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Kicking Big Rocks: Extreme Park Factors and Batted Ball Data

Today, let’s make some useful data available for reference on a couple of a subjects — park factors and pitcher batted ball data.

Extreme Park Factors

With Carlos Quentin getting traded to the Padres and the resulting discussion on how his power translates to San Diego, I decided to make available the most extreme park factors in the league. I used the handedness data available at Statcorner.com. I only looked at values that are 10% higher or lower than the league average. Also, I made available the factors that are the most relevant to fantasy baseball, Line Drives (major component of BABIP and AVG) and Home Runs. The park factors are for hitters with a value over 100 helping a hitter and a value below 100 hurting a hitter.

Line Drives

PF Team Hand
129 COL RHH
128 COL LHH
118 TEX RHH
113 DET LHH
113 STL LHH
113 DET RHH
113 STL RHH
111 TEX LHH
110 TBR LHH
89 LAD LHH
87 LAD RHH
82 HOU RHH
81 ATL RHH
80 HOU LHH
80 ANA RHH
78 ANA LHH
78 ATL LHH

Notes
•  It is not surprising to see Colorado and its thin, dry air at the top of the leader boards. These high park factors almost makes any decent hitter playing at Colorado a must fantasy start.
•  At the other end of the spectrum are some teams that play in humid, heavy air like Houston and Atlanta.

Home Runs

PF Team Hand
143 NYY LHH
138 CWS RHH
133 CIN RHH
126 CWS LHH
123 BAL RHH
120 PHI RHH
120 CIN LHH
119 TEX LHH
119 CHC LHH
118 BAL LHH
118 MIL LHH
117 COL RHH
117 HOU RHH
116 TOR RHH
116 PHI LHH
116 LAD LHH
115 NYY RHH
114 TEX RHH
114 TOR LHH
114 AZ LHH
113 COL LHH
89 TBR LHH
89 OAK LHH
88 DET LHH
87 CLE RHH
85 KC RHH
82 STL LHH
82 SFG LHH
82 SEA RHH
80 OAK RHH
79 BOS LHH
74 STL RHH
73 PIT RHH
71 KC LHH
59 SD LHH

•  Eight teams (Reds, White Sox, Yankees, Orioles, Phillies, Rangers, Rockies and Blue Jays) show up for both right and left handed hitters for parks that help with home runs. Only 3 teams show up twice on the list of home run suppressing parks (Royals, Cardinals and A’s).
•  San Diego is a sinkhole for left handed home runs hitters. Don’t forget that KC is not that far behind. Last season Eric Hosmer hit 16 of his 19 home runs on the road.
•  Another interesting note is the level that St. Louis kept Albert Pujols‘ HRs suppressed. He may see a nice uptick in Anaheim.

A Pitcher’s Batted Ball Profile and Pitching Stats

You’ll often hear that a pitcher is an extreme ground ball or fly ball pitcher. Generally, the assumption is that ground ball pitchers are better than fly ball pitchers because they keep the ball in the park more often. Generally, that is the case, but extreme fly ball pitchers have their advantages. I looked at the average stats from a group of pitchers given a certain ground ball or fly ball rate to see how they compare.

I took all the pitchers since 2008 that pitched in over 120 innings in a season. Then I lumped them as close as possible into five equal groups. Here are the results:

Ground Ball Data

GB% Bracket BABIP WHIP HR/FB K/9 BB/9 HR/9 LD% GB% FB% ERA FIP xFIP
>50% 0.298 1.33 9.9% 6.4 2.9 0.79 19.1% 52.5% 28.4% 3.86 3.87 3.86
46% to 50% 0.297 1.34 10.0% 6.8 2.9 0.94 19.3% 47.2% 33.5% 4.05 4.01 3.96
42% to 46% 0.296 1.34 10.1% 6.7 3.0 1.02 19.4% 44.1% 36.5% 4.19 4.15 4.11
38% to 42% 0.290 1.31 9.7% 7.0 2.9 1.07 19.4% 40.6% 40.0% 4.14 4.16 4.16
<38% 0.278 1.28 9.4% 7.1 3.0 1.14 18.9% 36.1% 45.0% 4.04 4.26 4.33

Fly Ball Data

FB% Bracket BABIP WHIP HR/FB K/9 BB/9 HR/9 LD% GB% FB% ERA FIP xFIP
>42% 0.285 1.32 9.3% 6.9 3.0 1.15 20.1% 35.1% 44.7% 4.24 4.31 4.40
38% to 42%% 0.288 1.29 9.7% 7.3 2.9 1.06 19.6% 40.1% 40.3% 4.04 4.09 4.09
35% to 38% 0.294 1.33 9.9% 6.7 2.9 1.01 19.4% 44.0% 36.6% 4.10 4.11 4.08
32% to 35% 0.299 1.35 10.0% 6.7 3.0 0.93 19.2% 47.8% 33.0% 4.08 4.04 4.00
< 32% 0.293 1.31 9.9% 6.3 2.9 0.79 18.0% 53.7% 28.3% 3.80 3.87 3.85

•  The main advantage of the ground-ball pitchers is the low number of HR/9. Even though their HR/FB is higher, the ground-ball pitchers overcome this problem by just not allowing as many fly balls. For fantasy purposes, this value leads to an over 0.2 improvement in ERA compared to other pitchers.
•  Fly ball pitchers have the advantage that fly balls are easier to catch than ground balls for outs. With these extra outs, fly ball pitchers have a lower BABIP and therefore a lower WHIP. Also, this lower BABIP allows these pitchers’ ERA to be better then their FIP and xFIP.

When deciding between pitchers with similar K and BB numbers, I would take the pitchers in the following order (ranking is based mainly on ERA with the tie breaker going to WHIP)

1. >50% GB rate
2A. <38% GB rate
2B. Between 46% and 50% GB rate
3A. Between 38% and 42% GB rate
3B. Between 42% and 46% GB rate

First, I would always pick pitchers based on a combination of strikeouts, walks and health. After that, use the pitchers batted ball profile to decide which pitcher to pick if undecided between two.




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5 Responses to “Kicking Big Rocks: Extreme Park Factors and Batted Ball Data”

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  1. MauerPower says:

    Really good stuff man.

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  2. peachesnnuts says:

    Lots of good stuff here Jeff but I have one observation. BABIP is always used as the frame of reference for the advantage FB pitchers have over GB pitchers. The problem here is that batting average is a bad proxy for run prevention. If we use wOBA against on fly balls we find it is .343 while the wOBA against on ground balls is .212. I say this to disagree with your last point. I agree K’s and BB’s are the first hitngs we should look at but then I go with descending order of GB%.

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    • Stocktopus says:

      The only issue with that is that we’re talking about fantasy here. If we were being GMs, i would 100% agree with that. But in fantasy, when comparing pitchers with similar K and BB numbers, you’re just trying to figure out which pitcher is likely to have the better ERA, WHIP, and whatever else your league chooses to use. And in the “Ground Ball Data” table, <38% GB is second in ERA and first in WHIP.

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  3. Josh says:

    I COULD be wrong, but I’ve read that the LD numbers are a reflection on the scores or game charters. Some scores might call some fliners fly balls and some might call the same a line drive.

    If this is true then the bottom line is that most of this data is noise and we can’t determine which component is a true factor and which is just scorer bias, which obviously doesn’t help a player’s avg or BABIP.

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