Hornets Guards —- Passing

[Editors comment: @Maknusia started this thread, however, since @TTNN is the original author of the content and he is also a poster here, I have credited this thread to him/her.]

Original article from At The Hive fanposts

Kind of just play with our player stats from last season, and see what I get.

So the first set of numbers I was looking at are their assists vs. touches/game.

(players included are guards and small forwards who played at least 5 games last season and with minimum 10 min/game PT).

In general, the more touches a player had, the higher assist they will generat. The solid line indicated the league average assist number player generated with their touches. Each circle represent a player, with red ones are our currently players and blue ones were ex-Hornets played last season.

Players located above the solid line are above league average in generating assists with their numbers of touches , and players located below the solid line would be considered under league average.

High lighted yellow are players who touches the ball a lot during a game, and generates lots of assist. And you will find very familiar names in that cluster: Chris Paul, John Wall, Stephen Currry, LeBron James, Russell Westbrook, Rickey Rubio, Ty Lawson, Reggie Jackson. Interesting to see Michael Carter-Williams in Sixers (labeled green) really only had average capability in generate assists, but he was leading the league in touches/game, kind of wondering whether Sixers were just simply trying to boost his numbers.

Out of 6 players who had greater than 90 touches per game, Kemba Walker had the lowest assist number, by a big margin. Kemba had 0.3 ast more per game than Batum with 30 more touches. With similar touches per game, Westbrook (labeled orange in the graph) has 8.6 APG, which is 3.5 more than Walker’s 5.1 APG.

So why is that, is Kemba Walker a ball hog?

I like to use %pass to sort out ball hogs. I calculated %pass = pass per game/touches per game*100%. That is a good indication on chances a player tend to pass out the ball when they touch it.

The solid line is the league average of all the players who played more than 5 games last season and with > 10 min PT. And the dotted line were indicating 1*SD from the average.

I found this view is a good way to cluster super passer from ball stoppers. Players with super high pass percentages tend to cluster with players who promote ball movement, but don’t really like to shoot the ball. And players who have low percentage of passes tend to be volume shooters who likes to shoot everything. And I did not realize Nick Young pass less than 50% when he touches the ball.

So when we look at the players on Hornets team, first thing I noticed is that Batum passes a lot, and that also is consistent with his low USG rate of only 14.7% last season.

To my surprise, Kemba Walker not only not clustered with non-passer group, he actually had above average in terms of willingness to pass the ball. Lin and Mo Williams sit right at the league average there.

Then the question is, why Kemba had low assist when he is a willing passer?

There are two kinds of passes. 1) you use your court vision or play making skills, and you hit an open man, then that pass results in an assist opportunity or hockey assist. 2) you don’t have a good look so you just get rid of the rock either leave the work to your teammates or you reset and then demand the ball later on, so that’s just a simple pass.

So I calculated Assist opportunity percentage = (assist opportunity + secondary assist per game)/ pass *100%

This number is a good indication on how active a player is looking for helping their teammates to score.

In this graph, the solid line is the league average and the dotted line are 1*SD from the league average.

Players who has highest assist opportunity% numbers were playmakers who are good at creating for their teammates. Players clustered with highest AST Opp% are: (roughly by order: Kobe Bryant, Ty Lawson, James Harden, Chris Paul, Reggie Jackson, Jameer Nelson, Russell Westbrook, Rajon Rondo, Ricky Rubio, John Wall, LeBron James, Stephen Curry etc.)

Lin stands out here with way above league average assist opportunity percentages within this team. Pretty much 1 out of 4 passes is for his teammates to score. Batum was a bit lower than I anticipated but still above league average. And Kemba Walker is less active in looking for opportunities to set up his teammates. He would be below average in PG category, only 18% of his passes were trying to set his teammates up.

Last, I look for their assist success rate, which I calculated using following formula: (AST+FT assist)/assist opportunities*100%

This is an indication on how good a player is in generate looks for their teammates, and a good play maker would have better success rate, and a not so good play maker would have lower success rate. (Unfortunately, finishing capability of teammates could affect this number a lot too, thus that might need to take into consideration when player were compared across different teams.)

Again, the solid line indicate the league average, and dotted line indicated 1SD from league average.

Batum is really good in terms of assist success rate, and Lin is above average too. Kemba Walker was below average there.

