Projection: Wizards Will Win 45 and Return to the Playoffs

Improvement from the Washington Wizards youngsters, and an infusion of frontcourt depth will be offset by missed time due to injuries and declines from the team’s older players to keep the team in the middle of the NBA pack for another year. However, the team could be a dangerous playoffs matchup if they’re able to enter the postseason healthy.

Below, I’m offering up projections done a couple different ways. The first is built on the similarity scores I’ve posted after the past week. This approach finds players in NBA history with similar production at a similar age, and then looks at what they did in subsequent seasons. Those findings are then applied to the Wizards roster and combined with a playing time estimate for each player.

For this estimate (and the other one, which I’ll get to later in this post), I’ve used my overall metric Player Production Average (PPA), which is calibrated to explain individual player contributions to winning and losing. PPA is pace neutral, accounts for defense, and includes a “degree of difficulty” factor based on the level of competition a player faces while on the court. In PPA, 100 = average, higher is better, and 45 = replacement level.

Statistical Doppelganger Projection

POS PLAYER MIN LAST SEASON PPA PROJECTED PPA PROJECTED kWins
PG John Wall 2812 139 156 9.0
C Marcin Gortat 2464 154 132 6.7
SG Bradley Beal 2325 96 112 6.1
SF Paul Pierce 1800 139 108 4.0
SF Otto Porter 1800 15 85 3.2
PF Kris Humprhies 1608 132 133 4.4
F/C DeJuan Blair 1450 97 97 2.9
PF Nenê 1430 102 90 2.7
PG Andre Miller 1165 86 65 1.6
G/F Glen Rice Jr. 1029 20 78 1.2
F/C Drew Gooden 1002 106 100 2.1
G/F Martell Webster 1000 77 74 1.5
POS TEAM 19885 107 110 45.4

MIN = projected TOTAL minutes for the upcoming season

Projected kWins = how many wins that player will contribute based on his projected PPA and projected total minutes.

Over the past few seasons, the Wizards’ front office has converted the team from being one of the league’s youngest to being one of the league’s oldest. Gortat, Nenê, Pierce, Gooden and Miller all figure to play prominent roles this season, and all are past 30. Well past 30 for Pierce and Miller.

Projections for Porter and Rice were difficult because they played so little last season. For both, I elected to throw out their rookie numbers and rely instead on projections based on the rookie seasons of the players they were most like from college. Using the rookie numbers would lower Porter’s projected PPA to 66 and Rice’s to 30. That would drop Washington’s projected win total 43.8.

Note that I didn’t include Kevin Seraphin, Garrett Temple or Rasual Butler in the above table. They’ll get some minutes along the way, but — barring several catastrophic injuries — none should see enough court time to have a major effect on the team’s fortunes. For those interested, this is what I project for the end-of-the-bench trio:

  • Garrett Temple: 33
  • Kevin Seraphin: 49
  • Rasual Butler: 55

I can hear the cockeyed optimists already: “Why do you just assume the old guys will get worse? What if they maintain for a year?” Quick answer (and one I’ve given before): athletes past 30 tend to two things — get hurt and get worse.

But, let’s say the “Ancients” are able to do this season what they did last year. In that event, Washington’s projected win total would climb to 48.5. Which would be hella fun.

On the other hand, there are scenarios where there are more injuries than expected, younger players don’t make anticipated improvements and/or older players decline more steeply. That gives a potential “bottom” of 40.7 wins.

Simple Rating System Projection

POS PLAYER MIN LAST SEASON PPA SRS PROJECTED PPA PROJECTED kWins
PG John Wall 2812 139 143 8.3
C Marcin Gortat 2464 154 144 7.3
SG Bradley Beal 2325 96 111 5.3
SF Paul Pierce 1800 139 117 4.3
SF Otto Porter 1800 15 63 2.3
PF Kris Humphries 1608 132 133 4.4
F/C DeJuan Blair 1450 97 135 4.0
PF Nene Hilario 1430 102 102 3.0
PG Andre Miller 1165 86 108 2.6
G/F Glen Rice 1029 20 82 1.7
F/C Drew Gooden 1002 106 118 2.4
G/F Martell Webster 1000 77 102 2.1
G/F Rasual Butler 0 63 81 0.0
C Kevin Seraphin 0 35 53 0.0
G Garrett Temple 0 24 54 0.0
POS TEAM 19885 107 117 47.9

The guys over at Basketball-Reference have up Simple Rating System projected stats for everyone who played in the NBA last season. Go here for an explanation. They don’t estimate minutes, which I can understand because it’s frigging hard to do.

The SRS approach is more optimistic than the one I used — at least at the bottom line. SRS generally predicts less of a decline for older players, but also not as much improvement from the younger ones. The SRS approach would suggest the Wizards will win 48 games this season — with a low end prediction of 43 and a high of 52.

