Archive for September, 2014

Curtis Granderson: Another Mets Free Agent Bust?

The Mets took a chance last year and inked Curtis Granderson, age 33, to a four-year contract worth $60 million. Granderson was just coming off an injury plagued season with the Yankees in which he fractured his right forearm, and then the pinky in his left hand, sidelining him for over 100 games. In 2013 he posted a slash line of .229/.317/.409. Prior to his 2013 season, Granderson finished 4th in MVP voting in 2011, and was an All-Star in 2011 and 2012, finishing with more than 40 HR and 100 RBI’s.

So what can we expect from Curtis Granderson for the rest of his career with the Mets? Is there hope that he will be the big clutch hitter the Mets desperately need and come close to his 2011 and 2012 seasons with the Yankees? Or will his name be forever remembered by Mets fans in the same category as Jason Bay and Chris Young, forged in the hall of ineptitude? Here is a look at Curtis Granderson’s numbers after 2010 when Granderson turned 29 and started his stint with the Yankees. Here is a look at some of his numbers from 2010-2012, before his injury-riddled 2013 campaign:

Season Age G AVG OBP SLG wOBA HR R RBI BB SO
2010 29 136 .247 .324 .468 .344 24 76 67 53 116
2011 30 156 .262 .364 .550 .393 41 136 119 85 169
2012 31 160 .232 .319 .492 .346 43 102 106 75 195
Average 151 .247 .336 .503 .361 36 105 97 1 157

It is important to note that he is playing the majority of his games at notoriously hitter-friendly Yankees Stadium. Using a measure of the effect of Yankee Stadium called park index, it can found that Yankee Stadium has about a +3% increase on a left-hander’s average, and a +53% on a hitter’s home run total. Granderson hit 56 total homers at Yankee Stadium from 2010-2012. After the Mets reconfigured their outfield, their left-handed batters hit on average +2% more home runs. If we adjust Curtis Granderson’s home run total to playing at CitiField for these years, his adjusted home run total is somewhere between 26-27 per year.

This still is a great total, and I think any Met fan would welcome a 25+ home run season from Granderson with open arms. Right now there are 10 games left in the season and Granderson has 18 home runs. He could sit around 20 this season which would not be terrible unless we remember his atrocious .218/.320./.374 slash line. We also have to consider the unfortunate factor of Granderson’s age to this equation. Granderson has a little bit of a strange aging curve because of his incredible seasons at age 30 and 31. I decided to look at how similar players performed at ages 32, 33, 34, and 35 (no player that has a top-ten similarity score has played a season at age 36 yet). The similarity scores were calculated based on Baseball-Reference’s similarity scores equation.

All of my worst fears came true and I started having flashbacks of one of the all-time worst Mets busts as I saw the name that popped up at number 1 — Jason Bay. Here is what other similar players did at age 32, 33, 34, and 35 (I omitted information if a player played less than 70 games aside from Granderson’s season at age 32.):

Sim Player OPS- age 32 OPS- age 33 OPS- age 34 OPS- age 35
Curtis Granderson 0.72 0.69
922 Jason Bay 0.70 0.54 0.69
914 Wally Post 0.84 0.53
908 Jesse Barfield
906 Jose Bautista 0.86 0.92
903 Jose Cruz 0.73 0.69
901 Preston Wilson
899 Edwin Encarnacion
899 Phil Nevin 0.82 0.86 0.67 0.76
896 Larry Hisle
894 Jayson Werth 0.72 0.83 0.93 0.83

This does not paint a good picture of what we hope to expect from Granderson. For a player signed to the amount of money as Granderson, I would like to see an OPS around or above .800. There are only two out of ten players — Phil Nevin and Jayson Werth, that hit decently at the advanced ages of 34 and 35 (Werth is hitting pretty well with over 80 RBI’s with an OPS above .800, Nevin hit decently with a 0.76 OPS and 22 home runs at age 35). Six out of ten players ended their careers following a tremendous decline before getting to age 34 (I included Jason Bay whose career was arguably over before age 31, a year after signing with the Mets), Edwin Encarnacion is too young to make any conclusions about, and it is looking like Jose Bautista will play well, or at least decently at ages 34 and 35.

Even though most similar players did not have good seasons, or even reach seasons at ages 34, 35, and 36, similar players like Jayson Werth, Phil Nevin, and Jose Bautista give us a glimmer of hope. Similar players in no way give us a definitive look at a player’s future, so there is also always the possibility Granderson carves himself a much different path than any of the players on this list. To determine what might be causing Granderson’s decline, I’m going to look through Granderson’s batted ball statistics along with walk rate and strikeout rate:

Year Team Age BB% K% GB% FB% HR/FB BABIP
2010 Yankees 29 10.0% 22.0% 33.0% 47.2% 14.5% .277
2011 Yankees 30 12.3% 24.5% 33.8% 48.0% 20.5% .295
2012 Yankees 31 11.0% 28.5% 33.1% 44.0% 24.2% .260
2014 Mets 33 12.3% 22.0% 33.2% 48.3% 9.5% .255

The most glaring discrepancy between Granderson’s time with the Mets and Yankees is his HR/FB rate. His BABIP has gone down a little, but it is not that far removed from his numbers from 2010-2012. BABIP is a good statistic to look at to determine if a player is having a relatively unlucky season by comparing it to that player’s normal BABIP. It looks like he might have been a little lucky getting hits in 2011. Other than that, BABIP does not tell the story of what has happened to Granderson in 2014.

