatory and exciting, and something more speculative and, hopefully, systematic. Something like: If every team in Division I could play every other team 500 times on a neutral floor, here is how I think those teams would sort themselves out according to winning percentage.

If I’m going about things correctly, I can see two potential virtues in such an exercise. First, as a reader, I’m interested in what a list like that would look like in any given week. Second, you, as a reader, don’t need me to tell you that Kentucky and Syracuse have fewer losses than any other major-conference teams. You already know that. I’d like to offer something new if I can, even if the “something new” turns out to be merely affirmative and I say simply, “Well, what do you know, Kentucky and Syracuse look like the two best teams from this perspective too.”

For the record I’d like to see teams selected for the NCAA tournament according to a process that more or less aligns with the traditional “reward” approach, and then seeded according to a method informed by a modicum of “prediction” wisdom.

Part of a continuing series.

Twitter: @JohnGasaway. Contact: here.

14 Games With the Celtics

Filed under: Uncategorized — Neil Paine @ 11:34 am

From January 6 to January 20, the Boston Celtics had a stretch where they lost 6 times in 7 games. Here were the relevant numbers from those 7 games:

Player          G       GS      Min     PPG     TS%     APG     RPG    AWS36
-----------------------------------------------------------------------------
Ray Allen       7       7       251     11.6    58.2    2.1     3.9     3.5
Paul Pierce     7       7       239     13.6    47.7    4.0     4.0     2.3
Kevin Garnett   7       7       222     13.4    47.9    2.0     7.4     3.0
Rajon Rondo     6       6       221     14.7    52.7    7.8     5.0     5.7
Brandon Bass    7       1       189      8.7    43.5    0.9     5.9     2.7
Jermaine O'Neal 7       6       170      5.3    41.3    0.6     6.9     4.1
Mickael Pietrus 6       0       120      7.8    62.2    0.0     1.2     4.5
Avery Bradley   6       1        81      4.3    53.5    0.7     1.8     3.4
E'Twaun Moore   5       0        59      2.2    25.0    1.6     0.6    -2.2
Marquis Daniels 5       0        43      1.2    25.0    1.0     2.2    -2.2
Greg Stiemsma   7       0        37      0.4    52.1    0.3     1.3     1.3
Keyon Dooling   2       0        26      6.5    81.3    0.5     1.0     1.6
Chris Wilcox    3       0        11      1.7    64.4    0.0     0.3     3.7
JaJuan Johnson  4       0        10      2.3    65.4    0.0     1.3    17.9
Sasha Pavlovic  1       0         1      0.0            0.0     0.0     0.0
-----------------------------------------------------------------------------
OFFENSIVE RATING:  95.0
DEFENSIVE RATING: 100.8
-----------------------------------------------------------------------------

Rajon Rondo was also injured during that 7-game stretch. Yet, the C’s then proceeded to go on a run of 6 wins in 7 games:

Player          G       GS      Min     PPG     TS%     APG     RPG    AWS36
-----------------------------------------------------------------------------
Paul Pierce     7       7       242     22.9    61.8    7.7     6.4     9.7
Brandon Bass    7       3       234     13.1    52.9    1.4     6.7     3.8
Avery Bradley   7       7       219      6.3    43.2    2.9     2.9     0.6
Kevin Garnett   7       7       207     12.9    57.5    3.3     7.0     8.6
Mickael Pietrus 6       2       154      8.8    51.3    0.8     4.0     3.4
E'Twaun Moore   7       0       113      6.1    57.8    1.6     1.3     3.9
Ray Allen       4       4       108     13.3    68.2    4.3     1.8     8.2
Chris Wilcox    5       0        92      4.4    57.3    0.6     4.2     2.7
Sasha Pavlovic  7       1        89      3.6    52.1    0.3     1.6     2.4
Jermaine O'Neal 4       4        83      6.5    57.4    0.8     4.5     2.9
Marquis Daniels 6       0        82      3.7    52.7    2.2     1.3     3.1
Greg Stiemsma   6       0        34      0.7    28.6    0.3     1.5    -0.5
JaJuan Johnson  2       0        14      7.5    75.9    0.0     1.0    15.9
Keyon Dooling   1       0         9      3.0    75.0    0.0     0.0     5.3
-----------------------------------------------------------------------------
OFFENSIVE RATING: 105.4
DEFENSIVE RATING:  90.8
-----------------------------------------------------------------------------

