Daily Notes, Featuring an Experiment

Table of Contents
Here’s the table of contents for today’s edition of Daily Notes.

1. Experiment: SCOUT+ Batting Leaderboards
2. SCOUT+ Leaderboard: Spring Training Batters
3. SCOUT+ Leaderboard: Arizona Fall League Batters, Revisited

Experiment: SCOUT+ Batting Leaderboards
For the past year-plus, I’ve frequently published in these pages what I’ve called the “SCOUT leaderboards” for winter leagues and (recently) spring training. I’m quoting myself when I write that “SCOUT represents an attempt to derive something meaningful from small samples” and is the average of a player’s standard deviations from the league mean (or, z-score) either in regressed strikeout and walk rate (for pitchers) or regressed home-run rate, walk rate, and strikeout rate (for hitters). SCOUT builds off of work done by Pizza Cutter on when samples for different stats become reliable. By taking a batter’s strikeout rate, for example, after X plate appearances and figuring in the league-average strikeout rate for the remaining plate appearances — up to the reliable sample size for strikeout rate — we’re able to reach a conservative estimate of what that batter’s “true talent” strikeout rate is. (Click here for more on SCOUT.)

Yesterday, I wondered aloud whether it made sense to continue including walk rate in SCOUT for hitters. “There seems to be,” I suggsted “a significant-enough population of hitters who’re able to post high-ish walk rates against minor-league (and, presumably, spring-training) pitching based largely on selectivity, but whose walk rates decline considerably when they face more talented major-league pitchers.” If walk rates dry up upon reaching the major leagues, it follows that they shouldn’t be included in a metric designed to make some kind of comment on a player’s future.

A couple of readers suggested that this might not be the case, at all, however — and, in fact, a recent (and excellent) study by Chris St. John at the Platoon Advantage reveals that minor-league walk rate is actually a useful tool in attempting to analyze a prospect’s chances for major-league success. (Note that I say prospect. There are still likely older minor leaguers who, by virtue of experience, are able to post comparatively high walk rates in the high minors.) St. John’s work has convinced me that SCOUT should include walk rate.

Simulataneous to this, I’ve wondered less aloud whether it might make sense to weight the various elements of SCOUT. Walk rate might be important, but home-run rate is surely more important — and yet, per SCOUT, a batter at 0.5 standard deviations above the league mean in expected walk rate would be valued as highly as a player at 0.5 standard deviations above the league mean in expected home run rate.

In response to that, I submit this experiment: a version of SCOUT called “SCOUT+.” SCOUT+ builds off of work by Bradley Woodrum from last August. In a piece by Woodrum called “Defensive Independent Hitting, Or ShH” (the last bit standing for “Should Hit”), Woodrum found that using the three variables found in FIP — using those plus expected BABIP — that one could predict a batter’s “true talent” wRC+ with some accuracy.

True-talent BABIP is, of course, not something that we can reasonably predict for winter leagues or spring training; the other elements of ShH, however, have already served as the basis for SCOUT.

Accordingly, I’ve used my limited spreadsheeting skills to calculate what we’ll call SCOUT+ for the moment. SCOUT+ is essentially a heavily regressed estimate of what a player’s wRC+ should be — again, minus BABIP. By definition, this will undervalue players who are capable of sustaining higher BABIPs and overvalue players whose “true talent” BABIP is lower than league average. Put another way, SCOUT+ will likely undervalue players who hit the ball hard and/or are fast, while overvaluing players who are either slow or possesses an extreme fly-ball approach.

To calculate SCOUT+, I’ve used a simplified form of Woodrum’s equation — specifically, as proferred by Tom Tango, (12.3*xHR% + 3*xBB% – 2*xK% ) * 92, where xHR%, xBB%, and xK% stand for expected home run, walk, and strikeout rate. To that result, I’ve added a constant that sets the average for all players in the sample at 100. The results seem reasonable, and SCOUT+ appears to account for the different values between home runs and walks and strikeouts in a way that plain SCOUT did not.

SCOUT+ Leaderboard: Spring Training Batters
The idea for SCOUT+ is introduced in belabored fashion above. HR%, BB%, and K% are the raw rate stats so far from spring training. xHR%, xBB%, and xK% (i.e. expected home-run, walk, and strikeout rate) are the regressed versions.

Below is the SCOUT+ batting leaderboard for spring training, for the 149 batters who’d recorded at least 22 spring-training at-bats as of Thursday afternoon. Note, of course, that the samples in question are very small and that the results should be regarded with due restraint.