My initial thought from watching Granderson play daily was that he is striking out at a much higher rate. In fact, his K% is lower than it was in 2011 and 2012, and on par with what it was in 2010. And here is where we come to his HR/FB. Although Granderson is hitting about the same FB%, the percent of his fly balls that are going out of the park is dismally low compared to how it was when he was hitting 40+ home runs at Yankee Stadium. Although this could partially be age-related, it could be easily argued that a huge component of this is also the change in ballpark where Granderson plays. It is hard to determine if Granderson could possibly change his approach somehow to adjust to CitiField’s landscape when he is going to be 34 years old next year. The future is looking bleak for Mets fans unless Granderson can figure out how to turn things around next season.


Javier Baez: It Won’t Mean a Thing if He Don’t Fix His Swing

“It looks like he’s going to be able to stay in an up-the-middle position on the defensive spectrum,” added the National League scout. “When you have a combination of speed, defense and power, like he has, that’s hard to find in the middle of the diamond. In the end, he looks like a player who has a chance to legitimately contribute to a major-league club on both the offensive and defensive sides of the ball.”

No, that scout wasn’t talking about Cubs uberprospect Javier Baez, but rather about Cubs ex-uberprospect Brett Jackson , as told to David Laurila back in March, 2011. Before Baez, and Jorge Soler, and Kris Bryant, it was Jackson who was the Anointed Expurgator of Ruminant Curses. As you probably know, the goat turned out to be too strong for B-Jax, who struck out at an epic 41.5% rate with the Cubs before being exiled to the minors, where his bat continued to avoid contact with the same unerring purpose with which children avoid vegetables. Theo ultimately traded him to the Diamondbacks for a few Jerry Colangelo bobbleheads.

Here’s Jackson’s line from his fly-on-windshield season in 2012 with the Cubs:

144 PA, 41.5% K, .175/.303/.342, 78 wRC+.

And here’s Javy’s line as of September 14:

166 PA, 41.6% K, .174/.229/.387, 68 wRC+.

Scary stuff, kids. Now several caveats obviously apply here, including small sample size. The players themselves are quite different. Jackson was a five tool guy who was good at everything but exceptional at nothing. While Baez has certainly had to rearrange his garage to fit all his tools, his calling card is Sheffield-like bat speed. Baez is almost without doubt the most exciting .174 hitter the game has ever seen. But the question is whether the rapidly bleaching bones of Brett Jackson’s career stand as a warning to Baez, and to those in the Cubs front office that see him as an anchor tenant at Wrigley for years to come.

To examine this, I compared Baez’s progress from high-A to the majors with Jackson’s, and I also threw in two guys that have had immediate success in The Show. George Springer (another high K guy) and Soler (a much more disciplined prospect).

Starting at high-A, the players looked like this:

Baez:          337 PA, 23.1& K, .274/.338./.535, 145 wRC+

Jackson:    312 PA, 20.2%, .316/.422/.517, 170 wRC+

Soler:         236 PA, 16.1% K, .281/.343/.467, 128 wRC+

Springer:   500 PA, 26.2% K, .316./.398/.557, 143 wRC+.

This includes only Baez’s high-A appearances in 2013 — I’m leaving out 86 PAs from 2012 in which Baez was only modestly effective. B-Jax wins this round, although Soler’s advanced approach is already apparent.  All four had good years.

Here’s how they performed at AA:

Baez:          240 PA, 28.8% K, .294/.346/.638, 180 wRC+

Jackson:    297 PA, 24.9% K, .256/.373./.443, 123 wRC+

Soler:           79 PA, 19.0% K, .415/.494/.862, 265 wRC+

Springer:  323 PA, 29.7% K, .297/.399/.579, 174 wRC+

Jackson had two roughly equivalent AA seasons in 2010 and 2011 — I’m showing the latter here. Springer had 87 difficult appearances in AA in 2012 — I’m showing his breakout 2013 season. All four struck out more often in AA, but all except Jackson improved on their performances at high-A. Soler’s numbers were insane, and the Cubs quickly promoted him to AAA to give him some more challenging pitches to work with.

And speaking of AAA:

Baez:         434 PA, 30.0% K, .260/.323/.510, 108 wRC+

Jackson:   467 PA, 33.8% K, .256/.348/.479, 107 wRC+

Soler:         127 PA, 20.5% K, .282/.378/.618, 149 wRC+

Springer:  266 PA, 24.4% K, .311/.425/.626, 175 wRC+

This is Jackson’s 2012 line at AAA. He put up a better wRC+ of 128 in 2011, in 215 appearances. I’m showing Springer’s AAA numbers for 2013; he had 61 arbitration-delaying PAs in 2014, in which he performed even better before being promoted. Springer actually improved his whiff rate in AAA, turning in a dominating season. Soler’s ludicrous AA numbers came somewhat back to Earth, but he still raked, with a K% only slightly worse than in AA.

Baez and Jackson, on the other hand, began shipping water. Their seasons were not horrible, but they performed significantly worse than they had in AA, with rising (and in B-Jax’ case, skyrocketing) strikeout rates. Both would carry their decaying swings to the major league level, where they both have paid a huge price, whiffing over 40% of the time. Springer also added about 10% to his K rate on reaching the majors, but he started from a lower base, and retained enough on-base to be a plus hitter (.231/.336/.468) before injuries sidelined him.

