Ever wonder just how different the arsenals of knuckleballers are, relative to the rest of pitchers? Well, we already discussed how pitcher arsenals are analogous to ecological communities and how some of the statistical techniques community ecologists use can be transferred to describing and testing relationships for pitchers. Operating under that assumption, I used a technique called Nonmetric Multidimensional Scaling (NMDS) to graphically display pitcher arsenals.

I downloaded the pitch type usage of all pitchers last season who threw 150 innings or more and used a multivariate method called nonmetric multidimensional scaling to graphically display how pitch usage differs from one pitcher to another. The first step is to create a matrix of how different each pitcher's arsenal is from every other pitcher (called dissimilarity scores and based on the Bray-Curtis index of community dissimilarity). Once this matrix has been created, the technique compares each of those scores to one another and assigns each pitcher's arsenal a set of (x,y) coordinates in two-dimensional space. All that remains is to plot those coordinates and we see similarities and differences between pitcher arsenals. Now, the axes do not actually mean anything. Although we can test variables to see how they correspond to this ordination, all the figure below shows is how pitcher arsenals differ in relation to one another.

I think the results speak for themselves but I'll just quickly say that Dan Haren and Roy Halladay are separated due to cutter usage. R.A. Dickey, while still quite far removed from the rest of the pack is nowhere near as far away as Tim Wakefield because he uses his fastball more (22% of the time vs. 9% of the time) and his knuckleball less (75% vs. 89%).

Anyway, this doesn't really mean anything, I just thought you might find it interesting.

Thanks to The Smiths for today's title.

With Tom's recent post asking which former Jay folks would like on their team, the issue of, possibly the most polarizing (nah, that's not the right word, polarizing people can be liked) player the Jays ever had came up quite a bit. I will not say his name because then I'd be tempted to use foul language (which, according to our bylaws here, would be allowed, which, in turn, is why it would be so tempting).

Anyway, this comes up a lot when we discuss players who took advantage of some of the wonders of 20th century biochemical advancements, but I figured it was worth discussing -- if only to see what you all think about it. It's been posited that, because most players had access (and many also took advantage) of the aforementioned external subsidies to performance, it does not make sense to discredit the more exceptional players who did.

However, I think it behooves us to remember how baseball talent is distributed. Now, of course, as one gets to the upper levels, "baseball talent" refers to specialized talents, but I'm pretty sure you all get the idea.

Pardon the obviously crude illustrations.

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In light of some recent discussions around these parts about pitcher hit and walk sequencing, the tendency of pitchers to give up more or fewer hits when runners are on base or not, I wanted to look at how repeatable sequencing is. The topic came up, as I'm sure you probably know, from Brandon Morrow's seemingly very low strand-rate. The topic was first covered by our very own hugo and was more recently treated by Steve Slowinski over at Fangraphs, with hugo concluding that Morrow needs to mix in his offspeed pitches more against righties and Slowinski concluding that Morrow needs to develop a new pitch.

I tend to agree with hugo on this one more than Slowinski, though I do think that he'd benefit greatly from developing a new pitch (which he started to do with the cutter towards the end of last season anyway). I've said it before, I don't think that working in new pitches is necessarily going to help him strand runners. My guess is that Morrow's strand-rate last season had more to do with bad luck than anything else. Nonetheless, I think major league hitters will adjust to starters who don't mix up their offerings so Brandon's days as an above-average starter who can afford to rely so heavily on just two pitches are numbered. In any event, this piece is not meant to treat the Brandon Morrow subject so much as it is to look at the effects of sequencing throughout baseball.

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Hi again, everyone. As a follow-up to last week's piece on the effects of pitcher arsenal diversity on pitcher luckiness or unluckiness, I decided to do two things which might reduce some of the error. The first was to limit the sample size to include pitchers with 200+ innings in 2011 and the second was to evaluate effects of arsenal diversity on BABIP, instead of the difference between an ERA estimator (such as xFIP or SIERA) and the pitcher's actual ERA. There are drawbacks to both of these changes, of course. A reduction in sample size of roughly 100 pitchers greatly reduces the power of any test and focussing on BABIP only means that we would not be able to evaluate any effects that diversity may have on sequencing.

As per the comments, I used pitch types from texasleaguers instead of fangraphs (quite a pain, actually, since it seems like you can't export all the texasleaguers data at once) and built each pitcher's mean fastball velocity into the model. Given how small the sample size is and how volatile BABIP can be, it should come as no surprise that neither the effects of diversity (p = 0.961) nor the effects of velocity (p = 0.899) come out looking significant.

I also ran a model to determine the effects of each pitch type on BABIP. Although most pitch types did not seem to have an effect, there did seem to be an effect of increasing frequencies of sliders (p = 0.0595) and may have been one when two-seamers and sinkers were pooled (p = 0.0276), both of which corresponded with increasing BABIP. I expected these effects to be due mainly to the effects that these pitches have on batted balls -- essentially, pitchers who throw a lot of sliders or a lot of two-seamers are likely to generate a lot of groundballs and, consequently, have a higher BABIP. Unsurprisingly, this was the case.

Thanks to Bright Eyes for today's post title. Sorry, but I can't give you back the last five minutes of your life.

It's no secret that we are interested in determining what factors allow some pitchers to sustain lower ERAs than their strikeout-, walk-, and groundball-rates would suggest. Hence, this, another installment for this endless series in which we try to determine what factors allow pitchers to outperform their peripherals. In this installment, we'll be looking at whether pitchers with more diverse arsenals are able to keep hitters off-balance, allowing them to induce weaker contact. I have long considered this a possible reason that Shaun Marcum, who is notable for his array of pitches and his willingness to use any pitch in any count, has been able to outperform his peripherals for so long.