Here’s a table comparing results from the two approaches:

POS PLAYER DOPP PPA SRS PPA DOPP kWINS SRS kWINS
PG John Wall 156 143 9.0 8.3
C Marcin Gortat 132 144 6.7 7.3
SG Bradley Beal 112 111 6.1 5.3
SF Paul Pierce 108 117 4.0 4.3
SF Otto Porter 85 63 3.2 2.3
PF Kris Humprhies 133 133 4.4 4.4
F/C DeJuan Blair 97 135 2.9 4.0
PF Nenê 90 102 2.7 3.0
PG Andre Miller 65 108 1.6 2.6
G/F Glen Rice Jr. 78 82 1.2 1.7
F/C Drew Gooden 100 118 2.1 2.4
G/F Martell Webster 74 102 1.5 2.1
G/F Rasual Butler 55 81 0.0 0.0
C Kevin Seraphin 49 53 0.0 0.0
G Garrett Temple 33 54 0.0 0.0

DOPP PPA = projected PPA using the Statistical Doppelganger approach

SRS PPA = projected PPA using the Simple Rating System numbers published by Basketball-Reference

DOPP kWINS = projected individual wins contributed using the Statistical Doppelganger approach

SRS kWINS = projected individual wins contributed using the Simple Rating System numbers published by Basketball-Reference

Final Word

In the end, I’m using my own projection system. Last year, I projected the Wizards would win 43 — they won 44, albeit not exactly in ways that I expected. I’ve refined my projection system (I hope), although I’m hoping the team outperforms my prediction by a wide margin.

While still lacking an elite producer, the Wizards could be a tough out in the playoffs when rotations shorten and there’s more rest between games (especially in the first round). If they can get there healthy, of course.

I’m projecting the Wizards will win between 41 and 48 games this season. Final projection: 45 wins and the fifth seed in the Eastern Conference playoffs.

Wizards Doppelgangers: Bump and Bruise

NBA: Preseason-Washington Wizards at Chicago Bulls

So far, I’ve used my Statistical Doppelganger Machine to find “most similar” players in NBA history for the following:

Next through the machine: Bump (Kris Humphries, DeJuan Blair and Kevin Seraphin), and Bruise (Martell Webster).

For those interested, I discuss the method a bit in the first post. Basically, the Machine looks for most similar production at a similar age.

First up: Humphries.

PLAYER POS SEASON AGE TEAM SIM SCORE PPA SEASON PPA PEAK
Kris Humphries PF 2013-14 28 BOS 100 132 146
Chris Wilcox PF 2010-11 28 DET 90 121 165
Lawrence Funderburke PF 1998-99 28 SAC 89 120 120
Mark Strickland F/C 1997-98 27 MIA 89 111 111
Tom Owens PF/C 1977-78 28 POR 89 126 149
Taj Gibson PF 2011-12 26 CHI 89 105 105
Antonio McDyess PF 2006-07 32 DET 88 115 175
Marcus Camby C 2002-03 28 DEN 88 131 212
Will Perdue C 1994-95 29 CHI 88 140 140
Loy Vaught PF 1992-93 24 LAC 87 99 131
Clarence Weatherspoon PF 1999-00 29 MIA 87 108 168

What Humphries did last season generates a nice list of solidly productive role playing bigs, which is about what I’d have expected. The group’s average peak performance is 147 — for Humprhies it’s 146. Average peak age for this group was 26.5 — Humprhies appears to have peaked at 25. I say “appears” because Humphries, of course is still going.

In the seasons after those shown here, the similars were a mixed bag of ups and downs. As a group, they were steady. This suggests that Humphries may be in the “plateau” portion of his career production arc. (That arc typically follows a trendline of improving production early in a player’s career to a peak between ages 25 and 28, a plateau until the early 30s, and then decline.

PLAYER POS SEASON AGE TEAM SIM SCORE PPA SEASON PPA PEAK
DeJuan Blair PF 2013-14 24 DAL 100 97 145
Dennis Rodman* F 1986-87 25 DET 90 84 180
Craig Smith PF 2006-07 23 MIN 90 87 91
Matt Geiger F/C 1993-94 24 MIA 89 87 153
Gary Trent PF 2002-03 28 MIN 88 103 133
Channing Frye PF 2007-08 24 POR 88 77 118
Jerome Whitehead C 1982-83 26 SDC 88 104 128
Alan Henderson PF 1996-97 24 ATL 88 67 116
Robert Traylor C 2003-04 26 NOH 88 79 143
Greg Ballard SF 1978-79 24 WSB 88 114 157
Ivan Johnson PF 2011-12 27 ATL 88 73 83

I very much like seeing Dennis Rodman’s name on the list, although this is the pre-RODMAN Rodman. This was actually Rodman’s rookie season, and while he rebounded well, he wasn’t yet the outlandish board man he’d later become. This Rodman was even willing to shoot the ball now and then — he used roughly 19% of his team’s possessions while on the floor (average is 20%).

The rest is kind of an odd group at first glance. There are physical, burly types like Smith, Trent, Traylor and Johnson, and more finesse types Frye, Henderson and Geiger.

You may be wondering what Frye is doing on this list at all. But, this isn’t the stretch-4 Frye, this is the PF/C type in his second to last year in Portland. He had just 10 three-point attempts that season with the Blazers. Two years later, he’d hoist 392 for the Suns.

The average peak PPA for this group was 132 at age 25.6. Blair’s best season was a 145 as a rookie, but he’s just 25 years old this season. This group of similars was a mix of improvement and decline in the year following the one shown.

PLAYER POS SEASON AGE TEAM SIM SCORE PPA SEASON PPA PEAK
Kevin Seraphin C 2013-14 24 WAS 100 35 95
Stanislav Medvedenko PF 2002-03 23 LAL 90 17 84
Mark Alarie PF 1987-88 24 WSB 88 42 77
Keith Lee F/C 1986-87 24 CLE 87 55 77
Bill Martin SF 1985-86 23 IND 86 34 34
Greg Foster F/C 1990-91 22 WSB 86 36 42
Malik Allen PF 2006-07 28 CHI 86 35 76
Channing Frye PF 2008-09 25 POR 86 27 118
Melvin Turpin C 1986-87 26 CLE 86 50 130
Josh Powell PF 2008-09 26 LAL 86 29 61
Brad Lohaus F/C 1987-88 23 BOS 85 49 93

I know there’s a lot of hope that Seraphin may finally have a breakout season. Even before looking at his comps, I’d have recommended betting against that “breakout.” This list only solidifies that recommendation.

To me, Seraphin is a reflection of how brutal the NBA can be. He’s one of the better basketball players in the world — top 600, easy. Maybe top 500. But, he hasn’t been good enough to be a bona fide NBA player.

No matter what slice of time I examine, I keep finding the same problems with Seraphin’s play: too many turnovers, too few rebounds, rampant fouling. That was true, even during his “good” stretch a few years ago when he performed at about the level of an average NBA player. Since then, he’s been awful. The league’s least productive center two years ago. The second least productive center last season.

This group of similars can be characterized as peaking early (age 24.8) and low (average peak PPA: 81). The best of the group was Mel Turpin. That Frye season was his last in Portland, and the worst of his career.

The good news is that most of these players improved the season following the one that appears on the list. The bad news: the average improvement wasn’t much.

PLAYER POS SEASON AGE TEAM SIM SCORE PPA SEASON PPA PEAK
Martell Webster SF 2013-14 27 WAS 100 77 114
Keith Bogans SG 2007-08 27 ORL 95 81 89
Morris Peterson SF 2003-04 26 TOR 90 86 111
Richard Jefferson SF 2011-12 31 SAS/GSW 90 93 162
Rasual Butler G/F 2006-07 27 NOK 90 61 89
Pat Garrity SF 2002-03 26 ORL 89 79 89
Matt Carroll SF 2007-08 27 CHA 89 59 105
Brandon Rush SG 2010-11 25 IND 89 66 106
Quentin Richardson SG 2008-09 28 NYK 89 82 123
Gordan Giricek SF 2003-04 26 ORL 88 71 99
Ryan Gomes SF 2009-10 27 MIN 88 83 119

Webster will be out for a significant chunk of the season after back surgery during the summer. His list of similars is an unsurprising mix of role-playing G/F types. This group’s average peak was at age 26.7 — Webster is 28 and appears to have peaked at 26 (his first year in DC). The average peak PPA was 110 — barring a miraculous recovery, Webster’s was 114.

The guys on this list tended to decline a bit the following season, though not much. Assuming Webster gets healthy, it’s reasonable to expect him to play about as well as he did last season.

Just for those who might be interested, here are the “similars” for Trevor Ariza and Trevor Booker, the team’s significant free agent losses:

PLAYER POS SEASON AGE TEAM SIM SCORE PPA SEASON PPA PEAK
Trevor Ariza SF 2013-14 28 WAS 100 145 145
Nick Anderson SG 1995-96 28 ORL 93 151 164
Bryon Russell SF 1998-99 28 UTA 88 128 128
Bryon Russell SF 1999-00 29 UTA 88 127 140
Hersey Hawkins G/F 1995-96 29 SEA 88 148 173
Kerry Kittles SF 2003-04 29 NJN 88 136 179
Hersey Hawkins SG 1994-95 28 CHH 88 148 173
Brent Barry SG 1999-00 28 SEA 87 132 183
Dan Majerle SG 1992-93 27 PHO 87 133 135
Wesley Person SG 1997-98 26 CLE 87 150 150
Rashard Lewis PF 2007-08 28 ORL 87 138 165
PLAYER POS SEASON AGE TEAM SIM SCORE PPA SEASON PPA PEAK
Trevor Booker PF 2013-14 26 WAS 100 123 123
Gustavo Ayon PF 2011-12 26 NOH 90 134 134
Luc Mbah a Moute PF 2011-12 25 MIL 90 106 106
Chucky Brown PF 1995-96 27 HOU 90 117 117
Kent Benson C 1983-84 29 DET 89 119 137
Ben Poquette PF 1981-82 26 UTA 89 100 106
Rasho Nesterovic C 2006-07 30 TOR 89 120 140
Tyrone Corbin SF 1988-89 26 PHO 89 110 126
Nenad Krstic C 2009-10 26 OKC 89 107 118
Jason Thompson PF 2011-12 25 SAC 89 130 130
Olden Polynice C 1991-92 27 LAC 89 114 127

And finally, here’s the “All-Sims” team, so to speak. The method here is to take the guy from the top of each player’s list:

