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.

When Colby Rasmus was dealt to the Blue Jays from the St. Louis Cardinals in a blockbuster trade on July 27, 2011, there were mixed emotions in Toronto regarding the deal. On the one hand, he was (and arguably still is) just a few years removed from being a blue-chip, five-tool prospect with power and plus defense. On the other, there was the much-publicized family feud with then-Cards manager Tony La Russa, the seemingly lethargic attitude at bat, in the field and in media interviews (a reputation unaided by his laid back, southern drawl), the strikeouts, and most of all: the unshakeable stigma of not living up to his foretold potential.

The overwhelming consensus as the deal was struck and following it as well was one of relative indifference, and with good reason. After coming over from St. Louis in 2011 he didn’t exactly set the world on fire in a 35-game stint with the Blue Jays (.173/.201/.316). His slash line from last year (.223/.289/.400) seemed to be building on 2011 and the mounting strikeouts failed to endear him to a disgruntled Blue Jays fan base hungry for something to cheer about. With his average at .225 on April 28, this year looked to be more of the same. It has become increasingly clear however, that something has changed this season. Before being sidelined by an oblique injury, Colby was putting together an impressive season—despite receiving next to no credit for it. He hadn’t missed a step since coming off the DL either, homering in each of the four games after returning, becoming just the 10^th different Blue Jay to do so.  Unfortunately, it lasted just six games as Rasmus was sidelined by an errant Anthony Gose warm-up throw to the face.

Let’s start with the traditional measures: a .276 average, 22 home runs, and 66 RBI to go along with an .840 OPS in 118 games. He is now one of only four Blue Jays all-time to hit 20 bombs in back-to-back seasons (the others are Vernon Wells, Jose Cruz Jr., and Lloyd Moseby). He will finish up one off his career high of 23 home runs set last season and 9 off his best mark of 75 RBI also set last year. These totals would obviously be higher as well had Rasmus not missed a month due to an oblique strain and even more time because of his facial injury. As of September 19 (before going down for a second time and after missing a month), he was near the top of many major statistical categories for AL centre fielders: second in home runs (22), slugging (.507), and OPS (.845); third with 66 driven in and 4^th with 49 extra base hits. Last year, Colby went 24.2 plate appearances in between home runs and this season he was at 20.8, which practically equates him with Baltimore star Adam Jones (Jones went deep every 20.9 plate appearances). Colby’s 2013 home run prowess on average per game as a centre fielder is also superior to the likes of Carlos Gomez, Mike Trout, Shin-Soo Choo, and Andrew McCutchen. He went deep every 6.6 games in 2012 and every 5.4 on average this year. Even with the time he’s missed, only Jones, McCutchen, Trout, and Gomez have driven in more runs as a centre fielder in Major League Baseball. Rasmus’s .840 OPS is surpassed only by Trout, McCutchen and Choo. Those are impressive stats and equally impressive company to be grouped with.

Even given the aforementioned statistical information, there are always those who will refuse to qualify a player’s worth and contribution without the use of sabermetrics and so in fairness this aspect must be investigated as well. I cannot pretend I understand the drawn out calculations though I understand what the numbers mean. I will be firstly using Baseball-Reference’s WAR data summarized by ESPN. Colby Rasmus has a wins above replacement of 4.8, fifth best of any centre fielder in baseball. Simply put, the number is great and to put it in perspective, he trails just Trout, Gomez, McCutchen and Jacoby Ellsbury in this regard. He is ahead of players considered league-wide to be great, or at least above-average: Adam Jones, Shin-Soo Choo, Austin Jackson, Desmond Jennings, Andre Ethier, Matt Kemp (in limited time), Michael Bourn, Denard Span, and Curtis Granderson (in limited time, and might be over the hump, I know) to name a few prominent ones. FanGraphs also puts Rasmus at 4.8 WAR, and according to their rating system both Baseball-Reference and FanGraphs would qualify him as an All-Star (a player with 4-5 WAR is deemed All-Star-worthy).

