I celebrated tax day this year by taking a dive into the numbers behind the Detroit Tigers’ offensive production. Since last season, I had developed the feeling that this should-be offensive power was having trouble scoring late in games, hamstringing their ability to mount comebacks and generally secure a win in the later innings, and I wanted to investigate to see whether that really was the case.

The evidence I gathered in April appeared to support my hypothesis. The 2013 season was just twelve games old, though, and it was difficult to ascribe too much meaning to data gathered from such a small sample set. As of today, however, Detroit has played one hundred games in the 2013 campaign, so I decided to update the numbers:

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Dave Cameron recently posted an interesting article about Jhoulys Chacin. It’s all about how Jhoulys Chacin is defying the rules of HR/FB rates. His HR/FB rate this year is a mere 2.8%. Jhoulys Chacin has pitched 120 innings, had 106 fly balls, and allowed just three home runs. Very impressive. But it makes you wonder if there are other pitchers who are maintaining low rates while allowing more fly balls overall. Because while Jhoulys Chacin is obviously benefiting from his HR/FB ratio, it’s possible for a pitcher to have more fly balls while maintaining a slightly higher HR/FB and benefit more. So I invented CAIN, a counting stat to help measure that.

CAIN does not stand for anything. I’m just paying homage to a famous outlier.

CAIN = FB – (9.34 x HR)

To explain, the Fangraphs Glossary says that the league-average fly ball rate is “~9-10% depending on the year”. In fact, of the 91 qualified pitchers in Fangraphs database for 2013, the average HR/FB ratio is 10.7 percent. So there are 9.34 fly balls for every homer. So we can say that for most pitchers, if they had ten homers at this point in the season, they would have about 93.4 fly balls.  Ten homers and 93.4 fly balls would give you a CAIN of exactly 0. Make sense?

Now for what you came here for. Here are the top ten in CAIN this year:

Note that I’m not saying any players might actually be able to sustain their CAIN, I just think it’s an interesting little tidbit, and perhaps a nice follow on to Dave Cameron’s article.

And Jhoulys Chacin is not #1. It turns out that Eric Stults is in fact benefiting more from his HR/FB rate outlier this year. Of course, that’s partially happening in Petco. Petco is not Coors.

Poor Joe Blanton. His peripherals aren’t that bad this year. But he’s been posting some pretty high HR/FB rates for the last five years or so. I’ll leave it to someone else to puzzle that out.

After doing this analysis I wanted to know about exceptional seasons in the “UZR era” for pitchers’ CAINs. I am continuing to use 9.34 as the FB/HR value, not adjusted for year. If I was being very scientific I would probably break that constant out for league AND year, but I’m lazy and unpaid. Anyway, here, unsurprisingly, is Matt Cain:

So in summary, CAIN is a nice little tool if you are interested in seeing just how much a HR/FB rate is affecting a pitcher’s performance. If anyone can think of a better acronym, like one that actually is an acronym, please leave a comment.

Editor’s note: This piece was removed at the request of the author.

Earlier this season, while writing a piece on Rick Porcello’s breakout, I noticed he was mixing a certain combination of skills that seemed both interesting and valuable. He was striking out more than 7 hitter per 9, walking fewer than 2, and also inducing a ground ball rate above 50%. The story with Porcello was the increase in strikeouts, but it also put him in some excellent company. Going back to 2002 (when GB% becomes available) the list of qualifying pitchers to meet those criteria is short and impressive. Roy Halladay (4), Chris Carpenter (3), and Cole Hamels (1). Porcello is doing it this year and so is John Lackey. And Lackey’s story might be the most interesting.

(Note: K% and BB% tell a similar story, the cut-offs are just harder to express and are not the real focus of the article.)

Porcello’s key was an improved changeup and new curveball that induced more strikeouts, but Lackey has actually improved his numbers in all three categories pretty dramatically. Even if we accept that Lackey’s 2011 numbers were depressed due to the coming injury, if you take his career numbers and place them next to his 2013 season, the change is quite interesting.

