Archive for the 'Statgeekery' Category

Last week, I ran a post (prompted by this post at the Wages of Wins) wherein I tried to determine the offensive impact when a team loses its leading scorer. I found that, since 1986 at least, a team loses about 2 points of offensive rating relative to the league average when its top scorer by PPG doesn't play.

I got a lot of great feedback from that initial post, so I decided to try my hand at a sequel after making a number of improvements to the study:

• One complaint was that I was lumping efficient scorers in with inefficient ones in the original study. No one is really debating whether losing LeBron James will hurt an offense, but one of the core questions is whether losing Carmelo Anthony or Rudy Gay has a negative impact as well. To that end, I'm now isolating only teams with inefficient leading scorers. This means a team's PPG leader, minimum 1/2 of team games played, with either a Dean Oliver Offensive Rating or True Shooting % that was equal to or below the league's average that season.
• Another complaint was that I looked at offense alone, rather than the total impact of the player's loss. So now I'm looking at the change in team efficiency differential (offensive efficiency minus defensive efficiency) when a player is in and out of the lineup.
• While I accounted for strength of opponent in the last study, I didn't account for home-court advantage. Now I have added an HCA term to what we would predict an average team to put up vs. a given opponent (+4 pts/100 of efficiency differential to the home team), in addition to an SOS term (the opponent's efficiency differential in all of its other games).

What follows is a massive table that shows the results of this new study. The outcome (the bottom-right cell) is the average change in efficiency differential when an inefficient leading scorer plays vs. when he does not play, weighted by possessions without the leading scorer. If it is positive, it is evidence that even inefficient scoring is an attribute that teams find difficult to replace in a salary-capped economic system; if it is negative, it is evidence that scoring is overrated if it's not done efficiently, and that inefficient #1 options can be replaced with relative ease.

To the data dump (mouse over column headers for descriptions):

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Bill Simmons and BS Report HoF guest Chuck Klosterman are discussing Larry Bird vs. Dirk Nowitzki in a podcast. Simmons says that the advanced stats place Dirk in the same category as Bird, perhaps even giving Dirk the edge, and he's not sure how he feels about this.

I wasn't sure how I felt, either, so I looked up the numbers. Here is a monster table with their advanced stats -- each has played exactly 13 years:

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In gathering links for StatHead yesterday, I came across this post at the Wages of Wins, wherein Prof. Berri mentions that the losses of Allen Iverson, Carmelo Anthony, & Rudy Gay did not hurt their respective teams. He then writes:

"In each of these examples, the loss of a scorer led people to forecast doom.  In each case, the team losing the scorer managed to survive and even improve.

Readers of The Wages of Wins and Stumbling on Wins understand this basic story. Scoring is overvalued by many NBA observers.   Top scorers do not always have the impact on wins that people imagine.  But no matter how often this story repeats, each time a scorer is lost we still see the same arguments offered by adherents to the conventional wisdom (for example, this week the Grizzlies insisted they would never dream of letting Gay depart)."

That's anecdotal evidence, though. What if we looked at every instance of a team losing its leading scorer? Would the typical team in that situation be impervious, or are those just a few cherry-picked exceptions to a larger rule?

Well, luckily, at BBR we have boxscores for every regular-season game since 1985-86. So I gathered our data, considering a team's "leading scorer" to be the player who led the team in PPG among players who played more than half of the team's games. I then looked at each team's offensive rating in every game, noting whether the designated "leading scorer" played in that game or not.

I also accounted for the strength of the opposing defense in each game by measuring how many pts/possession the opponent allowed in every game of the season except the one at hand. The end result will measure how well each offense performed relative to what we would expect from a league-average team facing the same opponent -- split by whether the team's "leading scorer" played or not.

