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March 10, 2005
The Only Constant Is Change
Since then, there have been quite a few people talking about predictions for the upcoming season, and adjusting for players in new parks is an essential part of that process. Players like Shawn Green--heading from Los Angeles to Arizona--are likely to see a boost in their raw numbers despite no actual gain, or even loss, in their performance. Understanding these changes is essential to running both fantasy teams and actual teams.
Adjusting for park factors going forward is more difficult than it might seem for one simple reason: Park factors are not as constant as they should be. Anyone who used the 2004 Player Forecast Manager and drafted the entire Expos team understands this problem very well. Nate Silver summed the issue up nicely in Baseball Prospectus 2005:
Previously, we had worked under the assumption that park factors would be the same in the upcoming season as they were in the previous season. This seems like a neutral enough premise, but it ignored the fact that park factors have fluke seasons, just as ballplayers do.
Certain parks are more difficult to predict than others. Tampa Bay, for example, has been a rock of stability since it opened in 1998. Its batter park factor was 104 in 1998, but stayed exactly the same at 100 from 1999-2003 before dipping to 96 last year. Likewise, Florida was also very steady, notching park factors of 96, 93, 96, 96, 97, 94, and 95 since 1998. But while the state of Florida may be very good at keeping its parks steady, several other locales are not. Here are the parks with the highest standard deviation in park factor since 1998, excluding cities in which a new park was built.
TEAM 1998 1999 2000 2001 2002 2003 2004 STDEV Kansas City 104 101 104 110 117 113 95 8.2 Montreal 96 104 100 107 101 118 95 7.9 Colorado 119 129 131 122 121 112 120 6.8 Chicago (N) 103 107 90 96 98 99 106 6.4 Arizona 101 97 102 106 108 111 103 4.9 Texas 104 104 105 100 112 110 111 4.7 Anaheim 101 100 102 107 97 93 99 4.7
There are some reasons for the drastic changes in Kansas City and Montreal. The Royals moved their fences out prior to 2004 and Montreal added 20 games in San Juan; regardless, that's a lot of room for error. Colorado has long held a dramatic edge on other parks, especially when mentally remembering park factors--yet in 2003 it was a mere 112 after that extreme 131 in 2000. Rather than the singular outlier to which we've all become accustomed, Coors Field was only the third-most advantageous park to hitters in 2003, following Kansas City and Montreal and barely edging out Arizona and Texas.
Looking at how each a previous season's park factor correlates to the current season over the past 15 years, we see a coefficient of correlation of .5522, strong enough to be significant, but low enough to raise concerns.
Adding another year to the data could help things, since it will help mitigate those fluke seasons like Kansas City and Montreal in 2003.
While there is a little improvement, it's negligible at best. Perhaps we're still giving those occasional fluke seasons too much credit. Let's try three years worth of data.
Now our correlation has actually dropped from the single-year correlation. It doesn't appear that adding additional years to the previous park factor adds any additional accuracy.
Up until this point, virtually all of the variance in park factors has been assumed to be a result of sample size issues and noise. On the other hand, when looking at Kansas City in 2003, there were physical changes associated with the changing park factor, in this case moving the fences in the outfield.
Looking at all parks since 1990, I've added several measures to the analysis. There are several measures for park size: an average of the distances down the left-field and right-field lines, an average of the power alleys, an average of straightaway center and deepest center, and the distance from home plate to the backstop. These four measures should give us an idea of how big the park is--both in the outfield and in foul ground--and any changes to the fences. An average height of all fences in the outfield has been added as well.
Running a multivariable regression using the previous season's park factor as well as measures for the size of the ballpark yields a coefficient of correlation of .5870, barely better than the .5647 we found using the average of the previous two seasons. Of that 58.7%, 55.5% is explained by the previous year's park factor; essentially nothing comes from adding park dimensions to the regression. The average of straightaway and deepest center and the backstop distance showed more correlation than other metrics, but those were closer to zero than Mike Matheny's OBP. So while they're nice to have around, only a gross misallocation of resources would use them on a full-time basis.
Though many park factors have shown a high degree of variance over the past 10-15 seasons, there are quite a few--Tampa and Florida were mentioned, but also Los Angeles, St. Louis and Shea Stadium--that are quite stable from year to year. Still, when you're looking to gain an edge on your fantasy competitors, be sure to look back a few years or gather an entire division of park factors before making any rash decisions: There's more variance in year-to-year park factors than you may think.