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Quantifying Specific Statisticss

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Chuck Newhouse
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Re: Quantifying Specific Statisticss 

Post#21 » by Chuck Newhouse » Tue Jul 22, 2014 6:03 am

engageTHEmasses wrote:
Chuck Newhouse wrote:...if you think I can't do better than PER then you don't understand stats very well and you sure don't know what I can do with stats. I have a gift dude, it's obsessive and savant like when it comes to stats. I've been studying stats and quantification technique since the 70's and the only reason I don't do more with it is that I get so into it that it actually physically hurts my brain I get so engrossed.


That was a lot of effort to go to just to try to embarrass me. It is sad that some people who can't do anything think that because they have no special talents, then obviously nobody does!
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Re: Quantifying Specific Statisticss 

Post#22 » by ishoy123 » Tue Jul 22, 2014 2:38 pm

Chuck Newhouse wrote:
neurotik wrote:So instead of using empirical evidence to support this statistic you decided to use the opinions of an internet basketball forum?


Duh...why use just my own evidence when I can get a much larger sample size. TBH this is an elite level forum and usually from my experience the general consensus on this board has been as, if not more accurate then past GM's.


LOL WHAT
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Re: Quantifying Specific Statisticss 

Post#23 » by Psubs » Tue Jul 22, 2014 2:50 pm

Here's the formula I put together with another person about 10 years ago for a keeper league.

Points = +1.0
Rebounds = +1.5
Assists = + 2.0
Turnovers = -1.5
Blocks = + 2.5
Steals = + 2.0
- 0.5 x (FGA-FGM)
- 1.0 x (FTA-FTM)
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Re: Quantifying Specific Statisticss 

Post#24 » by hankscorpioLA » Tue Jul 22, 2014 3:05 pm

I don't want to crap all over this, but I also don't really understand what the point is.

If you are trying to determine the "real" value of a particular stat then the way to do it is through analysis of the data.

I like using blocks as an example because you can see easily how different outcomes affect the value of a blocked shot. In terms of ascending value, you have

1) a blocked shot that goes back to the opposing team - this likely presents them with a fairly easy opportunity to score

2) a blocked shot that goes out bounds - although the opposing team gets possession, they now have to run an inbounds play, often with little time on the clock (also a factor)

3) a blocked shot that goes out of bounds off an opposing player

4) a blocked shot that results in a change of possession - these are most likely to result in fast break opportunities and easy buckets.

Now you look at each of these scenarios and figure out how often they result in points for or against.

So you start with a blocked shot - that is worth 2 points because it prevents the opponent from scoring. But when you block a shot to the opposing team and you find that they score 70% of the time, then that kind of block is only "really" worth 0.6 points saved. On the flipside, if when you block the ball to your team and you score 70% of the time, then that block is worth 3.4 points.

This is just an example, but I would argue that this is the kind of approach that is needed.

Perhaps rather than soliciting opinions you could try to crowdsource this analysis.
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Re: Quantifying Specific Statisticss 

Post#25 » by engageTHEmasses » Tue Jul 22, 2014 5:08 pm

Chuck Newhouse wrote:
engageTHEmasses wrote:
Chuck Newhouse wrote:...if you think I can't do better than PER then you don't understand stats very well and you sure don't know what I can do with stats. I have a gift dude, it's obsessive and savant like when it comes to stats. I've been studying stats and quantification technique since the 70's and the only reason I don't do more with it is that I get so into it that it actually physically hurts my brain I get so engrossed.


That was a lot of effort to go to just to try to embarrass me. It is sad that some people who can't do anything think that because they have no special talents, then obviously nobody does!




a) You assume it was a lot of effort. It was actually quite pleasurable.

b) You assume I don't have any special talents. In reality, I have several talents.

c) People who make assumptions and broad generalizations are usually fantastic statisticians.

d) As well, if you are using the method described in your original post, then perhaps your statistical talent isn't as vast and special as you would like to think. Well... I suppose it does qualify as 'special'.






























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Re: Quantifying Specific Statisticss 

Post#26 » by JN » Tue Jul 22, 2014 5:17 pm

Chuck Newhouse wrote:What is more valuable an assist or a rebound? If a point is worth a point when trying to evaluate the positive effect it has on a game then what should a steal or a block be worth?

I am looking for some opinions so that I can try to work out an average and maybe create a new type of stat? Feel free to use decimals.

