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Testing Projections for 2011

Posted By __Matt Swartz__ On February 9, 2012 @ 10:15 am In __Daily Graphings__ | __48 Comments__

Each year, baseball fans and commentators across the nation make bold predictions about what they expect in the coming year. They frequently make outlandish claims like “Adam Dunn is going to hit 50 home runs in Comerica Park!” or “This is the year that Joe Mauer finally hits .400!” but such predictions are far more likely to be high than low. Sure, if you said Jose Bautista was going to summon greatness going into 2010, you looked pretty smart, but anyone who predicts performance seriously knows that you need to hedge your bets. While frequently accused of being overly pessimistic about whoever your Home Nine are, on average, they land high about as often as they land low. This field of “projection systems” grows by the year, but there are significant differences between them. Today, I’ll evaluate their 2011 projections for hitters and pitchers.

Firstly, lets peak at the candidates:

MARCEL: Tom Tango’s free projection system, intentionally using a simple formula as a challenge to forecasters.

PECOTA: Baseball Prospectus’ projection system available by subscription, run by Colin Wyers.

OLIVER: The Hardball Times’ projection system available by subscription, run by Brian Cartwright.

ZIPS: Baseball Think Factory’s free projection system, run by Dan Szymborski.

CAIRO: Revenge of the RLYW’s free projection system, run by “SG.”

STEAMER: Free projection system, run by Jared Cross, and his former students, Dash Davidson and Peter Rosenbloom.

You can learn more about these projection systems here.

HITTERS

The projection systems differ significantly with respect to their standard deviations of wOBA, with some hitting projection systems being particularly more risky in estimating the performance of players. The more risky a projection system, the more likely it will be wrong by a lot, which hurts its performance, particularly with respect to its Root Mean Square Error. Thus, riskier projection systems may be right more often, but when they’re wrong, they’re very wrong. So, before we do anything, let’s rank the projection systems in terms of how risky they are:

Projection | StDev of WOBA |
---|---|

Oliver | .0309 |

Steamer | .0289 |

ZiPS | .0287 |

Cairo | .0283 |

PECOTA | .0278 |

Marcel | .0234 |

Marcel is going to have fewer “big misses” than Oliver will, so we’ll want to look at both RMSE (which will punish risky guesses) and Correlation (which will reward better player rankings), as well as average absolute error (which will fall somewhere in between in terms of punishing and ignoring risky projections).

Here is the RMSE table, weighted by PA, and only including guys with at least 200 PA. As you see, PECOTA, a relatively safe projection comes out ahead, even further ahead than Marcel which is even safer. I’ll also include a row for “last year’s stats” to see how predict they are.

Projection | RMSE |
---|---|

PECOTA | .0317 |

ZiPS | .0318 |

Oliver | .0321 |

Steamer | .0322 |

Marcel | .0330 |

Cairo | .0333 |

Last Year’s Stats | .0388 |

Oliver fared pretty well, despite its risky nature. It takes a step forward when you look at absolute average error.

Absolute average error and root mean square error are differing in terms of how much they punish bad performance. Take System A that misses on Player X by 20 points of wOBA and misses on Player Y by the same amount. Take System B that guesses Player X exactly but misses on Player Y by 30 points. Average absolute error will favor System B, but RMSE will favor System A.

Projection | AAE |
---|---|

ZiPS | .0244 |

Steamer | .0247 |

Oliver | .0247 |

PECOTA | .0248 |

Marcel | .0257 |

Cairo | .0264 |

Last Year | .0303 |

ZiPS is the champion of AAE, with its somewhat risky projections. They may be wrong by more when they’re wrong, but they’re right more often.

If we then jump forward and look at correlation, we get a whole new winner. Correlation is going to be different because all correlation cares about is rankings for the most part. If you projected Ryan Braun to have a .530 wOBA and Adrian Beltre to have a .430 wOBA, you would have had a great projection year using correlations, despite the fact that Braun’s wOBA was closer to .430 and Beltre’s was closer to .380. Correlation just wants you to rank the guys well. Using correlation, we get the following rankings.

