Archive for June, 2015

Bogaerts Shoo-in for All-Star Snub

When Xander Bogaerts was a 16-year-old kid playing baseball down in Aruba, looking up to his idol Derek Jeter, I’m sure he too had dreamed of one day playing in an All-Star game. Well, that dream may soon become real for the 22-year-old shortstop.

In 2009, when Bogaerts was just 16-years-old he was offered a professional contract by the Boston Red Sox, a contract that featured a $410,000 signing bonus.

Cruising through the minor leagues, Bogaerts soon became the talk of the town in Boston when he was ranked the 6th overall prospect in baseball by Entering the 2013 season, it wasn’t expected that Bogaerts would see any time at shortstop that season due to the acquisition of Stephen Drew.

He was brought up though, and good thing he was. Bogaerts exceeded most expectations in the 2013 postseason batting .296 in 12 games eventually helping the Red Sox to their third World Series title in 10 years. He returned to his homeland of Aruba and was treated to a red carpet as well as a parade with the natives lining the streets.

In 2014, he was shuffled back and forth from shortstop to third base, messing up his development and also taking a toll on him offensively. He did, however, manage to hit 12 home runs in 144 games. His average though dropped considerably from what many thought it would be. Bogaerts also showed how you can have awesome plate discipline in Triple-A, but it doesn’t always translate right away into your big-league career.



This year, Bogaerts is excelling in all aspects of his position. On offense and defense, the youngster has the temperament like that of a seasoned veteran. And it’s great. He doesn’t take his at-bats into the field and he certainly doesn’t take his fielding miscues (or lack thereof) into the batters box.

In 56 games this season, Xander is batting .297/.343/.401. Not to mention he has been one of the hottest hitters in the game in the last 30 days batting .352/.383/.466 while posting a 137 wRC+ in that span as well. And, Bogaerts also is getting base hits on pitches all over the strike zone. That shows a huge improvement in his plate discipline and pitch-selection skills over the past season.


On the defensive side, Bogaerts has shown a strong improvement from last year when he posted a Defensive Runs Saved of -9. This year, thanks to his offseason work in Arizona with Dustin Pedroia, and also adjusting his footwork with infield coach Brian Butterfield, Bogaerts has a Defensive Runs Saved of -1.

So why isn’t Bogaerts being talked about as if he’s an All-Star? His numbers show that he is certainly All-Star-Game worthy. The top five vote collectors for the All-Star Game at shortstop are Alcides Escobar, Jose Iglesias, Marcus Semien, Jed Lowrie and Jose Reyes. How does this make sense? I’ll tell you — because for some reason, this year in particular, the All-Star Game is a popularity contest, and it’s not based on skill. What do the numbers say to you? They don’t lie.

If it was based on skill, there wouldn’t be seven Kansas City Royals in the lead to start the All-Star Game this year. Pablo Sandoval wouldn’t be third in the AL third baseman voting. David Ortiz wouldn’t be fourth in the DH voting. And Dustin Pedroia most definitely would not be trailing Omar Infante in the second baseman voting. We will wait and see if Bogaerts has a shot at the All-Star Game this year, but if not, we can then tell that this system is flawed and is not based on skill and skill alone.

A Nightmare Scenario for Pedro Alvarez: Playing for Cleveland

With apologies to Clevelanders everywhere, I can think of a number of reasons why Pedro Alvarez would rather be in Pittsburgh than in Cleveland, or C-Town, or The Mistake on the Lake, whichever you prefer. Pittsburgh is located where the first Europeans reached the “Golden Triangle” at the confluence of the Ohio, the Monongahela, and Allegheny rivers. “Golden Triangle” not only sounds very appealing, in a slightly sexual way, but to my knowledge none of the three rivers confluence-ing in Pittsburgh have been on fire like the Cuyahoga River that runs through Cleveland.

Pittsburgh has regularly finished near the top of lists of “Most Livable City in the U.S.”, including last year when the ‘Burgh narrowly beat out Honolulu for the top spot. It was the third time in seven years that The Economist had Pittsburgh at or near the top of the list of the most livable cities in the continental United States. Cleveland, on the other hand, was named the most miserable city in the United States, according to a 2010 poll by

So it’s clear the Pedro Alvarez is quite fortunate to be with the Pittsburgh Pirates as opposed to the Cleveland Native Americans, just for the pure livability factor of the city.

When it comes to baseball, this pattern continues. The Pirates are 31-25 (.554) and in second place in the NL Central. Cleveland is 27-29 and in fourth place in the AL Central (all data is through June 7th). According to the playoff odds at FanGraphs, the Pirates currently have a 67.6% chance of winning their division or being a wild card team. The Indians are at 51.5%. If not for the parity of the American League, this gap would be even greater.

More importantly, and the point of this article, there’s something very specific to Pedro Alvarez that makes it fortunate that he’s with Pittsburgh this year and not Cleveland. Pedro Alvarez does not hit well against left-handed pitching. This is not a major revelation. I think most people reading FanGraphs know that Pedro struggles against lefties. In his career, Pedro has hit .193/.263/.315 against lefties, with a walk rate of 8.3% and strikeout rate of 36.7%. Yikes! His wRC+ against lefties in his career is 61. Against right-handed pitchers, Alvarez has hit .248/.321/.474, with an improved walk rate of 9.5%, a much improved strikeout rate of 26.9%, and a 118 wRC+. For reference, based on his career wRC+, Alvarez hits like the 2015 version of Starling Marte (.256/.317/.473 this year) against right-handed pitchers and like the 2015 version of Lonnie Chisenhall (.209/.241/.345 this year) against lefties. Chisenhall was just sent to the minor leagues. Pedro Alvarez, like former Pirate Andy Van Slyke before him, is fortunate that he lives in a predominantly right-handed world.

Adding to the good fortune for Alvarez this season is the limited number of left-handed pitchers the Pirates have faced. The average team in the Major Leagues has had 24.8% of their plate appearances against southpaws. The Pirates have had the lowest percentage of plate appearances against lefties, just 17.5%. That’s more than one standard deviation below the average. The Cleveland Indians are on the opposite side of the coin, having had 38.0% of their plate appearances against lefties, which is more than two standard deviations above the average. Consider the handedness of the starting rotations of the non-Pittsburgh and non-Cleveland teams in the AL Central and NL Central:

The non-Cleveland teams in the AL Central have 12 right-handed starters and eight left-handers (40% lefties) currently in their starting rotations. The non-Pittsburgh teams in the NL Central have 17 righties and just three lefties currently in their starting rotations (15% lefties).

I was curious how big a difference this would make for Pedro Alvarez, so I decided to look at it in two different ways.

For the first scenario, I took Pedro’s batting line against lefties (20 PA) and righties (173 PA) this year. I figured out what percentage of the team’s plate appearances against each type of pitcher Alvarez has had. I then applied those percentages to Alvarez if he were to play on a team that has seen the league average number of lefties and on the Indians, who have seen the most lefties this year. Here are the results:

Alvarez goes from a .244/.316/.453 hitter with the Pirates to a .220/.289/.403 hitter with the Indians. Not only would he be moving from one of the most livable cities in the U.S. to one of the most miserable, his production would take a big hit. He also loses some playing time because the Pirates have limited him against lefties and the Indians and the league-average team have faced fewer righties.

