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Introducing a Simple New Plate-Discipline Metric

Not all walks are created equal. This is codified most directly in the concept of the intentional walk, which became such standard and tedious practice that it can now be accomplished with a simple signal. While some intentional walks are strictly based on setting up a better opportunity (say, it allows for a double play, more convenient force outs, or the pitcher is batting next), they often indicate that simply allowing the hitter to take first is a safer alternative than giving them the chance of doing anything else.

More generally, while walks are good for the batter, they're worse than getting a hit. On the flip side, while walks are bad for the pitcher, they're better than giving up a hit. To put that another way, getting a walk instead of getting a hit is a missed opportunity for the batter and a minor victory for the pitcher, but getting a walk instead of getting an out is a minor victory for the batter and a missed opportunity for the pitcher. This is pretty straightforward, but the distinction isn't really captured in most plate-discipline statistics. On one hand, something simple like walk percentage very intentionally sets aside the question of what else might have happened in the plate appearance, or if those walks are coming at the expense of hits, given the uncertainty of a batted ball becoming a hit. On the other hand, swing decision data combined with Statcast batted-ball data could provide a pretty good idea of what the alternatives could have been for a given walk - potentially even to the level of whether there was a hittable pitch earlier in the plate appearance and what the batter could have been expected to do with it - but would require some fairly detailed analysis to determine how beneficial the walk was compared to what could have been. Is there a middle ground there?

One approach is to reframe the issue slightly. If a batter gets a hit instead of walking, that shouldn't reflect negatively on their plate discipline. If a batter gets a hit instead of getting out, that doesn't directly relate to plate discipline either. With that in mind, you can focus directly on the ratio between how often a batter walks and how often they get out. While there are various ways to represent the resulting statistic, the simplest is to just divide walks by outs for a direct Walks Over Outs value. Since most data sources don't have 'outs' as a direct batting statistic, the formula would be as follows:

WOO = BB/(1-BA)*AB

If that feels too results-oriented, you could substitute xBA for BA (this would probably be the more useful version, but the end results are generally fairly similar, so I'll stick with the simpler version here). Either way, leaders in WOO generally line up pretty closely with leaders in BB%, but good hitters aren't 'punished' for getting hits instead of walks, and those anomalies who walk a lot but do relatively little else (sorry, Vogelbach) are held in less high regard. For reference, here are the top 30 seasons by WOO over the past five full seasons, along with the BB% for those players.

PLAYER YEAR WOO BB%
Joey Votto 2015 0.382 20.6
Mike Trout 2018 0.376 20.1
Bryce Harper 2015 0.355 19
Joey Votto 2017 0.353 19
Mike Trout 2017 0.337 18.5
Mike Trout 2019 0.33 18.3
Aaron Judge 2017 0.327 18.7
Bryce Harper 2018 0.315 18.7
Mike Trout 2016 0.308 17
Paul Goldschmidt 2015 0.306 17
Alex Bregman 2019 0.305 17.2
Joey Votto 2018 0.3 17.3
Matt Carpenter 2017 0.289 17.5
Joey Votto 2016 0.288 16
Bryce Harper 2016 0.282 17.2
Yasmani Grandal 2019 0.282 17.2
Juan Soto 2019 0.278 16.4
Miguel Cabrera 2015 0.271 15.1
Paul Goldschmidt 2016 0.27 15.6
Jose Bautista 2015 0.27 16.8
Jose Bautista 2016 0.269 16.5
Brandon Belt 2016 0.265 15.9
Josh Donaldson 2016 0.264 15.6
Rhys Hoskins 2019 0.263 16.5
Carlos Santana 2019 0.262 15.7
Carlos Santana 2015 0.255 16.2
Carlos Santana 2018 0.255 16.2
Edwin Encarnacion 2017 0.253 15.5
Ben Zobrist 2016 0.252 15.2
Daniel Vogelbach 2019 0.251 16.5

I also thought it would be interesting, given the large number of high-leverage walks in yesterday's game, to look at a quick snapshot of this metric on a team level for the entertainingly small sample size of one game.

Team AB BB AVG WOO
PIT 35 11 0.257 0.423
LAD 36 8 0.417 0.381
TEX 40 9 0.375 0.36
KCR 38 8 0.395 0.348
SEA 32 9 0.156 0.333
HOU 33 6 0.273 0.25
MIN 39 7 0.256 0.241
CLE 32 6 0.188 0.231
SDP 32 5 0.313 0.227
CIN 34 5 0.294 0.208
NYY 35 6 0.171 0.207
DET 30 5 0.167 0.2
CHC 26 4 0.077 0.167
LAA 30 4 0.2 0.167
PHI 36 4 0.25 0.148
SFG 39 4 0.256 0.138
OAK 30 3 0.2 0.125
COL 35 3 0.314 0.125
TBR 32 3 0.156 0.111
STL 37 3 0.27 0.111
CHW 33 2 0.212 0.077
TOR 36 2 0.222 0.071
MIL 40 2 0.275 0.069
ATL 37 2 0.189 0.067
MIA 30 1 0.1 0.037
ARI 40 1 0.3 0.036

Which I think confirms what we were all suspecting: the Mariners are currently among the league leaders in WOO.





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