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Articles Tagged Forecasting 

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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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Examining the accuracy of PECOTA's past, and talking about its future.

Yesterday, Dave Pease went ahead and talked about the process of generating the PECOTA forecasts in the past. Now I’m here to talk about how PECOTA has fared, and where PECOTA is headed.

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My name is Nate, and I am a forecaster. I forecast how baseball players are going to perform. And I pretty much get the worst of it. Tell somebody that their childhood hero is going to hit .220 next year, or that the dude they just traded away from their fantasy team is due for a breakout, and you're liable to get called all kinds of names. A bad prediction will inevitably be thrown in your face, (see also: Pena, Wily Mo) while a good one will be taken as self-evident, or worse still, lucky. The truth is, though, that those of us who make it our business to forecast the performance of baseball players have it pretty easy. For one thing, we've got an awesome set of data to work with; baseball statistics are almost as old as the game itself, and the records, for the most part, are remarkably accurate and complete. For another, it's easy to test our predictions against real, tangible results. If we tell you that Adam Dunn is going to have a huge season, and instead he's been demoted to Chattanooga after starting the year 2-for-53, the prediction is right there for everyone to see in all its manifest idiocy. Not so in many other fields, where the outcomes themselves are more subject to interpretation.

My name is Nate, and I am a forecaster. I forecast how baseball players are going to perform. And I pretty much get the worst of it. Tell somebody that their childhood hero is going to hit .220 next year, or that the dude they just traded away from their fantasy team is due for a breakout, and you're liable to get called all kinds of names. A bad prediction will inevitably be thrown in your face, (see also: Pena, Wily Mo) while a good one will be taken as self-evident, or worse still, lucky.

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January 16, 2004 12:00 am

PECOTA Takes on the Field

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Nate Silver

Last year at this time, when we were first unveiling PECOTA, I was besieged with questions about the system's accuracy. From the very start, the system has always had its believers and its skeptics; all of them wanted to know whether the damn thing worked. My evasive answers to these questions must surely have seemed like a transparent bit of spin doctoring. One of my readers suggested to me, quite seriously, that I had a future in PR or politics. But I was convinced--and remain convinced--that a forecasting system should not be judged by its results alone. The method, too, is important, and PECOTA's methodology is sound. It presents information in a way that other systems don't, explicitly providing an error range for each of its forecasts--which, importantly, can differ for different types of players (rookies, for example, have a larger forecast range than veterans). Its mechanism of using comparable players to generate its predictions is, I think, a highly intuitive way to go about forecasting. Besides, all of the BP guys seemed to appreciate the system, and getting the bunch of us to agree on much of anything is an accomplishment in and of itself. Now that it has a season under its belt, however, we can do the good and proper thing and compare PECOTA against its competition.

Last year at this time, when we were first unveiling PECOTA, I was besieged with questions about the system's accuracy. From the very start, the system has always had its believers and its skeptics; all of them wanted to know whether the damn thing worked.

My evasive answers to these questions must surely have seemed like a transparent bit of spin doctoring. One of my readers suggested to me, quite seriously, that I had a future in PR or politics. But I was convinced--and remain convinced--that a forecasting system should not be judged by its results alone. The method, too, is important, and PECOTA's methodology is sound. It presents information in a way that other systems don't, explicitly providing an error range for each of its forecasts--which, importantly, can differ for different types of players (rookies, for example, have a larger forecast range than veterans). Its mechanism of using comparable players to generate its predictions is, I think, a highly intuitive way to go about forecasting. Besides, all of the BP guys seemed to appreciate the system, and getting the bunch of us to agree on much of anything is an accomplishment in and of itself.

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Reasonable Person Standard

I got BP rolling many years ago in large part because of a forecasting system I had created called Vladimir. Vlad was basically a two-step system. The first step was categorization: What type of player is this? What is the shape of his performance? Is he a slow masher? A waterbug? A power-and-speed guy? How old is he? The second step was a neural-net system, which basically "walked" the player in question down their expected career path. I used Clay Davenport's DTs as the inputs for the system, because it helped me out in terms of removing park and league effects.

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There are a lot of problems with a system like this.

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