Creation of the FPCT+ Statistic
Author: Cade Nelson, Exercise Science Major at University of Kansas, Normal Cornbelters Baseball Analytics Intern
What is FPCT+?
Fielding Percentage Plus (FPCT+) is inspired by stats like OPS+ and ERA+, which takes the player’s ERA or OPS and normalizes and compares it to the entire league. For example, the average FPCT+ is 100, and an FPCT+ of 105 would mean that the fielder is five percent better than the league average.
Why was FPCT+ Created?
FPCT+ was created to compare the fielders of the Kernels Collegiate League (KCL) to the average fielder of the KCL. FPCT+ is the closest estimate to FWAR (fielding wins above replacement) available with the current level of data in the KCL.
How is FPCT+ Calculated?
First, find the league fielding percentage by taking the total amount of successful fielding plays, Assists + Putouts, divided by the total chances of the entire league, Assists + Putouts + Errors. Once the fielding percentage of the entire league is
calculated, find the fielding percentage of the individual(s) needed using the formula above. The individual(s) fielding percentage is then calculated, followed by taking (individuals fpct% / league fpct%) * 100 to yield FPCT+. No park factors are included in this stat for now, but in the future, it could be added to see if home ballparks have any effect on fielding percentage. However, the league this stat is created for, the KCL, has no ballpark factors as the league uses one field for all the games.
League FPCT%= (league assists + league putouts) / (league assists + league putouts + league errors)
Individual FPCT%= (indiv. assists + indiv. putouts) / (indiv. assists + indiv. putouts + indiv. errors)
FPCT+ = (individual FPCT% / league FPCT%) *100
How is FPCT+ Used?
FPCT+ should be used to compare players who play similar positions, if not the same position consistently, like OF versus OF, MIF versus MIF, or C versus C. Comparing players of similar positions gives a better representation of their fielding abilities. For example, comparing a usual left fielder and a first baseman isn’t fair to either player. On the other hand, comparing two fielders that usually play shortstop is fine because, over a season-long time, the plays’ complexity should even out to where the
players have had similar types of plays to make. For example, compare two usual shortstops in the KCL, Camden Porter (Merchants) and Cale Steinbaugh (Ground Sloths), who have similar total chances.
Camden Porter – 12 PO, 11 A and 2E
- Current league FPCT%=.961
- FPCT+= 95.7
Cale Steinbaugh – 10 PO, 11 A, and 3E
- Current league FPCT%=.961
- FPCT+= 91.0
Then, look at Evan Hutson- 43 PO 3A
- Current League FPCT%=.961
Evan Hutson primarily plays first base, which isn’t as tricky as a shortstop, so he should have a better fielding percentage. Both of these shortstops are below-average league fielders, but also shortstop is the third most challenging defensive position according to Sabermetrics defensive spectrum behind catcher and pitcher (James). This is why it is essential to compare players with similar positions because it would create false
beliefs in players. Therefore, the next update to FPCT+ is to create PFPCT+ (Positional Fielding Percentage Plus), which would be used to compare players in the same position groups.
Potential Source of Error
A potential error that could be included in FPCT+ is scoring decisions made by official scorers, which could skew the fielding percentage of the league as well as the fielding percentage of an individual player. However, this should not have a large impact on results, as errors are usually easy to identify and score with a basic knowledge of what an error is.
FPCT+ does a great job of comparing defensive fielders to league average fielders and other fielders in the league. However, the most important thing when using FPCT+ is to compare players who play similar positions to get an accurate view of each fielder. To make FPCT+ better, creating PFPCT+, which compares the fielding percentage of an individual for a specific position to the fielding percentage of the league for the same position.
I first would like to acknowledge the Normal Cornbelters and my internship mentor Jarrett Rodgers, who allowed me to use the KCL statistical database to work on a team and individual research projects. I would also like to thank the other KCL analytics interns (Matt Bowerman, Jeffrey Brover, Jacob Hallowell, Clark Heideman, Christian Taylor, and Ian Thompson) for the help so far during the internship; without them, it wouldn’t be possible to collect the statistics from the KCL games. Finally, I would like to
thank Jeffrey Brover for posting this research to the Kernelytics Blog. Don’t hesitate to contact me at firstname.lastname@example.org with any further questions. Thank you for reading!
James, Bill. Baseball Abstract. 1981.
Normal Cornbelters. “KCL Stats.” Corn Crib.
“On-Base plus Slugging plus (OPS+): Glossary.” MLB.com,
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