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Sky Kalkman 

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All of the stats about your 32 prediction contest ballots.

This year’s 32 Predictions contest had almost the exactly the same number of entries as last year’s, with 686. Forty-nine entries left at least one question blank, and 11 of you decided not to name your entry. (At least we can never make fun of you for it.)

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March 31, 2014 6:00 am

32 Predictions Contest

12

Sky Kalkman

Put your predictive powers to the test.

This is the sixth year I’ve run this contest, and the second at Baseball Prospectus. (You can find the questions and results from the 2013 contest here.) I stole the idea from my college differential equations professor, Doug Drinen of Pro-Football-Reference, and heck, I might as well steal his directions, too (translated into baseball-speak):

Below you will find 32 pairs of numbers. In each case, you tell me which number will be bigger. One point for each correct answer. Most points wins.

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How good were BP readers at predicting events last season?

The number have finally been crunched on the 2013 32 Predictions contest. Five entrants tied with 26 points, but with the earliest entry, Vitaly Vinar wins the tie-breaker. Congrats! Full results can be found here.

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All the stats about the 32 prediction contest ballots.

A total of 694 people entered the 32 Predictions contest, including Ben Lindbergh and me, but not including the nine of you who didn't leave a name and the 10 of you who entered after the deadline. Those weren't good strategies for winning.

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March 29, 2013 5:00 am

32 Predictions Contest

16

Sky Kalkman

Put your predictive powers to the test.

This is the fifth year I’ve run this contest, but the first here at Baseball Prospectus. I stole the format from an old differential equations professor of mine, Doug Drinen of Pro-Football-Reference, and heck, I might as well steal his directions, too (translated into baseball-speak):

Read the full article...

Updated depth charts plus the basics of the in-season plan.

The Depth Charts have been updated through Jerry Sands' opening night. While my favorite change was swapping the second and sixth spots in the Braves batting order, the most interesting one was adjusting the Rangers projections for Josh Hamilton's injury. It was interesting because our final pre-season projection already took into account the likelihood that Hamilton would miss time to injuries sometime in 2011. We had him at 549 plate appearances, or about 75 percent of a 162-game season, batting third in the Rangers high-octane lineup. Therefore, he was only docked about five weeks worth of playing time for this injury, even though he'll miss six to eight. In other words, we were already expecting him to miss part of the next two months.

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Another massive round of changes to the Baseball Prospectus Depth chart. Don't forget about our tips hotline: dc@baseballprospectus.com.

We've made some smaller changes since the last big update, but I'm here to announce another round of mass changes. You can find the most significant of them below.

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A listing of recent depth chart playing time changes, plus we've added an email address dedicated to depth chart-related news. As news rolls out of spring training, send us your tips, including a link.

I've volunteered to give Marc a hand with the Depth Charts. After surfing through the comments for all thirty teams, we've made the changes listed at the end of this post. But we also have an announcement:

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Sky lays out what he's looking for in a Scoresheet trade. Hint: you can keep your pitching prospects.

An open letter to my fellow BP Kings Scoresheet league members:

Hi, I'm the new guy—well, one of the new guys. Many of you are quite aware of that fact, having offered some, ahem, creative trade proposals over the last few weeks. Rather than take offense, I'll  assume we just have philosophical differences. (That's certainly the case for fellow noob Paul Swydan.) To help open the lines of communication, here's some friendly advice for offering me trades.

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Building on Matt Swartz' recent analysis of ERA estimators, we look to see which are more accurate when there's little historical information to work with.

Last week Matt Swartz published an updated analysis of ERA estimators. He was kind enough to share his data so I could take a look at the accuracy of ERA estimators as a function of innings pitched. In other words, is there a difference in accuracy between the estimators given 100 historical innings pitched versus 500?  (Hint: yes.)

We can measure a lot of things that happen while a pitcher is on the mound, but it takes a while for the real information to show itself. As we collect more data, the random noise is more likely it is to cancel itself out. For example, during any one season you'll see a lot of .270 BABIPs, but once we look at careers over five-season stretches, .270 BABIPs are few and far between.

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