PER and Usage Rate

[Minstrel]

Any statistic is on an unrelated scale from PER. That seems obvious, doesn't it?

It should also be obvious that dividing two numbers that are on different scales from one another has very little chance of being meaningful. Having two statistics or metrics on the same scale is far from unusual or impossible. Assist Rate and Turnover Rate are on the same scale: percentage of a team's possessions. Thus, there's a much more compelling case for multiplying or dividing those two numbers.

You were wrong on your 1.0 Usg/PER ratio assumption, but that's not a big deal to me.

Generous of you, but it wasn't anything so large as an assumption, just a wondering. The fact that three wildly dissimilar players all happened to come out to within 1.0-1.2 was interesting, but I didn't research it much further. A 1:1 ratio was unlikely (since they're on different scales) but there's still a pretty good chance that there's some kind of mean that players don't vary too widely from. What constitutes "too widely" would be based on the standard deviations from the league (or starter) mean.

but I do feel that there is value in looking at the components of an equation, and how they may impact the answer of that equation.

Sure, I do too. That's what we do all the time when we reference Usage, Rebound Rate, Assist Rate, pace, etc. These are all components of PER. I'm not sure that there's an inherent value in simply dividing the result of a formula by one of its constituent parts. I'm open to it, but I'd like to hear a logical case for why it's meaningful. Not just "Here are a few players' Usage/PER...maybe this means something."

I'm not saying that usage is or is not a valid statistic; I am saying that it may seem to play a role in PER inflation

And I said/agreed with that in my first response to you. PER and Usage are definitely correlated to some extent; that higher Usage will usually yield a higher PER, because PER does reward gross production, not just efficiency. So using more possessions will result in more shot attempts, points and assists. Of course, if the player is not very good, this will only have limited effect because more shots put up and more possessions used (by an inferior player) will result in worse scoring efficiency and more turnovers, both of which hurt PER.

 

[Minstrel]

Sabermetrics was born out of taking different sets of data and then trying to combine them into a new answer. You know this, right? You take as much data that is available to you as possible, and you try to make sense of them. At times, you try to combine them.

Sabermetrics didn't begin with someone randomly combining two statistics and "asking" if it was meaningful and then raging when people were dubious. Sabermetrics, whether you go with Bill James or Pete Palmer, began with people making logical guesses/contentions about baseball and then crunching vast amounts of data to see whether there was statistical evidence for them.

Neither of them said "What if we divided RBI by batting average? Does that give us something? Help me out here, guys."

 

[BlazerCaravan]

Usage is a dirty statistic. It has a lot of stuff that is either good or bad. Every time a player touches the ball within a possession - it is added to his usage.

This is not true. Usage is an estimate that takes a player's shots, turnovers, etc., and divides them by the team's shots, turnovers, etc. here's the official definition:

Usage Percentage (available since the 1977-78 season in the NBA); the formula is 100 * ((FGA + 0.44 * FTA + TOV) * (Tm MP / 5)) / (MP * (Tm FGA + 0.44 * Tm FTA + Tm TOV)). Usage percentage is an estimate of the percentage of team plays used by a player while he was on the floor.

Using Usage as a piece to a formula that attempts to measure Net Positive Impact isn't wrong. I'm making this argument separate from PapaG, because I want to make it clear that while I am not a professional statistical analyst (that's my wife) by job description, I am a BI engineer, so I wasn't just putting legos together out of stats and wondering what I was coming up with. Impact tells a story (though I admit the stat isn't yet normalized because I'm not going to go through the effort of tracking every player with it).

Anyway, I'm washing my hands of this thread.

 

[andalusian]

This is not true. Usage is an estimate that takes a player's shots, turnovers, etc., and divides them by the team's shots, turnovers, etc. here's the official definition:

Thanks. I assumed it was based on real data that is logged, but this makes it even more interesting - since this is much more of a team-based statistic - and not a real touches statistics - which makes it's use even more ambiguous to determine a player's specific contribution. Still, even if the statistic specifically matches the real world - the meaning behind it is the same - and the inconclusive nature of it (was it a beneficial or a harmful touch) makes using it with PER rather inconclusive (other than as an exercise for one's Excel skills).

