Data Analytics: The Future of Legal
Abstract
During the 2002 season of Major League Baseball (MLB), the Oakland Athletics and New York Yankees both won an astounding 103 games, a feat only topped three times by any team in the last 24 years. Despite identical records, the New York Yankees payroll on opening day was $125,928,583 compared to only $39,679,746 for the Oakland Athletics. On a per win basis, the Oakland Athletics paid $385,240 per win compared to the New York Yankee’s $1,222,608. The New York Yankees were not an outlier. All other MLB teams (excluding Oakland) paid on average $853,252 per win in 2002. So how were the Oakland Athletics able to generate wins with over two to three times the cost efficiency of other teams in the league? By now, the answer has been well documented by Michael Lewis in the book and subsequent movie Moneyball, in which Lewis recounts how Oakland Athletics’ General Manager Billy Beane replaced traditional approaches to scouting and roster management with a rigorous use of data and statistical models. This is often referred to as “data analytics.”
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