Before the season started we learned that Yao Ming wasn’t going to play. Furthermore, Ron Artest had departed for LA and the availability of Tracy McGrady was questionable. These three players led the Rockets in points per game in 2008-09. With so much scoring exiting the building, many NBA observers thought Houston was destined for the 2010 lottery.
In looking over Houston’s roster, though, it didn’t appear this team was quite as bad as people thought. Entering this season the Rockets roster included the following players who were above average performers in 2008-09: Trevor Ariza, Shane Battier, Chuck Hayes, Kyle Lowry, and Carl Landry. Although these players were not scorers, their respective WP48 [Wins Produced per 48 minutes] numbers suggested the cupboard wasn’t bare in Houston. In fact – although the loss of Ming and McGrady was going to hurt some (notice I left out Artest) – it seemed that the playoffs were still a possibility for the Rockets.
The Rockets Today
The Rockets have now played 27 games in 2009-10. And if the season ended today, the Rockets would not have a seat at the NBA lottery. So how was this possible?
Table One reports the Wins Produced and WP48 numbers for the Rockets this season. As one can see, the following players have so far been above average (average is 0.100): Luis Scola, Carl Landry, Kyle Lowry, Trevor Ariza, and Chuck Hayes. Yes, five of the six players who were above average last season are still above average this season.
Again, the loss of Ming and McGrady (the latter only recently returned) didn’t help. But the Rockets are still on pace to win about 46 games this season (after winning 53 last season). So apparently losing your top scorers is not necessarily a death sentence.
The performance of the Rockets this season demonstrates an aspect of basketball performance often noted in this forum. Basketball players – relative to what we see in baseball and football – are very consistent over time. For the most part, the productivity of the players the Rockets are employing this season is not much different from what we saw last year. Yes, the loss of the team’s primary scorers has forced other players to take shots. In general, though, the increase in shot attempts hasn’t reduced each player’s effectiveness.
The Ariza Story
As a few commentators on the Miami Heat post from a few days ago noted, though, the exception is Trevor Ariza (how we went from a discussion of Dwyane Wade to Ariza, though, is a mystery to me). Last season Ariza took 14.1 field goal attempts per 48 minutes (FGA48) and his adjusted field goal percentage was 51.1%. This season, Ariza’s FGA48 has increased to 20.4 and his adjusted field goal percentage has fallen to 45.8%. And this has led some people to argue that Ariza is the classic example of how increasing shot attempts lowers efficiency.
In reading the comments I am somewhat convinced I cannot change everyone’s mind about the meaning behind Ariza’s numbers. In fact, one commentator explicitly stated: “If a statistical study suggests otherwise, it must be missing something.”
Such a comment forces me to lower my expectations. The following comments on Ariza’s declining field goal percentage, as I note, will not necessarily change minds (but they might give “true believers” something else to rationalize away). Here are some things to think about when you consider Ariza’s drop in efficiency:
- Ariza’s career adjusted field goal percentage is 48.4%. So his mark this year is not far from this career mark.
- Ariza, though, has dropped off. Looking at other players on Houston’s roster, though, reveals a different story. Carl Landry is taking 5.6 additional field goals per 48 minutes and his shooting efficiency has only declined from 57.5% to 57.0%. Chuck Hayes has seen his FGA48 rise by 4.9 and his shooting efficiency has improved by 7.7%.
- The discussion of Ariza, Landry, and Hayes is purely anecdotal. When you look at players from 1977-78 to 2007-08 (the sample includes over 5,000 season observations) you see that there is a link between a change in the number of shots he takes and his shooting efficiency. But the impact is quite small. Here is what we say in our next book: “…imagine a player who takes 16.3 shots per 48 minutes and has an adjusted field goal percentage of 48.4% (these are the league average marks). If that player increased his shots per 48 minutes to 25.3 (a two standard deviation increase), his adjusted field goal percentage would be expected to decline to 47.1%.”
Given all this, is the change we see in Ariza’s shooting efficiency simply due to the fact he is being asked to take more shots? I don’t think the evidence leads to that conclusion. We don’t see the same story when we look at the other players on the Rockets (who are also taking more shots). And we don’t see such a strong link between shot attempts and shooting efficiency when we look at a sample of over 5,000 NBA players.
All that being said, I don’t have a great story for why Ariza’s efficiency has declined. I will note that although NBA players are very consistent from season to season, shooting efficiency is one aspect of a player’s performance that is the most volatile (as we note in the next book, it’s about as volatile as OPS and Slugging Percentage in baseball).
The Bigger Story
Regardless of how you see the Ariza story, the primary observation remains. Most players on the Rockets are playing about as well as we would expect given their past performance. Yes, many of these players are playing more minutes. And many are taking more shots. But their overall effectiveness is roughly the same.
Now it’s important to remember that losing Ming and McGrady didn’t help. And although the Rockets would be in the playoffs if the post-season started today, they are only barely in. There’s still a chance Houston will be visiting the lottery. That being said, what we have seen so far does suggest that losing your top scorers doesn’t necessarily kill an NBA team. At least, that’s what we see when a roster has an abundance of productive non-scorers.
The WoW Journal Comments Policy
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.