Community Blog

Note: I have no idea if I’m the first to do this, but quite frankly I don’t care.

There’s always been something strangely romantic to me about being the absolute worst at something. And when I say worst, I don’t just mean one of the worst–I’m talking about being the absolute worst period whatever period ever period. I can’t explain why it is–perhaps it’s because I’ve been last at virtually everything throughout my life, or perhaps it’s because I’m a fan of the Orioles–but for some reason, I’m transfixed by the idea of being the floor, the ultimate, the person or entity that everyone else looks down upon.

Now, what do my strange, borderline masochistic feelings towards the awful have to do with three of the better all-time players, two of whom are enshrined in Cooperstown and one of whom probably should be? Well, they all have one thing in common, which no one has seemed to realize: At one point or another, they were all the worst qualifying position player in the majors.

How, you ask? When? Why? I’ll answer your questions, but first I’d like to share with you some of the other big names that fit this criteria. Since the season ended on the 29th of September¹, there are now 143 seasons, meaning 143 worst players (or LVPs, for the sake of this exercise). I gathered up each of them, and saw that of the 143 atrocious seasons, many of them involved players that had good–or in some cases, great–careers. I then proceeded to order each player season by career WAR, to present you, the unedumacated reader, with…The Best Of The Worst.

By that, I mean: the following post consists of the top-10 careers (as measured by career WAR) for position players that were the worst in the major leagues for a particular season. I classified the general area of their career that the LVP season happened in: start-of-career bump, middle-of-career fluke, or end-of-career decline; I also put in my attempt at an explanation as to why the bad season happened. Oh, and I also divided them into groups (as they were somewhat similar), à la Bill Simmons’ NBA Trade Value Rankings.

Off we go!

GROUP I: AGING IN THE OUTFIELD²

10. Marquis Grissom

Career WAR: 26.9

LVP year/WAR: 2000/-1.8

Classification: End-of-career decline

Grissom had a solid career for the most part–he won a World Series, with a club that doesn’t tend to do well in the postseason; he won four consecutive Gold Gloves (from 1993 to 1996); at the time of his retirement, he was one of only seven players all-time with 200 home runs, 400 stolen bases, and 2000 hits (a club later joined by Johnny Damon); and he is now, to paraphrase Drake, 46 sittin’ on 51 mil ($51,946,500, to be exact). Also, as you can see above, he compiled a decent career WAR total, including two 5-win seasons in 1992 and 1993 for the Expos. However, you sure as hell wouldn’t have known that from watching his horrid 2000 season.

Traded to the Brewers in 1998 after the Indians resigned Kenny Lofton, Grissom was never able to recapture the magic from his early days north of the border, or at the very least, his sufficing days in Atlanta or Cleveland. At this point, his fielding in center (which peaked at 20.7 Def³ in 1994) was in decline, but his glove work in 2000, while unsatisfactory (-3.8 Def), wasn’t particularly bad for him, as he’d proceed to have Defs lower than that in four of his next (and last) five seasons. His baserunning was also trending downward; his BsR (which peaked at 10.5 in 1992, when he stole 78 bases) had plummeted all the way to -0.5, as he swiped a mere 20 bags. Again, though, this is a relatively minute contribution.

His batting was the major reason for his hideousness in 2000: He put up a Starlinesque triple-slash of .244/.288/.351, in a season when 16 players hit 40 home runs, 15 batted at least .330, and the major league-average triple-slash was .270/.345/.437. This all added up to an Ichiroesque wOBA of .282 and an Hechavarriaesque wRC+ of 59, which, combined with the aforementioned poor baserunning and defense, was enough to give him -1.8 WAR and edge him past Mike Lansing (-1.7) for the LVP.

This disappointing offensive season, while not all that unusual, was certainly a fluke to some degree. In terms of plate discipline, he was basically the same in 2000 (6.1% BB, 15.5% K) as he was for his career (6.2% BB, 13.8% K); it was when he put the ball in play that he got in trouble. His ISO of .108 was his lowest since 1991 (his first full season), and it convalesced the next year to a much healthier .183 (albeit in 172 fewer plate appearances); in addition, his BABIP was at .270, the second-lowest of his career to that point, although it dropped even further, to .242, the next year. It wasn’t like he had a huge rebound in 2001, either; however, the increase in ISO (and not qualifying for the batting title) was enough for a lofty -0.9 WAR in 468 trips to the plate the next year.

9. Carlos Lee

 Career WAR: 28.2

LVP year/WAR: 2010/-1.5

Classification: End-of-career decline

El Caballo clearly had some late-career struggles (as Richard Justice sure liked to point out); the huge (for the time) $100 million dollar deal that Houston signed him to certainly didn’t help the fans’ image of him. The contract notwithstanding, Lee had a decent career–two Silver Sluggers (2005, 2007), a five-win season in 2004 with the White Sox, and the career WAR that precedes this section. He also had the rare (for this era) distinction of never striking out 100 times in a season, and to top it all off, he was a player who acknowledged, and embraced, his critics. Plus, you can’t blame him for signing the contract–blame that dipshit of a GM, Ed Wa-wait, what’s that you say? Lee wasn’t signed to the ridiculous contract by Wade, but by…Tim Purpura? The guy with one reference on his Wikipedia page? Who the hell is he? Oh, whatever. Where was I?

Ah, yes. Lee’s final years. It should be noted that Mr. Lee’s abominable antepenultimate⁴ season was bookended by respectable seasons in 2009 and 2011, when he put up a combined 3.9 WAR–not exactly Mike Trout, but also not Delmon Young. With aaaaaalll of that said, though, the fact of the matter is: Carlos Lee was really goddamn awful in 2010. He provided typical Carlos Lee defense (-23.2 Def, edging his -22.3 Def from 2006 for the worst of his career) and baserunning (-3.6 BsR…pretty much in line with his career numbers), which commiserated with a .246/.291/.417 triple-slash (.310 wOBA, 89 wRC+) to give him a WAR of -1.5.

I brought up earlier that his 2010 season was, to some extent, a fluke. The main reason for his poor offensive showing in 2010 was twofold: a low BABIP (.238, 21 points lower than his previous career low of .259) motivated by a 15.6% line-drive rate, well below his career average of 20%; and a decrease in free passes (5.7% BB, much lower than his career rate of 7.5%). Both of these measure bounced back the following season, to .279 and 9%, respectively.

And lest we forget, 2010 was a year with a looooot of bad players–Melky Cabrera (-1.4 WAR) getting run out of Atlanta, Adam Lind (-1.0) and Jason Kubel (-0.4) coming crashing down to Earth after breakout seasons, Jonny Gomes (-0.4) and Carlos Quentin (-0.3) showing off their fielding prowess, Cesar Izturis (-0.4) being Cesar Izturis…But I digress. The main takeaway: 2010 Carlos Lee=horrible. Basically all other years Carlos Lee=not (to varying degrees).

GROUP II: THEY DON’T RACKA THE DISCIPRINE

8. Ray Durham

Career WAR: 30.3

LVP year/WAR: 1995/-1.4

Classification: Start-of-career bump

The Sugarman’s career met a premature end, but he was a trusty player for most of it. He was never a star player–his career-high single-season WAR was 3.9 in 2001, in his last full year with the White Sox–but he was always a contributor, putting up at least a two-win season for nine of the eleven seasons from 1996 to 2006. He made two All-Star teams (in 1998 and 2000), scored 100 runs in every year from 1997 to 2002 (if you care about such things), and stole at least 23 bases a season over that span (plus 30 in 1996). When his career started, though, he was just a fifth-round draft pick by the White Sox in 1990, who worked his way up through the farm system and won the starting second baseman job in 1995 spring training. How did that first season go?

Well, let’s start by saying he was never a very good defender. His career high Def was 8.1 in 2001, and he followed that up with -11.9 in 2002; for his career, his Def was -62.5, 2nd-worst among second basemen over that time. In the year in question, though, he took his defense to a new level. He put up…a -20.7 Def. Now, that doesn’t sound particularly (or at least historically) shitty, at least at first glance; after all, three players had worse figures this year alone⁵. However, one must consider the position that Mr. Durham manned was (and is) one of the premier defensive positions on the field, such that there have only been two–count ’em, two–players ever with a Def lower than that at second: Todd Walker (-21.5) in 1998, and Tony Womack (-24.2) in 1997. What’s more, Durham achieved that in only 1049.2 innings (in 122 games played); supposing he played 20% more innings (~1250, a standard 7 of the 19 qualified second basemen reached in 2013), he could have easily had a -25 Def, a depth the likes of which no second baseman has sunk to.

As dreadful as he was with the glove, he was still pretty reprehensible with the bat. In 517 plate appearances, he posted a .257/.309/.384 triple-slash and a .306 wOBA. In this day and age, those numbers are all right; in 2013, Brandon Phillips was able to put up a 91 wRC+ with similar lines. During the height of the PAWSTMOMNEP⁶ Era, in the Cell? Those numbers are unacceptable, and this was reflected in Durham’s 82 wRC+ and -10.9 Off. His awfulness in these two areas was not offset by his relatively solid baserunning (1.3 BsR), and he finished the season with a WAR of -1.4, which tied him with Kevin Stocker for the honor of ultimate player.

Durham’s defense never became great, but his offense rebounded after this fluke rookie season. His 6% walk rate as a rookie was much lower than his career average of 9.7%, and was by far the lowest of his career; his .127 ISO as a rookie was also considerably lower than his career ISO of .158, and was the third-lowest of his career. He was inconsistent for a few years after 1995, alternating between solid (2.0 WAR in 1996, 3.2 in 1998) and not so solid (0.3 in 1997, 1.2 in 1999), before accruing 2.7 WAR in 2000, the start of a seven-year run of at least two wins. But, despite what this article might lead you to believe, he was not, in fact, a good rookie.

7. Ron Fairly

Career WAR: 35.3

LVP year/WAR: 1967/-0.8

Classification: Middle-of-career fluke

In terms of hitting, Fairly is akin to the players that precedes him (and the player that he precedes): They all have so-so averages and power numbers, in addition to undesirable defense, but have fairly good plate discipline, which allowed them to enjoy fruitful careers. Hence, the cheesy and racist title for this section⁷.

Anyway…Fairly was another player like Durham–never elite, but always productive. He had two 4-win seasons (1970 and 1973), made the All-Star team in 1973 and 1977, and owned a career .360 OBP in an era where there was a dearth of offense. He also won three World Series, in 1959, 1963, and 1965–all with the Dodgers, with whom he spent the first 11.5 years of his 21 years in the majors. He was adequate throughout most of his tenure in Los Angeles; after his first three seasons (1958-60) when he didn’t start, he put up at least 1.9 WAR in each of the next six seasons (1961-66).

And then along came his putrid 1967. “How putrid?” you inquire. Very putrid, I reply. His defense (-15.7 Def in 1167.1 innings) and baserunning (-1.5 BsR) were as weak as they’d ever been , but this year was all about the offense. As a well-conducted player at the plate, Fairly took the base on balls quite a bit in his career–about once in every eight trips to the plate (12.5%). While his BB% of 9.7% was down from that average, and the lowest of his career to that point, it was basically in line with the MLB average of 9.8%; in addition, his strikeout rate of 9.2% (compared to 10.4% for his career) was well below the MLB average of 15.8%.

Like with Grissom, Fairly’s issues were chiefly with balls in play. He didn’t have a whole lot in the way of power, as his .101 ISO was a good deal below the major league-average of .148, and below his respectable career ISO of .142. The luck dragons were also not particularly fond of him that year; his BABIP freefell to a hapless .224. Even in the year prior to The Year Of The Pitcher, the major leagues still managed to hit .255/.302/.404, with a .280 BABIP and a .148 ISO; Mr. Fairly “hit” .220/.295/.321, which gave him a .277 wOBA and an 82 wRC+ (as opposed to .329 and 113 for MLB). In the end, he had -0.8 WAR for the year, which tied him with Zoilo Versalles for the LVP title⁸.

In summary, ISO and BABIP were the main reasons for Fairly’s nauseating 1967; each was down from .177 and .288 figures the year before, and after another down year in 1968 (.066 and .259)⁹, they would rebound to .202 and .270 in 1969, and would remain high as Fairly enjoyed the best years of his career in Montreal¹º.

6. Eddie Yost

 Career WAR: 37

LVP year/WAR: 1947/-0.8

Classification: Start-of-career bump

As Matt Klaassen wrote last year, one can only express sorrow when looking back at the career of Mr. Yost, who played 60 years too early. For his career, the third baseman had a modest .254 batting average and .371 slugging percentage, but a phenomenal .394 on-base percentage, due to a 17.6% career walk rate that, as the article points out, is second only to Barry Bonds since 1940. However, this was in the pre-pre-pre-Moneyball days, before many people knew about any stats, much less “advanced” stats like OBP. So, sadly, Yost is doomed to walk the earth as a forgotten man. Well, not really–he died last year–but you get what I’m saying.

In 1947, however? Eddie Yost wasn’t underappreciated, as anyone who knew anything about baseball could see that he was awful. After logging 47 combined plate appearances in 1944 and 1946 (and joining the navy in 1945, just in time for the end of the war), Yost finally got a chance to start in 1947, getting 485 plate appearances for the Washington Senators. How did he do with those plate appearances? Well…

He accrued free passes, or so it would appear at first glance; his walk rate of 9.3% would be sterling in this era, what with our major league walk rate of 7.9%. Back then, however, the major league walk rate was 9.7%, so he was actually below average; moreover, the major league strikeout rate was 9.6%, meaning his 11.8% strikeout rate was worse that average. His defense–which would never be that good, as his -91.7 career Def shows–was also rather lousy, to the tune of -6 Def, as was his baserunning (-1.5 BsR). However, it’s possible to play at an elite level with poor plate discipline (as Carlos Gomez sure has shown) and with poor fielding (as Miguel Cabrera sure has shown); what, then, made him so dissatisfactory?

Well, as that annoying bundle of sticks that the women love might say, it was all about (hashtag) that power. He had a .054 ISO; even in a year where the average ISO was .117, that’s not exactly Miggy levels. His BABIP was right around average (.275, to .277 for the majors), but this Pierre-like power, coupled with the aforementioned above-average strikeout rate, gave him a batting average of .238 and a slugging percentage of .292. His on-base percentage was a solid .312; however, the major league-average OBP was .336, along with a .261 AVG and .378 SLG. All of this commiserated to give him a .277 wOBA, 84 wRC+, and -17.1 Off; when Green Day was awakened, Fairly had -0.8 WAR, which beat out Jerry Priddy (-0.6) for the LVP title.

From there, Yost only got better. Yeah, there wasn’t much worse he could get, but his WAR still improved in each of the next four seasons, and he was a two-win player for seven straight years (1950-56). He pinnacled with 6.2 WAR in 1959, when he even hit for decent power, socking 21 dingers (with a .157 ISO) in his first year after leaving The Black Hole Known As Griffith Stadium¹¹. It’s a shame he had to start out as shittily as it did.

GROUP III: A ROCK AND GUITAR PLACE¹² (OR, AGING IN THE OUTFIELD, PART II)

5. Dave Parker

Career WAR: 41.1

LVP year/WAR: 1987/-0.6

Classification: End-of-career decline

Paul Swydan reminisced on the career of Parker earlier this year after it was announced that he (i.e. Parker) had Parkinson’s disease. I’ll briefly rehash Swydan’s reporting here.

Parker’s job when called up was to replace the player who is #1 on this list, and obviously, those were some big shoes to fill. Parker did not shrink from the spotlight, however, as he produced 30.3 WAR over his first five full seasons (1975-79), which tied him with The Despised One for fourth-most in the majors over that span. He attained quite a bit of hardware over this time as well–the NL MVP trophy in 1978 and Gilded Gloves¹³ in 1977-79–and won batting titles in 1977 and 1978.

These five years were the best of his career, and after the fourth year (1978) he was rewarded with a five-year, $5 million-dollar contract–the largest in MLB history at the time. However, much like a certain power-hitting Pennsylvanian today, he would struggle to live up to this contract; after the first year (1979), when he put up 5.7 WAR and was instrumental in helping the Pirates win the Fall Classic, he would only log 1660 plate appearances over the final four years of the deal (and only contribute 1.6 WAR in those years). This was due to several factors, namely his affinity for a certain white substance that is, generally speaking, frowned upon in our society; his continued usage of said substance earned him a full-year suspension for the 1986 season, which he was able to circumvent via community service, donations of his salary, and submission to random drug tests.