As we have seen, Rasmus obviously brings quite a bit to the table offensively, but what about defensively? What if I were to suggest that he has a better defensive WAR and range factor than Mike Trout? Or that there are only three players with over 100 starts in centre (Leonys Martin, Ellsbury, and Gomez) that have a greater dWAR than Rasmus? And only three with a better range factor? These are all in fact true statements. He sits at 1.6 dWAR compared to -0.8 for Trout and has a 2.77 range factor compared to Trout’s 2.61 mark. Obviously Trout’s oWAR (10.1) and WAR (9.2) are off-the-charts good and this is not an attempt to bolster Colby Rasmus at the expense of Mike Trout. But a point needs to be made, so bear with me. Mike Trout’s dWAR was 2.2 last year in 108 starts in centre and as aforementioned, it is -0.8 this year in 106 starts. His range factor was 2.7 last year and 2.61 this season. He had 268 total chances in 886 innings in 2012. In 2013, he had only 273 in 937 full innings in centre field. He is less valuable defensively to the Angels, has apparently less range, and has gotten to fewer balls.

2013 Mike Trout vs. Colby Rasmus

	WAR	dWAR	oWAR	Range Factor
Mike Trout	9.2	-0.8	10.1	2.61
Colby Rasmus	4.8	1.6	3.5	2.77

 

However, a crucial point remains: Trout made a name for himself (and rightfully so) last year as an elite defensive player to complement his superb offensive skills. His reputation as a defensive wizard has stuck with him into this season—there has not been any mention about any defensive regression. Instead he is heralded as a possible MVP candidate despite the fact the Angels will miss the postseason as they did last year. And just as Trout’s reputation as an above-average fielder has outlasted his ability (only up until the end of 2013), the opposite has been true for Rasmus. His status as an underachieving strikeout machine has overshadowed his amazing progression as an all-around player. Consider the power, the average, runs driven in, and OPS combined with the much-improved wins above replacement numbers (overall, offensive, and defensive). His overall WAR of 4.8 is a career high by over one full win (3.6 in 2010), defensively he has improved every season since 2010 and now sits at 1.6. Offensively he is at 3.5 wins above replacement and has improved by at least two runs every year in that category since becoming a Blue Jay.

Colby Rasmus as a Blue Jay

	WAR	dWAR	oWAR
2011	-1.0	-0.0	-0.9
2012	1.7	1.0	1.1
2013	4.8	1.6	3.5

 

I think it is safe to say that he has become more of a well-rounded player but more importantly, he is on an upward trajectory. Conversely, take the highly-coveted, soon-to-be free agent Shin-Soo Choo, who at age 30 is seemingly regressing defensively (a career-worst -1.9 dWAR both this and last year). His offensive numbers are impressive, don’t get me wrong, but it remains to be seen how much longer he can be an effective outfielder. A .424 on base percentage with 20+ homers is nice, but Baseball-Reference reveals that his WAR (4.0) is still lower than Rasmus’s (4.8) with the latter seemingly on an upswing. I do think Choo is good, but it is all but certain that he will be overpaid and consequently Colby Rasmus will look like a far better option.

I believed I have put forth at least a half-decent argument that Colby Rasmus is extremely valuable and even elite. I argue that his numbers on average rival the best in the game at his position and that he should get a little more credit for his impressive body of work. Some would point out that perhaps I have not examined his numbers from all possible perspectives, which I plan to do now using various data presented by FanGraphs. A comparison of this season with his 2011 and 2012 campaigns reveal ominous similarities. He struck out 29.5% of the time in 2013, which is actually up from 23.8% last year, and walked an insignificant 0.6% of the time more often (he still only walks 8.1% of the time). His BB/K ratio is also down to 0.27 from 0.32 in 2012 and 0.43 in 2011. He swung at 29.3% of pitches out of the zone in 2013 compared to 31.8% last year, and while perhaps showing a bit more patience, the number from this season equals his career average exactly. He has swung at basically the same amount of pitches inside the zone this year and last, and 2013’s mark of 67.2% is slightly off his career average of 70.6%. As for the balls he made contact with: while the percentage of pitches he made contact with inside the zone is almost exactly the same as 2012, the pitches he made contact with outside the strike zone was at 55.4% from 62.2% last season. So is he simply getting lucky by swinging and missing more often, thereby not making weak outs and having a shot at the next pitch? There may be some truth to that considering (as we have seen) that he swings at almost the same amount of pitches out of the zone as last year. On the other hand, he did strike out more in 2013 than 2012, which may discount the luck idea. The main bullet point here is that there does not seem to be much deviation from this year and the two preceding it and that there must be another explanation to help explain his success.