Name	Org	PA*	HR%	BB%	K%	xHR%	xBB%	xK%	SCOUT+
Dan Uggla	ATL	32	9.4%	18.8%	18.8%	3.4%	8.3%	18.2%	114
Mat Gamel	MIL	25	12.0%	12.0%	12.0%	3.4%	7.0%	17.1%	113
Melky Cabrera	SF	30	10.0%	0.0%	3.3%	3.4%	5.4%	15.1%	111
Eric Sogard	OAK	35	5.7%	11.4%	8.6%	3.0%	7.2%	15.9%	111
Alex Gordon	KC	29	6.9%	13.8%	13.8%	3.1%	7.4%	17.3%	109
Jaff Decker	SD	29	6.9%	10.3%	10.3%	3.1%	6.9%	16.6%	109
Jemile Weeks	OAK	32	9.4%	3.1%	12.5%	3.4%	5.8%	16.9%	109
Lorenzo Cain	KC	25	8.0%	12.0%	12.0%	3.1%	7.0%	17.1%	109
Aubrey Huff	SF	27	7.4%	7.4%	7.4%	3.1%	6.5%	16.2%	109
Hector Luna	PHI	26	7.7%	7.7%	7.7%	3.1%	6.5%	16.3%	109

*Estimated.

And here’s the laggardboard:

Name	Org	PA*	HR%	BB%	K%	xHR%	xBB%	xK%	SCOUT+
Chris Gimenez	TB	25	0.0%	0.0%	40.0%	2.4%	5.5%	21.7%	89
Bryan LaHair	CHC	25	0.0%	0.0%	36.0%	2.4%	5.5%	21.1%	90
Ian Desmond	WSH	26	0.0%	0.0%	34.6%	2.4%	5.5%	21.0%	90
Dayan Viciedo	CWS	24	0.0%	4.2%	41.7%	2.5%	6.1%	21.9%	90
Chris Heisey	CIN	29	0.0%	10.3%	41.4%	2.4%	6.9%	22.6%	91
Danny Espinosa	WSH	25	0.0%	0.0%	32.0%	2.4%	5.5%	20.4%	91
Jai Miller	BAL	26	3.8%	3.8%	46.2%	2.8%	6.0%	23.0%	92
Brandon Wood	COL	24	0.0%	0.0%	29.2%	2.5%	5.6%	19.9%	93
Clete Thomas	DET	26	0.0%	0.0%	26.9%	2.4%	5.5%	19.6%	93
Tim Beckham	TB	23	0.0%	4.3%	34.8%	2.5%	6.1%	20.6%	93

*Estimated.

SCOUT+ Leaderboard: Arizona Fall League Batters, Revisited
Towards the end of November, I published the final SCOUT batting leaderboard for the Arizona Fall League using the original calculation of SCOUT. Here’s that same leaderboard, except using SCOUT+. (I’ve included some notes on the differences below.)

Name	Org	PA*	HR%	BB%	K%	xHR%	xBB%	xK%	SCOUT+
Robbie Grossman	PIT	124	5.6%	16.1%	14.5%	3.9%	13.8%	15.4%	133
Mike Olt	TEX	121	10.7%	12.4%	29.8%	6.0%	11.5%	27.8%	128
Michael Choice	OAK	75	8.0%	12.0%	16.0%	4.0%	10.8%	17.8%	121
Wil Myers	KC	106	3.8%	18.9%	17.0%	3.1%	14.7%	17.7%	121
Jefry Marte	NYN	90	4.4%	13.3%	13.3%	3.2%	11.5%	15.8%	117
Nolan Arenado	COL	129	4.7%	6.2%	10.9%	3.5%	7.6%	12.1%	117
Jedd Gyorko	SD	81	6.2%	12.3%	18.5%	3.7%	11.0%	19.0%	115
Brodie Greene	CIN	90	4.4%	11.1%	13.3%	3.2%	10.5%	15.8%	115
Bryce Harper	WAS	104	5.8%	10.6%	21.2%	3.8%	10.3%	20.6%	112
Aaron Hicks	MIN	120	2.5%	15.0%	17.5%	2.6%	13.0%	17.9%	110

*Estimated.

Notes
• Pittsburgh outfield prospect Robbie Grossman, who finished atop the original SCOUT leaderboard, finishes atop the SCOUT+ leaderboard, too.
• Unlike Grossman, Texas third-base prospect Mike Olt (No.2 here) didn’t even finish in the top 10 on the SCOUT leaderboard — finishing 18th overall, actually, out of 64 qualified batters.
• Gone from the original leaderboard are Milwaukee’s Logan Schafer (who moves down to 17th overall), Boston’s Alex Hassan (14th), and San Francisco’s Joe Panik (16th). Replacing them are Olt, San Diego third-base prospect Jedd Gyorko (who was 12th), and Bryce Harper (who was 19th originally).