After his contract expired in 1983, he signed with the Reds, and his production was essentially the same as in the last few years in Shitts¹⁴ Pittsburgh (save for his fluky, 5.4-WAR 1985 season) and as it would be for the rest of his career. There was one year in particular, though, where he really hit rock bottom: the year that followed the suspension year.

In the year in question–his last in Cincy–Parker was, shall we say, no muy bueno. A career .290/.339/.471 hitter, Parker hit .253/.311/.433 in 1987. While one might attribute this to his 16.1% strikeout rate and 6.8% walk rate (compared to 15.5% and 8.9%, respectively, for the majors), these numbers are actually pretty analogous to his career numbers (15.1% and 6.1%, respectively). His power was also comparable to his career figures (.180 ISO in 1987, .181 for his career).

For Mr. Parker (like for so many of the others on this list) the issues arose from two areas: when the other team fielded the balls he hit, and when he fielded the balls the other team hit. Parker’s .265 BABIP was the lowest of his career for a full season, not to mention being 49 points below his career BABIP of .314 and 24 points below the major league-average BABIP of .289. His glovework also left something to be desired¹⁵–he posted a -17.8 Def, which was second-worst in the majors.

Overall, his hitting wasn’t too dreadful–his triple-slash was good enough for a .316 wOBA and 87 wRC+. He wasn’t even that bad, period–his WAR was -0.6, a relatively good figure. Out of the 143 seasons, only 5 players were LVPs with a higher WAR; Parker was just unfortunate enough to have a down season in a year where the only serious competition for the LVP was Cory Snyder (-0.5).

One last thing on Parker: As painful as his 1987 season was, his last season (1991) was even worse–he had -1.2 WAR in only 541 plate appearances for the Angels and Blue Jays. Unfortunately, he was robbed of the LVP crown by some scrub named Alvin Davis. Hah! What a loser! It’s not like any site editor has ever vehemently defended Davis and would cancel my account if I was to insult Mr. Mariner!

(Your move, Cameron.)

4. Bernie Williams

Career WAR: 44.3

LVP year/WAR: 2005/-2.3

Classification: End-of-career decline

Williams’ case for Cooperstown has generated quite a bit of controversy, over everything from the impact and weighting of postseason play to the cost of defense to True Yankeedom. That point, however, is now moot, as Williams is now off the ballot; whether or not he deserves to be is a can of shit I’d rather not open right now.

With Williams, the superlatives are certainly present–five All-Star appearances (1997-2001), four Gilded Gloves¹⁵ (1997-2000), four World Series rings (1996, 1998-2000), a Silver Slugger (2002), and the ALCS MVP in 2002. He also had seven 4-WAR seasons over an eight-year period (1995-2002), and was the 12th-most valuable position player in baseball over that time. Plus, he was, y’know, a True Yankee.

It’s the years to which this period came prior that I am focusing on. After that eight-year run of dominance, Williams fell off a cliff (as Paul Swydan explained earlier this year), putting up…wait for it…-3.3 WAR over the next four seasons, dead last in the majors in that span. Most of that negative WAR came from one year: 2005, the second-to-last of Mr. Williams’ career.

In said year, Williams (he of the career .297/.381/.477 triple-slash) batted a mere .249/.321/.367. The PAWSTMOMNEP Era was beginning to transition into the Era of the Pitcher, but offenses were still favored–the major league triple-slash was .264/.330/.419–and Williams’ batting (and -1.4-run baserunning) was enough to give him a .305 wOBA, 85 wRC+, and -11.2 Off. His strikeout rate in 2005 (13.7%) mirrored his career rate (13.4%) and was much better than the MLB rate (16.4%), and while his walk rate of 9.7% was low by his standards (career walk rate of 11.8%), it was still considerably better than the MLB average of 8.2%; his batted-ball rates were also all right (19% LD/43.5% GB/37.5% FB). It was a poor BABIP (.270, as opposed to .318 for his career and .295 for MLB) and a poorer ISO (.118, as opposed to .180 for his career and .154 for MLB) that did him in.

But Williams’ offensive struggles pale in comparison to his defensive ineptitude. Never the greatest with the glove, Williams hit a new low in 2005¹⁶, as he had a -30.2 Def in 862.2 innings. In terms of UZR, he was 29.2 runs below average, and because he didn’t play that much, his performance extrapolates to 42.5 runs below average per 150 games. As a point of reference, Adam Dunn’s worst career UZR/150 as a outfielder was -39.2 in 2009. So, yeah.

The disconcerting work in the field by Williams, coupled with ineffectiveness at the plate, gave him a -2.3 WAR for the year; this put him in a class of his own, as the next-worst player was Scott Hatteberg, whose -0.7 WAR was a full 1.6 behind him. Maybe, if his production hadn’t completely deteriorated at the tail end of his career, Williams would be in the Hall right now, and the point would still be moot, but for a better reason.

GROUP IV: CENTRAL COMPETITORS

3. Ted Simmons

Career WAR: 54.2

LVP year/WAR: 1984/-2.4

Classification: End-of-career decline

Look, I’m not saying Ted Simmons should go to the Hall of Fame. What I will say is this:

	Career PA	Career WAR
Player A	9685	54.2
Player B	9278	54.9

Simmons is player A. Ichiro Suzuki is player B. Just sayin’.

Anyway, regardless of one’s opinion on Mr. Simmons, one cannot deny that he was an excellent all-around player for most of his career. People of his day certainly didn’t, as he was named to eight All-Star teams (1972-74, 1977-79, 1981, 1983) and won a Silver Slugger (1980). He was generally regarded as the second-best hitting catcher of his era, behind Johnny Bench, and while catchers are held to a lower offensive standard than most other players, he was no slouch with the bat–his career wRC+ was 116, better than Adrian Beltre and Andruw Jones. His defense was mediocre (53rd out of 113 catchers in Def over the course of his career), but he was still outstanding, as the above comparison should show.

Like all mortal men, though, Simmons’ production decayed as he aged; after contributing at least three wins in 12 of 13 seasons from 1971 to 1983, he was at or below replacement level in four of his last five years. In the first of those five years, he was a special kind of awful.

In 1983 (the last year of the 13-year period), Simmons was actually quite valuable, to the tune of 3.7 WAR for the Brewers. The next year…well, everything fell apart. His triple-slash collapsed from a healthy .308/.351/.448 to a sickly .221/.269/.300, which took his wOBA from .352 to .259, his wRC+ from 122 to 60, and his Off from 16.6 to -24.6. His plate discipline was fairly similar in both years (6.3% BB%, 7.8% K% in 1983; 5.6% and 7.5% in 1984), although his walk rate in both years was a good deal below his career average of 8.8%. This meltdown was primarily caused by a David Murphy-like¹⁷ dropoff in BABIP, from .317 to .233, and a near-halving of ISO, from .140 to .078; both of these numbers were a good deal below Simmons’ career numbers (.284 and .152, respectively). What’s more, he spent most of his time at DH, meaning he was held to a higher offensive standard; thus, these already bad numbers were reduced even further.

When Simmons did play in the field (at first and third, not catcher), his defense was somewhat rotten, as he posted -4 TZ in 457.1 combined innings. With the subtracted defensive runs for not fielding at all, his Def dropped from -3 in 1983 to -14.5 Def (12th-worst in the majors) in 1984.

When the dust had settled, Simmons was left with -2.4 WAR, which gave him a comfortable lead over Curtis Wilkerson (-1.1) for the LVP honor. Simmons never really got much better than this–he gave the Brew crew 1 WAR in 1985, before costing the Braves a combined 1 win as a utility man over the next three years. Retiring at age 39, Simmons might’ve been enshrined if he had kept up his consistency into his late 30s.

While Simmons was arguably Hall-worthy, there’s no arguing over these next two. Well, there actually is a fair amount of arguing over this next guy, but…never mind.

2. Pete Rose

Career WAR: 80.3

LVP year/WAR: 1983/-1.9

Classification: End-of-career decline

Yes, the LVP in back-to-back years was a very valuable player overall. Surprised? Well, that’s allowable–when you started reading this, you probably had no idea what you were reading in the first place, much less if it would involve two exceptional players performing uncharacteristically poorly in two consecutive years.

Moving on…Rose hardly needs an introduction, but I’ll give him one anyway. Seventeen All-Star nods (1965, 1967-71, 1973-82, 1985); a three-time batting champion (1968-69, 1973) and three-time World Series champion (1975-76, 1980); two Gilded Gloves (1969-70); a Silver Slugger in 1981; the NL ROTY¹⁸ and MVP in 1963 and 1973, respectively; and one of the better (and most deserved)¹⁹ nicknames in baseball. Plus, there was that whole 4,256 hits thing, but nobody cares about that.

Rose spent most of his career with the Reds, winning two of his three championships with them. The third? That was won with the Phillies, with whom he spent five mostly forgettable years at the end of his career. After averaging 4.7 WAR over his first sixteen seasons with the Reds (and being the fourth-most valuable player in baseball over that span), Rose only earned 3.8 WAR in the next five years combined. To be fair, he was at least middling for the first four years, finishing above replacement level in each. In the last year (1983), however, he was anything but middling–and not in a good way.

1983 wasn’t a particularly great year for hitters–the aggregate MLB triple-slash was .261/.325/.389; nonetheless, Rose was still considerably below thoe numbers. Never one to hit for power (his best ISO was .164 in 1969), he sunk to new lows in 1983, pulling a Ben Revere;²º his ISO at season’s end was a repulsive .041, considerably lower than the major league ISO of .128. BABIP also wasn’t too kind to him, as he posted a .256 mark in that department, a good deal below the major league BABIP of .285. His plate discipline was superb (9.4% and 5% walk and strikeout rates, compared to 8.4% and 13.5%, respectively, for the majors), but this could not compensate for his failings in the other areas, and he ended up with a .245/.316/.286 triple-slash, with a .277 wOBA, 68 wRC+, and -21.6 Off.

He didn’t do himself any favors on the basepaths (-1.5 BsR) or in the field (-8 TZ, -14.7 Def in 1034 innings between first and the oufield), but he was never the greatest in those categories. In this one year, he was an all-bat guy with no bat²¹, and that usually doesn’t have good results.

When all was said and done, Rose’s WAR was -1.9, a figure that easily bested Dave Stapleton (-0.7) for the LVP title. One last note on Rose: Unlike the #1 player on this list, people seem to be cognizant of Rose’s awfulness in his LVP year–probably because Rose’s year occured near the end of his career, and he never received a chance to outshine it. This next guy, though? Well…

1. Roberto Clemente 

Career WAR: 80.5

LVP year/WAR: 1955/-0.9

Classification: Start-of-career bump

Yeah, I’d say he made up for one bad season. Either that, or fifteen All-Star games (1960-67, 1969-72)²², twelve consecutive Gold Gloves²³ (1961-72), four batting crowns (1961, 1964-65, 1967), the NL MVP (1966), the WS MVP (1971), and 80.1 career WAR were all for naught.

Despite all of the undeniable awesomeness of Clemente’s career as a whole, there is still that one blemish on his record: his less-than-perfect rookie year. After being drafted by the Pirates in 1954, Clemente was given the opportunity to start immediately, and the outcome wasn’t very good.

Clemente was never a particularly patient hitter, with a career BB% of 6.1%; in 1955, however, he was especially anxious, with a 3.6% BB% that was a good deal below the major league-average of 9.5%, and was the second-worst individual figure in the majors²⁴. He didn’t strike out that much, as his 12% K% wasn’t too much worse than the major league-average of 11.4%, and was identical to his career rate.

Always a high-BABIP guy (with a career number of .343), Clemente’s balls were unable to find holes in 1955, as he had a BABIP of .282 that, while being higher than the major league BABIP of .272, was still the second-lowest of Clemente’s career. His power also hadn’t developed yet²⁵, as his .127 ISO (which was also similar to the major league ISO of .136) was much lower than his career ISO of .158. Overall, Clemente batted .255/.284/.382, with a .294 wOBA, 73 wRC+, and -18.5 Off. His baserunning wasn’t much of a factor (-1.3 runs, compared to 2.1 total for his career), but on defense, he was substandard, putting up a -4.6 Def that was 21st out of 31 outfielders²⁶.

This all coalesced into a WAR of -0.9, which allowed Clemente to beat Don Mueller (-0.7) for the LVP title by a narrow margin. It would take Clemente a few years to really get going; from 1956 to 1959, he was worth a modest 8.9 WAR²⁷ (44th in the majors) as he struggled through various injuries. By 1960, though, he was healthy, and would contribute 72.7 WAR (fourth in the majors) from then until…well, you know.

***

What does all of this mean? Well, the average major league player is worth 2.97 wins over the course of their career; the average player that won an LVP is worth 6.04 wins over the course of their career. Impacted by outliers, you say? Even when the ten players listed here are taken out, the average career WAR of the remaining 133 is 3.05–slightly better than for every player. Looking a little deeper, we can see that of the 16,292 players with a plate appearance, 852 (or 5.2%) are worth 20 or more wins over the course of their careers. For LVPs? 15 out of 143, or 10.5%²⁸.

Is this good news for Eric Hosmer and Adeiny Hechavarria? Possibly. Would assuming this is good news for Hosmer and Hechavarria be drawing a causation from a correlation? Probably. I don’t know. What do I know? I know that if you read this all of the way through, I just wasted a sizable chunk of your time. And in my book, that is a job well done.

——————————————————————————————————-

¹Or the 30th, technically. Whatever.

²As far as I can tell, that’s an original joke. Feel free to chastise me if I’m wrong.

³How exactly do I cite the Def stat? Is it a plural type of thing, like “Player X had 5 Defs last year”, or a singular, like…well, like I wrote in the post?

⁴Spellcheck, you have crossed a line.

⁵The worst fielder in the majors, according to Def? Carlos Beltran and his -21.4. Yes, the three-time Gold Glove-winning, two-time Fielding Bible-winning Carlos Beltran. Yes, I’m also unsure how to react.

Why has no one else realized this, though? Cameron? Sullivan? God forbid, Cistulli? I’m looking at you all! Write something about this, or else I’ll be forced to!

⁶You know, the Possibly Affiliated With Substances That May Or May Not Enhance Performance Era.

⁷Hey, it’s South Park’s words, not mine.

⁸Versalles’ career was also rather notable–and not just for his abnormal appellation.

⁹To be fair, every hitter had a down year in 1968.

¹ºOne doesn’t receive too many opportunities to type this (much to the chagrin of Mr. Keri).

¹¹According to the B-R bullpen, there were only two–count ’em, two–fair balls ever hit out of Griffith. TWO! In 51 motherfucking seasons! So Safeco ain’t so bad, I guess.

¹²Get it? ‘Cuz Parker did…and Williams did…oh, never mind.

¹³Not a typo (read on).

¹⁴Sorry, force of habit (I’m a Ravens fan).

¹⁵In a manner not dissimilar to a controversial shortstop of our era (or to the next player on the list), Parker was always one whose reputation overshadowed his production in the field, as neither basic statistics (.965 career fielding percentage, 137th out of 167 players over the course of his career) nor advanced statistics (-127.5 career Def, 688th out of 704 players over the course of his career) were particularly fond of his work with the glove.

¹⁶Actually, every Yankees defender hit a new low in 2005, as their team defense was the lowest of any team in the UZR era (-141.7 runs); this was due in no small part to the horrifying outfield of Williams, Gary Sheffield (-26 UZR), and Hideki Matsui (-15.2 UZR).

¹⁷I’ll have more on that in the coming weeks.

¹⁸How does one abbreivate Rookie of the Year? ROTY, RotY, or some combination of the two?

¹⁹Deserved on more than one level.

²ºI shouldn’t need to explain what I mean by that. Also, Rose pulled a Ben Revere in 1984 and 1986, albeit in non-qualifying seasons.

²¹I know I’ve heard that expression before–I think it was used to describe Jesus Montero–but I can’t seem to find where it was used.

²²No, I didn’t do my math wrong–from 1959 to 1962, MLB had two All-Star games.

²³I was going back and forth about whether to call this one gilded; over the indicated time span, Clemente was eighth among outfielders in Def–quite good, but not the best (as winning a Gold Glove in each year would imply).

²⁴It was not, however, the worst mark of his career, as he would post walk rates of 2.3% and 3.3% in 1956 and 1959, respectively.

²⁵In terms of power, Clemente was a late peaker, with five of his six highest ISOs coming during or after his age-31 season (1966). I’d be interested in knowing how common that is.

²⁶For whatever reason, defensive records for each individual outfield position only go back to 1956–any time before that, it’s all just lumped into “Outfield”. Also, for that matter, innings played on defense only go back to that date as well, which doesn’t seem to make sense, given that play-by-play data is available back then.