At 32, Lackey is getting better. Certainly he had some excellent seasons from 2005-2007 when he exceeded 5.0 WAR for three straight years, but this performance is quite something. He’s getting more strikeouts than in all but one year of his career and he’s posting the best walk rate and highest ground ball rate in his career by substantial margins. The results are better if you look at ERA, FIP, and xFIP as he’s never had a lower ERA or xFIP and only bested this year’s FIP during that run from 2005-2007.

Lackey is having a career renaissance. But the interesting thing on the surface is that Lackey’s velocity isn’t much different and his pitch selection isn’t either. He’s got the same repertoire and it seems to be of the same quality. But looks can be deceiving.

A word is needed up front regarding pitch classification. Pitch F/X seems to think he’s throwing a different mix of pitches, but it’s actually just classifying them differently. You can see in the two charts below, from 2011 and 2013, that the velocity and horizontal movements are quite similar, but the pitches are being called something different.

Lackey Velocity, H-Movement in 2011

Lackey Velocity, H-Movement in 2013

His velocity isn’t much different from his rough 2011 campaign and the location of the pitches aren’t terribly different either. He’s routinely working low and away to both RHH and LHH this year just like he did in 2011.

This doesn’t seem to be about stuff or location, and this isn’t an increase in performance driven by BABIP because the indicators I’m looking at are entirely within the pitcher’s control This is an analysis about an increase in strikeouts, a decrease in walks, and an improvement in GB%. Lackey is getting better results because he’s doing well in those categories. Yet he’s not throwing harder and he’s not throwing much differently than he did in one of his worst years.

Well, not so fast. There is one key difference that I’ve been able to find and I think it can explain why Lackey is doing so much better. Let me start by pointing out some even more interesting tidbits. First, Lackey is throwing fewer pitches in the zone overall while inducing much less contract overall with virtually the same swing percentage as in 2011. His first-pitch strikes are up but are not much different than his career norms. Lackey is getting more swings and misses, which could point to the strikeout increase. But Lackey is also hitting the zone less often, which means a good portion of the walk decline is coming from batters swinging at pitches outside the zone. That all makes sense to some degree.

Lackey is getting more swings and misses via pitches out of the zone, so his strikeouts are up and his walks are down. But the increase in GB% is the aspect about which I was most curious and it fits in with what I believe is cause the strikeout and walk transformation.

Lackey isn’t throwing harder, the ball isn’t moving more horizontally, and the general location of his pitches haven’t changed. What has changed is the vertical movement on a subset of his pitches. It’s hard to notice when you’re looking at season averages, but graphically it is quite striking. In certain situations, Lackey throws a variation of his fastball and cut fastball that moves with the same velocity and horizontal movement as normal, but with more vertical break. It’s almost like having another pitch that Pitch F/X doesn’t understand. Take a look and remember he’s thrown fewer pitches overall in 2013, so the cluster stands out even more:

Lackey Velocity, V-Movement in 2011

Lackey Velocity, V-Movement in 2013

I’m not a leading expert on Pitch F/X or pitching in general, but this is the kind of thing that catches my attention. Lackey’s pitches don’t seem different overall, but there is a group of them that are acting differently. The increased downward break is likely to blame for more ground balls and I can certainly imagine it’s part of what’s driving the strikeouts via hitters swinging and missing on pitches they didn’t except to drop so much.

In analyzing this particular cluster of fastballs, the results were striking. Of 86 such pitches, 27 were called balls, 17 were called strikes, 19 were fouled off, 10 were swung at and missed, 3 were hit for singles, and 10 resulted in ground outs. When Lackey throws this pitch, the worst thing that happens is a single and even those are pretty rare. If we consider this pitch in context, during at bats in which he threw one of these pitches, he walked 14 hitters, struck out 28, induced 3 line outs, got 33 groundouts, and allowed just eight hits. All singles.