Using Usage as a piece to a formula that attempts to measure Net Positive Impact isn't wrong.

No, it is not. It just makes little sense to use it with PER in a rigid formula (multiplication) that have had lots of fine tuning by a proper analyst using large data-sets.

I believe that my initial post on this was the one that touched on the way I would look at the combination - by looking at outliers for players that have a low Usage% but rather acceptable PER - as players that might not be used properly by their coaches. As a direct multiplication - no real value, imho.

 

[blazerboy30]

How does trying to see if a correlation exists between two different data sets not make sense?

You're essentially trying to do a guess-and-check multi-variable regression with these two stats. The problem is that doing a multi-variable regression with variables that are strongly correlated is usually not all that useful.

 

[andalusian]

I would like to publicly point out that what I said about this subject is not trying to make fun of anyone - I just do not think the two statistics go together in any way that makes sense with a direct formula. I apologize if anyone thought otherwise.

 

[PapaG]

You're essentially trying to do a guess-and-check multi-variable regression with these two stats. The problem is that doing a multi-variable regression with variables that are strongly correlated is usually not all that useful.

I've been told that the variables are not correlated. It's actually more of a binary-variable equation, though, since I'm using the end result of two different equations, and not the variable that comprise each numer.

At this point, you're assuming that the two are not strongly correlated. It's my belief that more work could be done to see how strong a correlating relationship between the two results may be.

We've already had one glaring incorrect assumption in this thread, and the two people who falsely used that data are still trying to spin that error.

 

[PapaG]

I would like to publicly point out that what I said about this subject is not trying to make fun of anyone - I just do not think the two statistics go together in any way that makes sense with a direct formula. I apologize if anyone thought otherwise.

Fine. Then why did you waste so much time arguing that, instead of actually addressing some of the results? BC's post yielded some interesting results, IMO?

 

[PapaG]

Sabermetrics didn't begin with someone randomly combining two statistics and "asking" if it was meaningful and then raging when people were dubious. Sabermetrics, whether you go with Bill James or Pete Palmer, began with people making logical guesses/contentions about baseball and then crunching vast amounts of data to see whether there was statistical evidence for them.

Neither of them said "What if we divided RBI by batting average? Does that give us something? Help me out here, guys."

You obviously have no clue on the history of sabermetrics. LOL

 

[PapaG]

Thanks. I assumed it was based on real data that is logged, but this makes it even more interesting - since this is much more of a team-based statistic - and not a real touches statistics - which makes it's use even more ambiguous to determine a player's specific contribution. Still, even if the statistic specifically matches the real world - the meaning behind it is the same - and the inconclusive nature of it (was it a beneficial or a harmful touch) makes using it with PER rather inconclusive (other than as an exercise for one's Excel skills).



No, it is not. It just makes little sense to use it with PER in a rigid formula (multiplication) that have had lots of fine tuning by a proper analyst using large data-sets.

I believe that my initial post on this was the one that touched on the way I would look at the combination - by looking at outliers for players that have a low Usage% but rather acceptable PER - as players that might not be used properly by their coaches. As a direct multiplication - no real value, imho.

You didn't even know how what usage rate measured? Good God. I hope you learned a few things in this thread.

 

[andalusian]

Fine. Then why did you waste so much time arguing that, instead of actually addressing some of the results? BC's post yielded some interesting results, IMO?

This is exactly what I said all-along, I guess I just got carried away in the "Fuck this thread, no-one answers me" and started to be more confrontational than needed. I still maintain that if you read my original posts in this thread, this is exactly what they say.

 

[andalusian]

You didn't even know how what usage rate measured? Good God. I hope you learned a few things in this thread.

Yes. I learned not to be affected by your theatrics. Thanks for the lesson.

 

[PapaG]

I believe that my initial post on this was the one that touched on the way I would look at the combination - by looking at outliers for players that have a low Usage% but rather acceptable PER - as players that might not be used properly by their coaches. As a direct multiplication - no real value, imho.

Actually, that was my idea, and I mentioned it regarding Batum earlier in this thread.