²⁷Interestingly enough, Clemente’s greatest defensive seasons were during this period. He had 20.7 Def in 1958, and 19.3 in 1957; his next-best season with the glove was in 1968 (16.8).

²⁸The other five (besides the ten listed here): Milt Stock (22.3 career WAR, -2.7 in 1924); Alvin Davis (21.1 career WAR, -1.6 in 1991); Raul Ibanez (20.5 career WAR, -1.7 in 2011); Jason Bay (20.3 career WAR, -1.1 in 2007); and Buck Weaver (20.3 career WAR, -1.1 in 1912).

Happy 21st birthday, Bryce Harper!

In two seasons to date, Harper has posted a 128 wRC+ while hitting .272/.353/.481 in 1094 plate appearances.

Steamer projections have Harper projected to hit .266/.347/.464 as a 21-year old, which would make for a 125 wRC+. But if Harper posts a lower batting average, OBP, and slugging than he did in either of his first two years, I imagine that would be a major disappointment, not just for fans of the Washington Nationals, but for fans of the sport of baseball. And also, perhaps mostly, for the player himself. (But at least the projections have him down for a career-high 23 home runs.)

Changing gears for a moment, how about that Mike Trout. You may have heard, but some people thought he was the American League’s most valuable player after he hit .326/.399/.564 in 639 plate appearances in 2012. Then he somehow got even better as a hitter in 2013, posting a .323/.432/.557 line in 716 PA.

But when Trout was 19, he hit .220/.281/.390 in a 40-game, 135-PA cameo in 2011. Harper would crush that line as a 19-year old rookie in 2012. Then, of course, Trout’s age-20 season set an impossible standard that Harper had about a 3.4×10^9 percent chance of surpassing, if we’re being optimistic.

Because of the one-year age difference, had Trout just ended up reasonably good rather than ridiculously great, he might have served as a decent guide for how Harper could develop. Sort of a one-year advance copy. But Trout’s 2013 season confirmed that he is ridiculously great, so that idea is out the window for now.

What about other players who got their starts as teenagers? According to the Baseball-Reference.com similarity scores, Harper through his age 20 season has posted numbers most similar to Tony Conigliaro (956), Ken Griffey (954) and Mickey Mantle (954). All three of these players debuted in their age 19 seasons.

Mantle was already a great hitter when he was 20, posting a .311/.394/.530 line in 626 PA (158 wRC+), but the other two players set more worldly, but still great-for-20, lines: Griffey a .300/.366/.481 (666 PA, 132 wRC+) and Conigliaro a .269/.338/.512 (586 PA, 131 wRC+).

Harper’s wRC+ in 2013 was 137, slightly better than either Griffey or Conigliaro, but he only put in 497 plate appearances. Still, the three players had awfully similar age-20 seasons.

When he turned 21, Conigliaro’s effectiveness decreased to a 123 wRC+ and .265/.330/.487, before a recovery when he turned 22 (144 wRC+, .287/.341/.519, 389 PA) prior to the disaster that occurred on August 18, 1967, when he was hit in the face by a pitch.

Griffey’s improvement was steadier, as he posted a .327/.399/.527 line when he was 21 and a .308/.361/.535 one at 22 years old, with wRC+ marks of 148 and 145, before experiencing his first two 160 wRC+ seasons the next two years.

One more player I want to talk about in this context is Giancarlo Stanton. He fiddled around in A+ and AA when he was 19, because the universe doesn’t just up and grant every great talent the ability to hit Major League pitching as a teenager. Stanton instead debuted in his age 20 season and hit .259/.326/.507 (118 wRC+ in 396 PA) before hitting 34 home runs in his age 21 season with a 141 wRC+ and a .262/.356/.537 line in 601 PA.

So where the heck are we now? I just shared a lot of names and players and numbers and slashes, but none belong to Bryce Harper. He’ll have a heck of a lot more to do with his development than Mickey Mantle’s ghost.

I think the record shows, however, that players who hit well when they are 19 and 20 generally don’t stagnate at 21. The projected line from the beginning of this post still seems low.

To conclude, here is a possible range of outcomes for Bryce Harper in 2014:

Worst-case: His health remains an issue. His stats end up about as projected…or worse.

Mid-case #1: He actually gets healthy but still faces a Conigliaro-like decline between his age 20 and age 21 seasons. (Although, Conigliaro’s decline still left him hitting at a darned good level.)

The Steamer projection is somewhere between this and the prior case.

Mid-case #2: Ken Griffey. Don’t let the version of Ken Griffey from his mid-20s in the mid-90s, the version who hit 56 home runs in consecutive seasons, interfere with the classification of this as a “mid-case.” A 10-20 point jump in Harper’s wRC+, as Griffey experienced when he turned 21, would be a welcome development and continue Harper on his perennial all-star path.

Best-case: Mike Trout. I might have skipped a couple mid-cases, but let’s get back to Trout. It’s going to always get back to Trout, I think, for years when we have conversations like this. But if Trout could struggle when he was 19–unlike Harper, Mantle, Conigliaro, Griffey (sorry Stanton)–and then explode when he turned 20, why can’t the other once-in-a-generation talent of this generation experience a similar jump? (Please allow me a “why can’t” when talking about best-case scenarios.) It wouldn’t be a change from bad to great, but good to unfathomable, and it would come a year later, but maybe instead of having Griffey’s age-20 season and Griffey’s age-21 season, Harper can skip right to Trout or Mantle’s age-21 season.

The “Griffey-Griffey” path is still a more realistic hope for those looking for Harper to exceed the computed expectations set by Steamer. I don’t think a 150 wRC+ is out of reach, but even a 140 or 145 wRC+ or so would be a nice continuation for Harper’s career.

Adam Wainwright pitched a decent game Monday night in Game 3 of the NLCS, throwing 7 innings and giving up 6 hits, no walks and striking out 5. He had a game score of 62, usually a sign of a well-pitched game, and he ended up with the loss because the Cardinals offense chose to take the night off. Brian Kenny (@MrBrianKenny) of the MLB Network started a movement called KillTheWin, his quixotic effort to have the win eliminated as a baseball statistic. I wrote a couple posts at my blog Beyond The Scorecard because I thought it was an interesting idea and seemed like a fun issue to research and will include the links at the end of this post, but Wainwright’s game got me thinking–how often in the postseason is a pitcher not justly rewarded for a good effort?

As the use of starting pitchers has changed over time, the win has become a far less effective metric in judging pitcher effectiveness. I don’t remember how I stumbled across using a game score of 60 as my marker of effectiveness (probably at Kenny’s suggestion) and like any other single number it’s not the entire story of a pitching performance, but it grants the opportunity to separate pitching effectiveness from a lack of offensive production or bad defense. Including Monday’s game there have been 1,393 postseason games played since 1903, meaning there have been 2,786 starts in postseason history–this chart shows the breakdown of wins, losses and no-decisions for those starters in that time frame:

In the postseason, starting pitchers won almost 36% of their starts. This covers the entire spectrum of postseason play, from the games in the early 1900s when a pitcher typically finished what he started all the way to examples like Saturday where Anibal Sanchez was removed after 6 innings (and 116 pitches)…and throwing a no-hitter. Different times, to be sure. With this context, this chart shows how often a pitcher who had a game score of 60 or greater was credited with the win:

Definitely an improvement over the general trend, but still, a pitcher who pitches well enough to attain a game score of 60 or greater has done all he can–he’s given up few hits and walks and struck out a decent number of hitters. In short, he’s kept base runners off base, the primary job of a pitcher and almost 35% of the time has nothing to show for it, or even worse, is tagged with a loss. This chart shows these numbers since the playoffs were expanded in 1969:

The introduction of relievers definitely hurt the cause of these starting pitchers, with almost 40% of pitchers who threw very good games not receiving a win. On the flip side, it is gratifying to see that only around 9% of wins go to pitchers who were the beneficiaries of being on the right side of 13-12 scores or games along those lines–justice exists somewhere. This last chart shows the record by game score stratification:

Who was that unlucky pitcher with a game score greater than 90 who received the loss? Nolan Ryan in Game 5 of the 1986 NLCS.

The 10-15 regular readers of my blog hopefully are aware that I typically write with my tongue firmly lodged in my cheek, and the win is so entrenched in baseball lore that removing it as a point of discussion simply won’t happen, but it doesn’t mean that it has to receive the emphasis it does. When we have the wealth of data that sites like FanGraphs places at our fingertips, we don’t have to rely on a metric that was formed at the inception of organized baseball that is a relic today, particularly one that doesn’t give an accurate portrayal of pitching performance around 35% of the time. Kill The Win–maybe not, but we can certainly de-emphasize it.

#KillTheWin blog posts:

The first one, which lays out definitions and rationale

The second one, which expands it

A final one, an exercise in absurdity

Talcott v. National Exhibition Co., 144 A.D. 337, 128 N.Y.S. 1059 (2 Dept., 1911)

What was Merkle’s Boner?

On September 23, 1908 the Chicago Cubs played the New York Giants at the famed Polo Grounds.  Al Bridwell came to bat with two outs and the game tied 1-1 in the bottom of the ninth.  He laced a single to the outfield and the runner on third trotted home, thinking he had just scored the winning run.  The Cubs second baseman Johnny Evers, of the famed “Tinkers to Evers to Chance” double play combination and future Hall of Fame inductee, however, called for the ball from the outfield because Fred Merkle, the Giants runner on first, had not touched second base.  Although there is controversy regarding whether Evers got the actual ball back, the umpire ruled Merkle out at second and due to the force, the apparent winning run was erased.

As was common at the time, the fans at the Polo Grounds would walk across the field after the game to exit the ballpark.  By the time the play was decided and the winning run nullified, however, the fans believing the Giants had won were already streaming across the field and it was impossible to resume the game before the game was called on account of darkness.

On October 6, 1908, the National League Board of Directors made its final ruling that because Merkle had failed to reach second, the force rule was applied correctly and the game was a tie.  At the end of the season, the Cubs and Giants were tied for first place and a makeup game was needed to determine which team would play in the World Series.  This game was played on October 8, 1908 at the Polo Grounds and reportedly drew 40,000 people, the largest crowd ever to have attended a single baseball game at the time.

The Cubs won this game over the Giants and went on to beat the Tigers 4-1 in the World Series, their last World Series victory.

The play that forced the makeup game was dubbed “Merkle’s Boner” and Fred Merkle was tagged with the nickname “Bonehead.”  Years later, Merkle admitted that he never touched second base but claimed he had been assured by umpire Bob Emslie that the Giants had won.  Despite a solid 16-year Major League career, including four seasons with the Cubs, Merkle was never able to shake the stigma of the play.

What does Merkle’s Boner have to do with this case?

As a result of the play and the October 6^th mandate for the makeup game, the Polo Grounds played host to the makeup game on October 8, 1908.  This game was “of very great importance to those interested in such games, and a vast outpouring of people were attracted to it.”  On the morning of the game, the ticket booths at the Polo Grounds were inundated with people trying to secure reserved seats for that afternoon’s game.

Plaintiff Fredrick Talcott, Jr. went to the ballpark intending to buy tickets for the game and entered an “inclosure” where the ticket booths were located.  After finding that the tickets were sold out, he tried to leave the inclosure along with a great number of people also trying to exit at the same time.  As he attempted to leave, however, ballpark attendants prevented his exit and he was “detained in the inclosure for an hour or more, much to his annoyance and personal inconvenience.”  Mr. Talcott brought this lawsuit seeking damages for false imprisonment.  He further claimed to have been pushed by the defendant’s “special policemen.”

The Giants countered that plaintiff simply could have used one of the other exits available.  Mr. Talcott alleged, however, that he was not aware of any other exits to the inclosure and none were pointed out to him.

Who won?

The case went to a jury trial and Mr. Talcott was awarded $500 in damages (approximately $12,000 today) with judgment entered on May 19, 1910.

The Giants appealed but the appellate court affirmed the judgment in favor of Mr. Talcott.

Why?

The jury found that that plaintiff’s detention was unwarranted.  The appellate court agreed with this finding, ruled that the award was not excessive and found no reason to interfere with the jury’s verdict.

Additionally, the court found that Mr. Talcott was not required to demonstrate that he incurred any special or actual damages as a result of the detention.

Pitch sequencing is a complicated topic of study. Given the previous pitch(es) to a batter, the next pitch may depend on factors such as the game-based information (e.g., count, number of outs, runners on base); the previous pitch(es), including their location, type, and batter’s response to them; and the scouting report against the batter as well as the repertoire of the pitcher. In order to approach pitch sequencing from an analytical prospective, we need to first simplify the problem. This may involve making several assumptions or just choosing a single dimension of the problem to work from. We will do the latter and focus only on the location of pitches at the front of the strike zone. Since we are interested in pitch sequencing, we will consider at-bats where at least two pitches were thrown to a given batter. The idea is to use this information to generate a simple model to indicate, given the previous pitch, where the next pitch might be located.

We can start with examining the distance between pitches, regardless of the location of the initial pitch. If this data, for a given pitcher, is plotted in a histogram, the spread of the data appears similar to a gamma distribution. Such a distribution can be characterized many ways, but for our purposes, we will use the version which utilizes parameters k and theta, where k is the shape parameter and theta is the scale parameter. With a collection of distances between pitches in hand, we can fit the data to a gamma distribution and estimate the values of k and theta. As an example, we have the histogram of C.J. Wilson’s distances between pitches within an at-bat from 2012 overlaid with the gamma distribution where the values of k and theta are chosen via maximum likelihood estimation.

Author’s note: I started working on this quite a few weeks ago and so, at the time, the last complete set of data available was 2012. So rather than redo all of the calculations and adjust the text, I decided to keep it as-is since the specific data set is not of great importance in explaining the method. I will include the 2013 data in certain areas, denoted by italics.

While this works for the data set as a whole, this distribution will not be too useful for estimating the location of a subsequent pitch, given an initial pitch. One might expect that for pitches in the middle of the strike zone, the distribution would be different than for pitches outside the strike zone. To take this into account, we can move from a one-dimensional model to a two-dimensional one. Also, instead of using pitch distance, we are going to use average pitch location, since this will include directional information as well. To start, we will divide the area at the front of the strike zone into a grid of three-inch by three-inch squares. We choose this discretization because the diameter of a baseball is approximately three inches and therefore seems to be a reasonable reference length. The domain we consider will be from the ground (zero feet) to six feet high, and three feet to the left and right of the center of home plate (from the catcher’s perspective).

We will refer to pairs of sequential pitches as the “first pitch” and the “second pitch”. The first pitch is one which has a pitch following it in a single at-bat. This serves as a reference point for the subsequent pitch, labeled as the “second pitch”. Adopting this terminology, we find all first pitches and assign them to the three-inch by three-inch square which they fall in on the grid. Then for each square, we take its first pitches and find the vector between them and their associated second pitches (each vector points from the first pitch to the second pitch). We then average the components of the vectors in each square to provide a general idea of where the next pitch in headed for the first pitches in that square.

In areas where the magnitude of the average vector is small, the location of the next pitch can be called isotropic, meaning there is no preferred direction. This is because average vectors of small magnitude are likely going to be the result of the cancellation of vectors of similar magnitude in all directions (from the histogram, the average distance between pitches was approximately 1.5 feet with most lying between 0.5 and 2.5 feet apart). One can create contrived examples where, say, all pitches are oriented either left or right and so there would be two preferred directions rather than isotropy, but these cases are unlikely to show up at locations with a reasonable amount of data, such as in the strike zone. In areas where the average vector has a large magnitude, the location of the next pitch can be called anisotropic, indicating there is some preferred direction(s). Here, the large magnitude of the average vector is due to the lack of cancellation in some direction. For illustrative purposes, we can look at one example of an isotropic location and one of an anisotropic location. First, for the isotropic case:

In this plot, the green outline indicates the square containing the first pitches and the red arrows are the vectors between the first and second pitches. The blue arrow in the center of the green square is the average vector. For the grid square centered at (-0.375,2.125), we have a fairly balanced, in terms of direction and distance, distribution of pitches. Therefore the average vector is small in magnitude. In other cases, we will have the pitches more heavily distributed in one direction, leading to an anisotropic location:

As opposed to the previous case, there is a distinct pattern of pitches up from the position (-0.125,1.625), which is shown by the average vector having a substantially larger magnitude. This is due to most of the vectors having a large positive vertical component. Running over the entire grid where at least one pitch had a pitch following it, we can generate a series of these average vectors, which make up a vector field. In order to make the vector field plot more legible, we remove the component of magnitude from the vector, normalizing them all to a standard length, and instead assign the length of the vector to a heat map which covers each grid square.