Projection | Correl. |
---|---|

Oliver | .6151 |

ZiPS | .6139 |

PECOTA | .6136 |

Steamer | .6039 |

Cairo | .5685 |

Marcel | .5614 |

Last Year | .4740 |

Oliver comes out in front if you use correlation. Despite having perhaps overly aggressive estimates of talent level, scaling back your Oliver projections might have been the best way to predict hitters.

PITCHERS

What about pitchers? Well, the leaderboard will look quite different there. Following some of my previous work, I include some ERA Estimators among pitching projections. This time, I’ll convert them into projections by regressing ERA in 2011 against 2010 and 2009 versions of this ERA estimators. This produced the following formulas:

SIERA_proj = .59*SIERA(’10) + .26*SIERA(’09) + 0.47

xFIP_proj = .65*xFIP(’10) + .24*xFIP(’09) + 0.29

FIP_proj = .43*FIP(’10) + .30*FIP(’09) + 0.94

tERA_proj = .38*tERA(’10) + .29*tERA(’09) + 1.08

The projections now have the following standard deviations of ERA among all pitchers with 40 IP in 2011:

Projection | StDev of ERA |
---|---|

ZiPS | .7322 |

PECOTA | .7238 |

Oliver | .6356 |

Cairo | .5314 |

Steamer | .5207 |

Marcel | .4453 |

SIERA_proj | .4188 |

xFIP_proj | .3854 |

FIP_proj | .3829 |

tERA_proj | .3807 |

Starting off with RMSE—which should punish riskier projections, we see that it does exactly that:

Projection | RMSE |
---|---|

Steamer | .8324 |

Cairo | .8736 |

SIERA_proj | .8746 |

xFIP_proj | .9014 |

FIP_proj | .9033 |

tERA_proj | .9050 |

Marcel | .9066 |

PECOTA | 1.024 |

ZiPS | 1.030 |

Oliver | 1.042 |

Last Year’s Stats | 1.282 |

ZiPS, PECOTA, and Oliver all had the riskier projections and all fared the worse. Interestingly, despite being more risky than scaled back ERA estimators, Steamer and Cairo outperformed them at RMSE.

What about average absolute error? The rankings look similar, though a few projections swap places.

Projection | AAE |
---|---|

Steamer | .7067 |

SIERA_proj | .7281 |

Cairo | .7331 |

FIP_proj | .7333 |

xFIP_proj | .7360 |

tERA_proj | .7361 |

Marcel | .7474 |

ZiPS | .7749 |

PECOTA | .7905 |

Oliver | .8009 |

Last Year’s Stats | .8766 |

Steamer again comes out ahead. Moving to correlation, we see the same type of thing, though surprisingly, Marcel does better and Oliver does worse with correlation, despite its punishment of conservative projections.

Projection | Correl. |
---|---|

Steamer | .4581 |

Cairo | .4213 |

SIERA_proj | .4089 |

xFIP_proj | .3763 |

Marcel | .3744 |

FIP_proj | .3739 |

tERA_proj | .3715 |

PECOTA | .3705 |

ZiPS | .3701 |

Last Year’s Stats | .3265 |

Oliver | .3163 |

But on all three, Steamer comes out ahead. I asked Jared Cross what was making his projections so good, and he explained that he was using velocity (as well as handedness) in his pitcher projections, and that was giving them a leg up. He wasn’t the only person to suggest doing something like this. I only started thinking seriously about it recently, but I think it really is the “next big thing” in pitcher projections. Unlike hitter projections which seem to come down to which metric you want to use to test them, pitcher projections come back Steamer in all three tests. Perhaps more interestingly, the better-known projections such as Oliver, PECOTA, and ZiPS, despite doing the best on hitters, they fare the worst with pitchers. Perhaps being good at projecting both pitchers and hitters is as rare as being good at doing both of them.

Of course, these are all just one-year tests, so there is a lot of luck involved for any of these. However, as each of these systems moves forward to their next race, this is where they stand.

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