Before we can start looking at possible effects of arsenal diversity, we need to quantify the diversity of each pitcher's arsenal. As such, I chose the Shannon-Wiener index, a measure commonly used to estimate biodiversity in ecological communities. I used a sample of all pitchers who pitched 100+ innings in 2011 (a total of 145 pitchers) and exported their pitch type data from fangraphs and used the vegan package in R to calculate Shannon-Wiener diversity. Essentially, pitchers were analogous to communities and pitch types were analogous to species. The index takes both the number of different types of pitches a pitcher throws and the evenness of his usage of those pitches into account. The pitch distributions is an important factor here -- the index should be less influenced by a "see-me" changeup used two or three times a start than by a pitcher who uses his changeup five or six times as frequently. The index scales from zero (which would be a pitcher who uses the same pitch 100% of the time) to the natural logarithm of the number of different pitches a pitcher throws. As an example, a realistic maximum might be a pitcher who throws seven different pitches and uses them all equally. His arsenal would have a diversity index of log(7) = 1.946, so we can say that the index (for pitchers) scales from roughly 0 to 2.

Ever wonder which pitchers have the most diverse arsenals? Well, at the top of the list is actually our old friend, Shaun Marcum at 1.525 (mean diversity = 1.084; see the end of the article for the entire list of pitchers). Remember that these values are calculated on a log-scale, so Shaun Marcum has a much more diverse arsenal than the average pitcher. At the bottom of the list, as you might have guessed, you'll find extreme one-pitch specialists, like Tim Wakefield and Justin Masterson. Due to the innings exception, Mariano Rivera is not included, but his diversity score is 0.407. If you were unconvinced about the method before, I hope seeing Marcum near the top and these other pitchers near the bottom has assuaged your fears.

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Our collective interest in the nature of BABIP is no secret around these parts. A few months ago, we quantified a fairly weak, but highly significant link between BABIP and flyball-rate. As a long-delayed follow-up, I wanted to look at the actual correlation of a pitcher's babip from one year to the next.

I constructed a simple linear model attempting to fit 2011 pitcher BABIP to 2010 pitcher BABIP. I excluded pitchers with fewer than 170 IP in either season (a total of 57 pitchers in the sample). The model did not incorporate batted ball profiles, k-rates, or anything else that might be correlated with pitcher BABIP.

A significant relationship was not established (F = 1.085, df = 55, p = 0.302, R**2 = 0.02). With larger samples, I bet we would see a significant relationship, but I don't think the correlation would be any stronger (R**2 = 0.02 is extremely weak).

You'll likely notice that the "perfect correlation" is slightly off. That's because of a very slight decrease in BABIP leaguewide in 2011, relative to 2010. The "no correlation" line shows a horizontal line at league-average BABIP in 2011 (0.28815). Essentially, a strong correlation would be much more closely aligned with the "perfect correlation" line than with the "no correlation" line. That a. the points are not clustered around the actual correlation line; and b. the actual correlation line is quite similar to the horizontal, the correlation between a pitcher's BABIP in 2011 and his BABIP in 2010 is extremely weak.

So what does this mean for predicting BABIP in 2012? Personally, I think we can probably throw a pitcher's 2011 BABIP out the window and concentrate on his flyball-rate instead. In fact, after incorporating a pitcher's 2010 GB-rate into the model, the R**2 value increased to 0.08 and the relationship became much more significant (F = 3.447, df = 54, p = 0.039). Unsurprisingly, the relative importance of 2010 GB-rate (92%) on fitting the model was far greater than the relative importance of 2010 BABIP (8%). As it is commonly held that the longer into his career a pitcher has pitched, the better read we have on his hit-suppressing tendencies, the next topic I want to look at is whether a pitcher's single season batted ball profile is a better predictor of his next season's BABIP than his career BABIP.

What do you all think would likely be the better predictor?

Thanks, by the way, to the Silver Jews for today's post title.

In a discussion some folks were having about trying to put together an acceptable trade package for Joey Votto, Ricky Romero's trade value was brought up.  The logic was that Ricky's value is not as high as we might perceive it to be because, even though he was an all-star this season, his fWAR value (2.9) was not all that high this season.  Of course, the first problem is that FIP, and thus fWAR, likely underrates Ricky.

His peripherals (K%, BB%, GB%) were all very much in line with what he did last season (perhaps even a very slight tick better).  Now, some folks will look at his xFIP last season (3.64) and his xFIP this season (3.80) and say I'm wrong but, remember, xFIP is not only influenced by peripherals.  Balls in play can have a very large effect in either direction (because they skew the number of innings pitched and, thus, skew K-rate, BB-rate, and GB-rate).  In any event, the point is that his peripherals are very much the same as last year, when he was a 4-win pitcher without anything to suggest that it was an outlier.  This season, Ricky pitched 15 more innings.  Now, assuming that his BABIP rate returns to where we'd "expect" it to (via statcorner) he'd lose 20 outs and still have pitched slightly more innings than last year, statcorner does this and, using tRA, calculates Romero at 3.4 wins for the year.  However, I think statcorner WAR also slightly underrates Romero, since it uses real HR/fly-rate instead of normalizing it somewhat.  All told, offence was down this year from last but I'd think Romero was still about a 4 fWAR pitcher (a 4fWAR pitcher is roughly equivalent to a 3.5 rWAR pitcher).

Since it is generally assumed that 5 rWAR is roughly equivalent to an all-star season and fWAR should scale linearly to rWAR (the difference between fWAR and rWAR is in the y-intercept (replacement-level), not the slope (number of wins above it)), 5.5 fWAR should be all-star, right?  Wrong.  If 5.5 fWAR were all-star level, there would have been -- MLB-wide -- only 11 pitchers who put up all-star seasons.  Assuming there are 20 all-star slots filled by starting pitchers (10 for each team, there were 17 pitchers on the AL roster in 2010, so 10 starters is not that many), we should be looking at all-star level as 4.7 fWAR.  With the recalculated and (in my opinion) more accurate 4 fWAR, Ricky still doesn't quite make the cut, but he's quite close, so Ricky was actually very close to an all-star quality pitcher this year.  So what about Votto?