If Jackson represents the sum of all Baez’ fears, Springer represents the hope. Springer actually struck out more frequently than Baez in the lower minors, but Springer found a way to reduce his strikeout rate at AAA, and has found a way to produce at the major league level even while whiffing a third of the time. While Springer may not be able to sustain this productivity unless he once again addresses his contact problems, his strikeout rate isn’t unheard of in the majors. Baez’ rate, at 41%, lies largely outside the realm of civilized baseball discourse.

As of this writing, no qualifying hitter has a K rate anywhere near 40%. Indeed, there are only four hitters with a K rate exceeding 30% (Chris Davis, Chris Carter, Adam Dunn, and B.J. Upton). Two of these guys (Carter and Dunn) are have a wRC+ over 100; the other two do not. The worst career strikeout rate (minimum 1000 PAs) belongs to Tyler Flowers at 34.8%. No player has long survived in the majors beyond this forbidding boundary. The worst career K rate for a player with a career wRC+ over 100 is the aforementioned Chris Carter, checking in at 33.6%. Baez has a long way to go to even reach this dismal rate.

He has perhaps taken some baby steps: after striking out at a 42.2% clip in August, he’s shaved that to 40% in September. His last golden sombrero was on September 5, so it’s been over a week. Umm … yeah … these are the flimsiest of straws to grasp. With Addison Russell, Starlin Castro, and Kris Bryant all staking claims on the Cubs infield, Baez may be running out of time to prove that he can prevent strikeouts from getting his goat.


When Teams Collapse

Watching a team struggle in key games in September is possibly the most painful part of being a baseball fan.  Sometimes they turn it around, but on some occasions a fan base watches a team go from a near certain playoff birth to watching October baseball.  If it looks like your team might fall apart what is it that should worry you most?  My guess is that it should be mental lapses, which would be the most likely thing to increase if the team is feeling pressure.

Mental lapses have a couple of possible proxies in baseball statistics, and one would be errors.  Teams that are on the path to collapse might be identifiable if they start having more blunders in the field than they had earlier in the year.  Historically it looks like this might be true.  Coolstandings has a list of some of the greatest collapses from a playoff odds standpoint.  Eight of the top ten collapses show an increase in errors during the month of September.

 photo Errors_zpsedfd5cd7.png

 

Only the 2011 Braves and the 1999 Reds had lower errors per game in September while collapsing and the Reds were pretty close to the same as the season as a whole.  These gaps are also somewhat conservative since I included September in the whole season number, so the differences from the rest of season would be greater.  Also, the September number includes regular season games that end up in October.  As you an see, the difference on average for the collapsing teams is .117 more errors per game or 17.6% more errors per game than their season as a whole.  The 2011 Braves might be the exception that proves the rule as they were way, way better in September at avoiding errors only having 5 the entire month.  If you take them out the average difference shows almost 25% more errors for collapsing teams in September.

This could be something other than mental issues.  It is possible that errors are higher in general in September due to things like expanded rosters, but of course contending teams aren’t going to be giving a lot of opportunities to unproven talent and shouldn’t be subject to that sort of thing.  Errors  don’t need to be the only proxy either, as I think making outs on the base paths or throwing to the wrong base/missing cutoff men sorts of mental lapses might work too.  Maybe it work better to add up all “mental mistakes” and then look for differences.  We could also look at it in a sort of contagion effect, but I am going to need a site to start giving monthly splits for all team data in an easily accessible way first.

Pressure and other intangible sorts of ideas are always hard to directly study, but we have all felt it manifest in our own lives so we can’t expect professional athletes to be immune to such things.  Watching the Royals the last two weeks or so I have felt like this is happening at times (though Lorenzo Cain literally just smashed a three run bomb off of Chris Sale).  Any Oakland fans feel like they have seen this too?


Streaking with Phil Hughes

Phil Hughes is currently enjoying his most fruitful season as a starter. Indeed, he has already received considerable attention for his improved control  and refined repertoire. Nonetheless, several recent feats merit additional attention.  Indeed, Phil Hughes’ most recent start against the Chicago White Sox saw several notable streaks come to an end.

 

 

Hughes certainly wasn’t pleased with himself, and for good reason: he had just issued a free pass and put a runner on first base. Perhaps Hughes grasped the historic implications of that BB — he hadn’t issued a walk since August 10th against the A’s. That streak spanned 160 consecutive batters faced, including five walk-free games. Hughes pitched 37 innings without giving up a walk over those five games — the average MLB starting pitcher, posting a BB/9 of 2.7, would have walked over 11 batters during that span.

Hughes’ streak certainly appears impressive, but exactly how does it compare to his peers? Well, no other starting pitcher has managed such a streak this season… except for Phil Hughes. That’s right — Hughes had already posted a streak of 178 consecutive batters faced without a walk. Spanning from April 20th to June 1st, that streak included six walk-free games!

Hughes’ refusal to issue walks puts him in some pretty elite company. Observe the table below:

Table 1: For Starting Pitchers from 1969-2014, Longest Consecutive BB-Free Game Streaks, Sorted by IP.