This leads me to the second scenario. The Pirates have rightly limited the number of plate appearances against lefties for Alvarez this year. Just 20 of his 193 plate appearances have been against southpaws. If Alvarez was transplanted to Cleveland, or to a league-average team, perhaps they would also keep Alvarez riding the pine when a lefty is on the mound. In this second scenario, I moved Alvarez to the Indians and to a league-average team and limited his plate appearances against left-handers to his actual number of 20.

If he were limited to 20 plate appearances against lefties, Alvarez would hit similarly well but would lose playing time. In the case of a hypothetical move to Cleveland, Alvarez would have 41 fewer plate appearances, the equivalent of around 11 games based on his current 3.6 plate appearances per game played.

Pedro Alvarez, you are a fortunate man. Not only do you live in a world with many more right-handed pitchers than lefties, but you also play for a team that has seen more right-handers than any other team in baseball. Now go enjoy the spectacular view of Pittsburgh while riding on the Duquesne Incline, then get a Primanti Brothers sandwich and head over to Point State Park near the “Golden Triangle” and enjoy one of the country’s largest fountains on a beautiful Pittsburgh day.

(I feel like I should maybe apologize to any Clevelanders who might be reading this, but I don’t know if they have Internet in Cleveland yet, so it’s probably not necessary.)

Delino DeShields and the Baseline BABIP for Speedy Players

Delino DeShields currently has a .395 BABIP en route to a .291 average. A .395 BABIP is probably unsustainable, but I was shocked when I saw the Steamer projection of .287 BABIP for DeShields, going forward. A .287 BABIP for a guy like DeShields is just unreasonable. He’s one of the fastest guys in the league, and his baseline BABIP should be well above .300 as he can turn groundouts into infield singles.

So I decided to crunch some numbers, which ultimately confirmed my suspicions. A BABIP of .286 is too low.  Looking at batted ball data, .315 is what I calculated his expected BABIP to be going forward. I’ll explain below:

DeShields has 13.6% infield hit%.

League average is 6.7%.

DeShields is more than twice as likely to get an infield hit, which is 6.9% more likely than average to get a hit in general. As a side note, he’s also 50% on bunting for hits, which is astounding (also more than twice league average).

Baseline BABIP for groundballs is .232

Add a DeShields speed .069 infield groundball advantage, and therefore you’re looking at a DeShields baseline groundball BABIP of .301.

Line drives are the best — .690 baseline BABIP according the source above. Fly balls have .218 baseline BABIP. Speed shouldn’t have much of an effect on these so I’m not adjusting them, other than accounting for infield fly balls which are guaranteed outs.

I’m going to calculate the expected BABIP for DeShields based on the above data. The expected BABIP will equal the summation of the following:

Flyballs — .218 x .261 (26.1% FB, minus the difference between DeShields IFFB and league average, which is .111 minus .095 = .016; .261-.016=.245) = .05341

Groundballs — .301 x .638 (63.8% GB) = .192038

Line Drives — .690 x .101 (10.1% FB) = .06969


We can take that average and take away his strikeouts/walks to determine his expected batting average/OBP going forward.

22.1% Ks. So we’ll take the baseline BABIP multiplied by .779. = .245 expected batting average.

13.1% BBs. So we’ll take the baseline BABIP multiplied by .648 (Ks and BBs out) = .204. Add back the BBs. = .335 expected OBP.

I haven’t even gotten into directional placement of grounders, so it could be true that DeShields is even better than these projections I just calculated.

Regardless, league averages are .252 average and .314 OBP. DeShields is proving to be roughly a league-average hitter by expected batting average, and clearly above-average hitter if you’re looking at expected OBP.

In other words, DeShields is here to stay.

Closer by Conference Committee: The Stats Behind the Congressional Baseball Game

The 2014–2015 offseason was not kind to Mike Doyle. The 10-year manager lost two of his team’s best hitters, and his ace pitcher is coming off shoulder surgery. Meanwhile, his opposite number, Joe Barton, has problems of his own. He has the impossible task of unearthing a pitcher capable of stopping Doyle’s offense, or else face a seventh straight loss to their archrivals in this year’s championship game. Yes, against all odds, and despite all your preconceptions, there’s a lot on the line at this year’s annual Congressional Baseball Game.

There’s plenty of uncertainty about what will happen this Thursday night, when Doyle’s Democrats meet Barton’s Republicans under the Nationals Park floodlights. But one thing we don’t have to be unsure of is the numbers. One year ago, I posted here at FanGraphs about a groundbreaking new dataset: advanced metrics for the most legit office baseball league of all time. (Thanks to those of you who responded favorably—and who didn’t immediately laugh me out of the virtual room. Your reward is 1,500 more words on the subject!)

The CBG’s own mini FanGraphs Leaderboard—looking suspiciously like a Google spreadsheet—is now updated with the past six years of statistics (as always, many thanks to the game’s dedicated scorekeepers who provided the data). Like the real FanGraphs Leaderboard and individual player pages, it is divided into Standard, Advanced, and Value statistics, all calculated according to this site’s official methodology. Figures earlier than 2009 and more advanced than those three sections are sadly unavailable (my FOIA for Pitch F/X data is taking forever…).

Of course, any statistics are meaningless without context, so I’ll give you some. Here’s how the teams break down for what’s likely to be the closest Congressional Baseball Game in years.

Projected Democratic Lineup

Player Slash Line wRC+
SS Tim Ryan .500/.500/.600 130
2B Raul Ruiz .333/.429/.500 107
P Cedric Richmond .833/.882/1.167 238
CF Patrick Murphy .600/.750/1.000 193
LF Jared Polis .583/.600/.750 153
1B Joe Donnelly .286/.412/.357 92
C Chris Murphy .250/.333/.250 67
3B Hakeem Jeffries .333/.333/.333 74
RF Kurt Schrader .500/.667/.500 144

The once-mighty Democratic offense (averaging 15.2 runs per game the past six years) has major holes to fill this year at third base and in the leadoff slot. Since 2009, 3B Tim Bishop and OF Adam Smith have each generated 8 wRC, a mark exceeded by only one other congressional ballplayer; both are gone this year. Bishop, a patient-but-lumbering Adam Dunn–type, was designated for assignment by the voters of New York last November, while veteran tablesetter Smith (.444/.565/.500) is a casualty of hip surgery.

However, that still leaves the Democrats with four elite hitters—the top four, in fact, going by WAR for position players. Florida’s Patrick Murphy (.687 wOBA) and Colorado’s Jared Polis (.556 wOBA) have demonstrated impressive power, while the more speed-dependent Tim Ryan of Ohio feels like a natural successor to leadoff. But these swing-state swingers don’t even play in the same universe as Louisiana congressman Cedric Richmond. The man does everything: walk (29.4% BB%), hit for power (.333 ISO), and, oh yeah, pitch (spoiler alert!; see below). His offensive runs above replacement, at 6.7, is higher than the rest of the Democratic roster combined (6.0). It’s little wonder that GOP manager Barton opted to intentionally walk him three times in last year’s game. When a guy’s slugging percentage (1.167) indicates he averages over a base per plate appearance, he probably deserves a free pass every time he’s up this year.