So here is a template you can cut and paste:

Point - 1.0
Off Rebound -
Def Rebound -
Assist -
Steal -
Block -
Turnover -


Your basically trying to create something like PER without any shooting efficiency..... and shooting efficiency is pretty damn important so not sure why you want to ignore it.

But as others have mentioned the evaluation of boxscore stats has been beaten to death.
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Re: Quantifying Specific Statisticss 

Post#27 » by Chuck Newhouse » Tue Jul 22, 2014 5:49 pm

hankscorpioLA wrote:I don't want to crap all over this, but I also don't really understand what the point is.

If you are trying to determine the "real" value of a particular stat then the way to do it is through analysis of the data.

I like using blocks as an example because you can see easily how different outcomes affect the value of a blocked shot. In terms of ascending value, you have

1) a blocked shot that goes back to the opposing team - this likely presents them with a fairly easy opportunity to score

2) a blocked shot that goes out bounds - although the opposing team gets possession, they now have to run an inbounds play, often with little time on the clock (also a factor)

3) a blocked shot that goes out of bounds off an opposing player

4) a blocked shot that results in a change of possession - these are most likely to result in fast break opportunities and easy buckets.

Now you look at each of these scenarios and figure out how often they result in points for or against.

So you start with a blocked shot - that is worth 2 points because it prevents the opponent from scoring. But when you block a shot to the opposing team and you find that they score 70% of the time, then that kind of block is only "really" worth 0.6 points saved. On the flipside, if when you block the ball to your team and you score 70% of the time, then that block is worth 3.4 points.

This is just an example, but I would argue that this is the kind of approach that is needed.

Perhaps rather than soliciting opinions you could try to crowdsource this analysis.


What you did was basically what I was looking for...different perspectives. I know what you are getting at but there are flaws in your theory as well, like for example if you block a shot it usually means that the opposing team has already been able to get the ball inside and if that happens there needs to be an adjustment to the probability of scoring on a possession because they are already more likely to score on that possession than if they were not able to penetrate.

Another thing to consider is when you are using advanced analysis and you get a large enough sample size (a couple years should be fine) all the different types of blocks will level themselves out to some degree so an over riding exponent will do the trick basically.

I am not trying to argue with you at all I am just saying that certain quantification techniques have loopholes in logic, someone may find a hole in my theory too.

I was just hoping that a discussion would start and I could cherry pick some good points that were made and include this in my overall vision.
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Re: Quantifying Specific Statisticss 

Post#28 » by Chuck Newhouse » Tue Jul 22, 2014 5:51 pm

JN wrote:
Chuck Newhouse wrote:What is more valuable an assist or a rebound? If a point is worth a point when trying to evaluate the positive effect it has on a game then what should a steal or a block be worth?

I am looking for some opinions so that I can try to work out an average and maybe create a new type of stat? Feel free to use decimals.

So here is a template you can cut and paste:

Point - 1.0
Off Rebound -
Def Rebound -
Assist -
Steal -
Block -
Turnover -


Your basically trying to create something like PER without any shooting efficiency..... and shooting efficiency is pretty damn important so not sure why you want to ignore it.

But as others have mentioned the evaluation of boxscore stats has been beaten to death.


I didn't reveal the full picture.
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Re: Quantifying Specific Statisticss 

Post#29 » by JN » Tue Jul 22, 2014 6:01 pm

Chuck Newhouse wrote:I didn't reveal the full picture.


I hope the full picture involves a statistics that measures a player's value beyond those of the traditional box score statistics.

The beauty with basketball is that many meaningful things happen during a game. Many of them can be counted and measured (pts, reb, ast, stl, blk, fg%). Many of them cannot be counted and so easily measured. Trying to evaluate the latter is the real challenge.
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Re: Quantifying Specific Statisticss 

Post#30 » by 416 » Tue Jul 22, 2014 6:33 pm

Chuck Newhouse wrote:
hankscorpioLA wrote:I don't want to crap all over this, but I also don't really understand what the point is.

If you are trying to determine the "real" value of a particular stat then the way to do it is through analysis of the data.

I like using blocks as an example because you can see easily how different outcomes affect the value of a blocked shot. In terms of ascending value, you have

1) a blocked shot that goes back to the opposing team - this likely presents them with a fairly easy opportunity to score

2) a blocked shot that goes out bounds - although the opposing team gets possession, they now have to run an inbounds play, often with little time on the clock (also a factor)

3) a blocked shot that goes out of bounds off an opposing player

4) a blocked shot that results in a change of possession - these are most likely to result in fast break opportunities and easy buckets.

