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If everyone on the Astros played to their 90th-percentile projections, and everyone on the Angels played to their 10th-percentile projections, which would win more games?

Last year around this time I had plans to compare the Astros’ teamwide PECOTA projections to those of a variety of lower-level squads: the best Triple-A roster, the best Double-A roster, an All-Star High-A team, etc. I didn’t get to it, and then the season started, and I still didn’t get to it, because the Astros started off hot and it would have been weird to have run that piece about a team that was 22-23 in mid-May. I was sort of glad I didn’t run it, because the longer I lived with the idea the more it started to feel mean.

So this year, I have a similar idea, and I’m rushing it out before the guilt kicks in. Again I’m going to be exploring just how bad the worst team in baseball is. Or just how good the worst team in baseball is. That’s the point of it, after all. It’s not to prove that the Astros are as bad as, say, a team of High-A All-Stars. It’s to see if the Astros are as bad as a team of High-A All-Stars, and if they’re significantly better (as I suspect they would have been), then we’ve learned a little something about baseball.

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Announcing the arrival of the 2013 PECOTA percentiles.

PECOTA percentiles are now available to subscribers.

Those of you new to BP, or to PECOTA, might wonder why we publish percentiles in addition to the weighted-mean projections for players, which we’ve already released. The answer is that forecasting is an inexact science; the future is not exactly what you'd call certain. The percentiles allow us to put a range of outcomes around a single-point forecast, to illustrate how uncertain the forecast is and what range of outcomes are most likely.

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The PECOTA cards are here, including 10-year projections and percentiles.

Without further ado, we present to you the PECOTA cards. Debuting on the cards are the 10-year forecasts and the percentile forecasts.

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How good are the PECOTA percentiles?

Let's talk percentiles.

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June 30, 2003 12:00 am

Estimating Pitch Counts


Ted Kury

Using a database of 30,000 starts from 1994 to 2000, BP corrspondent Ted Kury introduces a new model to estimate pitch counts per start from historical and minor league games.

Using data commonly available in newspaper box scores, e.g., innings pitched, hits, runs allowed, earned runs allowed, walks, and strikeouts, we can derive estimated pitch counts. In addition, we will look at how the designated hitter impacts pitch counts.

The Raw Data

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