Beyond the starting nine, the Democrats have a few nice complementary pieces off the bench. Pinch-running artist Eric Swalwell has scored five runs and stolen five bases in just two games, causing him to lead the league in wSB and Base Running value. Jersey number IX (for Title IX) Linda Sánchez, the only woman on either roster, is a feared pinch-hitter with her .857 OPS.

The one weak spot in the order—as in many an MLB lineup—may be catcher, where Connecticut Senator Chris Murphy has OPSed just .583 since 2009. However, his job is safe, as Democratic coaches swear by his defense and game-calling ability. Defense has been a team-wide Democratic focus during their current winning streak; the team hasn’t made an error in its last two games. More tellingly for the FanGraphs crowd, Republican batsmen have a .338 BABIP off Dem pitcher Richmond—pretty low for a league of 50-year-old fielders covering a big-league-sized field. (By comparison, Democratic hitters have a .476 BABIP the past six years, reflecting a less polished GOP defense.)

Projected Republican Lineup

Player Slash Line wRC+
3B Jeff Flake .286/.286/.500 85
2B Kevin Brady .313/.421/.375 95
P John Shimkus .429/.429/.429 99
SS Steve Scalise .500/.750/.500 156
RF Bill Shuster .235/.263/.294 58
1B Tom Rooney .167/.167/.250 39
LF Dennis Ross .111/.200/.111 32
C Rodney Davis .250/.400/.250 82
CF Rand Paul .200/.200/.200 144

Democrats may aspire to switch places with the majority GOP in the halls of Congress, but they’d never trade their baseball lineup for this one. Yet Republicans aren’t as bad as they look; our six years of data overlap neatly with their six-year losing streak, and those wRC+ numbers are dragged down by an overall offensive environment grossly inflated by Democratic blowouts.

The GOP’s one hitter who rates above even that lofty baseline is Majority Whip Steve Scalise of Louisiana. One of the Republicans’ hardest-working players, Scalise has forced his way into the starting lineup after years as a bench player with the league’s fifth-highest wRAA—behind only the Democrats’ four elite sluggers. Texan Kevin Brady and Illinois’s John Shimkus have played in the CBG since the 1990s, when their stellar play (in 1997, Shimkus hit the game’s most recent over-the-fence home run) fueled a 12-year Republican dynasty. The grizzled veterans may have lost a step since then, but they have slumped less than the Republicans’ other players. Finally, Senator Jeff Flake, like former fellow Arizonan Mark Trumbo, has a real gift for power (.214 ISO) but doesn’t get on base well (zero walks in his last 14 plate appearances). It makes him a curious choice for leadoff—one that Barton will hopefully reconsider in 2015.

The bottom of the lineup drops off sharply and features the bottom three CBG players by WAR. Bill Shuster, Tom Rooney, and Dennis Ross each clock in at –0.2 wins above replacement, although bad luck has been a factor. Ross, who represents the Tigers’ spring training home of Lakeland, FL, sports an unfortunate .167 BABIP and has at least displayed the ability to draw a walk (10% walk rate). Yet putting the ball in play at all has proven to be a problem. In 41 combined plate appearances, Shuster, Rooney, and Ross have combined for 10 whiffs. (By contrast, in 40 plate appearances of their own in the Congressional Baseball Game, Democrats Murphy, Polis, and Richmond have never struck out.)

A poor eye is a theme for Republican hitters. Their active roster has a 7.1% walk rate and a 27.6% strikeout rate; that’s bad even if you’re facing Major League pitching, let alone the still-good-but-not-Clayton Kershaw Democratic staff. Barton should be preaching patience to his team, noting that, in last year’s game, Democrats actually had more walks than hits en route to 15 runs.

Projected Democratic Pitchers

Player ERA FIP K/7 BB/7
RHP Cedric Richmond 2.59 5.64 9.85 2.59

For four years running, only one man has taken the hill for the Democrats—and one is all they’ve needed. The team’s best hitter, Richmond, is also their workhorse pitcher, and he is in absolute control of the game when he’s on. An unparalleled two-way threat, Richmond has a total WAR (combining offensive and pitching value) of 1.5—in just four games! In four complete games pitched (caveat: the Congressional Baseball Game is seven innings long, not nine), he has taken a no-hitter into the final inning as well as thrown a shutout (and that was in two separate games). His Game Scores by year have been 77, 55, and 76 before dipping to 33 last year. Ominously, Richmond was pitching through an injury last year, and he is still recovering from November shoulder surgery here in 2015. The GOP will take another game like last year’s, when they were able to hang six runs on him, while Democrats are just holding their breath for the long-term health of their 41-year-old ace—still a spring chicken by CBG standards.

Projected Republican Pitchers

Player ERA FIP K/7 BB/7
RHP John Shimkus 8.08 7.30 4.04 3.23
RHP Pat Meehan 7.74 7.53 11.05 7.74
RHP Marlin Stutzman 14.44 9.71 7.88 9.19

To put it gently, the Republicans are better at twirling government shutdowns than shutdown innings. Though their hitting may not be top-shelf, that’s not their real obstacle in trying to reclaim congressional bragging rights; their (in)ability to get Democrats out is.

It’s unclear whom Barton will tap to start the 2015 game. Pennsylvania righty Pat Meehan has an impressive strikeout rate but a scary walk rate, and he has only ever been used in relief. Marlin Stutzman of Indiana probably won’t get a second chance after giving up six runs and only getting four outs in his 2014 start. I endorsed him for the start last year on the strength of a good K-BB%, but I fell victim to small sample size; he now has thrown more balls than strikes in his (slightly longer) career.

For whatever my recommendation is worth nowadays, the Republicans should start Shimkus. As unsightly as that FIP is, a 100 FIP- tells us that it’s actually league average (remember, this is a really hitter-happy league). He’s also the only GOP hurler with good command—his 7.8% BB% is even lower than Richmond’s. Like many ageing pitchers, he’s reinvented himself as a control artist who doesn’t miss many bats (9.8% K%). As with his offense, Shimkus used to be more dominating on the mound; he pitched the Republicans to multiple wins in the mid-2000s. If Barton does indeed give Shimkus the ball on Thursday, he’ll see a very different approach, but he hopes it can still add up to the same old result.

The Curious Case of Alex Guerrero

June is here and summer has been kicked into full swing. And of course you can’t have summer without baseball and with about a third of the season gone, we now have an idea of how the year is shaping up. There have been some surprises — at the beginning of the year many were wondering if Bryce Harper would regress even more, and of course they’re not talking about that now. Many had A-Rod not producing at all but so far this year, he’s returned to A-Rod form. We have rookie sensations who are delivering right away in Joc Pederson and Kris Bryant but there’s another rookie who has put up great numbers but hasn’t seen the same hype or support from analyst and in ways, even his team, that the others have. I’m talking about Alex Guerrero of the Los Angeles Dodgers of course. Technically a rookie with clause in his contract that keeps him from being sent to the minors, at the beginning of the year some thought it would hurt the Dodgers to have Guerrero on the roster but so far he’s been an offensive surprise (and for not playing third base or the outfield much, defensively he’s done better then let’s say former Dodger, Hanley Ramirez.) So what I want to know is, where’s the love for Alex Guerrero?

