The Impact on Hitters Who Change Parks

(Special thanks to Tom Tango for working through the conceptual and analytical issues on this article with me)

After seven outstanding seasons as one of the National League’s premier hitters, Prince Fielder signed a nine-year $214 million deal to play first base for the Detroit Tigers. During his years in Milwaukee, Fielder averaged a .391 wOBA, 32 home runs (.0546 HR/PA) and posted a .257 ISO. Certainly, no one could argue about his productivity. But with a change to a new team —and more importantly, a new park — there are questions about whether Fielder’s offense will be impacted.

If Park Factors are to be believed, he should be in for a decline. By just about any model, Detroit is roughly even offensively overall, but a much tougher hitting environment for left-handed hitters than Milwaukee. That means we should expect Fielder’s offensive performance to decline more than basic aging and regression would predict. Since the Park Factor change only impacts half of a player’s games each year, the theoretical ratio between change in factors and change in performance is 2:1. Essentially, we’d expect a wOBA to decrease by 1.5% and home runs to decrease by 15%. There are a number of different Park Factor formulas, but the general pattern looks similar regardless of the factors you look at.

To get a sense of the real impact that changing parks has on hitters, I created a data set with two consecutive seasons worth of figures for all hitters with more than 100 plate appearances from 2003 to 2010. From there, I  looked at changes in players’ wOBA and in home runs per plate appearance from the first year to the second. Then I compared Park Factors and Home Run Factors for the parks in which they played in both years. (Park and Home Run Factors were borrowed from Seamheads, as I have done previously.)

There are a number of comparisons below: players with more than 100 PAs in both years; players with more than 300 PAs in both years; players with more than 300 PAs in both years and a first-year ISO greater than .145; and players with more than 300 PAs in both years and a first-year ISO less than .145. For each segment, I looked at the percent change in Park Factors (or Home Run Factors) and the percent change in wOBA (or HR/PA) for the top 100 or top 30 players based on the size of the change in their Park Factors or Home Run Factors. I looked at both positive and negative changes in those factors in each category.

The lower the ratio between factors and metrics, the greater the impact of the park change. Here are the results for wOBA:

Park Factors Change wOBA Change Ratio (PF Change:wOBA Change)
AVE_%Change AVE_%Change
Top 100 [>100 PA both years)] Positive Change 18.30% 2.20% 8.3
Negative Change -15.80% -3.30% 4.8
Top 100 [>300 PA (both years)] Positive Change 14.20% 1.50% 9.5
Negative Change -11.40% -3.40% 3.4
Top 30 [>300PA (both years)>.145 (YR1)] Positive Change 16.40% 1.40% 11.4
Negative Change -15.10% -5.80% 2.6
Top 30 [> 300PA (both years)< .145 ISO (YR1)] Positive Change 20.50% 7.20% 2.9
Negative Change -14.70% 0.30% 54.7

Remember that the actual ratio of Park Factor Change to wOBA Change should be around 2:1. But it isn’t. The closest we find is 2.6:1 for regulars with a first-year ISO greater than .145 when moving to a more pitcher-friendly park (2.6 ratio). And what about for hitters moving to a more hitter-friendly park? That ratio is 11.4:1. The impact on wOBA is far more muted than theory would lead us to believe. Not only that, but the magnitude of the impact varies quite a bit depending on the type of hitter you are and whether you’re moving to a more friendly or less friendly hitting environment. Power hitters don’t seem to be affected that much by moving to a more hitter-friendly park (11.4:1 ratio). But move to a more pitcher-friendly park, and the impact is easier to see (2.6:1 ratio).

But do we get any closer to the theoretical ratio for HR/PA?

