Tag Archives: players

Advanced statistics agreed about those NBA players

Yesterday I explored topic of the biggest differences between perceived value for some NBA players among five advanced statistics and I don’t want to be only negative ;-)
So for a change let’s spotlight those examples where metrics mostly agree, shall we?

Again based on great database prepared by Alex at and for the last 5 years I ranked each player [with at least 1000 minutes played] according to five different metrics [PER, Win Shares per 48 minutes, Wins Produced per 48, new RAPM and ASPM] and then I calculated standard deviation of each player’s ranking and below you will see only those with the lowest number each year meaning the differences in evaluation between statistics were the smallest.

Again let’s start with the most recent season…
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Posted by on January 26, 2012 in Expanding Horizons


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Players at heart of disagreement between metrics

Recently Alex at collected a great dataset with multiple advanced statistics in one file and used it for comparison how different advanced metrics relate to each other. It was a great post and one I had in mind for some time so I’m glad someone else did it thus saving me some time ;-)

But as a fan of curiosities I wanted to explore the differences between them.
Obviously if you know how those advanced statistics are created [and you can read more about it in Alex’s description] you should know what to expect but I don’t think it can be substitute for details and specific names.
So which NBA players were the toughest for advanced metrics to evaluate in recent years?
Which played have caused problems each year or multiple times?
How extreme were differences?

Here’s what I’ve done for this post:
based on Alex’s database and for the last 5 years I ranked each player [with at least 1000 minutes played] according to five different metrics [PER, Win Shares per 48 minutes, Wins Produced per 48, new RAPM and ASPM] and then I calculated standard deviation of each player’s ranking.
Today I’ll focus on those players with the highest number which means their perceived value varied the most significantly between those 5 statistics. Tomorrow will be the opposite side, where stats value players similarly.

Let’s start with 2010-11, 245 players qualified and here’s the list of those with standard deviation above 60…
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Posted by on January 25, 2012 in Expanding Horizons


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NBA Players who Learnt to Make Three-Point Shots

After I explored sudden mid-career improvement in free throw accuracy a natural follow-up was to check another shooting skill: three point shots. Who did have the biggest improvement?
Which players did have the most impressive improvement late in their career?
Who did attempt the most shots before sudden jump in accuracy?

You will be able to find all the answers in one table ;-)

And it was created in a following way: I used a file with historical data from basketballreference.comand I found all players who had a season with 100 attempts with improvement by at least 3% [up to at least 33%] when compared to a previous one and they played a minimum 1000 minutes in both years.
Finally I sorted it by the size of improvement. Also I excluded players from 1979, their sudden jump was possible… because three point shot became available ;-)

Interesting fact: 2 recent winners of Most Improved Player Award are in the Top40 [Love, Diaw] and 6 of the last 7 winners are in the Top350 [additionally Brooks, Granger, Turkoglu and Bobby Simmons all with at least 4% improvement].

Anyway, here’s the full list…
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Posted by on January 20, 2012 in Expanding Horizons


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NBA Players with Mid-Career Improvement in FT%

As a fan of unusual events I’m both excited and fascinated by the career of Tyson Chandler.
Let me explain why…

  • he was drafted second overall… yet he played less than 25 minutes in each of first three seasons.
    Obviously he came straight out from high-school which didn’t help but still it’s pretty unusual treatment of such high draft pick.

  • he changed teams 5 times which is already well above average and he is 29 years old.

  • his toe was the main reason for a rescinded trade which is a very rare event in itself
    [according to there were only SEVEN of them!].

  • last year alone he was traded as a salary dump… and he was the key piece for a championship team.
    Quite a rollercoaster ride, huh? And I can’t count how many times it happened but I’m pretty sure that’s not a typical way to acquire one of the best players on championship team.

  • most importantly [at least for this post], he did something which most big guys [or maybe even all people] can only dream about: he learnt how to shoot free throws very late into his playing career!

    In his first 8 years Tyson Chandler attempted 1789 free throws and made 1071 of them for a 59.87% mark but in his ninth season he suddenly became 73+% shooter. And it wasn’t a fluke either, in all following seasons he made at least 73% of his freebies.

