Can Big Data Predict Your Future Stars?

10 comments

Within one week, Jeremy Lin, has gone from a relative unknown to a global sensation. In the same week, he went from near unemployment to a sporting sensation now referred as “Linsanity“.

Interestingly, about 1 ½ years ago, the Hoops Analyst blog nearly predicted his imminent success. When analyzing his college performance, particularly 2-point field goal percentage and his RSB40 (a combined score of rebounds, steals and blocks per 40 minutes) he ranked among the elite. These numbers are important as they highlight the potential for future performance and “show NBA athleticism better than any other, because a high score in both shows dominance at the college level on both ends of the court.”

When circumstances change, in this case a promotion to the NBA, opportunity and situation play an important factor that is hard to measure. Additionally, one week is yet statistically significant and time will tell if his performance can be sustained over time. Nonetheless, those that analyzed his past performance shouldn’t be surprised by his rising star.

That begs the question – why aren’t most companies analyzing their employee data to find the rising stars. One could argue that Jeremy Lin’s heritage (Chinese) and experience (Harvard) didn’t necessary sound the bell in an industry where pedigree and success looks very different. Every company must have many Jeremy Lin’s running around their offices. The answer, though, is most won’t be found (and likely lost) until performance focuses on the outcome instead of the process.

Tagged as: cornerstone ondemand, Jeremy Lin, linsanity, NBA, Talent Management

  • Iammanagerx

    It’s interesting that no one is talking about how his previous employer, and his current manager and team executives, failed to provide him with any expanded opportunity up until this week – and then it happened by chance.  Enough talent to get hired but not enough to proactively get a real chance by anyone in management that thinks they know what they are doing…

  • Kevin

     This is a very meaty post.

    Linsanity has certainly taken the pro basketball world by storm this week, but we really don’t know as of right now whether his high level of performance can be sustained. One of the things that is going to happen is that NBA coaches that play against the Knicks now have film on him, and they’ll make adjustments to defend him. If they make adjustments and they are successful, Lin could be a short term flash in the pan.

    In sports, baseball has been known for being more data oriented than basketball. Baseball measures everything and I think it is a more data measurement friendly sport because it is a little easier to isolate individual performance.

    In terms of businesses using data, I’m a proponent of it. I have experience with a program called WorkMeter to evaluate employee performance. With proper data, you’ve got the ability to develop the right insights to beneficially measure and improve performance. Without data, you can’t get to the right insights.

    There was data out there on Lin, it just wasn’t properly interpreted or implemented.

  • Anonymous

    This is a very important point, Jason, and I’m glad to see you highlighting it. I tend to think, however, there isn’t enough performance focus on the outcome OR the process. The essence of the challenge is simply not enough data points. I don’t need to hash through (again) the failures of the annual performance review as a one-time feedback from one point of view.

    The question becomes, how do you build the data on your talent so you can find your Jeremey Lins? WHat’s the RSB40 score for your talent?

    Of course, I come down on the side of strategic employee recognition in which all employees are encouraged to praise their colleagues in very specific and detailed ways for their contributions and successes. This gives a much broader picture of your talent on both an individual and aggregate basis.

    Do you need your most innovative product designers for huge new product development push? Simply parse the data to find which product designers – anywhere in your organization – have been recognized the most for innovation. If you structure your program properly for what matters most in your organization, you can turn your recognition data into your own form of “moneyball” for your hidden talent.

  • http://twitter.com/EvolvOnDemand Evolv On-Demand

    It’s not just the NBA that has a problem with this. The NFL scouting combine turns out to be completely terrible at predicting who will make a great football player.  And, Moneyball aside, baseball has a similar issue. The Israeli military developed a complex methodology for spotting officer material, and then that turned out to also be completely non predictive. 

    (http://www.evolvondemand.com/blog/2011/11/03/the-israeli-army-predicting-performance-and-confidence-vs-data-2/)

    Big data can absolutely be used to predict performance.  But there is tremendous resistance to using this approach.

    Why? Several reasons. 

    First, people are bought into the process they’ve been supporting.  They don’t want their judgement to be replaced by some algorithm.

    Second.  It can be really hard to get the right data.  It’s a huge challenge.   Then doing the math to get the scoring right is very complex stuff.

    Finally, and this is the worst part: the people who are doing the hiring are generally measured on time to fill and cost per fill. Because quality of hire is so hard to measure, recruiters are never measured on it.  So they don’t need to do it. 

    Anyways, I’ve been blogging on this one too. 

    http://www.evolvondemand.com/blog/2012/02/15/great-article-on-selecting-talent/

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  • http://basissap.com martin_english

    It doesn’t have to be ‘big data’, it just has to be the RIGHT data; After all, the numbers were there all along :) I did notice that http://hoopsanalyst.com/blog/?p=487 didn’t give us stats on the rest of the 2010 draft field (even though it appears that Lin was the best by the numbers).  Apart from that, there are some interesting analogies with Moneyball (and business) -
    ** the important numbers are not measured (or are ignored)
    ** the wrong numbers are measured anyway,
    ** ‘gut instinct’ is based on irrelevancies (like race and experience)
    ** Once you optimise for the first three conditions, the numbers don’t lie.

    thanks

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  • Anonymous

    It seems that the most probable reasons for failing to notice talents(like Lin for example) are a lack of proper internal communication and inappropriate employee management which can’t produce any real data about employee performance,dedication or productivity at work.
    That all can lead to miss-judgment about available workforce and their abilities 

  • http://www.kaptasystems.com/ Kapta

    The world revolves around numbers and they very rarely lie. Big data can very often provide glimpses to what is to come.

  • http://twitter.com/TopherJRyan Christopher Ryan

    This is a wonderful adaptation of Pop culture to the business arena. Great content! Metrics will be different depending on the organization and goals, but: does anyone have any business insight to add to this discussion?

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