So, looks like Walker, Batum and Lin are all willing passers, with Batum and Lin being better play makers on this team. Lin is more active in looking for opportunities to set up his teammates, and Batum somehow generate better looks to have better success rate. Other guards are not much play makers on this team unfortunately.

Looking forward to see Walker/Batum, Walker/Lin Batum/Lin pairing in game situation, and I would really like to see all three of them on court together, could be a really dynamic line up with three ball handlers, two play makers on the floor, would be fun to watch.



  1. Hehe…

  2. Btw, I will just put the author to be TTNN……..can I? @mak?

  3. lol…not asleep yet!

  4. I had it stated above under the header as “by @TTNN“…or am I misunderstanding you?

  5. if you ever need to find Brent, just post a new thread and he will magically appear

  6. word!

  7. Oh I just meant since you just posted the whole content and TTNN is also a poster here. I will just credit this thread to TTNN…..I hope it is ok

  8. sure!

  9. Brent made the author of the post actually show as TTNN.

  10. As I commented before, incredible analysis. Grantland worthy! You should definitely do this again in the future and publish to SBNation!

  11. got it ๐Ÿ™‚

  12. Love the numbers and the reasoning behind it. I believe TTNN welcomes different perspectives too. So maybe poster here can suggest what metrics can be more indicative, or anything that can make this analysis more complete. I believe lin should be a stand out under good analytics.

  13. Totally agree unless he decides to “disappear”:-)

  14. One thing I was missing from the graphs was the x-axis label. Anyone know what the unlabeled x-axes are?

  15. great, now our forum will be inundated with DOTA fans!

    jk =)

  16. LOL Absolutely no idea what it’s all about:-) Just know it’s e-game:-) Glad that JLin enjoys something besides bb.

  17. @brentyen:disqus can you remove the remarks…it looks funny ๐Ÿ˜‰

    [Editors comment: @Maknusia started this thread, however, since @TTNN is the original author of the content and he is also a poster here, I have credited this thread to him/her.]

    BTW, TTNN is she ๐Ÿ˜‰

  18. Yup…me too….you are not alone ๐Ÿ˜‰

  19. Kemba Walker is an interesting case.

    To me, he is a pass first point guard who’s forced to shoot.

    How can a player be forced to shoot, one might ask? Because this is the NBA where defenses force a player to play to his weaknesses.

    In the case of Walker, he’s a small slow guard who can really jump but cannot truly shoot over or drive past people. That causes opponents to be able to passively defend him with just a single player while also playing his passing lanes.

    Just to open up those clogged passing lanes, Walker has to force dribble penetration despite not having the footspeed to truly make it happen. This results in him wildly dribbling on the perimeter and heaving the ball at the rim just to get opponents to commit to him enough to step out of those clogged passing lanes. Thus Walker seems like a ballhog when actually all he’s doing is trying to unclog the jammed passing lanes.

    This is the opposite of Lin who’s so athletic and so proven as a big time scorer that he has a gravitational pull on opposing defenses even when he doesn’t have the ball. The reason every Lin game features wide open passing lanes is because Lin draws more defense than any guard in the NBA. It’s just up to Lin’s coaches and teammates to move to those open spots created by Lin siphoning defenses away from the other 4 teammates on the court.

  20. @TTNN would be the best person to explain

  21. @brentyen:disqus or @psalm234 could you let me edit the article…I missed out one of the graph…my bad!

  22. @Maknusia, I have 6 things that came to my mind after I read your post:
    1) Unless your full time job is a stat bball analyst, you must have A LOT of time on your hands to do this analysis. How long did it take you?
    2) Where did you get the stats? you take it from a .csv file and crunch them in matlab? Or you copy and paste or have a software that does this stat analysis.
    3) This is a very good (maybe even unbias) numerical detail analysis of ALL players..not just lin..now we know where lin really stands objectively instead of lovers and haters eye test.

    4) You must be a stat or technical major, unless you are still in HS.

    5) If you really have these stats analysis ability AND TIME, you might want to apply it to something more monetarily awarding..like quantitative trading in stocks or futures.
    6) Thank you for the analysis so that others didn’t have to do it.

  23. The writer of the piece is actually @TTNN so you are addressing to wrong individual.

  24. oh sh–t, I just read the top and realized that.. guess his name stood out but it’s ttn. Thanks for the correction.

  25. @maknusia:disqus Nevermind, as PHillycheese corrected me, it’s ttnn.
    Oh well, and I thought you were that good !!

