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.
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.
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).
If a measurement is horribly inconsistent over time, there are two possibitities (sic):
Whatever you are measuring is itself wildly inconsistent over time.
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.
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.
I’ve been writing about college hoops now since the Harding Administration, but this season I’ve been asked to do something that I’ve never done before. This season I’m filling out a top 25 each week.
If you’ve ever wanted to yell at a writer about how incomprehensibly stupid their top 25 so obviously is (guilty), I would ask that you do one thing. Create your own top 25. You’ll find that it’s an intrinsically unsatisfactory exercise. What is plain and indeed obvious to the naked eye can’t be captured by a listing of teams from No. 1 to No. 25. Invariably there’s a top tier of elite teams that everyone rightly recognizes as Final Four material, and then there’s a big drop-off in quality before you get to the next set of programs. But of course in rankings there’s no such thing as a “big drop-off,” you just go from No. 8 to No. 9. Yell at your own rankings all you want, but someone has to be the No. 9 team in the country.
My main objection to top 25’s as they’re traditionally crafted, however, has less to do with their innate nature and more to do with how pollsters rank teams. The problem with any top 25 is that it’s little more than a list of the longest current winning streaks, combined with teams that are losing and which were likely overrated to begin with. (Did you know that Louisvilleis ranked No. 25 in this week’s Coaches’ Poll? That’s astounding to me. The Cardinals have recorded the Big East’s tenth-best per-possession performance to date.)
Now, there’s nothing wrong with winning streaks. Kansas, to take one example, has a pretty good one going, and I believe the Jayhawks really are one of the best teams in Division I. But once we surrender to the notion that a team stays where they are in the rankings until they lose, we’ve forfeited about 75 percent of our discretionary freedom of movement right there.
Which is why I’m going to employ a different method for ranking teams. I figure if I’m going to the trouble of tracking thousands of possessions played by 157 different teams I might as well use what I have, right? And every year starting right now, in late January, I feel like a critical mass has been reached in terms of possessions played. I feel like I now have enough information to dissent from the conventional wisdom on a given team with some degree of confidence. The dissent may well turn out to be wrong, of course, but at least my erroneous dissents will be less boring than parroting what everyone else says. Anyway that’s the hope.
So much for the white paper, let’s look at some actual instances where the world will think I’m nuts.
Kentucky
The Wildcats will not be No. 1 in my top 25 that comes out tomorrow. Shocking, I know. UK is plainly one of the best teams in the country, but you don’t need John Gasaway to tell you that the Cats haven’t lost since December 10. You know that already. The question that needs answering is: among these best teams in the country, who’s been performing at a level better than anyone else? That’s a close question, but my answer there is not to be found in Lexington.
Murray State
I think the proper reward for the Racers is my hearty congratulations, offered here, on an amazing 20-0 season. I do not think, however, that people truly believe MSU could go .500 on a neutral floor against, say, the six teams currently ranked right below them in the polls (UNLV, San Diego State, Florida, Creighton, Indiana, and Marquette). I think it’s simply the “0″ in “20-0″ that’s driving this ranking, which is more of a reward and a pat on the back than a true estimation of the team’s heft. Well, I’m going to offer my true estimation: It’s close and I’m open to persuasion but right now the Racers are not a top-25 team.
Mississippi State
The Bulldogs have been far more impressive to humans than they are to laptops all season long. Fortunately my business card reads “Mediating human-laptop disputes since the Harding Administration,” so I’m qualified to tell you the truth here is somewhere in the middle. Winning at Vanderbilt is nothing to sneeze at, certainly, but then again struggling to beat a strugglingTennessee team on your home floor or losing on the road to Ole Miss are data points as well. MSU’s been outscored by their SEC opponents thus far, and in fact they’re just 12 points away from an 0-5 record in-conference. Rick Stansbury’s team may well be better than laptops think, but their defense has been frighteningly bad and they are not yet top-25 caliber.
