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Archive for the 'Analysis' Category

Mailbag: The Redd-Randolph All-Stars

11th April 2011

Here's an idea sent my way courtesy of BBR reader Rob P.:

"Can you think of players who had excellent 'per-36-minute' stat lines on limited
minutes, and who either outperformed or seriously underperformed those 'per-36'
numbers once given an increase in minutes?

I'm a Celtics fan, so Glen Davis comes to mind as being a good example of
someone who produced close to their per-36 averages upon being given a larger
role.

I'm curious about some of the extremes; players whose averages were seriously
impacted by an increase in minutes. Basically examples that make you think, 'it
was a bad idea to give this guy more minutes' OR 'I can't believe he's been
coming off the bench all this time instead of starting!'"

One of the big early battlegrounds of APBRmetrics was the philosophical debate between per-minute and per-game statistics. Per-game was the traditional standard, but analysts like John Hollinger began to tear that way of thinking down after realizing per-minute performance held over for most players who received more playing time. From Hollinger's seminal 2004-05 Pro Basketball Forecast:

"It's a pretty simple concept, but one that has largely escaped most NBA front offices: The idea that what a player does on a per-minute basis is far more important than his per-game stats. The latter tend to be influenced more by playing time than by the quality of play, yet remain the most common metric of player performance.

[...]

Unfortunately, many NBA execs and fans still believe that somebody can be a '20 minute player' -- that he's only useful in short stretches but can't play a full game. With the exception of the rare few who are scandalously out of shape (Oliver Miller, for example), this is profoundly untrue. [Michael] Redd was the perfect example -- he was thought of as a bench player simple because that's what he'd always been, but there was no reason he couldn't play 40 minutes a night. There's a supposition that some players' production will decrease with increased minutes, but within reason that's completely untrue. The first Prospectus emphatically proved this with research showing that most player's [sic] performance improves with greater playing time."

Hollinger's examples of predictable "breakouts" from per-minute stats included Redd, Zach Randolph, Carlos Boozer, and Andrei Kirilenko, all of whom held onto their low-MPG production when thrust into bigger roles. In fact, Hollinger featured Redd on the cover of his 2nd book as an example of a player with great per-minute stats who was underrated because of a lack of playing time.

So, to answer Rob's original question, and in honor of Hollinger's early per-minute darlings, here are the "Redd-Randolph All-Stars". To qualify, a player had to:

  • play in the "Hollinger Era" (the 1990s, 2000s, or 2010s)
  • play at least 41 games in back-to-back seasons
  • play less than 24 MPG in the first of the back-to-back seasons, and more than 24 MPG in the second
  • see an increase of at least 7 MPG between the two seasons

Of that group (which included 320 players since 1990), I'll list 3 top-5 lists: players who improved their PERs the most when given increased playing time, players whose PERs were the closest to what they had been before when given increased playing time, and players whose PERs declined the most with an increase in PT. This will capture all of the possible extremes Rob mentioned, plus the Hollinger prototype of players whose PERs didn't change at all.

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Posted in Analysis, BBR Mailbag, History, Statgeekery, Totally Useless, Trivia | 33 Comments »

So Who’s the MIP? (Episode II)

1st April 2011

About this time last year, I developed a method for identifying the leading candidates for the Most Improved Player (MIP) award. Since we are nearing the end of the 2010-11 season, I thought it might be interesting to revisit this topic. I made some minor tweaks to last year's method, so let me outline the process once again before reporting this season's results.

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Posted in Analysis, Awards, Statgeekery | 29 Comments »

NY Times: Why Derrick Rose Should Not Win M.V.P.

31st March 2011

A general overview of the MVP race and the statistical argument against Derrick Rose:

Keeping Score: Why Derrick Rose Should Not Win M.V.P. - NYTimes.com

You can catch it in tomorrow morning's paper as well.

And for a similar (longer) take on Rose -- albeit with a different conclusion about who the real MVP is -- John Hollinger also had a good piece at ESPN today.