    Anyways.. that analyst can and should apply it to quantitative stocks trading. He must have software to crunch and therefore TIME.

  26. I’m glad this analysis got it’s own thread. Great piece of work and easily understood.

  27. most importantly, it’s the graphical display that ppl can easily visualize and compare where all nba players stands.

  28. He may have been forced to shoot since there was no one else on the team that was a better shooter. But next year there will be more shooters so he will not have any excuses not to pass more. Kobe always said he was forced to shoot because his teammates were not good. Seems reality with Kobe was he had trust issues.

  29. that teammate has to have a 100% fg percentage in order for k to trust him.
    he just likes to shoot.

  30. So you think I’m not good?! ๐Ÿ™

  31. Nicholas Batum is the classic case of a player with no discernable skills other than his pogo stick athleticism being maximally highlighted in a modern day free flowing basketball system.

    Batum played for the Portland Trail Blazers under coach Terry Stotts. Stotts has a system that has the wings either setting picks or making quick simple decisions off a pick. Batum wasn’t tasked with doing much and was as much a screener as he was a decisionmaker.

    What Batum was not asked to do was dribble penetrate against opposing defenses. We shall see if Batum can not only dribble penetrate on this Charlotte lineup that desperately needs it but also cut and make SG decisions that would free up Walker and MKG on the perimeter.

  32. My interpretation is that BOTH x and y axis of the last 3 graphs are the same pass percentages, assist opportunity & asst success rate so that the graphs can depict players who have below the league’s average, the x-axis shows descending order (highest to the lowest) percentage while y axis starts from 0.

    Only the 1st graph has the x axis showing different category of touches/game vs y axis assist/game.

  33. I’m a bit confused about you saying M Williams is a better SF than Batum but I’m under the impression that you’ve singing Batum praises. What makes you changed tune?

  34. hahahaha not many can be that good at stat analysis..they need to have that skill and also software to do it.
    If you don’t have the software to do it, you will need matlab to code it up.

    For ppl in general, to code it up in matlab and with valid data will require a lot of time. So I thought you some how had it all set up

  35. My tune hasn’t really changed.

    I like Nicholas Batum as a small forward which is his natural position. I DON’T like him at SG which to me would be highlighting his weaknesses and diminishing his strengths.

    To me, Batum is a very talented SF while Marvin Williams is an even more talented SF who’s also more fundamentally sound than Batum. They’re two different players because Williams also has the bulk to play PF and has a post game that Batum does not have.

    I firmly feel that between Marvin Williams and Nicholas Batum, Lin will have a far easier time working with the more athletic and more fundamentally sound Williams than with the more proven Batum.

  36. You dont need that sophisticated sw; excel with pivot table would do…will explain above

  37. Ok but before this season, you hardly talked about M Williams but you were so high on Batum…in any case, Jeremy fans should be happy that he has 2 very good SFon this team to play PnR with, right?

  38. Yes, but you still need time..AND valid nba data.. to convert them all to csv and everything else.
    And all those percentages are thought out.. so you need at least 8 hours if you are skill full enough.

  39. 1) I will let TTNN to explain ;). TTNN and wu kong does great analysis on JLin and the team that he plays for. Both of them had

    done tons of analysis. BTW, I have done my share as well, when he was at Rox…its buried deep somewhere ๐Ÿ™ This is where I hate disqus,…prefer forum format which has search and sort functionality )

    2) stats are basically available at couple of sites, where one need to take those and do the correlations. Basically I would get it from NBA VU. For example, if you need the passing stat, then pull it down from http://stats.nba.com/tracking/#!/player/passing/ and export it to Excel to perform the analysis. There are couple of other sites, that I would the data such as

    There are way many more than those…If you are interested…I will get them

    3) Yes

    4) As for me, I uses tons of numbers in my line of work, so excel works perfectly

    5) Nope…not even enough time to get my “Hot-N-Now”

    6) Thanks to @TTNN

  40. Right.

    The two SFs Lin has are both starting caliber players that Lin will enjoy playing with.

    Frankly, the only reason I did not mention Marvin Williams right away was because I didn’t even remember he was a Hornet! His play as a Hornet last season had been so ineffectual, it’s like he wasn’t with the team. On this Hornets team that had ineffectual point guard play, I give Marvin Williams a pass.

