The Thin Line Between 1B and DH
Posted: Fri Feb 22, 2019 9:50 am
Despite being a front office that prioritizes ground ball pitchers, I've never really prioritized 1B defense. That's where your put your "2nd DH", right? Well, I'm re-thinking that a touch after seeing just how brutal Norris Rutledge was last season - a -14.9 ZR. And that shouldn't be a surprise, he had a -15.6 the year prior, -14.9 before that and -12 two seasons before that. He's *always* been a brutal defensive 1B - I just never even bothered to check since I didn't think it mattered. So, why do I think it matters now? Well, because across the rest of my team (C, 2B, 3B, SS, LF, CF, RF) we were "pretty average" (noting that I am assuming a '0' is relatively "average" - which may be a very poor assumption) - netting a -2.1 ZR - yet were 12th in BABIP in the JL. So, it seems Rutledge is the low hanging fruit for why we were near the bottom of the barrel defensively as a team. So, that got me asking myself... if 1B "matters" after all, then what is the 'cut-off' for when a player needs to be a DH instead of a 1B? Below is a compilation of the 1B that logged the most innings at the position for their teams in the JL last year with a relevant plot attached.
ZR IP ZR/IP Range Error Player with most innings at 1B on team
MON -12.5 819 -0.015262515 1 4 Ettienne LaFitte
HSV -14.9 1350 -0.011037037 1 4 Norris Rutledge
WIC -5.8 615 -0.009430894 1 3 Brian Clough
JAC -4.6 1294 -0.003554869 2 3 Manuel Martinez
RCK -2.1 1313 -0.001599391 2 5 Manuel Marino
LOU -1.6 1262 -0.001267829 3.00 4.00 Pedro Debesa
AC -0.4 1084 -0.000369004 2 5 Adrian Salazar
NSH 0.6 1251 0.000479616 4 2 Miguel Suarez
LV 0.8 995 0.00080402 3 7 Gervasio Ridder
BRK 2.6 1320 0.001969697 3 5 Cisco Guerrero
SA 2.1 658 0.003191489 2 5 Manuel Nunez
PHO 4.5 1366 0.00329429 3 4 Mario Deortez
MC 4.1 1138 0.003602812 3 6 Mike Ellis
CC 2.7 492 0.005487805 6 7 Wendel Clark
NO 10.4 1367 0.007607901 4 7 Yancy Cravat
Looking at the plot attached, it seems pretty clear that 1 range is a no-go as all 3 players that had it were brutal at 1B (though, granted, we didn't get a 1 range, high error rating sample to see how that might affect the level of disfunction). In case you're having trouble interpreting ZR/Inning, a -0.01 ZR/Inning is a -14.58 pace over a full season (162 games * 9 innings = 1458 innings). On the next level of range (2), you start to see a pretty wide range of competence. The poor error rating sample is the worst of the four - as you might expect; however, the other three are pretty widely spaced despite the same error rating. If I had to guess, I'd say the one sample with a positive ZR at 2 range is an outlier (either in-season luck or a rating that is close to the next level on the 1-250 scale). It isn't until we get to 3 range that you start to see consistent positive ZR (4 samples positive vs. 1 sample negative). You also see almost no consistent trend in error rating vs. performance, though with a dataset so small that doesn't necessarily lend to any conclusions. At 4 range you have both positive with the low error rating being just barely so and the high error rating being the best of the dataset - even better than the 6 range sample with the same error rating (not that this should be a conclusive comparison).
So... what's my conclusion from this? Blankly, 1 range is unacceptable. 2 range is fine, but don't except solid, consistent defensive contributions even if the error rating is good. 3 range is when you start really getting into the realm of competence and 4+ range is when you start to see "good" defense with, potentially, some diminishing returns on range from there. Within that, it seems like <=3 error will start to tank your range's contribution to defensive performance. 4-5 is where you start to feel confident in competence - though the tangible fluctuation in performance in this particular SSS leads me to believe this isn't a 'comfort' zone. That makes >=6 the real benchmark for "good".
Error color scale for chart:
2 = red
3 = orange
4 = yellow
5 = light green
6 = dark green
7 = cyan
ZR IP ZR/IP Range Error Player with most innings at 1B on team
MON -12.5 819 -0.015262515 1 4 Ettienne LaFitte
HSV -14.9 1350 -0.011037037 1 4 Norris Rutledge
WIC -5.8 615 -0.009430894 1 3 Brian Clough
JAC -4.6 1294 -0.003554869 2 3 Manuel Martinez
RCK -2.1 1313 -0.001599391 2 5 Manuel Marino
LOU -1.6 1262 -0.001267829 3.00 4.00 Pedro Debesa
AC -0.4 1084 -0.000369004 2 5 Adrian Salazar
NSH 0.6 1251 0.000479616 4 2 Miguel Suarez
LV 0.8 995 0.00080402 3 7 Gervasio Ridder
BRK 2.6 1320 0.001969697 3 5 Cisco Guerrero
SA 2.1 658 0.003191489 2 5 Manuel Nunez
PHO 4.5 1366 0.00329429 3 4 Mario Deortez
MC 4.1 1138 0.003602812 3 6 Mike Ellis
CC 2.7 492 0.005487805 6 7 Wendel Clark
NO 10.4 1367 0.007607901 4 7 Yancy Cravat
Looking at the plot attached, it seems pretty clear that 1 range is a no-go as all 3 players that had it were brutal at 1B (though, granted, we didn't get a 1 range, high error rating sample to see how that might affect the level of disfunction). In case you're having trouble interpreting ZR/Inning, a -0.01 ZR/Inning is a -14.58 pace over a full season (162 games * 9 innings = 1458 innings). On the next level of range (2), you start to see a pretty wide range of competence. The poor error rating sample is the worst of the four - as you might expect; however, the other three are pretty widely spaced despite the same error rating. If I had to guess, I'd say the one sample with a positive ZR at 2 range is an outlier (either in-season luck or a rating that is close to the next level on the 1-250 scale). It isn't until we get to 3 range that you start to see consistent positive ZR (4 samples positive vs. 1 sample negative). You also see almost no consistent trend in error rating vs. performance, though with a dataset so small that doesn't necessarily lend to any conclusions. At 4 range you have both positive with the low error rating being just barely so and the high error rating being the best of the dataset - even better than the 6 range sample with the same error rating (not that this should be a conclusive comparison).
So... what's my conclusion from this? Blankly, 1 range is unacceptable. 2 range is fine, but don't except solid, consistent defensive contributions even if the error rating is good. 3 range is when you start really getting into the realm of competence and 4+ range is when you start to see "good" defense with, potentially, some diminishing returns on range from there. Within that, it seems like <=3 error will start to tank your range's contribution to defensive performance. 4-5 is where you start to feel confident in competence - though the tangible fluctuation in performance in this particular SSS leads me to believe this isn't a 'comfort' zone. That makes >=6 the real benchmark for "good".
Error color scale for chart:
2 = red
3 = orange
4 = yellow
5 = light green
6 = dark green
7 = cyan