For the 2013 data set:

By computing these vectors over the domain, we are able to produce a vector field, albeit incomplete. Computing this vector field based on empirical data also lends itself to outliers influencing the average vectors as well as problems with small sample size. We can attempt to handle these issues and gain further insight by finding a continuous vector field to approximate it. To do this, we will begin with a function of two variables, to which we can apply the gradient operator to produce a gradient field. We can zoom in near the strike zone to get a better idea of what the data looks like in this area:

Note that as we move inward, toward the middle of the strike zone, the magnitude of the average vector shrinks. In addition, the direction of all vectors seems to be toward a central point in the strike zone. Based on these observations, we choose a function of the form

P(x,z) = (1/2)c_x(x – x_0)^2 + (1/2)c_z(z – z_0)^2.

The x-variable is the horizontal location, in feet, and z the vertical location. This choice of function has the property that there is a critical point for P and when the gradient field is calculated, all vectors will radially point toward or away from this critical point. The constants in the equation of this paraboloid are (x_0,z_0), the critical point (in our case, it will be a maximum), and (c_x,c_z) are, for our purposes, scaling constants (this will be clear once we take the gradient). The gradient of function P is

grad(P) = [c_x(x – x_0), c_z(z – z_0)].

Then c_x and c_z are constants that scale the distances from the x- and z-locations to the critical point to determine the vector associated with point (x,z). Note that grad(P)(x_0,z_0) = [0,0]. In fact, we will give this point a special name for future reference: the pitching sink. For vector fields, a non-mathematical description of a sink is a point where, locally, all vectors point toward (if one imagines these vectors to be velocities, then the sink would be the point where everything would flow into, hence the name). This point is, presumably, the location where we have the least information about the direction of the next pitch, since there is no preferred direction. Again using Wilson’s data as an example:

For the 2013 data set:

The gradient field is fit to the average vectors using linear least squares minimization for the x- and z-components. This produces estimates for c_x, c_z, x_0, and z_0. For the original vector field, if we are interested in the location where the average vector is smallest in magnitude (or the location where there is the least bias in terms of direction of the next pitch), we are limited by the fact that we are using a discretized domain and therefore can only have a minimum location at a small, finite number of points.

One advantage to this method is that it produces a minimum that comes from a continuous domain and so we will be able to get unique minimums for different pitchers. Another piece of information that can be gleaned from this approximation is the constants, c_x and c_z. If c_x is large in magnitude, there may be a large east-west dynamic to the pitcher’s subsequent pitch locations. For example, if a first pitch is in the left half of the strike zone, the next pitch may have a proclivity to be in the right half and vice versa. A similar statement can be made about c_z and north-south dynamics. Alternatively, if c_x is small in magnitude, then less information is available about the direction the next pitch will be headed. For Wilson, the constants obtained from the best fit approximation are a pitching sink of (-0.163,2.243) and scaling constants (-0.925,-1.055).

For C.J. Wilson’s 2013 season, we have the sink at (-0.109,2.307) and scaling constants (-0.902,-0.961), so the values are relatively close between these two seasons.

We can now obtain this set of parameters for a large collection of pitchers. For each pitcher, we can find the vector field based on the data and then find the associated gradient field approximation. We can then extract the scaling constants and the pitching sink. We can run this on the most recent complete season (2012, at the start of this research) for the 200 pitchers who threw the most pitches that year and look at the distribution of these parameters.

The sinks cluster in a region roughly between 1.75 and 2.75 feet vertically and -0.5 and 0.5 feet horizontally. This seems reasonable, since we would not expect this location to be near the edge or outside of the strike zone. Similarly, we can plot the scaling constants:

The scaling constants are distributed around a region of -1 to -0.8 vertically and -0.7 and -0.9 horizontally.

One problem that arises from this method is that since we are averaging the data, we are simplifying the analysis at the cost of losing information about the distribution of second pitches. Therefore, we can take a different approach to try to preserve that information. To do so, at a grid location, we can calculate several average vectors in different directions, instead of one, which will keep more of the original information from the data. This can be accomplished by dividing the area around a given square radially into eight slices and calculating the average in each octant.

However, since each nonempty square may contain anywhere from one to upwards of thirty plus pitches, using octants spreads the data too thin. To better populate the octants, we can find pitchers with similar data and add that to the sample. To do this, we will go back to the aforementioned average vectors and use them as a means of comparison. At a given square, with a pitcher in mind whose data we wish to add to, we can compute the average vector for a large collection of other pitchers, compare average vectors, and add the data from those pitchers whose vector is most similar to the pitcher of reference. In order to do this, we first need a metric. Luckily, we can borrow and adapt one available for comparing vector fields:

M(u,v) = w exp(-| ||u||-||v|| |) + (1-w) exp(-(1 – <u,v>/||u|| ||v||))

Here, u and v are vectors, and w is a weight for setting the importance of matching the vector magnitudes (left) and the vector directions (right). For the calculations to follow, we take w = 0.5. The term multiplied to w on the left is an exponential function where the argument is the negative of the absolute value of the difference in the vector magnitudes. Note that when ||u|| = ||v||, the term on the left reduces to w. As the magnitudes diverge, the term tends toward zero. The term multiplied to (1-w) is an exponential function with argument negative quantity 1 minus the dot product between u and v, divided by their magnitudes. When u and v have the same direction, <u,v>/||u|| ||v|| = 1, and the exponent as a whole is zero. When u and v are anti-parallel, <u,v>/||u|| ||v|| = -1 and the exponent is -2 so the term on (1-w) is exp(-2) which is approximately 0.135, which is close to zero. So when u = v, M(u,v) = 1 and when u and v are dissimilar in magnitude and/or direction, M(u,v) is closer to zero.

We now have a means of comparing the data from different pitchers to better populate our sample. To demonstrate this, we will again use C.J. Wilson’s data. First, we will run this method at a point near his sink: (-0.125,2.125). Since we will have up to eight vectors, we can fit an interpolating polynomial in between their heads to get an idea of what is happening for the full 360 degrees around the square. The choice of interpolating polynomial in this case will be a cubic spline function. This will give a smooth curve through the data without large oscillations. Working with only Wilson’s data, which is made up of 30 pitches, this looks like:

The vectors are spread out in terms of direction, but one vector which extends outside the lower-left quadrant of the plot leads to the cubic spline (light blue curve) bulging to the lower left of the strike zone. Otherwise, the cubic spline has some ebb and flow, but is of similar average distance all around.

When we remove the vectors and replace them with the average vector of each octant (red vectors), we have a better idea of where the next pitch might be headed. We also color-code the spline to keep the data about the frequency of the pitches in each octant. Red indicates areas where the most pitches were subsequently thrown and blue the least. We see that the vectors are longer to the left and, based on the heat map on the spline, more frequent. However, a few short or long vectors in areas that are otherwise data-deficient will greatly impact the results. Therefore, we will add to our sample by finding pitchers with similar data in the square. We will compute the value of M between Wilson at that square and the top 200 pitchers in terms of most pitches thrown for the same season.

For Wilson, the top five comparable pitchers in the square (-0.125,2.125), with the value of M in parentheses, are Liam Hendriks (0.995), Chris Young (0.986), A.J. Griffin (0.947), Kyle Kendrick (0.943), and Jonathan Sanchez (0.923). Recall that this considers both average vector length and direction. Adding this data to the sample increases its size to 94 pitches.

For this plot, the average vector (the blue vector in the center of the cell) is similar to that of Wilson’s solo data. However, since the number of pitches has essentially tripled, the plot has become hard to read. To get a better idea of what is going on, we can switch to the average vector per octant plot:

Examining this plot, most of the average vectors are in the range of 1-1.5 feet. The shape of the interpolation is square-like and seems to align near the edge of the strike zone, extending outside the zone, down and to the left.

We can also run this at points nearer to the edge of the strike zone. On the left side of the strike zone, we can work off of the square centered at (-0.875,2.375) (note that we drop the plots of the original data in lieu of the plots for the octants).

For the original sample, the dominant direction (where most of the vectors are pointed, indicated by the red part of the spline) is to the right, with an average distance of one to two feet in all directions. Now we will add in data based on the average vectors, increasing our sample from 15 to 97 pitches.

For the larger sample, the spline, which is almost circular, has average vectors approximately 1 to 1.5 feet in length. The preferred directions are to the right (into the strike zone) and downward (below the left edge of the strike zone). Also note that comparing the two plots, the vectors in the areas where there are the most pitches in the original sample (between three and six o’clock) have average vectors that retain a similar length and direction.

Switching sides of the strike zone, we can examine the data related the square centered at (0.875,2.375). For the original sample, the dominant direction is to the left with little to no data oriented to the right. Since there are octants that contain no data, we get a pinched area of the cubic spline. This is due to the choice of how to handle the empty octants. We choose to set the average distance to zero and the direction to the mean direction of the octant. This choice leads to pinching of the curve or cusps in these areas. Another choice would be to remove this octant from the sample and do the interpolation with the remaining nonempty octants.

Adding data to this sample increases it from 9 pitches to 67, and the average vector and spline jut out on the right side due to a handful of pitches oriented further in this direction (this is evident from the blue color of the spline). In the areas where most of the subsequent pitches are located, the spline sits near the left edge of the strike zone. Again, the average vectors in the red area of the spline maintain a similar length and direction.

Moving to the top of the strike zone, we choose the square centered at (0.125,3.375). The original plot for a square along the top contains 11 pitches and no second pitches are oriented upward. There are only have four non-zero vectors for the spline and the dominant direction is down and to the left.

In this square, the sample changes from 11 to 72 pitches by adding similar data. Note the cusp that occurs at the top since we are missing an average vector there. Unsurprisingly, at the top of the strike zone, the preferred direction for the subsequent pitch is downward, and as we rotate away from this direction, the number of pitches in each octant drops.

Finally, along the bottom of the strike zone, we choose (0.125,1.625). Starting with 27 pitches produces five average vectors, with the dominant direction being up and to the left.

With the additional data from other pitchers, the number of pitches moves up to 87. The direction with the most subsequent pitches is up and to the left. In areas where we have the most data in the original sample (the red spline areas), the average vectors and splines are most alike.

There are several obvious drawbacks to this method. For the model fitting, we have some points in the strike zone with 30+ pitches and as we move away from the strike zone, we have less and less data for computing the averages. However, as we move away, the general behavior becomes more predictable: the next pitch will likely be closer to the strike zone. So the small sample should have less of a negative effect for points far away. This is also a potential problem since we use these, in some cases, small samples to calculate the average vector in each square, which is used as a reference point for adding data to the sample. It may be better to use the vector from the gradient field for comparison since it relies on all of the available data to compute the average vector (provided the gradient field approach is a decent model).

Another problem is that in computing the average vector, we are not taking into account the distribution of the vectors. The same average vector can be formed from many different combination of vectors. However, based on the limited data presented above, adding to the sample, using M and the average vectors, does not seem to have a large effect on octants where there is the most data in the original sample. These regions, even with more data, tend to retain their shape. These are also the areas that are going to contribute most to the average vector that is used for comparison, so this seems like a reasonable result.

A smaller problem that shows up near the edge of the zone is that we still occasionally, even after adding more data, get directions with only one or two pieces of data and this causes some of the aberrant behavior seen in some of the plots, characterized by bulges in blue areas of the spline. One solution to this would be to only compute the average vector in that octant if there were more than some fixed number of pitches in that direction. Otherwise, we could set the average vector to zero and the direction to the mean direction in that octant.

Obviously, an analysis of one pitcher over a small collection of squares in the grid does not a theory make. It is possible to examine more pitchers, but because the analysis must be done visually, it will be slow and imprecise. Based on these limited results, there may be potential if the process can be condensed. The pitching sink approach gives an idea of where the next pitch may be headed. As we move toward the sink, we have less information on where the next pitch is headed since near this point, the directions will be somewhat evenly distributed. As we move toward the edge of the strike zone, we get a clearer picture of where the next pitch is headed if only for the reason that it seems unlikely that the next pitch will be even further away.

While this model seems reasonable in this case, there may be cases where a more general model is needed to fit with the behavior of the data. To recover more accurate information on the location of the next pitch, we can switch to the octant method. Since some areas with this method will have very small samples, we can pad out the data via comparison of the average vectors. This seems to do well at filling out the depleted octants and retains many of the features of the average vectors in the most populated octants of the original samples. At this point, both these models exist as novelties, but hopefully with a little more work and analysis, they can be improved and simplified.

The loss to the Pirates, the recent removal of Dusty Baker, and the upcoming free agency of Shin-Soo Choo has overshadowed Bronson Arroyo and his status with the Reds. It seems that if there is one player who never receives enough attention, it is him. But while the baseball world may not seem to realize that he is a free agent, there is no doubt that Walt Jocketty and his staff are very much aware of the 36 year-old starter’s expired contract.

Bronson Arroyo, with the exception of 2011, has been not only one of the Reds best starters, but one of the most consistent pitchers in baseball. He has not been a Cy Young candidate and he is not the ace of the Reds by any means. But the one thing that cannot be denied is his innings pitched per year. Since joining the Reds, he has thrown over 1600 innings and has averaged about 211.1 innings pitched per year. They have started to dip recently but throwing 202 innings each year of the past two seasons shows that despite the age, he still has his durability. He has managed to avoid the DL in his career which is something to be marveled at. Every year that he has pitched with the Reds, he has started at least 32 games and averaged 6-7 innings per start. This kind of reliability is something to be desired out of a starter in this day and age where there is at least one Tommy John surgery or one pitcher who is on a strict innings limit.

One of the things that allow Arroyo to be so durable is the fact that he does not waste his time out of the zone with his pitches. His goal is to go right at the hitters. This season, he was fifth in the majors in walks per nine with 1.51. During his tenure with the Reds (2006-2013), he has averaged 2.31 BB/9 which is good for 14th among pitchers who have thrown at least 1000 IP during that time frame. He seems to be trying to improve those numbers as his BB/9 has been 1.54 over the past two seasons. He indicates that he refuses to beat himself by giving up the free pass (which can help him out seeing as how does not strike out a lot of batters and he does tend to give up home runs).

Bronson Arroyo has made himself a very good pitcher due to great durability and his ability to change speeds when he pitches. Last season his fastball averaged 87 mph and his curveball averaged about 70 mph. The change of speeds helps him to keep most batters off balance because they have no idea what kind of speed is going to be released from his arm or what kind of arm slot the baseball is going to be thrown at. While watching a Reds game, one of the guests in the booth said that he would rather face a pitcher like Aroldis Chapman because he knows what speed and arm slot to expect most of the time. Chapman will throw his fastball about 85.4% of the time and his off-speed pitch (slider) about 14.6% of the time. Once the batter stands in the batter’s box, he can expect to see that heater for the majority of the time. Bronson Arroyo throws his fastball (or sinker) last season for 44.1% of the time. That is 55.9% of the time that he throws one of his 3 other off speed pitches that ranges anywhere from 70 mph to 77.6 mph.

Despite the fact that Arroyo is such a good pitcher, it is unlikely that he will return to the Reds. The Reds, I’m sure, would like nothing more than to have Bronson Arroyo return to their team. The problem is that the Reds are going to have a full rotation and none of the other pitchers are going to the bullpen any time soon. Tony Cingrani has emerged as a phenomenal young left-handed starter that has earned a starting spot. Homer Bailey and Mat Latos have proven to be durable aces that on their best day can match up with anyone and shut down the best of offenses even in Great American Ballpark. Mike Leake probably would have been sent to the bullpen to make room for Arroyo but because of the great bounce-back season that he had, he has re-solidified his spot in the rotation as well. Cueto could be an option to be sent to the bullpen because of his long list of injuries but it is true that when healthy, he is one of the best pitchers in the game. The Reds also have several very talented pitching prospects in the minors in Robert Stephenson, Daniel Corcino, and Nicholas Travieso who are just waiting for an excuse to be called up to the majors. And because of Arroyo’s proven track record it is almost a solid guarantee that he will not be sent to the bullpen.

If you take away anything from these past few paragraphs, it should be that Arroyo is a solid and dependable starter. Maybe on certain teams (I’m looking at you, Houston) he could be an ace but on most teams he will be a solid mid-bottom of the rotation starter for any team. His tendency to give up home runs could be cured in a more pitcher-friendly ballpark but it is unlikely that the problem will go away all together. He is a good pitcher who might get his 3 years, and 30+ million dollars somewhere but he will not find it in Cincinnati. Cincinnati is a mid-market team who is going to have to worry about signing up Homer Bailey, Mat Latos, and Tony Cingrani in the future and they have already spent a lot of money to keep Jay Bruce and Joey Votto locked up for the long haul. Their depth in pitchers allows them to look elsewhere for places on where to spend all of the money that they would have to spend in order to resign Arroyo. Perhaps they could use it to get La Russa out of retirement . . .