Based on these findings, one might think Rasmus would have had a similar year in 2013 to 2012 and 2011. But the numbers do not corroborate this as we have clearly seen. So what is different? BABIP. Rasmus has the worrisome distinction of having an unusually high batting average on balls put in play. BABIP can have a profound effect on a player’s batting average and a player with an unusually high or low BABIP will likely regress back to their career rate the following season. Proponents of sabermetrics will also convey that a very high BABIP may suggest that a player is having a fluky season. As for Rasmus, his batting average on balls in play was .356 this season compared to .259 last year and .267 in 2011. During his breakout 2010 campaign, it was .354. These are not small discrepancies. He hit .276 both this year and in 2010 and .223 and .225 last year and 2011, respectively. There is a definite link and it seems to have to do with BABIP. He has averaged .298 over his career in that department, which is considered normal.

So for the most part, he has been either well above or below it throughout his five years in the big leagues. Is he just having an especially good year? We won’t know until next season if he will regress but there are a few reasons to think he will be fine. His 2010 and 2013 numbers are more of what people expect than the years in between based on his ability. Maybe a .356 batting average on balls in play isn’t outrageously high and maybe 2012 was the fluky year. This season Rasmus hit a greater percentage of balls in play for line drives (22.0%) than ever before in his career (average: 19.5%). Also, more of his fly balls left the yard this season (13.2% last year and 17.3% this season). So maybe he is hitting the ball harder, and a few extra fly balls are hanging up just long enough to clear the fence. Although, ESPN’s Home Run Tracker considers just three of his home runs to have “Just Enough” distance while the other 19 were no doubters or had cleared the wall by “Plenty”. Another interesting point is that Mike Trout’s batting average on balls in play over the last two years is .379. Will he be able to keep that up? It is as much a question for Trout as it is for Rasmus.

This analysis of course not definitive but it merely is alternative to the fluke theory. It is possible that Rasmus can repeat his stellar 2013 season. One thing is clear though: this year, he was up there with the best centre fielders in the business. This was shown using traditional measures as well as new-age sabermetrics. He was near the top in most significant offensive and defensive categories and had he not been hurt he would have set career-highs and perhaps received a little more (and well-deserved) credit. He flew under the radar and it’s unfortunate that he is not appreciated as he should be. If he has a good 2014, I believe he will finally shake the lackadaisical, under-achieving, strikeout machine stigma and instead be seen as a quietly confident, budding star with an ability to hit for average and power to go along with graceful and effortless defense.

I’ll admit it: I feel like a trendsetter most of the time.

Usually it’s how I justify to myself my terrible clothing taste, or bad haircut, or lack of interest in Breaking Bad, or why I don’t get invited to many social events.  But this time, my friends, surely this trend will carry on, for at least a year!  And it will all be because of me, or at least I will tell myself that.

So what trend is it that I’ll be initiating today?  I’m going to abjectly speculate on who the next baseball commissioner should be.  And I’m going to do it a year in advance, before you get tired of hearing all of the names mentioned that have no chance at ending up on the 31st floor of 245 Park Ave.   So, without further ado, let’s begin speculation season, shall we?

One of the names surely to draw a lot of attention in the “Commish Search 2k14” (as it will surely be named) is one Robert Q. Costas, known to many as NBC Sports Anchor Bob Costas.  Bob has had a long love affair with baseball, including writing a 197 page manifesto of objective revelations in his year 2000 book, “Fair Ball: A Fan’s Case for Baseball.”  For those that haven’t read it, it’s a fascinating read, even thirteen years after writing.  In it, he proposes numerous ideas, many of which took over a decade to be implemented, such as instant replay in the playoffs (obviously still not fully implemented), daily interleague play to balance the size of each league, and even tweaks to the wild card system to punish teams for not winning their division, a practice that started in 2012.

