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  1. What’s the correlation between P/PA and K/PA? I imagine it’s quite high. If you do things that lead to a lot 2 strike counts, you’re likely to strike out. And the way to get to 2 strikes it to either take a lot of strikes (which generally means taking a lot of pitches) or swinging and missing.

    The guys who exceed expectation do both. The guys who are below expectation swing a lot but don’t take a lot of pitches. Seems pretty straight forward. Strikeout rate and pitch taking both drive striking out. The homoscedasticity seen here would seem to suggest a very low correlation between the two. It would be interesting to see the scatterplot of SwStr% and Sw%

    Comment by Rick — January 31, 2011 @ 2:59 pm

  2. Great stuff Albert, you outed yourself as a Simpsons fan as well.

    Comment by AlexS — January 31, 2011 @ 3:03 pm

  3. A few weeks ago, Eno Sarris took a look at a few batters with high swinging-strike rates and average strikeout rates, showing that a batter with a penchant for (or weakness in) whiffing on pitches doesn’t necessarily post as a high number of strikeouts as you would expect.

    I love how this is some sort of revelation. Thank god for the groundbreaking work of Eno Sarris.

    Comment by Telo — January 31, 2011 @ 3:38 pm

  4. I think these two studies highlight the need for Klooking% and Kswinging% on fangraphs.

    Comment by philosofool — January 31, 2011 @ 3:44 pm

  5. I remember an article (here I think, though it might have been over at The Hardball Times) that I’m having trouble finding right now, that examined where pitchers got their swinging strikes and compared that with their strikeout rate. It found that the ability to generate swings out of the zone correlated better with K% than the ability to miss bats within the zone. It’s sort of interesting, then, that the batters who were able to “beat” their swinging strike percentage and avoid strikeouts were a group prone to chasing.

    Comment by Whelk — January 31, 2011 @ 3:54 pm

  6. missed that

    Comment by t-lonious munk — January 31, 2011 @ 4:29 pm

  7. Sounds like reverse correlation. Guys with good breaking stuff who get batters to chase, will get more Ks. Not the other way around.

    To your second point:

    There are two factors involved in a batter striking out:

    - Swinging and missing
    - Taking a lot of pitches

    If you do em both? You strike out a lot.

    Do just one? You strike out an average amount.

    Do neither? Well, you ain’t striking out very often.

    This is obviously rough and dirty, but that last Eno Sarris article was a joke the way people reacted surprised to it (Albert included.) It’s such a basic concept it’s hard to believe some of the people reacting to it had ever played or watched a baseball game.

    Comment by Telo — January 31, 2011 @ 4:43 pm

  8. Could there be a ‘swing score’ the same way there is a ‘speed score’?

    Or measure attributes of hitting apart from box-score results.

    First strike % could be high due to a patient hitter taking more (those who don’t mind hitting from behind in the count) or an aggressive hitter swinging at what might be the best pitch of the at bat.

    Same ratio (first strike %), but deeper analysis based on how a batter gets there.

    Apply similar logic to every possible count combination, then devise a patience index. Or does that already exist?

    Then you might be able to understand what it means when D. Barton and P. Fielder both post a 16% bb%. Completely different value if you can determine Barton’s mark is primarily HIS approach to pitchers, but Prince walks as much as he does due primarily to the pitchers’ approach to HIM.

    Sort of like FIP is to era, I would imagine in depth analysis along the lines mapped out in the article would eventually lead to similar bb/k visibility.

    Great stuff!

    Comment by Jimbo — January 31, 2011 @ 6:02 pm

  9. Example of where this would apply…

    Mark Reynolds “looks” like he walks a lot for a power bat. Granted, he does excel over some, but if you knew that his mark was more from being avoided than having a great eye…then regression is all the more likely if even a single ‘hole’ is found and starts to get challenged.

    Comment by Jimbo — January 31, 2011 @ 6:09 pm

  10. Very interesting article…after I read it I spent some time playing around with some data in Excel, and using my rudimentary knowledge of statistics, it seems that swing% and contact%, in tandem (using multiple linear regression), seem to predict K% fairly well. At a high level this makes sense to me because:

    1) The batter can’t strike out if they make contact
    2) The more often the batter swings, the more often the at-bat will end early in the count (when they can’t strike out)

    Of course there would definitely be some correlation between the two percentages (swing% and contact%) which might make it hard to develop a reliable model using multiple linear regression (just like you mention in the article with swing% and SwStr%).

    Comment by rodgers37 — January 31, 2011 @ 6:35 pm

  11. Word. Would love to see this, if it’s possible.

    Comment by Anon — January 31, 2011 @ 11:23 pm

  12. “it seems that swing% and contact%, in tandem (using multiple linear regression), seem to predict K% fairly well.”

    The formula is actually a lot simpler: 1-ct% = K%.