I haven’t watched many Lackey starts, so Red Sox fans might be able to speak more confidently on the subject, but it appears as if Lackey has turned himself into one of these special pitchers who can maintain high K%, low BB%, and a high GB%. We only have a couple of months of data, so this could still vanish from in front of our eyes.

My interest in Lackey, Porcello, and these pitchers at large comes from my belief in DIPS theory, but also a more general belief that limiting walks and extra base hits will help prevent runs and a pitcher can play a role in limiting extra base hits even if some of it is out of their control. If you’re inducing ground balls when you allow contact, you’re not going to get hurt nearly as often. Whether you like metrics like SIERA for this, or simply like to read FIP alongside GB%, it makes good sense.

John Lackey is becoming one of those guys. Along with Porcello (and Fister and Felix who have been hovering around these somewhat arbitrary cutoffs), he is headed for the club occupied only by Doc, Carpenter, and Hamels. I can’t tell you how often this happened prior to 2002, but the fact that only one of the eight recorded seasons is anything short of great makes me think this is worth tracking. The specific numbers aren’t hugely important, but they allowed me to discover the new and improved John Lackey.

He gets more strikeouts, allows fewer walks, and induces more ground balls. He’s had a bit of a rough stretch since his peak five years ago, but with this new approach, and occasionally different fastball, John Lackey is pitching himself back into the upper reaches of the American League.

Plate Discipline Correlations, 2008-2013 

In fall 2008 FanGraphs was kind enough to release new plate-discipline metrics, including first-pitch strike percentage (F-Strike %), outside-the-zone swing rate (O-Swing %), and inside-the-zone swing rate (Z-Swing %).  At the time, Eric Seidman was even kinder when he investigated the correlation of these plate-discipline statistics with standard pitcher metrics like WHIP, FIP, BB/9, and K/9. Very thoughtful indeed.

Now we have another 4.5 years of plate discipline data, compiled by Pitch f/x rather than Baseball Info Solutions. It may be worthwhile to see how these numbers compare with Seidman’s, as well as add a measure of uncertainty to the correlations. It is possible for two factors to have a strong relationship, but because of small sample sizes or other forms of variability, the correlation value may not be as precise a measure as a high R-value may suggest.

Bootstrapping

Correlation coefficients, which fall between -1 and 1, allow us to measure the strength of linear dependence between two variables, such as O-Swing % and K %. We can use bootstrapping techniques to obtain 95% confidence intervals for these correlation coefficients. Calculating confidence intervals for correlations adds a measure of uncertainty to the process—narrow intervals indicate we can have greater confidence that the R-value we obtain represents the true correlation between the two metrics.

Bootstrapping is a statistical technique in which we resample our current sample, in this case 500 times. This repeated process allows us to assign measures of accuracy to sample estimates, such as medians, means, or correlation coefficients. For our purposes here, it is only important to note that we can be 95% confident that the true R-value lies between the intervals. If the interval includes 0, meaning absolutely no correlation, we can conclude that there is not enough evidence to indicate any relationship between the two variables.

First Strike %

These correspond well enough to the values obtained by Seidman, with one exception worth noting. While he used K/9 and BB/9 to correlate with F-Strike %, here we examine the correlation with strike and base on balls percentages. Our correlation coefficient is similar in magnitude at .24 versus .19, but its wide confidence interval approaches the null value and suggests the estimate is not very precise. This is worth noting, especially considering that BB % appears to have such a strong correlation with F-Strike % of -.79 with relatively narrow confidence intervals. Seidman observed a similar pattern—pitchers who get into an 0-1 count are more prone to not walking batters than striking them out.

O-Swing %

O-Swing % is the percentage of pitches a pitcher pitched outside the zone but still generated a swinging strike. Think anyone facing Pablo Sandoval. Here we again see relatively moderate correlations with relatively tight confidence intervals ranging from 0.30 to 0.19. Pitchers who induce swings at pitches outside the zone may be especially tricky for hitters to do damage against. So far this season Adam Wainwright and Matt Harvey are both in the top three in O-Swing %, and top two in both WHIP and FIP.

