Corrected Pitcher Efficiency

[QuickJones81]

Part of the fun for me in following baseball is diving into the stats, and thinking through new ways to skin the cat and understand performance. I wanted to bring up one stat I’ve developed to get some critiquing. One approach I’ve been tossing around is something I'm calling CPE (corrected pitcher efficiency). The logic is that the main purpose for a pitcher is to get outs as efficiently as possible. One crack at measuring this is to look at batters faced per out produced. Similar to WHIP in some respects, though this takes a little bit more into account.

 

Further diving into efficiency would say that you probably want to look at things from a pitch / out standpoint. This would value a contact Buehrle type pitcher over a strikeout pitcher, but to me I am OK with that as the former projects to last longer in games, and maintain longer careers, and therefore in the end, to me at least, offer more value to a team.

 

Since I haven’t found a site that offers pitch counts in an easily digestible form (if people know of a good site for milb let me know) I initially stuck with the BF/O approach. I then took the step of removing the defense of the players behind the pitcher, or luck factor if you will, and created a correction factor whereby actual outs is reduced by the number of hits that should have occurred if the balls put into play were retired at a BABIP of .295 as opposed to the actual BABIP for the pitcher.

 

The resulting equation is:

CPE = Batters Faced / { Outs – [(Hits / BABIP)*.295 – Hits]}

 

I then applied this stat against the White Sox minor leaguers with at least 10 IP (I eliminated some people from the list due to my own subjective non-prospect assessment made mostly because of age at level). With all of this factored in, your leaderboard would be:

1. Euclides Leyer: 1.27

2. James Dykstra: 1.31

3. Andre Wheeler: 1.33

4. Jeffrey Wendelken: 1.34

5. Tyler Barnette: 1.36

6. Tony Bucciferro: 1.42

7. Tyler Danish: 1.43

8. Chris Beck: 1.43

9. Robinson Leyer: 1.46

10. Brad Goldberg: 1.48

 

Your Bottom 5 would be:

1. Nick McCully: 1.64

2. Jefferson Olacio: 1.60

3. Andrew Mitchell: 1.57

4. Braulio Ortiz: 1.56

5. Myles Jaye: 1.54

 

Thoughts? Do you find this useful? Did I inadvertently rip something else off that works better?

 

[NorthSideSox72]

Interesting. It seems the numbers rougly parallel walk rates inverse, though not quite exactly.

 

[danman31]

Why are you subtracting hits twice? You correct for BABIP and then subtract hits a second time.

[QuickJones81]

Why are you subtracting hits twice? You correct for BABIP and then subtract hits a second time.

 

Hits / BABIP = batted balls in play, multiplying by .295 determines how many hits they would have if they had an average BABIP, subtracting hits again gives you the differential between actual hits and expected hits given a normal BABIP. Those additional hits (or negative) are then taken away from the actual outs recorded since the outcome would have changed.

 

Not sure if I explained my logic well.

[danman31]

Ahh, I missed the extra brackets.

 

I think you should probably add the adjustment (whether it be positive or negative) to batters faced and not outs. Outs is the constant. How many batters to get this number of outs. Batters faced is the variable that evaluates the pitcher.

 

Not convinced the stat has any value either way.