Now you look at each of these scenarios and figure out how often they result in points for or against.

So you start with a blocked shot - that is worth 2 points because it prevents the opponent from scoring. But when you block a shot to the opposing team and you find that they score 70% of the time, then that kind of block is only "really" worth 0.6 points saved. On the flipside, if when you block the ball to your team and you score 70% of the time, then that block is worth 3.4 points.

This is just an example, but I would argue that this is the kind of approach that is needed.

Perhaps rather than soliciting opinions you could try to crowdsource this analysis.


What you did was basically what I was looking for...different perspectives. I know what you are getting at but there are flaws in your theory as well, like for example if you block a shot it usually means that the opposing team has already been able to get the ball inside and if that happens there needs to be an adjustment to the probability of scoring on a possession because they are already more likely to score on that possession than if they were not able to penetrate.

Another thing to consider is when you are using advanced analysis and you get a large enough sample size (a couple years should be fine) all the different types of blocks will level themselves out to some degree so an over riding exponent will do the trick basically.

I am not trying to argue with you at all I am just saying that certain quantification techniques have loopholes in logic, someone may find a hole in my theory too.

I was just hoping that a discussion would start and I could cherry pick some good points that were made and include this in my overall vision.


This is what hank was saying: http://en.wikipedia.org/wiki/Expected_value

You should know the distribution types of all the variables you are considering because certain statistical concepts assume a certain distribution and will not work with other types. http://en.wikipedia.org/wiki/Distribution_(mathematics)

What I would love to see happen with bball stats is quantifying risk. If you could figure that out, that would be great. For example a steal looks good on paper, but if a player is gambling all the time to boost his steal stats (at the expense of playing poor defense), then its not really a good measure of performance.
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Re: Quantifying Specific Statisticss 

Post#31 » by vini_vidi_vici » Tue Jul 22, 2014 6:44 pm

416 wrote:This is what hank was saying: http://en.wikipedia.org/wiki/Expected_value

You should know the distribution types of all the variables you are considering because certain statistical concepts assume a certain distribution and will not work with other types. http://en.wikipedia.org/wiki/Distribution_(mathematics)

What I would love to see happen with bball stats is quantifying risk. If you could figure that out, that would be great. For example a steal looks good on paper, but if a player is gambling all the time to boost his steal stats (at the expense of playing poor defense), then its not really a good measure of performance.


Have you seen the ghost data (re:SportsVU)? Via a Zach Lowe article.

• One more fun ghost video from a Pacers-Raptors game: Keep an eye on Kyle Lowry (No. 3 in white) and Ghost Lowry as Indiana swings the ball around. Real Lowry gambles for a steal, as real Lowry is prone to do, and Ghost Lowry frowns upon this risky behavior. Fun times.


[youtube]http://www.youtube.com/watch?v=UIwPkG7T4mg[/youtube]
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Re: Quantifying Specific Statisticss 

Post#32 » by 416 » Tue Jul 22, 2014 6:58 pm

vini_vidi_vici wrote:
416 wrote:This is what hank was saying: http://en.wikipedia.org/wiki/Expected_value

You should know the distribution types of all the variables you are considering because certain statistical concepts assume a certain distribution and will not work with other types. http://en.wikipedia.org/wiki/Distribution_(mathematics)

What I would love to see happen with bball stats is quantifying risk. If you could figure that out, that would be great. For example a steal looks good on paper, but if a player is gambling all the time to boost his steal stats (at the expense of playing poor defense), then its not really a good measure of performance.


Have you seen the ghost data (re:SportsVU)? Via a Zach Lowe article.

• One more fun ghost video from a Pacers-Raptors game: Keep an eye on Kyle Lowry (No. 3 in white) and Ghost Lowry as Indiana swings the ball around. Real Lowry gambles for a steal, as real Lowry is prone to do, and Ghost Lowry frowns upon this risky behavior. Fun times.


[youtube]http://www.youtube.com/watch?v=UIwPkG7T4mg[/youtube]



vvv thanks man! good stuff =)

by the looks of it, seems like lots of smart people with resources have already done what chuck is trying to accomplish. Maybe its best for him to start digging through this stuff rather then trying to re-invent the wheel from sctratch :dontknow:
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