Big Winners of the Offseason So Far: AL

As all of baseball convened in San Diego this past week, there were a lot of holes to fill. There are some teams that have been very active in free agency and trades over the past weeks and this article means to look at three teams in the American League that have enhanced their rosters over that span of time.

These teams did not make the playoffs in 2014 and they added players that may make them playoff caliber teams in 2014.

2014 Regular Season Record (73-89)

There has been a lot of pressure on the White Sox to build a winner as the Detroit Tigers and Kansas City Royals have made the World Series in the past three years and the Cleveland Indians made the playoffs in 2013. The White Sox made a couple big splashes this offseason to boost their profile in the AL Central.

A bit before the Winter Meetings, they inked Adam LaRoche to bolster their weak lineup and provide left-handed power to match Jose Dariel Abreu’s right-handed power in the middle of the lineup. LaRoche has averaged 27 home runs per 162 games in his career and twice in the past three years has had an OPS over .800. LaRoche may not be an All-Star caliber player, but, other than an awful 2011, LaRoche has consistently been a strong performer with an OPS+ of 114 for his career.

The White Sox have an ace in Chris Sale, with a 9.8 K/9 and a 2.76 ERA since entering the league in 2010. He had a 2.17 ERA over 26 starts last season, but the Sox needed a second top pitcher to compliment Sale in the rotation. They did just that by moving prospect Marcus Semien, along with other minor league prospects, for Jeff Samardzija. The 29 year old veteran has struck out 200 or more batters in each of the past two years and posted a sub-3.00 ERA last season. His ERA went up and strikeouts went down as he went from the Cubs in the National League to the Athletics in the American League, but did see his WHIP drop strongly to beneath 1.00 and struck out 99 while walking only 12. The White Sox now have two top-25 starters coming into the 2015 season, as Sale will be top-5 starter and Samardzija will comfortably sit in the 22-24 range.

The White Sox needed some help in the bullpen as Zach Putnam or Jake Petricka were set to be the closer for 2015, so they dipped into their pockets, signing two former All-Stars to multi-year contracts. Zach Duke signed a bit before Winter Meetings and the former All-Star starter has a 2.20 ERA in his last 88 appearances and the White Sox needed a left-handed relief option as the entire bullpen was right handed before signing Duke. The big splash for the White Sox, though, was signing former Yankee All-Star closer David Robertson. Since 2011, Robertson has a 12.3 K/9 and from 2011-2013, had no higher than a 2.67 ERA. He only has 46 saves in his MLB career, as he was the setup man for Mariano Rivera coming into 2014. But Robertson had 39 saves last year, and has seen his BB/9 go from 4.7 in 2011 to a 2.8 average from 2012-2014. Duke will provide left-handed relief help that the White Sox were devoid of and Robertson will be the All-Star caliber closer that the White Sox have been without since Bobby Jenks left.


2014 Regular Season Record (83-79)

The Blue Jays play in the most active division and have been active in the market. They signed a Gold Glove caliber catcher, an MVP candidate at third base, and freed up space on the roster for a top prospect.

Russell Martin is a highly underrated player who is very strong in intangibles, like his blocking of pitches and elite game calling skills, and will bring his veteran experience to Toronto. Martin’s game calling abilities are well known; his catching abilities will enhance the entire Blue Jays staff, as he led a Pirates staff to back-to-back playoffs with top five ERAs in each season. Martin may never steal double-digit bases again, as he did each season from 2006 to 2009, but he had a .832 OPS last year and hit 39 home runs in his two previous seasons in the AL East, both with the Yankees. His .402 OBP of 2014 may be a bit of a misnomer of his abilities; he had a .332 OBP in the previous five seasons, but he will have much more than 45 runs as a top of the lineup hitter in a lineup with three MVP candidates behind him. Martin may be in a lineup with MVP caliber talent, but could end up being the most vital piece of a playoff run for the Blue Jays.

Josh Donaldson is the newest MVP candidate in the Blue Jays lineup, adding to the already formidable combination of Edwin Encarnacion and Jose Bautista. The Blue Jays had to trade three prospects and starting third baseman Brett Lawrie to get Donaldson, but Donaldson is well worth the investment. He has been the starting third baseman for the Athletics for two years and over that time he hit 53 home runs and was a top-10 MVP finisher in both 2013 and 2014. Donaldson broke out in 2013 with a .883 OPS and 64 XBH and had a bit of a letdown in 2014; he still finished with 29 home runs and 98 RBI in 2014, even though he struck out 20 more times and saw his OPS drop to .798.  There are not many power hitting third basemen in baseball and the Blue Jays are fortunate to have Donaldson, a top five 3B option.

The Blue Jays saw a couple needs in the offseason and two were filling a gap in the outfield left by free agents Melky Cabrera and Colby Rasmus, as well as finding a place in the rotation for top prospect Daniel Norris. By trading fifth starter J.A. Happ for Michael Saunders, and allowing Norris to slide into the rotation, both gaps were filled. Norris is the #25 ranked prospect according to, with a 2.53 ERA last year and a 10.7 K/9 over his three minor league seasons. He may struggle a bit earlier in the season, but he could have a similar impact to 2014 rookie star Marcus Stroman with his power fastball and a strong slider/changeup combination. Norris may not have a huge impact to start the season, but could be an impact player later in the season.

Saunders was a bit undervalued in Seattle, but has a very interesting profile. He slots into the bottom of the projected Blue Jays lineup and has a little bit of a better profile than the man he is replacing, Colby Rasmus. Saunders is a very good defensive outfielder, but has had two seasons with more than 10 home runs and steals, while also posting three consecutive seasons with an OPS above league average. The only season where Saunders had 500 or more at bats, 2012, he posted 19 home runs and 21 steals; his OBP has risen from .306 in 2012 to .341 in 2014, so there is potential for Saunders to be even better with more opportunity in Toronto. Saunders was obtained for a very movable piece in Happ; if the Blue Jays are able to fill a major need in the outfield and only have to give up a fifth starter to do so, this would be a huge victory for the Blue Jays.


2014 Regular Season Record (71-91)

The 2013 champion Red Sox bore no resemblance to the 2014 team that finished last in the AL East. As the Red Sox are a financial juggernaut, they were able to flex their muscles adding two former All-Stars and then traded for two All-Star pitchers in San Diego.

Pablo Sandoval has been an instrumental part of three Giants World Series and, after disappointment from Will Middlebrooks, will bring his talents to the Red Sox in 2o15. Much has been written about Sandoval’s streaky play and his free swinging ways, but Sandoval is a .294 hitter over his seven MLB seasons and averaged 44 extra base hits over the past four seasons. The switch hitting Sandoval will get a serious boost from the left side by hitting doubles off of the Green Monster; this is a needed boost as Sandoval has not had 30 or more doubles in a season since back-to-back 30 double seasons in 2009 and 2010. Only once in his career has Sandoval had more than 80 RBI and twice has he had 20 or more home runs; Sandoval’s value comes from his postseason experience and is a top 15 3B in a weak 3B crop.

Rick Porcello was a top prospect coming through the Tigers system, but really never broke through as a stable pitching option until his 15 win 2014 season where he had a 3.43 ERA. The Red Sox need a lot of pitching help, as they finished 10th in the AL in ERA, and Porcello’s ground ball tendencies may fit the Red Sox well. Xander Bogaerts will be more prepared at shortstop this season and Dustin Pedroia‘s defense up the middle will absolutely suit Porcello’s skills. Porcello is coming off of his first 200 inning season and has seen his WHIP go from 1.41 in his first four seasons to 1.25 in the last two seasons. He has seen his K:BB ratio rise over 3 as well and he is only 26 years old going into his seventh MLB season. That experience should be great for him coming into the grinder that is the AL East. Porcello has a career FIP that is 30 points less than his career ERA, showing that the talent is there for Porcello; look for him to breakthrough as an All-Star caliber pitcher this year.

Hanley Ramirez was the top hitter available and has been one of the most polarizing players over the past five seasons. Coming into 2010, he was the top fantasy baseball prospect, but saw his OPS go from .853 in 2010 to .742 combined in 2011 and 2012; he then posted a .907 OPS in 2013 and 2014, including a white hot 1.040 OPS in 88 games of 2013. Ramirez has twice before been a 50+ SB player and led the NL in BA in 2009, so the talent is there. But Ramirez has averaged only 121 games played since 2010 and has had two seasons where he played in less than 100 games.

Ramirez will also move to left field this season which should be a very interesting move for fantasy purposes; had Ramirez stayed at third, or even shortstop, he may have been a third round pick, but as an outfielder it is very questionable. There is a chance that Ramirez has less wear and tear in the outfield and becomes a top-10 hitter again, but a .282/.358/.467 slashline in the outfield is not worthy of a top-10 OF spot. A lot will be expected from Ramirez, but this may be the season that he is able to play 150 games of All-Star caliber play in the outfield, regaining his reputation as an MVP candidate.

2015 Fantasy Sleepers: Starting Pitching

The key to winning at fantasy baseball is finding players who will outperform their draft position.  This will be the first of a series of articles addressing undervalued and overvalued players that you should be targeting in your draft.