That wacky Missouri Valley
If I feel like I now have enough tracked possessions in the bank to dissent from the conventional wisdom, I really feel that way when it comes to the Valley, where teams are already at the halfway mark in their conference schedules. Creighton is tied for the league lead at 8-1 with Wichita State, but the Bluejays own the bragging-rights tiebreaker there, having recorded a 68-61 win over the Shockers in Wichita on December 31. Now the weird part: Wichita State, even taking that loss into account, is recording perhaps the most dominant performance by a Valley team in the past five years. Nationally the Shockers will of course be filed cognitively under what we’ve seen from the Valley’s best or second-best team in the past, but in truth we haven’t seen a team outscore the MVC by very nearly a quarter of a point per trip, at least not recently. In other words there’s a chance that Creighton and Wichita State are the two best teams the Valley has produced in a long while, and, coincidentally, they both came down the pike the same year.
Keep all of the above in mind when my top 25 drops tomorrow. Also keep in mind this isn’t college football, thank goodness, so none of this matters anyway.
I’m not quite sure what to say about today’s news that the Washington Wizards have relieved Flip Saunders of his duties of head coach. In this case, relieved seems like an especially apt adjective, as Saunders no longer has to deal with one of the most dysfunctional rosters in recent NBA memory. At the same time, Saunders has to answer for the failure of the Wizards’ young players–particularly John Wall–to develop under his watch, as our Bradford Doolittlechronicled while explaining why Paul Westphal needed to go in Sacramento.
When the coach of a 2-15 team getting outscored by 10.6 points per game gets fired, it’s easy to predict improvement. Regression to the mean all but ensures the Wizards will play better the rest of the way, because almost nobody is actually this bad. Since the merger, just 11 NBA teams have been outscored by double digits on average, and Washington’s record would be a 10-win pace for an 82-game season, threatening the 9-73 record of the 1972-73 Philadelphia 76ers.
At the same time, the track record of Saunders’ replacement (his long-time assistant, Randy Wittman) does not exactly inspire confidence, to put it politely. Friend of BBP Tom Zillerhas the ugly numbers on SBNation.com.
The most intriguing coaching rating available is done by Stats for the NBA, which treats coaches as the sixth player on the court as part of the regularized adjusted plus-minus process, quantifying their effect on players. There are many issues with this process, most notably the assumption that player ability is static throughout the 10-year period during which players and coaches are rated, but it’s a pure measure of coaching ability unlike any other. Saunders, by this measure, comes out as one of the league’s better coaches in this period, which is consistent with his track record in Minnesota and Detroit. Wittman, by contrast, lurks at the bottom of the list with Lawrence Frank (?!?) and a bunch of names who will never coach in the NBA again.
The Wizards are still overwhelmingly likely to get better the rest of the way, but promoting Wittman may not have helped their chances.
I’ve been thinking a lot recently about “fit” when it comes to building a basketball team.
Too often, statheads are guilty of plugging in players’ metrics and expecting a certain outcome without giving a thought to how the skills of each player mesh together. Dean Oliver’s “Skill Curves” are a simple way of modeling these teammate interactions, but they often fall short because Possession % is not always an adequate explanation for how a player produced points at a given efficiency.
A better way to look at fit would be to grade players’ skill sets and see how certain combinations of skills predict lineup efficiency. That’s a project for a later date, but today I thought I’d take a stab at generating ratings for players based on their skills. Here’s the process:
Generate projections for each player. In this case, I used Basketball-Reference’s Simple Projection System and regressed to positional means instead of league means. I also left out the age adjustment, since I want to estimate player skills in year Y, not year Y+1.
Decide which skills to measure, and how to measure them. I settled on the following “tools” as base skill sets for an NBA player:
Shooting - Pure shooting ability; measured by Free Throw Percentage
Scoring - Ability to create & make shots; measured by Points per 36 minutes
Floor Game - Passing & ball handling skill; measured by Pure Point Rating
Defense - Size + blocks, steals, & other stats that predict defensive +/-; measured by Defensive SPM
Rebounding - Ability to grab missed shots; measured by Total Rebounds per 36 minutes
Determine a grading scale. First, I calculated each player’s percentile rank (among players at the same position that season) in each category. Then, in true scouting tradition, I set up a 2-8 scale for each skill rating, corresponding to the following percentiles:
2 = 0-3%
3 = 4-13%
4 = 14-35%
5 = 36-65%
6 = 66-87%
7 = 88-97%
8 = 98-100%
The average player at a position will have a rating of 5 in a given skill. Remember, players are rated relative to others at the same position, so a 5 Floor Game point guard is still likely to be a better passer/ballhandler than an 8 Floor Game center.