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Dallas Mavericks

57-25, 2nd in NBA Southwest Division (Schedule and Results)
Coach: Rick Carlisle (57-25)

PTS/G: 100.2 (11th of 30) ▪ Opp PTS/G: 96.0 (10th of 30)
SRS: 4.41 (8th of 30) ▪ Pace: 91.3 (18th of 30)
Off Rtg: 109.7 (8th of 30) ▪ Def Rtg: 105.0 (8th of 30)
Expected W-L: 53-29 (8th of 30)

Arena: American Airlines Center ▪ Attendance: 803,968 (6th of 30)

Playoffs:
Won NBA Western Conference Semifinals (4-0) versus Los Angeles Lakers
Won NBA Western Conference First Round (4-2) versus Portland Trail Blazers

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Chicago Bulls

62-20, 1st in NBA Central Division (Schedule and Results)
Coach: Tom Thibodeau (62-20)

PTS/G: 98.6 (20th of 30) ▪ Opp PTS/G: 91.3 (2nd of 30)
SRS: 6.53 (2nd of 30) ▪ Pace: 90.4 (23rd of 30)
Off Rtg: 108.3 (11th of 30) ▪ Def Rtg: 100.3 (1st of 30)
Expected W-L: 61-21 (1st of 30)

Arena: United Center ▪ Attendance: 893,462 (1st of 30)

Playoffs:
Won NBA Eastern Conference Semifinals (4-2) versus Atlanta Hawks
Won NBA Eastern Conference First Round (4-1) versus Indiana Pacers

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2010-11 NBA power rankings through the games played on May 12, 2011:

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Since we're in the thick of the playoffs, it seems appropriate to revisit this post from last June regarding the importance of each game in a best-of-7 series:

We can try to quantify [the relative importance of each game] by looking at the potential swings in each team's probability of winning the series based on the outcome of a given game. Let's establish a scenario where two morally .500 teams are playing each other in a 7-game series; the home team in any game has a 60% chance of winning (60% traditionally being the NBA's home-court advantage), and the away team has a 40% chance. At the beginning of a series in the 2-2-1-1-1 format, the team playing Game 1 at home has a 53.2% probability of winning the series (go here for the formulae I used to arrive at these numbers). If that team wins Game 1, their probability of winning the series suddenly increases to 66%, a boost of 12.8%, and if they lose, their probability drops to 34%, a decrease of 19.2%. Since there are only two possible outcomes in any game (win or loss), we can say that the average swing in series win probability for the home team in Game 1 is +/- 16% (12.8% plus 19.2%, divided by 2).

Do this for both teams and every possible situation in a 7-game series, and you can establish which games produce the biggest swings in series win probability:

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Oklahoma City Thunder

55-27, 1st in NBA Northwest Division (Schedule and Results)
Coach: Scott Brooks (55-27)

PTS/G: 104.8 (5th of 30) ▪ Opp PTS/G: 101.0 (18th of 30)
SRS: 3.81 (9th of 30) ▪ Pace: 92.7 (13th of 30)
Off Rtg: 111.2 (5th of 30) ▪ Def Rtg: 107.2 (15th of 30)
Expected W-L: 51-31 (9th of 30)

Arena: Ford Center ▪ Attendance: 726,071 (14th of 30)

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Los Angeles Lakers

57-25, 1st in NBA Pacific Division (Schedule and Results)
Coach: Phil Jackson (57-25)

PTS/G: 101.5 (9th of 30) ▪ Opp PTS/G: 95.4 (8th of 30)
SRS: 6.01 (3rd of 30) ▪ Pace: 90.7 (21st of 30)
Off Rtg: 111.0 (6th of 30) ▪ Def Rtg: 104.3 (6th of 30)
Expected W-L: 58-24 (3rd of 30)

Arena: STAPLES Center ▪ Attendance: 778,777 (8th of 30)

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Miami Heat

58-24, 1st in NBA Southeast Division (Schedule and Results)
Coach: Erik Spoelstra (58-24)

PTS/G: 102.1 (8th of 30) ▪ Opp PTS/G: 94.6 (6th of 30)
SRS: 6.76 (1st of 30) ▪ Pace: 90.9 (20th of 30)
Off Rtg: 111.7 (3rd of 30) ▪ Def Rtg: 103.5 (5th of 30)
Expected W-L: 61-21 (2nd of 30)

Arena: AmericanAirlines Arena ▪ Attendance: 810,930 (4th of 30)

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