Z-Swing %

We can see from the results below that Z-Swing %, the rate of inducing swings at pitches in the zone, bears little relationship with any of these metrics. Seidman’s analysis showed that the correlations were negligible at best. The confidence intervals for all of these measure metrics include 0, meaning that we cannot be 95% confident that there is any relationship present. A quick glance at the leaderboards shows that Ian Kennedy and Miguel Gonzalez are near the top of the list this season, and these guys aren’t exactly shoving.

All data courtesy of FanGraphs.

 Because I’m a believer in open data, you can find my R code here.

Pitching is a fascinating aspect of the game of baseball. Talent is required, but a lot of the results that come after the ball leaves the pitcher’s hand are luck. Defense plays an important role as well. Rosters are not constructed in the same way, so defense isn’t uniform accross the league. Many pitchers that don’t miss many bats need the help of the defense behind them in order to get solid results. Other pitchers, such as Matt Harvey, can rely on themselves a little more due to their ability to strike out more batters. I decided to look at which pitchers have been fortunate in 2013 and the ones whose fate has been a little less positive. I took xFIP and subtracted ERA from it to get my data. The range was from 2.1 to -1.85.

Luckiest

Jeff Locke – J.P. Breen has a good post up about how fortunate Locke has been to pitch with the Pittsburgh Pirates defense behind him. He had the largest gap in xFIP and ERA of anyone in the 2013 sample at 2.10. His ERA currently sits at 2.15 while xFIP sees him at 4.25. While he does pitch in front of a very good defense he has also been very lucky regarding the home-run ball. His HR/FB rate currently sits at 6.7%. I’d bet on that regressing to at least the 10% xFIP uses in the second half. Luckily for him, the defense isn’t going anywhere.

Travis Wood – Wood essentially came out of career purgatory for the 2013 campaign. After spending most of his career being yo-yoed back and forth between AAA and the bigs he put together a solid first half. Currently his xFIP is 1.61 points higher than his ERA. Like Locke, Wood also has seen his HR/FB rate decrease dramatically this season to 5.7%. While that looks extremely low he posted  HR/FB percentages of 6.3% and 6.7% in 2010 and 2011 before it jumped to 12.7% last season. I’m not quite sure what to make of this. I lean towards his home-run rate settling in closer to 12.7% than I do the rates in other seasons. He might have a knack for keeping the ball in the park though. The Cubs defense is actually quite good according to their UZR so that also helps Wood keep his stats much lower than his peripherals. I’m interested to see how he fares in the 2nd half.

Mike Leake – Leake’s put together another Mike Leake-type season. His ERA is currently at 2.69, which will definitely regress to his career norms. Unlike the two pitchers above we have plenty of Major League data on Leake. His LOB% is at a career high along with a HR/FB that would be the best of his career. Leake’s 2013 campaign looks pretty similar to his 2011 except he is striking out less batters while also getting a few more fly balls. Leake is hurt by his home park and the Great American’s effect on his ability to keep balls in the yard is pretty incredible. His HR/FB rate at home is 5% higher than it is away, but this season the split is nearly 13 percentage points. His 4.6% HR/FB rate away from home this year should regress to settle a little closer to his career split of 11.0%.

Unluckiest

I won’t go straight down the list of the unluckiest because the first few names are, to put it mildly, not very good pitchers currently. Those pitchers were: Wade Davis, Joe Blanton, and Edinson Volquez.

Rick Porcello – Porcello checked in at number 3 on the unluckiest pitchers. His xFIP sits at 3.07 compared to his 4.80 ERA. He’s seemingly made a habit out of his peripherals constantly being much better than the stats that show in their traditional form. It’s no secret that the Tigers are not very good on defense. Porcello has shown some signs of life this season with his K% trending up while his BB% is trending down a tad. He’s gotten a little unlucky with his HR/FB% this season (15.7%) compared with his career average coming into 2013 (11.4%). Porcello does get a ton of groundballs though, and it’s likely we’ll always consider him “unlucky” as long as he has the Tigers defense behind him.