The most “complete” players (highest simple sum of ratings) in the dataset seem to be Larry Bird and Kevin Garnett. In 2005, KG was an 8 (relative to other PFs) in Scoring, Floor Game, Rebounding, and Defense, with a 7 in Shooting, for a total of 39. Bird actually had three separate 39 seasons — 1984, 1985, and 1989; in the former two he was 8 across the board except in Scoring, and in the latter he pushed his Scoring to 8 (with his Defense falling to 7)… Chris Paul’s 2009 and 2010 seasons were phenomenally complete, as he posted 8s in Scoring, Floor Game, & Defense, to go with 7s in Shooting & Rebounding… LeBron James is known for his all-around talent, which is borne out with 8s in Scoring/Floor Game and 7s in Rebounding/Defense. But the well-known hole in his game is also exposed here: he’s merely a 5 Shooter… Kobe Bryant’s complete skills seem to have peaked in 2004, when he was an 8 Scorer, 7 Shooter/Rebounder, and 6 in Floor Game/Defense. In 2011 he remained an 8 Scorer (as he’s been every year since 2002), but his Shooting/Rebounding have fallen to 6s, and his Floor Game & Defense only score 5s… Arvydas Sabonis was a surprisingly great all-around player. Even at ages 34-35 in 1999 & 2000, he was an 8 Shooter relative to other centers, with 7s across the board in the other categories… During the prime of his first stint in the league, Michael Jordan was consistently an 8 Scorer/6 Shooter/7 Floor Game/8 Defender, with his rebounding varying between 7 & 8. When he returned from retirement, MJ’s Floor Game & Defense fell to 6, and in the twilight of his Wizards days he was a 7 Scorer/5-6 Shooter/6-7 Floor Game/7 Rebounder/5-6 Defender.
In the wake of Saturday’s overtime New York Knicks loss to the Denver Nuggets, SBNation.com’s Tom Ziller had an intriguing column about how criticism of the Carmelo Anthony trade is based on the faulty premise that the performance of the Knicks and the Nuggets since the deal can be compared, which makes little sense given how much better Denver was before the two teams swapped players. I don’t think Tom went far enough. I think there’s another false comparison here, between New York before and after Anthony’s arrival.
The Knicks are, by all available statistical evidence, worse now than they were this time a year ago. However, to assume that they would have continued to play at the same level had they not made the trade ignores a number of key outside factors. First, whether because of injury or simply because he’s lost a step, Amar’e Stoudemire has been terrible this season. I was appalled watching the Phoenix game last week how much difficulty Stoudemire had beating his defender (usually Marcin Gortat) off the dribble, which was a key staple of the New York offense last season. Before the Anthony trade, Stoudemire was responsible for using about 32.0 percent of the Knicks’ plays. If he was doing that with his current .495 True Shooting Percentage, how big a problem would that be?
Second, Landry Fields morphed from a budding star to a borderline starter right about the time of the Anthony trade. Part of that, surely, is the way Anthony’s arrival changed Fields’ role in the office. At the same time, Fields was a second-round pick who was playing out of his mind for three and a half months. While Fields’ translated college statistics suggested he was underrated, they still projected him as barely better than replacement level as a rookie. It’s certainly possible that Fields’ last 44 games reflect his true talent level more than his first 50.
Having a true point guard in Raymond Felton would make a huge difference to the Knicks, and their depth would be stronger with Timofey Mozgov in the middle. Still, my suspicion is that a New York team without the Anthony trade would be struggling nearly as much as the current group. Since that team does not exist, however, it’s easy to apply to it whatever expectations best serve your conclusion about the Anthony trade. If you believe the Knicks would be in the middle of the Eastern Conference Playoff race with Felton and Danilo Gallinari, that can’t possibly be disproved.
