Monday, July 22, 2013

Mid-season catcher framing data, and farewell

 Sorry for that I haven't written up any posts in a while... If you only want mid-season catcher framing results being tempted by the title of this post, just jump directly to the bottom of the page. Today I'm not going to talk about baseball, let alone framing. You may be tempted to know a little more about some of the results after glancing at the table below - e.g. the goodness of rookie catcher Mike Zunino, Matt Wieters performing badly after several good seasons, or maybe Carlos Santana - but I can't add any more of useful notes. I should also notify that this post is likely to be my last post in this blog; as some of you may already have known, I have currently been building up another, my own web site for past a couple of months. It is still not near on the production stage and probably won't come along until at least the end of the regular season, as I started completely from scratch without any background even in basic HTML structures. That said, I've been doing my best to found as fabulous, sophisticated one as possible, attempting to go in public during this year at the very latest. Just stay tuned for a while...


  *** If you want to read how I can computed my catcher framing values, just click on and read this linked post.
 
  *** The main reason I decided to publish my mid-season framing data here is that the above linked post have gotten by far the most page views among all of my blog posts (I know it's linked in multiple places around the Web) and hence I realized that some of you would like to check out the in-season update, which I suppose has not been published that much. I'd like to inform, however, that I wrote up two more PITCHf/x posts in the past. One is on the situational differences on getting called strikes, derived from the aforementioned catcher framing post. The other is on what kind of pitches are harder or easier when it comes to executing a sacrifice bunt. I have some more topics to write and actually wrote halfway on some of those topics on my editor. When I finished building up and published the new site, I'll resume doing the work.


 # Minimum pitches required to cut through: 1,500

firstlastnruns
JoseMolina3,83813.8
AlexAvila4,44612.3
HankConger2,4239.8
YanGomes2,7558.9
RussellMartin5,1008.8
MikeZunino1,6608.5
ErikKratz2,7627.8
DavidRoss1,7556.7
JonathanLucroy5,4186.4
CarlosSantana4,9726.1
YasmaniGrandal1,9225.2
BrayanPena2,8694.4
DerekNorris4,1454.1
ChrisStewart4,2024.0
JoeMauer4,5313.2
YadierMolina5,5052.5
JarrodSaltalamacchia5,4162.3
CarlosCorporan2,5251.9
YorvitTorrealba2,1391.8
BrianMcCann3,2801.7
TylerFlowers5,2731.2
J.P.Arencibia6,0301.0
JeffMathis1,8370.7
SalvadorPerez5,6400.5
EvanGattis1,989-0.2
WilsonRamos1,502-0.4
GeovanySoto2,245-0.6
DionerNavarro1,990-0.7
JoseLobaton3,758-0.7
MartinMaldonado1,803-0.7
MichaelMcKenry1,766-0.8
HectorGimenez1,724-0.8
BusterPosey5,381-1.0
RyanHanigan3,024-1.8
MiguelMontero5,764-1.8
AustinRomine2,035-2.3
RobBrantly3,854-2.6
A.J.Pierzynski4,935-3.0
CarlosRuiz2,640-3.0
KellyShoppach2,525-3.6
JesusMontero1,866-3.9
A.J.Ellis4,614-4.0
JasonCastro5,321-4.3
WelingtonCastillo4,996-5.2
JohnJaso2,708-5.6
JohnBuck5,383-6.2
MattWieters6,340-6.5
WilinRosario4,900-6.9
KurtSuzuki4,480-7.2
NickHundley4,431-7.2
ChrisIannetta5,178-7.8
RyanDoumit2,592-8.0
DevinMesoraco3,418-9.6

Friday, January 25, 2013

My thoughts on Itoi trade, and Fighters


 If you were a general manager in MLB and got a trade proposal that you acquire Evan Longoria and Wade Davis from the Tampa Bay Rays by giving away Joe Blanton, Juan Uribe, and Jerry Hairston, what kind of a response or a reaction would you show? Would you accept the offer, either?

 Everyone -- Yeah, EVERYONE -- , would pounce on the proposition. In no minute would he spend thinking about the offer, as he would be nothing but anxious to think that while he was pondering the relative impact of the trade for his team, the opposing team realized the silliness of their thought and pulled the offer back before your answer. That kind of trade would never occur in real life, ever.

 But the world around you is too small to know the entire world. And where there are more than half a day time difference happened that apparently ridiculous player swapping. Three days ago the Nippon Ham Fighters, a team run by a lot of idiots in the front office, yet somehow considered to be by some dunces, strangely enough, "a smart team whose design and construction of being competitive is rooted in the sabermetric-type thinking", sent Yoshio Itoi, the best player in the league yet carrying with arguably one of the most lucrative contract under his belt, along with a crappy lefty starter to the Orix Buffaloes to acquire starter Hiroshi Kisanuki and shortstop Keiji Obiki, both at most average in their respective position, and one scrap-heap outfielder to give a great shock nationwide. Unlike MLB, the league in which stars are often transferred to another team even during the regular season via a trade, rarely happens the kind of trades which involves at least one MVP-caliber in Japan. Yet it occurred, in too ludicrous, cryptic a way.

 Some reports said that Itoi demanded to be posted this winter, that it ended up in forcing the Fighters to dump him in preparation down the road or as a "disciplinary action". But it misses a vital point; posting system can be ruled out if not for a club's consent. Suppose that to post him this winter doesn't bring sufficient profits they think is sufficient enough, then just keep him in the roster to play for them going forward. Unless he retires from playing in Japan as did Hideo Nomo in early-1990s, he's never be permitted to play in the United States under the current arrangement in the first place. And the likelihood of him making the determination is, of course, none.

 Their decision doesn't make any sense from the monetary perspective, either. The salary of three players they got summed up to 168 million yen. Itoi hasn't actually signed a contract yet, but reports said he was close to signing with 200M, which, in relation to his overall competence, is incredibly profitable to the team. The gap, 56M yen (equivalent to about 600K in dollar-term) can be easily made up for by trading other slightly cheaper but much less talented veterans eating up still bigger portion of their budgets. Not to mention that should they be in desperate need for relieving their sluggish payroll of some salaries, why the heck does such a patchy and crappy closer still see himself in the roster?

 They are the Nippon Dumb Fighters, a team which picked up Tomoyuki Sugano in the first-round at 2011 draft despite no promise of signing, a team which picked up Shohei Otani next year despite his official statement of no will of playing in Japan, little or no promise of reaching an agreement once again, but saw the good fortune in a counterfeit presentation, a team which said after the two straight proclamation of forcing their way through that it was their philosophy to select the best player available, nonetheless ending up in fact in picking up Yuki Saito two years ago. They are those who signed with a obscure softball player (I don't necessarily disparage the decision but just list a set of their deeds), give a high praise to a horrible tactician just because he's a knockout, and archived great success in brainwashing fans into their sect.

 Some dumb jocks are already entrapped into believing that the Fighters did the right thing as they thought they had done, the decision of which was a natural result of the output returned by their bragging operations system implemented some years back with investment of up to one million dollar. They are pretty convinced that that the team has done eccentric actions for the past couple of years is a natural consequence of their sagacious and audacity, ending up in an assumption that they ought to be right again in this trade as they think they have been. Not everyone falls into a crappy gin like this, as those jocks take only up around five percentages out of the population. But they are obsessed, haven't yet apostatized.