Edwin Jackson – Jackson is an interesting case to me. Every year I’ll watch him have a few electric games and expect him to finally make a jump into a more consistent front of the rotation of the starter, but it never happens. Jackson is simply who he is at this point. This season his xFIP is 3.74 compared to his ERA of 5.11. The first thing I noticed was his LOB%; currently at 62.3% this season compared with 70.8% in his career. His batted-ball profile is very strange. He’s actually getting more groundballs than any time in his career. He has come around of late, so maybe his luck is turning more in his favor.

Matt Cain – Cain’s struggles have been well documented this season, by many people including Fangraphs’ own Eno Sarris. Cain’s ERA is currently1.16 points worse than his xFIP. Unlike Porcello, Cain has made a career out of always outperforming his peripheral stats. Eno’s piece can explain the reasoning on why 2013 has been somewhat of a bust for Cain infinitely better than I would be able to. The Giants continue to insist that Cain is healthy, so unless something changes it’s safe to assume they’ll keep running him out there every fifth day. Hopefully adjustments can be made to get back his career norms.

Josh Willingham hasn’t been very good this year. He’s on pace to put up his worst full-season WAR mark. I’m secretly glad about this because I’ve never really liked Josh Willingham as a player. (Well, I guess it isn’t a secret anymore.)

One of the reasons Willingham has been considered a good player is his selectivity at the plate. He’s certainly a patient hitter: this year, he’s swung at a lower percentage of pitches than any other qualified batter. But has he been selective?

One simple selectivity approximator is O-Swing%. The less pitches a hitter swings at outside the strike zone, the better he probably is at identifying where pitches will end up and which ones not to swing at. And sure enough, Willingham is third-best by this measure for 2013.

But O-Swing% isn’t the best way to measure selectivity. After all, an infinitely unselective batter could take every single pitch he saw and record a perfect 0.00000 O-Swing% — but a 0.00000 batting average as well. Last year, Carson Cistulli noticed this and constructed a leaderboard of hitters with the greatest discrepancies between their O-Swing and Z-Swing rates, in order to highlight batters who swing at lots of hittable pitches but lay off balls. And guess what? Josh Willingham proved to be baseball’s 8th-most selective hitter.

So there’s no doubt, then, that Josh Willingham is selective? Well, it depends how you define “selective”. First, we should notice that Willingham ranks in the bottom 20 of Z-Swing% this year — which means that he made it onto Cistulli’s leaderboard not because he swung at a lot of pitches in the zone, but just on the strength of his amazing ability to lay off pitches out of the strike zone. This is a useful skill — it leads to a high walk rate — but the low Z-Swing% is concerning.

There are two possibilities that I can make out: either Josh Willingham is only swinging at pitches he likes in the zone, but taking “bad” strikes, which would make him a true selective hitter; or he’s just indiscriminately taking too many pitches, kind of like that hypothetical 0.00000 O-Swing% guy.

Which is it?

Here are the pitches swung at by Josh Willingham in 2013, courtesy of Texas Leaguers:

If we just look at pitches in the strike zone, it looks to me like he’s avoiding pitches down and in, as well as pitches down and away. So there’s a pattern, then. That’s a good sign.

Now let’s take a look at his career swing rates, this time via Baseball Prospectus:

Looks similar, right? So Willingham must know what he’s doing. Throughout his career, he’s made sure to lay off not just balls, but also pitches in the bottom-left and bottom-right parts of the strike zone. Those must be the kinds of pitches he’s bad at hitting.

Here’s his BABIP chart:

Well, that changes things, doesn’t it? He seems to be pretty good at hitting those down-and-in pitches he selects against. Willingham appears to have a good eye, but it doesn’t look like he’s choosing his pitches wisely.