A big reason for the sudden turnaround is scheduling:

Date        Opponent            SRS     Result
 1/6/2012   Indiana Pacers     16th    L , 74-87
1/11/2012   Dallas Mavericks    8th    L , 85-90
1/13/2012   Chicago Bulls       4th    L , 79-88
1/14/2012 @ Indiana Pacers     16th    L , 83-97
1/16/2012   Okla. City Thunder  6th    L , 88-97
1/18/2012   Toronto Raptors    26th    W , 96-73
1/20/2012   Phoenix Suns       21st    L , 71-79
                        
1/22/2012 @ Washington Wizards 28th    W , 100-94
1/23/2012   Orlando Magic      19th    W , 87-56
1/26/2012 @ Orlando Magic      19th    W , 91-83
1/27/2012   Indiana Pacers     16th    W , 94-87
1/29/2012   Cleveland Cavs     24th    L , 87-88
1/31/2012 @ Cleveland Cavs     24th    W , 93-90
 2/1/2012   Toronto Raptors    26th    W , 100-64

However, I find their play in Rondo’s absence to be interesting. During the losing skid, Rondo was their best player, possibly the lone player on the team playing at an acceptable level. Garnett & Pierce struggled to make shots; Allen couldn’t even get shots. When Rondo went down, Avery Bradley took over at PG and has been terrible, giving the team practically none of Rondo’s production… But Garnett, Allen, and (especially) Pierce are suddenly playing extremely well. This is not the “Ewing Theory” in action, of course; it’s more a reminder that seasons are filled with strange streaks. Most of them get smoothed out in the long run, but they sure can be mystifying in the moment.

Email Neil at np@sports-reference.com. Follow him on Twitter at @Neil_Paine.

February 2, 2012

Mythbusting: Home Court Advantage

Filed under: Uncategorized — Kevin Pelton @ 1:12 pm

Along with David Locke, the radio voice of the Utah Jazz, I’ve started a weekly podcast we call NBA Mythbusting. Each week, we’ll discuss a certain NBA belief and look at how the numbers assess it. Then I’ll post that audio here along with some additional supporting stats.

To start things off, we discussed a hot topic in Portland I touched on last week: Do some teams play better on the road than at home, even after accounting for the typical home-court advantage? In the podcast, the main bit of evidence I utilize is that there is little year-to-year correlation between adjusted home-court advantage, as well as that over the four years for which I have data, this values largely converge toward zero. Here are the complete four-year rankings:

 Team    HCA

DEN     4.3
UTA     4.0
GSW     2.2
CHA     1.5
POR     1.4
IND     1.4
TOR     1.0
CLE     0.7
SAC     0.5
PHX     0.4
WAS     0.4
HOU     0.2
MIL     0.2
CHI    -0.1
ORL    -0.2
ATL    -0.2
SAS    -0.3
NOH    -0.3
LAC    -0.6
MEM    -0.8
DAL    -0.9
OKC    -0.9
PHI    -0.9
LAL    -1.2
DET    -1.2
NJN    -1.3
MIN    -1.5
NYK    -1.9
MIA    -2.0
BOS    -2.4

Just two of these values are larger than the typical home-court advantage (a little more than three points most seasons, but over four this year). There is ample evidence, here and elsewhere, that Denver and Utah do enjoy a unique advantage because of their altitude. This makes sense since we see a similar effect in college hoops. At the risk of denigrating home crowds, their impact doesn’t seem to be large enough over time to make a meaningful difference. There are some results here that make sense casually (Golden State and Portland both ranking in the top five), but other counterintuitive results that can’t be ignored (Charlotte is in between them; Oklahoma City, even without the lame-duck Seattle season, rates as a reverse advantage).