    I think of Nicholas Batum as a poor man’s Grant Hill with Hill’s athletic talent but not Hill’s fundamental soundness or hoops intelligence.

    I think of Marvin Williams as the Eastern Conference Kawhi Leonard, though Williams is actually more athletically gifted and more skilled than Leonard.

    Lin will run all sorts of plays with either forward.

  41. yup, I use to spend like 16-20 hours doing it for Rox game

  42. Yeah, I sense the same thing…but I cant say for sure, until I see it real time. And KW has good speed as well, from the little highlights that I have seen

  43. In the last 20 years, I’ve never taken seriously anything that Kobe Bryant says.

  44. Part of the reason teammates couldn’t hit shots is than their defenders did not have to abandon them to prevent the unathletic Walker from scoring.

    Having shooters on the court does not help if they’re tightly guarded.

    This is the NBA where the key to getting guys open is to have a player that scores so well that he cannot be single covered. Walker is not that player, whether Lin is there or not.

  45. I know, just not sure if she want it to be known….or not..lol

  46. This is a very good point.

  47. Ha..it is ok…I will fill it later

  48. Great analysis, extremely helpful! Keep up the great work TTNN

  49. ok…thx

  50. Just curious… the first graph is using per game data… What happens if that data is ‘normalized’ to a per 36 or per 48 min basis??? There are problems when using the per 36 or 48 numbers because they are just linear scaling the box score numbers and when you look at someone like JLin in fact his box score numbers do not increase linearly with more playing time his increase and reach peak performance in the 35 min range…Some players decrease with more time ( they truly are better in short burst of time and get worse with more playing time) In these cases the simple linear scaling will give inaccurate projections – either too low or too high values… However it is still typical when comparing players to use the linear scaling of per 36 or per 48.

  51. Wow, Kawhi Leonard is my 3rd fav(a very distant 3rd though) player after Timmy, and of course my #1 fav is Jeremy. I can imagine Jeremy will have much better stats this year and also this season is most likely a breakout season for Jeremy!

  52. sorry for babbling… essentially I like looking at both per game data and the linear scaling… each give you some different data.

  53. I think the normalization is somewhat done because it is plotted against touches per game.

  54. I take Kobe Bryant’s statement about him channelling dark sides very seriously.

  55. But Bryant as the all time leader in bricks DOESN’T SHOOT WELL ENOUGH to be channeling anything!

  56. Kemba Walker would prefer to pass to wide open guys even though there are no wide open guys due to Walker not being athletic enough to draw a double team.

  57. Then you will enjoy Marvin Williams even though Williams won’t play as many minutes as Leonard would.

    By the way, I am unconvinced that signing LaMarcus Aldridge truly helps the Spurs. He’s gotta really toughen up mentally and physically in order to play on that team.

  58. A correct analysis, wu kong.

    With all analytical basketball stats, there’s never such thing as a true apples to oranges measurement.

    Stats are only a part of the big picture.

  59. I’m awaiting the new season excitedly yet patiently in anticipation..*.*

    About LMA, how so, KHuang?

  60. I just threw together some fake numbers trying to make this make sense in my head… blue numbers are basic per game values… red are scaled to per 36 basis… plotted each set against each other and you get a vastly different curve… refresh to see image… what am I missing??? have not slept much lately so I am sure my brain is not working up to normal capacity… need more coffee…. yes more coffee should do it

  61. ” need more coffee…. yes more coffee should do it”
    You are doubling the coffee now?! ๐Ÿ˜‰

  62. only had two cups… need 3rd or forth cup to get brain up to speed ๐Ÿ™‚ not my normal caffeine intake but today I might need a full pot to keep going… I don’t sleep much usually anyway but dog kept me awake last night so my normal few hours was more like a few minutes ๐Ÿ™

  63. I am confused now…what are you trying to do here?

  64. Lwt me try to explain….so I think the first graph should not use per game data but instead use ‘normalized’ box scores with either per 36 or per 48 scaling. When comparing players it s normal to use the scaled data… You suggested that the ‘normalizing’ of the data was already inherent because the graph was pergame vs pergame data…. So…
    I made up values of per game Ast and touches and the then ‘normalized’ that data to a per 36 minute basis ( simple linear scale)… then plotted the per game data as was done in the first graph… and plotted the per 36 data and they are VERY different…

  65. It is possible ( very possible) I am missing something basic here b/c I am very sleepy and in need of more coffee. ๐Ÿ™‚

  66. Oh I see….ha my bad I should have elaborate more. What I meant is like you said…..linear scaling is one way to normalize…but it is not accurate in many cases. What I was trying to say is simply plot against touches per game might be a more accurate way to “normalize” the curve.