 

[BlazerCaravan]

AS I UNDERSTAND IT (and I'm sure I'll be corrected if I'm the least bit wrong) Andalusian is right; because the constituent pieces of Usage and PER are pretty much the same, the two will correlate strongly. And, because they correlate strongly, they either amplify or cancel each other out, depending on your actions (multiply, divide, etc.) If two stats like that do correlate strongly, they basically should reduce out. It's not PER and Usage we should be looking at.

There is this quest to find a super-advanced stat like QB Rating that gives you One Stat To Rule Them All, a single stat that encompasses both efficiency and real-world usefulness. PER alone isn't that stat. If such a stat existed, it would be made available from people who know what they're doing.

 

[PapaG]

AS I UNDERSTAND IT (and I'm sure I'll be corrected if I'm the least bit wrong) Andalusian is right; because the constituent pieces of Usage and PER are pretty much the same, the two will correlate strongly. And, because they correlate strongly, they either amplify or cancel each other out, depending on your actions (multiply, divide, etc.) If two stats like that do correlate strongly, they basically should reduce out. It's not PER and Usage we should be looking at.

There is this quest to find a super-advanced stat like QB Rating that gives you One Stat To Rule Them All, a single stat that encompasses both efficiency and real-world usefulness. PER alone isn't that stat. If such a stat existed, it would be made available from people who know what they're doing.

They don't, though. We've had a Usg/PER range of 0.94(Batum) to 1.57 (Brandon Jennings) in the 6 or so players mentioned. I use those two as an example because they were within 0.4 of each other in PER (17.3 to 16.9).

That's a big difference, and suggest that there isn't a consistent correlation. The question then becomes, what does this large difference mean?

 

[PapaG]

Anyhow, thanks for the input from those trying to be sincere. I think there is something here. If not, no biggie.

 

[Ed O]

They don't, though. We've had a Usg/PER range of 0.94(Batum) to 1.57 (Brandon Jennings) in the 6 or so players mentioned. I use those two as an example because they were within 0.4 of each other in PER (17.3 to 16.9).

That's a big difference, and suggest that there isn't a consistent correlation. The question then becomes, what does this large difference mean?

It doesn't mean anything.

It means that some players produce because they score or create scoring opportunities for others, while other players get rebounds and block shots.

Why is this news?

Ed O.

 

[blazerboy30]

Anyhow, thanks for the input from those trying to be sincere. I think there is something here. If not, no biggie.

Why don't you just pull the PER and USG stats for every player and provide the correlation results? Wouldn't that be the easiest way to answer this? Or are you looking for somebody to write you a script to scrape the data and do it for you?

 

[PapaG]

It doesn't mean anything.
It means that some players produce because they score or create scoring opportunities for others, while other players get rebounds and block shots.

Why is this news?

Ed O.

A large statsitcal variation doesn't "mean anything"? Maybe not to you; I have a curious mind and wonder if Jenning's PER is inflated by a high usage rate, or if Nic is being used right by the coach, if Jennings is extremely overrated, and only gets his stats in an inefficient manner because of volume, if PER isn't the great comparative tool I thought it was prior to this exercise...

all kinds of questions pop into my head. If you aren't interested, that's cool.

 

[PapaG]

Why don't you just pull the PER and USG stats for every player and provide the correlation results? Wouldn't that be the easiest way to answer this? Or are you looking for somebody to write you a script to scrape the data and do it for you?

I provided links to somebody who did that earlier in this thread for last year. If you didn't click on the data, that's not my fault. It shows how ridiculous the entire 'correlation' argument is. It's rare to find a player with a 1/1 Usg/PER ratio.

 

[PapaG]

I thought this was funny.

WHAT. A. TRAIN. WRECK.

 

[PapaG]

LOL

VINCE CARTER IS THE ROOT OF ALL THAT IS WRONG WITH THIS WORLD.

 

[PapaG]

I'm stuck on a conference call. I think I'll run some more plot graphs like these. I'll start with Portland.

 

[PapaG]

I found this comment interesting. I noted Nic's Ortg earlier in the thread.