But maybe BABIP isn’t the right thing to look at. After all, it leaves home runs and whiffs out of the equation. So here’s the same chart, but using TAv this time (TAv is basically Baseball Prospectus’s version of wOBA; wOBA is better, though):

This seems to justify him swinging at those high, inside pitches, but it still doesn’t explain him laying off pitches down and in, or swinging at pitches up and away.

So I think we can tentatively conclude that Josh Willingham isn’t as good at selecting pitches to swing at as one might think. This analysis is incomplete, of course, because there are a whole lot of factors — pitch type, count, base-out states, etc. — which I haven’t taken into consideration. I also haven’t looked at other hitters’ data — maybe Willingham is actually good at this compared to other major league hitters.

Why not take a look at another hitter? Say, Marco Scutaro. Here’s his Swing% chart:

And TAv:

I would say Scutaro’s selectivity is a little better. He’s good at hitting pitches inside and he knows it, so he mostly swings at pitches inside. Now, he’s inexplicably not very good at hitting pitches right down the middle, but Scutaro probably figures he should swing at those anyway because, well, they’re easy to hit, in theory.

Marco Scutaro is more selective than Josh Willingham. Intuitively, that seems right, as it would explain (to some degree) Scutaro’s league-best SwStr%.

Conclusion

I guess the moral of the story is don’t trust O-Swing% and Z-Swing% as selectivity indicators. And PITCHf/x data is fun to look at.

On the surface, Jason Marquis looks like he’s having a good season. He’s 9-4 with  a 3.77 ERA for the San Diego Padres and if you’re looking at how well his team performs collectively during his starts and how effective he has been at limiting runs, you might even say Jason Marquis is having one of the best seasons of his career. If you look more closely, however, he’s actually having one of the worst seasons in baseball history.

That’s a crazy juxtaposition. We’re used to evaluating players by advanced statistics like FIP, xFIP, WAR, and others, but it’s pretty rare that they tell us something totally different from the basic descriptive stats. Usually we look at a player’s FIP and think their ERA might be due for some regression. We don’t often look at a player’s FIP only to find that their ERA is propped up with toothpicks and Scotch tape.

Such is the case with Jason Marquis. His numbers this season are actually quite remarkable. He’s 48th among qualified starters in ERA, good for an ERA- of 104, which is just a bit below average. If you turn to FIP, he’s dead last at 5.70, good for a 156 FIP-. His xFIP is better, but it still remains fourth-worst in MLB at 4.77 to go along with an MLB-worst 125 xFIP-.

No qualifying starter’s ERA is outperforming their FIP as much as Marquis’ is this season. Jeff Locke and Jeremy Guthrie are in the conversation, but Marquis is decidedly ahead. Perhaps even more noteworthy is that Jason Marquis’ 156 FIP- is the second-worst number for a qualifying starter since 1901, and the record is within reach at 159.

Marquis is essentially having an all-time worst season in terms of strikeout rate, walk rate, and home-run rate when adjusting for park and league average. Yet he’s allowing a very average number of runs in a very respectable number of innings. Only two players in history have had their FIP- outperform their ERA- by more than Marquis’ difference of 52 and they both played before 1910.

I don’t mean to belabor Marquis’ 2013 season among the all-time clunkers in MLB history, but rather to simply set the stage for the remainder of this analysis. Marquis is doing a pretty decent job preventing runs, but is doing a terrible job at the aspects of preventing runs a pitcher can most control. What’s going on here?

First, let’s consider the Padres defense. By DRS, UZR, and UZR/150, the Padres rank between 15th and 21st in MLB this season. They aren’t a terrible defensive team, but this doesn’t appear to be a club that should systematically deflate their pitchers’ ERA. It’s possible that they are playing amazing behind Marquis and not for everyone else, but that seems unlikely. If you consider the Padres starters as a whole, their ERA is higher than their FIP and have individual starters on both sides of the divide. Additionally, it doesn’t appear as if any of this can be explained by the GB/FB ratio of each pitcher, which might have pointed to a particular aspect of the Padres defense.

