Time-warping: How can it help predict baseball?

December 2, 2009
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A couple of months ago, I was talking to Anne Milley, director of analytical intelligence strategy at SAS. She was telling me about time-warping. That’s a method for assessing greater significance to events that happen in certain times.

The most common is to give more weight to the most recent events. The book I looked for yesterday is probably more a predictor of my interest tomorrow than one I searched for in 2004. But how much more relevant is it? Statisticans can study patterns across large populations and come up with time-warping formulas. I would imagine that they vary from sector to sector. A three-year-old search for hospice treatment probably has close to zero predictive power at this point. But if you were looking for Bob Dylan songs back then, you’re probably still interested.

This type of analysis is going to become ever more pervasive as we generate more time-stamped data with our smart phones. Of course, the trick then will be to warp for both time and place. The variations are endless.

Adrian Beltre

I would imagine that Nate Silver, the baseball and political statistician I interviewed last spring at South by SouthWest, has sophisticated time-warping



A couple of months ago, I was talking to Anne Milley, director of analytical intelligence strategy at SAS. She was telling me about time-warping. That’s a method for assessing greater significance to events that happen in certain times.

The most common is to give more weight to the most recent events. The book I looked for yesterday is probably more a predictor of my interest tomorrow than one I searched for in 2004. But how much more relevant is it? Statisticans can study patterns across large populations and come up with time-warping formulas. I would imagine that they vary from sector to sector. A three-year-old search for hospice treatment probably has close to zero predictive power at this point. But if you were looking for Bob Dylan songs back then, you’re probably still interested.

This type of analysis is going to become ever more pervasive as we generate more time-stamped data with our smart phones. Of course, the trick then will be to warp for both time and place. The variations are endless.

Adrian Beltre

I would imagine that Nate Silver, the baseball and political
statistician I interviewed last spring at South by SouthWest, has
sophisticated time-warping models for baseball players. Since the Phillies are in the market for a third baseman, I’ve been thinking recently about Adrian Beltre, who had one great year at the hot corner for the Dodgers. As a
25-year-old, he hit 48 home runs in 2004 — but hasn’t hit more than 26
in a season since then. I would think that time-warping would almost
discount that one season as a near meaningless blip. Now that I think
about it, there’s a chance it’s not meaningless at all: After 2004, baseball
started testing much more vigorously for steroids.

That raises another challenge for statisticians: Drug warp.

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