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Latest insights and trends in Personal Analytics, Operational Intelligence, and Workplace Productivity.

Jun 21
Sports Analytics 10 Years After Moneyball
Brad Null, Chief Data Scientist at, told a packed house at the recent San Francisco Data Mining Meetup that he was inspired by the popular 2004 business book Moneyball: The Art of Winning an Unfair Game, which dramatized how the Oakland Athletics fielded a competitive, low-budget professional baseball team using Big Data analytics, to get started in sports analytics and to pursue his Ph.D. degree in Systems Engineering and Management Science from Stanford University, where his thesis focused on Stochastic Modeling and Optimization in Baseball.  Also known as a founder of, an analytical engine for evaluating post-season college basketball play, Null’s hour-long talk was filled with a series of insights that went beyond how teams are put together (the primary focus of the Moneyball book) to show how teams can use his analysis based on transitional probability to optimize team performance, player performance and real-time game decision-making.
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For example, Null showed his model could make specific predictions about whether it makes sense to walk (or to pitch to) dominating baseball hitters like Barry Bonds or the advisability of bunting in different situations.  Null conclusion about bunting, where a hitter will tap a pitched ball into fair territory rather than hitting it squarely, is that it is an underused offensive technique that teams should consider doing more of.  Likewise, his model advises that football coaches should "go for it" more on fourth down, rather than following the standard practice of punting to the opposing team.  

Null then demonstrated his method could model Major League Baseball (MLB) player abilities and offer predictions for each of following fourteen outcomes whenever a specific hitter (excluding pitchers) made a typical plate appearance:
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  1. home run, 
  2. ground out, double play with runner on first base, 
  3. runner reaches base on error, 
  4. fly out, all runners advance, 
  5. triple, 
  6. ground out, all runners advance, 
  7. single, runners advance one base, 
  8. fly out, runner on third base scores, others hold, 
  9. walk (base on balls), 
  10. strikeout, 
  11. double, 
  12. ground out, runners advance if forced, 
  13. single,runners advance two bases, and 
  14. fly out, runners hold their bases

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Other Uses Besides Sports
Null also briefly discussed how his approach for sports analytics can be used to design and implement forecasting and optimization platforms in the areas of advertising, marketing, finance and operations—although the overwhelming majority of questions Null fielded from the 200 or so sports lovers in the audience were sports-related, many of which dealt with Null’s extension to his model that he claims takes into account age effects and year-to-year variance in a player's underlying abilities.

How OmniContext™ Personal Analytics Can Help You Stay Active

Recent Hypersoft blog posts have shown how the analytical revolution is playing an ever-increasing role in both baseball and basketball, where star NBA athlete Kevin Durant admitted that he’s integrating Personal Analytics into his work habits.  But you don’t need to employ your own numerical expert if you want to live a more dynamic lifestyle.   OmniContext™ Personal Analytics works by automatically and discreetly collecting and analyzing real-time PC data about what applications, websites, and documents you’re spending time with, in addition to tracking your mobility using GPS and accelerometer measurements.  As a result, you’re able to track and improve your personal goals like becoming more active and not wasting time on a couch watching professional athletes have all the fun.  Begin reshaping your future now by downloading OmniContext™ Personal Analytics.



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