According to 111 members of the sports media (out of 12o voters), Derrick Rose was the very best rookie in 2009. Of the nine voters who differed from this conclusion, five preferred O.J. Mayo, two preferred Brook Lopez, and two preferred Russell Westbrook.
Of these choices, none was actually the most productive rookie. But before I get to that story, let me discuss why the Rookie of the Year in 2009 was merely average.
An Average Rose
Let’s first compare what Rose did this past season to what we see from a typical point guard.
Adjusted Field Goal Percentage: 47.4% avg. PG, 48.2% Rose
Free Throw Percentage: 78.8% avg. PG, 78.8% Rose
Points Scored per 48 minutes: 18.4 avg. PG, 21.8 Rose
Rebounds per 48 minutes: 4.7 avg. PG, 5.1 Rose
Steals per 48 minutes: 2.0 avg. PG, 1.1 Rose
Turnovers per 48 minutes: 3.4 avg. PG, 3.2 Rose
Blocked Shots per 48 minutes: 0.3 avg. PG, 0.3 Rose
Assists per 48 minutes: 8.6 avg. PG, 8.2 Rose
Personal Fouls per 48 minutes: 3.6 avg. PG, 2.0 Rose
Win Score per 48 minutes: 6.3 avg. PG, 6.6 Rose
From these numbers we see Rose is below average with respect to steals and assists. He is above average with respect to shooting efficiency (barely), points scored, rebounds, turnovers, and personal fouls. But except for scoring and personal fouls, the difference between Rose and the average point guard is quite small. And his advantage with respect to scoring is only due to his propensity to call his own number (i.e. he is above average with respect to field goal attempts). Consequently – when we put the whole picture together – we see there is little difference between the Win Score of Rose and the average point guard.
In sum, all these numbers tell us that Rose was essentially little better than an average player in 2008-09. And this is because he really didn’t do anything exceptionally well. Yes, Rose has his fans. But I think even his fans would be hard pressed to find any facet of the game where Rose currently excels.
I should emphasize that I am not talking about the future. At some point, Rose might develop into an above average player. But as a rookie, this didn’t happen.
So why was he named Rookie of the Year?
The key issue is point score per game. Rose had the second highest scoring average among rookies. And since the leading scorer – O.J. Mayo – played on a losing team (and was also drafted after Rose), we should not be surprised that most of the media focused on the point guard from Chicago.
The Most Productive Rookies
Now what happens if we look at all rookies via Wins Produced? To answer this question we turn to Table One.
In the past I noted that Kevin Love would probably lead all rookies in Wins Produced (see HERE and HERE). And as we see in Table One, that’s where Love finished in the rankings. When we focus on WP48 [Wins Produced per 48 minutes], we see that Love was the only rookie to surpass the 0.200 mark (if we ignore what Hamed Haddadi did in 120 minutes).
Love, though, was not the only above average freshman (average WP48 is 0.100). Of the rookies who logged 1,000 minutes, Greg Oden, Rudy Fernandez, Brook Lopez, Marc Gasol, Anthony Randolph, Nicolas Batum, Mario Chalmers, and Rose all surpassed the average mark (again, Rose just barely went past this mark). Typically rookies struggle to get past the 0.050 mark, so this many above average rookies – with three playing for one team (Portland) – is not the story we generally see.
The Top Rookies at Each Position
All of this tells us that the media had a few other choices beyond Rose. In fact, as both Table One and Table Two illustrate, if all they looked at was point guards, Rose may still not have been the best choice.
Mario Chalmers did not take as many shots as Rose and he didn’t get as many rebounds or assists. But he was the more efficient scorer and he did a much better job of getting steals. Consequently, Chalmers was slightly more productive.
When we turn to the shooting guards, O.J. Mayo was considered the best by the writers. In fact, five sportswriters thought Mayo was the best rookie. And Mayo – as Table Two reports — was above average with respect to scoring and personal fouls. But he was below average with respect to every other aspect of the game. Hence Mayo’s overall production – which was below average — was eclipsed by Rudy Fernandez (who was well above average for the Blazers).
When we turn to the forwards, the media selected Michael Beasley as the top forward. Again – as Table Four reveals – the media missed the mark. Beasley – like Mayo – was above average as a scorer. But – like Mayo — he was below average with respect to everything else. Consequently, Beasley’s overall per-minute production was eclipsed by Anthony Randolph and Nicolas Batum (another Blazer).
When we turn to the centers we see yet another center. Table Five includes Greg Oden, who the media completely ignored. On a per-minute basis, though, Oden (again, of the Blazers) was the second most productive rookie. His primary problem was personal fouls, which reduced his minutes (the play of Przybilla also kept him on the bench). But beyond this issue, Oden is already above average with respect to rebounds and shooting efficiency.
What All This Means
Let me close by noting again what these numbers tell us. Last year Kevin Durant was not the most productive rookie. In fact, it wasn’t even close. This result made fans of Durant very angry and many of these feel vindicated by Durant’s sophomore performance. But as I noted a few weeks ago, regardless of what Durant does for the rest of his career, his below average rookie campaign will remain.
The same story can be told about Rose. Someday Rose might be one of the top point guards in the game (although he will probably never be as productive as Chris Paul). But that was not the case during Rose’s first season. And no matter what Rose does going forward, his future production will not change the fact he was just about average as a rookie.
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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.