The best team in the NBA this season has been the Cleveland Cavaliers.  And yet I have not written about this team since last December.  So let me remedy this oversight by answering some questions about the Cavaliers (this is a trick I have seen writers use which essentially requires that I interview myself).

Let me start with the basic data. 

Table One: The Cleveland Cavaliers after 77 games in 2009-10

Table One reports what the Cavaliers have done after 77 games this season.  The two standard views are presented.  First we see that the Wins Produced for the Cavaliers stands at 56.2 this season.  If the veteran players had maintained what they did last year, the team’s Wins Produced would be 63.9.  So although the Cavs are the best team in 2009-10, had these players did what the did in 2008-09 this team would be even better. 

Okay, that’s the team picture.  Here are a few questions.

How much has LeBron meant to the Cavaliers?

Of the team’s 56.2 Wins Produced, 27.1 can be traced to King James. So LeBron is producing nearly half the team’s wins. 

How does LeBron compare to Michael Jordan?

LeBron is 25 years of age and is posting a 0.443 WP48 [Wins Produced per 48 minutes].  In 1987-88, MJ was 24 years old.  Here is what he did that year, and each of the next three seasons:

• 1987-88: 27.2 Wins Produced, 0.394 WP48
• 1988-89: 32.9 Wins Produced, 0.486 WP48
• 1989-90: 28.7 Wins Produced, 0.431 WP48
• 1990-91: 27.6 Wins Produced, 0.437 WP48

After the 1990-91 season, Jordan never surpassed the 0.400 mark or produced more than 25 wins in a single season. In other words, MJ peaked around 25 and then slowly declined (and it was – as we generally see – a slow decline).  If LeBron follows this same pattern, then he will not quite get to what MJ did in 1988-89.  But the difference is not very large.

Who is helping King James?

Just like Jordan was not a one-man team, King James is also getting help.  But who we think is helping depends upon where we look.  If we consider per-game scoring, the key helpers on this team are Mo Williams (15.5 points per game), Antawn Jamison (15.5 points per game), and Shaquille O’Neal (12.0 points per game). No other player is averaging in double digits.

If we look past scoring, though, we see that two of these three scorers aren’t helping much.  The lone exception is Jamison.  Last year, Jamison posted a 0.178 WP48.  This season his mark is 0.176.  So Jamison – despite his advance age – is still helping.

An average player posts a WP48 of 0.100.  In addition to Jamison, we see that the Cavaliers are getting above average production from Jamario Moon [0.190 WP48], Anderson Varejao [0.185 WP48], Delonte West [0.123 WP48], and J.J. Hickson [0.119 WP48]. With six above average performers, it’s clear the Cavaliers are not just LeBron. And that is why this team leads the NBA.

Who is not helping the Cavaliers?

Although LeBron is getting quite a bit of help, as noted above this team should be even better. The two players primarily responsible for the team’s decline are Shaq and Zydrunas Ilgauskas.  This is actually an observation made last December.  At the time I argued that these two players needed to do better if Cleveland was going to separate themselves from the other teams in the league.   Fortunately for the Cavaliers, Boston (and perhaps LA) has declined.  Still, the lead Cleveland enjoys would be more impressive if the biggest players on this team could play bigger.

Will Cleveland win the NBA title in 2010?

And now for the big question.  Last year the Cavaliers were the best in the regular season and failed to reach the NBA Finals.  The team that tripped them up was the Orlando Magic.  This year, the Magic are somewhat better.  And the Cavaliers have decline a bit.  Still, Cleveland is the better team and they will have homecourt advantage.  So I would favor Cleveland to come out of the East and for LeBron and company to advance to another NBA Finals. And in the Finals, I expect Cleveland to be victorious.

As you consider this prediction let me note that I was victorious the True Hoop Stat Geek Smackdown last year.  So that might suggest I know something.  Then again, I picked Cleveland to beat Orlando last year (so what do I know?).   In other words, I think Cleveland should be favored.  But the playoffs are not a foregone conclusion. 

- DJ

The WoW Journal Comments Policy

Our research on the NBA was summarized HERE.

The Technical Notes at wagesofwins.com provides substantially more information on the published research behind Wins Produced and Win Score

Wins Produced, Win Score, and PAWSmin are also discussed in the following posts:

Simple Models of Player Performance

Wins Produced vs. Win Score

What Wins Produced Says and What It Does Not Say

Introducing PAWSmin — and a Defense of Box Score Statistics

Finally, A Guide to Evaluating Models contains useful hints on how to interpret and evaluate statistical models.

