joec32033 wrote:

Bonn1997 wrote:

joec32033 wrote:

Bonn1997 wrote:One could teach a whole class on test validation but one way to validate a measure (which it looks like the guy did with this measure) is to correlate it with the outcome(s) you're trying to predict--in this case, correlating the total of each individual's WP score on a team with the team's overall win-loss record. (Teams whose 12 players combine for the high wins produced totals should have more wins.) In addition, you'd want to show that your measure correlates better with that outcome than do other measures like Hollinger's PER. Normally, when you get an anomalous piece of data, you don't dismiss it but you don't obsess over it either. If you get a surprising blood test result, you repeat the test. Here, if a player has a surprising WP score, I'd look at his score in each of the past several seasons. I'd also keep in mind that a test doesn't have to be perfect to be useful. If a blood test is correct 60% of the time, it's more useful than flipping a coin, and in the long-run you'd be better off going with the blood test than coin flipping to make decisions. That means a test can be wrong sometimes and still be useful.

Bonn, I understand what you are saying but I have to disagree with the assessment. Some things are so fluid and have so many variables that a test measuring an individual aspect is not useful as is the case with this. This seems to skew in favor of multidimensional players and seems to value as a per 48 minute stat. I can best explain it as a hypothetical example:

Player A:
40 mins per game. 22 pts-8 rebs-3 ast-1 stl-1blk. The player scores his points on say 7-16 from the floor(on average) and 4-7 from the line (on average). Say the player averages 35% from 3. Say this player is the main cog on a .500 team

Player B:
20 mins per game. Say 12pts-4 rebs-2 ast-1 stl-.5 blk. The player scores his points on 5-10 from the floor, 2-3 from the line and shoots 37% from 3. Say this player is a role player on a .600 team.

This stat seems to value efficiency and multidimensional ability. I bet if you plugged in these numbers player B would have a higher score. There are guys who put up numbers who just don't seem to be winners (Jamison, Abdur-Rahim) and there are guys who don't put up numbers who seem to win all the time (Robert Horry comes to mind). Just because a guy happens to be on a winning team doesn't mean he should be measured in forming a baseline for a test on winning (Hi Nate).

I think the guy who formed this test had good intentions but at the same time I think he limited his information and did not include ALL variables.

It's not clear there is much of a difference between those two players. We humans are just so used to looking at stats without considering mpg. I'd agree that multidimensionality helps players in any statistical analysis, but I think it *should* help them. I think the major reason Jamison and Abdur-Rahim is that statisticians haven't (yet) developed good ways to measure man-to-man and help defense (and you're right that this measure doesn't take into account every factor--I don't think any test in any field does). If Carmelo was playing outstanding man to man and help defense or hustling after every loose ball, I'd say that he's bringing a lot to the table that this test is missing. That's obviously not the case though. None of this is saying that he's a bad player, and I think some are interpreting it that way. His win production was not bad; it just wasn't at superstar/max contract level.

But there are huge differences in roles in those 2 players. If player A has to score and the other team game plans for that and doubles him vs player B who gets his points against the 2nd team. Player A has more value.

We agree that no test can measure everything, but there are tests that measure out so bad or whose method is severely flawed that they shouldn't be used as a measuring stick.

In this particular test/method, roles are not accounted for. Scores against double teams are not accounted for which could lend itself a little to Carmelo's inefficiency.

I can see where you say the players are similar but only in regards to a per minute statistical analysis. If teams are focusing on player A, and player B is getting his stats while a team is focusing on someone different the stats should be weighted as such.

I purposely made the stats look the way they do with no players in mind, but player A looks like a guy like Carmelo while player B puts up stats similar to a Carlos Delfino or Ilyasova.

This stay just has way to many variables out of the referenced players control for ot to have any meaning to me.

If one guy is putting up 10 points in 20 min and the other 20 points in 40 min, teams should focus equally on the two while the on the floor. If they don't, they're just paying too much attention to points and too little to min (like most fans).

