The Western Conference, from top to bottom, is clearly the best conference this season. But as I noted a few days ago, the stars of the West were not clearly better than their counterparts in the East. So we should not be surprised that the Eastern stars prevailed in the 2008 All-Star game.
Was LeBron James, though, the game’s MVP? To answer that question, let’s turn to PAWS [Position Adjusted Win Score]. Table One reports Win Score, Win Score per minute, PAWS, and PAWS per minute for each of the participants in the 2008 NBA All-Star Game.
Table One indicates that LeBron certainly played well. But he was not the M2P (Most Productive Player). That honor went to (drum roll, please)… Brandon Roy. Yes, with a mark of 16.3, Roy was the most productive star in the All-Star game.
But Roy’s team lost. On the winners, the M2P was the player Doug Collins identified as the MVP of the game. Yes, Ray Allen – the last addition to the team – was the M2P in the East.
As for King James, he placed fourth, right behind Mr. Slam Dunk (or Superman), Dwight Howard.
Thoughts on the Slam Dunk Competitors
Speaking of the slam dunk competition, a thought occurred to me as I watched this on Saturday night. Clearly the competitors for this title are extremely athletic. I imagine few people can blow out a candle and slam dunk a basketball at the same time. But how does this athletic skill translate into wins?
For the winner of this competition, Dwight Howard, we know his skills do produce wins. After all, Howard was the M2P of the first half of the season. For the other competitors, though, the answer is not that clear.
Table Two reports the productivity of Rudy Gay, Jamario Moon, and Gerald Green. All three players are primarily small forwards, and we see that relative to average at this position, all three have shortcomings. Gay is an above average scorer, but not particularly good at any other aspect of the game. Moon falls a bit short in terms of shot attempts, but is exceptional with respect to net possessions (rebounds + steals – turnovers). And Green can be average as a scorer (at least he was in 2006-07), but he is below average with respect to most other aspects of the game.
Of the three, I suspect Gay is the preferred player for most NBA fans. Certainly he improved tremendously between his rookie and sophomore season. And Gay is easily the most prolific scorer. In terms of wins, though, Moon is somewhat more productive. No, Moon cannot score like Gay. At least, he hasn’t shown this skill yet. But his work with respect to the non-scoring aspects of the game, give him the nod over Gay.
Although people might debate the merits of Gay and Moon, I think it’s clear that Green has not been a productive NBA player. This is clear when you look at Wins Produced, Win Score, or even NBA Efficiency. Green’s shortcomings should be used as an illustration for young basketball players. Yes, Green has amazing athletic ability. But NBA players are not asked to blow out candles while dunking basketballs. The game of basketball requires that you offer something beyond scoring. Rebounds, steals, and turnovers are all correlated with team wins. And when a player ignores these aspects of the game, both his productivity and his team will suffer.
The Other News
The big news over the weekend was not the All-Star festivities. The Hawks and Kings swapped players and hence signaled to their fans that the playoffs are the team’s priority. Of course, the Hawks are focused on the 2008 playoffs, while the Kings are looking at 2009 (or 2010, 2011, etc…). My plan is to offer some thoughts this week on whether this move allows each team to achieve its objectives.
In addition, the Nets might be moving Jason Kidd to the Mavericks. Will this make Dallas the favorites in the West? Or will this move derail the title aspirations of the Mavericks? Again, I will try and answer these questions this week as well.
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.