Posted in Analysis, Awards, Layups, NY Times | 101 Comments »

The Unlikeliest Final Four

28th March 2011

Note: This post was originally published at College Basketball at Sports-Reference, S-R's College Hoops site, so when you're done reading, go over and check it out!

Just how unlikely is this year's Final Four of Kentucky, UConn, Virginia Commonwealth, and Butler?

Well, going by one measure, the odds of it happening were 0.00003% -- only two entries (out 5.9 million) correctly picked the four teams in ESPN.com's Bracket Challenge. But I decided to see how this year's improbable group matched up against other inexplicable Final Fours since the tournament expanded to 64 teams in 1985. Here were the Final Fours with the highest average seed # since then:

Year Team A Seed Team B Seed Team C Seed Team D Seed Avg #1s
2011 KEN 4 CONN 3 VCU 11 BUTL 8 6.50 0
2000 UNC 8 FLA 5 WISC 8 MICS 1 5.50 1
2006 GEOM 11 FLA 3 LSU 4 UCLA 2 5.00 0
1986 KAN 1 DUKE 1 LSU 11 LOU 2 3.75 2
1992 IND 2 DUKE 1 MICH 6 CIN 4 3.25 1
2010 MICS 5 BUTL 5 WVIR 2 DUKE 1 3.25 1
1985 STJO 1 GTWN 1 VILL 8 MEM 2 3.00 2
1990 ARKA 4 DUKE 3 GEOT 4 UNLV 1 3.00 1
1996 MIST 5 SYRA 4 UMAS 1 KEN 1 2.75 2
2005 LOU 4 ILL 1 MICS 5 UNC 1 2.75 2

Aside from 2011, two other years stand out at the top of the list: 2000, when two 8-seeds crashed the Final Four, and 2006, when no #1 seeds made it (but George Mason did). In terms of pre-tournament likelihood, how do those years stack up to 2011?

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Posted in Analysis, History, NCAA, Statgeekery | 7 Comments »

D-Rose and Iverson

23rd March 2011

With Derrick Rose's 2011 MVP looking like a foregone conclusion, it seems only natural to compare his campaign to that of Allen Iverson in 2001, the year another popular guard won the MVP despite not being the game's most talented player.

Here's the numerical tale of the tape for A.I. and D-Rose, with Rose extrapolated to 82 team games: (Glossary)

Player G MP ORtg %Pos DRtg OSPM DSPM SPM
Iverson 71 2979 106.3 33.8 99.2 6.79 0.07 6.86
Rose 81 3025 111.5 32.6 102.2 6.16 -0.96 5.20

Statistically, the two players are incredibly comparable. If you translate Iverson from the 103.0 league-ORtg environment of 2001 to the league ORtg of 107.1 in 2011, his ORtg/%Poss/DRtg becomes 110.5/33.8/103.0, production that is basically equivalent to Rose's after adjusting for usage.

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Posted in Analysis, Awards, History, Statgeekery, Statistical +/- | 183 Comments »

CBB: Which Coaches’ Teams Underperform Their Seeds?

21st March 2011

Note: This post was originally published at College Basketball at Sports-Reference, S-R's College Hoops site, so when you're done reading, go over and check it out!

Watching Texas and Pitt destroy my bracket for what seems like the fifth or sixth time in the last 10 years, I was compelled to ask: is it just perception, or do Rick Barnes' and Jamie Dixon's teams always significantly underachieve in the NCAA Tournament?

Luckily, I can answer that question two ways. The first is to look at every NCAA Tourney game since the field expanded to 64 teams in 1985, and measure the probability of a team winning any game based on the seeds of the two teams involved. The logistic regression formula, based on 1,686 games (including Sunday's results), is this:

Expected W% ~ =1 / (1 + EXP(0.1738176 * Seed Diff))

Where Seed Diff is simply the team's seed # minus the opponent's seed #. For instance, when a 4-seed plays a 5-seed, as Texas did Sunday, their seed difference is (4 - 5) = -1, which yields an expected win % of 54.3%. And when a 1-seed (like Pitt) plays an 8-seed (like Butler), the seed difference is -7, giving an expected W% of 77.1%.