    In the end, I was comparing between two different normalization process. They do not necessarily leads to the same result/curve.

    Btw, touches per game is not really strictly a normalization per SE….but I think it has part of the effect.

  67. ah ok… got it… agree each version of ‘normalizing’ the data has benefits and drawbacks. Still would like to see the per 36 numbers plotted…. and of course there is always the inherent problem that all of this is based on box score values which we know are full of errors and manipulations. ๐Ÿ™

  68. The good about touches per game may be that it takes away the uncertainties of how you are utilized when you are on the floor. Of course just partly. But it is definitely more indicative than what your mpg is telling u. That is my take.

  69. comparing a player that has avg 35-40 minutes per game and another player that has avg 19 minutes per game it is problematic. if a player has say 15 touches in a game was that because they have a low usage in 35 minutes or a high usage in 19 minutes you don’t know because you are only looking at the per game value and not per minute value… maybe it would be better to have the first graph data ‘normalized’ to per minute values and then plotted…. Ah numbers and the joy of manipulating them…. I am such a geek ๐Ÿ™‚

  70. Yes, that is why I think per touch is better than per minutes…….because minutes is not indicative. Only minutes count here is the time you actually get to touch the ball.

  71. plot ast/avg minutes vs touches/avg minutes… that would ‘normalize’ the data for each player. that way players that have high minutes per game you can then see are the touches per game for them because they are playing more than average minutes or are they in fact ‘hogging’ the ball when they are on the floor.

  72. Yes, and when you plot like that, you basically took out “per game”. It becomes ast per touch…..which…is less relevant to you being a ball hog or not…

  73. if you take each players ast and touches and divide them by their individual avg minutes you can then compare player to player ast and touches per minute played instead of per game played. Looking at the data on a per minute played versus looking at the data per game is a vastly different analysis. I would like to see the per minute played data. I think the per minute played data tells you who who is using their minutes on the floor more efficiently.

  74. and I would also like the scaled per 36 or per 48 data… IF @TTNN has the values I would run the numbers and plot… too lazy right now to go look up all that data

  75. LMA hasn’t played for a tough coach like Popovich before.

    Aldridge will be expected to body up and play hardnosed fundamental basketball in a way he never did in Portland.

    In Portland, Aldridge could drift to the perimeter and wait for shots to come to him off screens. But in San Antonio, Aldridge will be asked to plow through defenses and make intelligent reads with the ball in his hand.

    We’ll see if Aldridge can adjust.

  76. If Clifford really does play Lin and Kemba together, Kemba’s efficiency will definitely improve because Lin will be drawing double teams and Kemba will get open looks. Also, having so many better 3 pt shooters this year will also help Kemba too.

  77. Isn’t Ast/G vs Touches/G the same as Ast/min vs Touches/min? Don’t both end up as Ast/Touches?

  78. Irrelevant to basketball but for the soccer fans in this forum here’s my predictions for the upcoming Premier League campaign https://jhaposts.wordpress.com/2015/08/04/premier-league-title-race-predictions-20152016/ . Have a new Jeremy Lin article out soon aswell ๐Ÿ˜€

  79. You might be right. I just don’t see the point of the x-axis or what it means. If it is just a percentile, why does it only go from 90% to 50%, why not 100% to 0%?

  80. Since the discussion for this thread is heavy in stats, people can still post non-technical stuff at the other thread

    so that way we can have multiple threads going at the same time.
    I made the other thread a Sticky so it will show up first for general discussion

  81. Oh okay, now I get what you’re saying. The x-axis is identical to the y-axis in all the graphs, except for the first one. That was very confusing. #1 of graphs: label all the axes.

    I still don’t see the point in doing that. You don’t need a graph if only have 1 axis! You just use a list! My only guess is that it gives more room to add labels on data points?

    But then, if x-axis is same as y-axis, shouldn’t it be a straight line? Why is the line curved?