Rk Name Strk Start End IP Games W GS CG H ER BB SO HR ERA HBP Tm
1 Greg Maddux 6/25/2001 8/7/2001 65.1 9 8 9 1 69 22 0 45 3 3.03 0 ATL
2 Randy Jones 5/21/1976 6/18/1976 60 7 5 7 5 53 16 0 14 5 2.4 0 SDP
3 Greg Maddux 8/3/2007 9/13/2007 53.2 9 5 9 0 56 19 0 30 2 3.19 1 SDP
4 David Wells 9/6/2002 4/16/2003 53 7 6 7 2 42 11 0 36 4 1.87 4 NYY
5 Javier Vazquez 5/1/2005 6/4/2005 50 7 3 7 2 51 19 0 41 4 3.42 3 ARI
6 Greg Maddux 6/9/1995 7/6/1995 47 6 4 6 2 39 5 0 36 1 0.96 0 ATL
7 Bob Tewksbury 6/20/1993 7/17/1993 44 6 4 6 0 43 12 0 21 2 2.45 1 STL
8 David Wells 8/24/2004 9/18/2004 41 6 5 6 0 36 14 0 28 6 3.07 0 SDP
9 Phil Hughes 4/26/2014 5/27/2014 40.1 6 4 6 0 38 7 0 30 1 1.56 0 MIN
10 Paul Byrd 5/4/2007 5/30/2007 40 6 4 6 0 49 16 0 21 6 3.6 1 CLE
11 Randy Jones 4/23/1980 5/16/1980 39.1 5 3 5 3 26 4 0 17 1 0.92 0 SDP
12 Bob Tewksbury 6/20/1992 7/9/1992 38.2 5 3 5 2 37 4 0 17 1 0.93 0 STL
T-13 LaMarr Hoyt 7/13/1983 8/7/1983 38.1 6 5 6 1 44 18 0 24 6 4.23 0 CHW
T-13 Brian Anderson 8/28/1998 9/19/1998 38.1 5 3 5 1 37 12 0 13 5 2.82 0 ARI
T-15 Cliff Lee 9/23/2012 4/9/2013 37.2 5 2 5 0 30 7 0 37 5 1.67 0 PHI
T-15 Moose Haas 4/16/1982 5/10/1982 37.2 5 1 5 0 37 12 0 19 2 2.87 2 MIL
T-17 Phil Hughes 8/16/2014 9/6/2014 37 5 3 5 0 31 9 0 31 3 2.19 2 MIN
T-17 Curt Schilling 5/13/2002 6/3/2002 37 5 4 5 0 26 9 0 47 1 2.19 2 ARI
19 Brad Radke 4/19/2005 5/10/2005 36.2 5 2 5 2 41 12 0 24 6 2.95 0 MIN
T-20 Brian Tollberg 7/16/2001 8/22/2001 36.1 6 3 6 0 44 19 0 24 6 4.71 2 SDP
T-20 Curt Schilling 8/20/2004 9/10/2004 36.1 5 5 5 0 28 9 0 34 3 2.23 1 BOS

Since the mound was lowered 45 years ago, Hughes’ streaks rank 9th and T-17th respectively. Notice the other pitchers who have multiple streaks in the top 20: Greg Maddux, David Wells, Randy Jones and Curt Schilling. For a guy who signed for $8M/year, that’s some impressive company (and Randy Jones). While Phil Hughes certainly isn’t Greg Maddux, his ability to limit walks has helped him post an xFIP of 3.17 this year, giving the Twins the closest thing to a true No. 1 starter they’ve had since Johan Santana.

Interestingly enough, Hughes made even more history against the Chicago White Sox, this time snapping a team-wide streak for the Minnesota Twins.

 

At first glance, there is hardly anything remarkable about this outcome. Hughes has struck out 175 other batters faced this season, and Tyler Flowers has struck out in 152 other plate appearances. This, however, was Hughes’ 10th strikeout of the day — an arbitrary but nonetheless impressive feat.

With this punch-out, Hughes finally put an end to an ugly streak in Twins’ recent history: a Twins’ starting pitcher hadn’t fanned 10 batters in an outing since Francisco Liriano’s 10K performance against the Baltimore Orioles on July 18th, 2012. The Twins’ streak of 379 games without 10 punch-outs from a starting pitcher was the longest active streak in the league. During that 379-game drought, starting pitchers from the league’s 29 other teams amassed a total of 497 10-strikeout performances.

It’s no secret that Twins’ starters have been remarkably inept at missing bats in recent history. The table below depicts the depth of their woes over the past five seasons.

Table 2: From 2009-2014, Starter K/9 Including Mean & Standard Deviation

Rank Team K/9
1 Giants 7.85
5 Cubs 7.38
10 Braves 7.23
Mean 6.96
15 Marlins 6.93
20 Angels 6.81
25 Athletics 6.64
29 Orioles 6.28
30 Twins 5.84
σ 0.44

At more than 2.5 standard deviations below the mean K/9, Twins’ starting pitchers have been tremendously poor at striking hitters out over the last five seasons. Whether or not this has been a function of design or merely ineffectiveness, the Twins’ rotation has severely hurt the team, posting an ERA of 4.88 during that period. Within this context, Hughes’ outing is truly shocking.

Perhaps Hughes’ outing is a sign of better fortunes to come for the Twins. Perhaps it was an anomaly. Both Hughes (11K) and Quintana (13K) set career-high strikeout totals in their respective starts. At one point, the never-prone-to-hyperbole White Sox broadcast team proclaimed, “You give Chris Sale this visibility, starting every game at home…he would re-write the strikeout record book.”

Regardless of the game conditions, Hughes’ start featured several remarkable feats. Ironically, while Hughes’ lone walk (a negative outcome) allows us to appreciate his greatness, his 10th strikeout (a positive outcome) allows us to contextualize the Twins’ incompetence. Here’s to you, Phil.

