Archive for September, 2013

Is Using Wins + Quality Starts the Answer?

Rotograph’s venerable duo Mike Podhorzer and David Wiers recently contemplated aloud a new statistic, formulated by Ron Shandler, that replaces Wins (W) and Quality Starts (QS) by simply adding the two (W+QS). Chandler decided to use this approach in monthly fantasy leagues, and its useful to look at how using this combination could best be used to solve an implacable problem, the overall crappiness of using wins to evaluate a pitcher’s ability.

W+QS is interesting because it weights QS more than W, since a pitcher usually has considerably more QS than W. With a mean of 19 QS and only 12 W, a starting pitcher is more likely to throw at least six innings with 3 earned runs or less than he is to get the W. Wins are capricious and depend greatly on the pitcher’s offensive support. As a way to measure a pitcher’s ability, one might argue that wins are a waste of time. In fantasy baseball, a pitcher is most often valued by his ERA, WHIP, number of Ks and W and Saves. Some more progressive leagues use QS in place of the W.

As evidenced by the table below, ranking a pitcher by W+QS instead of wins alone certainly helps many a fine pitcher, especially James Shields, who leads the league in QS but only is ranked 38th in wins, while also penalizing others like Shelby Miller who has even more wins (14) than quality starts (12). Stephen Strasburg and Cole Hamels see the greatest percent increase jumping from wins to QS+W, while Jeremy Hellickson and Shelby Miller’s total changed the least.

Conversely, Shelby Miller and Jeff Locke saw the greatest increase from quality starts to W+QS, again showing that Mr. Miller, while pitching well his first full season, got the W more often that he made a quality start. A quick glance at his game log shows the innings-limited young pitcher often earned the win when pitching less than the 6 innings needed to record a quality start.

