Part of statistics is that there are many interpretations and not all are right and wrong ways of looking at it. And I would hope you could correct me on my stats as seeing another person's view on a statistic is how one improves in their own future intpretation and adjusts the variables to accomodate. And I don't try and "pretend" to be anyone. You can call a zebra a horse, but it's still a zebra. I've never tried to pretend to be a horse.
Well, that is a refreshing post! Thank you for acknowledging that there may be more than one way to assess the impact of a statistic, and how other variables may impact that statistic. Who would be the "best" player under the PER system, in your opinion? A player with a usage of 25 and PER of 25 A player with a usage of 25 and a PER of 15 A player with a usage of 15 and a PER of 15 A player with a usage of 15 and a PER of 25 These questions interest me, and coaching can also be called into question when assessing this statistical ratio.
That's a mighty high zebra you are perched on. I told you exactly why I find it meaningless. Forget the uniform part - what I should have said was that Minstrel showed you vastly different players with pretty close ratio - not letting you learn much from it. I am pretty much out of this. You want to take Hollinger's careful work on PER and make it more meaningful by multiplying it with a noisier and less precise bit of information that's already in there - go for it and enjoy it. I think I gave you a pretty good idea why I think this is not relevant - but if you do not like it - so be it.
In other words, you were completely incorrect on your 1.0 gaffe. Thanks for somewhat admitting it! No wonder you're "pretty much out of this". Hollinger himself admits that an inefficient shooter who puts up a high volume will have an inflated PER. You also glossed over my posts on a PER multiplier being worth looking at, but that doesn't surprise me, either.
On a pure "best" scenario, I'd say the 25/25 guy. He's performing at a high level, and doing it with a) a large sample size and b) presumably more attention paid from the defense. I think the 15/25 guy would be an excellent 3rd/4th option. Maybe like Pippen in 91...where he eventually proved (in 94) that he COULD be the man, but he hadn't shown it yet. I think there are a lot of reasons someone might be a 25/15 and it's "ok", but he's not the best. If his 15PER is the highest, then he should be getting more of the touches. But that just means that your team isn't good.
Though I've been going with the premise that PER generally goes down as usage goes up for a mediocre player, and PER goes up with usage if you're an elite player. I'm not sure that assumption is correct. In Haberstroh's article about Horford today he makes it out to be relatively remarkable that Horford's PER, shooting% and turnover% have improved as his usage went from "4th-option" 17.6% last year to 20.4% this year.
Uniform does not mean 1. It means that it is pretty consistent, and for the most part, it is. I actually told you that it is 1.0 +/- 0.2 which would be my guess. Just to look at it - I randomly chose 125 players that play at least 20 MPG and have at least 20 games (just to get a proper sample size) from this year, and only 9 of those did not fall within the +/- 0.2 I told you. Let's say I got really lucky - and there are 3 times as many that do not fall within this range - you are still looking at the vast majority falling within this range and even those that are out of it are very close. It is pretty uniform, as I have said. But again, you are missing the big pictures. Who cares about that, what is important is that you are adding a number that is already implicitly in the formula, only doing it with a much less precise version of it - which makes it worse than PER by itself - and somehow think this adds value. Large data sets have lots of noise in them. The value of statistics is to try and minimize that noise and learn something from it. You are doing the exact opposite and somehow get all offended when we tell you that.
I also wonder about the usage rate straight-up as a factor. Jordan's highest usages were in the 38% range, Nic's at 17% now (highest of his career). Roy was between 23 and 28. Blake's hovered around 15% the last few years. Travis was low-20s. I just don't know how to work with it, I just think that there may not be a linear effect
I'm not saying it's a linear effect; hence some sort of multiplier that tries to explain high PER/ high usage players.
Actually, a value of statistics is to try and understand how a given variable correlates (or may even cause?) to the statistic. I'm not at all trying to minimize "noise". I'm trying to understand some "noise", and how it impacts the final result of the equation. I see a correlation that jibes with my own observation. If you're not interested in actually addressing it, please feel free to start your own thread.
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. Unfortunately, you do not know if it was good (score, assist, rebound, block) or bad (turn-over, miss). PER tries to use the stuff we know for sure - and put the correct sign (+ or -) and some kind of a constant according to it's "value". Since we do not know what usage contains (the good or the bad parts) - it is not always direct correlation between more usage and better PER or the other way around. Also - possessions where the player touches the ball and are not accounted for by the normal direct statistics (scoring, missing, assisting, turnovers, blocks etc...) - can still be good (the player has so much attention on him that he started swinging the ball until a free player is found for an uncontested shot) or bad (dominated the ball, pounding it and allowing the defense to get set and protect the basket better). In other words - it really does not give you too much information all by itself...
A multiplier would still be linear. A non-linear function is one that changes slope at different times during the output...simply adding a multiplier doesn't create that effect. If an extra percentage of Usage doesn't always mean the same thing in terms of how it impacts the team, then it's a non-linear factor and dividing it by PER will still yield a non-linear function...no matter what multiplier you put on it. In which case, just looking at Usage/PER is meaningless unless you know where the value of Usage changes and how. Since Usage is on a completely unrelated scale from PER and, as andalusian said, incorporated into PER, it isn't at all clear that simply dividing Usage by PER is ever meaningful. Any more than dividing steals per game by winning percentage is particularly meaningful.
It would be linear in the sense that the multiplier would add a linear effect, so long as you stay consistent in your equation. I should hope that any advancement of statistics is linear in its application. Also, I'm noticing a correlating trend, not some sort of mathematical fact, and I'm asking for it to be explained. Thus far, all I have is a lie about how a 1.0 Usg/PER ratio is close to a mean. Clearly, it isn't. Any statistic is on an unrelated scale from PER. That seems obvious, doesn't it? Do you work with numbers for a living? You were wrong on your 1.0 Usg/PER ratio assumption, but that's not a big deal to me. That another poster seems stuck on your false assumption and has ended up hijacking this thread is unfortunate, 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. 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, that it is a part of the PER equation, that it may be able to separate players of a similar PER in terms of their on-court value, or even (gasp!) their "usage" by their own coach. That's all I'm asking in my brainstorm. The non-linear answers that are all over the board, yet don't attempt to refute the Usg/PER statistic, don't surprise me.
I never said it was clear. I offered up a simple equation. One that you falsely refuted by guessing about a ratio that you thought was ordinary, but was actually quite extraordinary. This place is crazy. A bunch of people who think that they are much smarter than they are in reality. I'm asking for help, which BlazerCaravan actually tried to give and did well, IMO, and the rest of the "experts" lie about a statistic that doesn't even exist and continue to dwell on that false argument.
Fuck this place. Someone asks for help, you explain to him what something does not make sense mathematically - and he goes all upset at you for trying to answer him.
So ... usage is a bad stat? Great That wasn't at all the point of the thread. I noticed a correlation that may exist between PEr and usage. That's the only point.
How can unrelated mathematical components make "sense" mathematically unless you explore the supposed connection? You lied/were ignorant about Usg/PER and the 1.0 ratio being consistent. Admit that and we can proceed. As it is, I can only think that you're full of shit, which surprises me, because I've always enjoyed your posts in the past.
How does trying to see if a correlation exists between two different data sets not make sense? Do you really work with numbers, andlusian? 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. That you were so far wrong on your Usg/PER assumption tells me that you're not capable of advanced mathematics.