Warning: Very long post ahead.

At some point in time, maybe you’ve complained about the lineup your favorite team’s manager used. Maybe you’ve heard of or considered the concept of lineup optimization. Maybe you’ve heard that an optimized lineup, over the course of a full season, wouldn’t make that big of a difference.

It really doesn’t, but that doesn’t make it any less interesting.

In elementary school I spent precious class time attempting to optimize kickball lineups. I suppose that was my first foray into the world of sabermetrics and general baseball nerdiness.

Now, I tend to visit BaseballPress on a daily basis to check the lineups of every team, just because. Even more now, I am writing a long post regarding lineup optimization in the MLB.

Sky Kalkman wrote a great piece on his interpretation of The Book’s findings on lineup optimization. He summed it up with this:

“…we want to know how costly making an out is by each lineup position, based on the base-out situations they most often find themselves in, and then weighted by how often each lineup spot comes to the plate. Here’s how the lineup spots rank in the importance of avoiding outs:

#1, #4, #2, #5, #3, #6, #7, #8, #9

So, you want your best three hitters to hit in the #1, #4, and #2 spots. Distribute them so OBP is higher in the order and SLG is lower. Then place your fourth and fifth best hitters, with the #5 spot usually seeing the better hitter, unless he’s a high-homerun guy. Then place your four remaining hitters in decreasing order of overall hitting ability, with basestealers ahead of singles hitters.”

Following the conclusion of the Major League Baseball regular season, I took to the task of finding each team’s most common starters and lineups, hypothetically optimizing them and comparing the results by which team theoretically cost themselves the most runs by straying from optimization.

I sorted each team’s hitters by plate appearances, made sure there was a representative of every position and used Baseball-Reference’s batting order archive to find the most common order those eight/nine players appeared in to find each team’s hypothetical “most common” lineup.

Then I plugged that lineup into Baseball Musing’s lineup optimization tool, along with their 2013 OBP and SLG to find the optimized lineup for each team.

It’s far from a perfect science, especially with teams like Oakland who often change their lineup by utilizing platoons, but it’s good enough and I wanted an opportunity to tell people much smarter and more qualified than me how to better do their job.

Behold, the results (where rpg is runs per game, season difference is the amount of runs “lost” from a season’s worth of theoretical lineups to optimized lineup, and rank is the most optimized to least optimized lineups):

Boston Red Sox

Common rpg: 5.448. Optimized rpg: 5.547. Season difference: -16.038 runs.

Rank: 10th AL, 24th overall

2013	OBP	SLG	Optimal	OBP	SLG
CF Ellsbury	.355	.426	LF Nava	.385	.445
RF Victorino	.351	.451	DH Ortiz	.395	.564
2B Pedroia	.372	.415	RF Victorino	.351	.451
DH Ortiz	.395	.564	1B Napoli	.360	.482
1B Napoli	.360	.482	C Saltalamacchia	.338	.466
LF Nava	.385	.445	SS Drew	.333	.443
C Saltalamacchia	.338	.466	CF Ellsbury	.355	.426
3B Middlebrooks	.271	.425	3B Middlebrooks	.271	.425
SS Drew	.333	.443	2B Pedroia	.372	.415

 

 

 

 

 

 

 

Oh, man. Off to a rocky start. Bear with me, folks, they aren’t all this jarring. This is probably the wackiest one that got spit out. The Red Sox obviously would never hit Dustin Pedroia ninth. The Book likes the nine-hitter to be a high OBP, low SLG guy so the top-of-the-order hitters have guys on base when they come to bat. And to be fair, Dustin Pedroia pretty much had the batting profile of a slap-hitter this season. He had the lowest SLG on the team, and his ISO puts his power production below guys like Brandon Crawford and Chris Denorfia. While his great .372 OBP is likely being put to waste in this lineup, Pedroia’s 2013 numbers fit the bill of an optimal #9 hitter when the rest of the lineup is this good.

Tampa Bay Rays

Common rpg: 4.689. Optimized rpg: 4.779. Season difference: -14.580 runs.

Rank: 8th AL, 17th overall

2013	OBP	SLG	Optimal	OBP	SLG
CF Jennings	.334	.414	2B Zobrist	.354	.402
DH Joyce	.328	.419	RF Myers	.354	.478
2B Zobrist	.354	.402	DH Joyce	.328	.419
3B Longoria	.343	.498	1B Loney	.348	.430
1B Loney	.348	.430	3B Longoria	.343	.498
RF Myers	.354	.478	CF Jennings	.334	.414
LF Johnson	.305	.410	SS Escobar	.332	.366
C Molina	.290	.304	LF Johnson	.305	.410
SS Escobar	.332	.366	C Molina	.290	.304

 

 

 

 

 

 

 

I always like Joe Maddon’s lineups. He mixes things up a lot and isn’t afraid to push the envelope. He’s batted catchers high in the order. He’s led off Ben Zobrist, an excellent – but unconventional – leadoff hitter. For a while this year he batted Evan Longoria second, which is quite smart and probably never would have been considered a decade ago. However, Desmond Jennings isn’t an ideal leadoff hitter with a .330 career OBP and Matt Joyce’s .252 BABIP left him with the lowest OBP of his career. Zobrist is the Rays best leadoff hitter and Wil Myers, arguably the Rays most productive hitter, should be higher in the order.

Baltimore Orioles

Common rpg: 4.724. Optimized rpg: 4.814. Season difference: -14.580 runs.

Rank: 9th AL, 19th overall

2013	OBP	SLG	Optimal	OBP	SLG
LF McLouth	.329	.399	LF McLouth	.329	.399
3B Machado	.314	.432	1B Davis	.370	.634
RF Markakis	.329	.356	SS Hardy	.306	.433
CF Jones	.318	.493	CF Jones	.318	.493
1B Davis	.370	.634	3B Machado	.314	.432
C Wieters	.287	.417	DH Flaherty	.293	.390
SS Hardy	.306	.433	2B Roberts	.312	.392
DH Flaherty	.293	.390	C Wieters	.287	.417
2B Roberts	.312	.392	RF Markakis	.329	.356

 

 

 

 

 

 

 

Chris Davis started the season out as the Orioles #5 hitter, because no one yet knew he would transform into some sort of robot humanoid. Once the transformation was well underway, Buck Showalter continued batting Davis fifth and a struggling Nick Markakis third, likely because “if it ain’t broke, don’t fix it,” and the idea that moving a hot batter to a different spot in the order could somehow throw him out of his groove. It took the Orioles until the middle of August to move Davis into the three-hole and by then Davis’ low spot in the order relative to his production likely cost them a handful of runs. Given the disparity of his OBP compared to his teammates, he’s even better suited for the two-hole.

New York Yankees

Common rpg: 3.978. Optimized rpg: 4.077. Season difference: -16.038 runs.

Rank: 1th AL, 25th overall

2013	OBP	SLG	Optimal	OBP	SLG
CF Gardner	.344	.416	CF Gardner	.344	.416
RF Suzuki	.297	.342	2B Cano	.383	.516
2B Cano	.383	.516	1B Overbay	.295	.393
DH Hafner	.301	.378	DH Hafner	.301	.378
LF Wells	.282	.349	SS Nunez	.307	.372
1B Overbay	.295	.393	RF Suzuki	.297	.342
SS Nunez	.307	.372	3B Nix	.308	.311
3B Nix	.308	.311	LF Wells	.282	.349
C Stewart	.293	.272	C Stewart	.293	.272

 

 

 

 

 

 

 

This isn’t the Yankees lineup we’re used to after the last couple months, or really after the last decade. But as Yankees fans well know, it is the lineup we saw for the majority of the season. Sorry you had to see this again, Yankees fans. The Yankees did hit Robinson Cano in his more deserved second-position for a period of time, but it was basically out of necessity as they had no other real hitters to work with. Instead, Ichiro Suzuki spent the majority of the time in the two-hole seemingly on reputation alone, despite being the third-worst candidate for the spot on a team full of Jayson Nix, Eduardo Nunez and Lyle Overbay’s.

Toronto Blue Jays

Common rpg: 4.791. Optimized rpg: 4.914. Season difference: -19.926 runs.

Rank: 14th AL, 29th overall

2013	OBP	SLG	Optimal	OBP	SLG
SS Reyes	.353	.427	SS Reyes	.353	.427
LF Cabrera	.322	.360	DH Encarnacion	.370	.534
RF Bautista	.358	.498	CF Rasmus	.338	.501
DH Encarnacion	.370	.534	RF Bautista	.358	.498
1B Lind	.357	.497	1B Lind	.357	.497
C Arencibia	.227	.365	3B Lawrie	.315	.397
CF Rasmus	.338	.501	LF Cabrera	.322	.360
2B Izturis	.288	.310	C Arencibia	.227	.365
3B Lawrie	.315	.397	2B Izturis	.288	.310

 

 

 

 

 

 

 

Like the Rays and Yankees, the Blue Jays experimented for a bit this season and batted Jose Bautista #2. Like the Rays and Yankees, this was very smart. Like the Rays and Yankees, they inexplicably stopped their experiment and reverted to a more traditional lineup. Melky Cabrera was not a good hitter this year, yet there he sits in the most important spot of our hypothetical lineup, while the Blue Jays have three great #2 candidates in Edwin Encarnacion, Bautista and even Adam Lind, who was basically “slow Jose Bautista” this season. Burying Colby Rasmus‘ .500 SLG in the seven-hole also didn’t help. And, no, that isn’t a typo. J.P. Arencibia really finished with a .227 OBP this year.

Detroit Tigers

Common rpg: 5.375. Optimized rpg: 5.510. Season difference: -21.870 runs.

Rank: 15th AL, 30th overall

2013	OBP	SLG	Optimal	OBP	SLG
CF Jackson	.337	.417	DH Martinez	.355	.430
RF Hunter	.334	.465	3B Cabrera	.442	.636
3B Cabrera	.442	.636	2B Infante	.345	.450
1B Fielder	.362	.457	1B Fielder	.362	.457
DH Martinez	.355	.430	SS Peralta	.358	.457
LF Dirks	.323	.363	CF Jackson	.337	.417
SS Peralta	.358	.457	C Avila	.317	.376
C Avila	.317	.376	RF Hunter	.334	.465
2B Infante	.345	.450	LF Dirks	.323	.363

 

 

 

 

 

 

 

OK, this one is actually kind of genius. Although OBP is far more important than speed in regards to a leadoff hitter, speed still kind of matters. You probably don’t want your slowest player batting leadoff, especially if you have a burner in the two or three spot. But the Tigers already have the slowest team in baseball, by far, and Miguel Cabrera is their ideal two-hitter. Since Victor Martinez won’t be holding Miggy up on the basepaths, putting his .355 OBP in front of Miggy is actually really smart, especially considering Miggy hits a first-inning homer like half the time anyway. Austin Jackson’s baserunning ability is better suited towards the bottom of the lineup for singles hitters like Alex Avila, Torii Hunter and Andy Dirks. Because of this wildly unconventional lineup, the Tigers ranked last in the study, and I would really love to see this lineup actually get played out.

Cleveland Indians

Common rpg: 4.456. Optimized rpg: 4.509. Season difference: -8.586 runs.

Rank: 2nd AL, 7th overall

2013	OBP	SLG	Optimal	OBP	SLG
CF Bourn	.316	.360	C Santana	.377	.455
1B Swisher	.341	.423	2B Kipnis	.366	.452
2B Kipnis	.366	.452	LF Brantley	.332	.396
C Santana	.377	.455	1B Swisher	.341	.423
LF Brantley	.332	.396	SS Cabrera	.299	.402
SS Cabrera	.299	.402	DH Reynolds	.307	.373
DH Reynolds	.307	.373	RF Stubbs	.305	.360
3B Aviles	.282	.368	3B Aviles	.282	.368
RF Stubbs	.305	.360	CF Bourn	.316	.360

 

 

 

 

 

 

 

As an Indians fan who was constantly frustrated by Terry Francona’s lineups, their rank in the study surprised me. However, the Indians problem was more with player selection, not lineup order, which isn’t reflected in the study. The Indians best statistical hitter, Ryan Raburn, amassed only 277 PA’s and didn’t make the cut. Yan Gomes, the Indians second best hitter, eventually began receiving his well-deserved playing time, but still finished with just 322 PA’s and missed the cut. To start the season, the Indians buried Carlos Santana‘s great OBP in the six-hole and wouldn’t move Asdrubal Cabrera’s putrid OBP out of the top of the order. But Francona fixed his mistake early enough for it not to be reflected in the years end most common lineup. And in that lineup, the Indians did a good job by having their top five hitters be their highest OBP guys. Michael Bourn was not the leadoff hitter the Indians thought they were signing, and was actually a pretty bad one with a .316 OBP. Santana and Jason Kipnis are much more deserving choices to lead off, though in real life I would likely flip-flop them, considering speed.

Kansas City Royals

Common rpg: 4.094. Optimized rpg: 4.204. Season difference: -17.820 runs.

Rank: 13th AL, 27th overall

2013	OBP	SLG	Optimal	OBP	SLG
LF Gordon	.327	.422	DH Butler	.374	.412
1B Hosmer	.353	.448	1B Hosmer	.353	.448
DH Butler	.374	.412	LF Gordon	.327	.422
C Perez	.323	.433	C Perez	.323	.433
CF Cain	.310	.348	RF Lough	.311	.413
3B Moustakas	.287	.364	SS Escobar	.259	.300
RF Lough	.311	.413	CF Cain	.310	.348
2B Getz	.288	.273	3B Moustakas	.287	.364
SS Escobar	.259	.300	2B Getz	.288	.273

 

 

 

 

 

 

 

Despite performing poorly in the study, the Royals two lineups were actually pretty close, and theoretically they could have earned themselves a handful more runs by simply swapping Billy Butler and Alex Gordon’s spots in the lineup. I have always loved Gordon as an unconventional leadoff hitter, but this season he stopped taking walks and getting hits on 35% of his balls in play, leading to a pedestrian .327 OBP after posting marks of .376 and .368 the last two seasons. Butler had a weird year, too, as he started walking all the time and lost all his power, posting a lower isolated slugging percentage than David Lough. But Butler is one of the slowest players in baseball and Eric Hosmer is a pretty good baserunner, especially for a first basemen, so swapping their orders in the optimized lineup might make more sense.

Minnesota Twins

Common rpg: 4.301. Optimized rpg: 4.379. Season difference: -12.636 runs.

Rank: 5th AL, 12th overall

2013	OBP	SLG	Optimal	OBP	SLG
2B Dozier	.312	.414	LF Willingham	.342	.368
C Mauer	.404	.476	C Mauer	.404	.476
LF Willingham	.342	.368	DH Doumit	.314	.396
1B Morneau	.315	.426	1B Morneau	.315	.426
DH Doumit	.314	.396	2B Dozier	.312	.414
3B Plouffe	.309	.392	3B Plouffe	.309	.392
RF Arcia	.304	.430	SS Florimon	.281	.330
CF Thomas	.290	.307	RF Arcia	.304	.430
SS Florimon	.281	.330	CF Thomas	.290	.307

 

 

 

 

 

 

 

Blame slugging percentage for this one. Joe Mauer should really be the Twins leadoff hitter. But, since slugging percentage is flawed in its attempt to represent power by including singles – something Mauer hits a ton of – Mauer has over 100 points of SLG on Josh Willingham, leading the generator to believe Willingham is a more ideal leadoff hitter despite Mauer’s .404 OBP. We all know that Willingham is more of a power hitter than Mauer, which is why we should always use ISO to measure power, where Willingham edges Mauer .159 to .156 even on a down season. Other than the mistake of batting Brian Dozier leadoff, though, the Twins real-life lineup does a pretty great job, with their OBPs falling in descending order after Dozier. If this lineup generator used ISO instead of SLG like I wish it would, flip-flopping Mauer and Willingham at the top would likely be the optimal order for the Twins.

Chicago White Sox

Common rpg: 3.950. Optimized rpg: 4.030. Season difference: -12.960 runs.