Sometimes it’s just about the situation, but on the surface it doesn’t look like this is a good explanation either. Marquis allows a .318 wOBA with the bases empty to go with a .380 wOBA with men on and a .306 wOBA with men in scoring position. He’s more or less the same pitcher with men in scoring position as he is with no one on so pitching from the windup versus the stretch isn’t the answer. Let’s look at each base situation.

Obviously, some of the samples are really small, but notice how much worse Marquis is when a runner is on first base, but not also on second base. Could this have something to do with holding the runner? A couple of possibilities spring to mind. One, Marquis is distracted by the baserunner. Two, having the first baseman holding the runner creates a hole where Marquis often allows hits. Three, the presence of the baserunner and location of the runner cause Marquis to pitch differently in order to avoid the hole on the right side, resulting in pitches that get smashed. I’m not sure if any or all of these are factors, but they are possible factors. If something like this is the case, it’s possible that Marquis isn’t actually as bad as his FIP tells us overall, but rather just really terrible in certain situations and reasonably average most of the time.

Some of this timing argument is dispelled if we consider that he’s actually allowing a higher percentage of his home runs with men on base (48%) than league average (40%) so his high walk rate and high home-run rate should be costing him dearly. But they are not. Marquis is pitching like he should allow close to 6 runs per 9 innings but he’s allowing fewer than 4.

His strikeout rate is 12th-worst in baseball at 14.7% and his walk rate is easily the worst at 13.2%. Only 1o pitchers have a higher HR/9. Yet he’s right around league average in ERA. Metrics like SIERA don’t rate him any better, as he comes in 2nd-worst at 5.11.

He shouldn’t be doing this well. He’s leaving runners on base like he’s Felix Hernandez, but walking guys like he’s Carlos Marmol, giving up home runs like Jose Valverde, and only striking guys out like he’s Bronson Arroyo. He’s getting a lot of ground balls, but he has a low BABIP against.

If you look at Lucas Harrell and Jason Marquis, most of the stat line is nearly identical.

If anything, based on K/9, BB/9, and HR/9, Harrell should be doing better. But somehow, Marquis is getting a much lower BABIP and a higher LOB% despite getting pretty much the same number of ground balls and having a worse HR/FB rate. They have essentially identical xFIP and Marquis has a worse FIP. The fact that Harrell has a 5.07 ERA and Marquis has a 3.77 ERA defies understanding (the story is the same with park-adjusted numbers).

This is likely just one of those small sample size mirage/miracles. Marquis has lost a tick on his fastball this year and his changeup is acting more like a splitter according to Pitch F/X, but nothing appears fundamentally different that would allow him to actually sustain this low BABIP (last year it was .311). Perhaps baseball fans who watch the Padres more regularly can offer some insight into what exactly is the driving force behind his low BABIP this season.

If you’re someone who likes to look at FIP, you’re looking at one of the worst seasons in baseball history. If you’re someone who cares more about overall run prevention, you’re looking at an average year. Granted, it’s not uncommon for a player to over or under perform their peripherals over 100 innings, but it is amazing how dramatically it is happening for Marquis.

It’s not unusual for BABIP to drive over- and under-performances by 20 or so points in on the ERA/FIP- scale, but what Marquis is doing is beyond the typical variation. For every qualifying season since 1901, the mean FIP-/ERA- differential is around 2.3 and the standard deviation is about 13.8. Marquis’ 2013 season is 3.6 standard deviations above the mean. (For just 2013 those numbers are a mean of 1.3, SD of 17.6, and Marquis is 2.8 SD above the mean)

The simple takeaway of this entire exercise is this. Jason Marquis is over-performing his peripherals this season and there isn’t a clear explanation for why this might be the case other than standard variation in BABIP. It’s a perfectly reasonably explanation. Marquis is getting some good fortune regarding where baseballs have been hit during key moments that have allowed his ERA to stay relatively low despite the fact that based on his other numbers it should be much higher. That happens. What is so amazing about this is the degree to which it is happening.