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Over the past couple weeks we've been talking a lot around here about what factors affect pitcher batting average on balls in play (BABIP).  So far, the only factor we've found that I feel sufficiently comfortable saying is both under a pitcher's control (to a large degree) and has a large impact on pitcher BABIP is batted ball type, since flyballs in play are more likely to become outs than groundballs.

There are a number of reasons for this.  First, flyballs that go over the fence in fair or foul territory don't count as "balls in play."  So homeruns, which would often be hits even if they didn't go over the fence (there are occasionally the ones that just clear and would have been caught on the track, but plenty of homeruns would be gappers), are removed from the BABIP calculation.  This is not an insignificant event as somewhere around 10% of fair flyballs leave the park each year.  Second, the hardest hit balls in the air are often scored as linedrives, which don't technically count into the fangraphs flyball-rate (though, as Woodman pointed out the other day, they do count into StatCorner's Balls in Air).  Third, it is much easier for fielders to get to flyballs.  They have more time to get underneath them and do not have to throw a baserunner out once they've made the play.  Finally, and probably least importantly, a flyball in foul territory can count as an out but cannot count as a hit.  Compare this to a groundball in foul territory, which can count as neither, and flyball-babip is again suppressed relative to groundball-babip.  Combine these factors and it is easy to see why It is so much easier to get outs on flyballs.  In fact, even though misplays on flyballs typically do not get scored as errors (increasing BABIP) and misplays on groundballs typically do result in being scored as errors (reducing BABIP), flyballs still result in lower BABIP.  Also, by no means was this an exhaustive list so it's also possible that I've missed some other reasons flyballs become outs more frequently

However, although we've established that pitcher groundball-rate can inflate pitcher BABIP, we haven't actually looked at all at the degree to which BABIP is affected.  In 2011, AL pitcher, BABIP amongst qualified starters has ranged from Jeremy Hellickson's .223 to CC Sabathia's .318.  It should come as no surprise that Hellickson is an extreme flyball pitcher.  So was Hellickson lucky or is it simply the result of him inducing groundballs just 35% of the time and Rays having an exceptional defence?  Was Sabathia unlucky or was his AL-worst BABIP simply the result of an above-average groundball-inducing tendency?

I used a sample of all pitchers MLB-wide who pitched at least 300 innings over the past three seasons (since 2009) to test the relationship between groundball-rate and BABIP.  Here are the results:

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There are probably still some folks out there who have never heard of WAR, but they're increasingly becoming few and far between and very few of them likely are at this site.  However, what actually constitutes this "replacement player"?  It's been fairly widely-cited that a replacement-player is roughly two wins twenty runs per 600 plate appearances worse than a league-average player.  In fact, that's supposedly what our "replacement-level" is based on -- not who is actually readily available, but what "league-average" production is.

Thus, we say that a two-2.5-win player is "league average."  Of course, note the tautology here.  We're saying that a two-2.5-win player is league-average based on the fact that he's worth two 2.5 wins more than a replacement player, but we're saying that a replacement player is worth two wins less than league average.  However, it's entirely possible that the relationship between WAR and wins actually changes from year-to-year because there's no perfect way to define "average."  Even though WAR is based on the statistical mean of player performance in any given year, the distribution of that performance may change.  This is particularly true when looking at the game across long timescales.  Consider for a moment how much the base level of play to be considered "replacement level" changed during wartime, league expansion, integration, etc.

Part of the reason that Honus Wagner was able to accrue four consecutive 10-WAR seasons was that he was an incredible ballplayer but another part of it was how many players were systematically excluded from baseball at that time.  Thus, there was a smaller talent pool available, leading to a lot of players in the league who would not be in a similarly-sized league today.  Of course, the leagues have expanded, so we kind of assume these things even out, but they don't necessarily.

To make matters even more confusing, the two most frequently-cited sites for WAR (fangraphs, FWAR, and baseball-reference, rWAR) calculate it differently.  In fact, not only do they calculate WAR differently, they calculate replacement-level differently.  But which method actually scales more closely-related to actual numbers of wins?  I decided to try and figure out what the relationship between fWAR, rWAR and wins has been the past three seasons.

Before we get started, I'm not going to go into details about the differences between the two metrics because other folks have done that already.  If you're interested, there are plenty of folks who'd be more than happy to explain in the comments section.

So, first, I downloaded the past three seasons-worth of team WAR across MLB (a total of 90 team-seasons).  Then, using R, I constructed linear models to determine the relationship between wins above replacement-level and actual wins.

Here is the relationship between wins and fWAR:

and here is the relationship between wins and rWAR:

These graphs might look similar at first glance, but note the differences in scale on the x-axis (!).  As expected, the slopes are similar (one more WAR should mean one extra team win), but where that relationship begins is very different depending on whether we're looking at fWAR or rWAR.  So a main reason we see a big difference between fWAR and rWAR is that rWAR assumes that replacement-level is higher than fWAR assumes it is.  How much higher?

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Earlier today (or yesterday, depending on which timezone you're in and when you're reading this), woodman663 posted a really interesting article demonstrating that changeup specialists may have a predilection for sustaining low babip.  Many of the examples of pitchers that he looked at (for example, Ted Lilly) were extreme flyball pitchers.  Since flyballs are more likely to turn into outs than grounders, it forces us to disentangle these two factors from one another.  

Woodman and I have talked back and forth on the piece a bit and I suggested that we run some statistical analyses so that we could tease out whether the changeup effect was truly meaningful or if it was just an artifact of the flyball effect.  I said much of what is in this post in the comments section, but here it is full-blown and with the output (which is important, in case I'm making mistakes here -- please let me know if you notice any).