Read the rest of this entry »

Using Gifs to Visualize Curveballs on the Scouting Scale

After reading Kiley McDaniel’s articles on explaining the scouting scale, I thought that I would take a different approach in trying to explain it–namely the approach of gifs.  Although the scale is normally reserved for players who haven’t lost their rookie status in the majors, perhaps a visualization can better illustrate what “major league average,” “plus,” or “below average” looks like.

To show the differences between curveballs in different positions along the scouting scale, major league curveballs must first be graded.  To do this, I pulled up Baseball Prospectus’ pitch f/x leaderboard and set it to filter for pitchers who threw at least 100 curveballs in 2014.  The way in which pitch movement is measured and the fact that lefties’ curveballs’ horizontal movement appeared to be measured lower than righties forced my methodology.

I split up righties and lefties into separate groups for analysis.  Pitch movement was recorded based on the inches a pitch broke more than the the pitch with the least break.  Total movement was recorded as the square root of the combined horizontal and vertical movement squares (C2 = A2 + B2).  Z Scores were recorded for each curveball’s velocity and movement, and then were added (with movement receiving a 1.5 times greater weight) to form a grade.  The grades were then transferred into Z Scores with a median of 50 and each standard deviation being 10.  Finally, the righties and lefties were combined to make a final scouting scale.

Name / Throws / Total Movement Z Score / Velocity Z Score / Scouting Grade

Garrett Richards R 2.081 0.289 73.4
Drew Pomeranz L 0.882 1.398 69.0
Tyler Skaggs L 1.757 0.047 68.8
Blaine Hardy L 1.356 0.410 67.1
Gio Gonzalez L 1.406 0.317 66.9
Alex Cobb R 0.907 0.948 66.0
Roenis Elias L 0.949 0.771 65.3
Sonny Gray R 0.589 1.187 64.4
Jarred Cosart R 1.125 0.289 63.8
Felix Hernandez R 0.812 0.712 63.5
Jake Arrieta R 0.972 0.428 63.2
Charlie Morton R 1.191 0.066 63.0
Carlos Torres R 0.973 0.350 62.7
Craig Kimbrel R -0.474 2.430 62.1
Sean Marshall L 1.795 -0.970 62.0
Yoervis Medina R -0.471 2.330 61.5
Joe Kelly R 0.852 0.341 61.4
Mark Melancon R 0.312 1.116 61.2
Stephen Strasburg R 0.633 0.612 61.0
Justin Grimm R 0.404 0.915 60.8
Robbie Erlin L 1.711 -1.022 60.7
Jamey Wright R 0.958 0.057 60.6
Adam Wainwright R 1.731 -1.106 60.6
Wandy Rodriguez L 1.401 -0.639 60.1
Kevin Jepsen R -0.315 1.861 59.9
Jeremy Hellickson R 1.367 -0.664 59.9
Clay Buchholz R 1.007 -0.166 59.6
Scott Atchison R 0.429 0.683 59.5
Will Harris R 0.538 0.518 59.5
Michael Bolsinger R 0.524 0.521 59.3
John Axford R 0.857 0.015 59.3
David Robertson R -0.245 1.619 59.0
Jeremy Affeldt L 1.197 -0.497 59.0
Ian Kennedy R 0.943 -0.186 58.8
Yu Darvish R 0.941 -0.182 58.8
Eric Surkamp L 0.624 0.320 58.7
Nick Masset R 0.680 0.182 58.6
Tom Koehler R 0.551 0.360 58.5
Marcus Stroman R -0.189 1.455 58.4
Zack Wheeler R 0.604 0.263 58.4
Jose Fernandez R -0.255 1.532 58.3
Scott Downs L 1.405 -1.064 57.2
Edinson Volquez R 0.240 0.609 57.1
Felix Doubront L 1.093 -0.629 56.9
Juan Gutierrez R 0.110 0.744 56.7
Jesse Chavez R 1.076 -0.731 56.5
Jeremy Jeffress R 0.264 0.476 56.4
Danny Duffy L 0.446 0.272 56.4
Cody Allen R -1.180 2.630 56.4
Mike Leake R 0.210 0.515 56.2
Dellin Betances R -0.552 1.635 56.0
Trevor Bauer R 0.416 0.147 55.8
Jose Veras R 0.924 -0.638 55.6
Adam Warren R -0.220 1.022 55.2
Clayton Kershaw L 1.119 -0.906 55.2
Chris Tillman R 0.987 -0.828 55.0
Cole Hamels L 0.142 0.517 54.9
Miles Mikolas R 1.158 -1.093 54.9
Jeff Locke L 0.268 0.317 54.9
Gerrit Cole R -0.845 1.884 54.7
Josh Fields R 0.233 0.257 54.7
Nick Tepesch R 0.370 0.018 54.4
Brandon Workman R 0.743 -0.544 54.4
Carlos Carrasco R -0.275 0.954 54.2
Cesar Ramos L 1.941 -2.290 54.2
Tom Wilhelmsen R 0.309 0.034 53.9
Jenrry Mejia R 0.117 0.308 53.9
Trevor Cahill R 0.202 0.153 53.7
Odrisamer Despaigne R 0.813 -0.770 53.6
Collin McHugh R 1.350 -1.635 53.2
Jesse Hahn R 1.150 -1.345 53.2
Phil Hughes R 0.573 -0.502 53.0
Samuel Deduno R -0.389 0.906 52.8
Brett Cecil L -1.373 2.483 52.8
Ian Krol L -0.094 0.555 52.7
Wade Davis R -1.260 2.184 52.6
Jordan Zimmermann R -0.034 0.308 52.3
Andre Rienzo R 0.052 0.170 52.3
Junichi Tazawa R 0.731 -0.857 52.2
Jason Hammel R 0.477 -0.512 52.0
Wesley Wright L -0.302 0.764 52.0
Mike Fiers R 1.376 -1.890 51.8
Nathan Eovaldi R 0.542 -0.644 51.8
Alex Wood L -0.385 0.851 51.7
Jake Buchanan R 0.570 -0.709 51.6
Dillon Gee R 0.926 -1.261 51.5
Trevor May R 0.298 -0.328 51.4
David Buchanan R 0.247 -0.263 51.3
Hyun-jin Ryu L 1.074 -1.395 51.3
Rick Porcello R 0.171 -0.189 51.1
Yordano Ventura R -1.045 1.603 50.9
Anthony Ranaudo R 0.136 -0.195 50.7
Aaron Loup L -0.110 0.282 50.6
Brad Hand L -0.491 0.851 50.6
Brandon McCarthy R -0.757 1.116 50.5
Will Smith L -0.282 0.526 50.5
Vidal Nuno L 0.094 -0.043 50.5
Justin Verlander R -0.301 0.415 50.4
Tommy Hunter R -1.053 1.539 50.4
Santiago Casilla R -0.726 1.038 50.3
Brad Peacock R 0.257 -0.447 50.2
Madison Bumgarner L 0.013 0.043 50.2
Fernando Abad L -0.223 0.397 50.2
C.J. Wilson L 0.077 -0.066 50.1
Francisco Rodriguez R 0.334 -0.586 50.1
A.J. Burnett R -0.847 1.167 49.9
Erik Bedard L 0.572 -0.842 49.9
Tanner Roark R 0.889 -1.455 49.8
Kevin Quackenbush R 0.264 -0.531 49.7
Casey Janssen R 0.767 -1.287 49.7
Yovani Gallardo R -0.366 0.376 49.5
Matt Cain R -0.007 -0.173 49.4
Cory Rasmus R 0.332 -0.683 49.4
J.P. Howell L -0.488 0.652 49.2
Shelby Miller R 0.046 -0.328 48.9
Joba Chamberlain R -0.417 0.331 48.7
Mike Minor L -1.028 1.373 48.6
Lance Lynn R -0.505 0.441 48.5
Josh Beckett R 0.835 -1.587 48.4
Daisuke Matsuzaka R 0.510 -1.109 48.3
Chase Anderson R -0.043 -0.318 48.1
Jorge De La Rosa L 0.397 -0.845 48.0
Danny Farquhar R 0.214 -0.725 47.9
Nick Martinez R 0.107 -0.599 47.7
Jerry Blevins L 0.348 -0.825 47.6
Matt Garza R 0.444 -1.141 47.5
Franklin Morales L 0.340 -0.874 47.2
Craig Stammen R -0.732 0.563 47.1
Javy Guerra R -0.179 -0.266 47.1
Scott Feldman R 0.326 -1.041 46.9
Anthony Varvaro R -0.810 0.638 46.8
Hector Noesi R -0.910 0.741 46.5
Miguel Gonzalez R -0.144 -0.428 46.3
John Lackey R -0.520 0.128 46.3
Kevin Correia R -0.427 -0.015 46.3
Kyle Kendrick R -0.313 -0.186 46.3
Tyler Thornburg R -0.364 -0.118 46.2
Colby Lewis R -0.216 -0.344 46.2
Donn Roach R 0.008 -0.683 46.2
Tim Lincecum R 0.226 -1.022 46.1
Chris Capuano L -0.013 -0.507 46.0
Josh Tomlin R -0.062 -0.618 45.9
J.A. Happ L -0.568 0.275 45.7
James Paxton L -1.519 1.643 45.3
Vance Worley R -0.360 -0.289 45.1
J.J. Hoover R 0.093 -0.973 45.1
Tim Hudson R 0.007 -0.854 45.0
Wei-Yin Chen L 0.065 -0.809 44.7
Marco Estrada R -0.419 -0.286 44.5
Kyle Lohse R 0.091 -1.109 44.1
Vic Black R -1.482 1.248 44.1
Gavin Floyd R -1.285 0.951 44.1
Bruce Chen L 0.405 -1.421 44.0
Phil Coke L -1.232 1.003 43.8
Jon Lester L -0.249 -0.478 43.7
Paul Maholm L 0.340 -1.492 42.8
Jose Quintana L -1.420 1.134 42.7
David Phelps R -1.211 0.622 42.7
Zach Duke L -0.491 -0.288 42.5
Brett Oberholtzer L -1.197 0.751 42.4
Homer Bailey R -1.197 0.538 42.2
Jon Niese L -0.078 -0.954 42.2
Alfredo Simon R -0.751 -0.179 41.9
Jim Johnson R -1.275 0.605 41.9
Zack Greinke R 0.308 -1.903 41.0
Scott Carroll R -0.685 -0.431 40.9
Jason Vargas L -0.469 -0.562 40.8
Travis Wood L 0.088 -1.443 40.5
Heath Bell R -1.696 0.948 40.0
David Price L -1.564 0.954 39.9
Doug Fister R 0.071 -1.726 39.8
Jordan Lyles R -1.731 0.961 39.7
Michael Wacha R -0.378 -1.112 39.4
Masahiro Tanaka R -0.204 -1.413 39.2
Joel Peralta R -1.008 -0.208 39.2
James Shields R -1.514 0.518 38.9
Jake Odorizzi R 0.579 -2.759 38.0
Andrew Heaney L -1.515 0.568 37.7
Max Scherzer R -1.307 -0.163 36.5
Anibal Sanchez R -1.676 0.357 36.2
Julio Teheran R -0.450 -1.539 35.9
Scott Kazmir L -1.244 -0.124 35.7
Yusmeiro Petit R -1.253 -0.402 35.4
Mat Latos R -1.151 -0.586 35.2
Jacob deGrom R -1.879 0.473 35.0
Tommy Milone L -0.975 -0.626 35.0
Joe Nathan R -2.412 1.271 35.0
Edwin Jackson R -1.821 0.370 34.9
Matt Shoemaker R -1.126 -0.802 34.0
Drew Smyly L -1.762 0.330 33.4
Dan Haren R -1.545 -0.292 33.2
Hector Santiago L -1.609 0.069 33.2
Grant Balfour R -2.631 1.274 32.8
Ryan Vogelsong R -1.631 -0.405 31.6
Mark Buehrle L -0.593 -1.691 31.5
Erasmo Ramirez R -2.280 0.528 31.3
Jeremy Guthrie R -1.384 -0.854 31.1
Jacob Turner R -2.033 0.102 31.0
Hiroki Kuroda R -1.637 -0.528 30.7
Johnny Cueto R -2.591 0.880 30.6
Jake Peavy R -2.414 0.573 30.3
Josh Collmenter R -0.839 -1.884 29.7
Sam LeCure R -1.215 -1.461 28.7
Bronson Arroyo R -1.320 -1.416 28.0
Aaron Harang R -1.461 -1.319 27.2
John Danks L -1.548 -1.051 25.9
Eric Stults L -0.448 -2.837 24.9
Fernando Salas R -3.656 1.613 24.8
Carlos Villanueva R -2.102 -0.718 24.8
Jered Weaver R -0.725 -2.840 24.4