For the purpose of this post I’d like to focus on the last part and explore it: How often does such sudden improvement happen? Who did have the most impressive mid-career improvement in FT% in history?

Also you may not know this from this blog but I like playing basketball a lot and it’s the first topic which not only I can relate to personally but it also makes me appreciate it even more than just a curiosity because I feel like in terms of veteran’s basketball skills “you are who you are”.
Yet somehow at least one old dog learnt a new trick… maybe there is a light at the end of tunnel for all of us ;-)

OK, let’s start with basics, I downloaded a file from with historical data and I compared FT% in every season after 5th [with minimum 150 free throw attempts] to Free Throw Percentage based on all combined makes and attempts at this point of player’s career. And here are the results…
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Posted by on January 18, 2012 in Casually Unique


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Popularity of NBA Players according to Google

Latest surveys revealed that Kris Humphries shouldn’t be the advocate for long marriages or that you shouldn’t leave long-term relationship if you want to be liked by the public… OK, frankly I don’t know what was the point of this research [advertisement?] but it did raise a couple interesting questions for me:
who is the most known NBA player? Who is the most obscure one? How can we measure it?
Jersey sales could only work if it weren’t for huge differences in market sizes and for obvious reasons I’m not gonna ask a lot of random people so I focused on a well-known but rarely used number: Google search results.

Please don’t confuse it with a serious research, as far as I know even Google admit those are only estimates and they are not reliable but I was curious anyway. Also it may be a sign I use google too often but what the hell, I can’t be afraid to take chances like that, otherwise this blog will be more useless than it is now.

So today based on rosters from ESPN I entered phrase “NBA [Player’s Name] [Player’s Surname]” 377 times.
I included all rotation players but I ignored most guys on non-guaranteed contracts or camp invitees or guys whom even I didn’t know and I threw-in a couple of legends for a comparison sake.
Here is the list for Top40 and Bottom40 Google Search Results
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Posted by on December 28, 2011 in Expanding Horizons


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NBA Players’ Contracts under Latest Owners’ Offer

David Stern tried so hard to convince players to his latest offer that he even published a memo to the players with full explanation, or dare I say advertisement, of all important changes. I’ve read it and…
here’s my question: how would it really change NBA landscape?
What kind of differences would happen under those rules?
And which teams and players would be the most affected?

I didn’t want to use any semi-guessed projections how teams could look 3-4 years in the future so I’ve used actual NBA contracts. In other words, I assumed that EVERY current NBA contract was signed under new rules and I’ve changed every contract as it would have been signed under latest owners’ offer.

Obviously, it wouldn’t work that way but I think it’s a nice simulation about NBA rosters given those changes.

Here are the highlights:

[other than the fact it was a good exercise how NBA contracts work]

1) For most teams there would be only two differences

A couple of shorter by one year contracts and savings worth a couple of millions thanks to lower raises. It could make a real difference in some cases [like Joe Johnson’s] but it would have any effect in 2-to-4 years.
In all those shorter contracts and lower raises would be probably the most helpful to Magic’s roster.

2) Huge majority of bad contracts still would be signed

Obviously they would be a little less bad thanks to 1) but still if any GM wanted to overpay bench guy or pseudo-star, rules wouldn’t disallow him that opportunity.

3) The most affected teams would be

[in order of team’s city name]:

Denver Nuggets – their two rotation players [Harrington and Andersen] would either earn a lot less money or they would have to sign somewhere else. Also there’s a sizeable chance they would still have Carmelo.

LA Lakers – their core of Kobe, Gasol, Odom, Bynum would be the same but they would have to find some cheaper alternatives to play at PG and SF. Or players whom they signed [Artest, Blake, Barnes] would have to agree to significantly lower salaries.

Miami Heat – their window to win a championship would be 2 years shorter!
That’s because of a change regarding sign-and-trades rules.