After filling in at third for an injured Juan Uribe, Guerrero quickly impressed with his bat going 4-10 with one homer and six RBI. Once Uribe came back however, Guerrero was relocated to do what some consider to be one of the hardest things to do it in sports, pinch-hit. It didn’t seem to stop Guerrero who continued to hit, going 3-5 in a five-game stretch, hitting two homers with five RBI. It was easy to understand everyone’s apprehension when Guerrero came out hitting this way. He was operating at a Superman-like pace and the logical thought would be he’d eventually come back down to earth, so neither analysts nor even the Dodgers themselves fully committed to Guerrero. The Dodgers also had a clubhouse favorite and adequate third baseman in Uribe, a full outfield and a deep bench; it seemed like there was no place for Guerrero in the starting lineup. So as April turned to May, Guerrero would find himself jumping all around the left side of the field, playing third, left field, and of course, pinch-hitting. It still didn’t seem to stop Guerrero. From April 23-May 13, when Carl Crawford went on the DL, he hit .310 with three homers. He did have, as many predicted, a drop-off in production, but still put up numbers that warranted playing time and with the injury to Crawford, it seemed like he would have just that.

Guerrero is a swinger. It’s hard to say he’s a free swinger because he seems to have a pretty good understanding of the strike zone. He doesn’t walk much or steal bases and in the baseball world that generally doesn’t result in runs scored. But I’d look at where he’s batting in the Dodgers lineup to explain some of his less appealing numbers. In 2015 he’s batted fifth six times with Ethier, Heisley, Grandal and Van Slyke batting behind him. He’s batted sixth eight times, seventh eight times, eighth six times, and pinch-hit nine times. He’s never started in the top part of the order.

That seems odd for a guy who has put up the offensive numbers Guerrero has. Joe Maddon has made waves this season batting his pitchers eighth. One of his reasons is to get the nine-hole hitter better pitches to see in order to get on base and turn the lineup over to their best hitters. I’m not suggested the Dodgers bat their pitchers eighth but I do think Guerrero would benefit from having the production of someone like Adrian Gonzalez behind him. Forcing pitchers to challenge Guerrero in the strike zone in order to hopefully keep him off base and minimize any damage Gonzalez may inflict. Guerrero is definitely susceptible to the slider off the plate but I wonder if he would see less of those if he were batting third?

And although Guerrero swings a lot, 60.3% of the time to be exact, he’s also got a contact percentage of 77.9% better then Josh Donaldson, Paul Goldschmidt and Joc Pederson. And when Guerrero does make contact, he is generally hitting the ball hard, with an ISO of .371, second only to Bryce Harper. Guerrero is averaging a home run every 10.8 at bats. The Dodgers lead the majors with 23.7 at bats per home run but they’re also second in the league with 21 solo home runs — Guerrero has hit three of them. It’s obvious the Dodgers have a good offense but I wonder if it’s as productive as it could be and I wonder if Guerrero can play a bigger role?

Another reason for apprehension with Guerrero is the sample size we have. Guerrero didn’t put up these numbers in the minors and many didn’t expect him to contribute the way that he has in the show. All that leads to doubt from the outside. Guerrero has about 100 fewer at bats that the top hitters in the league. That being said however, it’s interesting to note how similar they are anyway. When added to the top hitters in the league, Guerrero is fifth in wOBA, third in SLG and as I mentioned before second in ISO.

With the rate that Guerrero is on, if he gets another 300 at bats would be 37 HR/ 59 R/ 93 RBI. If he got another 400 at bats it would be, 46 HR/ 74 R/ 116 RBI. As realistic or unrealistic as the projections may be, Guerrero even with a regression can put up solid major-league numbers. Would anyone say no to 25 homers and 80 RBI? I think the answer to the season-ending stats lie in how the Dodgers choose to handle the situation. They’ve already dealt Uribe to free up third base and with Crawford being moved to the 60-day DL, it looks like left field is Guerrero’s for the summer. But Yasiel Puig is coming back soon and Ethier has been playing better then expected this year, so is Mattingly going to platoon Ethier and Guerrero in left?

In many ways this is a great problem to have for the Dodgers — they’re a veteran team that wants to win now and having a versatile bench helps shift people around and keep everyone healthy. That being said, this is baseball and with the trade deadline less then two months away and the Dodgers with a beat-up starting rotation, who’s to say some of that offensive depth can’t be flipped for some pitching help? The question then becomes, who gets traded? But that’s a topic for another day. Until then we’ll just have to hope Mattingly and the Dodgers give Guerrero a chance in the top part of the order.

The Mariners Need to Help Robinson Cano Help Himself

The struggles of Robinson Cano in 2015 have been talked about frequently, especially as the Mariners’ struggles continue. Recently, Mariners hitting coach Howard Johnson suggested that Cano is pressing at the plate. Cano disagreed with the assessment, but the numbers back up Johnson.

The good news is that when Robinson Cano is making contact, it’s been pretty good. Cano is hitting the ball harder than he has over his career. His hard hit percentage is 35.2%, compared to his career 32.9% mark.  The 24.4% of line drives on batted balls would be the third highest mark of his career, exceeding his 21.4% career average.

The bad news is where Cano is hitting the ball.  Cano is hitting out of character. In particular, Cano has had some difficulty, or aversion, to hitting the ball to the opposite field. The chart below shows Cano’s 2015 batted-ball locations and his career batted-ball locations.

Contact Location Pull% Cent% Oppo%
2015 38.6% 42.0% 19.3%
Career 37.5% 35.7% 26.8%

This is a big issue because he is muting his best hitting ability. Cano is a .369 hitter when hitting the ball to the opposite field. Last year he hit .417 when going the opposite way; in 2013 he hit .455. This year he is hitting .303, but he is not giving himself the opportunities to take advantage of the success that has been consistent throughout his career and stellar in his most recent seasons.

The impact of this shift can be displayed by taking Cano’s 174 plate appearances in which he has not walked or struck out, and allocating the results of where the ball is hit by his career average Pull%, Center%, and Oppo%. I then applied his career batting averages for the batted ball location to those figures.

Batted Ball Location Career Batted Ball Location Averages Batted Ball  Location At Bats Ending in Batted Ball Loaction Career Batting Average in Batted Ball Location Projected Hits in Batted Ball Location
Pull 37.5% Pull 65 .327 21
Center 35.7% Center 62 .370 23
Opposite 26.8% Opposite 47 .369 17

The following would be the resulting average on batted balls, batting average, and on-base percentage based upon Cano’s 40 strikeouts and 12 walks:

Average on Batted Balls 0.354
Batting Average 0.290
On Base Percentage 0.327

These numbers are good, but they are still not remarkable, and they don’t look like the numbers we would expect from Cano.