For our first gifs, we’ll look at two of the top curveballs on the by-the-numbers scouting scale.  At an slightly above average velocity of 79.7 mph with the highest amount of break above than the baseline (sorry, Fernando Salas), Garrett Richards’ curveball is a sight to behold and grades out as a 73:

Next, we’ll look at Tyler Skagg’s curveball, which, features great horizontal and vertical movement while maintaining average velocity.  It grades out as a 69:

With the plus-plus-type (70) curveballs out of the way, we’ll take a look at some plus (60) curveballs.  Wandy Rodriguez fits this category.  His curveball has slightly less, but similar, movement as Skagg’s but it’s lesser velocity makes it a lesser-quality pitch.  Notice how it has defined break, but it appears a bit loopy due to its velocity:

With Kevin Jepsen, we see a curveball that is graded similarly but looks much different than Wandy’s.  Although its break looks sharp because of its velocity, the total movement is slightly below average.  This pitch may actually be a slider, but Jepsen’s breaking pitches tend to run together so consider it a representative picture of his curveball.

Next, we’ll look at an average (50 on the scale) curveball.  Erik Bedard gets slightly above average break, but with below average velocity.  It has solid two-plane break, but it doesn’t look very sharp:

Next, we’ll look at a below average (40 on the scale) curveball.  Jordan Lyles has good velocity on his curveball, averaging 81.75 mph, but he also averages nearly two standard deviations less break than an average curveball in this sample.  The following is a tough angle to see the break, but it lacks the sharp break of a better curveball.

Finally, we’ll look at Jered Weaver’s well below average (24 on the by-the-numbers scale!) curveball.  His curveball was the slowest in the sample and also featured below average break (much of the perceived break is from its low velocity).  It’s still an effective pitch for him, but it’s probably a result of his height and delivery combination than his curveball.  With just about any other pitcher, it would likely be ineffective.


Seth Smith Would Be Great Fit for Mariners

With Wil Myers headed to San Diego, and only a clean bill of health keeping Matt Kemp from joining him (which may or may not magically appear of LA is willing to pay a larger portion of Kemp’s remaining $105 million on his contract…), the San Diego Padres find themselves with an opportunity to move 2014 right fielder Seth Smith. Kemp’s days as an everyday center fielder have long passed, and Myers is likely better suited for a corner outfield spot as well. This leaves nowhere to play Smith, and the Padres could deal Smith to acquire some help at a spot other than the corner outfield.

The biggest issue with Smith has always been his splits. No matter how you slice it, Smith should relegated to a platoon role.

Smith’s splits:

                  RHP          LHP

wRC+     123              63

wOBA   .362            .274

ISO         .204           .109

A team that has been in the hunt for a corner outfielder this offseason is the Seattle Mariners. So far we’ve seen Seattle add Nelson Cruz to serve as the everyday DH, and over the past month we’ve seen the Mariners linked to names like Melky Cabrera, Dayan Viciedo, Justin Upton, and Kemp. The big hangup with Kemp, as well as Upton, was the issue with teams trying to pry away young pitchers like Taijuan Walker and James Paxton. Fortunately for the M’s, a player like Seth Smith would not cost them these young arms.

With Kemp and even more cash expected to head to San Diego, Upton will likely become the top corner outfielder available this offseason. Upton has just one year remaining on his current contract, and will make nearly $15 million in 2015. Meanwhile, Smith will make nearly $13 million over the next two years, and his contract contains a club option for the 2017 as well. Now we all know that Justin Upton is a better baseball player than Seth Smith, but is one year of Upton (and no Walker or Paxton for the next 6 years) better than two years of Smith and Justin Ruggiano (with Walker and Paxton still in Seattle for the next 6 years)?

Earlier this week, the Mariners acquired Ruggiano from the Cubs for minor league reliever Matt Brazis. Ruggiano is two years removed from a career year in Miami, in which he posted a 2.6 WAR in just 91 games. Even though his WAR over the last two seasons combined for just 1.3, he still profiles as an excellent platoon player. In 2013, Ruggiano posted a 130 wRC+ vs LHP, as well as a .362 wOBA. For 2014, Ruggiano’s numbers were nearly identical, posting a 129 wRC+ and a .362 wOBA vs LHP. Take a look at the career numbers below, with Smith and Ruggiano’s being their career splits:

                     Upton     Smith     Ruggiano

wRC+         121            123           128

wOBA        .359         .362          .360

ISO             .202         .204          .241

Whether or not the Mariners add Justin Upton, or Seth Smith, or go with some sort of Brad Miller/Ruggiano platoon that we’ve seen rumored, they will get solid production from that right field spot. Smith would cost them some value, and the Smith/Ruggiano platoon may not be the sexiest, but Smith would not cost them a Walker or Paxton.

Under the Radar: John Mayberry

Amidst the expensive December fireworks being set off by Andrew Friedman and Theo Epstein, the cash-strapped New York Mets quietly took another step towards correcting a major 2014 deficiency with the addition of John Mayberry for $1.45 million.

Removing the historically bad hitting performance of their pitching staff (they started the season with a major league record 0-for-64), the often maligned Mets lineup actually generated a respectable 104 wRC+ against right-handed pitching in 2014, good enough for 5th best in the National League.

Their offense vs. left-handed pitching was another story however as an 89 wRC+ (14th NL) and 22 HR (MLB worst) left the Mets scrapping to find runs in the late innings of games against deep lefty-heavy bullpens.  Leading the struggles vs lefties were Eric Young Jr (84 PA, 60 wRC+), Lucas Duda (125 PA, 54 wRC+), and Chris Young (83 PA, 51 wRC+).

