Daily Notes: A Loathsome Semantic Point in re Spring Stats

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

1. A Loathsome Semantic Point Regarding Spring Stats
2. SCOUT Leaderboards: Spring Training
3. Mostly Unhelpful Video: Michael Wacha Strikes Out Five

A Loathsome Semantic Point Regarding Spring Stats
It has been noted by people smarter than the present author — and also by Jeff Sullivan — that it’s best to approach spring numbers with a great deal of caution so far as drawing conclusions is concerned regarding what they might suggest about a player’s true-talent level. While research from last March by Mike Podhorzer and Matt Swartz reveals that, in certain cases, spring numbers actually do possess some predictive value, this is likely an instance of exceptions proving rules.

Still, to say that spring numbers are “meaningless” because they lack predictive value for the upcoming season is likely not quite right, either. As Dave Cameron recently noted in a February piece on the relevance of WAR, every stat “is simply the answer to a question.” A question that the present author has some interest in answering is “Which players are performing the best this spring?” — not necessarily with a view to how it might inform their regular-season production (although I’m willing to become irrationally exuberant with little provocation), but merely in and of itself. Any numbers that answer that question have “meaning” to that end.

Of course, some metrics are still unpalatable in small samples. Clayton Kershaw‘s 9.00 ERA in 8.0 spring innings probably tells us less about his performance so far than the 12:1 strikeout-to-walk ratio he’s posted against 42 batters (for a strikeout and walk rate of 28.6% and 2.4%, respectively). Likewise, knowing what we do about certain metrics and the rates at which they become reliable, it’s also manifestly the case that Royals third baseman Mike Moustakas will not continue to bat .545 (on the strength of a BABIP nearing .600). Walks and strikeouts, though — and home runs for batters — are all readily available, become stable rather quickly, and account a great deal for a player’s ability either to produce or prevent runs.

All of which concludes the author’s lengthy and unnecessary justification for publishing below the SCOUT leaderboards for spring.

SCOUT Leaderboards: Spring Training
SCOUT Leaderboard: Spring Hitters
Below is the current SCOUT batting leaderboard for spring training. SCOUT+ is calculated using regressed home-run, walk, and strikeout rates, where 100 is league average (in this case, for all spring hitters) and above 100 is above average. xHR%, xBB%, and xK% stand for expected home run, walk, and strikeout rate, respectively.

Player Team Pos PA xHR% xBB% xK% SCOUT+
Ryan Raburn CLE 2B 20 3.8% 9.0% 16.9% 116
Nolan Arenado COL 3B 17 3.9% 8.2% 16.6% 115
Domonic Brown PHI RF 29 3.4% 10.6% 17.8% 114
Jason Bay SEA LF 12 3.2% 10.4% 17.2% 113
Miguel Cabrera DET 3B 22 3.5% 8.4% 16.0% 112
Allen Craig STL 1B 15 2.9% 10.8% 16.2% 111
Shane Robinson STL CF 21 3.2% 9.5% 16.1% 111
Steve Pearce BAL 1B 12 3.6% 8.9% 17.9% 111
Craig Gentry TEX CF 18 2.9% 10.1% 15.8% 110
Prince Fielder DET 1B 21 3.2% 10.0% 17.4% 110

SCOUT Leaderboard: Spring Pitchers
Below is the current SCOUT pitching leaderboard for spring training. SCOUT- is calculated using regressed strikeout and walk rates where 100 is average and below 100 is above average. xK% and xBB% stand for expected strikeout and walk rate, respectively.

Player Team IP TBF xK% xBB% SCOUT-
Michael Wacha STL 7.2 28 21.4% 8.4% 90
Brandon McCarthy ARI 5.0 19 21.1% 8.3% 90
Vidal Nuno NYY 5.0 19 21.1% 8.7% 91
Donnie Joseph KC 3.0 10 20.8% 8.5% 91
Clayton Kershaw LAD 8.0 42 20.5% 8.1% 91
Rick Porcello DET 8.0 31 20.4% 8.1% 91
Matt Magill LAD 4.0 16 20.8% 8.5% 91
Ryan Mattheus WSH 4.0 13 20.5% 8.4% 92
Simon Castro CWS 5.0 18 20.6% 8.5% 92
Michael Olmsted MIL 4.0 17 20.7% 8.9% 93

Mostly Unhelpful Video: Michael Wacha Strikes Out Five
Here’s mostly unhelpful video of Cardinals right-handed prospect and SCOUT pitching leader Michael Wacha striking out five minutes over 3.0 innings on February 27th:

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Dave (UK)
Dave (UK)

It is a shame there is no PitchF/X data for spring games as that data might actually enable us to see a change of approach at the plate for hitters and could actually prove useful in predicting performance in the upcoming season.


I don’t think I buy that, I remember back in 2000, the newly remade Mariners showed up in spring training and Edgar Martinez and John Olerud both started spring by taking absurd numbers of pitches to “re-learn” the strike zone. I used to try to do something similar though I never really played in games that you could safely say didn’t count in order to be safe in doing it. Some batters do drastically different things when they’re attempting to get comfortable than they normally do. If you know you’ve got a job locked up for the season, why not essentially watch pitches for a game or two, get used to seeing the ball without worrying about chasing anything, instead of chasing that curveball in the dirt, learn what it looked like, prevent yourself from developing bad habits early when it doesn’t matter. Rookies and minor leaguers (and some old guys) obviously don’t have that luxury, they’re trying to impress the big league squad and make the team, but if your job is locked up I could see taking an abnormal approach at the plate in an effort to get comfortable before the season starts.