This leads to Cano’s second issue: increased strikeouts. Cano’s 17.5% strikeout rate is well above his career average of 11.2%.

The Baseball Info Solutions Plate Discipline data shows two figures that stand out. (1) Cano’s Contact% is down 3.9% from his career average and (2) Cano is seeing 5.4% more first-pitch strikes than he has over his career.

Contact% F-Strike%
2015 82.7% 65.9%
Career 86.8% 60.5%

Lets start with the second figure. This is nothing Cano has control over and the cause is almost certain to be the presence of Nelson Cruz behind him in the lineup. But how can Cano adjust to this? He’s a batter that’s used to being pitched carefully, particularly last year, when he was a hitting oasis in the desert that was the Mariners’ lineup.

The first figure, Cano’s drop in Contact%, may be tied back to where this article started and the point mentioned above: hitting approach and batting count. Cano has performed pitifully when facing sliders and changeups this year, two pitches he has handled well over his career (see the chart below displaying Baseball Info Solution’s runs above average/100 pitches for each pitch type Cano has faced). This makes sense if he is seeing pitches behind in the count, and if he is aggressively seeking to pull the ball, for additional power; to be worth $24 million a year, or whatever reason that may be causing the change in hitting approach.

2015 -0.44 -1.71 -1.46 1.92 -4.07 3.67 -4.66
Career 0.65 1.58 -0.3 1.65 1.65 1.65 0.66

Howard Johnson is probably right. Robinson Cano is pressing. Cano needs to approach at-bats like he has his whole career and he’ll see a return to what we would expect from Robinson Cano. However, the Mariners can make it easier on him by changing up the order. Maybe Cano isn’t a hitter that thrives on being pitched to. It may benefit the Mariners to swap Cruz and Cano in the order. While Cruz has been great, the Mariners and Cano have been the opposite. A change couldn’t hurt.

But first, Robinson Cano needs to accept the hitter he is, because that hitter is very good.

Hardball Retrospective – The “Original” 2002 Toronto Blue Jays

In “Hardball Retrospective: Evaluating Scouting and Development Outcomes for the Modern-Era Franchises”, I placed every ballplayer in the modern era (from 1901-present) on their original team. Therefore, Jim Edmonds is listed on the Angels roster for the duration of his career while the Astros declare Rusty Staub and the Athletics claim Lefty Grove. I calculated revised standings for every season based entirely on the performance of each team’s “original” players. I discuss every team’s “original” players and seasons at length along with organizational performance with respect to the Amateur Draft (or First-Year Player Draft), amateur free agent signings and other methods of player acquisition.  Season standings, WAR and Win Shares totals for the “original” teams are compared against the “actual” team results to assess each franchise’s scouting, development and general management skills.

Expanding on my research for the book, the following series of articles will reveal the finest single-season rosters for every Major League organization based on overall rankings in OWAR and OWS along with the general managers and scouting directors that constructed the teams. “Hardball Retrospective” is available in digital format on Amazon, Barnes and Noble, GooglePlay, iTunes and KoboBooks. The paperback edition is available on Amazon, Barnes and Noble and CreateSpace. Additional information and a discussion forum are offered at

Don Daglow (Intellivision World Series Major League Baseball, Earl Weaver Baseball, Tony LaRussa Baseball) contributed the foreword for Hardball Retrospective. The foreword and preview of my book are accessible here.


OWAR – Wins Above Replacement for players on “original” teams

OWS – Win Shares for players on “original” teams

OPW% – Pythagorean Won-Loss record for the “original” teams


The 2002 Toronto Blue Jays         OWAR: 51.4     OWS: 312     OPW%: .572

GM Pat Gillick acquired 65% (35/54) of the ballplayers on the 2002 Blue Jays roster. 43 team members were drafted by the club. Based on the revised standings the “Original” 2002 Blue Jays captured the American League Eastern Division title by nine games over the New York Yankees and topped the Junior Circuit in OWAR and OWS.

The middle of the Blue Jays’ batting order was stacked. Shawn Green (.285/42/114) scored 110 runs and placed fifth in the MVP balloting. Jeff Kent (.313/37/108) laced 42 doubles and recorded a career-best in home runs. Carlos Delgado tallied 103 runs scored and blasted 33 round-trippers in the midst of a ten-year streak with at least 30 home runs per season (1997-2006). John Olerud (.300/22/102) rapped 39 two-base knocks and garnered his second Gold Glove Award. Shannon Stewart contributed a .303 BA and registered 103 tallies from the leadoff spot. Alex S. Gonzalez slashed 27 doubles and clubbed 18 circuit clouts while fellow shortstop Chris Woodward batted .276 with 13 dingers. Vernon Wells produced a .275 BA with 23 four-baggers and 100 ribbies.

Kent placed 48th at the keystone position in “The New Bill James Historical Baseball Abstract” and Olerud ranked 53rd among first sackers.

Shannon Stewart LF 2.37 18.47
Alex Gonzalez SS 2.78 14.36
Shawn Green RF 6.18 32.07
Jeff Kent 2B 6.04 29.93
Carlos Delgado DH/1B 4.76 25.97
John Olerud 1B 4.64 25.92
Vernon Wells CF 0.83 16.7
Greg Myers C 0.57 5.57
Chris Stynes 3B -0.02 3.46
Chris Woodward SS 2.17 11.74
Josh Phelps DH 1.46 9.8
Orlando Hudson 2B 1.17 5.89
Craig Wilson RF 0.95 10.78
Jay Gibbons RF 0.59 11.97
Ryan Thompson LF 0.14 2.84
Felipe Lopez SS 0.08 5.8
Pat Borders DH 0.06 0.36
Abraham Nunez 2B 0.04 4.88
Casey Blake 3B -0.11 0.11
Kevin Cash C -0.14 0.08
Mike Coolbaugh 3B -0.17 0.16
Brent Abernathy 2B -0.44 4.99
Michael Young 2B -0.63 10.72
Cesar Izturis SS -0.68 3.77
Joe Lawrence 2B -0.83 1.48

Roy “Doc” Halladay (19-7, 2.93) led the American League with 239.1 innings pitched and merited the first of eight All-Star invitations. David “Boomer” Wells equaled Halladay’s win-loss record. Billy Koch amassed 11 victories and saved 44 contests while Jose Mesa closed out 45 games with a 2.97 ERA. Steve Karsay (3.26, 12 SV) and Ben Weber (2.54, 7 SV) provided solid relief in the late innings.