    Comment by EK — January 31, 2011 @ 11:40 pm

  13. I don’t think this is strictly true. Fangraphs defines contact% as follows:

    Contact% – Total percentage of contact made when swinging at all pitches

    At first glance I don’t think this should have the “1-ct% = K%” relation to K%. Checking a bunch of player profiles seems to confirm this.

    Comment by rodgers37 — February 1, 2011 @ 9:52 am

  14. Just tried it out on some data – you’re right that 1-ct% seems to be a decent predictor – however using swing% as well in the regression better accounts for hitters like:

    1) Brett Gardner (see above) who have a very high ct% but strike out a decent amount because they take more pitches than most.

    2) Vladimir Guerrero who has an average ct% but strikes out infrequently because he swings at a lot of pitchers early in the count.

    Comment by rodgers37 — February 1, 2011 @ 10:20 am

  15. *applauds*

    This is what we should be doing during the offseason. Not complaining about comments.

    It seems to me that there should some sort sweetspot for lack of a better word. Batters who take a lot of pitches would probably strike out more than their projected k rate because they’ll strike out looking more often. Batters who have a high swSt% would underperform their projected k rate because they’d put the ball into play more (because they’re obviously swinging more). The guys in the middle would probably correlate well, but the guys on either end of the spectrum wouldn’t.

    I’m thinking.

    Comment by Mike Savino — February 1, 2011 @ 10:27 am

  16. I think we should look at pitches per plate appearance as well as swSt%. I think that’ll tell a better story. lol, I’m actually sitting in Math 312 at university (which is statistics) so I probably should just run it myself.

    Comment by Mike Savino — February 1, 2011 @ 10:29 am

  17. This sounds familiar, so I’ll just repeat what I said in the last comments section, with an addiiton. I didn’t present the piece as ground-breaking work, nor do I position myself as a pre-eminent researcher. I’m a noob writer just trying to take a critical eye to baseball.

    On that tip, doesn’t it make sense to question even basic assumptions about the game? Isn’t that how we got the genius that is The Book? I’m not comparing myself to Tango, I’m just saying that we can’t just say, oh, that’s obvious, no need to go look at that phenomenon.

    Especially in January and February haha.

    Comment by Eno Sarris — February 1, 2011 @ 12:14 pm

  18. Three 2010 D-backs (Upton, LaRoche, Reynolds) on the “struck out more than expected” list. Wonder if that is a 2-strike approach thing or if their is some other correlation. Also wonder how much fluctuation their is year-to-year for individual players. Good stuff.

    Comment by mymrbig — February 1, 2011 @ 12:40 pm

  19. Having followed Dunn for a couple seasons here in DC, I’m not surprised to find the data match the impression that he takes a lot of third strikes. It appears that when comparing him to a guy like Vlad, two things stand out:

    First, Vlad’s plate coverage (and tendency to use it to go for anything) is well known. Perhaps guys like Dunn don’t have that ability, whether because of weight, back problems, etc. Jim Edmonds struck me as a guy that took a lot of third strikes as well, and I know he had back problems.

    Second, Dunn seems to take the same approach to every ball, namely, if it looks in a good part of the zone, swing HARD! He rarely seems to change up an approach to go poking balls the other way, even when a massive shift is on. Perhaps guys like Dunn also can’t/won’t take slapping swings that may provide better coverage over a wider variety of locations. If so, Dunn’s approach makes sense given his below average speed and possible reluctance to turn his back on a reach or alter his normal timing and stroke.

    Comment by Cory — February 1, 2011 @ 1:37 pm

  20. I’m sorry Rodgers37, you are correct. I assumed contact % was contact rate, but that’s not the measure used in this site. 1-K% = Contact rate, which is a more important measure and which (as far as I can tell) does not seem to be reflected in a players page on this site.

    Comment by EK — February 1, 2011 @ 1:58 pm

  21. Don’t forget that it’s not just guys that take strikes and guys that swing and miss that strike out a lot. There are also the guys that hit a bunch of foul balls. Four foul balls, then a swing and miss. Not a bad contact rate. 100% strikeout rate.

    Comment by cardhorn — February 1, 2011 @ 2:37 pm

  22. I did a fair amount of work probably over a year and a half ago regarding pitchers and their expected k rate based on pitch outcomes; in zone ss, oz ss, foul, call str, etc.

    I posted it over on draysbay. Im on my mobile so Im not going to hunt for the link. My handle there is matthan.

    Either way there is a strong relationship between pitch outcomes and k rate. Some variables sizeably stronger than others.

    In theory, it can be built on to minimize FIP by increasing K rate, due to pitch outcome optimization. Which can be accomplished by throwing different pitches in different locations maximizing their k rate.

    Comment by Matt — February 1, 2011 @ 6:24 pm

  23. I wonder what Ted Williams would have done to any of the theories (or to possible future theories)…lol.

    BB% 20.6 %
    K% 9.2 %

    Comment by bcp33bosox — February 2, 2011 @ 3:15 am

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