We’re all open to the idea that some players will over- and under-perform, but Marquis is over-performing at such a rare level. He’s in the top 0.25% of all over-performances when comparing ERA- and FIP-, which are statistics that control for league average and park effects. When you strip away the context, Jason Marquis’ 2013 season stands out as the third-biggest over-performance in the last 113 years, which includes more than 8,000 individual seasons.

Everything I know about baseball tells me Jason Marquis won’t maintain this ERA if he maintains these K, BB, and HR levels, but part of me is really hoping that he does. I like when things make sense and can be easily explained, but sometimes it’s a lot of fun to watch a player defy the odds for no other reason than that the Gods of probability have chosen that player to be the exception that proves the rule. Jason Marquis and Padres fans are  hoping he can keep it up. Anything can happen, but as we should note, it usually doesn’t.

While I’ve agreed with his rankings thus far — not that my assessment means much to anyone other than me — I must at least challenge Mr. Cameron on his omission of the mighty Bruce.

To be fair, Cameron spent just a couple of sentences on the Cincinnati slugger in his Just Missed the Cut post, so a detailed reasoning wasn’t available. Regardless, I spent some time looking through Bruce’s numbers in an attempt to craft a credible, albeit tentative argument against his exclusion.

Here’s what I found:

Truth be told, the Reds outfielder has not done a whole lot to help his cause recently. While his power numbers remain streakily Brucian, they do not make up for his sliding peripherals: His strikeouts are way up. His walks are way down. His good-not-great batting average is buoyed by a career-high and likely unsustainable BABIP, and his defense has gone from Gold-Glove caliber to doesn’t hurt to have him out there.

So, what reasoning could I possibly have to combat that mountain of evidence? Well, let me channel my inner Hawk Harrelson and talk about Bruce and The Will to Win … Don’t stop reading! I was just kidding!

Bruce’s value truly begins with his durability. From 2010 to the 2013 break, he has played in more than 95 percent of Cincinnati’s scheduled games, almost 10 percent more than Cameron’s No. 50 — I told you this was tentative — Austin Jackson and +10 percent more than No. 43 Jason Heyward

I know 10 percent doesn’t seem like a whole lot, but when the Tigers and Braves have to plug in replacement level players like Andy Dirks and Reed Johnson for a month, the loss stings.

Complementing Bruce’s durability is his age. Despite six seasons in the bigs, he turned 26 years old a few months ago, and it can be argued he has yet to enter his peak years.

I’m not trying spin any yarns about the mythical breakout of players turning 27, but I am saying Baseball-Reference lists Reggie Jackson as Bruce’s No 1. comparable player through their age-25 seasons. A quick look back at the HOF’s numbers tells us it took him quite a few years to get those strikeouts under control.

Maybe Bruce never will, and maybe he, as many predict, becomes Adam Dunn (No. 7 on the same list), but let’s not be so quick to dub him Big Donkey Part Deux just yet. He still has plenty of time to right the ship and develop into a more well-rounded player.

Finally, Bruce’s contract is relatively team-friendly, considering the two-time All-Star has been in the majors in this his sixth season. This year, he’s a bargain at $7.5 million, and while his contract jumps to an average of about $12 million per season for the next three — and a team-controlled fourth — years, that’s not out of line for what sluggers of his caliber are paid.

Consider No. 45 on Cameron’s list, Edwin Encarnacion (breakout age, 29, by the way), whose track record is essentially 2012, is making about $10 million the next three seasons.

So, are these factors and his strong counting numbers evidence enough for Bruce’s inclusion on Cameron’s list? Maybe. However, more convincing arguments admittedly could be made for Max Scherzer and Jordan Zimmerman.

But without a doubt, Bruce is a fringe top-50 trade value player; his durability, youth and contract certainly warrant the debate, if not a spot on Cameron’s list.