The San Antonio Spurs currently have a record of 44-20. Across 82 games, such a record projects to 56 wins, or just two fewer regular season wins than the Spurs had last season when it eventually took the NBA title. So it looks like the Spurs in 2007-08 – despite losing badly to the Hornets badly on Wednesday night – look to be just as good as the 2007 NBA champs.
The Efficiency Differential Story
When we delve deeper into the numbers, though, we see a different story. In 2006-07 the Spurs scored 106.4 points per 100 possessions. On defense, again per 100 possessions, this team allowed 97.3 points. Hence its efficiency differential – offensive efficiency minus defensive efficiency – was 9.1. As Table One indicates, this was the best efficiency differential the Spurs have achieved in the Tim Duncan era (and although not indicated, also in team history).
After 64 games this season, the Spurs are only scoring 104.57 points per 100 possessions. And per 100 possessions, the team is allowing 99.64 points (the worst defensive effort in the Duncan era). So its efficiency differential is only 4.9. This is the worst differential the Spurs have had since Duncan’s rookie season. In sum, if we believe that efficiency differential trumps won-loss record as an assessment of team quality (and there is some reason to think this) then the Spurs have slipped quite a bit this season.
How did the Spurs Slip?
And of course we wonder how this happened. For an answer, we first turn to Table Two.
Table Two offers two projections of the Spurs. The first assumes that what the players did last year on a per-minute basis will be offered again this year. The second projects what we have seen so far to the end of the season.
The top two players on this team – Tim Duncan and Manu Ginobili – are essentially unchanged from last season. The third most productive player this season, Fabricio Oberto, is much improved from 2006-07. So obviously Duncan, Ginobili, and Oberto are not the problem.
However, when we look at the fourth most productive player – Tony Parker – we start to see a problem. Parker posted a 0.194 WP48 [Wins Produced per 48 minutes] last season, a mark that is well above average (average is 0.100). This season his WP48 has dipped to 0.119, which is right around average. The drop-off in Parker’s productivity accounts for 3.5 of the 10.6 difference we see in the two projections for this team.
Why Did Parker Decline?
Now where has Parker declined? For that answer we turn to Table Three.
From Table Three we see that Parker is essentially the same player with respect to assists and turnovers. Where he is not the same is in the areas of shooting efficiency (from the line and the field), rebounds, and steals. The drop in these three areas explains virtually all of the declines we see in Parker’s output.
And of course we wonder: why has Parker regressed with respect to scoring, rebounds, and steals? Parker plays for the same coach, in the same city, and with many of the same teammates. Judging by his shot attempts, assist, and turnover numbers, his role on the team is also unchanged. Given all that’s the same, it’s hard to understand why Parker is offering less.
Well, there is one obvious change. Parker was a single man last year. After last season, though, he married actress Eva Longoria. As we know from the Tony Rom0-Jessica Simpson saga in Dallas, when an athlete fails, we need to look no further than his love life for an explanation. Yes, this is clearly Longoria’s fault.
Okay, there’s one other possibility. In Parker’s first six seasons he only missed 21 games. This season he has already missed 13. It could be that Parker’s feet and ankle problems have reduced his productivity. Yes, that’s not a very sexy answer. But it probably is closer to reality. Unless Longoria can be blamed for Parker’s injuries, Parker’s marriage has nothing to do with his statistical drop-off. In fact, blaming the wife or girlfriend for a player’s performance is more than a bit silly. And that applies to the Romo-Simpson story as well.
The decline in Parker’s output only explains a part of the team’s regression. The Spurs have also received less from Michael Finley, Jacque Vaughn, Matt Bonner, and Francisco Elson.
The issue with Elson and Bonner has been resolved (at least partially) with the Kurt Thomas acquisition. Thomas has been above average for much of his career, and after ten games in San Antonio he has remained above average.
Additionally, Brent Barry – who I noted a few days ago is a very good player – is schedule to return to the line-up. If Barry can take some of Finley’s minute (or minutes from Vaughn), it’s possible much of the problems in San Antonio can be resolved before the playoffs.
Summarizing the Spurs Story
Here is a quick summary of the tale being told.
1. The Spurs won-loss record says this team is as good in 2007-08 as it was last year.
2. The team’s efficiency differential, though, tells us the team is much worse.
3. A significant chunk of the team’s decline can be traced to Tony Parker.
4. It would be an interesting story if we could link Parker’s decline to his marriage to Eva Longoria, but the truth is that it’s probably just Parker’s injuries.
5. The recent moves by the Spurs probably fix much of the non-Parker problems.
So if Parker can be healthy for the playoffs, the Spurs can expect to contend for another title. And that should be good news for fans of this team.
Of course, there is some bad news. Given how much the Western Conference elite have improved (and the improvement of the Celtics and Pistons in the Eastern Conference), another title will be quite difficult. It’s entirely possible – despite the trade for Kurt Thomas, the return of Brent Barry, and Parker’s improving health — that the Spurs will once again fail to win a title in an even-number season. Such odd luck must also have an explanation. Is there a wife or girlfriend that can be blamed for the Spurs failures in even-numbered years?
Our research on the NBA was summarized HERE.
Wins Produced, Win Score, and PAWSmin are also discussed in the following posts:
Finally, A Guide to Evaluating Models contains useful hints on how to interpret and evaluate statistical models.