POS Wizards Similars
PG John Wall Kemba Walker
SG Bradley Beal Brandon Jennings
SF Paul Pierce Toni Kukoc
PF Nenê Danny Manning
C Marcin Gortat Bill Laimbeer
BENCH
PG Andre Miller Rod Strickland
SG Martell Webster Keith Bogans
SF Otto Porter Lance Stephenson
PF Kris Humphries Chris Wilcox
C DeJuan Blair Dennis Rodman
G/F Glen Rice Jr. Scott Padgett
G/F Rasual Butler Steve Kerr
C Kevin Seraphin Stanislav Medvedenko
THE DEPARTED
SF Trevor Ariza Nick Anderson
PF Trevor Booker Gustavo Ayon

Wizards Doppelgangers: The Enigmas

otto porter

In case you missed them, I’ve done two installments using my Statistical Doppelganger Machine to look at the player seasons most similar to Wizards (similar production at similar age):

Here’s a look at low-minute Wizards for whom finding comps was challenging because of their scant playing time: Otto Porter, Glen Rice Jr., and Garrett Temple.

Let’s start with the guy likely to play the biggest role for the team this season: Porter.

PLAYER POS SEASON AGE TEAM SIM SCORE PPA SEASON PPA PEAK
Otto Porter SF 2013-14 20 WAS 100 15 15
Lance Stephenson SG 2011-12 21 IND 87 14 116
Cory Joseph SF 2011-12 20 SAS 86 18 94
Jumaine Jones SF 2003-04 24 BOS 86 24 98
Quincy Pondexter SF 2010-11 22 NOH 86 38 67
Shawne Williams SF 2006-07 20 IND 85 39 53
Kareem Rush SG 2002-03 22 LAL 85 3 53
Brandon Bass PF 2005-06 20 NOK 85 37 116
Kevin Martin SG 2004-05 21 SAC 84 32 162
Kedrick Brown SF 2002-03 21 BOS 84 44 97
Gerald Henderson SG 2009-10 22 CHA 84 36 99

Wizards fans know Porter had what amounts to a lost rookie season. He missed training camp with a hip injury, barely played, and was terrible when he did play. When I post my projections Wednesday, I’ll go through a couple different ways I dealt with predicting his performance, but the exercise today is NBA similars.

The list of similars was something of a pleasant surprise. It’s basically a list of players who had a disastrously bad season and then (for the most part) went on to become contributors. That said, “contributors” is a broad term. As a group, Porter’s similars tended to peak young (average age 23.4) and low (average peak PPA: 88). But, they all improved the following season — generally by a bunch.

And, the list includes Kevin Martin, who’s been a good player for nearly a decade, and Lance Stephenson who became an above average performer this season — and is likely to continue improving.

PLAYER POS SEASON AGE TEAM SIM SCORE PPA SEASON PPA PEAK
Glen Rice Jr. SG 2013-14 23 WAS 100 20 20
Scott Padgett PF 1999-00 23 UTA 90 20 96
Will Barton G/F 2012-13 22 POR 87 28 60
Quincy Douby SF 2006-07 22 SAC 86 34 34
Orlando Johnson SG 2012-13 23 IND 85 60 60
Reggie Jackson G 2011-12 21 OKC 84 32 101
Bostjan Nachbar SF 2003-04 23 HOU 84 24 84
Ronnie Price G 2006-07 23 SAC 84 40 54
Thabo Sefolosha SG 2006-07 22 CHI 84 31 129
Nolan Smith G 2011-12 23 POR 84 10 10
Kevin Brooks SF 1992-93 23 DEN 84 18 34

While Porter’s comps were somewhat comforting, it’s difficult to say the same about Rice’s. Like Porter’s list, Rice’s similars tended to peak early (average age: 24.0), but even lower (average peak PPA: 62). But, Will Barton and Reggie Jackson are both still works in progress. And, a few more had a productive season or two in which they helped their team, and one (Sefolosha) became a decent role-playing starter.

All that said, Rice played just 109 total minutes last season. The players who performed like him (similar age, similar production) isn’t a list all-time greats, but that shouldn’t be expected from a second round pick. There’s little reason to think Rice will be unable to work himself into being a contributor.

PLAYER POS SEASON AGE TEAM SIM SCORE PPA SEASON PPA PEAK
Garrett Temple G 2013-14 27 WAS 100 24 60
Rick Carlisle G 1985-86 26 BOS 89 28 28
Pace Mannion SF 1987-88 27 MIL 88 30 61
Pace Mannion SF 1983-84 23 GSW 88 32 61
David Wingate SG 1994-95 31 CHH 88 32 89
Dudley Bradley SG 1985-86 28 WSB 86 60 102
Jason Hart PG 2005-06 27 SAC 86 13 119
Ronnie Price PG 2012-13 29 POR 86 14 54
Randy Brown PG 1995-96 27 CHI 86 50 86
Royal Ivey PG 2006-07 25 ATL 86 38 38
Reece Gaines PG 2003-04 23 ORL 85 11 20

Temple may have been the most difficult player to project because so many of the guys who produced like he did last season didn’t have a follow-up year. In other words, the league took a collective look at players like Temple, and decided to sign Someone Else.

