Park Factors & Park Factors & Park Factors, Oh My

We all know a park’s dimensions, foul territory, hitter’s backdrop, atmospheric effects, etc. play a significant role in shaping our projections and on a player’s performance. Collectively, we know these effects as park factors. We are probably most aware of a park’s home run park factor. I’m sure that for many parks, you have a perception in your mind as to its home run friendliness. The data might say otherwise, but at least you think you know, unlike, say, triples, which I’m sure most haven’t a clue which parks are best for boosting the three-bagger. Unfortunately, while the idea of park factors is sound, they are extremely problematic to rely on.

Why is this the case? Because the park factors you find from various sources don’t even agree with each other! Every source uses a different methodology to calculate factors and since the sources don’t always divulge all the fun math and logic behind their methods, you have to just put blind faith in its accuracy.

Park factors aren’t opinions though. Whether we have developed the most perfect way to calculate park factors or not, a park factor is a fact, like how many homers Aaron Judge hit last season. There should be a correct way to calculate them and many incorrect ways to calculate them.

So let’s get right to this problem. Here, presented in front of you, are the home run park factors, separated by batter-handedness, from three different sources — FanGraphs, Baseball Prospectus, and StatCorner. The FG factors are halved on the Guts! page, so I unhalved them to compare to the other two sets. If you want to dive into the methodology, here are the two sources that provide them:

FanGraphs
StatCorner

2017 HR Park Factor Comparison
Team RHB LHB Diff, Large to Small
FG BP StatCorner FG BP StatCorner RHB LHB
ARI 108 109 120 108 110 106 12 4
ATL 90 91 88 108 108 109 3 1
BAL 110 111 117 122 106 108 7 16
BOS 102 94 101 80 86 77 8 9
CHC 110 104 112 88 115 85 8 30
CHW 110 102 113 116 97 120 11 23
CIN 112 102 105 120 108 120 10 12
CLE 98 94 99 110 106 110 5 4
COL 126 106 115 116 112 113 20 4
DET 100 108 101 104 98 101 8 6
HOU 102 102 105 106 103 108 3 5
KC 84 86 74 86 99 86 12 13
LAA 98 103 105 90 93 94 7 4
LAD 92 97 91 110 106 114 6 8
MIA 80 92 80 78 94 90 12 16
MIL 110 102 107 122 103 127 8 24
MIN 108 108 116 94 104 95 8 10
NYM 96 90 98 98 92 98 8 6
NYY 124 111 124 128 124 141 13 17
OAK 94 102 89 82 100 87 13 18
PHI 132 119 133 116 116 131 14 15
PIT 78 90 62 96 96 94 28 2
SD 90 93 97 92 88 86 7 6
SEA 96 94 98 100 101 108 4 8
SF 76 86 75 64 75 54 11 21
STL 88 90 82 94 98 98 8 4
TB 88 101 96 98 89 97 13 9
TEX 100 103 100 104 105 105 3 1
TOR 104 100 97 108 95 99 7 13
WAS 100 107 111 90 96 87 11 9

The three columns after the team abbreviation are the right-handed home run park factors from each of the three sources, followed by a trio of columns representing the left-handed home run park factors. The last two columns indicate the difference between the highest and lowest park factors for each team by handedness. Those are the focus of today’s discussion.

Ideally, there would be a correct way to calculate park factors and everyone would know it. Every site would be familiar with this correct method and publish the same park factors, just like every site publishes the same numbers of home runs Aaron Judge hit in 2017. But this clearly isn’t the case. This is far from the case.

Check out some of the differences between the highest and lowest factors!

We’ll start with the right-handed batter factors. PNC Park in Pittsburgh is either a standard home run suppressing park (according to BP) or possibly one of the most difficult places to hit a home run in over the entire history of baseball (according to StatCorner). How could the park factors in that one park vary so drastically?!

Coors Field in Colorado is the other park with a gap of at least 20 between top and bottom. That’s biazarre, didn’t we all agree that Coors was one of the game’s great home run parks? Well apparently BP disagrees. FanGraphs’ Coors park factor ranks it second in baseball behind Citizens Bank Park in Philadelphia, while the other two suggest it’s just another strong home run park, but doesn’t stand out.

Moving onto the left-handed home run factors, we find the biggest gap in all the land in Chicago, the North side to be exact. Wrigley Field is either a terrible place for lefties to mash one out of the park, or one of the best places. How could this park be so off? It’s not even like one factor is just over 100 and another is just below. The lowest is at 85 and the highest at 115! Something is wrong here.

It must be the Chicago wind, as Guaranteed Rate Field on the South side is another with a gap above 20. Milwaukee also joins that list either as fairly neutral or one of the best home run parks in the league, along with AT&T Park in San Francisco, where the factors can’t agree on exactly how torturous the park is for left-handed home runs.

Another interesting observation about these park factors is the highest and lowest from each source, and the gap between those two marks. Let’s compare:

2017 HR Park Factor High-Low Gap Comparison
RHB LHB
FG BP StatCorner FG BP StatCorner
Max 132 119 133 128 124 141
Min 76 86 62 64 75 54
Gap 56 33 71 64 49 87

So for both sides of the plate, Baseball Prospectus’ home run park factors have the narrowest range, StatCorner’s the widest, and FanGraphs is in the middle. I have no idea what this means about each source’s numbers.

Between my Pod Projections and my xMetrics like xHR/FB rate which drive my HR/FB forecasts, I use park factors extensively. But if every source calculates different factors (sometimes wildly different) for the same statistic, they become completely useless.

Let’s brainstorm, pick apart the methodology, and come up with the best, most accurate park factors we can use for all our future projecting activities and backwards-looking analysis.



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Mike Podhorzer is the 2015 Fantasy Sports Writers Association Baseball Writer of the Year. He produces player projections using his own forecasting system and is the author of the eBook Projecting X 2.0: How to Forecast Baseball Player Performance, which teaches you how to project players yourself. His projections helped him win the inaugural 2013 Tout Wars mixed draft league. Follow Mike on Twitter @MikePodhorzer and contact him via email.

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Samuel
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Samuel

Yankees vs Giants in the WS would be so interesting. It’s almost two different sports they play at their home stadiums.