Editor’s Note: As I conclude this article, the Twins’ Trevor May has just fanned 10 batters in his Sunday start against the White Sox. Here’s to you as well, Trevor.

Statistics courtesy of FanGraphs, historical data courtesy of Baseball-Reference, and gifs courtesy of MLB.TV.

Ben Cermak lives in Manhattan and spends far too much time thinking and writing about baseballYou can contact him via email at bcermak14@gmail.com


Response to “A Nice Problem to Have”

Normally, one would leave a comment in response to an column, rather than writing a full blown piece, but that FanGraphs is devoid of response pieces may mean that FanGraphs is devoid of a possible method of furthering our understanding of baseball. Different opinions and viewpoints lead to different ideas, possibly allowing other readers to think about the game in different ways. So, without further ado…

Jose Ramirez has certainly had an adventurous 2014 professional season. After starting the year in Triple-A Columbus, the 21 year old Dominican had a brief and unsuccessful stint for Cleveland, and was promptly sent back down after the recovery of second baseman Jason Kipnis. Since Asdrubal Cabrera was traded, Ramirez has been an everyday player, and from that point onward, Ramirez has been batting at a 105 wRC+ with a .328 BAbip, a reasonable number for someone with his kind of speed and spray hitting ability. Additionally, while not sterling, his 5.8 BB% and 12.7 K% are acceptable numbers for a rookie shortstop, particularly when compared to the average shortstop, who measures at 6.8% and 18.1% respectively.

Of course, as promising as Ramirez has appeared, he has only accumulated 173 PAs since the Cabrera trade, and his true value offensively may be less than he has shown. As Sarris points out, his defense though is where Ramirez truly shines. His defensive ratings statistically check out, and though it takes years for these ratings to stabilize, there is some possibility that these numbers are accurate or they even undersell his value. Already this year, Ramirez has been worth 1.4 WAR, thanks to his 5.8 UZR (placing him fifth amongst shortstops on the 2014 season). And as Sarris also pointed out, Ramirez has passed the eye test with flying colors.

It appears that Cleveland’s future would be more successful with Ramirez than without him. The Indians also have Francisco Lindor waiting in Triple-A Columbus ready to take his throne as long-term shortstop. Since his defensive value is supposed to make up the majority of his overall value, his floor would seem to be higher than the average shortstop prospect. Even if his bat is just league average, his defense should elevate him to an All-Star level, if everything goes according to plan. Sarris’s metric of 69.3% bust rate amongst shortstops rated in the top 100 prospects includes players at all levels of the minors. Of course, players in the lower minors have more volatile futures as their high praise is based more upon projection than offensive or defensive output. Lindor has made it through the minors, and is ready to assume his throne.

Ramirez’s defensive value lies in his cavernous range and sure-handedness, traits that will suit him almost as well at second base. Kipnis’s skills at second base have been only so-so in his career, and unless he learns the secret of Jhonny Peralta, he is unlikely to improve as his career transpires. A switch to the outfield, or even first base (with Santana switching to a full-time DH role), would be acceptable, as Kipnis’s value lies in his offensive game. However, if anyone should be traded, it is Kipnis, who, like Starlin Castro in Chicago, may be usurped by better, younger players, and whose trade value lies in past success.


Are All “Wins” Created Equal?

WAR is considered by many members of the baseball community to be the best all around evaluator of a player’s value to his team.  It is used to evaluate player’s of different positions and from different eras.  However this might not be as useful in looking at players from different position.

I have developed a model that shows that one win at each position is not actually created equal.  This season Buster Posey and Ben Zobrist have a similar WAR — 5 and 4.9 respectively — but I don’t think anyone will argue that Posey is the better player. In order to determine how much more valuable Posey actually is I created a regression equation.  In order to develop this equation I took stats from the past 5 full seasons (2009-2013).  I took each team’s total number of wins and found the average win total over those 5 seasons.  Then I used the FanGraphs section that allowed me to look at each team’s total WAR by position.  For each position I took the total WAR and divided it by number of games “played” at that position and then multiplied it by 162 to find a season equivalent for each team at each position.  For starters and relievers I just took the WAR numbers and divided by 5.  Then I took these numbers for each team and regressed it against average wins.

Note: I did not include DH as the stats that come from the DH are included in other positions (ex: If Joe Mauer DHs then his stats are included in the catcher WAR).

The resulting equation is as follows:

Wins= 49.3870 + 3.3251 * C + 0.9527 * 1b + 1.5122 * 2b + 1.4703 * SS + 1.5447 * 3b + 1.0027 * Rf + 1.4031 * Cf + 0.4450 * LF + 0.7521 * SP + 0.5137 * RP

R: 0.95

R-squared:0.91

A few quick observations of the equation make sense. An additional win at the catcher position is worth much more than any other position because teams value catchers who can both hit and play solid defense but are extremely willing to sacrifice offense if the guy can play defense. Additionally it supports the theory that the best teams are strong up the middle with SS, 2B, and CF being more valuable than corner OF spots and 1B.

While it is regressed against wins I don’t feel the best application of this model is to predict a team’s wins.  The best application of this will be to evaluate the players to sign in free agency.  This past offseason the Yankees did not sign Robinson Cano to a large contact and instead signed players like Jacoby Ellsbury, Brian McCann, Kelly Johnson, and Brian Roberts.  Johnson and Roberts were supposed to split time at second and Ellsbury and McCann were supposed to be upgrades and C and CF over what the Yankees had had.