  82. I am going to try to answer this… it will take several pictures so please refresh…to see them all.

    I started by making fake data for for three fake players #1,2 and 3 AST touches and minutes per game. I used that data to generate AST per 36, Touches per 36 data typically used when comparison players… as well as the AST/avg min played and touches/avg min played data here:

  83. so once I had enough coffee in me I looked at the per 36 and the per avg min data and realized mathematically they would end up being the same once plotted per 36: (12*36/20) vs ( 4*36/20) equivalent to (12/20) vs (4/20) so ignore the third set of data… plots given here for fun…. ๐Ÿ™‚

  84. but the per game data plotted and the per 36 min data plotted do not give the same results… in fact you can have players switch positions…. here is a chart of the per game ast vs touches plotted and the per 36 ast vs touches plotted… player #1 and player # 3 change drastically player #2 not so much.

    the blue set of data is the per game numbers… lets look at player #1: 12 touches and 4 ast (but he does this in 20 minutes)… player # 3: has 20 touches and 8 ast (in 42 minutes)… so if you ignore the time it took for them to generate these touches and ast and just plot the per game values you would think player #3 is the one generating more assist per touches….equivalent to the yellow dotted values above….

    However if you plot the per 36 values ( red set of data)… player #1 has a slight edge on player #3… player #1 now is higher than player#3 on the plot…

    So which should we look at???

    hmm… So let’s just say I am confused… and I welcome input… I will look again once I have had some sleep.

  85. I took your raw numbers and calculated AST/G divided by TOUCH/G. Then I calculated AST/36 divided by TOUCH/36. I also calculated AST/TOUCH. They all come out to the same number 0.3.

    It’s the same concept as AST/TOV ratio. It doesn’t matter whether you take AST/G divided by TOV/G or AST/36 divided by TOV/36, they’ll be the same as AST/TOV (aside from any minor rounding error).

    See my image (refresh page if needed):

  86. You are plotting each game’s data point. So yes, they will look different if you compare game vs 36 mins.

    But we’re looking at an aggregate number of AST/TOUCH, not game to game. For the aggregate, it doesn’t matter whether you take AST/g or AST/36 or total AST. They are all normalized the exact same.

  87. yes. but the plots below…help my slow sleepy head out here… looks at the combined plot with red and blue below…

  88. my fake data I have ast, touches and a time… indicating averages for that player for say a season… and then I take those averages and put them on a per 36 basis… not each games data point plotted…

  89. I think multiple threads is a bit confusing in the offseason…

  90. Ok let me try again… when you plot ast vs touches you are mainly looking for who is above and who is below the average and either method will get you that result… the ratios are the same whichever set of data you use… and you are simply looking for who exceeds the average and who is below the average….

    However, if you plot the per 36 basis data instead of per game basis you can more easily visualize that player #1 and player#3 are actually very similar in their AST vs Touches as the red dots for their data are both above the line and clustered closer than blue dots for player #1 and #3…

    (blue values of 12/4 for player #1 and 20/8 for player #3) vs
    (red values of 21.6/7.2 for player #1 vs 17.1/6.8 for player #3).

    Yes you can get all fancy and say for instance that player #1 and #3 are a very similar distance ( measured perpendicular from the average line) away from the average ( in either the blue or red set of data) but the casual viewer will not know this… they will simply look at player #1 far to the left and player # 3 far to the right of the graph and then most likely make an erroneous conclusion when comparing player #1 and player #3. It is highly likely that player#1 can perform as well as player #3 if given the same minutes… I think this is easier to see on a per 36 basis.

    It depends on what you care about… just showing who is above and below average it does not matter how you plot the data. but if you want to go further and consider which players give you better efficiency of ast per touches a per 36 basis helps to clarify that.

    plotting on a per 36 basis lets you look at the values for each player and compare them. you can now compare (red values of 21.6/7.2 for player #1 vs 17.1/6.8 for player #3) and more easily see they are similar players….

    I am questioning the statement here :”High lighted yellow are players who touches the ball a lot during a game, and generates lots of assist.” and then names of the generally accepted great players….That cluster only tells you really who has lots of touches but not necessarily who is the best at generating ast per touches… There are plotted points far above the average that are not in the yellow cluster there are other players that if plotted on a per 36 basis may also be in that cluster… you can get to this data by plotting a parallel line above the average and looking at all players above this and say they are great at using their touches see attached plot and fuchsia added line. but I think this is difficult for the casual observer. and most people do not know this…

    I just think visually the per 36 helps to visualize certain aspects…

  91. Oh, #1, #2, #3 are 3 different players, I thought it was 3 different games for the same player. I’ll have to go back and look at it again then.

