If you have been within range of ESPN, the New York media, or a NY sports fan over the last 10 days, you have undoubtably heard of Jeremy Lin, the Harvard graduate who has catapulted off the Knicks' bench into Super Lintendo stardom.
While my Boston area loyalties prohibit any Knicks' bandwagoning, I too love the story of a Harvard athlete breaking into the national consciousness.
For the vast majority, Lin's breakthrough is a complete surprise. However, for numbers hobbyist Ed Weiland, Lin's breakthrough was merely a matter of time. As described in a WSJ article:
"In May 2010, an unsung numbers hobbyist named Ed Weiland wrote a long-term forecast of Jeremy Lin for the basketball website Hoops Analyst. At the time, Lin was a lightly regarded, semi-known point guard who had completed his final season at Harvard. But Weiland saw NBA material.
He emphasized how well Lin played in three nonconference games against big schools: Connecticut, Boston College and Georgetown. He noted how Lin's performance in two unsexy statistical categories—two-point field-goal percentage (a barometer of inside scoring ability) and RSB40 (rebounds, steals and blocks per 40 minutes) compared favorably with college numbers put up by marquee NBA guards like Allen Iverson and Gary Payton.
Weiland concluded that Lin had to improve on his passing and leadership at the point, but argued that if he did, "Jeremy Lin is a good enough player to start in the NBA and possibly star.""
Further in the WSJ article, Weiland's daughter is quoted:
"As long as I remember, he's had spiral notebooks full of numbers," ... "He had so much random knowledge of players and teams."
What I find interesting in both excerpts is the emphasis on data collection, and particularly the use of outlier data points (two-point field goal percentage, RSB40). As I shared last week:
"Finally, big data scientists touch data in more ways. They are twice as likely as those working with normal data to work across the data life cycle, everywhere from acquiring new data to business decision making..."
And of course, I love that this story supports my data scientists are hiding in plain sight theory. Need a data scientist? Check out the fantasy sports forums.