Rank: 6th AL, 14th overall

2013	OBP	SLG	Optimal	OBP	SLG
CF De Aza	.323	.405	CF De Aza	.323	.405
SS Ramirez	.313	.380	RF Rios	.328	.421
RF Rios	.328	.421	3B Gillaspie	.305	.390
1B Dunn	.320	.442	1B Dunn	.320	.442
DH Konerko	.313	.355	LF Viciedo	.304	.426
3B Gillaspie	.305	.390	SS Ramirez	.313	.380
LF Viciedo	.304	.426	DH Konerko	.313	.355
2B Keppinger	.283	.317	C Flowers	.247	.355
C Flowers	.247	.355	2B Keppinger	.283	.317

 

 

 

 

 

 

 

Alejandro De Aza isn’t a great leadoff hitter with a .323 OBP, but when you have the fourth worst team OBP in baseball, .323 will do. The main problem with the White Sox order is their two-hole, as is the problem with most MLB lineups. Alexei Ramirez’s offensive skill set is basically the exact one that MLB managers are beginning to move away from in the 2-hole, with his .313 OBP, complete disappearance of power and newfound penchant for stealing bases. Contrary to conventional wisdom, good base stealers are better suited for the 6/7 spots in the lineup. Risking outs with your best hitters at the plate, who are more likely to drive you in with extra base hits anyway, is not a good idea. With that aside, the White Sox did well by choosing the correct leadoff hitter and keeping their worst hitters at the bottom of the order.

Oakland Athletics

Common rpg: 4.933. Optimized rpg: 4.989. Season difference: -9.072 runs.

Rank: 3rd AL, 8th overall

2013	OBP	SLG	Optimal	OBP	SLG
LF Crisp	.335	.444	3B Donaldson	.384	.499
SS Lowrie	.344	.446	SS Lowrie	.344	.446
CF Cespedes	.294	.442	DH Smith	.329	.391
1B Moss	.337	.522	1B Moss	.337	.522
3B Donaldson	.384	.499	C Norris	.345	.409
DH Smith	.329	.391	CF Cespedes	.294	.442
RF Reddick	.307	.379	LF Crisp	.335	.444
C Norris	.345	.409	RF Reddick	.307	.379
2B Sogard	.322	.364	2B Sogard	.322	.364

 

 

 

 

 

 

 

Surprise! The Oakland Athletics scored well in a SABR-slanted study. And this doesn’t even take into account how well the A’s optimize their lineup on a daily basis by correctly utilizing platoons. But either way, in this theoretical lineup, the A’s do a good job by getting their second and fourth hitters correct. Though breakout player and MVP-candidate Josh Donaldson is better suited to lead off, Coco Crisp was still a good option. And whether incidental or not, Yoenis Cespedes‘ low OBP in the three-hole doesn’t hurt them too much, as OBP isn’t as important in the three-hole as conventional wisdom would tell you. The A’s do well in this study with the lineup provided for them, and do even better in real life by putting the right guys on the field every day.

Texas Rangers

Common rpg: 4.481. Optimized rpg: 4.582. Season difference: -16.362 runs.

Rank: 12th AL, 26th overall

2013	OBP	SLG	Optimal	OBP	SLG
2B Kinsler	.344	.413	2B Kinsler	.344	.413
SS Andrus	.328	.331	3B Beltre	.371	.509
RF Cruz	.327	.506	1B Moreland	.299	.437
3B Beltre	.371	.509	RF Cruz	.327	.506
C Pierzynski	.297	.425	C Pierzynski	.297	.425
1B Moreland	.299	.437	CF Martin	.313	.385
LF Murphy	.282	.374	DH Profar	.308	.336
DH Profar	.308	.336	LF Murphy	.282	.374
CF Martin	.313	.385	SS Andrus	.328	.331

 

 

 

 

 

 

 

Ian Kinsler is another guy who doesn’t scream “prototypical leadoff hitter,” basically in the sense that he’s not a speed-first centerfielder, but he is a pretty great one and easily the Rangers best option. So you have to give them credit for sticking with him instead of going to the more conventional, “easy” choice of Elvis Andrus or Leonys Martin. However, the Rangers lose a lot of value by keeping the speedy Andrus in the two-hole, a spot he really wasn’t suited for this season with a career-worst .327 OBP. Adrian Beltre is the perfect fit for the Rangers #2 hitter, and Andrus is better suited for the bottom of the order. With Andrus’ basestealing abilities, I think it would be wiser to switch his spot with Jurickson Profar’s in this optimized lineup, giving Andrus the opportunity to attempt steals with the 8th and 9th hitters up, rather than the 1st and 2nd.

Los Angeles Angels of Anaheim

Common rpg: 4.864. Optimized rpg: 4.945. Season difference: -13.122 runs.

Rank: 7th AL, 15th overall

2013	OBP	SLG	Optimal	OBP	SLG
LF Shuck	.331	.366	C Iannetta	.358	.372
CF Trout	.432	.557	CF Trout	.432	.557
1B Pujols	.330	.437	RF Hamilton	.307	.432
RF Hamilton	.307	.432	1B Pujols	.330	.437
DH Trumbo	.294	.453	2B Kendrick	.335	.439
2B Kendrick	.335	.439	SS Aybar	.301	.382
3B Callaspo	.324	.347	LF Shuck	.331	.366
C Iannetta	.358	.372	DH Trumbo	.294	.453
SS Aybar	.301	.382	3B Callaspo	.324	.347

 

 

 

 

 

 

 

This one is similar to Detroit’s, but unlike Detroit’s, this one probably only works in theory. When Miggy is batting second and the entire team is slower than molasses in an igloo, I think you can get by with a slow-running, high-OBP guy like Victor Martinez leading off. When your #2 hitter is Mike Trout, you’re probably costing yourself extra bases on would-be Trout doubles and triples by having Chris Iannetta on first in front of him, likely having just drawn a leadoff walk. If Iannetta weren’t so slow and Trout weren’t so fast, Iannetta would actually be a pretty great leadoff hitter. Of all players with 350+ PA this season, only Joey Votto posted a higher BB% (18.6) than Iannetta (17.0). The Angels did do the right thing by putting Trout where he belongs in the two-hole, though. A .432 OBP is great for leadoff, but when you hit for more power than Giancarlo Stanton and Adam Dunn, some of those extra base hits go to waste leading off.

Seattle Mariners

Common rpg: 4.382. Optimized rpg: 4.439. Season difference: -9.234 runs.

Rank: 4th AL, 9th overall

2013	OBP	SLG	Optimal	OBP	SLG
SS Miller	.318	.418	3B Seager	.338	.426
2B Franklin	.303	.382	DH Morales	.336	.449
3B Seager	.338	.426	RF Saunders	.323	.397
DH Morales	.336	.449	1B Smoak	.334	.412
LF Ibanez	.306	.487	LF Ibanez	.306	.487
1B Smoak	.334	.412	2B Franklin	.303	.382
RF Saunders	.323	.397	C Zunino	.290	.329
C Zunino	.290	.329	SS Miller	.318	.418
CF Ackley	.319	.341	CF Ackley	.319	.341

 

 

 

 

 

 

 

The Mariners began the season with Dustin Ackley at second base and Brendan Ryan at shortstop. By the beginning of June, Ackley had hit himself back to AAA and not much later, the Mariners cut ties with Ryan’s offensive deficiencies in favor of rookies Brad Miller and Nick Franklin. Both held their own with the bat from the get-go, earning themselves the top two spots in the Mariners everyday lineup. However, despite holding their own, neither are really top of the order hitters with sub-.320 OBPs and just average power. Better suited for the top spots are the Mariners best player, Kyle Seager and most productive hitter, Kendrys Morales. Still, the Mariners performed well in the study, likely due to the similar profiles of most of their hitters.

Houston Astros

Common rpg: 4.133. Optimized rpg: 4.176. Season difference: -6.966 runs.

Rank: 1st AL, 3rd overall

2013	OBP	SLG	Optimal	OBP	SLG
LF Grossman	.332	.370	LF Grossman	.332	.370
2B Altuve	.316	.363	C Castro	.350	.485
C Castro	.350	.485	2B Altuve	.316	.363
1B Carter	.320	.451	1B Carter	.320	.451
DH Pena	.324	.350	3B Dominguez	.286	.403
RF Martinez	.272	.378	CF Barnes	.289	.346
CF Barnes	.289	.346	DH Pena	.324	.350
3B Dominguez	.286	.403	RF Martinez	.272	.378
SS Villar	.321	.319	SS Villar	.321	.319

 

 

 

 

 

 

 

The Astros place third in the study basically by default. It’s not hard to identify your best players and construct a near-optimal lineup when you’ve only got two league-average bats. Just put your best hitter, Jason Castro, in the two-hole, bat Chris Carter fourth to drive in runs and lead off your next highest OBP guy, who believe it or not is a one “Robbie Grossman” and the rest basically doesn’t matter because none of them are very good. Robbie Grossman is actually the most deserving leadoff batter on a real team in the Major League of Baseball. #Astros

Coming soon: Part 2, with National League lineups and conclusion.

Introduction

What is the best pitch of all-time?  Is it Mariano Rivera’s cutter?  Is it Randy Johnson’s slider?  Is it Walter Johnson’s fastball?  I do not know.  What I do know is that this question is nearly impossible to answer, so let’s simplify things a little.  What was the best pitch thrown during the 2013 regular season?  On a rate basis, PITCHf/x would lead us to believe that the best pitch thrown by a qualifying pitcher was Yovani Gallardo’s cutter with a wFC/c of 4.95.  In other words, for every 100 cutters thrown by Gallardo, he saved 4.95 runs above a pitcher who throws an “average” cutter.  What does this really mean though?  This system of calculation is based off the changes in run expectancy due to the outcome of each pitch, which is extremely complicated and tedious to calculate.  I felt that there had to be a simpler way to quantify the quality of a pitch. 

Background

Back in August, I posted an article entitled “Baseball’s Most Extreme Pitches from Starters, So Far” that posited the idea of total bases per hit allowed.  In other words, I wanted to look at who was getting hit the hardest.  Now, it was rightly suggested in the comments that this wasn’t the greatest way to determine a pitch’s quality.  For example, let’s look at the following two extremely hypothetical examples.  One pitcher throws his fastball exactly 100 times.  In those 100 pitches, he throws 99 of them for strikes.  On the 100^th pitch, he gives up a home run.  Now, by looking at TB/H, this pitch has a rating of 4.00, which is the worst possible rating.  However, he only gave up 0.04 total bases per pitch, which is excellent.  By comparison, the second pitcher throws exactly 100 fastballs as well.  He gives up 100 singles.  By TB/H, his fastball has a rating of 1.00, which is significantly better than the first pitcher.  However, he gave up 1.00 total bases per pitch, which is awful.  If a pitcher gave up a base runner each time he threw a pitch, he probably would cease throwing that pitch very quickly. 

That got me to thinking that total bases per pitch may be a much better way to determine the quality of a pitch, but there are also glaring problems with this method as well.  For example, 100 balls thrown in 100 pitches would a value of 0.00 total bases per pitch.  Clearly, a pitcher’s ability (or inability) to throw a pitch for a strike needed to be incorporated as well. 

Proposed Solution

To try and solve the problems suggested above, I propose the following simple formula:

adjTB/P = [1B + 2*2B + 3*3B + 4*HR + xBB] / Pitches

where,

xBB = Balls/4

With that said, I know some pitches are thrown out of the strike zone intentionally (i.e. the waste pitch).  At the end of the day, a waste pitch only puts you one step closer to walking a batter and adds one pitch to the pitch count.  Every coach would prefer their starter to throw a Maddux each time out, so efficiency is the name of the game.  In order to test this formula, let’s look at a sample calculation.

According to Baseball Prospectus and their PITCHf/x leaderboards, A.J. Burnett threw 614 four-seam fastballs this regular season.  On those 614 pitches, he allowed 10 singles, nine doubles, five home runs, and had 202 of those pitches called balls.  Burnett allowed 58 total bases and 50.5 xBB.  Doing some quick arithmetic, he allowed 0.1767 adjTB/P. 

At first glance, I’m sure your reaction is similar to my initial reaction.  Okay, so what does that mean?  On its face, a correct response may contain the words “I’m not really sure”.  If we look at the summation of each four-seam fastball thrown by starters this year, we find that the league allowed 0.1800 adjTB/P, so A.J. Burnett threw a slightly above average four-seam fastball this year.  To come to that conclusion though, you’d have to know both a player’s rate and the league rate.  We can present this information in a much nicer and easier to understand way. 

To do this, I decided to turn to the old standby from every scout in baseball, the 20-80 scale.  As you’re probably well aware, the 20-80 scale attempts to rate a player’s skills numerically.  50 is average.  60 represents exactly one standard deviation above average.  30 represents exactly two standard deviations below average, and so on and so forth.  By taking the weighted standard deviation of the data set, we can determine how many standard deviations above or below average a certain pitch is.  Looking at the full season data, the weighted standard deviation for four-seam fastballs is 0.0262 adjTB/P.  Another quick calculation tells us that A.J. Burnett rated as 0.13 standard deviations above average.  Converting that on a 20-80 scale rating, Burnett’s four-seam fastball gets a rating of 51.  On quick glance, the 51 rating makes much more sense than 0.1767 adjTB/P, which helps solve one of our problems.

Results

Now that we understand how to calculate the values and what they mean, let’s look at a scale for whose four-seam fastball really excelled and whose really was problematic.  To qualify for the full season, 600 total four-seam fastballs had to be thrown.  This gave me 103 qualified starting pitchers.  The Top 10 qualified starters were:

 

For comparison, the Bottom 10 qualified starters were:

 

On a monthly basis, a minimum of 100 four-seam fastballs had to be thrown.  The best and worst pitches each month this season were:

 

Only three starters qualified as above average in each month of the regular season.  Their monthly ratings are shown below.  No starter qualified as below average in each month this season. 

 

I plan to continue this study by analyzing both other pitch types and relievers.  Baseball Prospectus provides data for the following pitches: four-seam fastball, sinker, cutter, splitter, changeup, curveball, slider, screwball, and knuckleball.  At the completion of all the pitch types, I’ll post the ratings for complete repertoires as well.  If well-received, I’ll try and provide monthly updates as next season rolls along.      

wRC is a very useful statistic.  On the team level, it can be used to predict runs scored fairly accurately (r^2 of over .9).  It can also be used to measure how much a specific player has contributed to his team’s offensive production by measuring how many runs he has provided on offense.  But it is rarely used for pitchers.

Pitching statistics are not so much based on linear weights and wOBA as they are on defense-independent stats.  I think defense-independent stats are fine things to look at when evaluating players, and they can provide lots of information about how a pitcher really performed.  But while pitcher WAR is based off of FIP (at least on FanGraphs), RA9-WAR is also sometimes looked at.  Now, if the whole point of using linear weights for batters is to eliminate context and the production of teammates, then why not do the same for pitchers?  True, pitchers, especially starters, usually get themselves into bad situations, unlike hitters, who can’t control how many outs there are or who’s on base when they come up.  But oftentimes pitchers aren’t better in certain situations, as evidence by the inconsistency of stats such as LOB%.  So why not eliminate context from pitcher evaluations and look at how many runs they should have given up based on the hits, walks, and hit batters they allowed?

To do this, I needed to go over to Baseball-Reference, as FanGraphs doesn’t have easy-to-manipulate wOBA figures for pitchers.  Baseball-Reference doesn’t have any sort of wOBA stats, but what they do have is the raw numbers needed to calculate wOBA.  So I put them into Excel, and, with 50 IP as my minimum threshold, I calculated the wOBA allowed – and then converted that into wRC – for the 330 pitchers this year with at least 50 innings.

Next, I calculated wRC/9 the same way you would calculate ERA (or RA/9).  This would scale it very closely to ERA and RA/9, and give us a good sense for what each number actually means.  (The average wRC/9 with the pitchers I used was 3.95; the average RA/9 for the pitchers I used was 3.96).  What I found was that the extremes on both sides were way more extreme (you’ll see what I mean soon), but overall it correlated to RA/9 fairly closely (the r^2 was .803).