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"Hey, man of science, with your perfect rules of measure, can you improve this place with the data that you gather?"
-- Bad Religion, "I Want to Conquer the World"

Many of us around here like to joke that objective analysis through sabrmetrics reduces baseball to binary code.  Why would we watch games when we're likely to glean just as much pleasure (and, more importantly, knowledge!) from simply glancing at a series of strings of ones and zeroes?  Like all great jokes, it's funny 'cause it's true.  Though, if we are going to be 100% honest with ourselves and each other, there is a certain kernel of truth to this.  As a general rule, of course, given the option to spend three hours watching a ballgame (four hours, if the Yankees or Red Sox are playing!) or spend that time poring over boxscores and statistics, I choose watching firsthand over stats pretty much every time.  Still, note the qualifiers.

In any event, first and foremost, I'd like to stress that, although some might have you believe otherwise, there's nothing normative about sabrmetrics.  Whether one defines oneself as sabrmetrically-inclined or not is neither intrinsically good nor inherently damning.  In fact, whether you know Yunel Escobar's aggregate defensive rating or you think a relief pitcher's performance should be judged on how many saves he has, in a large-scale reductionistic sense, you're using an objectively determined statistic to aid you in your characterization, which, like it or not, makes you a sabrmetrician.  Now, naturally, most of us take a bit more of an holistic viewpoint on this, defining "sabr stats" as the "meaningful" ones (basically, the ones that analyses have shown are best correlated with what the statistic is purported to characterize).  As a baseball fan, my personal goal is to find simple solutions to complex questions.  And, make no mistake, most questions that plague baseball fans are complex.  The criteria that define the best player in baseball, for instance, can be interpreted many ways.  Just three things we might want to consider when we make our decision include: 1) if the player must play only one position capably or if he receives bonus points (and how many?) for varying degrees of versatility; 2) how we judge defensive skills; and, though it is infrequently discussed, 3) whether we’re filling our team with 25 doppelgangers of that player, because, if so, there must be a premium on players who can both hit and pitch (or who at least have strong arms).

So this seemingly simple question now has answers that could range from Jose Bautista to Micah Owings (no kidding!).  Now, Owings clearly doesn’t seem to pass the smell test for being considered the best player in baseball.  There’s a good reason for this – while he provides a degree of versatility that few players could even hope to match (Shaun Marcum's grand slam notwithstanding) – Major League Baseball rosters have 25 slots, so there is a premium on specialization at the elite level.  Owings does not provide the kind of specialization that would make him the best player at that level, but he could very well be the most useful MLB ringer on a college team – he’d hit the cover off the ball and be an exceptionally good pitcher.  The reason Micah Owings does not come to most of our minds first (or at all) when we consider the best players in baseball is because we are not interested in who would be the best ringer on a college team, we're interested in who would be the best player on an MLB team.  In a sense, the answer to every question depends on what specific criteria we use to answer that question so, before we can find our answers, we have to define our questions.

And that’s the rub.  Defining our questions forces us to simplify them in scope but increase them in number, complicating matters by requiring us to find lots of answers.  As humans, we are predisposed to biases when we attempt to simplify those questions.  These biases can distort our initial questions (and, in turn, distort our ultimate answers).  Objective analyses (essentially, the basis of sabrmetrics) attempt to remove (or, at least, account for) those biases.  At the same time, objective analyses are only as reliable as the data on which they are based and the ways that the analyses are designed and interpreted.  In many cases, those data are far from perfect and those studies are poorly-designed and incorrectly interpreted.  This can be truly frustrating when "bad data" and poorly conceived research do not merely compromise our capacity to reach meaningful conclusions, but actually cause us to draw incorrect ones.

However, as a group, we can identify where we've made errors and correct them, synergistically answering questions we could never answer alone.  In my opinion, what's truly fascinating about sabrmetrics is not simply minimizing distortion in condensing baseball to statistics, it's minimizing distortion in amplifying statistics to baseball.  Next time it seems like sabrmetricians are trying to reduce the game to binary code, remember that the real endgame is turning those ones and zeroes you see today back into frozen ropes and dying quails tomorrow.

FOR IMMEDIATE RELEASE

MONDAY, SEPTEMBER 12, 2011

TORONTO, ONTARIO

 

 

BLUE JAYS RHP JON RAUCH DONE FOR THE SEASON

 

            The TORONTO BLUE JAYS have placed RHP JON RAUCH on the 15-day disabled list with right knee cartilage tear, retroactive to September 5^th.

            RAUCH, 32, is 5-4 with 11 saves with an ERA of 4.85 in 53 games this season, his first with the Blue Jays.  The 6-10, 250 lb. right-handed pitcher will visit knee specialist Dr. Richard Steadman in Vail, Colorado to be evaluated early next week.  Should Dr. Steadman confirm the Blue Jays medical staff recommendation of surgery, he will perform the procedure at that time.  The recovery time should be six to eight weeks which will allow for full recovery prior to the 2012 spring training season.

What would you all do with him this offseason?

In an excellent piece this past week, hugo looked at some of the difficulties Brandon Morrow's been having this season.  He noted that Morrow has seemed to have had a lot of trouble stranding runners, possibly as a result of yielding a greater number of flyballs with runners on, which may or may not be related to having problems locating his pitches at the bottom of the strike zone when pitching from the stretch.  The article got me to thinking -- what factors actually control a pitcher's strand-rate?