Roy Halladay SP 6.74 21.67
David Wells SP 3.99 14.79
Woody Williams SP 3.2 9.65
Mark Hendrickson SP 1.23 4.01
Chris Carpenter SP 0.41 2.73
Steve Karsay RP 2.01 11
Billy Koch RP 1.44 18.37
Ben Weber RP 1.33 10.48
Jose Mesa RP 1.28 12.4
David Weathers RP 1.02 6.68
Mike Timlin RP 1 8.04
Giovanni Carrara RP 0.62 6.77
Kelvim Escobar RP 0.53 9.14
Carlos Almanzar SW 0.24 0.94
Jim Mann RP 0.18 1.02
Jose Silva RP 0.11 1.38
Brian Bowles RP 0.04 1.37
Gary Glover SP 0.03 4.54
Mark Lukasiewicz RP 0 1.17
Aaron Small RP -0.08 0
Pasqual Coco RP -0.13 0
Tom Davey RP -0.36 0.17
Todd Stottlemyre SP -0.38 0
Scott Cassidy RP -0.43 1.67
Mike Smith SP -0.45 0
Bob File RP -0.47 0
Graeme Lloyd RP -0.53 1.89
Pat Hentgen SP -0.54 0
Brandon Lyon SP -0.56 0

 The “Original” 2002 Toronto Blue Jays roster

NAME POS WAR WS General Manager Scouting Director
Roy Halladay SP 6.74 21.67 Gord Ash Bob Engle
Shawn Green RF 6.18 32.07 Pat Gillick Bob Engle
Jeff Kent 2B 6.04 29.93 Pat Gillick
Carlos Delgado 1B 4.76 25.97 Pat Gillick
John Olerud 1B 4.64 25.92 Pat Gillick
David Wells SP 3.99 14.79 Pat Gillick
Woody Williams SP 3.2 9.65 Pat Gillick
Alex Gonzalez SS 2.78 14.36 Pat Gillick Bob Engle
Shannon Stewart LF 2.37 18.47 Pat Gillick Bob Engle
Chris Woodward SS 2.17 11.74 Pat Gillick Bob Engle
Steve Karsay RP 2.01 11 Pat Gillick
Josh Phelps DH 1.46 9.8 Gord Ash Tim Wilken
Billy Koch RP 1.44 18.37 Gord Ash Tim Wilken
Ben Weber RP 1.33 10.48 Pat Gillick Bob Engle
Jose Mesa RP 1.28 12.4 Pat Gillick
Mark Hendrickson SP 1.23 4.01 Gord Ash Tim Wilken
Orlando Hudson 2B 1.17 5.89 Gord Ash Tim Wilken
David Weathers RP 1.02 6.68 Pat Gillick
Mike Timlin RP 1 8.04 Pat Gillick
Craig Wilson RF 0.95 10.78 Gord Ash Bob Engle
Vernon Wells CF 0.83 16.7 Gord Ash Tim Wilken
Giovanni Carrara RP 0.62 6.77 Pat Gillick
Jay Gibbons RF 0.59 11.97 Gord Ash Tim Wilken
Greg Myers C 0.57 5.57 Pat Gillick
Kelvim Escobar RP 0.53 9.14 Pat Gillick Bob Engle
Chris Carpenter SP 0.41 2.73 Pat Gillick Bob Engle
Carlos Almanzar SW 0.24 0.94 Pat Gillick
Jim Mann RP 0.18 1.02 Pat Gillick Bob Engle
Ryan Thompson LF 0.14 2.84 Pat Gillick
Jose Silva RP 0.11 1.38 Pat Gillick Bob Engle
Felipe Lopez SS 0.08 5.8 Gord Ash Tim Wilken
Pat Borders DH 0.06 0.36 Pat Gillick
Brian Bowles RP 0.04 1.37 Pat Gillick Bob Engle
Abraham Nunez 2B 0.04 4.88 Pat Gillick Bob Engle
Gary Glover SP 0.03 4.54 Pat Gillick Bob Engle
Mark Lukasiewicz RP 0 1.17 Pat Gillick Bob Engle
Chris Stynes 3B -0.02 3.46 Pat Gillick Bob Engle
Aaron Small RP -0.08 0 Pat Gillick
Casey Blake 3B -0.11 0.11 Gord Ash Tim Wilken
Pasqual Coco RP -0.13 0 Pat Gillick Bob Engle
Kevin Cash C -0.14 0.08 Gord Ash Tim Wilken
Mike Coolbaugh 3B -0.17 0.16 Pat Gillick
Tom Davey RP -0.36 0.17 Pat Gillick Bob Engle
Todd Stottlemyre SP -0.38 0 Pat Gillick
Scott Cassidy RP -0.43 1.67 Gord Ash Tim Wilken
Brent Abernathy 2B -0.44 4.99 Gord Ash Tim Wilken
Mike Smith SP -0.45 0 Gord Ash Tim Wilken
Bob File RP -0.47 0 Gord Ash Tim Wilken
Graeme Lloyd RP -0.53 1.89 Pat Gillick
Pat Hentgen SP -0.54 0 Pat Gillick
Brandon Lyon SP -0.56 0 Gord Ash Tim Wilken
Michael Young 2B -0.63 10.72 Gord Ash Tim Wilken
Cesar Izturis SS -0.68 3.77 Gord Ash Tim Wilken
Joe Lawrence 2B -0.83 1.48 Gord Ash Tim Wilken

Honorable Mention

The “Original” 2001 Blue Jays           OWAR: 51.5     OWS: 297     OPW%: .547

Toronto outpaced Boston to claim the A.L. East by a four-game margin. Shawn Green dialed long distance 49 times and plated 125 baserunners. John Olerud (.302/21/95) earned his second All-Star nod. Carlos Delgado launched 39 moon-shots and Jeff Kent drilled a career-high 49 two-baggers.

On Deck

The “Original” 1953 Braves

References and Resources

Baseball America – Executive Database


James, Bill. The New Bill James Historical Baseball Abstract. New York, NY.: The Free Press, 2001. Print.

James, Bill, with Jim Henzler. Win Shares. Morton Grove, Ill.: STATS, 2002. Print.

Retrosheet – Transactions Database

Seamheads – Baseball Gauge

Sean Lahman Baseball Archive

Selling David Price

I’ve been thinking about this one a lot, and I think people in general still view Price as a top-10 pitcher. I’ve seen him appearing in expert lists as such, and that’s the general vibe I’ve gotten from the fantasy community. I just think top-10 at this point is too high, especially when we’ve got such talented young stars ranked below him, both according to the expert lists and public perception (I’m talking about guys like Archer, deGrom, and Cole).

I’d actually have him closer to top 20-25 at this point (there are so many great pitchers). His K/9 has plummeted to 7.6 and K-BB% has fallen nearly nine percentage points to 14.3%.

At 14.3%, David Price is the No. 39 pitcher in the league in K-BB%.

Am I putting too much stock into a small sample, or has the decline begun, but people haven’t realized it yet (he still sports a solid 3.15 ERA)?

His peripherals also support his regression, as his xFIP is 3.94 and SIERA is 3.87.

Encouraging signs: FIP still has him at 3.27. Swinging strikes are similar to last year at 10.4% (only 0.2% difference). No velocity loss — in fact, his fastball is faster this year than last year.

Over his career, however, Price has been only slightly better than average at giving up/suppressing home runs, so I think xFIP and SIERA are the better ERA estimators than FIP. League average HR/FB is 10.8%, and Price was at 9.7% last year, 8.6% in 2013, 10.5% in 2012. So he may be slightly better than average, but unlikely to maintain 6.6% going forward.

It’s also worth noting that last year’s 9.8 K/9 was a career high. In 2013, he had a 7.3 K/9. From 2010-2012 his K/9 hovered in the 8s (and in 2008 and 2009 his K/9 was also sub-8, although I don’t give any weight to that at all as he was still developing as a pitcher). It could be that his high K/9 last year was an aberration.