I prepared for this first FanGraphs Community article of mine by studying Mayberry a little closer.  As a fan who has witnessed plenty of NL East action over the years, I was well aware of Mayberry’s established platoon splits.  What I wasn’t aware of was the massive amount of growth he had in 2014.

John Mayberry Splits vs LH Pitching

2011 – 6.7 BB%, 15.0 K%, 0.44 BB/K, .288 ISO, .306 BABIP, 157 wRC+
2012 – 5.6 BB%, 17.8 K%, 0.32 BB/K, .223 ISO, .289 BABIP, 116 wRC+
2013 – 7.4 BB%, 15.7 K%, 0.47 BB/K, .220 ISO, .244 BABIP, 106 wRC+
2014 – 13.4 BB%, 12.2 K%, 1.10 BB/K, .329 ISO, .214 BABIP, 151 wRC+

After 3 seasons with respectable peripherals, Mayberry took his platoon game to another level in 2014 with career-best numbers across the board except for an inexplicable .214 BABIP.  Over 534 career plate appearances against LHP, Mayberry carries a .269/.324/.533, 30 HR, 130 wRC+.

In addition to Mayberry is the aggressively acquired Michael Cuddyer (career 132 wRC+ vs LHP), and the Mets are now in position to be significantly strengthened vs. left-handed pitching without making headlines or gutting their very deep farm system.



The Ballad of Brett Lawrie

He’s not a good enough 3B and he doesn’t hit well enough to play at any of the easier defensive spots.

1261 PA, .273/.348./450, 102 OPS+

“He” is Edwin Encarnacion, then of the Cincinnati Reds, and those are his stats through his age 24 season (2005-2007). Just three years into his major league career, Encarnacion had yet to attain 600 PA in any one season, and questions were already be raised about his viability as an every day player. The quote above comes from here, and, to be fair, it represented the judgment of only some E5 observers. But despite having the opportunity to act out one of the best baseball revenge fantasies ever, Encarnacion never fully put those doubts to rest while he was with Cincinnati. Following what seemed to be a possible breakout season at age 25 in 2008, E5′s power disappeared the following year, and the disgusted Reds shipped him midseason to America’s Hat in exchange for the Ghost of Rolen Past, who gave Cincinnati the final 3.5 seasons of his career, 1.5 of which were useful.

North of the border, Encarnacion’s power returned in 2010 even as his OBP continued to regress; his production overall rebounded to the level it had been in 2008 (109 OPS+ and wRC+). He continued to maneuver around third base as though it were a point singularity, however, so in 2011 the Blue Jays began transitioning him to a 1B/DH, giving him 92 starts in those slots as opposed to just 30 at third. The results at the plate were encouraging: his average and OBP made substantial gains without giving away too much power. Then, in 2012, Encarnacion finally went off, commencing a three year tear during which his OPS has never been below .900 and his worst HR total was 34. Today, Encarnacion can hit in the middle of any major league lineup. If Alex Anthopoulos is working for the MLB Network in 2016, it won’t be Encarnacion’s fault.


He started off cold as ice … before getting hurt sliding into second base.

1361 PA, .250/.331/.415, 97 OPS+

“He” is Alex Gordon, and those are his stats through his age 24 season (2007-2009). Just three years into his major league career, Gordon’s stellar offensive production in college already seemed a distant memory, and questions were being raised about his viability as an every day player. The quote above comes from here, and while that writer was ready to give up on Gordon, to be fair, many others were calling on the Royals to remain patient with the second overall pick in the 2005 draft. In 2009 Gordon’s power, never substantial in the majors to that point, really began circling the drain, and after getting off to an anemic .685 OPS start in April 2010, the disgusted Royals demoted Gordon and banished him to left. He would never appear at third base again.

He would, however, rediscover his stroke. Gordon hammered 16 homers in just 321 PAs at Omaha, good for a steak-sized 164 wRC+. He returned to The Show on July 23, and while the remainder of his 2010 season did little to quiet his critics (he finished with a wRC+ of 85, actually two points worse than the previous year), in 2011 he began a four year rampage, headlined by 96 doubles during 2011-2012, as well as ironman durability. Since opening day 2011, Gordon is third in the majors in plate appearances, behind only Ian Kinsler and Elvis Andrus. Gordon’s durability and on-base skills have made him a key offensive cog in the Royals somewhat surprising resurgence. He’ll never have to go to back to Omaha unless he has relatives there.


He’s been injured numerous times, suspended and has underperformed with the bat once pitchers began taking advantage of his lack of patience at the plate.

 1431 PA, .265/.323/.426, 104 OPS+

“He” is Brett Lawrie, and those are his stats through his age 24 season (2011-2014). Just four years into his career, his first 171 blistering plate appearances of his career have disappeared in the rearview, and questions are being raised about his viability as an everyday player. The quote above comes from here, and was written before Lawrie strained his oblique (once again) last year, ending his season and, as it turned out, his tenure with the Blue Jays.

Lawrie plays baseball as though he’s being chased by an enraged Sumatran tiger. In his first brief season in the bigs (when, incidentally, he replaced Encarnacion as the Jays’ starting third baseman) this paid off with a .293/.373/.580 slash line.  Since then, however, he has been unable to translate all that energy into baseball achievement. That kind of intensity can wear thin unless it’s backed by production, and Lawrie’s rate stats have gone generally backwards since 2011. He’s had a fractured finger, repeated oblique injuries, and a bad slide into second, among other injuries.  Lawrie blames the turf at Rogers Centre, but the turf lawyered up, and Lawrie’s case proved at best inconclusive.

As the career paths of Encarnacion and Gordon suggest,  one way to resuscitate Lawrie’s bat might be to move him to the left end of the defensive spectrum. That won’t happen in Oakland; after the Donaldson trade the A’s third base depth chart (Renato Nunez aside) looks like the Fallujah skyline. Billy Beane has little incentive to try Lawrie anywhere other than third. And maybe it will work. The hopeful comp here might be Gary Gaetti, whose stat line through age 24 (1981-1983) looks similar to the other guys in this post, viz:

1241 PA, .237/.293/.428, 94 OPS+

But Gaetti was already a superior defender, and became a durable, full time starter at age 23. His plate appearances are light because he only had a shotglass of coffee in 1981.

Gaetti was good. Real good: a 42 WAR career during which he amassed 2,280 hits and 360 homers while adding defensive value almost right up to the end. It’s certainly worth Beane’s time to see if he has that kind of player on his roster, especially since it appears that the A’s are going to be running a talent show rather than a pennant race next season. My guess is that if Lawrie develops at third, he’ll have slightly more bat and slightly less glove than Gaetti, though to be fair, both men had exactly the same career minor league OPS (.851).

It’s less clear whether participating in this particular rat race is the best outcome for Lawrie. Like E5 and Gordon, he might be better served by moving to a safer corner where he can concentrate on developing his offensive skills without placing his body’s soft tissue in excessive danger. I’m sure if you asked him he’d say he wants to stay at third. My guess is that Encarnacion and Gordon once thought that way too, and Lawrie’s career to this point looks more like theirs than Gaetti’s.

Brett Lawrie, like Repo Man, is always intense. I have a hard time not rooting for him; he attacks his job with an explosive, exuberant passion that would get me (and probably you) fired. I want him to succeed. I’m not at all sure he will.

Adjusting to the New Reality

Adjusting to the New Reality

The level of offense in baseball has been dropping for some time now. In the 1980s and into the early 1990s, teams scored around 4.3 runs/game (with the exception of 1987, when offense jumped up to 4.7 runs/game for one year, then went right back down in 1988). Offense started to rise in 1993 and first jumped over 5 runs/game in 1996. Run-scoring peaked at 5.1 runs/game in 2000, then leveled off to around 4.8 runs/game through 2007. Since 2008, offense has gone down steadily, with 2014 seeing an average of 4.1 runs/game. You have to go back to 1981 to find fewer runs per game in baseball (4.0 runs/game).

This has implications in the world of fantasy baseball. Consider the table below that shows the ERA in Major League Baseball by year, going back to 2001:

2001 4.42
2002 4.28
2003 4.40
2004 4.46
2005 4.29
2006 4.53
2007 4.47
2008 4.32
2009 4.32
2010 4.08
2011 3.94
2012 4.01
2013 3.87
2014 3.74


Some would point to PED testing for the lower level of offense, some would blame a bigger strike zone, some would peg it on the increasing number of relievers throwing 95+ for an inning or two. Whatever the reason, this is the new reality and sometimes it can be hard to adjust to new realities.

Let’s look at the numbers shown above in more detail.

Over the stretch of years from 2001 to 2009, MLB had an ERA of 4.39. Over the three-year stretch from 2010 to 2012, ERA dropped to 4.01. The last two years have seen big drops each year, from 4.01 to 3.87, to 3.74.

This has repercussions in fantasy baseball. With ERA dropping quickly, we need to reevaluate the pitchers we take on draft day and during the season.