Home Run Factors Change HR/PA Change Ratio (HRFactor Change:HR/PA Change)
AVE_%Change AVE_%Change
Top 100 [>100 PA both years)] Positive Change 30.10% 21.00% 1.4
Negative Change -23.10% -21.40% 1.1
Top 100 [>300 PA (both years)] Positive Change 22.00% 9.10% 2.4
Negative Change -16.90% -18.00% 0.9
Top 30 [>300PA (both years)>.145 (YR1)] Positive Change 28.60% 3.60% 7.9
Negative Change -23.20% -23.70% 1
Top 30 [> 300PA (both years)<.145 ISO (YR1)] Positive Change 28.80% 59.00% 0.5
Negative Change -19.10% -8.60% 2.2

The short answer is yes. In fact, we see a number of scenarios where moving to a park with a higher or lower home run factor has an impact of 1:1 or better on a player’s HR/PA. What’s interesting, though, is that power hitters (>.145 ISO) moving to a more hitter-friendly park don’t reap the same benefits as weaker hitters who do the same. On average, above-average power hitters in the sample moved to parks that were on average 28.6% more home-run friendly, but only increased their HR/PA by 3.6% — an almost 8:1 ratio. But for hitters with less-than-average power? They moved to parks that were 28.8% more home-run friendly and increased their HR/PA  by almost 60% — a .5:1 ratio.

For example, take a hitter who is below average in power (say, five home runs in 600 PAs) and a hitter who is above average in power (40 home runs per 600 PAs). Move both of them to parks that are 10% more home run friendly, and we’d would see the less-powerful hitter to increase his home runs by about 50% (from five homers to eight homers per 600 PAs) and the more powerful hitter to increase his home runs by only 1.3% (from 40 to 41 per 600 PAs).

A change to a less-friendly offensive park produces slightly different results. The less-powerful hitter in the above example might see his home run total stay roughly the same; the more powerful hitter would see the park have a greater impact on his total.

So what does this mean for Fielder new digs in Detroit?

Using the data above to estimate, we might expect Prince’s wOBA to increase by roughly .4% — from .408 to .410 — since Detroit had an overall run park factor 5% higher than Milwaukee (101 versus 96). However, with natural aging and regression his wOBA will likely decline from last year, but park factors may not play as big of a role as we think. But  if we look at home run factor, things begin to change.

Overall, Comerica is 8% harder on home runs than Miller Park. That means Fielder could see his home run totals dip from 33 HR per 600 PAs to 30 HR per 600 PAs. The other way to look at it is that Comerica is 23% harder on left-handed hitters when it comes to home runs. I didn’t run the analysis with home-run-factor splits, but just to throw it out there, Fielder could see his home run rate drop from 33 per 600 PAs to 25 per 600 PAs. So, we may not expect a massive drop in Fielder’s overall production, his home run totals could see a decline.

None of this is to say that the ratios I’ve listed are concrete. Still, they suggest that applying park factors in a straightforward way (i.e. assuming a 2:1 ratio between factors and performance) can be problematic for hitters depending on the type of player (above-average power or below-average power) and whether that player is moving to a higher or a lower run environment. The data suggest that we need to do more work in this area if we want our park-neutral estimates for players to truly reflect reality.


To make life easier on folks, I have created a Google Doc with all the data I used for this study as well as a calculator. For the calculator, all you need to do is input the wOBA or HR/PA from YR1 in the green cell and then the percent change in park of home run factor in the other green cell. Again, I used Seamheads factors for the study. You will automatically see the percent change and estimated YR2 outcomes for each type of player calculated for you based on the ratios noted above.

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Bill works as a consultant by day. In his free time, he writes for The Hardball Times, speaks about baseball research and analytics, consults for a Major League Baseball team and appears on MLB Network's Clubhouse Confidential. Along with Jeff Zimmerman, he won the 2013 SABR Analytics Research Award for Contemporary Analysis. Follow him on Tumblr or Twitter @BillPetti.

26 Responses to “The Impact on Hitters Who Change Parks”

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  1. Delino DeShields says:

    Well, Bill, if “respectable baseball writers” link to you when they’re high, I’ll take this analysis as gospel.

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  2. DD says:

    Pretty sure this would be difficult to prove, but I think players like Fielder (big power guys who hit long HRs) are less affected by changing parks, as most of their HRs would be out almost anywhere anyway. For a player like, say, Ichiro (trivial-to-modest power guys who don’t hit bombs), moving from Safeco to Yankee Stadium should increase his HR total by a larger percent, since he would be hitting “just enoughs” in Yankee Stadium that would be outs in Safeco. Not sure if this was considered, but it seems the HR/PA chart bears that out.