TMS wrote:

Bonn1997 wrote:

TMS wrote:

Bonn1997 wrote:Gallo is significantly below average for a starter in the NBA though. Does he rebound well? No. Does he create shots for his teammates well? If so, I see no statistical evidence of it. Does he block shots well? No. Does he get a good number of steals? No. The only two things he does better than the average starter are shoot from the perimeter and draw fouls. Although he has good skills in those two areas, he only utilizes those skills well enough to put up 15 PPG.

first of all i disagree w/u if u think Gallo's a below average starting NBA player... but let's stick to the topic here... if you think Gallo's so below average, then why even hedge on trading him, AR & filler for Carmelo Anthony? does that make sense to you?

http://arturogalletti.files.wordpress.com/2010/12/untitled37.png

because of the contract Carmelo will get. Regarding the other stuff you put, I've already explained (and so has Martin) why visually inspecting for anomalies is not a good way to test the validity of a measure. (That doesn't mean this measure is valid; I don't know enough about it.)

using a stats chart with as many anomalies as this one demonstrates is an idiotic way of judging an NBA player's value to a team.

broken record?

Bonn1997 wrote:

TMS wrote:

Bonn1997 wrote:

TMS wrote:

Bonn1997 wrote:Gallo is significantly below average for a starter in the NBA though. Does he rebound well? No. Does he create shots for his teammates well? If so, I see no statistical evidence of it. Does he block shots well? No. Does he get a good number of steals? No. The only two things he does better than the average starter are shoot from the perimeter and draw fouls. Although he has good skills in those two areas, he only utilizes those skills well enough to put up 15 PPG.

first of all i disagree w/u if u think Gallo's a below average starting NBA player... but let's stick to the topic here... if you think Gallo's so below average, then why even hedge on trading him, AR & filler for Carmelo Anthony? does that make sense to you?

http://arturogalletti.files.wordpress.com/2010/12/untitled37.png

because of the contract Carmelo will get. Regarding the other stuff you put, I've already explained (and so has Martin) why visually inspecting for anomalies is not a good way to test the validity of a measure. (That doesn't mean this measure is valid; I don't know enough about it.)

using a stats chart with as many anomalies as this one demonstrates is an idiotic way of judging an NBA player's value to a team.

broken record?

scratch CD

Bonn1997 wrote:

joec32033 wrote:

Bonn1997 wrote:

joec32033 wrote:

Bonn1997 wrote:One could teach a whole class on test validation but one way to validate a measure (which it looks like the guy did with this measure) is to correlate it with the outcome(s) you're trying to predict--in this case, correlating the total of each individual's WP score on a team with the team's overall win-loss record. (Teams whose 12 players combine for the high wins produced totals should have more wins.) In addition, you'd want to show that your measure correlates better with that outcome than do other measures like Hollinger's PER. Normally, when you get an anomalous piece of data, you don't dismiss it but you don't obsess over it either. If you get a surprising blood test result, you repeat the test. Here, if a player has a surprising WP score, I'd look at his score in each of the past several seasons. I'd also keep in mind that a test doesn't have to be perfect to be useful. If a blood test is correct 60% of the time, it's more useful than flipping a coin, and in the long-run you'd be better off going with the blood test than coin flipping to make decisions. That means a test can be wrong sometimes and still be useful.

Bonn, I understand what you are saying but I have to disagree with the assessment. Some things are so fluid and have so many variables that a test measuring an individual aspect is not useful as is the case with this. This seems to skew in favor of multidimensional players and seems to value as a per 48 minute stat. I can best explain it as a hypothetical example:

Player A:
40 mins per game. 22 pts-8 rebs-3 ast-1 stl-1blk. The player scores his points on say 7-16 from the floor(on average) and 4-7 from the line (on average). Say the player averages 35% from 3. Say this player is the main cog on a .500 team

Player B:
20 mins per game. Say 12pts-4 rebs-2 ast-1 stl-.5 blk. The player scores his points on 5-10 from the floor, 2-3 from the line and shoots 37% from 3. Say this player is a role player on a .600 team.