2013 2014 Diff
C 0.72 2.6 1.88
CF 3.6 4.7 1.1
2B 6 0 -6

Looking at this chart this shows the WAR by position extrapolated for 162 for the three positions where the Yankees made major changes this offseason. Using the model the moves the Yankees made have actually led to a decrease of over one win.  While that may not seem like a very large difference the Yankees are in the middle of the wild card chase and could fall around one game out the playoffs.  Additionally, the lack of Cano and the struggles of Johnson and Roberts forced the Yankees to go out and trade for Martin Prado and Stephen Drew.  Without the contributions from Prado the Yankees second base position would actually have a WAR of below 1 which would have created an even bigger difference caused by not re-signing Cano.

This model is extremely useful for teams with limited budgets as it could help them determine what players and what positions they should sign in order to maximize their win totals.


Could It Be Time to Update WAR’s Positional Adjustments?

It’s been quite a week for the WAR stat. Since Jeff Passan dropped his highly controversial piece on the metric on Sunday night, the interwebs have been abuzz with arguments both for and against the all-encompassing value stat. One criticism in particular that caught my eye came from Mike Newman, who writes for ROTOscouting. Newman’s qualm had to do with a piece of WAR that’s often taken for granted: the positional adjustment. He made the argument that current WAR models underrate players who play premium defensive positions, pointing out that it would “laughable” for Jason Heyward to replace Andrelton Simmons at shortstop, but not at all hard to envision Simmons being an excellent right fielder.

This got me thinking about positional adjustments. Newman’s certainly right to question them, as they’re a pretty big piece of the WAR stat, and one most of us seem to take for granted. Plus, as far as I’m aware, none of the major baseball websites regularly update the amount they credit (or debit) a player for playing a certain position. They just keep the values constant over time. I’m sure that whoever created these adjustments took steps to ensure they accurately represented the value of a player’s position, but maybe they’ve since gone stale. It’s certainly not hard to imagine that the landscape of talent distribution by position may have changed over time. For example, perhaps the “true” replacement level for shortstops is much different than it was a decade or so ago when Alex Rodriguez Derek Jeter, Nomar Garciaparra, and Miguel Tejada were all in their primes.

I decided to try and figure out if something like this might be happening. If the current positional adjustments were in fact inaccurately misrepresenting replacement level at certain positions, we’d expect the number of players above replacement level to vary by position. For example, there might be something like 50 above-replacement third basemen, but only 35 shortstops. Luckily, the FanGraphs leaderboard gives you the ability to query player stats by position played, which proved especially useful for what I was trying to do. For each position, I counted the number of plate appearances accumulated by players with a positive WAR and then divided that number by the total plate appearances logged at that position. Here are the results broken out by position for all games since 2002.

Ch1

Based on this data, it seems like the opposite of Newman’s hypothesis may be true. A significantly higher portion positive WAR plate appearances have come from players at the tougher end of the defensive spectrum, which implies that teams don’t have too difficult of a time finding shortstops and center fielders who are capable of logging WARs above zero. Less than 13% of all SS and CF plate appearances have gone to sub-replacement players. But finding a replacement-level designated hitter seems to be slightly more difficult, as teams have filled their DH with sub-replacement-level players nearly 30% of the time. Either teams are really bad at finding DH types (or at putting them in the lineup), or the positional adjustments aren’t quite right. The disparities are even more pronounced when you look at what’s taken place from 2002 to 2014.

Ch2

The share of PAs logged by shortstops and center fielders hasn’t changed much over the years, but the numbers have plummeted for first basemen, corner outfielders, and DH’s. From Billy Butler and Eric Hosmer, to Jay Bruce and Domonic Brown, this year’s lineups have been riddled with sub-replacement hitters manning positions at the lower end of the defensive spectrum. Meanwhile, even low-end shortstops and center fielders, like Derek Jeter and Austin Jackson, have managed to clear the replacement level hurdle this season if we only count games at their primary positions.

The waning share of above-replacement PA’s coming from 1B, LF, RF, and DH has caused the overall share to drop as well, with a particularly big drop coming this year. Here’s a look at the overall trend.

 

Ch3

And here it is broken down by position…

 

Ch4

And just between this year and last…

 

ch5

 

Frankly I’m not sure what to make of all of this. I’m hesitant to call it evidence that the positional adjustments are broken. There could be some obvious flaw to my methodology that I’m not considering, but I find it extremely interesting that there’s been such a shift between this year and last. We’re talking an 8 percentage point jump in the number of PAs that have gone to sub-replacement-level players. Maybe its been spurred the rise of the shift or maybe year-round interleague play has something to do with it, but it seems to me that something’s going on here. And I’m interested to hear other people’s thoughts on these trends.


Corey Dickerson Doesn’t Care About Your Stupid Strike Zone

Rockies outfielder Corey Dickerson is quietly having an excellent season at the plate. Believe it or not, the 25-year-old is hitting an impressive .315/.371/.577, which even after adjusting for the effects of Coors Field, is still good for a 144 wRC+ — 13th highest among players with at least 400 plate appearances. Dickerson’s batted pretty sparingly against lefties, which has certainly played a role in his gaudy stat line, but platoon or no platoon, a .405 wOBA is certainly nothing to sneeze at.