The unknown alternative applies to all sorts of other decisions, too. I’ve been thinking about it lately in the context of forward/center Darnell Gant taking the final shot of Washington’s 69-66 loss to California on Thursday. Lorenzo Romar drew up the play to free Gant for an open shot despite the fact that he had missed his previous eight shot attempts on the evening, and Gant in fact missed at the buzzer to seal the loss. Inevitably, Romar was criticized for not getting the ball to star forward Terrence Ross, who had hit a three moments earlier to get the Huskies in position to tie.
Setting aside the question of whether Gant’s previous results mattered (aka “Hot Hand” theory), the interesting question to me is what chances Ross would have had of making the possible shot. Ross is making 39.4 percent of his triples this season, but Cal expected him to take the final shot and was prepared to take him away. We also know that percentages dwindle in the closing seconds because time pressures players to take more difficult attempts. Would Ross have had a 30 percent chance of tying the game? 25 percent? 35 percent? That’s the thing about end-of-game decisions: They’re usually a choice between a variety of unpleasant alternatives, which is why a missed shot should really be the expectation in such situations.
Using the in-season projection method combining current performance and SCHOENE described in today’s column, here’s a look at our projections for the end-of-season per-game leaderboards.
Player Tm PPG
LeBron James MIA 28.8
Kobe Bryant LAL 27.4
Kevin Durant OKC 26.7
Carmelo Anthony NYK 24.3
Dwyane Wade MIA 24.1
Blake Griffin LAC 22.5
Derrick Rose CHI 22.4
Kevin Love MIN 22.3
Monta Ellis GSW 22.1
LaMarcus Aldridge POR 21.8
Kobe Bryant came into Thursday (not reflected in these stats) with a lead of a point per game over LeBron James, but the expectation is he’ll fall al little bit off his current pace. So too may Kevin Love, who is currently fourth–though SCHOENE can’t account for the Ricky Rubio factor.
Player Tm RPG
Kevin Love MIN 14.6
Dwight Howard ORL 13.6
Andrew Bynum LAL 12.0
Blake Griffin LAC 11.2
David Lee GSW 10.4
Anderson Varejao CLE 9.9
Kris Humphries NJN 9.9
Marc Gasol MEM 9.8
Tyson Chandler NYK 9.7
Marcin Gortat PHX 9.5
This is Love’s and Dwight Howard’s category to lose, though Andrew Bynum could make a run if he keeps playing 35 minutes a night.
Player Tm APG
Chris Paul LAC 10.1
Rajon Rondo BOS 9.8
Steve Nash PHX 9.7
Deron Williams NJN 9.1
Jose Calderon TOR 8.4
John Wall WAS 7.9
Ricky Rubio MIN 7.9
LeBron James MIA 7.7
Derrick Rose CHI 7.6
Kyle Lowry HOU 7.2
The most interesting names here are Rubio, who could easily finish higher with more minutes, and Kyle Lowry. Lowry was averaging 8.7 assists through Thursday after averaging just 6.7 in similar minutes last season, so this method is understandably a bit skeptical he can keep it up.
Player Tm SPG
Chris Paul LAC 2.6
Iman Shumpert NYK 2.4
Ricky Rubio MIN 2.2
Rajon Rondo BOS 2.1
Monta Ellis GSW 2.0
Jeff Teague ATL 2.0
Mike Conley MEM 1.9
Brandon Jennings MIL 1.9
Dwyane Wade MIA 1.8
The NBA added two world-class thieves this season in Rubio and New York’s Iman Shumpert. Rubio is probably the biggest threat to Chris Paul leading the league.
Player Tm BPG
JaVale McGee WAS 2.7
Dwight Howard ORL 2.5
DeAndre Jordan LAC 2.4
Serge Ibaka OKC 2.2
Andrew Bynum LAL 2.0
Andrew Bogut MIL 1.9
Marc Gasol MEM 1.9
Roy Hibbert IND 1.8
Tyrus Thomas CHA 1.8
Josh Smith ATL 1.7
DeAndre Jordan and JaVale McGee are both averaging three blocks a night thus far, but the conservative projection doesn’t see anyone finishing that high. Jordan in particular could come back to Earth, having averaged just 1.8 blocks per game as a starter last season.