This stat seems to value efficiency and multidimensional ability. I bet if you plugged in these numbers player B would have a higher score. There are guys who put up numbers who just don't seem to be winners (Jamison, Abdur-Rahim) and there are guys who don't put up numbers who seem to win all the time (Robert Horry comes to mind). Just because a guy happens to be on a winning team doesn't mean he should be measured in forming a baseline for a test on winning (Hi Nate).

I think the guy who formed this test had good intentions but at the same time I think he limited his information and did not include ALL variables.

It's not clear there is much of a difference between those two players. We humans are just so used to looking at stats without considering mpg. I'd agree that multidimensionality helps players in any statistical analysis, but I think it *should* help them. I think the major reason Jamison and Abdur-Rahim is that statisticians haven't (yet) developed good ways to measure man-to-man and help defense (and you're right that this measure doesn't take into account every factor--I don't think any test in any field does). If Carmelo was playing outstanding man to man and help defense or hustling after every loose ball, I'd say that he's bringing a lot to the table that this test is missing. That's obviously not the case though. None of this is saying that he's a bad player, and I think some are interpreting it that way. His win production was not bad; it just wasn't at superstar/max contract level.

But there are huge differences in roles in those 2 players. If player A has to score and the other team game plans for that and doubles him vs player B who gets his points against the 2nd team. Player A has more value.

We agree that no test can measure everything, but there are tests that measure out so bad or whose method is severely flawed that they shouldn't be used as a measuring stick.

In this particular test/method, roles are not accounted for. Scores against double teams are not accounted for which could lend itself a little to Carmelo's inefficiency.

I can see where you say the players are similar but only in regards to a per minute statistical analysis. If teams are focusing on player A, and player B is getting his stats while a team is focusing on someone different the stats should be weighted as such.

I purposely made the stats look the way they do with no players in mind, but player A looks like a guy like Carmelo while player B puts up stats similar to a Carlos Delfino or Ilyasova.

This stay just has way to many variables out of the referenced players control for ot to have any meaning to me.

If one guy is putting up 10 points in 20 min and the other 20 points in 40 min, teams should focus equally on the two while the on the floor. If they don't, they're just paying too much attention to points and too little to min (like most fans).

Bonn, the point is one player is a bench player the other is a top two option on their team. This is in relation to this measure, not basketball strategy.

Not too many guys actually average 40 mind, but...Let's say you have Monta Ellis (25 pts in 40 mins) and say C.J. Miles (12 points in 22.5 minutes), who are you paying attention too?

crzymdups wrote:http://fivethirtyeight.blogs.nytimes.com/2011/01/15/why-carmelo-anthony-is-the-ultimate-team-player-and-what-advanced-stats-miss-about-him/?src=tptw

January 15, 2011, 3:21 AM
Why Carmelo Anthony Is the Ultimate Team Player (and What ‘Advanced’ Stats Miss About Him)
By NATE SILVER
Carmelo Anthony, whom the Knicks are considering acquiring in a trade, is sometimes thought of as a selfish player. Indeed, he is the center of the Denver Nuggets’ offense: when he is on the court for them, about 30 percent of their possessions end in Anthony shooting, going to the foul line, or committing a turnover. Nor is Anthony much of a passer: over his career, he’s accumulated 3.1 assists per 36 minutes played, considerably less than that of other high-volume scorers like Kobe Bryant (4.6 assists per 36 minutes) or LeBron James (6.2).

In taking all of those shots, however, Anthony has also done something else: he’s made his teammates much more efficient offensive players.

Anthony is a controversial player among those who devote their time to analyzing basketball statistics. The reason is as follows: although he scores a lot of points, he does not do so especially efficiently. His True Shooting Percentage (TS%) – which accounts not just for two-point buckets but also for three-point shots and drawing fouls, neither of which are a particular strength of Anthony’s – is .527 this year and .543 for his career. Those figures are roughly at the league average, which is about .540 in most years.

