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"Muthuball": How To Build an NBA Championship Team 94

First time accepted submitter Quillem writes "Muthu Alagappan, a 5'9" biomechanical engineering undergraduate at Stanford, made a presentation at this year's MIT Sloan Sports Analytics Conference which might well do to basketball what Moneyball did to baseball. His contribution revolves around a topographical analysis of NBA games which contends that there are really 13 positions in basketball — not just five. Besides a rather patronising — but informative — read in Gentlemen's Quarterly, there are earlier stories over at Wired and NYT blogs. Muthu's talk and slides are also available."
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"Muthuball": How To Build an NBA Championship Team

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  • by Hermanas ( 1665329 ) on Saturday June 16, 2012 @02:14AM (#40342601)

    a 5'9" biomechanical engineering undergraduate

    I, for one, welcome our new 5'9" cyborg overlords.

    • by Anonymous Coward

      Is this like a biomechanical Engineer (ie, Space Jockey?)

    • I, for one, welcome our new 5'9" cyborg overlords.

      For some of us, 5'9" is an underlord.

  • by evangellydonut ( 203778 ) on Saturday June 16, 2012 @02:21AM (#40342623)

    The statistics currently being tracked is more offense focused. Bad Boys of Detroit, the Bulls, and the Spurs had solid defense that helped them win but not necessarily show up in statistics unless you do a game-to-game analysis of the opponent's average offense performance vs performance against a specific team.

    Other than that, it's a pretty interesting thought/analysis... Just incomplete... but I'm sure someone can do a much more complete PhD thesis on this and get funded by some NBA team :-P

    • Blocked shots, defensive rebounds, steals...however I have my doubts basketball will ever get sabermatic. Single players dominate too much, lineups are smaller, and playing strategies are less strategic.

  • Patronising? (Score:5, Informative)

    by Anonymous Coward on Saturday June 16, 2012 @02:41AM (#40342693)

    How is the GQ article "patronising" - because the opening summary says, "A Stanford undergrad's new super-nerd study"? That's the only thing I see that could be remotely considered patronizing. And frankly, this *is* a "super-nerd" study - how is a statistical analysis of NBA players NOT super nerdy?

    Can we change the Slashdot motto to "butthurt editorializing for nerds," instead of "news for nerds?" The "news" part implies a factual focus, and the summaries are increasingly flamebait of the first order.

  • Emulating a brad pit movie in order to advance the profitability of a stupid game that already rakes in tens of millions per team.

    Great work, humanity will place you among the legends

  • Imagine, a 5'9" oneno teaching all the short guys how to beat the tall guys. One of the short guys tells his friend, Jemima's baby tall brother Jenkins, all about it. Jenkins, who is 6'7" know knows all the shit the 5'9" oneno knew, and beats him big time.

  • As Ethan "Bubblegum" Tate will have already remarked someday, whilst pondering the time dilation effects of Basketball wherein time passes progressively slower as less time is left "on the clock":

    I thought you knew that algebra was all razzamatazz

  • What the hell does his height have to do with anything?

  • Duh, 5 players on a team (X2) and 3 officials do add up to 13.
  • by excelsior_gr ( 969383 ) on Saturday June 16, 2012 @08:39AM (#40343605)

    You should all hand over your geek cards at the front desk, if you ever were in position of one.

    You have a geek making a presentation about an idea on how to bring together an optimum team of items depending on their statistical profiles, and you argue about how interesting basketball/baseball is? I have never witnessed people miss the point all at once that badly ever before in my life...

    Here, I will boil it down for you:
    1. Gather statistical data on the items of which you want to build a new group of.
    2. Do some data-mining and graphing to figure out how these items cluster. Do not predefine clusters, but let them surface themselves.
    3. Depending on a free, non-mapped variable (e.g. cost) make an optimum choice of individuals from each group. Alternatively, base your choice on a given pattern that you want to match or counter-act (e.g. the opposing team).
    4. Profit!
    5. Gather new data and update your graphs to keep up with times.

    How about starting to come up with ideas on how to apply this concept to physics, medicine, engineering and economics? Jeez...

    • by garcia ( 6573 )

      The problem is that, like Moneyball showed, it will only be an advantage as long as only a few teams (and not a majority) are using the methodologies. Once that happens, it's just the new norm.

      • Actually it is worse than that because of what Moneyball did not show. The movie Moneyball completely ignored the role played in the success of the team it followed by players who were stars by traditional baseball standards. In addition, the team in Moneyball was not all that successful, it never won a championship using the Moneyball techniques. I suspect that this new analysis will be somewhat similar. It will add a few new wrinkles to the process that teams use to evaluate talent and choose players. In
        • Re: (Score:2, Informative)

          by Anonymous Coward

          What do you mean the team was not that successful?

          2000 91 70 .565 1st in AL West Lost ALDS to New York Yankees, 2–3.
          2001 102 60 .630 2nd in AL West Lost ALDS to New York Yankees, 2–3.
          2002 103 59 .636 1st in AL West Lost ALDS to Minnesota Twins, 2–3.
          2003 96 66 .593 1st in AL West Lost ALDS to Boston Red Sox, 2–3.
          2004 91 71 .562 2nd in AL West
          2005 88 74 .543 2nd in AL West
          2006 93 69 .574

          • Re: (Score:2, Interesting)

            by Anonymous Coward

            And they did it for a fraction of the price of the Yankee's team. To use the Slashdot mandated car analogy, this is the same thing as racing against a 911 with a chevy accent and almost winning. Sure, you lost, but you didn't have to spend nearly as much money as your opponent to compete.

            The system employed in Moneyball were great for team owners and front offices. They've been HELL for fans and players though.

            Traditionally, better players were kept around and you only traded marginal players. Now thoug

    • When you show how this is anything at all new, and also applicable to medicine and other fields, I'll get excited. But it's not, so I won't hold my breath. Also, PLAYING sports is great. WATCHING sports is for paraplegics, fat people, and/or parents who have kids in the game.
    • by gknoy ( 899301 )

      Exactly. I really don't give a fig about basketball, likely as I don't understand it, and I found this talk (and slides) fascinating! I thought it was interesting to see how he could correlate players similarity by statistics, and then use that to expose cheaper versions of awesome players, or to show how your team is missing a certain skillset.

      Now if only I knew what a paint protector was and why it's important. :)

    • How about starting to come up with ideas on how to apply this concept to physics, medicine, engineering and economics? Jeez...

      How about RTFA? He was presenting on behalf of Ayasdi, a company run by Stanford mathematicians, whose proprietary software is used by physicians, environmentalists, and the government to understand cancer, diabetes, and oil spills.

      • I RTFA. I also watched the presentation where he makes a similar mention. But he does not give any examples. The only example that we have is the one for basketball. So how does this work in other fields? Just to mention that it is "used by x to understand y" is not enough!

        • That's what their company does, the primary uses of their software is in exactly the fields you mentioned. He simply applied it to BB as his pet project.

  • i play basketball on a rec league team and sometimes like to analyze our stats. does anyone know if there's a way to access these tools publicly? it would be interesting to see what kind of player we each are and then see how to complement our existing team. we know that we have some imbalances, but it would be cool to see if the tool picks out the same issues that we understand more intuitively.
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