Now, for the actual numbers:

	wRC/9	IP
Koji Uehara	0.08	74.1
Tanner Roark	1.04	53.2
Joe Nathan	1.08	64.2
Greg Holland	1.17	67
Alex Torres*	1.24	58
Craig Kimbrel	1.41	67
Luis Avilan*	1.42	65
Neal Cotts*	1.43	57
Mark Melancon	1.52	71
Kenley Jansen	1.55	76.2
Clayton Kershaw*	1.59	236
Paco Rodriguez*	1.60	54.1
Luke Hochevar	1.65	70.1
Matt Harvey	1.69	178.1
Tyler Clippard	1.69	71
Jose Fernandez	1.80	172.2
Tony Watson*	1.89	71.2
J.P. Howell*	1.94	62
Bobby Parnell	2.00	50
Clay Buchholz	2.04	108.1
Glen Perkins*	2.09	62.2
Justin Wilson*	2.13	73.2
David Carpenter	2.13	65.2
Casey Janssen	2.15	52.2
Sean Doolittle*	2.16	69
Brandon Kintzler	2.17	77
Aroldis Chapman*	2.24	63.2
Luke Gregerson	2.29	66.1
Steve Cishek	2.30	69.2
Joaquin Benoit	2.31	67
Max Scherzer	2.32	214.1
Madison Bumgarner*	2.35	201.1
Sonny Gray	2.39	64
David Robertson	2.42	66.1
Jean Machi	2.44	53
Dane De La Rosa	2.46	72.1
Tyler Thornburg	2.56	66.2
Drew Smyly*	2.58	76
Jason Grilli	2.59	50
Stephen Strasburg	2.60	183
Danny Farquhar	2.64	55.2
Michael Wacha	2.66	64.2
Joel Peralta	2.67	71.1
Brett Cecil*	2.68	60.2
Brad Ziegler	2.69	73
Johnny Cueto	2.69	60.2
Tommy Hunter	2.69	86.1
Addison Reed	2.69	71.1
Bryan Shaw	2.72	75
Casey Fien	2.73	62
Mariano Rivera	2.77	64
Sergio Romo	2.81	60.1
Hisashi Iwakuma	2.81	219.2
Jose Veras	2.81	62.2
Cliff Lee*	2.81	222.2
Darren O’Day	2.82	62
Tanner Scheppers	2.85	76.2
Trevor Rosenthal	2.87	75.1
Yu Darvish	2.87	209.2
Adam Wainwright	2.88	241.2
Anibal Sanchez	2.88	182
Mike Dunn*	2.89	67.2
Jeanmar Gomez	2.90	80.2
Brian Matusz*	2.94	51
Charlie Furbush*	2.96	65
J.J. Hoover	2.97	66
Francisco Liriano*	2.98	161
Grant Balfour	2.99	62.2
Alfredo Simon	2.99	87.2
Jonathan Papelbon	3.04	61.2
Jesse Chavez	3.04	57.1
Tyson Ross	3.07	125
Gerrit Cole	3.07	117.1
A.J. Ramos	3.07	80
Craig Breslow*	3.07	59.2
Tom Wilhelmsen	3.07	59
Andrew Cashner	3.08	175
Chris Sale*	3.10	214.1
Felix Hernandez	3.10	204.1
Vin Mazzaro	3.10	73.2
Zack Greinke	3.11	177.2
Jim Henderson	3.12	60
Matt Albers	3.13	63
Sam LeCure	3.14	61
Anthony Swarzak	3.16	96
Jerry Blevins*	3.16	60
Henderson Alvarez	3.16	102.2
LaTroy Hawkins	3.17	70.2
Tony Cingrani*	3.17	104.2
Mike Minor*	3.18	204.2
Jordan Zimmermann	3.18	213.1
Tim Stauffer	3.21	69.2
Travis Wood*	3.21	200
Edward Mujica	3.21	64.2
Alex Cobb	3.22	143.1
Rex Brothers*	3.23	67.1
Justin Masterson	3.24	193
David Price*	3.24	186.2
Santiago Casilla	3.26	50
Ryan Cook	3.26	67.1
Brett Oberholtzer*	3.26	71.2
Bartolo Colon	3.27	190.1
A.J. Burnett	3.29	191
Danny Salazar	3.30	52
Josh Collmenter	3.31	92
Nate Jones	3.31	78
Chad Gaudin	3.33	97
Jamey Wright	3.33	70
Joe Smith	3.33	63
Homer Bailey	3.33	209
Marco Estrada	3.35	128
Hyun-jin Ryu*	3.36	192
Anthony Varvaro	3.36	73.1
Chad Qualls	3.38	62
Tim Hudson	3.38	131.1
Jarred Cosart	3.41	60
Scott Rice*	3.41	51
Chris Archer	3.42	128.2
Jake McGee*	3.43	62.2
Ervin Santana	3.48	211
Will Harris	3.48	52.2
Aaron Loup*	3.48	69.1
Yoervis Medina	3.50	68
Fernando Rodney	3.51	66.2
Huston Street	3.51	56.2
Burke Badenhop	3.51	62.1
Patrick Corbin*	3.53	208.1
Mat Latos	3.53	210.2
Ryan Webb	3.54	80.1
Jered Weaver	3.54	154.1
Rafael Soriano	3.56	66.2
Bruce Chen*	3.56	121
Scott Feldman	3.57	181.2
Shelby Miller	3.57	173.1
Alex Wood*	3.58	77.2
Matt Cain	3.59	184.1
Gio Gonzalez*	3.60	195.2
Craig Stammen	3.61	81.2
Hiroki Kuroda	3.62	201.1
Matt Moore*	3.62	150.1
Ryan Pressly	3.64	76.2
Dan Straily	3.64	152.1
A.J. Griffin	3.68	200
James Shields	3.68	228.2
Adam Ottavino	3.68	78.1
Pedro Strop	3.68	57.1
Cody Allen	3.68	70.1
Alexi Ogando	3.72	104.1
Jhoulys Chacin	3.73	197.1
Kyle Lohse	3.74	198.2
Jake Peavy	3.74	144.2
Cole Hamels*	3.76	220
Nathan Eovaldi	3.76	106.1
Carlos Torres	3.76	86.1
Andrew Albers*	3.78	60
Ricky Nolasco	3.80	199.1
Robbie Erlin*	3.80	54.2
Ross Ohlendorf	3.82	60.1
Dale Thayer	3.82	65
Jarrod Parker	3.85	197
Jose Quintana*	3.86	200
John Lackey	3.86	189.1
Julio Teheran	3.87	185.2
Cesar Ramos*	3.88	67.1
Ernesto Frieri	3.88	68.2
Steve Delabar	3.91	58.2
Ivan Nova	3.91	139.1
Matt Belisle	3.91	73
Ubaldo Jimenez	3.92	182.2
Kris Medlen	3.93	197
Wandy Rodriguez*	3.94	62.2
Kelvin Herrera	3.95	58.1
Justin Verlander	3.97	218.1
Garrett Richards	3.97	145
Charlie Morton	3.97	116
Matt Lindstrom	3.97	60.2
Tom Gorzelanny*	3.97	85.1
Jared Burton	3.97	66
Jeff Locke*	3.99	166.1
C.J. Wilson*	4.00	212.1
Tim Collins*	4.00	53.1
Seth Maness	4.00	62
Matt Garza	4.03	155.1
David Hernandez	4.03	62.1
Lance Lynn	4.04	201.2
Rick Porcello	4.04	177
Miguel Gonzalez	4.04	171.1
Carlos Villanueva	4.04	128.2
Derek Holland*	4.04	213
Robbie Ross*	4.05	62.1
Jim Johnson	4.05	70.1
Kevin Gregg	4.06	62
J.C. Gutierrez	4.08	55.1
Bryan Morris	4.09	65
Mike Leake	4.09	192.1
Joe Kelly	4.11	124
Zack Wheeler	4.11	100
Jon Lester*	4.12	213.1
Taylor Jordan	4.13	51.2
Bronson Arroyo	4.14	202
Tim Lincecum	4.15	197.2
Eric Stults*	4.17	203.2
Chris Tillman	4.18	206.1
Doug Fister	4.19	208.2
Junichi Tazawa	4.20	68.1
Corey Kluber	4.22	147.1
Logan Ondrusek	4.23	55
Jaime Garcia*	4.25	55.1
Tyler Lyons*	4.25	53
Jorge De La Rosa*	4.27	167.2
Yovani Gallardo	4.28	180.2
Wade Miley*	4.29	202.2
R.A. Dickey	4.30	224.2
James Russell*	4.30	52.2
Tyler Chatwood	4.32	111.1
Sam Deduno	4.33	108
Andy Pettitte*	4.35	185.1
Michael Kohn	4.37	53
Josh Outman*	4.38	54
Dillon Gee	4.38	199
Martin Perez*	4.39	124.1
Jake Arrieta	4.39	75.1
Shawn Kelley	4.39	53.1
Drew Storen	4.41	61.2
Preston Claiborne	4.42	50.1
Tommy Milone*	4.45	156.1
Wily Peralta	4.46	183.1
Scott Kazmir*	4.46	158
Felix Doubront*	4.54	162.1
Jeff Samardzija	4.55	213.2
Shaun Marcum	4.56	78.1
Dan Haren	4.58	169.2
Alfredo Figaro	4.58	74
Troy Patton*	4.60	56
Hector Rondon	4.62	54.2
Oliver Perez*	4.62	53
Trevor Cahill	4.63	146.2
Wei-Yin Chen*	4.63	137
Todd Redmond	4.64	77
Zach McAllister	4.64	134.1
Jonathon Niese*	4.65	143
Tom Koehler	4.65	143
Ronald Belisario	4.66	68
Jeremy Hefner	4.66	130.2
Jacob Turner	4.68	118
Kyle Kendrick	4.68	182
Chris Rusin*	4.70	66.1
Brandon McCarthy	4.70	135
Freddy Garcia	4.70	80.1
Randall Delgado	4.70	116.1
Wilton Lopez	4.72	75.1
Mark Buehrle*	4.73	203.2
T.J. McFarland*	4.74	74.2
J.A. Happ*	4.79	92.2
Jason Vargas*	4.80	150
David Phelps	4.81	86.2
Brian Duensing*	4.82	61
Hector Santiago*	4.84	149
CC Sabathia*	4.85	211
Nick Tepesch	4.88	93
Jeremy Hellickson	4.89	174
Wesley Wright*	4.93	53.2
Chris Capuano*	4.95	105.2
Donovan Hand	4.97	68.1
Jerome Williams	4.99	169.1
Adam Warren	5.01	77
Paul Maholm*	5.04	153
Jeremy Guthrie	5.08	211.2
Jonathan Pettibone	5.08	100.1
John Danks*	5.09	138.1
George Kontos	5.10	55.1
Edwin Jackson	5.10	175.1
Ian Kennedy	5.14	181.1
Brad Peacock	5.15	83.1
Bud Norris	5.16	176.2
Erik Bedard*	5.17	151
Travis Blackley*	5.18	50.1
Ryan Dempster	5.19	171.1
Kevin Correia	5.19	185.1
Erasmo Ramirez	5.20	72.1
Roberto Hernandez	5.20	151
Kevin Slowey	5.20	92
Aaron Harang	5.24	143.1
Jason Marquis	5.25	117.2
Jake Westbrook	5.27	116.2
Juan Nicasio	5.29	157.2
Heath Bell	5.35	65.2
Josh Roenicke	5.35	62
Esmil Rogers	5.38	137.2
John Axford	5.42	65
Mike Pelfrey	5.43	152.2
John Lannan*	5.45	74.1
Andre Rienzo	5.46	56
Ross Detwiler*	5.54	71.1
Jason Hammel	5.55	139.1
Stephen Fife	5.63	58.1
Edinson Volquez	5.65	170.1
Dallas Keuchel*	5.68	153.2
Jordan Lyles	5.70	141.2
Phil Hughes	5.71	145.2
Tommy Hanson	5.74	73
Luis Mendoza	5.79	94
Jeremy Bonderman	5.82	55
Brandon League	5.82	54.1
Roy Halladay	5.85	62
Chris Perez	5.94	54
Scott Diamond*	6.01	131
Ryan Vogelsong	6.04	103.2
Wade Davis	6.05	135.1
Justin Grimm	6.10	98
Paul Clemens	6.14	73.1
Lucas Harrell	6.23	153.2
Jeff Francis*	6.39	70.1
Brandon Morrow	6.39	54.1
Joe Saunders*	6.39	183
Jon Garland	6.40	68
Josh Johnson	6.45	81.1
Mike Gonzalez*	6.50	50
Wade LeBlanc*	6.54	55
Brandon Maurer	6.58	90
Barry Zito*	6.63	133.1
Carter Capps	6.64	59
Dylan Axelrod	6.82	128.1
Kyle Gibson	6.92	51
Joe Blanton	7.00	132.2
Clayton Richard*	7.14	52.2
Alex Sanabia	7.29	55.1
Tyler Cloyd	7.40	60.1
Philip Humber	7.62	54.2
Pedro Hernandez*	7.68	56.2
Average	3.95	110.2

The first thing that jumps out right away is that Koji Uehara had a wRC/9 of 0.08.  In other words, if that was his ERA, he would give up one earned run in about 12 complete game starts if he were a starter, which is ridiculous.  The second thing that jumps out is that most of the top performers are relievers – in fact, 12 out of the top 13 had fewer than 80 innings, with the only exception being Clayton Kershaw.  Also, the worst pitchers by wRC/9 had a wRC/9 much higher than their ERA or RA/9.  Pedro Hernandez, for example, had a wRC/9 of 7.68, and there were 6 pitchers over 7.00.  Kershaw actually has a wRC/9 that is lower than his insane RA/9, so maybe he’s even better than his fielding-dependent stats give him credit for.

But wait!  There’s more!  The reason we have xFIP is because HR/FB rates are very unstable.  So let’s incorporate that into our wRC/9 formula and see what happens (we’ll call this one xwRC/9):

