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lefties 57 percent conclusions it has been interesting to watch david price develop into the ace of the rays pitching staff it has been fascinating for me to dive into how he accomplished that and to learn about the evolution of his repertoire across seasons it s rare that a pitcher is that willing to continuously experiment adapt and evolve he has a dominant 95 96 mph four seam fastball that serves him well against left handed batters and a four seamer two seamer pairing that has helped him improve against right handers in 2010 his breaking pitches are average offerings that fill out a solid repertoire though his performance on batted balls going forward may not be expected to be as good as it was in 2010 his stuff certainly seems to support his ability to carry the mantle of staff ace in the future may 24 2010 winning with an 89 mph fastball an analysis of brian bannister part 3 posted by mike fast under uncategorized 1 comment note this article was originally published at the statistically speaking blog at mvn com on february 28 2008 since the mvn com site is defunct and its articles are no longer available on the web i am re publishing the article here in part 1 of this series we examined brian bannister s suggestions for why he has been able to beat the league babip he indicated that it was probably due to pitching more often in favorable pitcher s counts and inducing balls in play with two strikes when the hitter is against the ropes however the evidence didn t show much advantage for bannister we noted that he did pitch a little more often in favorable counts but this led to him avoiding walks more than anything it had little salutary effect on his babip in part 2 of this series we learned about the pitches that bannister threw during 2007 and how he used them we saw that the fastball and curveball were good pitches against right handed hitters and the slider was a good pitch against left handed hitters part 1 part 2 part 3 in this final part of the series we re going to marry those two approaches to see if we can uncover any patterns that might explain bannister s babip performance in this portion i m not concentrating so much on evaluating bannister s own statements as i did on part 1 rather i m thinking more about what we can expect from bannister in the future i m also interested in investigating techniques that could prove useful for evaluating dips theory on a component basis as we accumulate more pitchf x data in the coming seasons should we expect bannister to maintain any of his babip edge and thus his 3 87 era from 2007 or are the projection systems like pecota subscribers only and chone more reasonable when they project an era of 5 19 or 4 74 more may 22 2010 winning with an 89 mph fastball an analysis of brian bannister part 2 posted by mike fast under uncategorized 1 comment note this article was originally published at the statistically speaking blog at mvn com on february 26 2008 since the mvn com site is defunct and its articles are no longer available on the web i am re publishing the article here in part 1 of this analysis we examined the league numbers for batting average on balls in play babip and whether bannister was able to beat the league babip by pitching in favorable counts we found that he did not gain any particular advantage by inducing more balls in play on two strike counts so we turn elsewhere to seek an explanation for his 2007 performance part 1 part 2 part 3 what pitches does brian bannister throw the scouting reports tell an interesting tale especially if you follow them back a couple years in the minor leagues the cut fastball was reputed to be his best pitch his four seam fastball was thrown in the high 80 s touching 90 although he was able to locate it well his curveball was a big breaker that was considered a plus pitch his changeup was a work in progress and his slider was regarded as a pitch likely to be scrapped but in the fall of 2006 in the mexican league bannister worked on a two seam fastball and after joining the royals in trade for ambiorix burgos he scrapped his cutter experimented with different speeds on his curveball and started throwing a slider again what can we see in the pitchf x data regarding his pitch repertoire in 2007 more may 22 2010 winning with an 89 mph fastball an analysis of brian bannister part 1 posted by mike fast under uncategorized leave a comment note this article was originally published at the statistically speaking blog at mvn com on february 24 2008 since the mvn com site is defunct and its articles are no longer available on the web i am re publishing the article here i ll warn you from the start that the title is a tad ambitious i don t know exactly how brian bannister wins in the major leagues with a below average fastball speed but i hope to share some of what i have learned on the topic this article will take the form of a three part series part 1 part 2 part 3 in case you ve been