While Dickerson’s out-of-the-blue breakout is interesting, the approach he’s used to get there is what makes him truly unusual. Since debuting last season, he’s swung at 62% of pitches inside the strike zone and 42% of pitches outside of it, making him about 1.5 times (62%/42%) as likely to swing at a strike than a ball. This is the lowest such ratio of any player with at least 600 PA’s these last two years. Dickerson’s not a free swinger, per se — his overall swing rate of 51% is 38th out of 251 players with at least 600 PA’s — but he just doesn’t discriminate based on whether or not a pitch is in the strike zone. Here’s a look at the hitters with the lowest Z-Swing%/O-Swing% these last two seasons:

Name O-Swing% Z-Swing% Z/O-Swing%
Corey Dickerson 42% 62% 1.47
A.J. Pierzynski 47% 74% 1.58
Salvador Perez 40% 65% 1.60
Dee Gordon 33% 53% 1.61
Shane Victorino 32% 53% 1.66
Alfonso Soriano 42% 69% 1.67
Scooter Gennett 40% 68% 1.68
Charlie Blackmon 39% 66% 1.70
Oswaldo Arcia 39% 66% 1.71
Juan Lagares 35% 59% 1.71
Evan Gattis 41% 70% 1.72
Pablo Sandoval 44% 76% 1.73
Ryan Zimmerman 31% 53% 1.73
Howie Kendrick 38% 65% 1.73
Chris Johnson 40% 70% 1.74

Dickerson’s contact rates tell a similar story. Just like his overall swing rate, Dickerson’s contact rate of 81% isn’t all that interesting. Here, he checks in at 151 out of 251. But also like his swing rate, it doesn’t change very much depending on a pitch’s location. He’s put wood on 83% of pitches he’s offered at in the zone, compared to 74% outside of it, making him 1.1 times as likely to connect on a pitch within the zone — fourth lowest out of 251.

Name O-Contact% Z-Contact% Z/O-Contact%
Victor Martinez 87% 93% 1.07
Pablo Sandoval 80% 87% 1.09
Dustin Pedroia 82% 92% 1.12
Corey Dickerson 74% 83% 1.12
Nick Markakis 83% 94% 1.13
Alexi Amarista 78% 90% 1.14
Brian Roberts 80% 92% 1.14
Eduardo Escobar 74% 85% 1.14
Dee Gordon 80% 91% 1.15
Adrian Beltre 78% 90% 1.15
Ichiro Suzuki 78% 90% 1.15
Yadier Molina 78% 91% 1.16
Denard Span 83% 96% 1.16
Jed Lowrie 77% 90% 1.17
Norichika Aoki 81% 95% 1.17

Multiplying these two metrics (Contact% x Swing%) gives us Dickerson’s contact rate over all pitches seen, regardless of that pitch’s location. Lets call this AllContact% to distinguish it from the traditional Contact%. This number shows just how much of an outlier he really is. For the average major league hitter, a pitch thrown in the strike zone results in contact 2.9 times as often as one outside of it, but for Dickerson, a pitch in the zone is less than 1.7 times as likely. Even if we set the bar as low as 70 plate appearances to include 577 players, this is still the lowest in baseball since the start of 2013.

Name Z/O-Swing% Z/O-Contact% Z/O-AllContact%
Corey Dickerson 1.47 1.12 1.66
Luis Sardinas 1.39 1.24 1.73
Reed Johnson 1.34 1.33 1.79
Alexi Casilla 1.67 1.10 1.83
Dee Gordon 1.61 1.15 1.84
Pablo Sandoval 1.73 1.09 1.88
Ramiro Pena 1.55 1.22 1.89
Jose Iglesias 1.54 1.24 1.90
Salvador Perez 1.60 1.19 1.90
C.J. Cron 1.52 1.25 1.90
Jeff Francoeur 1.63 1.18 1.93
Endy Chavez 1.61 1.20 1.94
Joaquin Arias 1.60 1.22 1.95
A.J. Pierzynski 1.58 1.24 1.96
Ryan Goins 1.46 1.34 1.96

And unsurprisingly, he also the all-time leader since 2007 (the earliest year with PITCHf/x data). Dickerson had the lowest among all players with 100 PA’s here, but I set the threshold to 600 PA’s to avoid having leader board filled with obscure players like Jesus Feliciano and Jordan Brown. In case you were wondering, Vladimir Guerrero checked in at 2.13.

Name Z/O-Swing% Z/O-Contact% Z/O-AllContact%
Corey Dickerson 1.47 1.12 1.66
Tony Pena 1.47 1.22 1.80
Dee Gordon 1.56 1.15 1.80
Salvador Perez 1.61 1.17 1.88
Garret Anderson 1.51 1.25 1.89
Pablo Sandoval 1.74 1.09 1.90
Joaquin Arias 1.58 1.22 1.92
Alexi Amarista 1.73 1.14 1.97
David Eckstein 1.71 1.15 1.97
Bengie Molina 1.74 1.13 1.97
Ichiro Suzuki 1.75 1.12 1.97
Erick Aybar 1.69 1.18 2.00
A.J. Pierzynski 1.68 1.20 2.03
Reed Johnson 1.50 1.36 2.03

Dickerson’s indifference to a pitch’s location means its probably only a matter of time before pitchers just stop throwing the ball in the strike zone, especially if he keeps slugging well above .500. So far this year, opposing pitchers have thrown Dickerson a strike just over 45% of the time. This is lower than the league average of 49%, but isn’t exceptionally low, especially for a free-swinging power hitter. Guys like Jose Abreu, Carlos Gomez, and Pablo Sandoval see strikes around 42% of the time, so pitchers could almost certainly get away with throwing Dickerson a few more balls. Sure, he’s shown that he’s able to hit those pitches, but even for a player like Dickerson, chasing after bad pitches is still a recipe for lots of swings and misses. His 74% O-Contact% is well above the league average of 63%, yet still lower than the overall Contact% of 80%.