I’m choosing to give weight to his current K-rate and peripherals (the sample size is now significant), while accounting for some improvement (this is David Price after all). Doing that, by my rough calculations, I’m looking at about a 3.5+ ERA ~8 K/9 pitcher going forwards.

Those are quality numbers, but not top-10 numbers, which is where people still value him. I’d flip Price for any top-20 pitcher with upside in an instant.

I don’t have an answer as to why the K-rate has plummeted so far. I did take a look at his usages, and he seems to have reduced the usage of his two-seam fastball. His entire career, that has been his most-used pitch. Last year he used it 40%. This year, he’s only throwing it 23% of the time, instead favoring a four-seam fastball as his dominant pitch. I believe this *may* be related to his K-rate drop, but it’s just an observation at this point. Regardless, we’ve reached the point in the season where it might be wise to be proactive.

Don’t Blame the Red Sox Trouble All On the Starters

A lot has been made of the Red Sox inability to win games after they spent $245 million this offseason on a bunch of hitters and middle-of-the-rotation starters. The Red Sox were unable to sign Jon Lester, and they made almost no effort to replace him in the rotation. Things have come to a front after Koji Uehara blew a save on Sunday to end a six-loss road trip at the hands of the Twins and Rangers.

With no defined ace in the squad, the Red Sox starting pitching has come under fire. In fact only the Blue Jays have a worse team ERA in the AL.[1] The pitcher from the Red Sox opening day roster with the lowest ERA is Clay Buchholz at an unsavory 4.33 and Justin Masterson’s is the worst at 6.37. The Sox won’t even sniff the playoffs if they don’t sort out their pitching situation, but I think the Red Sox starting pitchers have come under an unfair amount of criticism.

The Red Sox starting pitchers have had some horrendous outings, but despite their heinous ERAs the Sox starters have managed to put together 24 quality starts, a mark equal to the average in the AL and just one below the MLB average. Obviously quality starts are not a perfect metric for starting pitching, but considering the pre-season expectations for the Sox starting pitchers, being league average in keeping the team in the game (the basic idea behind quality starts) is not so bad.

In games where the Red Sox starter throws a quality start, the Sox are 14-10 (58%). Based on stats from all the quality starts from 1947-2006 the average team wins quality starts 67% of the time. At the current rate, the Red Sox will win 44/76 games in which their starters throw quality starts, seven games worse than they would if they won quality starts at the league average. In the worst AL East in recent memory, seven wins could make the difference for a Red Sox team that has struggled in the first third of the season.

What remains to be seen is if the bullpen or the batting lineup lets the starters down. The Red Sox bullpen has pitched 49 innings in games and the guys out of the pen have shone in those moments. The Red Sox bullpen has a 2.39 ERA and 1.04 WHIP in those games.[2] That compares favorably to the league averages out of the bullpen of 3.52 ERA and 1.27 WHIP. Koji Uehara has blown saves in a couple of these games, most recently on Sunday, but on the whole the bullpen pitchers have done very well protecting their starters’ quality starts.

The Red Sox were banking on being above average in their ability to carry their pitchers, but when their pitchers put them in a chance to win, they perform worse than the league average. In their 24 quality starts the Red Sox have averaged 3.75 runs per game. That’s close to the MLB average 4.14 runs per game, but not quite cracking the average is embarrassing for a lineup that was supposed to carry the team.

What’s more, the runs-per-game mark is buoyed by four outings of 8 runs or more (8, 8, 8, 9). If you exclude those four games, the Red Sox average only 2.75 runs per game, simply unacceptable for a team with playoff aspirations. In Red Sox quality starts, Red Sox batters have a weak 0.254/0.322/0.386 triple slash[3] and 0.249 batting average with RISP. Again this compares poorly with the MLB averages: 0.251/0.314/0.395 triple slash and 0.257 average with RISP.

Before the season ZiPS projected[4] the Red Sox batters would have a 0.265/0.333/0.407 triple slash. Until the Red Sox begin to bring their collective triple slash up to that level, particularly in games which their starters put together quality starts, they will continue to flounder at the bottom of the AL East. Paul Sporer and Eno Sarris pointed out the Red Sox failures at the plate in the May 28 episode of The Sleeper and the Bust. As the season goes on, analysts should follow their lead and consider the failures of the Red Sox batting order in addition to criticizing the low-hanging fruit that is the Red Sox starting rotation.



[1] All stats from ESPN unless otherwise noted. All stats are as of 6/1/15.

[2] As far as I could find there was no data complied on the Red Sox stats during quality starts so I compiled the statistics myself here.

[3] While this is bad, the Red Sox actually hit better in quality starts than on average – their triple slash for the season is 0.241/0.315/0.369. If I were arguing that the Red Sox are in last place because of their offense’s inability to perform in the same games that their pitchers do well, this stat would ruin my argument. However, since I am just using the stats in games when pitchers do well to highlight the fact that the pitchers get too much of the criticism, I feel that my argument is not undermined.

[4] I aggregated the zips projections of every players zips projected to get more than 25 at bats for this stat.

Using Batted-Ball Data to Measure Hitter Performance

Imagine a batter hits a long fly ball that’s destined for the right-field seats only for the outfielder on the other team to clear the wall and rob him of his home run. In traditional stat sheets, this is treated the same way as any other out and there’s no real way of distinguishing that from a dribbler down the third-base line. But intuitively we know that these are two very different things, and a batter who does more of the first is going to end up being more valuable than one who does more of the second. Thus, if we wanted to truly measure how well a player has performed, we need to separate the performance from the results. The best way of doing that is to break down a batted ball in the most granular way possible and look at the average performance for similar batted balls, and today I’ll reveal a personal tool to do this. This work was inspired by Tony Blengino’s terrific posts on batted-ball data, and I suggest reading his introductory post as background on the theory and methodology that I employ.

This tool uses information on the type, velocity, direction, and distance of a hitter’s batted balls to calculate an expected AVG, OBP, and SLG for him. It divides batted balls into buckets based on the type (GB, FB, LD, PU) and either the direction and velocity or the direction and the distance and calculates the resulting AVG and SLG for all batted balls that meet that criteria. It then goes through all of a batter’s plate appearances and uses these data to calculate both the observed and expected AVG/OBP/SLG for each PA. The table below shows the top 30 hitters by Expected wOBA (xwOBA) as of 5/26/2015.