BONUS NOTE ON YEAR-TO-YEAR VALIDITY: While looking for other references to Thinking, Fast and Slow and the NBA, I came across a post on the NBA Geek blog questioning plus-minus, with my treatise on evaluating basketball players as the (counter)example. The conclusion:

If a measurement is horribly inconsistent over time, there are two possibitities (sic):

  1. Whatever you are measuring is itself wildly inconsistent over time.
  2. You are not measuring what you think you are measuring.

I would submit that there is a third possibility, that the sample size is too small to measure the effect in question. Let’s consider three-point shooting. Last season, there was a 0.224 correlation between Ray Allen‘s three-point percentage from one game to the next. The standard deviation in his single-game shooting (.247) is large enough that we can barely predict within two standard deviations whether he’ll make all of his threes or none of them in a game. If you looked strictly at the single-game level, you’d think that three-point shooting was not a skill. But obviously this is not the case, once we aggregate it over more observations.

There are two requirements for a measurement to be meaningless: Not only must it be inconsistent, but when grouped over a longer sample size, it must converge to zero. This is, more or less, what we see with adjusted home-court advantage. If we used 10 years, the averages would be smaller than the four-year averages, and show little variation between teams. This is not the case with plus-minus. I have net plus-minus from BasketballValue.com handy for four seasons. During that time, Kevin Garnett has never rated as worse than 8.1 points per 100 possessions better than his teammates. If plus-minus wasn’t measuring something, we would not see such extreme values over such a long period.

Essentially, each single-season rating is made up of a signal (the player’s ability to help his team win while on the floor) and noise. Over time, the noise does converge to zero, so what remains is a much more reliable measurement. (This is in fact discussed in Thinking, Fast and Slow in terms of explaining the importance of regressing to the mean.) That’s how plus-minus can be unreliable for a single season, yet become more useful over time.

February 1, 2012

Sportsmanship requires sportsmen actually playing the sport in question

Filed under: Uncategorized — John Gasaway @ 1:46 pm

Last night I watched Michigan State play significant portions of a road game they ended up losing without Draymond Green, and then I watched Vanderbilt play significant portions of a road game they ended up losing without Festus Ezeli. Both stars were saddled with foul trouble that stemmed at least in part from what I will call “memo fouls.”

Last week NCAA national officiating coordinator John Adams highlighted the issue of sportsmanship in his monthly memo to officials. “You should have a very low tolerance for players who use profanity toward officials, or who ‘wave you off’ after a call,” Adams wrote. “These types of actions call for Technical fouls. Call them! Your coordinators and commissioners will support you.”

Sportsmanship is indeed a precious quality, a generous and ennobling aspect of an otherwise purely selfish and competitive spectacle. I’ll give a strange example. A week or two ago when Purdue played at Michigan State, an idiot at the Breslin Center heckled Robbie Hummel by saying he hoped to see the Boilermaker star tear his ACL yet again. After the game Tom Izzo made plain his wish to disembowel any such idiot, regardless of the fact that the idiot in question was wearing green. Izzo’s always been an enthusiastic practitioner of precisely this kind of angry sportsmanship, and I like that aspect of him a lot.

Izzo, Adams, Gasaway, you — we can all agree that sportsmanship merits recognition and even, at the margins, enforcement. The problem with Adams’ memo lies not in its veneration of sportsmanship. The problem lies in its solitary and incorrigibly crude mechanism for enforcement vis a vis players: assessing personal fouls.

Even pre-memo, we already saw too little of the game’s best players due to foul trouble, and I am already on the record as finding this surpassingly odd. Other sports go out of their way to make sure that their stars, you know, play. The NFL has done everything but equip quarterbacks with portable moats to make sure they stay healthy and in the game. Yet for reasons that have never been explained to my satisfaction, basketball alone tolerates an enforcement mechanism that is self-evidently self-defeating. Coaches choose to remove their best players from the game, for fear of being forced to remove their best players from the game.

It would make more sense to simply reward the opposing team with an escalating series of extra free throws and/or possessions. Meantime an allotment of a mere five fouls per player per 40 minutes cannot carry all of this rules-enforcement and behavior-modification water. No way.

The NCAA should have a very low tolerance for star players not playing. This type of situation calls for changing the rules. Change them! Your fans and your partner TV networks and their advertisers will support you.

Twitter: @JohnGasaway. Contact: here.

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