  92. this is so hard to do over messages… I would so rather be in a room with a white board and markers for everyone chatting away… oh and coffee of course always coffee ๐Ÿ™‚

  93. I looked at your fake example properly this time.

    Everything you said in the post above is correct. It doesn’t matter whether you use Per Game or Per 36, the data is the same. It just changes how things appear visually.

    Like you said, distance above or below the line is the key, not if you are towards the lower left or upper right. A casual observer might not know that, true. But they probably wouldn’t really get Per 36 chart either. They would probably be better off just looking at a simple ranking of Assists Per Touch:
    Player #3: 0.40

    Player #1: 0.33
    Player #2: 0.20

  94. you need double dose of coffee now!

  95. yes casual observers need a one number ranking… that is where all the Hollinger power rankings etc… come in… just give people on number for each player and list in order and be done with it.

  96. double shot espresso please ๐Ÿ™‚

  97. If your shooters are tighly guarded it willhelp with maintaining spacing. As long as the players are willing to swing the ball, it will bend the defense out of shape. Kemba has to shoot better though otherwise his defender will just sag off him, and he needs to make quicker decisions before the defense adjusts.

  98. have you jinx this thread?!!! ๐Ÿ˜‰ lol

  99. Not really……but can be close…I think

  100. that might be true.
    I’m trying to order threads automatically by latest comment so it will look like traditional forum
    Until then, maybe single thread would be easier to follow

  101. lol

    as I said to @disqus_hpbnOOqkJ6:disqus , I’m trying to order threads automatically by latest comment so it will look like traditional forum. For now, maybe single thread would be easier to follow

    It’s tough to balance simplicity of 1 thread & focused discussion of multiple threads

  102. you all are good…man!…I was just kiddin…..you know…buyin time til season starts…at least some pre-season and trainings…..

  103. Just ask Lin when he used to start in Houston but stood in the corner away from the ball.

  104. No you’re that good, since you didn’t do like a lot of bosses do and just take credit for other’s work!

  105. Excellent insight.

  106. Shows how much the game has changed since the illegal defence rule was scrapped. It’s now a shooter’s game and failure to create spacing is death.

    Most teams use multiple picks to free up shooters. Atlanta uses the elbow. A slow PG unable to keep his own man honest makes this even harder to execute. Of course, Lin has had to learn to play lots without any help from his coaches or teammates to help his fight off double teams. The fact Lin still gets decent numbers is testament to how quick he is to create his own spacing.

  107. “All in all, we’re just another brick in the wall”
    “We don’t need no Kobe Bryant”
    “We don’t need no death stare”
    “No pissing on a hydrant”
    “No BS control”

    “Hey Kobe, leave JLin alone”

  108. Hahaha…thx…nah…was just kiddin…just couldnt wait for the season to start…as usual! ๐Ÿ˜‰

  109. I think one reason is because Disqus uses threading of replies. So sometimes people will miss the newest replies if they are for older comments.

  110. Hey guys, feel free to check out my new article on Jlin. What will take him to the next level? https://jhaposts.wordpress.com/2015/08/06/what-will-take-jeremy-lin-to-the-next-level/ . spread the word and let me know if you agree with my points. Would be much appreciated.

  111. true…for the record, psalm did try the forum format, but during that period, many preferred disqus hence we gave the disqus a try…rest is history

  112. Defences are always set up to take away what you do best. Kemba lacks the speed to create separation and that allows defences to stand back and make it even harder for him to attack. His shooting isn’t anywhere the lethal ness of a Stef Curry so again they just stay back and face Kemba to commit.

    Stef Curry himself said that he isn’t the fastest or most athletic PG out there so he developed a super quick release and a great shot. This forces defences to load up on him and he creates spacing by the speed of his quick release and accurate shooting.

  113. Thanks…reading now…. ๐Ÿ˜‰

    BTW, you could mentioned the first 7 starts instead of 5 starts…just saying

    “What I can say for Lin is that you donโ€™t break the NBA record for the most points scored in your first 5 starts, performing at MVP level during Linsanity,”

  114. we had posted those numbers here long long ago…hmmm lemme see if I could get it

  115. I will just use these….

  116. First 5 starts and 7 starts

  117. BONUS!!!! ๐Ÿ˜‰

  118. Under the topic of Aggressive mind-set;

    you could have thrown couple of numbers, that would give better perception to the the story. The quote that you gave from Lin, is superb.