  Comparing Wins, Quality Starts, and Wins + Quality Starts

Name

W+QS Rank

W Rank

Change in Rank

W

QS

W+QS

% Change from W to W+QS

% Change from QS to W+QS

Max Scherzer

1

1

0

20

24

44

120

83

Adam Wainwright

2

3

1

18

26

44

144

69

Clayton Kershaw

3

8

5

15

26

41

173

58

Jordan Zimmermann

4

2

-2

19

21

40

111

90

C.J. Wilson

5

5

0

17

23

40

135

74

Bartolo Colon

6

4

-2

17

22

39

129

77

James Shields

7

38

31

12

26

38

217

46

Cliff Lee

8

12

4

14

23

37

164

61

Patrick Corbin

9

17

8

14

23

37

164

61

Chris Tillman

10

7

-3

16

20

36

125

80

Bronson Arroyo

11

20

9

14

22

36

157

64

Jon Lester

12

10

-2

15

20

35

133

75

Kris Medlen

13

16

3

14

21

35

150

67

Doug Fister

14

21

7

14

21

35

150

67

Hisashi Iwakuma

15

26

11

13

22

35

169

59

Madison Bumgarner

16

27

11

13

22

35

169

59

Mike Minor

17

31

14

13

22

35

169

59

Jarrod Parker

18

42

24

12

23

35

192

52

Anibal Sanchez

19

11

-8

14

20

34

143

70

Mat Latos

20

15

-5

14

20

34

143

70

Yu Darvish

21

28

7

13

21

34

162

62

Hyun-Jin Ryu

22

29

7

13

21

34

162

62

Justin Verlander

23

33

10

13

21

34

162

62

Chris Sale

24

45

21

11

23

34

209

48

Jorge De La Rosa

25

6

-19

16

17

33

106

94

Jhoulys Chacin

26

14

-12

14

19

33

136

74

Felix Hernandez

27

37

10

12

21

33

175

57

Travis Wood

28

66

38

9

24

33

267

38

Zack Greinke

29

9

-20

15

17

32

113

88

Justin Masterson

30

19

-11

14

18

32

129

78

Lance Lynn

31

24

-7

14

18

32

129

78

Jose Fernandez

32

36

4

12

20

32

167

60

Derek Holland

33

54

21

10

22

32

220

45

Ervin Santana

34

67

33

9

23

32

256

39

Cole Hamels

35

74

39

8

24

32

300

33

Jeremy Guthrie

36

23

-13

14

17

31

121

82

Julio Teheran

37

30

-7

13

18

31

138

72

R.A. Dickey

38

34

-4

13

18

31

138

72

Rick Porcello

39

35

-4

13

18

31

138

72

Gio Gonzalez

40

47

7

11

20

31

182

55

Homer Bailey

41

48

7

11

20

31

182

55

Mike Leake

42

18

-24

14

16

30

114

88

CC Sabathia

43

25

-18

14

16

30

114

88

Ricky Nolasco

44

32

-12

13

17

30

131

76

Mark Buehrle

45

43

-2

12

18

30

150

67

Hiroki Kuroda

46

46

0

11

19

30

173

58

Wade Miley

47

58

11

10

20

30

200

50

A.J. Griffin

48

22

-26

14

15

29

107

93

Scott Feldman

49

40

-9

12

17

29

142

71

Andrew Cashner

50

53

3

10

19

29

190

53

Kyle Lohse

51

55

4

10

19

29

190

53

John Lackey

52

57

5

10

19

29

190

53

Eric Stults

53

60

7

10

19

29

190

53

Matt Harvey

54

65

11

9

20

29

222

45

Dillon Gee

55

41

-14

12

16

28

133

75

Wily Peralta

56

51

-5

11

17

28

155

65

Andy Pettitte

57

59

2

10

18

28

180

56

Miguel Gonzalez

58

61

3

10

18

28

180

56

Felix Doubront

59

49

-10

11

16

27

145

69

Yovani Gallardo

60

50

-10

11

16

27

145

69

Kyle Kendrick

61

64

3

10

17

27

170

59

Matt Cain

62

75

13

8

19

27

238

42

Shelby Miller

63

13

-50

14

12

26

86

117

Ubaldo Jimenez

64

39

-25

12

14

26

117

86

Bud Norris

65

62

-3

10

16

26

160

63

A.J. Burnett

66

68

2

9

17

26

189

53

Jose Quintana

67

69

2

9

17

26

189

53

Jeff Samardzija

68

76

8

8

18

26

225

44

Kevin Correia

69

70

1

9

16

25

178

56

Joe Saunders

70

52

-18

11

13

24

118

85

Tim Lincecum

71

63

-8

10

14

24

140

71

David Price

72

73

1

8

16

24

200

50

Stephen Strasburg

73

79

6

7

17

24

243

41

Jeremy Hellickson

74

44

-30

12

11

23

92

109

Jeff Locke

75

56

-19

10

13

23

130

77

Dan Haren

76

72

-4

9

14

23

156

64

Ryan Dempster

77

77

0

8

14

22

175

57

Edwin Jackson

78

78

0

8

14

22

175

57

Jerome Williams

79

71

-8

9

11

20

122

82

Ian Kennedy

80

80

0

6

13

19

217

46

 

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

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

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

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

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


Putting Manny Machado’s 2013 in Context

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

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

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

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

So what can Orioles fans expect from Machado going forward?

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

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

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


A Pure Measure of Fielding Ability: Predictive Ultimate Zone Rating

image from thefarmclub.net

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

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

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

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

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

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

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

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

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

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

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


Probabilistic Pitch Framing (part 2)

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

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

IsCalledStrike + prob(CalledStrike)

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

Read the rest of this entry »


Roster and Gameday Strategies for One-Game Playoffs

Previously, I took a look at the benefits of a legally nebulous, but somewhat unlikely nine-man defense. In this piece, we’ll look at a group of other tactics that can be employed in the new one-game Wild Card Round that the MLB has created. This time, we’ll take a more traditional “outside the box” approach, if such a thing is possible.

With the addition of the new playoff round comes the opportunity for roster gaming. Being AL-centric here for a moment, we saw this last year in particular on the AL side. While the other three teams (Cardinals, Braves, Rangers) selected 3 starting pitchers to their Wild Card roster, the Orioles went with only 1, Joe Saunders. Sure, Arrieta, Hunter, Matusz all had starts in the year, but by September they were all in the pen. This freed up some roster room for Buck, which he primarily filled with other relief pitchers.

Now, that’s not the worst idea in the world, but given the uniqueness of the one-game playoff, why not make unique roster decisions?

First, as I mentioned above.  The selection and usage of pitchers seems paramount.  I’m of the opinion that one should almost play the entire game as if it were a game in extra innings.  Limit your pitchers to 2 innings or so, potentially even starting with your closer.  Now, that gets into the mental preparedness issues as to whether or not a closer could appreciate or handle coming into a game in the first inning. However, if he were aware that he is only going to be pitching the first inning, perhaps this may not be as big of an obstacle.

The main benefit to this is that you are able to rest your starters for a potential 5-game series against the best team in the league.  Additional benefits exist in the ability to play matchups, and remove a pitcher who gives up more than a run or two.   I would imagine this would result in selecting mostly (all?) relief pitchers, with an “emergency” starter, similar to how the All-Star Game has worked as of late. I would imagine employing this strategy would lead you to want to carry 11 or 12 pitchers on your roster.  That may limit your options for position players, which brings us up to point two.