 How can the team be so mad and foolish in decision-making process? My assumption is, their goal is not to make themselves competitive and vanquish all the other teams en route to winning a season; it is for the marketing. A set of their peculiar deeds has attracted an enormous amount of attention all over the country, obtaining the great eclat of an advertisement. Shouldn't we be astonished to see them greet Hideki Matsui as a next manager?

 Is it great time to stop using #LoveFighters hashtag and go with #DumbFighters instead? It may or may not.

Friday, January 18, 2013

Catcher Framing Runs, Part 2


 In the previous post, I introduced my methodology of analyzing catcher framing skills using complete Pitch f/x database and published and commented some observations, and cautioned the bias inherent in WOWY. Today's post is built off by the previous article, so if you haven't read it yet, go first to the linked page and read the article (around 2,500 words, a bit long post) entirely and then go back here and start reading this post.

 In the last chapter of Part 1, I promised to investigate framing through a variety of situations. For the purpose of this post, I controlled for the identity of yet another causative agent, a catcher, to make things irrespective of a player behind the plate. Since I added the identity of catchers in the form of their estimated skills computed from the estimated skills from their batterymates, basically little or no overlap happened that caused double-subtraction. I also want to state in advance that in order to make numbers represented look more intuitive, I express net called strikes scaled to per 100 pitches in this post.

 [Pitch Type]
 Do different types of pitch make any difference in terms of getting extra called strikes as long as the pitch is thrown to the same location in the zone?

Pitch TypeRuns
FF0.16
FT0.14
FC0.11
SI0.09
SL0.02
KC-0.18
CU-0.24
CH-0.49
KN-0.97
FS-1.32

 One thing worth noting is that right-handed pitchers' two-seamers and sinkers look different from those thrown by southpaws when it comes to getting borderline pitches called to his favor. Righties can benefit from extra called strikes by throwing such pitches whereas lefties can't. Actually, I remember seeing the two sinking fastballs have slightly different impact on whether a successful bunt is expected to occur by a pitcher's handedness in the middle of conducting bunt analysis last December. With a little wrangling, I found out the expectation of fastballs being called strike varies considerably dependent on the identity of opposing hitters' handedness, giving pitchers much more credit if his competitor of the matchup sits in the opposite side of the dish. Likewise, hitters are more likely to see himself in more favorable count after the call of the pitch is done if he stands in the same side of the plate as the ball is thrown. And the split is larger for left-handed pitchers. Nonetheless, pitchers, especially for lefties, throw the disproportionate amount of sinking fastballs to the batters sitting in the same side of the box. This leads to why lefties' sinking balls look below average pitches in terms of framing at first blush. Overall, the above table is a decent representation of the truth however, that I didn't separate it by throwing hand or hitter's handedness to eschew making it look more dirty. As to this pitch type and handedness issue, I would do separate analysis in the future with an expanded application of the analytic field.

 As to other type of pitch, change-ups and splitters are less likely to be called strike, which is no surprise here. So are knuckleballs. Overall, the more vertical movement a pitch has to the downward direction, the less likely it is to be judged strike by a chief umpire.

 [Base-Out Situation]
 We know the fact that different base-out situation has different impact on overall runs transition. For example, we know that on situations where a runner on 3rd and less than two outs, pitchers change his approach to try to defeat hitters by strikeouts to glue the runner to the base, while batters counter the approach to desperately make a contact to plate the runner home and gain a run for his team's scoreboard. When it comes to framing (or more properly, just called strike rate since we don't necessarily focus on catchers' skill, but to avoid confusion and I don't like to fall into such a political conflict with lack of cultural enrichment of English semantics, please take the two expression as an indication of exactly the same thing in this post), how do chief umpires act depending on 24 base-out situations?

OutsBasesRuns
0___0.41
01__-0.51
0_2_-1.1
012_-0.33
0__3-0.92
01_3-0.49
0_23-0.09
01230.26
1___0.11
11__-0.47
1_2_-0.56
112_0.1
1__3-0.83
11_3-0.58
1_23-0.52
11230.42
2___0.09
21__-0.3
2_2_-0.38
212_0.36
2__3-0.27
21_3-0.25
2_23-0.28
21230.55

 It looks like that in bases-loaded or empty, defensive teams can benefit from some extra strikes while offensive teams are more likely to get a favorable call if 1st base is open. Put it more clearly, the divided line can be ruled by whether potential intentional walks could happen. There would be some reasons behind it, such as umpires' unconscious awareness of pitchers' attempt to pitch around. In other words, the relative impact of issuing walks corresponds to the probability of a pitch being ruled strike to some degree. One of my favorite metrics in baseball is boLI, but to take one step further and check out bbLI, it gets itself modest correlation with overall run impact, at r=.45. However, keep in mind that the net called strike is expressed as per 100 pitches, so even the two extreme situation, a runner on 2nd and no out, and bases-loaded and two outs, the effect is mere 0.2 runs per 100 pitches. You may be able to better understand the above output as percentage increase, and even the extreme situation can only affect 1% worth of absolute called strike rate.

 [Park Effects]
 How about the differences between parks? Max actually threw suspicion of some park biases in the final part of his trilogy of catcher framing that some parks may have a different influence than others, citing Brain McCann and Ryan Doumit as an illustration. Actually, there are such bias inherent in how a borderline pitch is judged, and the effect is not so little as to make some catchers look more eminent than others. Taking an example of Turner Field, defensive teams can be given an extra credit to the tune of 0.26 strikes per 100 called pitches, which is extrapolated to no more than 0.03 runs per game. But if you, a catcher, play half of your games there, you would get additional 2 runs to your pocket solely through the context you are in that you have no control over.

 Year-to-year correlation on called strike rate for each specific park is 0.26, implying there are not, if any, much of a persistent effect in successive seasons. Standard deviation between each park is 0.33, which can be converted to a couple of runs per 81 games played. That is, most regular catchers can be subject to around four absolute runs or fewer playing half of games in his specific park, and even extreme workhorse starters like Felix won't be able to gain more than a fraction of runs dependent on a park he pitches in half of his starts. Comerica Park and Sun Life Stadium is the two most extreme instances, favoring defensive and offensive teams respectively to the tune of six or seven runs per 81 games played. It has to be regressed a bit to estimate true maximum amount of runs caused by parks, which I'm sure is around five runs in reality.

 [Home Field Advantage]
 We all know teams playing in their home park generally wins more games against visiting teams and in today's Major League baseball, the advantage is four additional wins in a hundred games played. Put this figure in perspective, if you play your baseball only in your home park through a entire season, you can be in as good a position as when you would get the best player in the market for free. So how much of this extra advantage is led by the chief umpire's peculiarity to patronage the host team?

Home/AwayRuns
Away-0.25
Home0.24

 As you can guess it easily, pitches typically are more likely to be called strike if they are thrown by home team's pitchers. When visiting teams attempt to score runs during the at-bat, they lose 0.25 potential strikes per a hundred called pitches, equivalent to a couple hundredth runs per game. In other words, we find that the effect of called strikes on home field advantage is only one ninth of the total amount of runs caused by HFA. So umpires slightly favor home teams' players in judging borderline pitches, but there still remain many more causes beyond the match in 18-feet distance.