	xwRC/9	IP
Koji Uehara	0.06	74.1
Paco Rodriguez*	1.13	54.1
Luke Hochevar	1.25	70.1
Tyler Clippard	1.25	71
Craig Kimbrel	1.51	67
Kenley Jansen	1.63	76.2
Aroldis Chapman*	1.68	63.2
Greg Holland	1.69	67
Casey Fien	1.88	62
Joe Nathan	2.06	64.2
Tanner Roark	2.06	53.2
Neal Cotts*	2.12	57
Clayton Kershaw*	2.13	236
Max Scherzer	2.17	214.1
Huston Street	2.18	56.2
Jose Fernandez	2.23	172.2
Alex Torres*	2.26	58
Yu Darvish	2.28	209.2
Glen Perkins*	2.29	62.2
Matt Harvey	2.32	178.1
Tony Watson*	2.35	71.2
Stephen Strasburg	2.35	183
Mark Melancon	2.36	71
Johnny Cueto	2.38	60.2
David Carpenter	2.39	65.2
Luis Avilan*	2.41	65
Justin Wilson*	2.48	73.2
Tommy Hunter	2.49	86.1
Joaquin Benoit	2.50	67
J.P. Howell*	2.51	62
David Robertson	2.52	66.1
Madison Bumgarner*	2.54	201.1
Hisashi Iwakuma	2.56	219.2
Tony Cingrani*	2.57	104.2
Jason Grilli	2.66	50
Darren O’Day	2.67	62
Jose Veras	2.68	62.2
Marco Estrada	2.70	128
Casey Janssen	2.71	52.2
Travis Wood*	2.76	200
Sonny Gray	2.80	64
Grant Balfour	2.81	62.2
Clay Buchholz	2.81	108.1
Danny Salazar	2.81	52
Cliff Lee*	2.81	222.2
Steve Cishek	2.83	69.2
Sean Doolittle*	2.83	69
Jim Henderson	2.83	60
Carlos Torres	2.84	86.1
Edward Mujica	2.85	64.2
Kelvin Herrera	2.86	58.1
Brett Cecil*	2.87	60.2
Jake McGee*	2.89	62.2
Mariano Rivera	2.89	64
Joel Peralta	2.89	71.1
Ernesto Frieri	2.93	68.2
Michael Wacha	2.95	64.2
Anibal Sanchez	2.95	182
Luke Gregerson	2.98	66.1
Brandon Kintzler	2.99	77
Tim Stauffer	2.99	69.2
Tanner Scheppers	2.99	76.2
Brad Ziegler	2.99	73
Alex Cobb	3.05	143.1
Dane De La Rosa	3.05	72.1
Addison Reed	3.06	71.1
Travis Blackley*	3.08	50.1
Jerry Blevins*	3.09	60
Bobby Parnell	3.09	50
Freddy Garcia	3.11	80.1
Jeanmar Gomez	3.13	80.2
Ervin Santana	3.17	211
Jean Machi	3.19	53
Trevor Rosenthal	3.20	75.1
J.J. Hoover	3.20	66
Chris Archer	3.20	128.2
Sergio Romo	3.20	60.1
Alfredo Figaro	3.21	74
Drew Smyly*	3.22	76
Alfredo Simon	3.23	87.2
Jonathan Papelbon	3.24	61.2
Charlie Furbush*	3.24	65
Mike Dunn*	3.26	67.2
Wandy Rodriguez*	3.26	62.2
Tyson Ross	3.27	125
Justin Masterson	3.27	193
Felix Hernandez	3.29	204.1
Mike Minor*	3.32	204.2
Rex Brothers*	3.33	67.1
Homer Bailey	3.33	209
Adam Wainwright	3.34	241.2
David Hernandez	3.34	62.1
Bryan Shaw	3.34	75
John Lackey	3.35	189.1
Danny Farquhar	3.36	55.2
Randall Delgado	3.37	116.1
Chris Sale*	3.37	214.1
LaTroy Hawkins	3.38	70.2
Chad Qualls	3.40	62
Jordan Zimmermann	3.41	213.1
Matt Cain	3.43	184.1
A.J. Griffin	3.45	200
Zack Greinke	3.45	177.2
Joe Smith	3.45	63
Burke Badenhop	3.46	62.1
Chris Tillman	3.47	206.1
Andrew Cashner	3.47	175
David Price*	3.49	186.2
Scott Feldman	3.49	181.2
Miguel Gonzalez	3.49	171.1
Francisco Liriano*	3.50	161
Nate Jones	3.51	78
Shelby Miller	3.51	173.1
Bronson Arroyo	3.52	202
Jake Peavy	3.52	144.2
Ross Ohlendorf	3.53	60.1
Tim Hudson	3.53	131.1
Logan Ondrusek	3.54	55
Yoervis Medina	3.54	68
Kyle Lohse	3.55	198.2
Tom Gorzelanny*	3.56	85.1
R.A. Dickey	3.58	224.2
Dale Thayer	3.59	65
Sam LeCure	3.60	61
Josh Collmenter	3.60	92
Aaron Loup*	3.61	69.1
Jesse Chavez	3.62	57.1
Hyun-jin Ryu*	3.62	192
A.J. Burnett	3.62	191
Brian Matusz*	3.62	51
Gerrit Cole	3.63	117.1
Bryan Morris	3.64	65
Pedro Strop	3.66	57.1
Patrick Corbin*	3.71	208.1
Hiroki Kuroda	3.72	201.1
Matt Moore*	3.74	150.1
Brett Oberholtzer*	3.75	71.2
Dan Straily	3.75	152.1
Julio Teheran	3.76	185.2
Alexi Ogando	3.76	104.1
Anthony Swarzak	3.76	96
Shawn Kelley	3.77	53.1
Jered Weaver	3.79	154.1
Ryan Webb	3.81	80.1
Jaime Garcia*	3.82	55.1
Gio Gonzalez*	3.82	195.2
Matt Albers	3.83	63
Kris Medlen	3.84	197
Matt Garza	3.86	155.1
Jamey Wright	3.86	70
Craig Breslow*	3.88	59.2
Cody Allen	3.88	70.1
Preston Claiborne	3.89	50.1
Cole Hamels*	3.91	220
Rafael Soriano	3.91	66.2
A.J. Ramos	3.92	80
Bruce Chen*	3.93	121
Santiago Casilla	3.93	50
Todd Redmond	3.94	77
Rick Porcello	3.94	177
Bartolo Colon	3.95	190.1
Dan Haren	3.99	169.2
John Danks*	3.99	138.1
Craig Stammen	4.00	81.2
Tyler Thornburg	4.00	66.2
Fernando Rodney	4.00	66.2
Chad Gaudin	4.01	97
Will Harris	4.01	52.2
Tommy Milone*	4.01	156.1
James Russell*	4.01	52.2
Jarred Cosart	4.02	60
Robbie Erlin*	4.02	54.2
Troy Patton*	4.03	56
Scott Rice*	4.03	51
James Shields	4.03	228.2
Mike Leake	4.05	192.1
Jared Burton	4.05	66
Ubaldo Jimenez	4.05	182.2
Seth Maness	4.05	62
Jeremy Hefner	4.06	130.2
Vin Mazzaro	4.06	73.2
Tim Lincecum	4.07	197.2
Mat Latos	4.08	210.2
Junichi Tazawa	4.10	68.1
Eric Stults*	4.10	203.2
Garrett Richards	4.12	145
Adam Ottavino	4.12	78.1
Zack Wheeler	4.13	100
Andrew Albers*	4.15	60
Carlos Villanueva	4.16	128.2
Andre Rienzo	4.16	56
Jeff Samardzija	4.18	213.2
Jake Arrieta	4.20	75.1
Tom Wilhelmsen	4.21	59
Jim Johnson	4.21	70.1
Brad Peacock	4.22	83.1
Corey Kluber	4.22	147.1
Heath Bell	4.22	65.2
Wade Miley*	4.25	202.2
Michael Kohn	4.25	53
Martin Perez*	4.26	124.1
Ricky Nolasco	4.26	199.1
Matt Belisle	4.27	73
Charlie Morton	4.27	116
Jon Lester*	4.27	213.1
Scott Kazmir*	4.27	158
Roberto Hernandez	4.28	151
Jarrod Parker	4.28	197
Justin Verlander	4.29	218.1
Derek Holland*	4.31	213
Henderson Alvarez	4.31	102.2
Ryan Cook	4.32	67.1
Cesar Ramos*	4.33	67.1
Ivan Nova	4.33	139.1
Jeff Locke*	4.34	166.1
Andy Pettitte*	4.35	185.1
Ryan Pressly	4.36	76.2
Yovani Gallardo	4.36	180.2
Donovan Hand	4.36	68.1
Dillon Gee	4.38	199
Drew Storen	4.39	61.2
Alex Wood*	4.39	77.2
Tyler Lyons*	4.40	53
Nathan Eovaldi	4.41	106.1
Kevin Gregg	4.42	62
Wesley Wright*	4.43	53.2
Jose Quintana*	4.43	200
Anthony Varvaro	4.44	73.1
Steve Delabar	4.44	58.2
Jason Marquis	4.46	117.2
Oliver Perez*	4.48	53
Wily Peralta	4.48	183.1
Joe Kelly	4.49	124
Lance Lynn	4.49	201.2
J.C. Gutierrez	4.53	55.1
Roy Halladay	4.54	62
Jhoulys Chacin	4.54	197.1
C.J. Wilson*	4.55	212.1
Chris Rusin*	4.56	66.1
Erasmo Ramirez	4.56	72.1
Doug Fister	4.58	208.2
Aaron Harang	4.59	143.1
Hector Rondon	4.60	54.2
CC Sabathia*	4.60	211
T.J. McFarland*	4.62	74.2
Jeremy Hellickson	4.62	174
Sam Deduno	4.64	108
Nick Tepesch	4.64	93
Ian Kennedy	4.65	181.1
Wei-Yin Chen*	4.68	137
Robbie Ross*	4.68	62.1
Chris Perez	4.69	54
Jerome Williams	4.69	169.1
Trevor Cahill	4.70	146.2
Adam Warren	4.71	77
Hector Santiago*	4.75	149
Taylor Jordan	4.77	51.2
Ryan Dempster	4.79	171.1
Esmil Rogers	4.80	137.2
John Axford	4.80	65
Tim Collins*	4.81	53.1
Jeremy Guthrie	4.81	211.2
Tom Koehler	4.83	143
Matt Lindstrom	4.84	60.2
Felix Doubront*	4.86	162.1
Jorge De La Rosa*	4.89	167.2
Jason Vargas*	4.89	150
Paul Clemens	4.95	73.1
J.A. Happ*	4.95	92.2
Erik Bedard*	4.96	151
Paul Maholm*	4.97	153
Josh Outman*	4.99	54
Jacob Turner	5.00	118
Tyler Chatwood	5.00	111.1
Shaun Marcum	5.00	78.1
George Kontos	5.03	55.1
Jason Hammel	5.04	139.1
Brandon McCarthy	5.06	135
Zach McAllister	5.06	134.1
Brandon Morrow	5.13	54.1
Jonathon Niese*	5.17	143
Brandon League	5.17	54.1
David Phelps	5.18	86.2
Chris Capuano*	5.18	105.2
Clayton Richard*	5.21	52.2
Carter Capps	5.21	59
Ronald Belisario	5.26	68
Wilton Lopez	5.27	75.1
Dallas Keuchel*	5.28	153.2
Jonathan Pettibone	5.28	100.1
Juan Nicasio	5.34	157.2
Stephen Fife	5.34	58.1
Edwin Jackson	5.36	175.1
Mike Gonzalez*	5.39	50
Kevin Slowey	5.40	92
Josh Johnson	5.42	81.1
Phil Hughes	5.42	145.2
Mark Buehrle*	5.45	203.2
Bud Norris	5.46	176.2
Brian Duensing*	5.51	61
Josh Roenicke	5.52	62
Jeff Francis*	5.62	70.1
Scott Diamond*	5.64	131
Jordan Lyles	5.65	141.2
Justin Grimm	5.66	98
Tommy Hanson	5.67	73
Kevin Correia	5.67	185.1
Edinson Volquez	5.69	170.1
Lucas Harrell	5.72	153.2
Joe Blanton	5.73	132.2
Brandon Maurer	5.80	90
John Lannan*	5.85	74.1
Ryan Vogelsong	5.85	103.2
Jeremy Bonderman	5.87	55
Luis Mendoza	5.88	94
Kyle Kendrick	5.90	182
Jake Westbrook	5.93	116.2
Mike Pelfrey	5.95	152.2
Dylan Axelrod	6.11	128.1
Jon Garland	6.21	68
Wade Davis	6.22	135.1
Ross Detwiler*	6.24	71.1
Joe Saunders*	6.29	183
Alex Sanabia	6.62	55.1
Barry Zito*	6.63	133.1
Wade LeBlanc*	6.65	55
Kyle Gibson	6.70	51
Philip Humber	7.19	54.2
Pedro Hernandez*	7.32	56.2
Tyler Cloyd	7.73	60.1
Average	3.99	110.2

Not a huge difference, although we do see Uehara’s number go down, which is incredible, and Tanner Roark’s – the second-best pitcher by wRC/9 – nearly double.  Also, Tyler Cloyd becomes much worse, and is now the worst pitcher by almost half a run per nine innings.  Kershaw’s wRC/9 goes up by a considerable amount, so much so that his xwRC/9 is now higher than his RA/9.  All in all, however, xwRC/9 actually has a smaller correlation with RA/9 (an r^2 of .638) than wRC/9 does, so it isn’t as useful. 

Now, logically, the people who outperformed their wRC/9 the most would have high strand (LOB) rates, and vice-versa.  So let’s look at the ten players who both outperformed and underperformed their wRC/9 the most.  The ones who underperformed:

	IP	LOB%	RA/9	wRC/9	RA/9 – wRC/9
Danny Farquhar	55.2	58.50%	4.69	2.64	2.05
Charlie Furbush	65	64.40%	4.57	2.96	1.61
Casey Fien	62	69.40%	4.06	2.73	1.33
Andrew Albers	60	60.40%	5.10	3.78	1.32
Nate Jones	78	62.90%	4.62	3.31	1.31
Joel Peralta	71.1	70.20%	3.91	2.67	1.24
Addison Reed	71.1	68.90%	3.91	2.69	1.22
Tom Wilhelmsen	59	69.90%	4.27	3.07	1.20
Jesse Chavez	57.1	66.90%	4.24	3.04	1.19
Koji Uehara	74.1	91.70%	1.21	0.08	1.13

We can see that everyone here – except for Koji Uehara, who had the fourth-highest LOB% out of all pitchers with 50 innings – is below the league average of 73.5%.  Only Uehara and Joel Peralta are above 70%.  Clearly, a low LOB% makes you allow many more runs than you should.  But what about Koji Uehara?  How did he allow all those runs (10, yeah, not a lot, but his wRC/9 was way lower than his RA/9) without allowing many baserunners to score and not allowing many damaging hits?  If you know, let me know in the comments, because I have no idea.

Now for the people who outperformed their wRC/9:

Rex Brothers	67.1	88.80%	2.14	3.23	-1.09
Donovan Hand	68.1	81.90%	3.82	4.97	-1.15
Stephen Fife	58.1	78.40%	4.47	5.63	-1.16
Jarred Cosart	60	85.90%	2.25	3.41	-1.16
Heath Bell	65.2	82.70%	4.11	5.35	-1.23
Chris Perez	54	82.30%	4.50	5.94	-1.44
Mike Gonzalez	50	80.30%	5.04	6.50	-1.46
Seth Maness	62	84.50%	2.47	4.00	-1.53
Adam Warren	77	84.70%	3.39	5.01	-1.62
Alex Sanabia	55.1	77.40%	5.37	7.29	-1.93

Just what you would expect:  high LOB%’s from all of them (each is above the league average).  Stephen Fife and Alex Sanabia are the only ones below 80%.

So what does this tell us?  I think it’s a better way to evaluate pitchers than runs or earned runs allowed since it eliminates context:  a pitcher who lets up a home run, then a single, then three outs is not necessarily better than one who lets up a single, home run, then three outs, but the statistics will tell you he is.  It might not be as good as an evaluator as FIP, xFIP, or SIERA, but for a fielding-dependent statistic, it might be as good as you can find.

Note:  I don’t know why the pitchers with asterisks next to there name have them; I copied and pasted the stats from Baseball-Reference and didn’t bother going through and removing the asterisks.

Mike Podhorzer has looked into the relationship of a batters’ average fly ball distance as it relates to their HR/FB ratio, and has found results that will allow others to more accurately project a hitter’s home run totals from year to year.

This got me thinking. Which can be a good or bad, but in this case, the authors’ labor produced a fruitful return. While a hitters’ HR/FB ratio can fluctuate indiscriminately from year to year, Podhorzer has proven a batters’ average fly ball distance is a better indication of a player’s true talent power production. In the same light, my study looks at how a player’s swinging strike rate (SwStr%) is a better indication of a pitcher’s strikeout potential than K/9.

My assumption was that K/9 and SwStr% have a strong relationship. But, how strong of a relationship is it? To find this out, I took all qualified starter seasons from 2003 to 2013, which gave me a sample size of 933 pitchers, and ran a correlation between their SwSTR% and their K/9. The results showed that there is an exceedingly positive correlation between SwSTR% and K/9, to the tune of a .807 correlation coefficient and a .65 R².

What is important to note is that there are very few pitchers present in the sample with a SwStr% above 13%, which may be symptomatic of something larger. Getting batters to swing and miss is difficult. The more often you can get a batter to swing and miss, the more valuable you are as a pitcher. As a result, the higher the SwStr%, the smaller the sample size becomes. For example, Johan Santana (2004) and Kerry Wood (2003) are the two lone dots to the farthest right on the graph with SwStr% of over 15: wow.

After the relationship between SwStr% and K/9 ratio became unmistakable, I calculated what a particular SwSTR%s translates into, as far as K/9, with the formula Y=68.473*x+0.8435, and got this chart:

The next step is to take what we have discovered and apply it to a sample. The chart below shows each qualified pitcher for 2013, their SwStr%, xK/9, K/9, and K/9-xK/9.  xK/9 is what we would expect a pitcher’s K/9 to be based off of their SwStr%, and K/9-xK/9 shows us how much a pitcher over-performed or under-performed their SwStr% and xK/9.The first set of ten names are the pitchers who outperformed their xK/9 the most, and the second list of ten names are the players who underperformed their xK/9 the most.

The results show that Ubaldo Jimenez, Yu Darvish, and Jose Fernandez are the pitchers who have outperformed their xK/9 the most in 2013. These three pitchers also have great a great amount of deception and/or command (deception in Jimenez’s case: because, no one has ever called Ubaldo a control artist). And, while they may have outperformed their true talent in 2013 to an extent—they all had remarkable years—maybe that deception and control, which SwStr% does not take into account, leads to less swings by batters and more pitches taken for strikes, as opposed to swung at for strikes.

Perhaps xK/9 is more helpful when we look at pitchers who underperformed their SwStr%, like Jarrod Parker and Kris Medlen. Both of these pitchers had down years compared to what their projections suggested, but their xK/9s seem to be optimistic about their futures. Parker showed a .18 improvement in his K/9 from the first half to the second half of the season, while Medlen showed almost a full point improvement going from a 6.81 K/9 in the first half to a 7.67 K/9 in the second half.

While xK/9 may miss something—deception and command—when it comes to pitchers that outperform their SwStr%, xK/9 seems to find a reason to be optimistic when it comes to pitchers like Kris Medlen and Jarrod Parker who have underperformed their SwStr% and strikeout potential.

Devon Jordan is obsessed with statistical analysis, non-fiction literature, and electronic music. If you enjoyed reading him, follow him on Twitter @devonjjordan.