First, very little is actually known about strand-rate.  To be honest, not only did I not know the method used to calculate it, I didn't even know what it actually purports to measure.  Does strand-rate even attempt to account for the effect of homeruns automatically scoring runners who are already on?  While the more sabrmetrically-inclined of y'all may not need this review, I certainly did.  Per Hardball Times, the stat is calculated as: (H+BB+HBP-R)/(H+BB+HBP-(1.4*HR)).  In words, this just means the number of baserunners a pitcher strands (the number he lets on minus the number he allows to score) divided by the number of baserunners he could potentially strand (total number of baserunners minus the number that score on homeruns).  Note that the method does not do a perfect job of describing the probability that a pitcher has stranded any given baserunner because it does not include hitters who have reached on errors and it uses a general formula to calculate how many of a pitcher's runs were scored on HR (essentially, it assumes each homerun scores 1.4 runners).  While this is not perfect in small samples, thanks to good friend to every statistician and half-cousin to Charles Darwin, Sir Francis Galton (to be honest, I'd never actually heard of him), who postulated the central limit theorem, in large samples, it's certainly good enough to allow us to make inferences.

To determine how closely strand-rate was correlated with (not affected by!) a number of factors, I performed separate simple linear regression analyses.

Predictions

Peripheral stats and fastball pitching:

I expected that K-rate affects strand-rate (positive correlation) more than any other single factor.  I also expected that BB% would be weakly positively correlated with strand-rate.  Upon reaching base on a walk, the hitter is at first base.  Since non-HR hits can go for doubles and triples (and thus those baserunners are inherently more likely to score), it makes sense that a pitcher who has a higher proportion of his baserunners reach via the walk should have a better strand-rate.  Additionally, walks are less likely to drive in runs than hits.  It is also possible that pitchers who throw harder would have better strand-rates.  As estimates of how hard pitchers throw, I used fastball velocity, fastball % (of pitches thrown), and weighted fastball value (per 100 fastballs) by linear weights.

BABIP, ERA, and ERA Estimators:
Strong negative correlations (as x increases, y decreases) should exist with BABIP (BABIP should drive strand-rate) and ERA (strand-rate should drive ERA).  As BABIP is likely driven by LD-rate, I expected strand-rate should also be correlated with LD-rate.  Since defence-independent (DIPS) ERA estimators/predictors take K% into account, I also predicted weaker, but still significant, negative correlations with DIPS metrics, such as FIP, xFIP, SIERA, and tRA. 

Batted Ball Types:
Since linedrives are much more likely to become hits, I expected a negative correlation between linedrive-rate (LD-rate) and strand-rate.  I did not expect to see a correlation with batted ball types besides linedrive-rate because, although groundballers are more likely to induce double plays and less likely to allow extra base hits, they are also more likely to allow singles.  Over extremely large samples, there must be an effect of pitcher batted-ball splits, but -- outside of LD-rate -- I expected that effect to be very small and obscured in one-year samples.

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Well, another frustrating game on Saturday afternoon.

Ricky Romero wasn't great but did manage to pitch through trouble, spreading out eight hits and three walks enough to give up just three runs over 6 2/3 innings (though he left two runners on base).  He was lifted after walking righty Alex Rodriguez.  Casey Janssen came in to face lefty Robinson Cano (that's how you leverage those platoon splits! . . not that hindsight isn't 20/20, of course).  Janssen gave up a double to Cano to put us behind (for good) by one.  He got a grounder that ate up Mike McCoy (who was in to replace Yunel Escobar, who was lifted with a jammed wrist), allowing Cano to score.  Janssen escaped with a long flyball to centre.  Camp pitched the 8th inning, which was notable for a very nice double-play started on a diving stab by Brett Lawrie.

The Jays had trouble bunching hits, scoring just four runs, in spite of eight hits, including two homeruns (both solo shots by Adam Lind and DeWayne Wise.  Wise also added a groundball triple down the right field line.  Aside from J.P. Arencibia, Yunel and Eric Thames (who was also pulled from the game early), the rest of the starters each got one hit.  Lawrie's double put the tying run at the plate in the top of the ninth, but that was as close as the Jays would come.

JotD go to Wise (.209) and Romero (.128).
The Tallet award goes to Janssen (-.475).
Hinske awards go to Arencibia (-.107) and Escobar (-.097). 

 Today's recap title continues the Beatles sleeping song vein started by Tom for the thread.

With Toronto Blue Jays fans hoping the team can contend in 2012, how to upgrade the team and reload for contention next season has been an important topic of discussion around here.  One position that the Jays will look to upgrade (have already upgraded?) is at second base, where just about anything would be better than what Aaron Hill (as much as I love him) provided (.221 / .267 / .309, wRC+ 63) before being traded for Kelly Johnson.

A fairly popular potential solution to our second base woes has been attempting to sign potential free agent Jose Reyes for (what we're assuming to be) about 5 yr / $100 M.  As I'm sure everyone knows, Reyes is an excellent player having by far the best season of his career (.336 / .377 / .507, wRC+ 149).  He does pretty much everything well on the offensive end -- makes good contact, hits for some power, has a nice batting eye, and is a phenomenal base stealer -- and plays league-average defence at one of the most valuable positions on the diamond, shortstop.  There are some grumblings that he'd be hesitant to move from shortstop back to second base (something he actually did do for the New York Mets back in 2004 to accommodate free agent signing Kazuo Matsui).

Of course, the Jays have been employing one of the best shortstops in the American League for the past season and a half, Yunel Escobar, who has hit well (.280 / .355 / .393, wRC+ 108) in 800 plate appearances while playing very good defence (metrics disagree but about 8 runs above average per season is fangraphs Aggregate Defensive Rating) since being traded to Toronto.  The Jays recently traded for the aforementioned Johnson, a second baseman in the midst of a down season (.210 / .290 / .408, wRC+ 91), but who has been solid at the plate overall (career: .260 / .342 / .441, wRC+ 107) and provides about league-average defence.  However, like Reyes, Johnson is a free agent after this season, which gives the Jays the opportunity to upgrade by signing Reyes and letting Johnson walk.  Is this a good idea?