 [Run Difference]
 Does specific game context have any impact on the likelihood of a called strike? Some assumption is that when you are in a position to win the game if no more runs are scored, you're less likely to be aided by umpires' judgement on each play in the field. Is this true or just an illusion?


 The answer is, yes, umpires in general give more favor to a team behind their opponent at the time when the pitch is thrown. There are a bit of variations in the graph, but if you assume the linear relationship, one run increase corresponds to .06 extra called strikes per 100 pitches, which is not meaningful quantity ever. Put it in another way, there are run differential effect certainly, the amount of which is not we pay a special attention to however. After all, if your team starts the game by a leadoff homer, preserve the run through the entire game, and end up winning in 1-0, the whole batters on your team are not fooled even one hundredth runs during those PAs collectively. It's also worth noting that tie-situation is an exception, where umpires aren't so motivated as to give some favors to the defensive side. Maybe they don't prefer extra innings and want to go home quickly?

 So I took a one more look and demarcated it by innings in order to check out tying-situation. What I did was mapped out average net called strikes per 100 pitches by innings, and connected it to draw a line, but I also separated it based on whether one team's score is identical to the other teams'. Green dotted line represents non-tying situation while red dotted line tie-situation. As you can see the below image, the points in 9th and later innings see further estrangement from each other. Blue solid line represents the magnitude of the difference of the two lines, which shows that umpires greatly favor offensive teams in 9th and later innings if the game is in great competition. Maybe umpires are excellent entertainer? Or are they influenced by the bigger cheers by fans of an offensive team? Or just abhor extra duties?


 [One more thing]
 In my last post, I only briefly correlated individual performers in my dataset to Mike's by comparing in career level. The reason I didn't do it season-wise was just a coding error and now that I fixed it well, let me try a comparison and report the result in this post. For 2008 to 2011, the correlation coefficient of total runs with weights on the number of pitches each catcher caught in Mike's dataset spitted out r=.87. Mean absolute difference of seasonal performance between Mike's and mine is three runs, with only 10% of them seeing more than five runs discrepancy. And it looks like Mike compiled Max's dataset and appended it in his file, so I did the same thing on Max's framing result and found that r=.78 and average absolute difference is five to six runs between Max's and mine, with seven out of 80 players-seasons see more than 10 runs discrepancy. Those seven consist of three McCann and two Yadier Molina, probably suggesting I didn't deal with pitcher-catcher combination bias capably. Keep in mind that Mike and I took the similar approach to estimate the contribution while Max used multilevel modeling, and Max reported only 80 catchers, mostly ranked top and bottom, so it's natural for there to be more deviations found in comparison with Max's estimated values.

 In part 3, I'll take a more detailed look at framing skills by breaking them down to individual level, like the sequence issue I stated briefly in the first post. However, it would not be out online, at least in the near future, since I'm not motivated enough to query at this time. If it were published, the content is focused on why and how some catchers are superior to others in the field of framing, but it's more unlikely to come out than likely right now, so do not pin your hopes on it for the time being.

Tuesday, December 25, 2012

Catcher Framing Runs, Part 1


 For some reasons, I didn't watch a lot of Major League baseball games this past year. There are some reasons behind it, but out of that little games, one team I saw play the most was the Texas Rangers, surely thanks in large part to their signing of Japanese legend Yu Darvish. It still didn't have me watch a large number of their games in 2012, and almost none after the All-Star game, but every time I saw Darvish pitch for them I always suspected that he was terribly fooled by umpires that he lost on some called strikes. Upon the perception I always went to the box score to check who was the pitching duo of him on the game, and when I found the guy was Mike Napoli, I always heaved a deep sigh in my mind. Napoli looked atrocious in all aspects of defensive games behind the plate, needless to say framing, standing in the way of Darvish having his pitches called strike in his favor. While we won't disagree with the idea that Napoli isn't overall a good or even decent defensive player behind the plate, how do other catchers compare in the field of framing in 2012?

 If you are a good follower on the Internet saber-world, it would be highly likely to have already read a couple of articles on catcher framing, notably by Max Marchi and Mike Fast. What I mean to do in this post is neither duplicate nor disparage their work. Rather, I just try to quantify catcher framing skills from the slightly different perspective and report the result here to share with you guys reading my blog right now.

 What I used to compute framing skills is five years worth of Pitch f/x data, the brief details of which is described in the previous post. For those not having read it, my dataset is corrected for velocity, movement, and location between parks. Reclassification of pitch type has not been done at this time, but will be implemented in the future. For the specific purpose of this post, I'll use called pitches only (i.e. strike out looking and balls, intentional or unintentional) to attempt to estimate framing contribution and ability for all catchers during the FX era. I also omitted all starting pitchers as batters, but sadly couldn't take relievers out of the population. Relievers don't usually come to the plate, so it doesn't make any meaningful influence.

 For each batter handedness and pitch count (or 'plate count', as Tango likes to call it; I'm personally fond of the term 'batting count' or 'hitting count'), I computed league average called strike rate on each actual pitch thrown with the identity of individual year also in mind (as I explained briefly in my last post, strike zone actually varies dependent on seasons). Then, I also figured out each pitcher, batter, and umpire's net called strike rate on each actual pitch thrown and got mean differences, lending itself to controlling for some bias inherent in each causative agent participating in the process. To give one example, here's the internal working process. Since I talked about Darvish and Napoli at the beginning of this article, let me feature the duo to illustrate the point. Darvish faces Mike Trout with Napoli behind the plate and Bod Davidson... - Oh! the Nemesis of all Japanese fans! - calling the game, and Darvish throws 93 mph fastball a bit far and away to Trout, that Trout kept the stick on his shoulder and Bob called the pitch 'Ball!'. In 2012, that 1-0 pitch to a right-handed batter is estimated to be judged strike 87.0% of the time according to naive model, and this probability functions as a reference point. Then, the estimated probability of called strike on that pitch is adjusted by the hitter, pitcher, and umpire's own rate. Net called strike rate for Yu Darvish is -0.1%, meaning every pitch Darvish throws is less likely to be called strike, on a very slight amount. Likewise, Trout's net called strike rate is -0.5% and Bob's 1.7%, so the 87.0% probability naive model spitted is adjusted that exactly the same pitch to the same-handed hitter in the same hitting count for the same year, is now estimated to be called strike 88.1% of the time. Since Bob is a pitcher-friendly umpire, that pitching combo could benefit from his wider zone. Nonetheless, that pitch is called ball, so Napoli is debuted -.09 runs (-.881 times 0.102 runs, the run value in 1-0 count) for this result. The same procedure is conducted through all called pitches for all years. The count-based run value is via my own calculation, that I figured out linear weights through the count for 2008 to 2012, weeding out all intentional walks, bunts, and pitchers as batters. I did some slight correction for the distribution of the quality of hitters in each specific count, since lots of Pujols, Fielder, Mauer, et al. see themselves in 3-0 count. Here's run values chart for all counts permutation.

