Sunday data/statistics link roundup (2/10/2013)

  1. An article about how NBA teams have installed cameras that allow their analysts to collect information on every movement/pass/play that is performed in a game. I think the most interesting part for me would be how you would define features. They talk about, for example, how many times a  player drives. I wonder if they have an intern in the basement manually annotating those features or if they are using automatic detection algorithms (via Marginal Revolution).
  2. Our friend Florian jumps into the MIC debate. I haven't followed the debate very closely, but I agree with Florian that if a theory paper  is published in a top journal, later falling back on heuristics and hand waving seems somewhat unsatisfying.
  3. An opinion piece pushing the Journal of Negative Results in Biomedicine. If you can't get your negative result in there, think about our P > 0.05 journal :-).
  4. This has nothing to do with statistics/data but is a bit of nerd greatness. Run these commands from a terminal: traceroute
  5. A data visualization describing the effectiveness of each state's election administrations. I think that it is a really cool idea, although I'm not sure I understand the index. A couple of related plots are this one that shows distance to polling place versus election day turnout and this one that shows the same thing for early voting. It's pretty interesting how dramatically different the plots are.
  6. Postdoc Sherri Rose writes about big data and junior statisticians at Stattrak. My favorite quote: " We need to take the time to understand the science behind our projects before applying and developing new methods. The importance of defining our research questions will not change as methods progress and technology advances".
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  • Colin

    I think you forgot to add a link to the first story. Can you add it?


    • jtleek

      Done, thanks for pointing out the missing link!

  • Kevin

    Regarding the NBA teams installing cameras, I imagine they have automatic detection algorithms given the turnaround time. I used to work for one of their competitors (synergy) and they paid people to annotate, like you said, that information. You'd have to code specific instructions for each play, depending on what happened on the court. I imagine they're identifying player positioning on the court and figuring out what sort of plays are being run based on that (pick and roll variants, isolations, post ups, fast breaks, cuts, etc).