New Orleans Hornets, New Jersey Nets, Orlando Magic – the same issue for all three of them:
thanks to a little tweak regarding player’s options either Dwight, Deron and Paul would sign shorter extensions or they wouldn’t be free agents until 2013.

New York Knicks – with Extension-and-trade prohibited Carmelo saga would look very differently.
Either he would have to wait until late August to sign his extension or trade with Nuggets wouldn’t happen and he would have to sign as a free agent.

Phoenix Suns and San Antonio Spurs – surprising amount of players would either earn a lot less money or they would have to sign somewhere else or in a different way than they did.

Washington Wizards – they would have a huge amount of cap space thanks to Rashard Lewis’ shorter contract. But there would also be a bad news about it: they would have to spend almost 30M$ to even reach new required minimum salary for a team!

4) The most affected players would be

Jermaine O’Neal, Matt Carroll, Al Harrington, Chris Andersen, David Lee, Ron Artest/Metta World Peace, Chris Bosh, LeBron James, Carmelo Anthony, Chris Duhon, Marcin Gortat, Channing Frye, Gerald Wallace, Antonio McDyess, DeJuan Blair, Gary Neal and Rashard Lewis.

So pretty much only stars who wanted to change teams and a middle class. All of them would either earn a lot less money or they would have to sign somewhere else or would have to sign in a different way than they did.

You can check and examine all the numbers below… although please keep in mind that:

  • I’ve used numbers from as a baseline so all marks are the same:
    (name) means free agent while [name] = rookie. You can find original file here,
  • I didn’t even try to fill out rosters so some of them looks empty,
  • I ignored most of the free agents [I included some of them just for fun and to show some new contracts],
  • “-PO” means that player wouldn’t have his player option.

And now a very, very long table with all NBA players’ salaries had they been signed under latest owners’ offer.
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Posted by on November 17, 2011 in Scrutiny


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Effects of No Rest between Games on NBA Players

There may not be NBA season but with a possibility of more packed schedule than usual due to the lockout
I wondered what are the effects of no rest between games and how important are those days providing a break. There are studies available about this issue on a team level but I haven’t seen one on a individual level.

So I’ve collected Yahoo’s split statistics for 953 player-seasons [those who played at least 41 total games in the last three years] and divided them into 5 groups based on a different situation:
“Total for a season”, “0 Days Rest”, “1 Day Rest”, “2 Days Rest”, and “3+ Days Rest”.

Here are basic averages of those groups:
[where WS/36 = Win Score per 36 minutes and GS/36 = Game Score per 36. Also please note that shooting percentages where calculated on a league level so FG% = “sum of all avg fgm” / “sum of all avg fga”]:

Situation GP Min/g FG% 3P% FT% Reb/g As/g Stl/g TO/g BL/g Fls/g Pts/g WS/36 GS/36
Season 68,7 24,73 46,04 36,19 76,79 4,22 2,20 0,75 1,39 0,49 2,11 10,33 6,32 10,81
0 days
of rest
15,7 25,18 45,86 36,58 76,91 4,24 2,17 0,73 1,40 0,49 2,17 10,43 6,11 10,58
1 day
of rest
34,0 25,12 46,02 35,94 76,88 4,30 2,25 0,78 1,41 0,50 2,13 10,47 6,37 10,85
2 days
of rest
10,8 24,67 46,37 36,54 76,81 4,21 2,23 0,75 1,39 0,49 2,10 10,38 6,34 10,85
3 days
of rest
8,2 23,01 46,08 36,26 75,68 3,99 2,08 0,73 1,32 0,49 2,02 9,73 6,31 10,82

4-5% loss maybe isn’t much of a difference but back-to-backs are indeed the worst and 1-to-2 days of rest seems the best time to play. On average that’s because of fouls, steals, rebounds, blocks, assists and FG%.

It pretty much confirms basic principles from a team level to an individual level so I’d like to dig in a little deeper.

For example, which group of players is affected the most by lack of rest? Those old veterans, right?
(Data about player’s age during each season was taken from,
“#” means “number of players in this range”)
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Posted by on November 14, 2011 in Scrutiny


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