BallStrikeRun Values
000.071
010.079
020.195
100.102
110.107
120.231
200.152
210.165
220.319
300.179
310.256
320.575

 So with the methodology being described, here's the result. Have a casual staring at the table below and go to the next paragraph.

NamePitchesRuns20122011201020092008
Jose Molina23363862211181719
Russell Martin449295861891214
Alex Avila2662241141963NA
Yorvit Torrealba27770395413610
Jonathan Lucroy21419366229NANA
Brayan Pena138623481556NA
Jeff Mathis281642774395
Jarrod Saltalamacchia26637271212-15-1
Geovany Soto392702664953
Gregg Zaun1307825NANA5154
Joe Mauer339032468441
Matt Wieters37193226772NA
Miguel Olivo33782176439-4
Taylor Teagarden939217-213131
Ryan Hanigan24471165428-3
Ivan Rodriguez2708315NA-1926
David Ross1566015-10178
Chris Snyder2514614-26111-2
Bobby Wilson100461410-1410
Miguel Montero3493812-32390
Eli Whiteside10915100253NA
Josh Thole1824410141-3-2NA
Yadier Molina4547494-5407
Paul Bako91228NANANA34
Francisco Cervelli11378802410
Wilson Ramos106767250NANA
A.J. Pierzynski459806-46-39-2
John Baker171676-20223
Henry Blanco136675-30-164
Brian Schneider18636503-214
Victor Martinez200955NA-17-32
Ronny Paulino186255-1-7147
Humberto Quintero2202040-8623
Kurt Suzuki499332-631-510
Jesus Flores14991072NA-2-6
Bengie Molina26324-1NANA34-8
Josh Bard13151-1NA07-6-3
Kelly Shoppach25856-11-3-1-13
Drew Butera11762-31-1-3NANA
Ramon Castro10166-4NA0-12-5
Wil Nieves14370-4-1-2-3-24
Brian McCann45845-402-1004
Lou Marson19314-4000-40
Buster Posey16529-6-2-3-10NA
Rod Barajas33616-11-8-12-383
Carlos Santana17674-11-9-64NANA
Dioner Navarro24181-13-1-22-1-11
Matt Treanor18052-15-3-1131-4
Kenji Johjima12984-15NANANA-9-6
Jason Varitek24643-16NA-9-71-1
J.P. Arencibia17510-16-2-12-2NANA
John Jaso14189-17-1-8-8NA0
Jason Castro9923-17-15NA-2NANA
John Buck37301-21-4-6-1715
George Kottaras13145-21-3-7-9-20
Rob Johnson15465-21-2-7-11-10
A.J. Ellis14250-22-10-5-81-1
Mike Napoli27429-23-100-3-4-5
Jason Kendall30160-28NANA-5-19-4
Koyie Hill14538-29-2-10-4-11-1
Nick Hundley25302-31-7-90-3-12
Ramon Hernandez29737-32-5-14-8-50
Carlos Ruiz37880-330-16-4-5-7
Chris Iannetta29701-35-7-8-1-4-15
Gerald Laird28067-39-8-4-7-11-10
Jorge Posada15817-44NA0-24-14-6
Ryan Doumit27070-69-16-8-2-10-33

 Our best friend Jose Molina is the King of Framer, leading also this past year with 22 runs saved. There are also some familiar names behind Jose, such as Jonathan Lucroy, Russell Martin, and Yorvit Torrealba. On the laggers, you can see some brutal framers such as Ryan Doumit and Gerald Laird, also well familiar if you already read Max and/or Mike's stuff. Actually, my model has good correlation with Mike's model, at r = 0.92, carrer-wise, despite the slight discord in years (I can't do a comparison season-wise with Mike's, and it seems that Max didn't publish his result, so I didn't compare mine to his). How about regression amounts? To make a fair comparison, I fitted mine to Mike's standard, that is, each catcher season is paired with two years sample (2009 and 2011 vs. 2008 and 2010) and is used only if both of those sample includes more than 6,000 called pitches. My model spitted correlation at around r = 0.81, requiring around 2,700 pitches for the signal and noise to see the same amount of variance. For your information, Mike reported that he found around r = 0.7 and about 4,500 pitches for the regression purposes. It looks like a huge leap from Mike's, but is this a fluke? However, I wrangled with different minimum requirements of the number of pitches caught, different permutation of years, even three-years interval (like 2008 and 2011 vs. 2009 and 2012), consecutive single year, leaping single year (like 2010 vs. 2012), and both usual correlation and weighted correlation, and both arithmetic mean and harmonic mean, most of the results returned around 1,600 to 2,500 regression amounts. The first sample spitted, in fact, one of the worst outcomes. Surely, if you raise the minimum pitch requirement to say 12,000 for both sample, the amount of regression needed skyrocketed, but very few catchers meet the criteria in the first place. So 2,500 to 3,000 pitches may be a realistic suggestion.

 As to the envelope of framing skills, standard deviation of yearly performance in framing runs with weights on the number of called pitches each catcher actually caught is about 7 or 8 runs, meaning 95% of all catchers reside within +/- 15 runs in a single year in framing performance. I cannot do any analysis in team-switchers right now, since I don't have any such data in handy. The next part is a bit technical, so if you don't like to get caught in such a swamp, feel free to skip to the "One More Thing" headline.

[Drawback of WOWY]
 While I was playing around with my computation of framing skills, however, I realized that some catchers are under the significant influence of notorious bias immanent in WOWY, whose values are unduly deducted from their fellow pitchers' skill in persuading umpires from calling strikes, and the more vital point is, that effect is far more marked one than I had ever imagined. Brian McCann is a catcher, the Braves' regular catcher for all of the covered years in the experiment. He caught three quarters of all called pitches the Braves pitchers threw during the period, and another one fifth of them were caught by David Ross, Atlanta's main backup catcher who, fortunately for the Braves fans but unfortunately enough for practitioners, is also estimated to be a competent framer who caught 78% of all called pitches he has actually caught as a member of the Braves. And to make the matter more vexing, lots of Atlanta's pitchers only pitched for the team. Tim Hudson, Jair Jurrjens, Tommy Hanson, Mike Minor, Kris Medlen, Kenshin Kawakami, and Brandon Beachy, all of them didn't pitch for any team but the Atlanta organization. After all, only pitchers who threw at least 1,000 called pitches (roughly equivalent to 400 TBF) both as a Braves and other organizations were Derek Lowe, Javier Vazquez, Jo-Jo Reyes, and Michael Gonzalez. You can have a good grasp on how large and serious the effect is by clicking on the below image.


 Out of all pitchers who threw more than 2,000 called pitched during the past five years that threw at least one as a member of the Braves, this graph shows each pitcher's net called strike rate attached a number implying the rate of the number of pitches that pitcher threw as a Braves compared to the total number of pitches he threw for all teams. Let's take a look at Derek Lowe, the fifth pitcher from the left. He looks like great in terms of getting extra called strikes in his favor, as you can see at the location of his number, further than 0.05 more than average (meaning he receives five extra strikes every 100th pitch compared to average, a tremendous amount). That 0.64 number attached to his location means he pitches 64% of the total number of pitches he threw as a Braves.