Let’s go back to 2009, when MLB had an ERA of 4.32. The top 60 starting pitchers in ERA (minimum of 160 IP) combined for an ERA of 3.54. The median ERA for this top 60 was 3.77. There were 11 pitchers with an ERA under 3.00.

Fast forward to 2014. Last year, MLB had an ERA of 3.74. The top 60 starting pitchers in ERA (minimum of 160 IP) combined for an ERA of 3.14. The median ERA for this group was 3.33. There were 22 pitchers with an ERA under 3.00.

2009 2014
ERA in MLB 4.32 3.74
ERA of Top 60 3.54 3.13
Median ERA of Top 60 3.77 3.33
Pitchers under 3.00 11 22


In 2009, the median guy in the top 60 was someone like John Danks (3.77) or Jarrod Washburn (3.78). Last year, the median guys in the top 60 were Jose Quintana (3.32) and Chris Archer (3.33). [Caveat: I know ERA isn’t the only way to judge a pitcher in fantasy baseball. I’m keeping it simple.]

Six years ago, when scouring the waiver wire, that pitcher with a 4.00 ERA was a potential pick-up. These days, you don’t want to look at that guy, he’ll just hurt your team. This may seem obvious, but it really is a change in mindset when you’re looking to improve your team. What we once thought was good is no longer good.

One of the side effects of a big drop in the run environment is the difficulty for projection systems to keep up. If we go back to the 2010 season, we can see a stark example. If a pitcher had league average ERAs in 2007 (4.47), 2008 (4.32), and 2009 (4.32), we could do a simple 3/2/1 weighted average for his three seasons and project an ERA of 4.35 for 2010. League-wide, though, ERA dropped from 4.32 in 2009 to 4.08 in 2010. Most projection systems will project ERAs that will be in line with the previous few seasons’ run environment. In this case, the projections will be well above what the actual ERAs were for the 2010 season (unless a projection system can anticipate such a drop in offense).

Let’s do the same for more recent seasons. If we take a pitcher with league average ERAs in 2011 (3.94), 2012 (4.01), and 2013 (3.87), and do a simple 3/2/1 weighted average, we get a 2014 projection of 3.93. The actual ERA in MLB in 2014 was 3.74, so pitchers as a group are going to be forecast with ERAs around 0.20 higher because the drop in offense was so drastic.

With this in mind, I looked at last year’s projections from four systems: Steamer, ZiPS, Davenport, and Oliver. I looked at all pitchers who were projected by each of the four systems who pitched 30 or more innings in 2014. There were 326 pitchers in this group and they finished 2014 with a combined ERA of 3.58. You can see how each of the projection systems forecast these players prior to the 2014 season:

Actual ERA 3.58
Davenport projection 3.76
Oliver projection 3.81
ZiPS projection 3.90
Steamer projection 3.91


When looking at the data, what you shouldn’t do is say that Davenport had the best projections. What is true is that Davenport best anticipated the run environment. Looking at the table, it would be easy to assume that Davenport and Oliver had the best projections, as they were closest to the actual ERA of this group of pitchers. In reality, if you are trying to assess which system better projected individual players, you would first want to adjust them all to the actual run environment, then compare the differences between projected ERA and actual ERA for individual pitchers.

In the case of the 326 pitchers used above, the table below shows the average absolute difference in actual ERA and projected ERA for each individual pitcher, using projections adjusted to the run environment of this group of pitchers.

Adjusted Projections
System AvgAbsDiff
Steamer 0.85
Davenport 0.86
Oliver 0.88
ZiPS 0.90


Looking at it this way, it’s easy to see that the different projection systems were very close on this group of 326 pitchers and Davenport and Oliver are in the middle of the pack, with Steamer moving from the bottom to the top.

What does this mean for 2015? If you’re the type of fantasy baseball player who likes to create your own projections by combining projections from other sources, you will first want to know what level of offense those projections are expecting (ERA in this example). If you think 2015 will be much like 2014 (3.74 league-wide ERA) but the projections expect an ERA much higher or lower, you should adjust all pitchers by the amount the projections are high or low. With these new adjusted projections, you can now combine your projections.

As an example, I took those same 326 pitchers from above and compared their actual combined ERA from 2014 to their 2015 Steamer projections. This group of pitchers had a combined ERA of 3.58 in 2014. Steamer is projecting them to have a 3.84 ERA in 2015. The difference is 0.26 in ERA. I don’t know the run environment Steamer is basing their projections on, but this would suggest that it’s higher than what we saw in 2014.

Based on the disclaimer that accompanies each team’s ZiPS projections, we know that ZiPS is projecting based on the AL having an ERA of 3.93 and the NL having an ERA of 3.75. This would be a slight increase from the 2014 season (AL: 3.82 ERA, NL: 3.66 ERA) and is, essentially, a 3/2/1 weighted average from 2012, 2013, and 2014.

I looked at the starting rotations for the five teams that we have ZiPS projections for so far. There are 25 pitchers and they are projected by ZiPS to pitch 3985 innings with a 3.73 ERA. These same 25 pitchers are projected by Steamer to pitcher 4039 innings with a 3.98 ERA. Steamer is high by 0.25. Steamer projects higher ERAs for 23 of these 25 pitchers. This is a small sample of just 25 pitchers, but it would appear that you will want to adjust the Steamer pitching projections down if you do any sort of combining of projections in your fantasy baseball prep.

In addition, if you’re in a keeper league and have access to last year’s data for your league, you may want to project your keepers and potential additions for 2015 and compare your team projections to last year’s stat categories. This way, you will have an idea of how competitive your team will be. For example, I’m in an 18-team, 25-man roster league. We have nine starters on offense, four starting pitchers, and two relievers in our active lineups, and a 10-man bench that can be made up of players from any position. Teams in this league averaged around 1000 innings last season, so when I create projections, I can plug in the stats for my keepers and potential additions to see how my team looks for the upcoming season. In order to compare my projected 2015 team to 2014 stat categories, I want my projections to be adjusted to the level of offense of 2014 (in this case, ERA).

Offense in baseball has been dropping for a few years now. Successful fantasy players will have to adjust to this new reality when doing their pre-season prep work, on draft day, and when adding players from the waiver wire.

Boston Should Turn Joe Kelly Into the Next Zach Britton

It seems that every MLB season, we witness a failed starter turn into a great reliever. The 2014 season was no different, and one of the biggest transformations came from the arm of Orioles RHP Zach Britton. Britton, a former 3rd round pick by the O’s in 2006, was twice named to Baseball America’s top 100 prospects list during his time as an Orioles’ minor leaguer. Britton would go on to join the Orioles’ rotation in 2011, posting a 4.86 ERA and a 4.23 xFIP over 46 starts from ’11-’13, while splitting time at AAA Nofolk. Due to Britton’s inconsistencies, the Orioles decided to try his hand in the bullpen for the 2014 season, and the results speak for themselves:

76.1 IP, 1.65 ERA, 2.82 xFIP, 75.3% GB rate

The ridiculous 75.3% GB rate comes after Britton posted a 55.5% rate over the previous three years. The key difference was Britton relying heavily on his sinking fastball, going from using it 69.6% of the time as a starter, to 91.5% as a reliever. As with most converted starters, Britton also saw a jump in his fastball velocity, going from an average of 92 MPH from ’11-’13, to 95.1 as a reliever. Is there another pitcher out there that could go from being a very average starter to a top notch closer? There is, and he also resides in the AL East.

Joe Kelly and Allen Craig were shipped to Boston in a 2014 trade-deadline deal that sent veteran John Lackey to the St. Louis Cardinals. In his seven starts with the Cards in 2014, St. Louis witnessed a regression from Kelly that was expected by anyone that had kept up with his peripheral statistics in the previous two seasons. Over the 2012 and 2013 seasons, Kelly posted a 3.08 ERA, which tied Madison Bumgarner and Stephen Strasburg, as well as topped teammate Adam Wainwright’s 3.39 ERA during that time frame. But when you look deeper, you see more numbers that don’t belong next to names like Bumgarner, Strasburg and Wainwright: 6.0 K/9, 4.12 xFIP, and 4.22 SIERA. Despite a fastball velocity that tied Jeff Samardzija’s 94.7, and only finished behind Strasburg and Matt Harvey’s 95.5, Kelly’s results were very mixed. In fact, they showed a great resemblance to someone else’s numbers:

               Kelly         Britton (starter)

FIP        4.11            4.25

xFIP      4.14           4.23

K/9        6.05           5.94

BB/9     3.35           4.00

GB%      52.4%        54.9%

While the numbers are not exactly identical, the results are very similar: two pitchers that had very good stuff, but were very inconsistent. As noted earlier, Britton’s move to the ‘pen was also a move to a primarily two-seam/sinking fastball that induced tons of ground balls. While it is unlikely that Kelly’s move to the ‘pen would turn him into a ground ball machine like Britton, it should be noted that Kelly already possesses an average fastball of 94.7 MPH as a starter, while Britton saw his jump from 91.6 as a 2013 starter to an average of 95.1 as reliever in 2014. A few other notable velocity spikes we’ve seen from pitchers with a history of working as a starter, as compared to  fastball velocity as a reliever:

Tommy Hunter 91.6 to 96.0

Andrew Miller 92.5 to 94.9

Wade Davis 91.8 to 93.7

Joba Chamberlain 92.5 to 94.6

Maybe Kelly puts it together as a starter this season. After all, Boston thought very highly of him if they were willing to give up John Lackey last season. Maybe we see Kelly cut down on his walk rates, and finally put together some peripheral stats that match his strong ERA numbers in 2012 and 2013. But what if he doesn’t? What if he continues to be a bottom-of-the-rotation type pitcher? Boston could move him to the ‘pen, see that 95 MPH fastball bump up to the 97-98 range, and reap similar rewards that the O’s received from Britton in 2014.

