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A Scientific View of the Game

baseball“Baseball thinking is medieval. It’s stuck in the Dark Ages. I have a more scientific view of the game,”  says character Peter Brand in the 2011 film, Moneyball. Based on a book by the same name, which is inspired by a true story, the film’s premise is that data mining helped the Oakland Athletics Major League Baseball team craft a winning team based on predictive analytics for each player’s performance. A perfect model for a small business with a limited budget, the story describes a complex analysis of a vast store of statistical data which allows the owners to select only players who will provide the highest return on investment.

This tale is a wonderful example of how data means nothing unless one is able to deduce meaningful connections and draw helpful information from it. The protagonist in our film example, for instance, recognizes that players with less experience are predictably less expensive, but, based on reliable statistical data, can be expected to play just as well as more experienced players. Further, one must determine which pieces of data are informative and which are misleading; in our example, RBI (runs batted in) totals, prized by most general managers, were deemed deceptive since, in order to have high RBI stats, other players must have first arrived on base ahead of the given player. Therefore, the most influential statistics in showing a player’s ability to score runs were decidedly on-base and slugging percentages. Such careful extraction of what the data is truly telling the individual is the heart of effective data mining.

As the story goes, in 2002, this method of careful deduction helped the Oakland Athletics to finish first in the American League West with an unexpected record of 103 wins and 59 losses for such a low-budget team. This exceptionally chosen group also set an American League record of winning 20 consecutive games.

As with any case of data mining, however, how the data is considered and compared with other elements determines its true value. “Statistics themselves can be very misleading,” writes Osama K. Solieman, in a book called Sports Data Mining, “Certain players are able to build impressive stats but have little effect on a game. On the other hand, there are players who make a significant impact on the game without having impressive statistics.” These statistics and the perception thereof has a strong effect on how players are rewarded for their efforts on payday, giving them great incentive to make plays which may affect their teams less and their personal statistics more. In this truly rich read from Spider Magazine, writer Salman Haqqi outlines how data mining influences a variety sports, including sports media attention and the multi-million dollar business thereof.

As Haqqi sums this lesson up, “Data and analytics [is] helping athletes and sports organisations improve their performance and take sports to new levels… The scary part of this field of analytics is that it is still in its infancy… Its usage in sports, with its neatly defined parameters, has been a perfect place to realise its practical potential [for use within other fields]. And time will reveal the endless possibilities it holds for the future.”

If you’re interested in learning more about how Data Mining can help you unlock hidden information in your own field, consider joining The Modeling Agency for a free webinar. If you’re ready to master this valuable tool more fully, perhaps one of TMA’s training seminars is for you!

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