Anthony’s TS% is also worse than all five of the Knicks’ regular starters, including Wilson Chandler (.579), Danilo Gallinari (.600), and Landry Fields (.611), the men whom he might replace in the lineup. This has led some to argue that Anthony could actually represent a step backward for the Knicks. David Berri, an economist at Southern Utah University who has developed a statistic called Wins Produced that places an extremely high premium on efficiency, told the Wall Street Journal that a Knicks roster with Anthony, Amare Stoudemire and Raymond Felton — but without Fields or Chandler — would win only 29 games per season.

This strikes me as highly implausible: the Nuggets, with a supporting cast that isn’t obviously any better than the one that Anthony would be joining in New York, have won an average of 48 games per season since Anthony’s rookie year, despite playing in the deep Western Conference. They have also been a relatively efficient offensive team. The year before Anthony joined the Nuggets, they ranked dead last in the N.B.A. in offensive efficiency (points scored per possession) on their way to winning just 17 games. But their offensive efficiently ranking shot up to 8th in the league in Anthony’s rookie season and has remained roughly at that level since.

What is missing from formulas like Berri’s is an account of what Anthony does to the rest of the Nuggets. Because he is able to score from anywhere in the court, Anthony draws attention and defenders away from his teammates, sometimes leaving them with wide-open shots. He also allows them to be more selective about the shots that they choose to take, since they know that Anthony can usually get a respectable shot off before the 24-second clock expires if needed.

These effects produce a profound increase in the efficiency of Anthony’s supporting cast when he is on the floor. In the 135 games that he played with the Nuggets, for instance, Allen Iverson’s True Shooting Percentage was 55.9 percent – much better than the 51.2 TS% that Iverson, a notoriously inefficient shooter, posted outside of Denver over the course of his career.

In fact, this is true of almost every Nugget who has played a sufficient number of minutes with Anthony. I identified 16 players who have accumulated least 2,000 minutes with the Nuggets in years when Anthony was on the team, and have also played at least 2,000 minutes in the N.B.A. without Anthony (either because they were playing for a different team or because they were on the Nuggets before Anthony’s rookie season). All but 2 of the players – Marcus Camby and Voshon Lenard – posted a higher TS% playing with Anthony than without him, and on average, he improved his teammates’ TS% by 3.8 points (to 55.0 percent from 51.2 percent).

The effect of a player who improves the rest of his team’s TS% by 3.8 points is extremely substantial: it is works out to their scoring about 5 or 5.5 additional points per game solely on the basis of this efficiency gain. That, in turn, translates into about 15 additional wins per season for an average team, according to a commonly-used formula. This is how Anthony creates most of his value — not in the shots he takes himself, but in the ones he creates for his teammates – and some of the “advanced” formulas completely miss it.

With that said, there is reason to question whether Anthony would have quite the same effect in New York that he did in Denver. With a few exceptions like Iverson, the Nuggets have generally surrounded Anthony with defensively-minded players like Camby who are not especially eager to shoot or who do not do so especially well. The Knicks, by contrast, are a run-and-gun team with lots of good shooters and they already rank fifth in the league in offensive efficiency.

There are some precedents for pairing several high-volume scorers together and seeing them thrive: when Kevin Garnett and Ray Allen joined Paul Pierce on the Celtics, for instance, all three players took fewer shots, but all three were rewarded with a significant increase in their TS%. On the other hand, Dwyane Wade, LeBron James and Chris Bosh have not seen an increase in their efficiency since joining together on the Miami Heat, even though they are shooting a bit less often.

So there are no guarantees – one would need to consider more carefully exactly how Anthony would integrate into Mike D’Antoni’s offense and exactly which type of shots he’d take. One would also need to think about Anthony’s defense and rebounding, where he is no standout. But upon a more careful examination, the argument that Anthony is a merely average offensive player turns out to be superficial.

Thanks; that's a very interesting article (even though the title is misleading, as the author himself is using advanced stats!) Advanced statistical analyses of basketball performance are still in their infancy. In the case of Carmelo, all the advanced stats say he's a good player--the only debate is over *how much above average* he is--some stats suggest slightly, otherwise substantially.