Trying to Improve fWAR: Part 1

FanGraphs Wins Above Replacement is considered by many in the sabermetric community be the holy grail of WAR.  And, even though I’m writing a piece that is critical of fWAR, FanGraphs is still the first website I go to when I want to get a basic understanding of a specific player or team’s value.  Don’t view this article as an attack on fWAR or FanGraphs, both of which I use frequently; instead, consider this article as constructive criticism.

fWAR, specifically for pitchers, is riddled with minor problems that together make the metric less valuable.  In Part 1 of the series, we’re going to look at a hotly debated issue regarding fWAR that has been brought up by other readers before: the fWAR park factors.

According to the FanGraphs glossary, a basic runs park factor is used when calculating fWAR.  Because FIP models ERA, using runs park factors for FIP shouldn’t be a problem.

Unfortunately, this idea simply isn’t true.  The inputs of FIP, HR/9, BB/9, and K/9, only include about 30% of plate appearances.  Some ballparks (Citi Field for example), inflate HR/9 and FIP despite suppressing runs in general.  If Pitcher fWAR is based on FIP, FIP park factors, not runs park factors, must be used.  Below is a table comparing runs and FIP park factors for different teams/ballparks, with FIP park factor equaling ((13*HRPF)+(3*BBPF)-(2*SOPF))/(14), with all of the data coming from the FanGraphs park factors.

Season Team Basic FIP Difference
2014 Reds 101 112 -11
2014 Brewers 103 111 -8
2014 White Sox 104 111 -7
2014 Yankees 103 110 -7
2014 Mets 95 102 -7
2014 Phillies 100 106 -6
2014 Dodgers 96 101 -5
2014 Orioles 102 107 -5
2014 Blue Jays 103 108 -5
2014 Astros 100 104 -4
2014 Indians 97 100 -3
2014 Padres 94 96 -2
2014 Mariners 97 97 0
2014 Rays 95 95 0
2014 Rangers 106 106 0
2014 Braves 99 99 0
2014 Diamondbacks 104 103 1
2014 Cubs 102 101 1
2014 Rockies 117 116 1
2014 Tigers 102 101 2
2014 Nationals 100 97 3
2014 Angels 95 92 3
2014 Athletics 97 93 4
2014 Cardinals 98 94 4
2014 Giants 93 88 5
2014 Royals 101 96 5
2014 Twins 101 95 6
2014 Pirates 97 89 8
2014 Red Sox 104 96 8
2014 Marlins 101 90 11

In addition, the standard difference between the Basic and FIP park factors was a staggering 5.5.  Clearly, using runs park factors on FIP significantly benefits and hurts certain teams’ Pitcher fWAR.

While the Marlins, Red Sox, Pirates, Twins, and Royals benefit from park factors that overestimate their ballpark’s FIP-inflating ability, the Reds, Brewers, White Sox, Yankees and Mets experience the opposite effect, falsely increasing/decreasing these teams’  Pitcher fWAR.

Looking at the team pitching leaderboards, the effect of this mistake is pronounced on several teams’ fWAR.  For example, the Mets, despite ranking 9th in the National League in FIP while playing in a ballpark that inflates FIP by 2%, rank dead last in the National League in Pitcher fWAR.  Similarly, the Red Sox rank 5th in the AL in Pitcher fWAR despite ranking 10th in the AL in FIP and playing in a ballpark that suppresses FIP by 4%.

Using FIP park factors instead of runs park factors is a simple change that would vastly improve the accuracy of Pitcher fWAR.  In the next segment of “Trying to Improve fWAR”, I’ll examine the league adjustments (or lack thereof) in both Position Player and Pitcher fWAR.

Progressive Pitch Projections: Four-Seam Fastballs (+ PITCHf/x Simulation)

Last time, we analyzed Yu Darvish’s sliders in terms of when they projected as strikes and how pitch movement affected perception, leading batters to swing at pitches outside of the strike zone in the direction of the pitch movement. This time, we will turn our focus to four-seam fastballs. As before, we are using the 2013 data set since the algorithms for this were run before the completion of the 2014 season. To start, we can examine a four-seam fastball from Yu Darvish, his second-most thrown type of pitch in 2013, via simulation using the nine-parameter PITCHf/x data for its trajectory. The chosen fastball from Darvish was thrown roughly down the middle of the strike zone and we also track the projection of the pitch as it approaches the plate.

 photo Darvish_FF_Middle.gif

Note that the pitch, in this case, is simulated at one-quarter actual speed. The strike zone shown is the standard width of the plate and 1.5 to 3.5 feet vertically. The red circle represents the projection of the pitch after removing the remaining PITCHf/x definition of movement from its current location (Note that while the simulation shown above is a GIF, the actual simulation is an interactive PDF where the controls at the bottom of the image can play, rewind,  slow down, etc. the simulation. This is discussed at the end of the article for the interested reader, including a link to several interactive PDFs as well as a tutorial for the controls and the source code written in TeX). Here, the movement causes the pitch to rise, giving the pitch in the simulation a “floating” quality as it never seems to drop.

As in the previous work on sliders, we will start by splitting the four-seamers into four groups based on the pitch location and the batter’s response: strikes (pitches with a 50% chance or better of being called a strike) and balls (lower than 50% chance of being called a strike), and swings and pitches taken. Working with the projections to the front of the plate after removing the remaining movement on the pitch, we can examine how attractive (in terms of probability that the projection will be called a strike) pitches in each of these four categories, on average, are to batters incrementally as they approach the plate.

To begin, for left-handed batters versus Darvish in 2013:

 photo Darvish_ST_BS_FF_LHB.jpeg

For both types of pitches in the strike zone (red=taken, green=swung at), the average probability of the pitch being called a strike levels off around 20 feet, with strikes swung at peaking at probability 0.919 at 9.917 feet from home plate, then dropping to 0.917 at the plate. Strikes taken reach their maximum at the front of the plate with probability 0.869. The four-seamers swung at outside of the strike zone (blue) average around 0.5 probability of being called a strike up until around 30 feet, before dropping off. The fastballs taken outside the zone (orange) tend to project as low-probability strikes initially and remain so to the front of the plate.

We can simplify this graph to include only swings and pitches taken.

 photo Darvish_ST_FF_LHB.jpeg

Once again, pitches swung at project as better pitches throughout than those taken. The peak for swings is at 14.083 feet with probability 0.782, and finishes at 0.777. The pitches taken keep increasing in attractiveness all the way to the front of the plate, reaching a called-strike probability of 0.332.

To further examine what is happening in these graphs, we can view the location of these projections from 50 feet to the front of home plate. The color scheme is the same as the four-curve plot above.

 photo Darvish_Pitch_Proj_FF_LHB_250ms.gif

Focusing on the blue projections for the moment (swings outside the strike zone), the projections down and to the right of the zone are carried by movement toward the strike zone and most end up as borderline strikes. Those up and to the left project further and further outside the strike zone as they approach the plate, since their direction of movement is roughly perpendicular to the strike zone contour. To get a better idea of the number of each of the four cases in nine regions in and around the strike zone, we can fade the data into the background and replace it in each region by an arrow indicating the direction that the average projection for that area is moving and the number of pitches of that case located there.

 photo Darvish_Pitch_Proj_Gp_FF_LHB_250ms.gif

Focusing first on the pitches in the strike zone, there is a dearth of projections in the upper-right area, which would be on the inside half of the plate to LHB. The pitches taken in the strike zone tend to skew slightly down and to the left, relative to those swung at. Note that in many of the regions around the strike zone, the samples are quite small so it may be difficult to draw any strong conclusions. With this in mind, these results can be summarized in the following table where the center cell represents the swing percentage in the strike zone and all other cells contain the percentage of swings in that region.

Darvish – Four Seamers vs. LHB
13.3 55.6 25
22.9 59 0
4.3 15.9 11.9

The region with the highest swing percentage is the strike zone, at 59%. The region with the next highest percentage is above the strike zone, which is in the general direction of movement, but here there are only nine data points to rely on for this percentage. It would seem that the regions that induce swings are those where the pitches project in the strike zone and are carried out by movement (above and above-and-left of the zone) and where the pitches project as balls but movement is carrying them toward the zone (below and below-and-right of the zone). Notice that the area below and left of the strike zone has 47 pitches thrown there and only 2 swings, which is where the movement parallels the strike zone.