Bryce Harper 151 191 89 0.331 0.471 0.722 0.505 29.1 0.298 0.445 0.650 0.467 23.3
Miguel Cabrera 164 195 93 0.341 0.446 0.610 0.453 21.7 0.304 0.415 0.665 0.457 22.3
Prince Fielder 182 199 93 0.363 0.417 0.571 0.425 17.7 0.349 0.404 0.640 0.443 20.5
Mike Trout 168 194 92 0.298 0.392 0.548 0.404 14.0 0.321 0.412 0.615 0.438 19.3
Anthony Rizzo 161 197 88 0.311 0.437 0.565 0.433 18.7 0.304 0.431 0.589 0.438 19.6
Ryan Braun 154 173 94 0.266 0.347 0.532 0.376 8.7 0.298 0.375 0.661 0.436 16.9
Paul Goldschmidt 160 190 93 0.338 0.442 0.631 0.459 22.0 0.290 0.402 0.615 0.433 18.1
Adrian Gonzalez 158 179 89 0.342 0.419 0.620 0.443 18.5 0.322 0.401 0.614 0.432 16.9
Todd Frazier 164 187 92 0.256 0.348 0.549 0.382 10.4 0.304 0.390 0.620 0.429 17.2
Yasmani Grandal 104 124 95 0.288 0.403 0.462 0.379 6.6 0.310 0.421 0.574 0.428 11.3
Brandon Crawford 151 170 93 0.298 0.376 0.510 0.383 9.5 0.316 0.393 0.608 0.426 15.2
Brandon Belt 139 156 93 0.302 0.378 0.496 0.379 8.2 0.316 0.391 0.606 0.424 13.8
Nelson Cruz 170 186 92 0.341 0.398 0.688 0.456 21.2 0.295 0.356 0.654 0.423 16.3
Alex Rodriguez 146 170 94 0.260 0.365 0.541 0.388 10.2 0.283 0.384 0.612 0.423 14.9
Joc Pederson 146 179 95 0.247 0.385 0.548 0.401 12.6 0.257 0.394 0.592 0.421 15.4
Mark Teixeira 147 177 87 0.231 0.362 0.551 0.390 10.9 0.281 0.402 0.560 0.414 14.2
Hanley Ramirez 158 170 94 0.259 0.312 0.468 0.336 3.2 0.318 0.366 0.590 0.406 12.6
Stephen Vogt 131 155 87 0.298 0.406 0.580 0.423 13.5 0.283 0.394 0.544 0.404 11.2
Cameron Maybin 109 126 92 0.248 0.349 0.404 0.332 2.0 0.304 0.398 0.537 0.403 9.0
Jose Bautista 133 165 92 0.211 0.364 0.444 0.353 5.4 0.252 0.397 0.530 0.401 11.5
Josh Reddick 153 170 90 0.314 0.382 0.536 0.395 11.1 0.302 0.372 0.561 0.399 11.6
Brian Dozier 174 196 90 0.247 0.332 0.494 0.355 6.6 0.284 0.365 0.572 0.399 13.4
Adam Jones 167 178 91 0.311 0.354 0.479 0.360 6.8 0.319 0.361 0.571 0.397 11.9
Freddie Freeman 169 188 92 0.302 0.372 0.485 0.372 8.9 0.304 0.375 0.553 0.397 12.6
Giancarlo Stanton 174 198 97 0.230 0.323 0.500 0.353 6.4 0.249 0.340 0.598 0.396 13.1
Matt Carpenter 165 184 91 0.321 0.391 0.582 0.416 15.0 0.293 0.366 0.557 0.394 11.9
Eric Hosmer 171 192 91 0.310 0.385 0.520 0.391 11.9 0.306 0.382 0.534 0.394 12.4
Lucas Duda 161 186 92 0.292 0.387 0.491 0.381 10.2 0.285 0.381 0.536 0.394 12.1
Mark Trumbo 144 152 93 0.264 0.303 0.507 0.345 3.9 0.298 0.335 0.600 0.394 9.8
Corey Dickerson 111 117 90 0.306 0.342 0.523 0.370 5.3 0.317 0.352 0.573 0.393 7.4

The tool uses the velocity and direction, rather than the distance and direction, of a batted ball to calculate the expected values with a few exceptions. If the velocity is not available for a fly ball or a line drive, it uses the distance and the direction of the batted ball to calculate the expected values. If the velocity of the batted ball is not available for a ground ball, the tool assumes it was of average velocity and only considers the direction it was hit when calculating the expected values. It does not consider distance for ground balls, as the distances are calculated using where the ball was fielded, so using distance would be describing what actually happened rather than what we expected to happen. For all line drives and fly balls hit over 375 feet it uses distance and direction rather than velocity and direction. The reason for this is that I do not have information on the hang time of batted balls, and in going through the data I found that fly balls and line drives that traveled over 375 feet but weren’t hit very hard were being severely underrated by the tool. As an example of the underlying data, the table below shows the reference data for fly balls hit to center field.

TYPE Velocity Range (MPH) Direction Range (90=CF) AVG OBP SLG
FB 105 150 85 95 0.732 0.732 2.511
FB 100 105 85 95 0.314 0.314 0.931
FB 97.5 100 85 95 0.082 0.082 0.247
FB 95 97.5 85 95 0.023 0.023 0.047
FB 92.5 95 85 95 0.000 0.000 0.000
FB 90 92.5 85 95 0.010 0.010 0.038
FB 87.5 90 85 95 0.025 0.025 0.063
FB 85 87.5 85 95 0.000 0.000 0.000
FB 80 85 85 95 0.020 0.020 0.050
FB 75 80 85 95 0.056 0.056 0.070
FB 70 75 85 95 0.220 0.220 0.231
FB 65 70 85 95 0.583 0.583 0.590
FB 60 65 85 95 0.145 0.145 0.145
FB 55 60 85 95 0.073 0.073 0.073
FB 0 55 85 95 0.073 0.073 0.073

I’m providing a link to a Google Sheets document with a leaderboard for all qualified batters, along with leaderboards broken down by each batted ball type. The document also contains a reference page that contains all the information for how batted balls performed in each bucket based on 2015 StatCast data for velocity references and 2014-2015 MLBAM data for distance references. The numbers in the reference page will continue to be updated as more data becomes available from StatCast. Feel free to look through this section and point out any inconsistencies you may see, and note that all data comes from BaseballSavant.

I’ve also provided a Methodology Example in the document so you can dig through what the behind the scenes data looks like as it’s being processed. Note that you may see some discrepancies in a player’s actual AVG seen here and his AVG seen elsewhere, as I treat sac flies as regular outs. The “Notes” tab gives a general outline of the procedure, and also contains a link to an Excel sheet that you can download to perform these calculations on your own.

Before I wrap up, I should also mention the limitations. It’s been noted elsewhere on FanGraphs that the StatCast data isn’t always completely accurate. Also, the tool currently doesn’t incorporate a player’s speed in any way, so guys like Dee Gordon are going to be fairly underrated in terms of their ground ball performance. I’ve been brainstorming ways to incorporate this and am open to any input you may have. Furthermore, I’ve noticed the tool can be pretty stingy with labeling balls as pop-ups and occasionally pretty generous with labeling them as line drives. I’ve noticed some fly balls with velocities over 95 MPH that only traveled 300 feet, indicating they were hit almost straight up in the air. Unfortunately, without data on the vertical angle of the ball off the bat or on the hang time of the ball in play, it will be difficult to fix this issue.

Even with these limitations, the tool works extremely well at determining how well guys have been hitting the ball and identifying who has been helped or hurt by factors beyond their control. Take the time to dig through the data and the code and point out areas for improvement, and I’ll incorporate them in future versions.