ESPN.com is reporting that Dennis Johnson has been elected to the Hall of Fame.   This selection — as discussed below — seems to be somewhat questionable.

Let’s begin with a model of Hall of Fame voting.  Justin Kubatko – at Basketball-Reference.com – reports a model of Hall of Fame voting that incorporates the following factors: height, points per game, rebounds per game, assists per game, All-Star game selections, MVP award shares, NBA championships won, and whether or not the player’s career ended before 1960.  The model ignores per-minute performance, shooting efficiency, turnovers, steals, blocked shots, and personal fouls.  This is not surprising since most player evaluations in the NBA focus on the per- game factors Kubatko included (i.e. don’t value per-minute performance highly) and ignore such issues as shooting efficiency and turnovers.  In sum, I think Kubatko’s model captures much of what goes in to the voting for the Hall of Fame.

That being said, according to this model, the probability that Johnson would be elected was only 0.498.  So Johnson – according to the limited list of factors generally noted in evaluations of NBA players — wasn’t a particularly strong candidate. 

If we shift our focus to Wins Produced (which I am sure would not be the best predictor of Hall of Fame voting) we see that Johnson did have some good seasons.  But he would not rank among the most productive players who ever played in the NBA.

• Seattle [1977-78]: 7.0 Wins Produced, 0.153 WP48
• Seattle [1978-79]: 8.4 Wins Produced, 0.149 WP48
• Seattle [1979-80]: 6.9 Wins Produced, 0.112 WP48
• Phoenix [1980-81]: 7.7 Wins Produced, 0.141 WP48
• Phoenix [1981-82]: 9.8 Wins Produced, 0.161 WP48
• Phoenix [1982-83]: 7.6 Wins Produced, 0.143 WP48
• Boston [1983-84]: 4.7 Wins Produced, 0.085 WP48
• Boston [1984-85]: 3.3 Wins Produced, 0.053 WP48
• Boston [1985-86]: 2.0 Wins Produced, 0.036 WP48
• Boston [1986-87]: 1.8 Wins Produced, 0.029 WP48
• Boston [1987-88]: 2.0 Wins Produced, 0.036 WP48
• Boston [1988-89]: 0.6 Wins Produced, 0.013 WP48
• Boston [1989-90]: 2.2 Wins Produced, 0.052 WP48
• Career [1977-1990]: 64.1 Wins Produced, 0.090 WP48

The data needed to calculate Wins Produced only goes back to 1977-78.  So we are missing what Johnson did his rookie season. We should note, though, that Johnson’s rookie season was the only time he did not play on a winning team. 

Johnson’s ability to find winning teammates was never more true than during his time in Boston.  An average player posts a WP48 of 0.100.  As one can see, Johnson was below average as a Celtic.  One suspects, though, that seeing Johnson celebrate at title as a Celtic in  1984 and 1986 led people to believe that he was an above average player.  Certainly Johnson was above average in 1979 when he won a title with Seattle.  But Johnson didn’t join the Celtics until he was 29 years of age.  And at that point, he was past his prime (and not an above average player).

One probably does not need Wins Produced to know that Johnson was not one of the all-time great NBA players.  And one also suspects that Johnson’s “skill” at finding great teammates drove this selection.  To illustrate, consider the top three producers of wins on the teams that Johnson won a title with during his career. 

The 1978-79 Seattle SuperSonics

• Jack Sikma: 10.6 Wins Produced, 0.173 WP48
• Gus Williams: 9.8 Wins Produced, 0.208 WP48
• Dennis Johnson: 8.4 Wins Produced, 0.149 WP48

The 1983-84 Boston Celtics

• Larry Bird: 24.2 Wins Produced, 0.383 WP48
• Robert Parish: 13.1 Wins Produced, 0.220 WP48
• Kevin McHale: 9.9 Wins Produced, 0.184 WP48
• Dennis Johnson ranked fourth on this team in Wins Produced

The 1985-86 Boston Celtics

• Larry Bird: 26.2 Wins Produced, 0.404 WP48
• Robert Parish: 11.7 Wins Produced, 0.219 WP48
• Kevin McHale: 11.6 Wins Produced, 0.232 WP48
• Dennis Johnson ranked sixth on this team in Wins Produced

Looking at these brief lists it’s clear that Dennis Johnson was a significant contributor to the title team in Seattle.  But in Boston – as noted above – Johnson was not the primary producer of wins.

One suspects that had Johnson spent his career with less successful teams that his selection to the Hall of Fame wouldn’t have happened.   In other words, this selection suggests that voters are simply not able to separate a player from his teammates.