Players like Temple peaked low (average peak PPA: 65) and fairly young (25.5). Two of the 10 most similar got significantly worse the following season, four got better, and four stayed about the same. The list certainly doesn’t offer much hope for improvement.

Later today: Bump and Bruise.

Wizards Doppelgangers: The Ancients

nene fights

In the classic mode of a franchise finished with rebuilding, and ready to repeat their overwhelming success of the previous season, the Washington Wizards went out this offseason and got older. Yes, I’m sure they’d much prefer if we all thought of it as adding “veterans,” and that’s a fine way of looking at it, if you like.

Understand, I’m not against “veterans,” I’m just aware of the reality that athletes over 30 years old typically do two things: get hurt and get worse. Hopefully, Father Time will give the Wizards a reprieve until the summer of 2016 when Kevin Durant is a free agent.

It could happen.

Last week, I ran the Wizards young backcourt (John Wall and Bradley Beal) through my statistical doppelganger machine. Today, let’s look at the oldsters Washington has added in recent years: Nenê, Marcin Gortat, Paul Pierce, Andre Miller, Drew Gooden and Rasual Butler. (Please take a look back at that link above for notes about the method.)

PLAYER POS SEASON AGE TEAM SIM SCORE PPA SEASON PPA PEAK
Nenê PF 2013-14 31 WAS 100 102 176
Danny Manning PF 1996-97 30 PHO 90 107 157
Clifford Robinson PF 1997-98 31 PHO 90 131 143
Ruben Patterson SF 2006-07 31 MIL 89 126 165
Mickey Johnson PF 1983-84 31 GSW 88 83 145
Elton Brand PF 2009-10 30 PHI 87 100 224
Christian Laettner PF 1997-98 28 ATL 87 127 146
Jermaine O’Neal C 2008-09 30 TOR/MIA 87 93 166
Matt Harpring SF 2005-06 29 UTA 87 105 161
Frank Brickowski C 1992-93 33 MIL 87 117 123
Antoine Carr PF 1992-93 31 SAS 87 106 109

The good news is that these doppelgangers were pretty good players. Nenê had the second highest peak PPA behind Elton Brand’s 224. He also peaked about a year later than the average for this group. Frank Brickowski and Clifford Robinson each peaked past 30.

But, Nenê’s production has slipped the past couple years, as has his availability, and there isn’t much reason to think he’ll regain something close to that peak performance. His comps offer an ideal optimist vs. pessimist test: five of his ten most similar players performed better the following season, give performed worse.

PLAYER POS SEASON AGE TEAM SIM SCORE PPA SEASON PPA PEAK
Marcin Gortat C 2013-14 29 WAS 100 154 186
Bill Laimbeer C 1986-87 29 DET 93 154 175
Mike Gminski C 1989-90 30 PHI 90 129 170
Bill Laimbeer C 1988-89 31 DET 90 144 175
Dave Robisch C 1979-80 30 CLE 90 132 132
P.J. Brown PF 1998-99 29 MIA 89 127 150
Bill Laimbeer C 1985-86 28 DET 89 156 175
Billy Paultz C 1977-78 29 SAS 89 164 164
Tom Gugliotta PF 1999-00 30 PHO 89 147 176
Maurice Lucas PF 1981-82 29 NYK 89 149 149
Luis Scola PF 2008-09 28 HOU 88 143 143

Obviously, Gortat isn’t really an “ancient.” He’s just 30 years old, he’s played relatively few minutes for a player his age, and he’s a fitness fanatic. But, eight of the ten most similar seasons to the one Gortat posted last year were followed by a less productive season.

If I throw out the two least similar seasons from Laimbeer, each of the 10 seasons most similar to Gortat last season were followed by a season that was less productive. Bright side here: it’s not like these comps became catastrophic failures. In general, they remained productive…just not quite as good.

PLAYER POS SEASON AGE TEAM SIM SCORE PPA SEASON PPA PEAK
Paul Pierce SF 2013-14 36 BRK 100 131 173
Toni Kukoc PF 2002-03 34 MIL 85 113 164
Vince Carter SF 2012-13 36 DAL 84 107 200
Chris Mullin* SF 1997-98 34 IND 84 156 182
Sam Perkins PF 1996-97 35 SEA 84 119 147
Bob Lanier* C 1983-84 35 MIL 83 152 138
Chris Mullin* SF 1998-99 35 IND 83 168 182
Sam Perkins PF 1994-95 33 SEA 83 132 147
Sam Perkins PF 1995-96 34 SEA 82 114 147
Chucky Atkins PG 2006-07 32 MEM 82 111 111
Terry Porter PG 1998-99 35 MIA 82 114 211

Gotta say that Pierce’s set of doppelgangers may be the weirdest assemblage of players I’ve gotten from The Machine. I think the challenge here is that not very many players even last to age 36 (plus), so the pool is shallow. Note that Pierce’s most similar player has a Sim Score lower than the 10th most similar for Wall, Beal, Nenê and Gortat. In other words, the list above are kinda similar, but not super close.

Overall, this is a terrific group of players that tended to peak young (around age 25-26 — Pierce peaked at 24), but had long-lasting careers. As would be expected for a group of mid-30s athletes, most declined the following season. However, most remained decent players for another year or two.

