## Introducing Statistics: Park Effects

Baseball games do not occur in a vacuum; instead they occur in particular stadiums. Park effects are the notion that stadiums have differential impact on individual baseball games. These effects can in turn skew a host of stats in order to make certain players look much or worse than they truly are, simply because of their home stadium. Each stadium is assign a park factor in order to account for these effects. Let me delve briefly into how park factors are derived, then we can conclude with a few applications.

Park factors adjust baseball events by how much they are dependent upon a given stadium. Thus, there is a park factor for home runs at Yankee Stadium which differs from the park factor for doubles at Yankee stadium. There are a variety of methods of producing park factors. The simplest method is the one used by ESPN, listed in the chart linked to above. To calculate the park factor for home runs:

**((homeHR + homeHRA)/(homeG)) / ((roadHR + roadHRA)/(roadG))
**

You add the number of home runs a team hits at home to the number of home runs it gives up at home, dividing by the total number of home games. That factor is divided by its road equivalent. This gets you a ratio in which a score of 1 is neutral, i.e. your park neither increases or decreases home run production. Numbers higher than 1 increase the number of home runs, while numbers lower than 1 decrease them. The same method can of course be applied to any other event, like hits, runs, doubles, etc., simply by switching home runs out for the other stat. Other methods of calculating exist, most commonly changing this basic model by using several years to determine a park factor, such as this historical example that I will use below.

Why do park effects matter? For starters, consider the 1995 NL MVP race. In 1995, Barry Larkin of the Reds edged Dante Bichette of the Rockies for the MVP. In the aggregate, Larkin hit .319/.394/.492, while Bichette hit .340/.364/.620. Given that, how did Larkin win? He had an advantage in OBP and nothing else. Bichette even lead in wOBA .413 to .405, and both teams made the playoffs. But consider Bichette’s home/road splits. At home, he hit .377/.397/.755, but on the road he hit .300/.329/.473. 31 of Bichette’s 40 home runs were hit at home. Larkin in contrast hit .328/.424//498 at home and .309/.360/.486 on the road. Larkin was the same player everywhere in 1995, while Bichette’s great overall numbers were a product of playing his home games in Coors Field. In 1995, Coors had a park factor of 1.23, while Cincinnati had a park factor of exactly 1. Bichette’s stats are inflated by a factor of 1.23, while with Larkin what you see is what you get. Because of this, Larkin received the edge in MVP voting, and Bichette settled for second place.

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This entry was posted on 11/04/2009 at 10:12 am and is filed under Baseball, Statistics. You can subscribe via RSS 2.0 feed to this post's comments.

**Tags:** Barry Larkin, Cincinnati Reds, Colorado Rockies, Dante Bichette, Statistics

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