It would appear, based on these observations, that the location of the pitch, relative to the direction of the movement, has an influence on generating swings outside the strike zone. As with the sliders in the previous article, we will use, as a measure of if the pitch is thrown outside in the direction of movement, the angle between the movement of the four-seam fastballs at 40 feet (the pfx_x and pfx_z variables in the PITCHf/x data set) outside the zone and a vector perpendicular to the strike zone extending to the final location of the pitch at the front of home plate. An angle of zero indicates that the movement of the pitch carried it perpendicularly away from the strike zone. Ninety degrees means that the pitch projection parallels the strike zone due to movement. A one-eighty degree angle means that the pitch is being carried by movement perpendicularly toward the strike zone. Further explanation, including a visual depiction, can be found in the link to the previous article at the top of this page.

To begin, we will look at the distribution of angle versus distance from the strike zone for all of Darvish’s four-seamers outside the zone to lefties.

 photo Darvish_Out_FF_LHB.jpeg

Darvish – Four Seamers Outside the Zone v. LHB
Angle Percentage Average Distance
Less Than 45 Degrees 28.9 0.546
Less Than 90 Degrees 58.6 0.517
All X 0.553

The distribution, in this case, seems slightly skewed toward having pitches thrown in the general direction of movement. This visual assessment is supported by the percentages in the table (sorted by angle and average distance from the strike zone contour in feet. e.g., 0.5 = 6 inches, 0.33 = 4 inches), with nearly 29% of pitches having an angle of less than 45 degrees and over 58% with an angle less than 90 degrees. The distribution does not seem to have definitive shape.

 photo MLB_Out_FF_RHP_LHB.jpeg

MLB RHP 2013 – Four Seamers Outside the Zone v. LHB
Angle Percentage Average Distance
Less Than 45 Degrees 37.5 0.557
Less Than 90 Degrees 61.9 0.501
All X 0.477

For all MLB right-handed pitchers in 2013, including Darvish, the distribution is much more clear. There is a swell of pitches thrown with angle between 0 and 90 degrees and within six inches of the strike zone, with 37.5% thrown with an angle of less than 45 degrees, and 61.9% with an acute angle. In conjunction, as the angle increases, the average distance from the strike zone decreases. To get a better handle on the ramifications of this choice of pitch locations, we can further sort the data into swings and pitches taken.

 photo Darvish_Out_Swing_FF_LHB.jpeg

Darvish – Four Seamers Swung At Outside the Zone v. LHB
Angle Percentage Average Distance
Less Than 45 Degrees 43.6 0.315
Less Than 90 Degrees 69.2 0.232
All X 0.248

For Darvish, nearly 44% of the pitches swung at had an angle between the vector perpendicular to the strike zone and the movement vector of less than 45 degrees. For those less than 90 degrees, this percentage jumps to nearly 70%. In addition, the average distance outside with angle less than 45% is an average of 4 inches outside whereas, overall, the average is about 3 inches in all directions. We can compare this to Darvish’s right-handed colleagues in 2013:

 photo MLB_Swing_Out_FF_RHP_LHB.jpeg

MLB RHP 2013 – Four Seamers Swung At Outside the Zone v. LHB
Angle Percentage Average Distance
Less Than 45 Degrees 46.9 0.267
Less Than 90 Degrees 66.8 0.248
All X 0.238

For MLB righties, the largest area of swings is right around a 30-degree angle. Close to half of the swings, 46.9% to be exact, occur when the angle is less than 45 degree and over two-thirds are for pitches in the general direction of movement. The average distance on four-seamers swung at outside is close to Darvish’s overall, but is almost an inch further out for Darvish for 45-degree or less angles. So for RHP to LHB, pitches thrown in the neighborhood of 30 degrees and within a half-foot of the strike zone tend to induce swings, which is also seen for Darvish. We can now look at the complement of this, pitches taken outside, to see how this distribution compares to swings.

 photo Darvish_Out_Take_FF_LHB.jpeg

Darvish – Four Seamers Taken Outside the Zone v. LHB
Angle Percentage Average Distance
Less Than 45 Degrees 26.4 0.611
Less Than 90 Degrees 56.8 0.577
All X 0.605

The distribution for Darvish on pitches taken has some semblance to that for all pitches, but the percentages have dropped in all cases. In addition, the average distances across the board are over six inches outside.

 photo MLB_Take_Out_FF_RHP_LHB.jpeg

MLB RHP 2013 – Four Seamers Taken Outside the Zone v. LHB
Angle Percentage Average Distance
Less Than 45 Degrees 35.1 0.654
Less Than 90 Degrees 60.7 0.571
All X 0.537

For all MLB RHP in 2013, the pitches taken by LHB outside the strike zone are largely located below 90 degrees, with a large number near 60 degrees. Compared to the case of all pitches outside the strike zone, the percentages are not all that dissimilar, but the distances are slightly larger. Putting the two hexplots together to see how they form the plot for all outside pitches, we see that what appears to be one large grouping of data below 90 degrees for all pitches separates into two smaller groupings: one around 30 degrees for swings and one around 60 degrees for pitches taken.

To examine why it might be the case that pitches thrown in the direction of movement, meaning a small angle between the movement vector and the vector perpendicular to the strike zone, are swung at more frequently and are more effective at inducing swings further from the strike zone than those that are not, we can take a four-seamer thrown by Darvish above the strike zone and examine both the trajectory of the pitch and its projection. We can again simulate such a pitch (at quarter speed) via the PITCHf/x data for Darvish. Note that since the below simulation does not possess the same computational capabilities as the rest of the code, which is done in R, we use the standard strike zone as a reference rather than the 50% contour.

 photo Darvish_FF_Top.gif

Based on the simulation and associated projection, we can see that the pitch projects as a strike early on and, late in its trajectory, appears to be a ball. The important observation for this is that, for some part of its flight, the pitch does appear that it may be a strike. Similarly, for a pitch below the strike zone, we see the opposite result.

 photo Darvish_FF_Bottom.gif

One can see the problem with getting a batter to swing at a pitch such as this. It starts out as looking like a pitch in the dirt and, through its path to the plate, only slightly improves its chances of being called a strike, and at no point really gives the batter much incentive to swing at it. Thus it makes sense that a batter might swing at a four-seam fastball high above the strike zone but not one a similar distance beneath.

Performing the same analysis for right-handed batters, we again start with Darvish’s results for the four-seam fastball in terms of ball/strike and swing/take.

 photo Darvish_ST_BS_FF_RHB.jpeg

Here, the swing/strike curve peaks at probability 0.94 at 11.667 feet and finishes at 0.937. These probabilities are slightly higher than those for lefties at the maximum and at the front of the plate. The pitches taken in the strike zone peak at the plate with probability 0.904, compared to 0.869 for LHB. For both cases of pitches outside the strike zone, they reach their maximum very early in the trajectory and drop off afterward.

 photo Darvish_ST_FF_RHB.jpeg

Changing to the two-curve representation for four-seam fastballs to right-handers, the swing curve reaches its apex of probability 0.814 at 19.833 feet and ends with probability 0.797 at the plate. For pitches taken, the average strike probability increases throughout the trajectory, ending at 0.411. Once again, these probabilities are higher than for left-handed batters.

 photo Darvish_Pitch_Proj_FF_RHB_250ms.gif

As before, we can switch to the discrete data and their projections as the pitches near the front of home plate. Of note is that the pitches taken (red data points) are, by and large, down and to the right of the strike zone from the catcher’s perspective, which is in the opposite direction that the movement influences the pitches as they approach the plate. In addition, the majority of swings outside the strike zone, the blue data points, leave the strike zone in the direction of movement. Also of interest is that the pitches fill up the strike zone more against RHB, while four-seamers to LHB were lacking for the inner half of the strike zone. For the pitches swung at outside the strike zone in the opposite the direction of movement, down and to the right, they end up very close the strike zone contour, making them boarderline strikes, and thus nominally classified outside the zone. To observe these phenomena more succinctly, we can switch to a vector representation indicating the number of pitches and the direction that the projections are headed for each of the nine regions in and around the strike zone.

 photo Darvish_Pitch_Proj_Gp_FF_RHB_250ms.gif

Of the 270 pitches in the defined strike zone, the average location of the 112 taken were down and to the right of those swung at, as represented by the red and green arrows. To quantify the percentage of swings in each of the nine regions, we can refer to the below table, aligned spatially with the data from the GIF (center square being in the strike zone).

Darvish – Four Seamers vs. RHB
37.5 47.4 13.3
X 58.5 0
50 13.8 3.7

Based on these results and for regions with more than a handful of pitches, the highest percentages of swings outside the strike zone are in the upper and upper-left regions, in the direction of movement. The lower-left corner is large as well but can be disregarded as it only contains two pitches, one of which was swung at. Also, it is hard to draw any conclusions to the left of the plate since there is no data.

We can now turn our attention to pitches outside the zone for both Darvish and other MLB righties in 2013:

 photo Darvish_Out_FF_RHB.jpeg

First, for Darvish, the distribution of pitches, when viewed by plotting distance from the strike zone versus angle between the perpendicular vector to the strike zone and the movement vector, appears bimodal with a large grouping both above and below the 90-degree mark.