hiding under the proverbial sabermetric rock the last few weeks maybe you re one of those weirdos who believe players are human or you ve been out of your garage recently to look at the sky brian bannister gave a fascinating three part interview to tim dierkes at mlb trade rumors last month in part 3 of the interview bannister talked about his opponents batting average on balls in play babip i think a lot of fans underestimate how much time i spend working with statistics to improve my performance on the field for those that don t know the typical babip for starting pitchers in major league baseball is around 300 give or take a few points the common and valid argument is that over the course of a pitcher s career he can not control his babip from year to year because it is random but over a period of time it will settle into the median range of roughly 300 the peak of the bell curve therefore pitchers that have a babip of under 300 are due to regress in subsequent years and pitchers with a babip above 300 should see some improvement assuming they are a major league average pitcher because i don t have enough of a sample size yet service time i don t claim to be able to beat the 300 average year in and year out at the major league level however i also don t feel that every pitcher is hopelessly bound to that 300 number for his career if he takes some steps to improve his odds which is what pitching is all about in the interview bannister postulated a reason for his success on babip so to finally answer the question about babip if we look at the numbers above how can a major league pitcher try and beat the 300 babip average by pitching in 0 2 1 2 2 2 counts more often than the historical averages of pitchers in the major leagues until a pitcher reaches two strikes he has no historical statistical advantage over the hitter in fact my batting averages against in 0 1 1 0 1 1 counts are 297 295 311 respectively very close to the roughly 300 average my explanation for why i have beat the average so far is that in my career i have been able to get a major league hitter to put the ball in play in a 1 2 or 0 2 count 155 times and in a 2 0 or 2 1 count 78 times that s twice as often in my favor i ll take those odds this interview has gotten a lot of buzz in sabermetric cyberspace several people have taken a look at babip at different ball strike counts including my colleague at statspeak pizza cutter there seems to be some ability for the pitcher to control the count on which hitters put balls into play but it looks like a fairly small effect on average pizza correct me if i m summarizing your conclusions incorrectly bannister also mentioned to dierkes that getting two strikes on the hitter gives him the strategic advantage in terms of pitch selection it is obvious that hitters even at the major league level do not perform as well when the count is in the pitcher s favor and vice versa this is because with two strikes a hitter has to swing at a pitch in the strike zone or he is out and he must also make a split second decision on whether a borderline pitch is a strike or not reducing his ability to put a good swing on the ball what this does is take away a hitter s choice if i throw a curveball with two strikes the hitter has to swing if the pitch is in the strike zone whether he is good at hitting a curveball or not he also does not have a choice on location we are all familiar with ted williams famous strike zone averages at the baseball hall of fame it is well known that a pitch knee high on the outside corner will not have the same batting average or obp slg ops as one waist high right down the middle here is a comparison of the batting averages and slugging percentage on my fastball vs my curveball fastball 246 404 curveball 184 265 we do know from john walsh s work something about batting average and slugging percentage against the typical major league fastball 330 521 and curveball 310 471 if bannister is correct in his numbers he s doing quite a bit better than the league with both the fastball and curveball but is bannister correct in the numbers he quotes and assertions he makes so far most people are accepting what bannister said at face value let s take a closer look and see if we should believe his numbers and conclusions we ll draw on two data sets from the 2007 season one is the standard pitch by pitch result data for all of bannister s 2603 pitches in 2007 with this data set we can examine results on balls in play and how bannister performed in various ball strike counts the second data set is the detailed pitchf x trajectory data recorded for 1304 of bannister s pitches or about half of his starts with this data set we can identify pitch types and reliable strike zone location information in order to gain a greater understanding of bannister s pitching strategies more may 21 2010 can we classify every pitch posted by mike fast under uncategorized 2 comments note this article was originally published at the statistically