Travis
January 25, 2010 at 5:42 pm # Usage rate influences PER by a good amount, which makes some of the high usage players like Monta Ellis, JR Smith and Vince Carter look much better than they actually are – and conversely, makes players like Anthony Parker or Anthony Morrow look less effective than their percentages may suggest. I think a better comparison would have been usage rate through the glass of offensive efficiency (offensive rating on basketball-reference). This would have given a better look at which players are truly efficient with their offensive touches.

 

[blazerboy30]

I provided links to somebody who did that earlier in this thread for last year. If you didn't click on the data, that's not my fault.

Try having a conversation without getting all upset and bitchy.

It shows how ridiculous the entire 'correlation' argument is. It's rare to find a player with a 1/1 Usg/PER ratio.

This doesn't make sense. You started this thread claiming there is a correlation between per and usg. Now you are saying the "correlation argument" is ridiculous?

Your link didn't provide any correlation or regression data that I could find. The hundreds of plots don't really tell much of a story, imo.

If somebody already pulled the data to find the correlation and regression statistics for PER and USG, then what is this conversation about?

 

[Ed O]

A large statsitcal variation doesn't "mean anything"? Maybe not to you; I have a curious mind and wonder if Jenning's PER is inflated by a high usage rate, or if Nic is being used right by the coach, if Jennings is extremely overrated, and only gets his stats in an inefficient manner because of volume, if PER isn't the great comparative tool I thought it was prior to this exercise...

all kinds of questions pop into my head. If you aren't interested, that's cool.

It's like asking what the difference between an apple and an orange is. They're just different.

A scorer gets a higher PER than a player who does the same things except scores less.

A rebounder gets a higher PER than a player who does the same things except rebounds less.

These are true statements but are truisms.

I think that your questioning of PER is absolutely fair. It can compare apples and oranges to a certain extent, but whether someone likes apples or oranges better is not a question that it can answer.

Ed O.

 

[PapaG]

It's like asking what the difference between an apple and an orange is. They're just different.
A scorer gets a higher PER than a player who does the same things except scores less.

A rebounder gets a higher PER than a player who does the same things except rebounds less.

These are true statements but are truisms.

I think that your questioning of PER is absolutely fair. It can compare apples and oranges to a certain extent, but whether someone likes apples or oranges better is not a question that it can answer.

Ed O.

Now hold on a minute. I've got some posters telling me that Usg and PER and too similar, and now you're telling me that they're too dissimilar? Neither of those really matter, though, if you control your equation and then try to derive information from the answers.

 

[PapaG]

Try having a conversation without getting all upset and bitchy.



This doesn't make sense. You started this thread claiming there is a correlation between per and usg. Now you are saying the "correlation argument" is ridiculous?

Your link didn't provide any correlation or regression data that I could find. The hundreds of plots don't really tell much of a story, imo.

If somebody already pulled the data to find the correlation and regression statistics for PER and USG, then what is this conversation about?

About what those results mean and how they could be used to evaluate players. Isn't that obvious? That's the entire point of the thread.

 

[blazerboy30]

About what those results mean and how they could be used to evaluate players. Isn't that obvious? That's the entire point of the thread.

Fair enough. But you haven't shown any results from a significant set of data. That is why I was asking for the correlation and regression data, not just a bunch of plots broken out on a per-team basis, or a few samples from players you selected.

People have been saying that per and usg are built on many of the same variables. It shouldn't be a huge surprise that they have some correlation.

If you're curious about the strength of the correlation, just pull the data and tell us what it is. Do you know how to do that?

 

[PapaG]

Fair enough. But you haven't shown any results from a significant set of data. That is why I was asking for the correlation and regression data, not just a bunch of plots broken out on a per-team basis, or a few samples from players you selected.

People have been saying that per and usg are built on many of the same variables. It shouldn't be a huge surprise that they have some correlation.

If you're curious about the strength of the correlation, just pull the data and tell us what it is.

I'm not claiming that I have an extensive data set that I have studied. I am pointing out an observation that I made regarding Usg/PEr, and if meaning can be derived from it. Also, why would I offer an extensive data set for review here? The primary critic in this thread didn't even know what Usage Rate measured, yet I'm supposed to offer up a data set that would literally take days to plot for a critical review. LOL

Do you know how to do that?

Speaking of being bitchy...