So what might sports teach higher education about data mining?In academe the stakes are higher than in baseball, but progress toward making good use of data has been uneven. Nonetheless, colleges are busymining students’ data trailsto build software that does things like suggest what mathematics problems they should work on or even what classes they should take.
During a panel on Wednesday about the cautionary side of Big Data, colleges got some insight from Steve Hirdt, a 45-year sports-data veteran who is executive vice president at theElias Sports Bureau,the official statistician to the major North American professional sports leagues. Elias records game statistics—hits in baseball, yards gained in football, points scored in basketball, etc.—and supplies data to teams and news-media clients. When you watch Monday Night Football, Mr. Hirdt is the guy off camera feeding the announcer facts like “Seattle 135 yards: fewest for a winning team in the NFL in the last three years."
First off, what you initially find in a given data set may turn out to be flat-out wrong upon closer scrutiny.
“A wrong conclusion from a cursory look—to me that’s the real cautionary side of Big Data,” Mr. Hirdt said. “If Big Data is going to amplify the possibilities for misapplication, as well as the possibilities for application, we might be in for a little bit of a rocky road.”
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