Or to put it another way… Dennis Johnson produced 64.1 from 1977-78 to 1989-90 and he is in the Hall of Fame.  Artis Gilmore – as we note in Stumbling on Wins – produced 130.3 wins from 1977-78 to 1987-88 (and likely many more wins in the ABA).  And Gilmore – who never won an NBA title — is still not in the Hall of Fame. 

- DJ

The WoW Journal Comments Policy

Our research on the NBA was summarized HERE.

The Technical Notes at wagesofwins.com provides substantially more information on the published research behind Wins Produced and Win Score

Wins Produced, Win Score, and PAWSmin are also discussed in the following posts:

Simple Models of Player Performance

Wins Produced vs. Win Score

What Wins Produced Says and What It Does Not Say

Introducing PAWSmin — and a Defense of Box Score Statistics

Finally, A Guide to Evaluating Models contains useful hints on how to interpret and evaluate statistical models.

Lenny Wilkens leads all NBA coaches in league history with 1,332 career regular season victories (vs. 1155 losses for a 0.536 career winning percentage).  Don Nelson’s career record, though, currently stands at 1330-1060 (a 0.556 career winning percentage).  So with just three more wins, Nelson will assume the top spot in league history.

Unfortunately for Nelson, this is one of the worst seasons in his career.  After 74 games, the Golden State Warriors record stands at 21-53.  With a 0.284 winning percentage, the Warriors are only on pace to win two more games in 2009-10.  And if that happens, Nelson and Wilkens will finish tied for first after this season.

When we look at the Warriors this season, it looks like this record should have been a bit easier to break. The Warriors efficiency differential – and Wins Produced (as Table One indicates) – suggests the Warriors should have already won 27 games.  So Nelson and the Warriors have been a bit unlucky this year.

Table One: The Golden State Warriors after 74 games in 2009-10

And when we consider the injuries on this team, we see even more misfortune.  Andris Biedrins – 0.200 WP48 [Wins Produced per 48 minutes] – and Anthony Randolph – 0.167 WP48 – have each missed 41 games this year.  Had these players been available the entire season, Nelson would now be adding to his record.

But injuries and bad luck did happen, and therefore, the Warriors may not be able to set the record for Nelson.  Despite this potential failure, there has been some good news in 2009-10.  Stephen Curry – the team’s lottery pick in 2009 – currently leads Golden State in Wins Produced and is posting a 0.124 WP48 [average is 0.100]. 

And Curry is not the only above average rookie. Reggie Williams left VMI in 2008.  And back in May of that year, Erich Doerr (who often does draft analysis at the WoW Journal), had this to say about Williams: NCAA scoring leader Reggie Williams posted a strong pace-adjusted .209 PAWS/M, but played a mere one game against a top 100 team.

Despite strong college numbers, Williams didn’t appear in the NBA until 2010.  And thus far – after just 441 minutes – Williams has posted a 0.146 WP48.  So it looks like the Nelson and the Warriors have found another above average performer.

The play of Williams (if it is for real) and Curry suggests the future in Golden State could look better. If Nelson returns next season, it looks like he could be coaching Curry, Williams, Randolph, and Biedrins.  All of these players are above average performers.  And if these players can be healthy and available, the Warriors should do much better than the 0.284 winning percentage seen this season.

Let me close with two observations. First, Nelson has not always been a victim of bad luck.  As noted last August – in The Squid that Saved Don Nelson (I still love that title) – one can argue that Nelson’s success as a coach was owed to the Milwaukee Bucks selection of Sidney Moncreif.  Had that not happened (and it woudn’t have happened if Dick Vitale and the Pistons made a different choice), Nelson’s career might not have been as notable.

That being said, it should be noted – as mentioned in Stumbling on Wins – Nelson is one of the few coaches we investigated who had a statistically significant impact on player performance.  So Nelson may not be the greatest coach ever (or he may be), but it certainly appears that he has made a difference.  And with a bit of good fortune this year, this particular difference-maker might become the winningest coach in NBA history.

- DJ

The WoW Journal Comments Policy

Our research on the NBA was summarized HERE.

The Technical Notes at wagesofwins.com provides substantially more information on the published research behind Wins Produced and Win Score

Wins Produced, Win Score, and PAWSmin are also discussed in the following posts:

Simple Models of Player Performance

Wins Produced vs. Win Score

What Wins Produced Says and What It Does Not Say

Introducing PAWSmin — and a Defense of Box Score Statistics

Finally, A Guide to Evaluating Models contains useful hints on how to interpret and evaluate statistical models.