In addition, as a baseball outsider (and by that I basically mean a non-owner or league official), Bob is able to objectively look at what is holding baseball back from a fan’s perspective, as well as some of the economic challenges.  In the Selig administration, we saw the Brewers, formerly owned by Selig, move to the National League, conveniently bettering the revenue situation of the team, now having a well-traveling rival fan base only 90 miles to the south.  In the Costas administration, the head of the organization would have no indirect benefits from having a team switch locales or leagues.  While this may seem like an outside chance of occurrence, it brings me to my next point.

Bob Costas is well regarded by the league.  Or at least, as a commentator.  In a sport that has so heavily relied on voices, with Vin Scully, Jack Buck, and the like, Bob Costas served as the “voice of a generation” for a short period in the 80s and 90s when NBC had somewhat robust MLB coverage.  Unfortunately now, Costas sits on the fringes of baseball, with a cameo gig at MLB Network, and the center of the NBC Sports lineup, which has not broadcast a Major League Baseball game this century.  His position within the MLB organization (albeit somewhat marginalized on MLB Network) should position him well to take a visible post within the Major League Baseball Executive Suite.

The last item, the “not really an outsider but not really on the inside” element to Bob is surely what will do him in.  The owners will largely want someone who will grow the game’s revenues, which doesn’t necessarily mean “make the sport more enjoyable for the fans.”  The precedent has largely been set, such as the addition of interleague play, which resulted in attendance spikes for the first 5 or so years, but have since returned to pre-interleague levels (outside of a few regional rivalry games).   A short term revenue increase is seen as a valuable addition to the sport, rather than the long term viability of America’s pastime.  This obviously is not a phenomenon unique to baseball, but one with which baseball struggles more than its sporting counterparts.

The only unfortunate thing is for us fans is that the man most suited to resolve those  philosophical struggles is the one most likely to be relegated to covering the appointment of the new Commissioner on the league’s own television network.

 

In fantasy, the 5 categories are meant to evaluate the overall value of a pitcher, and players that are best able to predict future value can win serious jelly beans. A pitcher accumulates Ks by defeating individual batters, while a low WHIP indicates that he can avoid putting opposing players on base. ERA evaluates a pitcher’s run prevention skill. Saves and wins are meant to measure a pitcher’s ability to dominate opposing teams, whether for an inning or an entire game. However, wins compare poorly with quality starts and W+QS when correlated with commonly used pitching statistics.

The chart below shows the correlation between wins, quality starts, and the combination of the two with other commonly used pitcher evaluation metrics. By calculating the correlation between these 3 categories and other pitcher metrics such as FIP, OPS allowed, batting average against, homeruns allowed per 9 innings, and runs above average by the 24 base/out states (RE24), we can measure not only the relationship between the variables, but also how much they differ from each other.

None of these statistics correlate as well with wins as they do with quality starts and W+QS. In fact, the difference between QS and W+QS is negligible in every case. This result makes sense—since QS make up the majority of the W+QS total, the two are almost identical in the chart. The actual values of each correlation are less important that the overwhelming conclusion that wins do not have much to do with pitcher skill, while the difference between QS and W+QS is negligible.

 Why, then, might it be useful to use W+QS? These results show that it may not be very different from using quality starts, but is far more reliable way to judge a pitcher’s performance than wins alone. W+QS double count the games when a pitcher goes somewhat deep into a game, pitches fairly well (3 ER or less), and exits the game while leading his opponent. This scenario might not be much different than the QS by itself, but it does retain an element of “winning the ballgame for your team”, which is what the win category somewhat accurately captures. A winning pitcher is generally on a winning team, although that statement may not mean much.

W+QS may be an unnecessarily complicated way to repeat the same evaluation standards as quality starts, but some players may prefer it simply because it retains the W while relegating it to a position of less importance. Maybe owning a great pitcher like James Shields doesn’t have to be so frustrating after all.