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Well, the Jays put up a decent fight but came up short again this afternoon.  Luis Perez managed to escape a few jams, allowing four walks and five hits, but yielding only two earned runs over five innings.  He got some help from the defence but overall did an acceptable, if not exceptional, job and managed to strand loaded bases in the bottom of the fifth by striking out Ben Zobrist.

The real damage occurred after Perez was lifted, first against Shawn Camp in the sixth and then off Jesse Litsch in the seventh.  Camp, to put it quite simply, was dreadfully ineffective today.  He only gave up one run, that is largely because the Rays managed to bunt into a double play.  Even with the two free outs, Camp still couldn't manage to escape the inning unscathed.  Litsch was also bad, walking two batters and then giving up a three-run homer to B.J. Upton, putting the Rays up 6-2.  Casey Janssen and Frank Francisco were solid, if unspectacular, to close out the game.

The Jays bats woke up some (seven hits, five for extra bases!, plus three walks), but were mainly scattered.  Jose Bautista drove in a run with a double in the first, but failed to score from second after Adam Lind lined a single and the Jays couldn't cash him from third with just one out.  J.P. Arencibia had two RBI.  He drove one run in on double in the fourth and drove in Brett Lawrie (who had tripled) in the seventh on a groundout.  Of course, an RBI groundout hardly helps the team when it is losing 6-2 in the seventh, but it is better than a strikeout.  Eric Thames and Edwin Encarnacion each hit back-to-back homers in the eighth to make it close but the Jays just couldn't come all the way back.

J.P. Arencibia had a great day behind the plate, making some nice blocks and gunning down three baserunners.  Brett Lawrie made a nice play on Kelly Shoppach's bunted double play, corralling a bit of a high throw from Camp to get the force at third and then firing a strike to Lind to get Shoppach at first.  Lind Kelly Johnson threw back to Lawrie and they almost ended up getting a triple play (Sean Rodriguez advanced from first to third on the play) but couldn't.  Bautista did throw out Rodriguez at third two innings later when he tried to advance two bases following an errant pickoff attempt from Janssen.  Aggressive baserunning is fun to watch, but I don't know how much it necessarily helps the team -- I loved Johnny Mac as much as the next guy (okay, maybe not quite as much), but it was certainly one thing that made me grind my teeth when I watched him.

JotD: No one had the numbers, but I'm giving it to J.P. (-0.004) for nabbing the three attempted steals and his RBI double.  Honorable mentions go to Encarnacion (.069) and Thames (.068), who were closest.

Suckage awards go to Litsch (-.240), Camp (-.099), Dewayne Wise (-.139) and Yunel Escobar (-.134), mostly because they were the last batters of the game (also why J.P.A. had a negative WPA, his last plate appearance lost him .092).

On the bright side, there's always tomorrow. Brandon Morrow for the Jays, David Price for the Rays.  We look to get back above .500.  Hopefully Bautista hits Price like he has so far this year.

Thanks to the Weakerthans for today's post title.

Not discussing this one too much.  The Jays threatened to put across a bunch of runs in the first inning but came away with just one, plated on an Edwin Encarnacion single.  Mike McCoy probably shouldn't have been leading off.  The Athletics answered right back with a run of their own in the bottom of the inning but Henderson Alvarez limited the damage.  From there, the Jays offence disappeared, managing just two hits the rest of the way.  They threatened to tie it in the top of the eighth, when John McDonald led off with a single and was bunted over to first by McCoy, but neither Yunel Escobar nor Eric Thames could cash him.

The A's plated a bit of a cheap run to take a 2-1 lead on a double play in the bottom of the fourth, after a groundball single and a rare Johnny Mac error.  Alvarez managed to get through six and pitched well, 3 K, 1 BB, 7 H, just the two runs, and 11 grounders of 17 total batted balls.  Jesse Litsch came on in relief in the seventh and looked shaky but escaped thanks to a nice play in right by McCoy.  The wheels fell off in the eighth, though, Litsch got into trouble and, with one run already in and a runner on was lifted for Rommie Lewis to face a pinch hitting Josh Willingham.  Willingham homered to put the game out of reach.

Notes: Jose Bautista was scratched due to a tight neck and Adam Lind was lifted due to injury resulting from a hit-by-pitch, but no word yet on how serious it is.

Jays of the Day: None, Encarnacion (.065) came closest.  Honourable mention to Alvarez for looking really good out there.

Suckage Awards to pretty much the whole offence, but notably Yunel Escobar (-.181), Brett Lawrie (-.111), and Mike McCoy (-.091).  I'm giving one to Rommie (-.047) because, even though the team was down by two when he came in, the homerun really was the nail in the coffin.

Thanks to Piebald for tonight's post title.  It's an afternoon game tomorrow (finally), 4:00 eastern.  Luis Perez starts against Guillermo Moscoso.  Hopefully the bats wake up against Moscoso, whose peripherals (4.9 K/9, 3.27 BB/9, 27.2% gb-rate) don't exactly scream 1337.

The past couple days, our fine site has been abuzz with talk about Ricky Romero's trade value, generating a lot of really interesting discussion from our fine users.  One particularly interesting piece, who has generated a lot of discussion is Clayton Kershaw, starting pitcher for the Los Angeles Dodgers.

Both pitchers have shown the capacity to log lots of innings and be effective.  Neither is considered an injury-concern.  The pitchers had similar seasons in 2010 (both pitched over 200 innings with 3.64 xFIP), but Kershaw is younger (23) and has stepped up his game, being far more effective this season (2.80 xFIP) than Romero (26, 3.63).  According to their fangraphs trade value series values, any team that would refuse to trade Romero (#37) straight-up for Kershaw (#11) would be nuts.