PLAYER POS SEASON AGE TEAM SIM SCORE PPA SEASON PPA PEAK
Andre Miller SG 2013-14 37 TOT 100 89 172
Rod Strickland PG 2003-04 37 ORL/TOR 90 81 176
Terry Porter SF 2001-02 38 SAS 88 87 211
Mark Jackson PG 2002-03 37 UTA 86 66 181
Don Buse PG 1983-84 33 KCK 86 96 148
Toni Kukoc SF 2004-05 36 MIL 85 84 164
Muggsy Bogues PG 1998-99 34 GSW 85 111 153
Maurice Cheeks PG 1992-93 36 NJN 85 89 180
Rod Strickland PG 2002-03 36 MIN 85 95 176
Gary Grant PG 1997-98 32 POR 84 108 115
Rickey Green PG 1991-92 37 BOS 84 57 157

Miller’s comps are an interesting assemblage of good-to-great players. I was surprised to see Muggsy Bogues make the list, primarily because I’d forgotten the 5-3 Bogues played so many seasons. Just about everyone on this list had a good career, but…as should be expected for a group this old — all 10 either declined or were out of the league completely the following season.

PLAYER POS SEASON AGE TEAM SIM SCORE PPA SEASON PPA PEAK
Drew Gooden C 2013-14 32 WAS 100 106 169
Nazr Mohammed C 2010-11 33 CHA/OKC 86 98 150
Chris Wilcox PF 2010-11 28 DET 86 121 165
Antonio McDyess PF 2006-07 32 DET 86 115 175
Joe Smith PF 2005-06 30 MIL 85 89 143
Nazr Mohammed C 2009-10 32 CHA 85 150 150
Antonio McDyess PF 2005-06 31 DET 85 91 175
Jermaine O’Neal C 2012-13 34 PHO 83 83 166
Chris Kaman C 2012-13 30 DAL 83 101 130
Frank Brickowski PF 1991-92 32 MIL 83 107 123
Chris Gatling PF 1995-96 28 GSW 83 82 131

For Gooden, I wonder whether these comps are particularly meaningful given that he was signed late in the season and played in fairly few games. This group basically split between those who declined the following season and those who got better. The net effect is that the group average was “about the same.” So, it could be a case of lather-rinse-repeat with Gooden this season — albeit with more total minutes.

PLAYER POS SEASON AGE TEAM SIM SCORE PPA SEASON PPA PEAK
Rasual Butler SG 2013-14 34 IND 100 63 89
Steve Kerr PG 1999-00 34 SAS 92 59 124
Jalen Rose SF 2006-07 34 PHO 89 72 117
Matt Bullard PF 1998-99 31 HOU 87 49 98
Jaren Jackson SG 2000-01 33 SAS 87 44 90
Anthony Bowie SG 1997-98 34 NYK 86 72 97
Eric Piatkowski SG 2004-05 34 CHI 86 79 121
Rashard Lewis PF 2012-13 33 MIA 85 60 165
Steve Kerr PG 2001-02 36 POR 85 64 124
Eric Piatkowski SF 2007-08 37 PHO 84 34 121
Eric Piatkowski SG 2005-06 35 CHI 84 16 121

Last up for today: the 15th man, Rasual Butler. You’ll notice this list is heavy on Eric Piatkowski, which isn’t exactly a great thing. Piatkowski hung around into his late 30s for some reason I don’t remember. Perhaps he had a contract that was being passed around. Perhaps he’s a really nice guy. There wasn’t much reason to keep him because of his on-court performance.

Otherwise, it’s mostly swingmen who peaked at the level of an average starter (Rashard Lewis excepted) and then declined. In limited minutes, Butler will probably play a little above replacement level this season. I’d have preferred this roster spot went to someone younger and with potential to improve.

Tomorrow: the rest of the roster.

A Quick Look At Something (Hopefully) Devoid of Meaning

otto porter

The Wizards exhibition season has ended and they’re hopefully in the training rooms and rehabilitation gizmos trying to get healthy for their regular season opener against the Heat next Wednesday.

What does the preseason mean? Roland Beech, then proprietor of 82games.com (and now working for the Dallas Mavericks) ran some correlations a few years back. His findings suggest that preseason provides some indication of a team’s regular season fortunes, but that the previous season’s winning percentage remains a better barometer.

Beech’s numbers showed the preseason matters most to teams who stunk the previous year. The strongest correlations between preseason record and regular season record were between teams that won 30 or fewer games the previous year. Second strongest were for teams winning 40 to 49 games (hello, Wizards).

The Wizards, of course, are going to be on the “preseason doesn’t matter” side of this discussion considering they were 11th in the Eastern Conference in preseason efficiency differential and 29th in offensive efficiency. The league scored 101.8 points per 100 possessions in the preseason; the Wizards scored 95.0.

On the bright side, Washington boasted the league’s 8th best defensive rating.

Below are the preseason estimated Player Production Averages (PPA) for Wizards who played at least 50 minutes. PPA is an overall rating metric I developed that credits players for things they do that help a team win, and debits them for things that don’t. It’s a per-minute stat that’s pace-neutral, (normally) accounts for defense, and (normally) includes a “degree of difficulty” factor based on the level of competition a player faces while on the floor. In PPA, 100 = average, higher is better, and (normally) 45 = replacement level.

