Strikeout to ERA Correlation

Those come from GBA on pitchers card, hitters card or X-outs on pitchers cards. GB(B) and GB(C) and FB(B-C-A) or lineouts or popouts do not add to double plays.

Those come from GBA on pitchers card, hitters card or X-outs on pitchers cards. GB(B) and GB(C) and FB(B-C-A) or lineouts or popouts do not add to double plays.

Sorry for the rookie question? Do all pitchers have the same number of GBA and X-outs on their cards? Or do high K pitchers have fewer of those?

Those come from GBA on pitchers card, hitters card or X-outs on pitchers cards. GB(B) and GB(C) and FB(B-C-A) or lineouts or popouts do not add to double plays.

Sorry for the rookie question? Do all pitchers have the same number of GBA and X-outs on their cards? Or do high K pitchers have fewer of those?

Xouts same on all cards GBA varies by card, independent of strikeouts

Do all pitchers have the same number of GBA and X-outs on their cards? Or do high K pitchers have fewer of those?

Should be noted that while all pitchers have the same number of X chances for each position on card as discussed previously.

However, high K pitchers will tend to have fewer gb()C than low K pitchers. So to some extent that could influence ERA.

But like the fb()B it will be small. The averages are 3.9 for those starters under 10 K on right side compared to 0.8 for those with more than 50. So a difference of 3.1 runner advancement with less than 2 out. So technically it could raise ERA but for practical purposes very little. I would not specifically target high K pitchers on theory of avoiding gb()C.

And gb()A is also fixed on the card and not really directly related to the number of strikeouts on card. But using the same query as used to get average flyball B and groundball C pitchers with under 10 Ks average 2.33 and those with >50 average 1.87. So the low K pitchers as a group on average get a little more benefit from double plays which would lower ERA for non strikeout pitchers. Hard to say whether that fully cancels the differences in fb()B and gb()C. Since double plays draw 2 outs effectively killing a rally my gut tells me they cancel out.

But having said all that since the 3 outcomes that fbB, gbC, and gbA all have fixed counts for specific cards if I wanted to target having or avoiding any of those rolls I would query my database directly for those fixed events instead of just high strikeouts and assuming one goes with or does not go along with it.

Do all pitchers have the same number of GBA and X-outs on their cards? Or do high K pitchers have fewer of those?

Should be noted that while all pitchers have the same number of X chances for each position on card as discussed previously.

However, high K pitchers will tend to have fewer gb()C than low K pitchers. So to some extent that could influence ERA.

But like the fb()B it will be small. The averages are 3.9 for those starters under 10 K on right side compared to 0.8 for those with more than 50. So a difference of 3.1 runner advancement with less than 2 out. So technically it could raise ERA but for practical purposes very little. I would not specifically target high K pitchers on theory of avoiding gb()C.

And gb()A is also fixed on the card and not really directly related to the number of strikeouts on card. But using the same query as used to get average flyball B and groundball C pitchers with under 10 Ks average 2.33 and those with >50 average 1.87. So the low K pitchers as a group on average get a little more benefit from double plays which would lower ERA for non strikeout pitchers. Hard to say whether that fully cancels the differences in fb()B and gb()C. Since double plays draw 2 outs effectively killing a rally my gut tells me they cancel out.

But having said all that since the 3 outcomes that fbB, gbC, and gbA all have fixed counts for specific cards if I wanted to target having or avoiding any of those rolls I would query my database directly for those fixed events instead of just high strikeouts and assuming one goes with or does not go along with it.

I guess I need a database

I started keeping a database on all the cards back in 2001. Back then there were few or no filters and sorting ability on the FA listing. I like to put together theme teams that target something. Had one where I used it to identify the pitchers with the most double plays on the card and hitters at each position with the least and then tried to get all those cards on one team in a draft. It was successful from point of view that I turned about 3 times as many double plays as I hit in to. But the won loss record was not good. I think now though that my initial balance between pitching and hitting leaned too much toward offense. If I do it again I will make more compromises on the offense side. Maybe settle for hitters in the top 10 for avoiding double plays instead of insisting on the leader in fewest DPs.

Plus I like messing with spreadsheets and databases. Did a lot of that in a prior job. Current job does not allow much opportunity to keep those skills sharp. So use this to keep in practice. So the type of ad hoc spur of the moment queries for this thread are really helpful.