What Can Jeremy Lin Teach Technologists?
He’s Lintastic. He has New York City going Linmanic. Jeremy Lin is a Harvard-groomed point guard, and two weeks ago he schooled Kobe Bryant. He’s exciting, unpredictable, and the ultimate example of how sports and data intersect. As such, the New York Knicks rookie point guard has a few lessons to teach the digital marketing business.
Conventional sports management (Golden State Warriors) cut Lin because they didn’t see the value in him. They were freeing up cash for a free agent. Very much like the old school coaches featured in the movie “Moneyball,” they made decisions from convention, not data.
Jeremy Lin’s got data. He was the first player in Ivy League history to record at least 1,450 points (1,483), 450 rebounds (487), 400 assists (406) and 200 steals (225). But just like old school know-it-alls, the Warriors knew it all. According to their conventions, Jeremy Lin wasn’t worth keeping.
Failure to pay attention to data: Big mistake.
Pat Riley is the sixth winningest coach in NBA history. He was a master of big data. If you read his book, “The Winner Within,” you’ll see that he collected stats on his players and players on opposing teams in similar positions, measuring performance versus opposing peers. Riley also tracked year-over-year individual improvement in 15 different areas. If a player grew 1% in five areas, the team collectively grew by a 60% increment. Data.
In Facebook’s S-1 filing, Mark Zuckerberg used the phrase “code wins arguments.” Facebook engineers aren’t rewarded by their ability to manage up or manage across; they are measured by the efficiency of their code. Performance metrics win arguments: like Riley, like Lin, like Zuckerberg.
But there’s also an art to improving metrics by motivating players, motivating engineers, motivating product teams to play on a higher level. And perhaps this is why Phil Jackson is the #1 winningest coach in NBA history (70.5% based on at least 500 games). Because Phil Jackson got more from his players -- he used data well, but it wasn’t everything.
Data won’t tell you everything. When Billy Beane of the Oakland As (who pioneered data driven management) recruited Scott Hatteberg, he didn’t rely fully on data. Hatteberg was a big hitter released by the Boston Red Sox because he ruptured his elbow and couldn’t throw a baseball. Not a good thing.
So Beane recruited him as a first baseman -- a position where he rarely had to throw a ball. It worked. Beane got great value (on-base average points vs. salary paid). But a model didn’t figure it out using data alone; Billy Beane did.
So as valuable as data is (the Golden State Warriors shouldn’t have ignored Jeremy Lin’s Ivy League records), there’s also the art of knowing how to leverage even better performance. Data is science, art and foresight.
So in the world of advertising technology, a world chock-full of data, who are the Jeremy Lins and Scott Hattebergs? Who are those companies below the surface but ahead of the curve in critically important data like viewable impressions (ads seen by consumers for at least 1 second per the impending IAB, ANA, and 4A’s standard)? Based on viewable impressions, we see five:
1. Acerno (Akamai)
5. Google Display
Ahead of the curve, for now.
But this can change if the Phil Jacksons of the world respond to viewable impression metrics (an important statistic in display media), and bring out even more from systems and technology to perform on a higher level, as data gets more visible and actionable.
Jeremy Lin felt tremendous pressure during Warriors home games, since Palo Alto was his home town and expectations were high (he played better on the road). But no coach figured out how to release that pressure. Perhaps the art of Phil Jackson mixed with the science of proven data could have worked.
At the Knicks, though, something elicited the underlying performance from Jeremy Lin that was always there with his Ivy League record: perhaps a coach, perhaps Lin himself, perhaps a teammate, or perhaps faith. In the world of data, it’s called a multichannel attribution model. It’s a team of influences -- hardly ever a single influence.
Data is powerful when captured and interpreted correctly, but data can’t do everything. People still need to unleash data’s power, and people have to visualize unleashing it. Then it becomes believable, then it becomes real. Welcome to the real Jeremy Lin.