    For example drive to the rim how he had improved and the drive percentage. He was one of the leader during the last season with Rox, behind Lebron

  119. As for shooting you did well, giving those 3pt percentage.

    Personally I would have been attracted to graphs and some NBA comparison from his past seasons

    For example back in Houston he was top 15 clutch player in NBA

  120. Overall, you recapped well…thanks for the writeup and sharing.

  121. Lin does hold the records for the most points by any player in his first 3, 4, and 5 career starts, but not first 7 starts. Lin had 172 points in his first 7 starts, so did Isiah Thomas (born 1961). I don’t know who has the most points in his first 7 starts, but Shaquille O’Neal had 187 points in his first 7 starts.

  122. There is an error in the first picture. Lin had 172 points in his first 7 starts, not 229 points.

    So Lin holds the records for the most points by any player in his first 3, 4, and 5 career starts since 1967, but not in first 7 starts.

  123. Oh, found another error! Lin had 62 assists in his first 7 starts, not 86 assists.
    I don’t know why ABC or ESPN would make such errors.

  124. hmmm thanks point it out…need to check the actual game logs, then

  125. No Matter How Much They Make, The Best Players In The NBA Are Vastly Underpaid

    NBA players do not get paid what theyโ€™re actually worth. Really young? Really good? Sorry โ€” for you, the market isnโ€™t truly an open one. Analysts โ€” and increasingly players such as Durant and Bryant โ€” can tell you that the maximum-contract rule suppresses the salaries of superstars, that inexperienced players are paid less than their contributions warrant, and that as a result, the NBAโ€™s middle class is paid far too much.

    In recent years, however, that popular notion appears to have been wrong. I built a model measuring how much NBA teams paid for their playersโ€™ wins above replacement (WAR), and it shows that the league has changed. During the 2014-15 season, the middle class was, in fact, paid far less for its production than max-contract players, accelerating a trend that began two seasons earlier. In other words, the role players were suddenly steals.


  126. To help model this and other matters of NBA interest, FiveThirtyEight editor-in-chief Nate Silver and I have been skunkworking a little model around here that will (theoretically, hopefully, god-willing) begin to do for basketball what PECOTA did for baseball. Using a playerโ€™s advanced metrics4 and his statistical tendencies, it can project a playerโ€™s development into the future by comparing him to similar players from the past. We call it CARMELO.5

    Using the beta version of CARMELO to analyze this summerโ€™s free-agent signings, I found that 10 of the 16 maximum-contract signees (as of July 12) project to bring a team a positive return on investment.6 As a group, it looks like theyโ€™ll be underpaid by an average of $5.6 million per year. (Leonard lords above them all: Heโ€™s projected to bring $26.9 million of extra value to the Spurs every year.7) Thatโ€™s quite a bit bigger than the average non-max signee of the summer, who thus far projects to bring his team just $850,000 of extra value per season.

    PS…since the posted below was in a build in table, I had extracted to Excel in order to post

  127. No problem! I checked before I posted it.
    In Lin’s first 7 starts, he has 172 points and 62 assists. I don’t know why ABC listed him 229 pts 86 asst in that comparison with other notable point guards.

  128. Thanks

  129. No problem, thanks for the nice comments Maknusia.

  130. @TTNN #AT THE HIVE many positive feedbacks, one huge comment from one Hornets fan:

    this could be a top 3 post at ATH the last couples years I have been here.
    by Championships on Aug 8, 2015 | 4:21 PM up reply

    The in-depth analysis and contents are way better than 99% of the sports articles floating around.

  131. Great analysis๏ผ๏ผ full of insights

  132. indeed

  133. My most memorable Jeremy Lin game https://jhaposts.wordpress.com… . Hope you all enjoy ๐Ÿ˜€

  134. wow, totally did not realize I have my own post. ๐Ÿ™‚ Thanks mod!

  135. answer question to Wu Kong about the first graph, why use per game number instead of per 36 min. I think per game number also gives indication about how important the player is to the team, which is reflected by playing time, the more touches per game, the more important this player is to the team, and also the longer they stay on court. If it is normalized to per 36 min, you get rid of that factor/information there, and they the non-rotation player or bench player will be crowded together with starters and main contributors.

  136. Rightfully deserved…nice work…really

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