And yet, I'm not so sure.  The first, and most obvious issue (and the only reason the Dodgers would even consider trading Kershaw) is their contract statuses.  As was discussed on the previous post, Romero has already been locked up long-term at a very cost-effective 5M in 2012 and 7.5M per season from 2013-2015, with a club option for 13.1M in 2016, totaling 4 yr / 27.5M if they do not pick up his option and 5 yr / 40 M if they do.  There is some risk that Romero could forget how to pitch or suffer a serious injury, but that contract is extremely team-friendly and is a great bargain.  

Kershaw's status, on the other hand, is up in the air.  He is eligible for free agencyarbitration for the first time this offseason, so he has three more seasons of team control.  His arbitration award this winter (assuming he does not ink a long-term deal) is likely to be somewhere in the neighbourhood of 12M.  The next two seasons, that award should increase, probably to about 15M and 18M, so Kershaw is likely to be paid about 45M over the next three seasons, after which he becomes a free agent.  While being on the hook for an injured Kershaw for multiple seasons is not a risk the team would have to take, that is a lot of salary to commit on a per-year basis, even though it's certainly worth it for a player of Kershaw's calibre.

For just over half of what Kershaw is projected to make over the next three seasons, the Jays could have Romero for an additional season with the club option for 2016.

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Last night, in the gameday thread, we discussed profiling pitchers based on how they attack hitters (or, more specifically, the results of their attacks on hitters).  An interesting question was posed this morning (or, I suppose, late last night, depending on when you go to bed, I did not see it until this morning) about what constitutes the ideal pitcher.  Now, the concept of the "ideal pitcher" is kind of a loaded term.  Theoretically, the "ideal" pitcher would be someone who could make batters swing and miss on every pitch, thus throwing 81 pitch, 27 K shutouts every start.  Of course, outside of Albert Brooks/Brendan Fraser films, this is impossible to do.

More practically, the <em>best</em> pitchers often have high groundball-rates to go along with high K-rates and low BB-rates.  Now, it's possible to make up for an average groundball-rate if the pitcher strikes more batters out or almost never walks anyone.  As an example, someone like Cliff Lee continues to be one of the best in the game in spite of league-average (or below) groundball-rates because of his strikeout- and walk-rates.  It is more difficult to compensate for low strikeout-rates (though pitchers with superlative groundball-rates, like Derek Lowe and Tim Hudson are solid, though unspectacular, arms).

As was mentioned earlier, pitching deep into games is an important factor to consider as well.  Considering that games are nine innings long (generally), pitchers who excel for five or six less innings still leave three or four innings for the opposition to score off the bullpen.  The knock against pitchers like Brandon Morrow is legitimate.  Morrow is an exceptional pitcher for six innings and an excellent pitcher overall.  Most pitchers pitch into the eighth inning by relentlessly pounding the zone.  Since it is difficult to strike batters out when everything is over the plate, there is a tradeoff associated with keeping the ball in the strike zone all the time.  However, to maximize pitch efficiency, pitchers need to keep the ball in the zone, otherwise they'll be lifted from the game with a pitch count of 110 in the sixth inning.  There is also a tradeoff associated with pitch location within the strike zone -- pitches up in the zone are more likely to generate swings and misses (and, consequently, strikeouts), but also more likely to generate flyballs (and, thus, homeruns).  Pitching down in the zone leads to more groundballs, but also more contact.  Even poorly hit balls can squeak through the infield, leading to baserunners and, eventually, runs.

Realistically, a pitcher's plan of attack should change with each batter that he faces.  While this is not an exhaustive list, some of the factors affecting his strategy (in addition to his personal repertoire and feel for pitches at that time) include the batter he is facing, the quality of his defence behind him, his pitch count that day, and the game situation (how many outs and how many bases are occupied).  Pitchers are likely to change their strategies between pitches within a single plate appearance:  if a pitcher has a batter at 1-2, why not try for the K?  Alternatively, if he's fouled off four or five pitches, maybe it's time to just try and get him to ground out.  Thanks to Jonathan Silvertown, plant population ecologists know that plant resource allocation strategies require tradeoffs between survival, growth, and fecundity.  In fact, pitching strategies are a result of a similar series of tradeoffs.

Thanks to The Clash for today's post title.

Well, that was a frustrating loss.  After putting up a good effort against Michael Pineda, the Jays fall short, giving up game-tying and -winning homeruns in the bottom of the eighth inning.

Henderson Alvarez put out a decent effort, pitching five innings and doing all right, but not exceptionally well.  It does bode well for the future, of course.

The Jays hit Michael Pineda fairly well (5 runs and 3 HR, by Eric Thames, Adam Lind, and Brett Lawrie, over 5 innings) but couldn't do much the rest of the way, merely holding serve until the bottom of the eighth inning, in which Trever Miller gave up a game-tying homerun and Jon Rauch gave up [another] homerun to allow the Mariners to take the lead and, eventually, the game.

I'd write more but: a. the game finished very poorly; b. i'm tired; and c. there is more interesting news tonight anyway, with the signing deadline here.

Jays of the Day: Lind (.244), Thames (.144), and Jesse Litsch (.242)
Suckage: Alvarez (-0.348), Rauch (-0.321), and Miller (-0.301)

We'll get 'em tomorrow . . . I hope.

jays up 5-4 thanks to lawrie's homerun

The past few weeks, as I'm sure y'all have, I've been thinking about the way Edwin Encarnacion has turned around his season.  Now that Brett Lawrie is up (and producing!), it seems that we can officially close the book on the EE at third base experiment.  Given that Edwin drove in the game-winning run yesterday afternoon, now's as good a time as any to take a look at the ups and downs of his season.