Darvish – Four Seamers Outside the Zone v. RHB
Angle Percentage Average Distance
Less Than 45 Degrees 31.2 0.633
Less Than 90 Degrees 41.1 0.619
All X 0.619

The four-seamers outside to righties are, on average, over 6 inches outside, with most thrown, 59.9% to be precise, in the opposite direction of movement. However, most of the pitches thrown in the direction of movement, 31.2%, are thrown with an angle of less than 45 degrees. Compared to LHB, the distances are greater and the percentage of pitches with an angle of less than 90 degrees is noticeably lower.

 photo MLB_Out_FF_RHP_RHB.jpeg

For MLB RHP, the distribution also appears bimodal, with two groupings of data near 30 degrees and 120 degrees. This roughly mirrors Darvish’s distribution, relative to angle versus distance.

MLB RHP 2013 – Four Seamers Outside the Zone v. RHB
Angle Percentage Average Distance
Less Than 45 Degrees 31.6 0.514
Less Than 90 Degrees 48.6 0.478
All X 0.476

As compared to Darvish, RHP threw about the same percentage of pitches with an angle of less than 45%, but more with an angle of less than 90 degrees. In all cases, the MLB RHP four-seamers outside were, on average, closer to the strike zone. Compared to pitches outside to lefties, the percentages for less than 45 and less than 90 degrees are down.

 photo Darvish_Out_Swing_FF_RHB.jpeg

Taking the subset of pitches swung at outside for Darvish, the distribution has become closer to having a single mode near 30 degrees. Despite reaching into small sample sizes for this subset, the below table reinforces these conclusions.

Darvish – Four Seamers Swung At Outside the Zone v. RHB
Angle Percentage Average Distance
Less Than 45 Degrees 67.6 0.351
Less Than 90 Degrees 73.5 0.347
All X 0.313

While only around 30% of Darvish’s pitches were thrown with an angle of 45 degrees or less, over two-thirds of his swings outside the strike zone were in this range of angles. This increases to nearly 75% when considering four-seam fastballs thrown in the general direction of movement, meaning 90 degrees or less. Of note here is that the distance that entices a swing decreases as the movement aligns less and less with the vector perpendicular to the strike zone. Here, the distances are greater compared to left-handed batters faced by Darvish in 2013, but the percentages are up.

 photo MLB_Swing_Out_FF_RHP_RHB.jpeg

Switching the larger sample of all 2013 MLB RHP, we retain only one of the modes observed for all pitches. The pitches that are swung at outside are clustered down near 15 degrees and within half a foot of the strike zone.

MLB RHP 2013 – Four Seamers Swung At Outside the Zone v. RHB
Angle Percentage Average Distance
Less Than 45 Degrees 53.9 0.286
Less Than 90 Degrees 74.2 0.268
All X 0.255

The percentage of swings with an angle of 45 degrees or less is over 50% and, like Darvish, those less than 90 degrees are up near 75%. The distance again decreases as the angle increases and, compared to Darvish, is much closer to the zone. Versus right-handed batters, the percentages for angles 45 and 90 degrees or less are greater but the distances do not differ greatly as compared to LHB.

 photo Darvish_Out_Take_FF_RHB.jpeg

The other half of the data, pitches taken outside, gives us the second mode seen originally in Darvish’s data. This mode is a cluster of data above the 90 degree level.

Darvish – Four Seamers Taken Outside the Zone v. RHB
Angle Percentage Average Distance
Less Than 45 Degrees 23.8 0.794
Less Than 90 Degrees 34.5 0.736
All X 0.681

While a quarter of the pitches taken are thrown with an angle of 45 degrees, only a little over one-third were thrown in the general direction of movement. Note that the pitches that are thrown in the direction of movement and are taken tend to average three-quarters of a foot outside, so it makes sense that they would not be swung at. The percentage of pitches taken with an angle of less than 90 degrees is down from 56.8% for LHB and, overall, the pitches are almost an inch further outside.

 photo MLB_Take_Out_FF_RHP_RHB.jpeg

For the MLB data set, the second mode is located around 120 degrees.

MLB RHP 2013 – Four Seamers Taken Outside the Zone v. RHB
Angle Percentage Average Distance
Less Than 45 Degrees 24.9 0.662
Less Than 90 Degrees 41 0.592
All X 0.542

As with Darvish, about one quarter of the pitches taken outside are at an angle of 45 degrees or less and 59% are thrown in the opposite direction of movement. When put up against the pitches taken by LHB, the percentages are down for both 45 and 90 degree or less pitches from 35.1% and 60.7%, respectively.

As with RHP versus LHB, the full distribution, in terms of the hexplots, separates into two clusters: one related to swings and one related to pitches taken. The cluster related to swings sits in the range of 15 degrees while pitches taken are closer to 120 degrees. This is similar to the case for lefties, except the cluster of pitches taken moves from the 60-degree area to the 120-degree area and the cluster related to swings moves down from 30 degrees to 15 degrees. However, in both cases, the swings appear to be separate clusters from the pitches taken.


For four-seam fastballs thrown by Yu Darvish in 2013, the maximum attractiveness on swings is in the range of 10-20 feet in front of home plate for left- and right-handed batters, possibly tying into how long a batter can reasonably project a pitch when deciding to swing. The four-seamers also tend to be swung at outside the strike zone in the general direction of movement, which we have seen previously with sliders. This is especially pronounced for RHB vs RHP, with pitches exiting the strike zone in the direction of movement causing swings, and pitches entering the zone opposite the direct of movement being taken. By simulating the PITCHf/x data, we can get an idea of why this might be true: pitches outside thrown in the general direction of movement project in the strike zone for some period of time before projecting outside of it and pitches thrown opposite this direction project outside and, while their probability increases, these pitches never appear as strikes and thus do not usually induce swings from the batter.

Next time, we will finish up with cut fastballs from Yu Darvish and see how movement affects perception in this case. After that, we can switch to the 2014 data set and also turn the algorithm around and apply it to a batter.

PITCHf/x Simulation

For those familiar with the previous installment, we covered a slider thrown by Yu Darvish to Brett Wallace and simulated the projected pitch location in R. To better represent how the pitch projection may tie into perception, we have switched to a more visually appealing representation of simulating the PITCHf/x data in the context of the catcher’s viewpoint (we could presumably display this from the batter’s point of view as well). For the aforementioned slider to Wallace, the simulated PITCHf/x data, based on the 9-parameter model, is:

 photo Darvish_Slider.gif

This would seem to be a better way to represent the data, including a backdrop and accurate scaling of the pitch size and location. As another example, we can simulate a random Darvish curve:

 photo Darvish_Curve.gif

In order to make the GIFs for simulating the PITCHf/x data, we are first using TeX to write the code and then compiling it using MiKTeX with the “animate” package handling the controls. To begin, we place a reference point 6 feet, 1 inch behind the tip of home plate, roughly approximating the location of the catcher (the one inch past six feet is not important but makes the distance to the front of home plate an even 7.5 feet).  The height of the reference point is taken to be 2.5 feet in the z-direction. This is the point by which we will determine perspective. Everything will be projected into the plane at the front of home plate, spanning three feet to the left and right of center and from the ground to five feet high. For a given position of the pitch, we find the associated spherical coordinates, relative to the reference point. To figure out where to display the pitch in the frame, we track the pitch along the line formed between the pitch location and the reference point until it reaches the frame. Since the two angle measures of the spherical coordinates will not change when tracking along this line, we need only find the distance along it that places it in the frame we are displaying.

Once we have the location of the pitch in the frame, we still need to find the size of the pitch as seen from that distance. To do this, we again use the reference point and find the distance to the center and to the top of the baseball. With a third side that goes from the top to the center of the baseball, this creates a triangle. Forming a similar triangle by adding an additional third side where the frame cuts the triangle at the front of the plate, we obtain a smaller triangle contained in the larger one. Using this geometry, we can find the size that the pitch will appear at this distance using trigonometric properties of similar triangles (namely that their sides have the same ratio).

To begin the simulaton, we find the times associated with 55 feet and the front of the plate. We then find the location of the pitch in three dimensions, incrementing in time from release to strike zone and adjusting the location and the size of the pitch to appear positioned and scaled correctly in frame. The simulation in the actual PDF is at 60 frames per second, with most animations lasting around a half a second. For the purposes of creating GIFs, we slow the pitches down to one quarter this speed and capture using a program called LICEcap. The code is written so as to work for any pitch by merely swapping in the chosen 9-parameter PITCHf/x data and recompiling. The projection is shown as a red circle, and is calculated as previously discussed. All background features are scaled appropriately, in a similar manner as the pitch.

Note that while this is, in many ways, an approximation of perception from the catcher’s point of view, it functions well for our purposes of providing a decent replacement for live video since we can overlay the projection and view it from the reverse of the traditional television angle from center field. Included is a link to a Google Drive containing a collection of interactive PDFs for pitchers and pitches from 2013 and 2014. There is also an interactive guide to the controls with the given example being a Clayton Kershaw slider. Finally, the source code is included so the interested reader/programmer can input any chosen PITCHf/x parameters and compile to get a representation of the pitch, that includes distance to home plate, the velocity of the pitch, and the time since release.