Even as a fan of a different AL East team, seeing Manny Machado go down with a knee injury this Monday saddened me. Fortunately, reports indicate the injury is not as serious as originally feared, and Machado could return for spring training. Machado is part of a class of young stars that have simultaneously taken baseball by storm and wrecked the grading curve for everyone to come after them. People are already giving up on Jurickson Profar because he isn’t a star at an age when most players are in Low-A ball. Bryce Harper ranks in the top 20 in the MLB in wRC+ at the age of 20, and hardly anybody notices.  Anyways, I digress. So where does Machado’s age-20 season rank?

Machado compiled 6.2 WAR in 2013, good for 10th in the MLB. In the last 55 years, only Alex Rodriguez in 1996 and Mike Trout in 2012 have posted a higher WAR in their age-20 season. Of course, there were some better seasons before then, but Machado probably wouldn’t have been allowed to play in those days.

Unlike Rodriguez and Trout, Machado’s offensive numbers, while impressive for a 20 year-old are league average overall. A-rod had a 159 wRC+ in ’96, and Trout had a 166 wRC+ last year. Machado managed a 101 wRC+, providing most of his value with the glove. UZR credited him with 31 runs saved, best in the majors. After a very hot start that was fueled by an inflated BABIP, Machado slowed down.

Month	wRC+	BABIP
Mar/Apr	122	0.355
May	156	0.387
June	107	0.372
July	42	0.210
Aug	122	0.340
Sept/Oct	39	0.227
1st Half	119	0.361
2nd Half	73	0.260

So what can Orioles fans expect from Machado going forward?

Machado is an aggressive contact hitter. His walk rate of 4.1% is one of the lowest in the MLB, and his strikeout rate of 15.9% is well below the MLB average. While Machado will never be Joey Votto, the walk rate will improve as he matures. His minor league walk rate was above 10%. Additionally, Machado should hit for more power. I could just say that he hit 51 doubles and those will turn into home runs. But, that would be lazy, and doubles don’t always turn into home runs as a player develops. Sometimes they turn into singles. Just ask Nick Markakis.

However, there are other reasons to believe Machado will hit for power. First of all, he has excellent bat speed, and there’s no lack of raw power. Some of the home runs he has hit are very impressive. Of the 14, ESPN Home Run Tracker classifies 10 of them as either No Doubters or Plenty.  The average speed off the bat was just a shade behind Robinson Cano. Furthermore, despite playing in one of the best home run ballparks in the league, and having an average fly ball distance on par with Nick Swisher, Machado’s HR/FB ratio of 7.9% is in the bottom third of the MLB. Bet on this ratio improving. While he does have a very high rate of infield flies (9th in MLB), he should be able to bring that down with improved discipline.

Hopefully for Orioles fans and baseball fans, Machado will have a complete recovery from his knee injury. It might be hard to live up to expectations after producing a 6.2 WAR season at age 20, but with improved offense Machado could be up to the task. Expect the plate discipline and power to improve, as the defense inevitably regresses from a season that stretched the upper bounds of UZR. It’s a very small group he’s in, but star players at age 20 tend to be stars at 25.

Throughout the pre-sabermetric revolution days of baseball, the statistics that determine fielding ability (namely errors and fielding percentage) had generated much criticism of fielding stats and undeserving gold glove award winners (Derek Jeter et al), and had kept fielding ability a mystery. However, this mystery in part led to the sabermetric revolution in baseball statistics. In the current day and age, with improved measures of performance available publicly, measuring fielding ability is somewhat less of an enigma, but still far from perfect.

One of the most often used fielding metrics in this day and age is UZR or Ultimate Zone Rating (click the link for an excellent FanGraphs explanation). Instead of counting perceived plays and errors, UZR records every batted ball hit to each of the numerous zones on the baseball field at each trajectory and the runs lost/saved as the fielder gets to the ball or falls short. This is found by matching the average result of the play with the Run Expectancy Matrix. Therefore, UZR provides a very accurate measure of how valuable that fielder was in terms of runs saved/lost over the course of the season.