I use “normally” in a few spots because the data necessary to calculate a player’s defensive contributions and/or the degree of difficulty factor aren’t available in the preseason. Plus, I don’t know if there can be such a thing as “replacement level” in the preseason.

PLAYER GMS MPG ePPA
james,damion 5 12.6 188
silas,xavier 3 17.7 145
porter,otto 7 26.6 110
miller,andre 6 18.3 96
temple,garrett 7 19.7 91
hilario,nene 5 19.4 90
wall,john 6 25.8 82
gortat,marcin 7 27.4 71
pierce,paul 5 19.6 51
seraphin,kevin 7 21.9 52
rice,glen 4 22.3 46
butler,rasual 5 15.6 32
blair,dejuan 7 21.3 -3
beal,bradley 3 20.7 -38

Do NOT use these numbers to make bold predictions or sweeping pronouncements. These represent a tiny sample size — a sample in which many players are basically going through the motions at least some of the time — and are presented just as a general barometer for how players performed in the preseason.

Looking back at last year’s preseason numbers and see that Bradley Beal had an outstanding preseason, but wasn’t as good in the regular season. Meanwhile, Trevor Ariza and John Wall were bad in exhibition games, but good in real ones. Some guys were about the same in both — Jan Vesely, Al Harrington, Eric Maynor, Kevin Seraphin and Garrett Temple.

This year…most of the team’s rotation players were unproductive. Wall had an ePPA below 20 most of the preseason until he had a good performance against the Knicks. Seraphin may yet have that breakout season so many are hoping for, but ended his preseason in the same general territory where he’s spent most of his young career.

The team’s bright spot in the preseason was Otto Porter. He was a little above average — encouraging progress from a second year player who was mostly overwhelmed as a rookie.

Now…real games.

Wizards Doppelgangers: The Young Backcourt

wall and beal

At this point in NBA history, there aren’t many “original” players. Today’s players are like players who have come before — in production, if not look and style. “This guy reminds me of…” is a game scouts and fans play, often to the point of absurdity.

Following in the footsteps of basketball statistical analysts like MikeG and Kevin Pelton, I’ve created a Statistical Doppelganger machine of my own. The Machine compares players across 14 categories, including age, minutes, box score stats and my own overall rating metric Player Production Average (PPA). Then, it combines those differences and…voila…players with the smallest differences leap to the top of the list — similar production at similar age.

The Machine uses pace-adjusted per-minute stats, but does not consider attributes such as height, weight or position.

I’ll post results for Wizards players leading up to the season. These “most similar” lists ultimately work their way into my projection for the team’s record this season, which I’ll publish closer to the season opener.

First up through the Statistical Doppelganger Machine: PG John Wall.

PLAYER POS SEASON AGE TEAM SIM SCORE PPA SEASON PPA PEAK
John Wall PG 2013-14 23 WAS 100 139 139
Kemba Walker PG 2012-13 22 CHA 89 131 131
Tony Parker PG 2004-05 22 SAS 89 136 187
Steve Francis PG 1999-00 22 HOU 89 133 172
Baron Davis PG 2005-06 26 GSW 89 132 163
Kenny Anderson PG 1993-94 23 NJN 89 129 161
Stephon Marbury PG 1998-99 21 MIN 89 130 164
Isiah Thomas* PG 1987-88 26 DET 88 130 181
Brandon Roy SG 2007-08 23 POR 88 143 189
Robert Pack PG 1995-96 26 WSB 88 143 143
Kenny Anderson PG 1994-95 24 NJN 88 135 161

While Wall’s overall production the past two seasons was flat (he posted a PPA of 139 in both seasons), his statistical similars is a good group for the most part. The average peak PPA for this group is 163, with Brandon Roy and Tony Parker at the high end and Kemba Walker (who’s still extremely young himself) and Robert Pack at the low.

Note the presence of Hall of Fame PG Isiah Thomas, as well as several dynamic performers like Steve Francis, Baron Davis and Stephon Marbury. And keep in mind that while Marbury ended up as The Official Selfish Player of the NBA, he was a first-rate talent who was highly productive.

For those who might be worried by seeing the names of Kenny Anderson and Robert Pack, well…stop it. Anderson was a good player, but didn’t possess anything like Wall’s elite athleticism. Plus, Anderson’s performance drop-off wasn’t really until he’d reached his thirties — and more than 23,000 career minutes. Wall isn’t even halfway there yet.

Pack was a not bad player when he could stay healthy, which really didn’t happen once he became a starter. In his best season, he managed to appear in just 31 games. Wall played more games than that in his third season, when he missed the first 33 games with a stress injury in his knee. In his four-year career, Wall has played every possible game for the Wizards twice.

While I’m not giving away my projection for Wall (yet), these comps suggest good things. I expect Wall to improve this season, and to peak at an All-NBA level in the next few years.

Next up, Bradley Beal.