Anyway, add all up of these expected wins for every coach's NCAA career, compare to his actual wins, and you can see which coaches have disappointed the most over their post-1985 careers:

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Posted in Analysis, NCAA, Statgeekery | 17 Comments »

Ten Thousand 2011 NCAA Tournaments

14th March 2011

Using Ken Pomeroy's ratings and the log5 formula, I set up a Monte Carlo Simulation and ran the 2011 NCAA Tournament 10,000 times. Here was the most likely bracket:

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Posted in Analysis, NCAA, Statgeekery | 12 Comments »

Time to Face Facts — Miami is Unlikely to Be a True .500 Team in Close Games

8th March 2011

The close-game struggles of this year's Miami Heat are nothing if not well-documented. A 5-13 record in games decided by 5 or fewer points has become the team's defining stat, far surpassing LeBron James' gaudy all-around numbers or the scoring brilliance of Dwyane Wade. As far as the mainstream media is concerned, it is now assumed this team will choke until they prove otherwise.

As statheads, we typically detest this sort of cliched, pseudo-psychological nonsense. Part of the sabermetric orthodoxy is to deny the existence of "clutch skills", or at least to minimize them relative to overall factors that impact every minute of the game. But with the Heat so dominant in blowouts and so vulnerable in close games, perhaps there is something to the old sportswriter aphorisms about certain teams being unable to close the deal when the margin gets tight.

As mentioned earlier, Miami is 5-13 (.278) in games decided by 5 points or fewer, while they sport a sterling 38-7 (.844) mark in games decided by 6 or more points. The Heat now have the biggest differential in NBA history between wpct in games decided by 6+ pts and games decided by 5 or fewer:

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Posted in Analysis, Rants & Ramblings, Statgeekery | 106 Comments »

Searching For the NBA’s Version of the Charlie Sheen Fiasco

2nd March 2011

In the wake of the ongoing Charlie Sheen chaos, I was (of course) racking my brain to find a comparable NBA analogy. Ideally you'd want to find a situation with the following parallels:

  • It involves a winning team. Although I have personally never seen an episode, Sheen's show Two and a Half Men is apparently wildly successful, as Sheen is quick to point out to anyone who will listen. So any NBA equivalent would have to involve a good team, probably one that had been a contender for multiple years.
  • It involves that team's best player. Monetarily speaking, Sheen is the #1 scorer on Two and a Half Men, and in fact the league's top player -- he made $1.8 million/episode in 2010, making him the highest-paid actor on television. The basketball equivalent would have to deal with a similar star in his prime.
  • The team releases that player mid-season. Production on Two and a Half Men's 8th season was halted midway due to Sheen's behavioral problems, so an NBA version would have to involve a team waiving their best player in the middle of the season.

Unfortunately, there isn't a single situation in NBA history that meets all of those requirements. In fact, as far as I can tell, there are only a few remotely comparable situations:

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Posted in Analysis, History, Insane ideas, Just For Fun, No Math Required, Rants & Ramblings | 21 Comments »

Where Would They Be Without Him?

28th February 2011

Tom Haberstroh had a great piece at ESPN last week in which he broke down the ongoing Derrick Rose-vs-LeBron James MVP debate. To me, the key passage was this:

"Oddly enough, what's not helping Rose's MVP case is his plus-minus numbers. And implicitly, this is where most Rose supporters state their case. When his advocates ask, 'Where would the Bulls be without Rose?' the question is meant to be a rhetorical one. The obvious implication is that a Rose-less Bulls squad would instantly become a basement dweller. But rather than blindly accept it, we can actually see how the Bulls have managed without him on the court. And how have they fared with Rose benched? By beating opponents by 51 points on the season, or an average of 4.9 points every 100 possessions. Why? Whether Rose is in the game or not, [Tom] Thibodeau’s game-changing defense remains."

I don't want to get into Rose-vs-James specifically here, but I do think what Tom wrote is a very important concept to apply to all NBA MVP debates in this modern age of plus/minus.

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Posted in Analysis, Awards, Rants & Ramblings, Statgeekery | 90 Comments »