 So here's a critical point. There are lots of points colored in green and attached number 1 at around 0.02 CS rate. Put this number into perspective, a true +.02 catcher can accumulate more than 20 runs in a year solely by framing, as good or better than the best fielder saves in a single year. Therefore, we basically threw away all records when Hudson, Jurrjens, Hanson, Minor, etc... is on the mound completely. And the end result? McCann is originally estimated to have saved eye-whooping 160 runs, by far the best figure and even about 50 runs more than runner-ups Martin and Lucroy. Ross is also a good framer, estimated to have helped his team near 70 runs by framing. But since the bias is destroying the two Atlanta's catchers, Ross is now estimated to have 15 runs and McCann is... negative 4 runs! Unreasonably huge drop. Not that we assume all of surplus values in Hudson, et al. should be credited to the Atlanta's catchers. However, the bias certainly exists, with tremendous extent. If you sort all catchers in descending order by the absolute difference between adjusted and non-adjusted runs, ten out of top 20 catchers caught for only one team and another four caught more than four fifth of all called pitches for a single team. And while it's no doubt that Jose Molina is the very talented framer, one of the reason his excellence shines in the ranking is he caught for three teams with not much of disproportionate amount of playing time, and two of the three teams of which he was a member employed lots of pitchers who transferred at least one time in the past five years. In summary, WOWY is a great approach, can be useful in lots of scientific fields, and one of my favorite methods in sabermetric analysis. It's very effective applied to analysis in career level (Tango did some catcher analysis back in 2008 in the THT Annual), and still good enough even for less years (five years in this case), but the bias is eroding more and more and the degree of amount is far greater than my initial expectation is. I hope to tackle with this issue in the future, but at this time I couldn't touch more on this topic.

 *** Actually, McCann is also an interesting subject in yet another area. Did anyone notice that Mike's model and mine have a starkly different view on his contribution, despite taking the similar approach? The reason is the occurrence of his contribution in terms of run values is skewed that he failed to succeed in counts where the impact of the call is important. It might be easier to get in relation to the timing of events in ERA, but unlike the sequence in ERA, we (or I?) have little or no knowledge on the degree of voices the signal has compared to those by noises in the timing of framing and it should be, I think, researched further down the road. For your information for the time being, if I set all run values in counts to a uniform, McCann is instead estimated to have saved 10 runs. Moreover, not all catchers are subject to such a steep variation. After all, the only catcher whose value is exposed to similar magnitude is Miguel Montero, in the same direction. If you take the mean absolute differences for all catchers with the weights on the number of called pitches, the calculator spitted out slightly less than 5 runs, and weighted standard deviation is a bit less than 4 runs.

[One More Thing]
 Having dove too far into the technical part, have you got exhausted? Then let's come back to the topic in the introduction paragraph. As I stated at the beginning I felt like Darvish was hugely fooled by umpires in getting his pitches called strikes early in the year (I wonder I watched only a few starts after the first three months). So did my gut hold true? Or was it I who was fooled by umpires?

 Lots of variation occurred throughout the year, but you can notice some remarkable patterns. First, Darvish certainly went badly off early in the year, and secondly, almost all of red-colored bars stretches toward downwards with relatively no shorter length. Third, he did rather benefit more often when Torrealba caught his pitches. I should also add that in a game May 11th, Torrealba started but exited after a head contusion by the splintered bat of Albert Pujols in the first inning, and caught only one inning before Napoli took the duty for the next five innings, so I decided to put Napoli as his partner on the day. Overall, Napoli is disproportionately responsible for the debt of runs caused by framing on games Darvish started, which would not be a surprise at all given his poor defensive reputation. And now that Napoli is gone to the Red Sox, we might be glad to be less irritated every time we see Darvish (and all the other pitchers of the Rangers) takes the hill in 2013 and beyond. Am I too harsh with Napoli?

 In part 2, I plan to dig into examining framing in a variety of situations, such as parks, base-out situation, home/away, run differences, pitch types, etc... It wouldn't be out at least in two weeks, so stay tuned and wait the next release without heed.

Tuesday, December 4, 2012

NPB park factors for 2006-2012


 One of my followers asked me yesterday on Twitter whether I published any park factors for NPB. Actually, I computed park factors and in consequence so-called "advanced" statistics (actually I don't like the idea of wOBA, WAR, FIP, etc... being classified in "advanced" category, but this post is not one centered around that way of thinking) for all players after the Japan Series last November. But because yesterday I noticed that somehow my script didn't snatch raw data correctly and instead stored mistaken values on my file (that's why I didn't realize until he asked me, since even if a script has some errors, it doesn't spit those errors if it stores something different on behalf of true one and the two values differ only a bit amount, at least through the quick and dirty eye test. I'd also like to point out that it was not my coding that caused the pain, but either a parse module or a site itself, though even if that was the case I'm not going to disparage them) and thought this was a good time to write a generalized script to compute park factors in order to utilize it again down the road, I decided to redo my park calculation and upload the result on this blog.

 Actually, as long as I know, all park factors you can find on the Web, even in Japanese (and I don't necessarily think you could do better by searching in Japanese than in English for NPB-related data), are raw factors, which is just computed with actual runs on the specific park in the specific year, and we all know that it should be tweaked a bit when implemented in actual players' statistics. I don't like to bother to talk about the internal structure of calculation that much, since most of you guys don't have much interest in NPB at least unless some players are going to head to MLB, let alone park factors, so here's a quick explanation. I figured out all runs logged on home and compared it to the league context. Then I scaled it so that league mean is 1.00 due to some games being played in rural stadiums, almost all of which are rather small and hence hitters park, and then averaged out values of each park with up to five years and some tiny weights dependent on the year, then regressed a bit to finish the calculation. The data below lists year, team (actually, I don't specify park name, though I accounted for park change for Hiroshima Carp prior to 2009), league, the number of home games, raw PF, and true, regressed PF in order. Note that YB on team abbreviation means Yokohama Bay Stars, and DB is DeNA Bay Stars, the same team but the team name changed due to a ownership change. Forgive me if you feel the way I show my PF below too dirty, but it lends itself better to you copying and pasting on your file.