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    • DD says:

      Also, could we do this same analysis comparing the players’ home/road splits and the change in them when changing parks? For example, if Carlos Gonzalez changed home parks to a neutral environment, would his home/road split variance shrink?

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    • Ben Hall says:

      I would think that big power guys who hit long home runs would be pretty well represented by guys with ISOs over .145

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  3. Baltar says:

    If the change of park does not have much effect in general, that brings the park factors themselves into question. Are they possibly wrong or at least overrated?

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  4. Jono411 says:

    Something here doesn’t make sense to me. Power hitters are more affected by the park change when moving to a less hitter friendly park, but less affected by the park change when moving to a more hitter friendly park? Shouldn’t the changes be symmetrical (as in the park’s attributes either always affect power hitters more, or always affect power hitters less)?

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    • Jono411 says:

      I think J. Cross’ post a couple down explains this – it’s because of regression to the mean.

      Assuming the positive change and negative change groups have the same samples size, we’re seeing an average -10% change in HR/FB rate for the high power group. And therefore the effect of the ballpark change is really around +/-13% with approximately a 25% change in park factors, which is the 2 to 1 ratio you’d expect.

      For the low power group, we have an average +25% change in HR/FB rate, and the effect of the ballpark change is really around +/-35%, with approximately 25% park factor changes. So in this case, we have a 0.7:1 ratio.

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  5. Julian says:

    I first read this as the impact of hitters who physically change a park. So like how Tampa changed the LF wall for Crawford and such. Probably not enough data on this, but would be interesting to see if this ends up helping a team or not.

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  6. byron says:

    What was the sample size here? Have we demonstrated that the expected random variance in HRs as measured by % of total is the same in low-power and high-power hitters? I’d want to see an enormous sample of <10 HR/600PA guys getting 5x the HR boost than park factors would lead you to expect. Adding lefty/righty splits (which fractures the sample) and using home/away data for all players, instead of just those who switch teams (which exponentially increases the sample) would, I think, make this a stronger study.

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  7. J. Cross says:

    Really cool stuff.

    One thing I’m concerned about here are the lines where hitters are split in >.145 ISO in year 1 and <.145 ISO in year one. We'd expect regression to the mean to play a role when we split hitters like this. The first group would see lower power numbers than expected (based on the park factor change) and the second group would see higher power numbers than expected based on park factors. In other words, I think this:

    [quote]On average, above-average power hitters in the sample moved to parks that were on average 28.6% more home-run friendly, but only increased their HR/PA by 3.6% — an almost 8:1 ratio.[/quote]

    can be explained in part by regression to the mean.

    Anyway, great study. So, hitters get a *bigger* HR swing than expected (less than 2:1 ratio) but a much smaller *wOBA* swing than expected (much larger than 2:1) ratio, right? This implies that non-HR components of wOBA must be *much* less affected by park changes than expected.

    I'm going to have to check the Seamheads site to see if their ballpark factors are regressed.

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  8. Mr Punch says:

    Does it matter that home runs are, famously, a “true outcome”? Overall park factors are quite complex, involving amount of foul territory, the hardness of the infield and height of the grass, speed of basepaths, walls to hit doubles off of, etc. All these affect team scoring, but they aren’t going to track consistently onto individual performance

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  9. KJOK says:

    “I’m going to have to check the Seamheads site to see if their ballpark factors are regressed.”

    The single-year factors are unregressed.

    The 3-year factors are regressed to the long-term, lifetime park factor for that park. So for example Coors averaged for 3 years (1 year before, current year, and 1 year after) then regressed 25% to the Coors specific lifetime factor. We won’t claim they’re perfect factors, but we believe regressing to a park specific number is more accurate than regressing all parks to average.

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  10. MikeS says:

    How does a massive outlier like Adam Dunn affect these data? He went from a bad hitters park to a great one, yet cratered. Does that appreciably affect the numbers?

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    • RC says:

      Another point of interest for me is how do Power hitters affect the park factors?

      Are the park factors completely independent of player performance? For instance, does Miller Park’s park factor go down without Prince in the Brewers’ line-up for the season?

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  11. pft says:

    I think the biggest issue for Fielder will be league pitching.

    Since players get 1/2 of their PA on the road, you might look at weighted road park factors as well.