After a somewhat unlucky 2010 season (.235 BABIP), it looked like there might be some potential for Encarnacion to be a real producer this year.  In just 367 plate appearances last season, Encarnacion hit 21 homeruns on his way to above-average production at the plate (.244 / .305 / .482; wRC+ 110), in spite of his atrocious BABIP.  His glovework, while still unspectacular, looked better (both anecdotally and statistically) and it was a surprise (at least, to some of us) when the Jays let him go for nothing to the Athletics, who then (also surprisingly) non-tendered him.  This offseason, when the Jays signed him to a nice little deal with an affordable team option for 2012, many of us here at BBB were glad, but did not understand why it had already been determined that he wouldn't play third.  He'd missed last spring due to an unfortunate fireworks incident and, given the strides he'd seemingly made last season, it only made sense to give him a shot to win the job out of the spring.  Even more bizarrely, after spending spring training at first base, the day before the season, it was announced that he'd be starting the season at the hot corner.

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Great game this afternoon, as the Jays took the home series from the Angels.  It was a bit of a nailbiter, requiring the Jays to come back in the ninth to tie it and plate a runner in the tenth for the walk-off, but that's part of what makes this game fun.

Brett Cecil started this one and had a rough first inning, escaping after a two-run homer by Torii Hunter.  Cecil bounced back to have a decent start, going 7 innings and yielding four runs.  Cecil struck out just three batters, but against no walks and just five hits, it wasn't so bad at all.  Relieving Cecil, Casey Janssen struck out a batter during perfect eight and Frank Francisco worked his way around a leadoff single by Bobby Abreu (who then stole second and third) during the ninth.  Rauch came on and pitched a perfect tenth (also striking out a batter) for the vultured win.

Facing Danny Haren, the Jays got about as much offence as expected: just five hits total over the first five innings, though two of those hits were back-to-back homeruns by Eric Thames and Jose Bautista.  The bats came alive against the bullpen, though, as the Jays almost (and, were it not for a Yunel Escobar sacrifice bunt and more overly aggressive baserunning by John McDonald, who had a nice day at the plate, would have) plated a run against Scott Downs in the eighth.  Jordan Walden was not as lucky, Colby Rasmus and Brett Lawrie (after a bad strike call on a 3-1 count prevented him from walking) hit back-to-back doubles to tie it up.  Lawrie stole third but J.P. Arencibia struck out and John McDonald flew out to shallow centre to end the threat.  The Jays put the deciding run across next inning, when Francisco Rodney walked Escobar to lead off the inning.  After failing to get the bunt down, Mark Teahen (who replaced an injured Rajai Davis on Mac's poor baserunning play) struck out, but Jose Bautista walked to put runners at first and second.  The Angels went to the lefty, Hisanori Takahashi, to face Adam Lind but the strategy backfired when lefty-masher Edwin Encarnacion hit an absolute bullet to left-centre.

Jays of the Day go to Brett Lawrie (.433, plus he started a great two-rundown double play), EE (.337), Bautista (.253), and Jon Rauch (.143).  I'm giving one to Thames (.083) for the homerun, too.

Suckage Awards go to Lind (-0.311), Arencibia (-0.284), and Davis (-0.129), though it seems unfair to penalize him for Mac's baserunning.  Cecil (-0.095) is close but I don't think he deserves one, anyway.

The Jays are in Seattle tomorrow night.  It's a late one (10 pm eastern start) but the pitching matchup (Henderson Alvarez vs. Michael Pineda) sure is intriguing.

Thanks to Freddie T and the People for today's post title.

Angels up 4-3 in the ninth

Hey all, just a quick update on xFIP-based WAR (as explained, here) for any interested parties.  Check them after the jump.  A quick methods update, I have included park effects by determining the difference between my estimated FIP-based WAR and fangraphs actual WAR and then adding it to xFIP-based WAR.  Roy Halladay is still on top.  Jays pitchers (Ricky Romero, Brandon Morrow, and Jo-jo Reyes) in black and bold.

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The Blue Jays got back to their winning ways tonight, defeating the Indians in a slugfest tonight 11-7.

Starting pitcher Jo-jo Reyes didn't exactly pitch well (5 2/3 IP, 0 K, 3 BB, 8 H, 3 R (0 ER), but he pitched -- as is so often said -- "well enough to win."  Well, show me someone who doesn't pitch well enough to win when your offence puts up 11 runs.  Of course, after some of the losses and no decisions he took this season, it is nice to get him an easy win.

While the bullpen didn't cough up the lead (it's tough to cough it up when you score 8 runs in the first six innings), as a unit, they didn't exactly look great.  Octavio Dotel came on in relief of Jojo in the sixth and pitched well, getting the last out (to strand two baserunners) and then pitched a perfect seventh.  For the eighth inning, Shawn Camp came in and then the wheels fell off.  Camp pitched to four batters, recording zero outs before being lifted for Jason Frasor with the bases loaded.  Frasor gave up a hit, allowing two runners to score but got out of the inning without any further damage, managing to preserve the three run lead.  After the Jays added a couple more insurance runs, Jon Rauch came in to close it out in the ninth and helped himself out of trouble with two strikeouts.

Well, obviously the bats were alive tonight against Mitch Talbot.  All the starters had at least one hit, except Jose Bautista (who walked twice) and Aaron Hill (who walked once).  Travis Snider (3-5, HR, 2B, 2 R, 5 RBI), Edwin Encarnacion (4-5, 2B, 4 R), and Rajai Davis (3-4, 2B. 2 R, 4 RBI) all had great days at the dish and Yunel Escobar (2-5, 2B, RBI) and Eric Thames (2-5) added two hits apiece.

Jays of the Day go to the Maharajai (.269), EE (.158), Yunel (.105), and Moonraker (.102)

I'm going to give an honourable mention to Dotel for stranding the two baserunners and Frasor for stopping the bleeding in the eighth.

J.P. Arencibia gets the Hinske and Camp gets the Batista

Tomorrow's another night game -- Brandon Morrow for the Jays and Josh Tomlin for the Tribe.