year,team,league,N_home,raw_pf,true_pf
2006,Bs,pl,34,1.01,1
2006,C,cl,65,1.15,1.09
2006,D,cl,70,0.84,0.91
2006,E,pl,62,1.06,1.02
2006,F,pl,57,0.94,0.94
2006,G,cl,64,0.98,1
2006,H,pl,65,0.81,0.93
2006,L,pl,65,0.98,1.03
2006,M,pl,68,0.88,0.96
2006,T,cl,60,0.92,0.91
2006,YB,cl,66,1.12,1.12
2006,Ys,cl,65,1.26,1.12
2007,Bs,pl,48,0.89,1
2007,C,cl,66,1.07,1.09
2007,D,cl,67,0.98,0.91
2007,E,pl,67,0.94,1.01
2007,F,pl,58,0.88,0.94
2007,G,cl,63,0.91,1
2007,H,pl,70,1,0.93
2007,L,pl,70,1.08,1.04
2007,M,pl,72,1.01,0.96
2007,T,cl,62,0.92,0.91
2007,YB,cl,66,1.05,1.12
2007,Ys,cl,68,1.19,1.12
2008,Bs,pl,48,0.89,1
2008,C,cl,66,1.1,1.09
2008,D,cl,67,1,0.91
2008,E,pl,70,1.04,1.02
2008,F,pl,59,0.88,0.94
2008,G,cl,63,1.05,1
2008,H,pl,68,0.94,0.93
2008,L,pl,68,1.04,1.04
2008,M,pl,72,1.09,0.96
2008,T,cl,61,0.76,0.91
2008,YB,cl,65,1.12,1.12
2008,Ys,cl,65,0.98,1.11
2009,Bs,pl,49,1.07,0.98
2009,C,cl,67,0.97,0.98
2009,D,cl,67,0.81,0.9
2009,E,pl,70,1.04,1
2009,F,pl,60,1.02,0.94
2009,G,cl,63,0.95,0.99
2009,H,pl,69,0.92,0.95
2009,L,pl,69,1.07,1.07
2009,M,pl,72,0.88,0.99
2009,T,cl,60,0.97,0.92
2009,YB,cl,65,1.22,1.14
2009,Ys,cl,69,1.07,1.1
2010,Bs,pl,51,1.14,1
2010,C,cl,68,0.96,0.98
2010,D,cl,69,0.83,0.86
2010,E,pl,68,0.97,1.02
2010,F,pl,58,0.91,0.95
2010,G,cl,64,1.06,1.01
2010,H,pl,69,0.91,0.92
2010,L,pl,67,0.98,1.07
2010,M,pl,72,0.89,1.01
2010,T,cl,60,0.89,0.9
2010,YB,cl,65,1.14,1.15
2010,Ys,cl,66,1.09,1.11
2011,Bs,pl,58,0.81,0.99
2011,C,cl,69,1.04,0.98
2011,D,cl,70,0.75,0.86
2011,E,pl,63,0.96,1.02
2011,F,pl,61,0.88,0.95
2011,G,cl,63,0.93,1.01
2011,H,pl,68,0.89,0.91
2011,L,pl,67,1.16,1.07
2011,M,pl,72,1.02,1.02
2011,T,cl,61,0.95,0.9
2011,YB,cl,64,1.17,1.15
2011,Ys,cl,63,1.18,1.12
2012,Bs,pl,58,1,1
2012,C,cl,68,0.85,0.97
2012,D,cl,67,0.77,0.86
2012,E,pl,68,1.04,1.02
2012,F,pl,58,0.94,0.95
2012,G,cl,64,0.99,1.01
2012,H,pl,68,0.79,0.91
2012,L,pl,68,1.06,1.07
2012,M,pl,72,1.15,1.02
2012,T,cl,60,0.76,0.89
2012,YB,cl,63,1.1,1.15
2012,Ys,cl,66,1.24,1.12


Monday, December 3, 2012

What kind of pitches are harder or easier to bunt?


 A sacrifice bunt has conventionally been featured on sabermetric blogosphere for its relative lack of importance to a baseball game and often criticized when it is dreadfully abused by a baseball manager on a given game. Even if you're a sabermetric newcomer who happens to be on this blog you would have already read at least a couple of articles on the validity of bunt usage citing run expectancy or that kind of stuff elsewhere as long as your passion for baseball is decent enough and you are always willing to cultivate your own baseball insights. The use of a sacrifice bunt has been decidedly one of the most controversial topics among baseball fans throughout the past couple of decades but there have been less articles published on it from the perspective of relative difficulties of executing successful bunts against different types of pitches. What I'm going to write today is focused on pitcher-batter confrontation on bunt attempts during the at-bats.

  Let me first show some data before cutting to the chase since it is one of areas where I was a bit interested in. Remember that most hitters feel more comfortable facing opposite-handed pitchers and vice versa, usually known as platoon splits. Managers construct their team's lineup paying some attention to an opposing starter's handedness on that day, and when the game is 'on' and they are pressed for calling a pinch hitter or new reliever, some consideration of a platoon advantage always resides at some place in their mind, even though they often treat all players as having identical splits and ignoring uniqueness of players, or stick excessively to a result of recent matchups and avert any warning uttered by Regression God. But is there any such predisposition when it comes to a sacrifice bunt and if that's the case, how much? From 1993 to 2011, I took all events I define as a sacrifice bunt attempt. My definition is all situations where 1) a runner on 1st and less than 2 outs, 2) a runner on 2nd and less than 2 outs, or 3) runners on 1st and 2nd and less than 2 outs. And out of those situations, I regard a successful bunt as 1) 1st runner advanced to 2nd or further, 2) 2nd runner advanced to 3rd or further, and 3) both 1st and 2nd runners advanced to at least one base ahead and no more than one out were recorded on that specific event, respectively. And all the other outcomes, along with any pitches batters attempted to bunt but missed (i.e. swinging strike, foul tip, and foul bunt on a bunt attempt) are considered to be a failed bunt. I should also point out that my bunt attempt definition is based on pitch-by-pitch, so if a bunter tried to conduct a sacrifice bunt but fouled off two straight pitches with bunt attempts, but finally succeeded in sending the runner on 2-0 count bunt, he is credit for one success and debuted for two failures. However, for pitches that a bunter squared to try to do a bunt, but a ball was off the zone and called ball, I don't include them in my analysis since I can't tell it from generic non-bunt approaches. Same is true of a called strike with squaring bunt attempt but drawing his stick back during the pitch flight. Then for each batter I crunched bunt success rate against right- and left-handed pitchers respectively, and calculated the differences weighted by a lesser side of his bunt attempts. OK, so have you caught up with so far? Let's check out the results.

 According to my research, right-handed hitters successfully sacrificed on a bunt against righties 47.7% of the time whereas facing lefties their success rate ascends to 48.9%, only 1.2% difference inspected. How about left-handed hitters? Their success rate against righties and lefties is 46.6% and 46.4% respectively, only 0.1% difference (rounding aside). So basically, when it comes to a sacrifice bunt, it has little or nothing to do with a platoon handedness advantage.

 And let me point out one more before going into details . How much is successful sacrifice bunting affected by an opposing pitcher's batted-ball tendency? I computed GB% (the rate of the number of ground-balls on the total number of batted-balls a pitcher allowed in play) for each pitcher/year going through 1993, exclusive to pitchers who were able to see at least 200 balls in play in the particular year (all bunt events are excluded). Then, I took maximum and minimum 15% out of the data set and defined them as GB and FB groups respectively. For your information, mean ground-ball rate for those two groups are 38.2% and 53.4% respectively, while among all pitchers, it's 45.4%. Bunters successfully bunted 44.2% of the time against GB group while against FB group, they succeeded in bunting 47.6% of the time, the 3.4% difference. Is this a result from any difference of quality of bunters on each group? Bunters whom pitchers on GB group pitched against were able to do a successful bunt against the rest of group pitchers (i.e. belong neither to GB nor FB group above) 47.1% of the time. How about bunters on FB group of pitchers faced? Their success rate against the neutral groups were also 47.1%. So basically, there are no bias on the quality of bunters each batted-ball group of pitchers faced. But how about the quality of pitchers within each group? Pitchers on GB group allowed .328 wOBA and those who belong to the other end of the spectrum allowed .341 wOBA. Definitely and as you would expect, GB pitchers on the whole were better hurlers in terms of total performance in confrontation. So I took a brute force approach, tearing the poorest performers off the group of fly-ballers until their performance as a group jibes with the better one. However, bunters still take a bit more pains to bunt against worm burners, as only 0.4 percentages of points got narrow between the two parties. I'll take a deeper look at this later, but keep this in mind for the time being.