However, there are major problems with UZR. Sample size issues cause large fluctuations from month to month and even year to year. Moreover, it does not provide a stable basis of fielding ability. Even when all players’ impacts are averaged to a constant, UZR/150, averaged to runs saved/lost per 150 defensive games, the metric is very volatile.

The reasons behind this might actually be easier to identify and correct than you might think. Let’s face it: not all fielders get the same amount of balls hit to them in the same place at the same trajectory within the same number of outs or innings. Infielders with a good knuckleballer on the mound and a slap hitter at the plate are going to get more grounders to each zone than infielders whose teams have fly ball pitchers on the mound and face lots of power hitters at the plate.

However, while the actual amounts may fluctuate from pitcher to pitcher and hitter to hitter, many fielders get a decent sample size of each batted ball to each zone over the course of multiple seasons. Even with a staff of fly ball pitchers, infielders will still handle their fair share of ground balls to each zone over the course of a season. So if there was a way to average all the pitchers and hitters together and measure the value and frequency of making a play in each zone based on the entire AL, NL, or MLB* average batted ball chart, then we could create a similar metric that would be more predictive, rather than purely descriptive.

*The purpose of separating the leagues is the discrepancy of hitting ability with the DH in the AL and the increased frequency of bunts (from pitchers) in the NL.

If we take the average percentage of batted balls to each zone with each trajectory for the AL, NL, or MLB and multiply that by the average runs saved/lost for plays made or missed in that zone, we can find a universal batted ball sample from which to apply the fielders’ impact. While this would not be directly proportional to the runs saved/lost for the fielder during that season for that pitching staff and the batters faced, it would be a metric independent of the impact that the pitcher and hitter has on the fielders. It would measure pure fielding ability over multiple seasons in the form of runs saved, but unbiased by the specific ratio of batted balls per zone and trajectory hit to the fielder over the seasons.

Predictive UZR will have sample size issues but when taken over multiple seasons, a starting fielder should get his fair share of batted balls hit to each zone with each trajectory. The percentages for his success rates at each zone and trajectory can then be applied not to the actual ratio of batted balls per zone hit his way (from his team’s pitching staff and hitters faced) but rather the average ratio of batted balls per zone hit in the entire AL, NL, or MLB.

Both UZR and Predictive UZR are very valuable for different things. UZR is a good reflection of the fielder’s direct impact on defense for the season. However, this might not accurately reflect the fielder’s true talent level because of the assortment of batted balls hit his way. Predictive UZR, while not a concrete reflection of the past runs saved, is a more pure measure of fielding ability. It can provide a number that, when compared to UZR, tells which fielder got lucky and which fielder did not, based on his pitching staff and the hitters faced. Another interesting twist the concept of Predictive UZR brings is that it can be based on the average batted ball chart of teams, divisions, and differing pitching staffs in addition to the AL, NL, or MLB. So a fielder’s projected direct impact, or UZR, can be transferred more easily as he moves from team to team, forming the basis of more accurate fielding projections.

Predictive UZR is not by any means a substitute to UZR, but rather complements it and works with it in intriguing ways. It is a concept worth looking into that has the potential to leave fans, media and front office personnel better informed about the game of baseball.

Nik Oza
Georgetown Class of 2016
Follow GSABR on twitter: @GtownSports

This is part two of a three-part series detailing a method of judging pitch framing based on the prior probability of the pitch being called a strike.  In part 1, we motivated the method.  Here in part 2, we will formalize it.

The formula we’ll use for judging catcher framing is pretty simple on its face. For each pitch delivered, we calculate a value

IsCalledStrike + prob(CalledStrike)

Here, IsCalledStrike is simply 1 if the pitch is called a strike, and 0 otherwise.  The second term is the probability that the pitch would have been called a strike, absent any information about the catcher’s involvement. We add up these values for every called ball or strike that a catcher receives, and report the resulting number.  Since this method is essentially identical to defensive plus/minus, I’ve taken to calling it Catcher Plus/Minus (CPM), although someone reading this can probably come up with something better.  I should mention the following: it has been brought to my attention that this method has been developed before.  However, I can’t find it written up anywhere on the web.  So you are welcome to consider this the documentation of an existing method, if you’d like.

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