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  12. byron says:

    Is there any reason not to expand this study to include the differences experienced by all players in the different environments they play in . . . also known as computing park factors?

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  13. MGL says:

    Several issues, most of them already touched upon:

    One, as Jared (Cross) points out, the regression towards the mean for the high and low ISO players is a critical factor which was overlooked by the author. As Jared points out, that would partly or completely explain the assymetrical park effects. All of the high ISO players will have their ISO go down in year 2 regardless of whether they changed parks or not or moved to a hitters or pitchers park. Same thing in reverse for the low ISO guys.

    Two, the park factors listed on Seamheads and used by Bill are by no means very accurate for several reasons. One, with inter-league games and the imbalanced schedule, computing park factors in the traditional manner, home and road team stats in one park divided by home and road team stats in all other parks, is not nearly good enough anymore.

    Three, the way they do the regression for the 3-year PF’s (I was not aware that they were regressed) is statistically unsound (you do not regress a sample mean toward a larger sample mean, you regress it toward a population mean, where the population is a large group to which the sample element belongs). There is some justification for doing it the way they do it, but not much. And 25% is not nearly enough for run factors, since as someone already pointed out, some aspects of PF’s, other than the HR’s, are very variable (i.e., they have a narrow range in “true” values and of course a large random variance component in any 1 or even a few years).

    Four, you cannot compare wOBA to a run factor, straight up! You have to convert wOBA into runs or square it as a short cut. For example, if a true PF were 2.0, i.e., it doubled run scoring, do you think that it would double wOBA also? If league wOBA were .330 and a team scored 5 runs per game, and then their wOBA were .660 in that same park, how many runs do you think they would score? Like 16 runs! In order to score 10 runs, you would need a wOBA of around .475. which is 1.44, Sqaure that and guess what? You get around 2! So Bill needs to square the wOBA in year 1 and year 2 before he computes the ratio!

    Five, and finally, park factors cannot be treated the same across leagues! They are strictly league dependent and league relative. There is no particular reason to think that if a player with a home park factor of 1.00 moves to the other league and to a home park with a park factor of 1.02, that is offensive stats will go up. First of all, the two PF’s are not equivalent. The entire set of parks in one league may be more or less hitter friendly than the entire set of parks in the other league. They likely are (by a little). Two, since the pitching is different (and we think that the AL pitching is much better), a player going from one league to the other may see his offensive stats go up or down because the quality of the pitching may change. Three, when as player goes from one league to the other, he likely has an adjustment period where his stats are not as good because he is not as familiar with the pitching.

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  14. Tim says:

    the pitching in the AL Central sans Detroit is craptastic….his hardest matchups could be against who? Danks? Ubaldo?

    78 games against staffs that pretty much suck….very weak on the high end talent and lots of #3 and 4 slot starters….not much top end talent in the bullys either….

    I like Prince for 42 dingers and 115 rbi and about 100 walks again

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    • pft says:

      You are forgetting the other 72 games against the AL East and AL West.

      Also, the NL Central sans Milwaukees was not much better than the AL Central will be in 2012. Indians and White Sox look to have decent staffs this year.

      3 yr HR PF (LHB)

      DET-89 (Milwaukee-116)

      (Avg sans Det-93, NL Central sans Milwaukee-102)

      AL West has neutral parks (avg HR LHB PF – 100) and tougher pitching. AL East has tougher pitching and smaller parks (Avg HR LHB PF 108).

      Add to this, Prince has not seen much of most AL pitchers, and that’s usually an advantage for the pitchers, so he should have an adjustment period. Also, players signing large contracts tend to press more to justify their contract and that can hurt performance (not always).

      I think Prince has a tough 1st year and will be lucky to hit 30 HR and get to a 900 OPS this year .

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  15. Brandon T says:

    Prince hits some of the longest HRs in all of baseball — which others have mentioned — so I don’t really expect the change in venue that to affect him that much. HitTrackerOnline backs this up to some extent: only 2 of his HRs wouldn’t have been gone in at least half the ballparks in baseball, and only one of those was at home (and that one still flew almost 400 feet). I would be far more concerned about age (and weight) related decline/injury. If he has a large decline in HRs, this will probably be the reason, not park effects.

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