 So with that knowledge in mind, let's ask always awesome Pitch f/x.

 Actually, here's my first post using Pitch f/x, so let me digress a bit to describe the underpinning of my resources. I use complete dataset from 2008 on, with pre- and post-season and All-Star games are omitted but called games are kept in. I also do some park-adjustments on location, movement, and velocity for all pitches on all stadiums where Pitch f/x cameras are implemented. As to each specific pitch type, I don't touch up any re-classification at this time, but would likely do in the future. Strike-zone definition is based on my own definition and computation, where the borderline is set at 50% probability of a called strike. This accords very well with Mike's horizontally, but differs slightly on vertical zone borderline, as I make use of raw sz_top and sz_bot parameters as well as a batter's own height. However, the reason of the slight discord sounds much more originating from the fact that I plug more recent years (strike zone is actually expanding a little, especially in the bottom, in recent years, especially 2012) than input of extra parameters fed to the equation. And even the difference is very tiny, around one inch wider to the bottom if average values are set to the three parameters.


[Pitch Type]
 First of all, what kind of pitch types are easier or harder to bunt? From here to the rest of this article I classify all pitches as either fastballs (four-seamers, two-seamers, sinkers, and cutters), breaking balls (sliders, curveballs, and knuckle-curves), or changeups (change-ups and splitters) and analyse it through these three partitioned categories. Here's the result.

ResultN
Breaking35.9%2,648
Changeups41.6%1,677
Fastballs44.6%14,736

Fastballs are considered to be the easiest pitch to bunt and on the other end of the spectrum lie breaking balls. As to platoon effects on pitch types, almost no difference can be detected other than fastballs, where 2 to 3 percentages of gap can be inspected, and right-handed pitchers' breaking balls to right-handed hitters, where the rate drops down to 33.7% on 1,413 pitches. If you wonder why so many fastballs are thrown with a bunt attempt (remember that number is composed of all pitches, not restricted to those consequent on successful bunts), to suspect the presence of selection bias is always a good thing. Here's wholly unrealistic example, but if Miguel Cabrera steps to the plate while Prince Fielder is dozing off on 1st base and you notice somehow (yeah, somehow!) Miggy tries to bunt with no supposed intention to pull his stick back to swing instead during the pitch flight, isn't all you can do in the situation throw 80s mph fastball down the middle to induce bunt in order? Actually this is an excessively impractical for the purpose of the illustration (and we know Jim Leyland is "smart enough" to not force Cabrera to bunt) but bear that sort of bias in your mind.

 Investigating inside each specific bin, four-seamers are easier to bunt than other pitches in the bin (around a couple of percentage points; this is one reason bunting against GB pitchers is tougher as you've seen first in this article).

[Location]

 When an opposing batter looks like trying to do a sac bunt, where in the zone should a pitcher throw? The above graph shows that bunt success rate sees its pinnacle down the middle horizontally while the further away pitches are from a hitter's body the harder they are to bunt, and in terms of vertical location the closer the ball is thrown to the ground, more failed bunts occurred. Actually, this trend holds true whether what types of pitches are thrown and is also irrespective of an opposing pitcher's handedness both vertically and horizontally.


[Velocity]
 How about velocity? Conventional wisdom says that the faster the fastball is thrown, the harder to bunt (or hit, anyway). Does this hold true?


 This theory is supported by the above graph. Successful bunt rate drops down if the fastball is thrown with lots of speed, though you have to caution yourself that there are great uncertainty at the speed of 95 mph or more because so fast pitches are not often seen in this range. Breaking balls are relatively constant in terms of relationship between velocity and bunt success rate (or rather a bit downward trend). On changeups, you can find a bit awkward dip around early-80 mph. I'll explain more on this later.

[Movement]


 As to vertical movement, it is generally harder to bunt if the ball is thrown with more downward movement. In fastballs, you can see the highest success rate around +9 value, where most four-seam fastballs can be seen. Change-ups also see its peak on a point where most pitches of that ilk are thrown (check out density estimate on the left side of the plot). Breaking balls, however, looks consistently declining irrelevant of distribution of pitches. This is caused by the fact that lots of pitches on the upper part of the distribution which are classified sliders are actually cutters, and remember that cutters are much easier to bunt than breaking balls.

 For an obvious reason, I restricted my sample to only righty-vs-righty match-up for horizontal movement. However, you're not able to see much of a meaningful result as my sample consistes of only 7,986 pitches and once you sliced it to three separated pitch category and given that on top of that there are a wide variety of values (from -10 to 10 mostly) seen in the range of pitch movement, you cannot take a look at the graph to come to any conclusion with decent certainty. However, doing nothing is always worse than attempting to detect some patterns even from less helpful dataset. So what kind of patterns can you see?


 Like the plot of the vertical movement, the success rate is highest at the point that most pitches are thrown within each specific bin other than breaking balls, which see its trough instead. As I stated in the last paragraph I extracted only right-handed hitters and pitchers, though if the handedness of hitters are deregulated the similar tendency can be seen. I doubt most pitchers, if any, can control the movement of their pitches at their disposal (at least until some comprehensive analyses are conducted and published), so anyway you would be better off just discriminating by each particular pitch types rather than pursing too far into movement values.

 And what does bring about the blip in change-ups you see in velocity graph? I rambled through a screen along with some codes and noticed that in early-80s mph, change-ups are more likely to be thrown in ahead count, with slightly more vertical movement (to the downward direction, of course) and slightly down in the zone. I'm never going to say that these are the sole factors driving the result in that way, but suspect that that pitchers throw more winning shots in that velocity range leads to the phenomenon. Also, in around 90s mph, you would see more misclassified pitches and it may raise the success rate in a bit amounts.

[One more thing]
 We all know that a pitcher's job as a batter when a runner is on is mostly send the runner farther to set the table for the top of the order if the number of outs is less than two. In those situations, pitchers as batters are likely to choose to sacrifice themselves to send the runner through bunt attempts. So are those bunters able to move the runner successfully?

HittersPitchersDiff
Breaking39.1%29.8%9.3%
Changeups41.7%41.6%0.1%
Fastballs45.9%42.3%3.6%
Overall44.6%40.6%4.0%

 Actually, Pitchers are made of only starters and relievers are included in Hitters, but who cares? If a reliever somehow comes to the plate with a runner(s) is already on the base, managers most often decide to pull him down by calling a pinch-hitter. Ah, okay, okay, forgive my laziness and check out the above table and you can notice an interesting finding; pitchers as batters are indefensibly awful when they are fed with breaking balls. I suspect pitchers in general, even if they are hard-working enough to practice bunting, do against only fastballs but not breaking balls. So why not pick on them by sliders-curves-and-sliders? And for those pedants out there, this is hugely statistically significant (after all, Pitchers account for more than one third of the total records). Rather, hitting count is a bigger causative agent for driving this effect, as there would be reasons behind it that opposing pitchers bother to throw breaking balls to the pitchers at the plate in the first place. The data bear it out well that pitchers as batters are more likely to be fed with breaking balls in behind count than in early and/or ahead count, but even after accounting for the effect, Pitchers still don't see themselves go well, to the tune of 7.5 percentages of points off Hitters.