PLAYER POS SEASON AGE TEAM SIM SCORE PPA SEASON PPA PEAK
Bradley Beal SG 2013-14 20 WAS 100 96 96
Brandon Jennings PG 2010-11 21 MIL 90 108 140
Brandon Jennings PG 2009-10 20 MIL 89 95 140
Mike Miller SF 2001-02 21 ORL 89 106 140
O.J. Mayo G 2008-09 21 MEM 89 81 96
Jason Richardson SF 2002-03 22 GSW 88 91 157
Quentin Richardson SG 2003-04 23 LAC 88 97 123
Michael Finley SF 1996-97 23 DAL 88 94 138
Calbert Cheaney SG 1994-95 23 WSB 88 88 88
Jamal Crawford SG 2003-04 23 CHI 88 107 113
Dennis Scott SF 1992-93 24 ORL 87 89 141

While I’m a big believe in Beal, I was not thrilled by this group of similars. Brandon Jennings? Twice? Really? OJ Mayo? Calbert Cheaney? Blech.

If you want to throw out one of those seasons from Jennings, be my guest. Next on the list was Klay Thompson last season, Eric Gordon, and then a season from Mahmoud Abdul-Rauf. None of whom significantly change the analysis.

There’s nothing particularly wrong with this group, except…none of them are Ray Allen or James Harden, or players of that caliber.

For the most part, though, this isn’t a particularly impressive group. The average peak PPA is 128, which is about the level of an average starter. Only Jason Richardson from this list peaked at a level that typically earns a spot on the All-Star team. What’s kinda interesting is that there are “repeaters” on Beal’s rookie and second year lists.

Players showing up as similar in both seasons include:

  • Mike Miller
  • Jason Richardson
  • Dennis Scott
  • Michael Finley.

Again, not a bad group…just not as strong a list as I’d have hoped. Still, Beal is young and seems to have the work ethic to improve. My gut says his ceiling is higher than what the numbers are saying…but that could just be the fan talking.

Next up: The Old Bigs — Marcin Gortat and Nenê.

Wizards 2014 Playoffs Wrap-Up

NBA Washington Wizards vs Chicago Bulls Play-Offs Game 4

Trevor Ariza dominated in the playoffs despite low-blow karate chop from Mike Dunleavy.

Just in time for the start of training camp, here’s a look back at the Wizards run in the playoffs this year. For those with short memories, Washington beat the Bulls in round one, and lost to the Pacers in round two. It was a good couple weeks for a franchise that’s been among the league’s worst the past several years.

I’ve finally gotten around to crunching the data to produce the Player Production Average (PPA) numbers. PPA is an overall rating metric I developed that credits players for things they do that help a team win, and debits them for things that don’t. It’s a per-minute stat that’s pace-neutral, accounts for defense, and includes a “degree of difficulty” factor based on the level of competition a player faces while on the floor. In PPA, 100 = average, higher is better, and 45 = replacement level.

Like any stat extracted from a small sample size, there’s a grain of salt factor. For example, Bradley Beal led the team with 458 playoff minutes — the cut when I look at regular season numbers is usually 500 minutes. Only 21 players reached 500 or more playoff minutes this year. That said, here are the numbers:

PLAYER GMS MPG RS PPA PS PPA
Trevor Ariza 11 37.0 145 193
Marcin Gortat 11 34.7 154 148
Bradley Beal 11 41.6 96 139
John Wall 11 38.2 139 82
Trevor Booker 9 16.2 123 75
Nene Hilario 10 32.5 102 49
Drew Gooden 10 14.6 106 37
Martell Webster 11 17.7 77 35
Andre Miller 11 9.8 86 12
Al Harrington 7 8.4 24 -22
Garrett Temple 10 .9 24 -33
Otto Porter 3 2.0 15 -49
Kevin Seraphin 4 1.5 35 -274

RS PPA = regular season

PS PPA = post-season

The numbers reflect Ariza’s tremendous playoffs performance. A 193 in the regular season would be worthy of All-NBA selection in most years. Among playoff performers with at least 100 total minutes, it ranked third overall behind Lebron James (263) and Chris Paul (211).

Gortat’s production improved as the playoffs went on. His first round PPA was a shade below average, but his play against Indy in round two pulled his full playoffs rating into the vicinity of his regular season performance.

The team’s only other above-average playoffs producer was Beal, who was terrific in round one (152) and solid in round two. A promising post-season debut for a talented kid who will still be among the league’s youngest players when he starts his third season in a few weeks.

The post-season wasn’t so kind to Beal’s backcourt partner, John Wall. In the first round, Wall’s overall production wasn’t overwhelming, but he thoroughly outplayed Chicago’s guards. Indiana did a better job of forcing him out of comfortable plays, and Wall struggled.

Now-departed Trevor Booker was solid in the first round, but played little in the second round. Friend of the blog Ben Becker wondered if Washington might have won against the Pacers if they’d played Booker instead of Gooden and/or Harrington. And, that’s definitely possible. The games were close and hard-fought, and the Wizards got next to nothing from Gooden and less than nothing from Harrington. Booker was fifth on the team in per minute production during the post-season, but 10th in round two minutes.

Against the Pacers, the Wizards got good production from Gortat, and little else from the front-court. Using the trio of Nenê, Gooden and Harrington with so little court time for Booker may well have cost Washington a trip to the Eastern Conference Finals.

Similarity scores coming soon.