We all know that pitcher BABIP is a difficult thing to predict. In Fact, we’ve got things like xFIP, and FIP, and tRA to help mitigate the unpredictability of it. However, for certain uses, however, it’s beneficial for us to pay attention to it. Fantasy baseball is one example of a case where FIP doesn’t necessarily do us a lot of good. In this case we’d rather get an idea of what their real ERA is going to look like. To that end, I’ve developed a way to predict a pitchers BABIP given a few other statistics that a pitcher has some control over.
Specifically, what I’ve done, is take 3 years of team data to predict BABIP for the various batted ball types. This helps factor in things like a slow infield, a high outfield wall, and other park based factors. It also factors in things like infield defense (on ground balls), and outfield defense (on outfield fly balls). Using 3 years of data isn’t perfect, as teams change over time, but I think you’ll find that it does a pretty good job. Another problem with my implementation, is that I’m assuming that IFFB’s are all outs, since I don’t have a statistic for the BABIP of infield flyballs.
So when is this useful? Well, it’s extremely useful when you’ve got a pitcher with a small sample of data (or none at all) playing with a particular team. When a pitcher switches teams, this helps give you a fairly good idea of how their BABIP will be effected. For instance, a groundball pitcher will be helped greatly by moving to a more groundball friendly environment (better park, better defense).
Let’s use an example to illustrate. Let’s say Ricky Nolasco get’s traded to the Rays. Using his career batted ball profile, the calculator gives him a .305 BABIP for his current team. Switch his team to the Rays, and suddenly he’s a .291 BABIP pitcher. Now let’s delve into the details of why this happened. Ground balls pitching for the Rays have a .230 BABIP, while the marlins have a .252 BABIP. Outfield Fly balls with the Rays are at .127, while with the Marlins it’s at .147. A lot of this is probably based on the Ray’s having a better defensive team, but park factor’s could come into play as well. The bottom line, Nolasco’s batted ball luck should improve with a change in teams, and with the calculator we can take a good guess at by how much.
How about another quick, more relevant example, Matt Garza. With the Rays, he shows at about .270 BABIP. With his move to the Cubs, he’s shown as a .287 BABIP pitcher, still well below league average, but not quite as elite as it was with the Rays. This of course, isn’t the entire picture of the move to the Cub’s. His strikeout’s, and walks will probably improve, and there could be a change in his HR/FB ratio as well. That’s all beyond the scope of this particular article, but still important to keep in mind.
How to Use it:
Step 1: Using one of the link’s below, you can download the spreadsheet
Open Office Link: http://www.mediafire.com/?hme601igqqu1215
Excel Link: http://www.mediafire.com/?z0vk84vhr5ug23r
Step 2: Open the spreadsheet, and input the LD%, GB%, IFFB%, and HR/FB% for your pitcher (these stats are easily obtained from www.fangraphs.com)
Step 3: Set the pitchers team, using the following lookup table:
ARI -> Diamondbacks
ATL -> Braves
BAL -> Orioles
BOS -> Red Sox
CHC -> Cubs
CWS -> White Sox
CIN -> Reds
CLE -> Indians
COL -> Rockies
DET -> Tigers
FLA -> Marlins
HOU -> Astros
KCR -> Royals
LAA -> Angels
LAD -> Dodgers
MIL -> Brewers
MIN -> Twins
NYM -> Mets
NYY -> Yankees
OAK -> Athletics
PHI -> Phillies
PIT -> Pirates
SDP -> Padres
SEA -> Mariners
SFG -> Giants
STL -> Cardinals
TBR -> Rays
TEX -> Rangers
TOR -> Blue Jays
WSN -> Nationals
Specifically, what I’ve done, is take 3 years of team data to predict BABIP for the various batted ball types. This helps factor in things like a slow infield, a high outfield wall, and other park based factors. It also factors in things like infield defense (on ground balls), and outfield defense (on outfield fly balls). Using 3 years of data isn’t perfect, as teams change over time, but I think you’ll find that it does a pretty good job. Another problem with my implementation, is that I’m assuming that IFFB’s are all outs, since I don’t have a statistic for the BABIP of infield flyballs.
So when is this useful? Well, it’s extremely useful when you’ve got a pitcher with a small sample of data (or none at all) playing with a particular team. When a pitcher switches teams, this helps give you a fairly good idea of how their BABIP will be effected. For instance, a groundball pitcher will be helped greatly by moving to a more groundball friendly environment (better park, better defense).
Let’s use an example to illustrate. Let’s say Ricky Nolasco get’s traded to the Rays. Using his career batted ball profile, the calculator gives him a .305 BABIP for his current team. Switch his team to the Rays, and suddenly he’s a .291 BABIP pitcher. Now let’s delve into the details of why this happened. Ground balls pitching for the Rays have a .230 BABIP, while the marlins have a .252 BABIP. Outfield Fly balls with the Rays are at .127, while with the Marlins it’s at .147. A lot of this is probably based on the Ray’s having a better defensive team, but park factor’s could come into play as well. The bottom line, Nolasco’s batted ball luck should improve with a change in teams, and with the calculator we can take a good guess at by how much.
How about another quick, more relevant example, Matt Garza. With the Rays, he shows at about .270 BABIP. With his move to the Cubs, he’s shown as a .287 BABIP pitcher, still well below league average, but not quite as elite as it was with the Rays. This of course, isn’t the entire picture of the move to the Cub’s. His strikeout’s, and walks will probably improve, and there could be a change in his HR/FB ratio as well. That’s all beyond the scope of this particular article, but still important to keep in mind.
How to Use it:
Step 1: Using one of the link’s below, you can download the spreadsheet
Open Office Link: http://www.mediafire.com/?hme601igqqu1215
Excel Link: http://www.mediafire.com/?z0vk84vhr5ug23r
Step 2: Open the spreadsheet, and input the LD%, GB%, IFFB%, and HR/FB% for your pitcher (these stats are easily obtained from www.fangraphs.com)
Step 3: Set the pitchers team, using the following lookup table:
ARI -> Diamondbacks
ATL -> Braves
BAL -> Orioles
BOS -> Red Sox
CHC -> Cubs
CWS -> White Sox
CIN -> Reds
CLE -> Indians
COL -> Rockies
DET -> Tigers
FLA -> Marlins
HOU -> Astros
KCR -> Royals
LAA -> Angels
LAD -> Dodgers
MIL -> Brewers
MIN -> Twins
NYM -> Mets
NYY -> Yankees
OAK -> Athletics
PHI -> Phillies
PIT -> Pirates
SDP -> Padres
SEA -> Mariners
SFG -> Giants
STL -> Cardinals
TBR -> Rays
TEX -> Rangers
TOR -> Blue Jays
WSN -> Nationals