[Conclusion]
 We dived into Pitch f/x and checked through whether there are some sort of patterns to make batters incompetent to do a sacrifice bunt. We got to know that pitchers can control lots of factors at their pleasure that have an impact on whether the outcome of bunts come to success or failures. Breaking balls are tougher to bunt than fastballs, and change-ups lie in the middle. Locating your pitches at your disposal do also have an influence and pitches thrown down and away are harder to bunt. Velocity also has a voice but it is actually a small voice. Movement is a bit tricky but basically if you can add more downward movement to your weapons (like picking out sinkers instead of four-seamers), you can tilt the odds a bit to make it happen to your favor. Finally, pitchers as batters are desperate bunters when being attacked by breaking balls, so if you have an ability to earn strikes by breaking balls, no reason whatsoever to turn only to your fastballs.

Tuesday, October 23, 2012

My rant on yesterday's Climax Series


 Yesterday's final game of the Central League climax series (equivalent to LCS in MLB, though a pennant winner is still the league champion even if they would miss a ticket to head to Japan Series as there is no division group in NPB) is definitely the game which gave one of the worst experience I have had since I first got absorbed into baseball world. This is NEVER a well-familiar "Don't bunt, Don't squeeze, Don't bunt" rebuke post you can find out elsewhere while talking about NPB. Here starts my rant.

 The game was Game 7, a death knell game for both teams. One which wins heads to Japan Series, and the other which loses will watch Japan Series on TV. Both the Giants starter D.J. Houlton and the Dragons starter Zyunki Ito started on three days rest. Remember NPB teams conventionally follow six-man rotation, which means there are usually five days between their assignments for starting. I should also note solely for fairness that both team's ace starter have been injured and not available on the series.

 So how awful were both managers on this game? Let me first rail against the Giants manager Tatsunori Hara. My first rant is on his decision to start Houlton on three days rest. Starters who are forced to start on shorter rest clearly deteriorate his performance on that day. How worse do they get? It would be somewhere from .7 to 1.2 runs per 9 IP! It's equivalent to making a legitimate 2nd rotation guy to below average, or average to near replacement. It's really a severe dip, but he treated them as if they have little or no talent lost even used on two days shorter break than his usual routine. Actually he also started Utsumi, a .530 starter, yesterday on three days rest, and found that Utsumi was far from his normal shape and got knocked out, only lasted on 4 1/3. What happened today? Houlton was too inconsistent from the beginning of the game, barely got out of the jam. Not to mention he is never a good starter! He's just an average. And even if the Dragons' order was stacked with lots of righties, you can easily figure that he has no platoon advantage from the way he attacks opposing hitters. And they in fact had another starter of average caliber on Dicky Gonzalez. Why didn't he get a chance to start this game? It totally makes no sense.

 Then occurred many more hilarious moves in succession. In the bottom of the 2nd, no out, and the Giants just scored two runs to lead the game and still had two runners on the bases, 1st and 2nd, Hara decided to leave Houlton in to do a sacrifice bunt. As I said previous two paragraphs, you should pull him out of the game as soon as possible, especially if you have a good chance to score more runs, as Houlton is no longer a good piece of their pitching relay and this is really a death knell game, the game where you should be desperate to be a winner. Also the Giants carry historically best bullpen this season and all they have to do is go into late in the game being ahead by a couple of runs. Nonetheless you still keep Houlton in the game? Huh. And then in the bottom of the 3rd with bases loaded and two outs, Hara still let again Houlton come to the plate. Houlton was pulled out of the hill finally in the bottom of the 5th via a pinch hitter, when the Giants already led on 4-0 and there were two outs and no runner on the bases. What a botched waste of pinch-hitting.

 With Houlton finally removed from the game, the Giants were going to preserve four runs lead with their bragging bullpen going forward. The next pitcher Hara send to the mound was, however, Hirokazu Sawamura, one of their starters who threw 108 pitches just two days ago! How awful a move it was, I did't know off the top of my head while watching the game. I can't get how any manager, a professional baseball manager, executes such a enigmatic move on the very very crucial match. I would like to be informed if there is anyone who gets a good grasp of this strategy and his insights, if any. Sawamura allowed two hits and gave up a run to be replaced at the beginning of the next inning.

 These string of hilarious moves only disclose half of the story. I haven't articulated the other half on this post yet.

 The Dragons manager, Morimichi Takagi also forced their starters Ito, never good, a common back of the rotation guy, to start the game on three days rest. The Dragons also had another starter available in this game as Yudai Ohno has taken four days break since starting on opening game of the series. Actually, Ohno is near one run per game better as he's .530 or such while Ito is awful, .420 or so and threw no more 10 innings this year. So why was Ito appointed as the starter on the game? Yeah, as inferred from an announcer's piece of information, it's solely because Ito pitched well enough on his last appearance to have brought them a win. That's his only one criterion on deciding who should serve as a starter. And the result? In the bottom of the 2nd, he got severe attack to be beat four successive hits to open the inning and knocked out easily, three runs surrendered in an instant and the Dragons could never come up with the opponents the rest of the game. Ohno on the other hand pitched last three innings, but who cares at that point?

 My rant still continues. In the 2nd inning above, the Dragons were driven to find themselves on bases loaded and no out. If I were the skipper I won't hesitate at all to bring Asao, one of the best reliever in the league and reigning MVP winner, in to face Terauchi (horrible hitter), Houlton, Chono (damn good), all right-handed hitters. Can you get the reason? Remember the game is a death knell and all you have to do is to win the game by whatever approach you take. And once the game is over, the next game (an opening game of Japan Series) will be played on five days later so you never have to care about pitchers' fatigue level seriously. And what you're encountering right now is exactly the low LI, high boLI situation. So basically what you only care about is a run(s). Even if that run came from a lead-off homer or a go-ahead single in the top of the 9th, a run is a run is a run. Once the game is over, it's irrelevant where that run came from in the game. So why not just use your best reliever to minimize the damage as little as possible to set the table for their offense knocking runs later in the game? Are you itching to mention comfort and adjustment factor? Nope! He's the single only Rich Gossage I've ever seen in NPB (that's the reason he won MVP last year as a non-closing reliever).

 Actually, the last assertion is not what I was quite disappointed at to see the manager didn't go that way; I suspect no more than three people out of half a million Japanese baseball fans could think it that way while having a good comprehension of the logic behind it. However, when ever was Asao brought into the game? None. Because at no point did the Dragons lead the game yesterday, exactly the same reason as he had been confined in the pen for three consecutive games. And despite winning three straight games to open the series and getting the right to heading to Japan Series almost in their hands, they weren't able to win not a single game thereafter and sadly found that their 2012 baseball season came to an end. See you 71 years old magical tactician next April.