Dickerson’s one-size-fits-all approach to swinging has worked well so far, but it remains to be seen what will happen when pitchers start exploiting it by throwing more balls out of the zone. Maybe he’ll be unfazed and keep on raking. Maybe he’ll turn into a strikeout machine, who needs to refine his approach to even stay in the big leagues. Either way, Corey Dickerson‘s a fascinating player, who’s unlike any we’ve seen in recent years, and it’ll be interesting to see if he’s able to keep succeeding going forward.


When Should I Steal?

The Stolen Base

Some consider the stolen base a “lost art.” Gone are the days of Vince Coleman’s back-to-back-to-back 100+ stolen base seasons of Whitey-ball folklore. Teams are stealing at the lowest rates (per game) since the 1950’s.

Stolen Bases by Year

Aside from the 2011 outlier, stolen base rates have trended downward at a serious pace, but stolen bases still have their place in the game, especially in increasingly shrinking run environments, but at what point is the value added from a stolen base worth the risk of an out?

Run Expectancy

Tom Tango’s handy-dandy run expectancy chart can give us this answer. In his run expectancy matrix, we can see how run expectancy can change from one state to another from a series of events. The basic guide that saberists abide by is that you should be able to steal bases twice as much as you get caught trying to steal to break even in expected runs, but every situation is different. With runners on first and third and two outs, you would actually have to steal bases at an almost 6:1 ratio to break even.

This is because of three factors: you are not adding any value to the runner that is already on third, making an out takes the bat out of someone’s hands, and making an out with someone already in scoring position is the most detrimental kind of out. Also, in any given situation, you are facing a battery with different characteristics. Stealing a base off of Kyle Lohse and Yadier Molina was nearly impossible back in 2011. On the other hand, stealing a base off of John Lackey and Jarrod Saltalamacchia would have been a lot easier. Accounting for the risk of your own baserunner, the defense, league rates, and base-out situation will lead to the most informed decision.

In the tool below, begin by picking your situation (the strings go: out, first base, second base, third base where “x” means no runner and a number means a runner occupies that base e.g. 0x2x means no outs and runner on second base). Then evaluate your baserunner’s steal rate against an average opponent (Steamer’s updated projection gives Kolten Wong a 21/24 chance of stealing a base). After that, evaluate your opponent’s steal rate against (lefty or righty pitcher, strong armed catcher). Then plug in the league average steal rate, and you should have an expected stolen base percentage for your given situation and the given change in run expectancy (RE24).

LINK


46 Lines About 7.7 Strikeouts

As of this writing the MLB K/9 stands at 7.7, the highest in recorded history.

>> Here is a list of HOF pitchers with a career K/9 over 7.7:

Nolan Ryan        9.55

Sandy Koufax   9.28

Yep, that’s it.

>> There are 27 active pitchers with at least 1,000 IP and a career K/9 over 7.7.

>> There have been 643 seasons in MLB history in which an ERA-qualifying pitcher put up 7.7 K/9.  Just under half of those (315) have occurred since 2003.

>> The five best 7.7+ seasons by FIP (FIP,ERA,ERA+, K/9):

Pedro Martinez             1999         1.39/2.07/243       13.20

Dwight Gooden             1984         1.69/2.60/137        11.39   19 years old

Clayton Kershaw          2014         1.89/1.70/2.11        10.74   MVP. Yes, I said it.

Sandy Koufax                     1965          1.93/2.04/160       10.24   26 HR allowed

Tom Seaver                      1971          1.93/1.76/194          9.08

>> The five worst 7.7+ seasons:

Brandon Duckworth    2002          4.39/5.41/72          9.22    26 HR allowed

A.J. Burnett                      2007          4.33/3.75/119         9.56    had winning record

El Duque                            2006           4.24/4.66/96         9.09

Tim Lincecum                 2012            4.18/5.18/68          9.19     lead league in losses

Jonathan Sanchez         2009          4.17/4.24/100        9.75

>> The major league strikeout rate has continuously been:

above 7 since 2009

above 6 since 1994

above 5 since 1982

above 4 since 1952

above 3 since 1930

>> The strikeout rate hasn’t decreased since 2005.

>> If the season ended today:

5 playoff teams would have a team K/9 over 7.7

Dodgers        8.4

Angels           8.2

Mariners       8.0

Nationals      7.9

6 playoff teams would have a team K/9 below 7.7

Cardinals      7.6

Athletics        7.5

Giants            7.5

Pirates           7.3

Royals            7.2

Showalters    7.1

>> From 2000-2008, only one World Series champion had a K/9 over 7.7: the 2001 Snakes at 8.0. Since 2008, only one world champ has had a K/9 under 7.7: the 2011 Cardinals (6.8).

I’m not sold on the idea that all these strikeouts threaten Our Way of Life (indeed, this is far more dangerous). But it will be fascinating to learn if some GM will be able to find an underpriced competitive advantage in scouting and developing guys whose bats can locate the ball more often.