Second, depending upon the comfort one has with their team’s starting lineup, the logical roster choice is to select speed.  In a one-game scenario, the likelihood of needing a hot bat to add to the lineup is low, and the value of a stolen base, potentially late in the game, can be incredibly high, as we saw in the 2004 ALCS.  Perhaps the inclusion of an emergency catcher would be a good idea, if you’re one of those who lives in perpetual fear of random foul tips and collisions.

The third and final element is for managers and players to put their ego at the door.  Here we live in the age of the immense infield shift, with the third baseman playing behind second base in some instances.  In a one-game playoff, the correct move is for the player to bunt the ball down the line where no defensive player exists.  Sure, I agree that over the long term of a season, you’re better off with the potential for a double or home run, but given the difference in value of having a player on the bases in one game (plus the potential that for the next at bat, the defensive team would not shift as dramatically) increases the likelihood of success for the team as a whole.  And besides, it even opens up the opportunity for the rare bunt double.   I’m not the first to make this argument, though.  This has existed since at least the 1946 World Series, when Ted Williams was out-dueled by Manager Eddie Dyer of the Cardinals.  For the record, Williams batted .200 that Series, with all of his hits being singles.

Ultimately, this boils down to one thing: small ball is the name of the game.  Even teams full of power hitters can benefit from not having to rely on the long ball to win a ball game, especially one as important as the Wild Card Game.   We only have to look back one year to see an example of a power team’s bats going cold at just the wrong time, with the Rangers, the MLB’s best offense in runs per game, only able to put together one run, while their opponents scored five with only one extra-base hit (and three sacrifices!).

What do FanGraphers think?  What strategies that are not typically employed would be worth the effort in a one-game playoff?


Robinson Cano and the Value of Turning Two

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

It’s no secret that Yankees second baseman Robinson Cano is an all-around excellent player, as he’s on his way to his fourth consecutive 5-win season. It’s also no secret that he’ll be a free agent after this season, and will certainly receive a contract in the nonuple figures. As the Angels have shown these past two offseasons, when you spend that much money on one player, you’d better be sure he’ll be worth it; the Yankees already have experience with terrible contracts (contracts they’re still due to pay for), so they’ll have very little room for error. Thus, executives of any and every team that might be interested in Cano will be doing their research, scouring the earth for any warning signs of a possible decline.

But back to Cano’s performance at the moment. While Cano is a superb player overall, much of his value comes from his bat; over this current 4-year 5-WAR streak, he’s been the seventh-best offensive player in the majors. The (relative) caveat in his game, therefore, is his defense: over that same span, he’s just 76th in fielding in the majors. Defensive statistics are subject to year-to-year fluctuations, and the fluctuations of Cano’s defense have been well documented. However, there’s a specific aspect of his defense that I’d like to focus on for the time being.

As you probably should know, UZR–the main defensive statistic at FanGraphs–is composed of four parts: RngR, which measures how many runs a player saves or costs his team with his range; ErrR, which measures how many runs a player saves or costs his team by committing or not committing errors;  ARM, which measures how many runs a player saves or costs his team with his arm in the outfield; and DPR, which measures how many runs a player saves or costs his team by turning or not turning double plays. This last segment is the one that is so interesting, at least to me, because it’s the one that Cano is the worst in the league at.

No, really. Among 79 qualified infielders¹, Cano’s DPR of -3.6 is the worst, and the next worse player (Neil Walker) is a full 1.2 runs away, at -2.4 DPR².

Now, the real question becomes: what (if anything) does this mean? Obviously, when you’re preparing to give someone a contract that could exceed the GDP of whatever the fuck this country is, you’d prefer if he wasn’t the absolute worst in the majors at something, even a seemingly trivial thing like turning double plays. Still, though, it’s worth asking: what, exactly, is the significance of this?

There are a few different ways of looking at this; for the purpose of this post, I divided my analysis into 5 main categories:

1. Is this a fluke?

As I mentioned before, year-to-year defensive statistics can be quite fickle, so it’s best to gain some historical perspective when evaluating a player’s defense³. So, does Robinson Cano have a history of being a bad double play turner?

Well, on the one hand: In 2011, he was 6th out of 73 qualified infielders in DPR; in 2010, he was 13th out of 81; and in 2007, he was 2nd out of 89. These numbers would suggest that his horrific 2013 has been a fluke, except…

Last year, he was 61st out of 76; in 2009, he was 77th out of 81; in 2008, he was 67th out of 78; in 2006, he was 62nd out of 89; and in 2005, he was 75th out of 77.

Add it all up, and since he entered the league in 2005, Cano is 83rd out of 95 qualified infielders in DPR. However, it should be noted that before this year (i.e. from 2005 to 2012), Cano was 55th, a much more respectable figure, if not a particularly great one.