speaking blog at mvn com on december 22 2007 since the mvn com site is defunct and its articles are no longer available on the web i am re publishing the article here what if we knew what type of pitches every major league pitcher threw what if we had detailed pitch by pitch data about how he used those pitches in every game situation what if this information was accurate and freely accessible to baseball researchers let s begin with some history since sportvision s pitchf x system was unveiled during the 2006 playoffs people have been thinking about using the detailed pitch data to classify pitches by type reference this comment by mlbam s director of stats cory schwartz when the system is installed in all 30 ballparks it will provide unprecedented accuracy consistency and depth of data to the measurement of speed and trajectory of each pitch schwartz said ultimately we ll be able to use this data to determine the pitch type in real time and with greater accuracy than ever before by recording all of this data in real time we can provide it to broadcasters such as fox in stadium scoreboards fans via enhanced gameday clubs and other business partners it wasn t long before baseball analysts joe sheehan was leading the public research down that path too publishing articles in the spring of 2007 about pitch classification for pitchers like jeff weaver mike mussina and kenny rogers using the data from the 2006 playoffs in april 2007 the pitchf x system was installed in nine ballparks and this produced a wealth of data that encouraged more people to join the analysis fun dan fox bill ferris and steve west were among the leading pitchf x researchers in the first half of 2007 and although the work in the field covered a number of topics pitch classification was often at the forefront soon the quest turned toward developing a set of rules to classify pitches for many pitchers perhaps for every major league pitcher john walsh published the early definitive article on this topic in august the analysis really began to heat up for example see these articles from john beamer and joe sheehan the quest for a pitch classification algorithm was on more may 21 2010 breakdown of balls in play by count posted by mike fast under uncategorized leave a comment note this article was originally published at the statistically speaking blog at mvn com on february 18 2008 since the mvn com site is defunct and its articles are no longer available on the web i am re publishing the article here recent evidence may suggest otherwise but i am still a contributor to statistically speaking i ve been working on an analysis that has been more difficult to bring to fruition than i expected that along with real life getting in the way more of late is what has severely cut into my posting frequency however in the process of number crunching for the analysis i m doing i came across some statistics that i haven t seen posted publicly anywhere not even in the baseball reference splits some of it is in the b r splits but not most of it maybe i ve just missed them in which case drop me a line and let me know where else you found them i thought these might be interesting to a few other people so i ll share them mostly i m just putting the numbers up here for the rest of you to enjoy but i ll also make a few comments on some trends that stuck out to me i m looking at pitch data broken down by ball strike count i m using the mlb gameday 2007 data as my source today i present the breakdown of types of balls put into play by the hitter ball strike total pitches total safe total out single double triple home run field error other safe 0 0 22029 0 341 0 659 0 214 0 069 0 007 0 039 0 012 0 001 0 1 17222 0 329 0 671 0 222 0 062 0 005 0 027 0 012 0 001 0 2 7878 0 319 0 681 0 228 0 049 0 005 0 022 0 013 0 001 1 0 14030 0 344 0 656 0 212 0 070 0 007 0 044 0 010 0 001 1 1 16576 0 334 0 666 0 214 0 066 0 006 0 034 0 012 0 001 1 2 14626 0 326 0 674 0 220 0 059 0 006 0 025 0 014 0 001 2 0 5015 0 355 0 645 0 202 0 077 0 007 0 056 0 012 0 000 2 1 10308 0 349 0 651 0 212 0 074 0 007 0 041 0 014 0 001 2 2 14861 0 330 0 670 0 215 0 062 0 009 0 030 0 012 0 001 3 0 251 0 402 0 598 0 167 0 120 0 008 0 092 0 012 0 004 3 1 4393 0 376 0 624 0 214 0 083 0 009 0 056 0 013 0 001 3 2 11019 0 351 0 649 0 216 0 070 0 007 0 045 0 012 0 001 total 138208 0 338 0 662 0 216 0 066 0 007 0 036 0 012 0 001 ball strike ground out fly out pop out line out force out ground into dp 0 0 0 208 0 195 0 073 0 043 0 036 0 034 0 1 0 270 0 183 0 067 0 047 0 034 0 034 0 2 0 291 0 181 0 070 0 047 0 039 0 033 1 0 0 225 0 206 0 078 0 048 0 031 0 032 1 1 0 267 0 194 0 070 0 046 0 031 0 030 1 2 0 293 0 181 0 076 0 047 0 033 0 028 2 0 0 218 0 217 0 077 0 051 0 028 0 027 2 1 0 254 0 198 0 075 0 049 0 026 0 025 2 2 0 278 0 194 0 076 0 051 0 031 0 025 3 0 0 171 0 219 0 096 0 040 0 024 0 020 3 1 0 213 0 213 0 081 0 049 0 023 0 021 3 2 0 264 0 212 0 080 0 055 0 009 0 012 total 0 254 0 195 0 074 0 048 0 030 0 029 ball strike sac bun...