Pages Navigation Menu

What Went Wrong in Detroit?

USA Today Sports

USA Today Sports

The Detroit Pistons are lucky in one, and probably only one, respect this year. The stench off the tire fires in bigger markets with New York Knicks and Los Angeles Lakers has obscured what a stinker of a season they are having. As of today the Detroit Pistons are 27-48, well on there way to a 50-loss season.

The Pistons aren’t another case of a tanking team throwing the season to position themselves for the future. The Pistons made one of the bigger off-season moves, signing Josh Smith to a four year $54 million dollar deal. Even at the time that seemed like too much money for Smith, but it certainly was not the move of a team that was expecting to rack up lottery ping-pong balls.

I did a post at the beginning of the season, entitled Highly Plausible Win Projections, some of which seem a little less plausible now than they did in October. The projections utilized different advanced player metrics with a guess-timate on playing time for each player. That was then converted to wins and losses via our old friend Pythagorean Wins.

In response to a post by Pistions blogger and Wins Produced enthusiast Ben Gulker, I happened to have highlighted the Detroit Bad at Basketball Boys, who all the metrics thought would be mediocre to good. The average win total for Detroit was 44 games.  

That isn’t going to happen.

So why are they so bad and where did my seemingly Plausible Projections go wrong? Before going into issues like fit and SPACING!!, I thought I would compare what the metrics, as interpreted by me, thought would happen and what did happen as measured by the same metrics, as well as who I thought would play and who actually has played. For a team like the Atlanta Hawks who have been missing their best player for most of the season and lack a vaguely comparable replacement, it is pretty easy to figure out why they’ve under performed.

So, I made a spreadsheet and did a bunch of algebra to quantify it all. Mostly it confirmed what we already know; Josh Smith has been a tremendous disappointment in Detroit. But a tire fire like this season takes more than one flaming radial.  You may or may not remember that Chauncey Billups signed with Detroit in the off season, this, unfortunately, is also the year Billups stopped being an NBA basketball player. Two NBA rookies signed by the Pistons in the offseason have yet to become NBA players and, after stellar rookie and sophomore  seasons, Greg Monroe has apparently made it his personal goal to prove regression to the mean, for which we stats people are no doubt grateful.

Using both the minutes played and estimated production for Wins Produced, ASPM and Win Shares I calculated the Advanced Disappointment Index for each player on the Pistons, which is essentially the difference between projected wins and actual wins created by the player. The first column is the difference between the player’s forecast percentage of games minutes played and actual actual percentage played.

Player Court Time Difference Advanced Disappointment Index
Andre Drummond 16.19% 3.12
Brandon Jennings -1.32% -1.44
Charlie Villanueva -19.34% -0.35
Chauncey Billups -29.92% -3.45
Greg Monroe -0.92% -1.82
Jonas Jerebko -14.47% -1.16
Josh Harrellson -10.34% -0.37
Josh Smith 3.47% -4.69
Kentavious Caldwell-Pope 6.48% -1.59
Kyle Singler 44.57% 2.22
Luigi Datome -10.92% -1.03
Peyton Siva -1.88% -0.18
Rodney Stuckey 0.61% -1.99
Tony Mitchell -0.70% 0.27
Will Bynum 19.15% -0.24

Looking at this again we see how much of a disappointment Smith’s season has been in Detroit, sort of the anti-Al Jefferson, producing five fewer wins than he would have basically if he had maintained the production he produced in the last couple of years in Atlanta.

We can use our friends at Basketball Reference to look at Smith’s decline this year, producing less than he has since he’s been in the league.

We can see here how fit and position matter. Smith is a somewhat above average power forward transformed into a decidedly sub-par small forward. Not only is Smith’s True Shooting percentage at a career low, his three-point attempt rate is at a career high while his three point percentage at a career low, and his free throw attempt rate is at a career low too. To top it all off, playing alongside Drummond and Monroe, his rebounding percentages are a career low too.

Season Age Tm Lg Pos G MP PER TS% eFG% FTr 3PAr ORB% DRB% TRB% AST% STL% BLK% TOV% USG% ORtg DRtg OWS DWS WS WS/48
2004-05 19 ATL NBA SF 74 2050 15.4 .506 .458 .394 .038 7.9 18.6 13.0 10.2 1.5 5.4 16.0 18.4 100 107 0.6 1.8 2.4 .055
2005-06 20 ATL NBA SF 80 2559 15.5 .500 .447 .377 .142 8.0 17.2 12.5 12.2 1.3 6.2 15.2 18.9 103 108 1.5 2.1 3.6 .067
2006-07 21 ATL NBA SF 72 2647 18.3 .506 .458 .390 .153 7.4 21.0 14.1 16.3 2.0 6.1 16.3 24.1 99 102 0.4 4.0 4.4 .079
2007-08 22 ATL NBA PF 81 2873 19.0 .520 .468 .414 .087 6.6 20.4 13.5 16.6 2.3 5.9 15.5 25.0 102 103 1.2 4.6 5.8 .097
2008-09 23 ATL NBA PF 69 2421 17.2 .533 .508 .423 .102 6.5 17.6 12.1 12.1 2.1 3.5 13.8 22.6 103 104 1.3 3.6 4.9 .097
2009-10 24 ATL NBA PF 81 2871 21.0 .536 .505 .422 .007 9.0 19.8 14.3 19.0 2.4 4.5 14.3 22.2 109 101 4.2 5.1 9.3 .155
2010-11 25 ATL NBA PF 77 2645 19.2 .540 .502 .304 .148 6.2 23.7 15.0 17.1 2.0 3.5 14.3 24.7 104 102 1.9 4.5 6.4 .116
2011-12 26 ATL NBA PF 66 2329 21.1 .499 .470 .292 .099 6.9 24.8 15.9 20.6 2.1 3.8 11.7 28.4 101 96 1.9 4.9 6.8 .139
2012-13 27 ATL NBA PF 76 2683 17.7 .501 .491 .272 .170 5.8 21.3 13.6 20.9 1.8 3.9 14.6 26.7 97 101 -0.3 4.5 4.2 .075
2013-14 28 DET NBA SF 75 2673 14.1 .460 .445 .247 .210 4.0 17.9 10.5 14.9 2.0 3.3 12.6 24.6 94 107 -1.5 2.7 1.2 .022
Career NBA 751 25751 17.9 .509 .476 .346 .121 6.8 20.2 13.5 16.2 2.0 4.6 14.3 23.7 101 103 11.1 37.8 48.8 .091
9 seasons ATL NBA 676 23078 18.4 .516 .480 .360 .109 7.1 20.5 13.8 16.3 2.0 4.8 14.6 23.5 102 103 12.5 35.1 47.6 .099
1 season DET NBA 75 2673 14.1 .460 .445 .247 .210 4.0 17.9 10.5 14.9 2.0 3.3 12.6 24.6 94 107 -1.5 2.7 1.2 .022
Provided by Basketball-Reference.com: View Original Table
Generated 4/4/2014.

In one sense this shows the limits of the metrics. It is well known that players stats are less stable when they change teams, changing positions also has an effect, roles matter. Then again, the metrics and my attempt make projections assume that teams aren’t going to play significant players out of position for the entire year along side teammates that are not the least bit complimentary to each other.  For that, I guess you need a Joe Dumars factor for your model.

%d bloggers like this: