The stats and the commentariat all agree, the Phoenix Suns are most unexpectedly good team and the Brooklyn Nets are the most unexpectedly bad team.
Recently over at Hickory-High, I looked at surprises in the Eastern and Western conference in terms of player production as measured by ASPM, box score based metric scaled like a plus/minus score. Here I am going to apply it to the whole team.
In the table below I have the average of the projected ASPM score for returning players that had at least 160 minutes, the average actual ASPM to date this year, and the difference in standard deviations. Then a couple of columns that I think put change in ratings in perspective, the percent of the players in the comparison returning, the average age of those players and whether the team has a new coach.
| Row Labels | Projected ASPM | 2013-2014 ASPM |
ASPM Diff (Std Dev) | Percent Starters & Role Player Vets on Same Team | Average of Age | New Coach? |
| East | -0.18 | -0.44 | -0.12 | 69% | 27.2 | |
| ATL | 0.47 | 0.2 | -0.13 | 50% | 27.5 | New |
| BOS | -0.79 | -0.11 | 0.31 | 60% | 26.7 | New |
| BRK | 0.42 | -1.54 | -0.9 | 45% | 31.1 | New |
| CHA | -1.34 | -0.63 | 0.33 | 75% | 25.3 | New |
| CHI | 0.65 | 0.99 | 0.16 | 86% | 29.4 | |
| CLE | -0.18 | -1.55 | -0.63 | 75% | 25.5 | New |
| DET | 0.57 | -0.19 | -0.35 | 71% | 25.4 | New |
| IND | 0.7 | 1.32 | 0.28 | 78% | 27.3 | |
| MIA | 0.72 | 1.39 | 0.31 | 90% | 30.9 | |
| MIL | -0.66 | -2.44 | -0.82 | 40% | 27.2 | New |
| NYK | 0.26 | -0.91 | -0.54 | 75% | 30.4 | |
| ORL | -1.4 | -0.22 | 0.54 | 86% | 25.7 | |
| PHI | -1.92 | -1.84 | 0.03 | 44% | 23.7 | New |
| TOR | 0.01 | 0.03 | 0.01 | 78% | 25.2 | |
| WAS | 0.29 | -0.54 | -0.38 | 89% | 26.6 | |
| West | -0.09 | 0.49 | 0.27 | 62% | 27.4 | |
| DAL | 0.88 | 0.91 | 0.02 | 40% | 30.6 | |
| DEN | 0.03 | 0.42 | 0.18 | 58% | 26.5 | New |
| GSW | -0.19 | 0.03 | 0.1 | 60% | 26.8 | |
| HOU | 0.44 | 1.11 | 0.31 | 64% | 26.7 | |
| LAC | 1.52 | 1.84 | 0.15 | 50% | 27.8 | New |
| LAL | -1.56 | 0.21 | 0.82 | 50% | 27.9 | |
| MEM | 0.64 | -0.4 | -0.48 | 67% | 28.3 | New |
| MIN | 0.44 | 0.88 | 0.2 | 75% | 26.3 | |
| NOP | -0.34 | 0.27 | 0.28 | 60% | 25.1 | |
| OKC | -0.39 | 0.35 | 0.34 | 83% | 30.4 | |
| PHO | -1.97 | 1.37 | 1.53 | 50% | 25.5 | New |
| POR | -0.73 | 0.03 | 0.35 | 50% | 25.7 | New |
| SAC | -1.04 | -0.29 | 0.35 | 70% | 26.5 | |
| SAS | 1.63 | 2.76 | 0.52 | 90% | 29.5 | |
| UTA | -0.95 | -1.92 | -0.44 | 63% | 26.5 | |
| Grand Total | -0.13 | 0.05 | 0.08 | 65% | 27.3 |
In the table there are a couple interesting results. There has been a great deal of talk about the historic gap between the Eastern and Western conferences. The players in the West came in with a slightly higher average projected ASPM, but just slightly -.09 for the West and -.18 for the East. However, to date players in the West as a whole has been out performing their projections .58 points while the East players are under performing their projection -.26.
The discrepancy gets even more pronounced when looking at average ASPM weighted by minutes played, indicating either that the Western teams have more star power and less 'flat' rosters or that the coaching desicions are better lined up with ASPM's measurements, or some combination.
As shown below:
| Team | AVERAGE ASPM | Ave ASPM Weighted by Minutes | Difference From Average |
| ATL | 0.24 | 0.67 | 0.43 |
| BOS | -0.25 | -0.26 | -0.02 |
| BRK | -1.59 | -1.42 | 0.17 |
| CHA | -0.72 | -0.85 | -0.13 |
| CHI | 0.52 | 0.58 | 0.06 |
| CLE | -2.04 | -1.75 | 0.29 |
| DET | -0.19 | 0.08 | 0.27 |
| IND | 1.32 | 1.95 | 0.63 |
| MIA | 1.39 | 1.89 | 0.50 |
| MIL | -2.33 | -2.13 | 0.20 |
| NYK | -1.42 | -0.96 | 0.46 |
| ORL | -0.22 | 0.21 | 0.43 |
| PHI | -1.88 | -1.44 | 0.44 |
| TOR | 0.11 | 0.42 | 0.31 |
| WAS | -0.64 | 0.02 | 0.66 |
| East Total | -0.53 | -0.17 | 0.36 |
| DAL | 1.00 | 1.10 | 0.10 |
| DEN | 0.46 | 0.95 | 0.49 |
| GSW | 0.75 | 1.48 | 0.74 |
| HOU | 1.26 | 1.50 | 0.24 |
| LAC | 1.56 | 2.15 | 0.60 |
| LAL | 0.03 | 0.23 | 0.20 |
| MEM | -0.18 | 0.48 | 0.66 |
| MIN | 0.88 | 1.68 | 0.79 |
| NOP | 0.23 | 0.65 | 0.41 |
| OKC | 0.73 | 1.43 | 0.71 |
| PHO | 1.50 | 1.34 | -0.16 |
| POR | 0.40 | 1.16 | 0.77 |
| SAC | -0.17 | 0.02 | 0.19 |
| SAS | 2.38 | 2.64 | 0.27 |
| UTA | -1.49 | -1.55 | -0.06 |
| West Totals | 0.62 | 1.04 | 0.42 |
| League | 0.05 | 0.41 | 0.36 |
Really interesting here we see that Hornacek, who has coached the team most overperforming their stats, has played his veteran players with the least correlation to ASPM (though the correlation moves positive when rookies and back benchers are inclyded). On the other hand Portland shows the highest correlation, reflecting, in part, the differential in talent between the bench and starters even after this year's upgrade.
Not only do thirteen teams have new coaches, on the other seventeen teams nearly 30% of the players included in the comparison changed teams, so 60% of the players in the comparison are in the first year with their coach, and that is not including rookies new to the league.
A great deal of blame and credit gets heaped on head coaches in the NBA depending on how the team is doing. But it is a real question as to how much of it is justified. It is the job of the coach to put their players in positions to be effective, take the best shots available, know when to crash the glass, where to be positioned on defense. Zach Lowe at Grantland had a nice piece on the progress of Steve Clifford, the new Bobcats coach, in that regard. We can also see that, for example, the Phoenix Suns under Jeff Hornacek have increased the their expected points per shot (Xpps) from 1.o27 per shot last year to 1.057 this year, putting the team in a better position to score efficiently.
However, one the things we know from examining player metrics over the years is that player performance is less consistent when players change teams and on teams that have high turnover in players have less consistent results. With the current CBA favoring shorter contracts and shorter coaching stint, it seems that player performance will be less predictable and the nuances of the game will be even more important for analytics.
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