So, overall, it’s fairly safe to conclude that Cano has something of a poor history of turning double plays. What next?

2. Does a poor DPR correlate to poor defense in other areas?

To answer this question, I’ll bring up a few graphs. These’ll show us how well DPR this year has correlated to RngR…

DPR-RngR

…ErrR…

DPR-ErrR

…UZR…

DPR-UZR

…and finally, whatever that Def stat is.

DPR-Def

In case you were wondering, the R-squared values for these graphs were .000669, .004252, .028772, and .032933, respectively.

So there’s clearly no correlation between DPR and any other defensive statistic, which brings up the original question: What’s the point of all of this? Well…

3. Just how bad is a -3.6 DPR?

Quite bad, it turns out. In the illustrious 12-year history of the stat, the only worse seasons were Jas0n Bartlett in 2009 (-4.2)⁴, Yunel Escobar in 2008 (-3.7), and Omar Vizquel in 2005 (-4.0).

Again, this takes me back to my original point: when a player’s going to be paid a yearly salary that will exceed the total gross for this shitty movie, you generally don’t want him mentioned among the worst players in history (albeit a very short history).

Still, though, these three were/are good defensive–and all-around–players for the majority of their careers. So what’s to worry about?

4. How have players with similarly poor DPRs done in their seasons?

For this one, I’ll expand the criteria to all seasons with -3 DPR or worse; other than Cano this year, there are 11 such seasons:

Player Year DPR
Neil Walker 2011 -3.2
Jason Bartlett 2010 -3.1
Yuniesky Betancourt 2010 -3.5
Jason Bartlett 2009 -4.2
Placido Polanco 2008 -3.1
Yunel Escobar 2008 -3.7
Brian Roberts 2007 -3.2
Luis Castillo 2006 -3.0
Omar Vizquel 2005 -4.0
Jimmy Rollins 2002 -3.1
Jose Vidro 2002 -3.5

Of these 11 seasons, the average WAR was 2.8, less than half of Cano’s WAR this year. The highest WAR was Bartlett’s 5.3 in 2009⁵, but overall the results were much lower.

So it would appear that Cano’s done something relatively new this season–play at a very high level while having a substandard DPR–but this still doesn’t answer the main question. I’ll answer that next, and the results are intriguing:

5. How have other players with DPRs this bad done for the rest of their careers?

Let’s continue to look at these 11 seasons. How were these players before and after their -3 DPR season?

Player WAR-Pre WAR-Post Off-Pre Off-Post Def-Pre Def-Post
Neil Walker 1.5 2.7 7.4 6.7 -6.8 -0.1
Jason Bartlett 3.5 0.8 4.4 -16.7 10.5 5.4
Yuniesky Betancourt 0.4 -1.4 -15 -23.8 -1.4 -7.7
Jason Bartlett 4.1 1 6.2 -11 14.1 0.9
Placido Polanco 3.3 2.2 1.8 -10.3 11.3 11.9
Yunel Escobar 3.6 3.1 10.2 -0.5 6.4 10.9
Brian Roberts 3.1 2.4 5.4 3.9 5.5 0.1
Luis Castillo 2.5 1.7 1 -0.8 4.6 -1.8
Omar Vizquel 2.4 1 -8.8 -24.5 12.8 14.6
Jimmy Rollins 1.4 3.4 -5.3 4.5 0.1 10.2
Jose Vidro 2.3 1.2 10.3 1.2 -5.2 -9.8
Average 2.6 1.7 1.6 -6.5 4.7 3.2

(All values are per 600 PAs. Year of DPR is included in Pre.)

They all saw a noticeable drop off in their WAR; the only ones whose WAR increased were Rollins and Walker, and they had their bad seasons when they were young. Given that Cano will turn 31 in October, it’s safe to say this will not happen to him. Since Cano is getting older, a decrease in WAR to some degree should be expected, especially considering the volatility of his position; this has been covered before, though.

What I found interesting, though, was that the players’ defense (as measured by that fancy new Def stat) didn’t really drop off much after the bad DPR year, but their offense seemingly fell off a cliff. This goes against the theory of player aging curves (that offense can get better as players get older, but defense tends to just decline overall).

Obviously, this is a very small sample size, and to extrapolate anything meaningful from it would be foolish. Also, it’s pretty unlikely that the decline was caused by one bad year turning double plays.

This post as a whole was probably rather cockamamie⁶, but then again, everything I post here tends to be. I just hope I was able to raise some interesting questions about how much turning two matters to a player’s overall worth. Perhaps, years from now, when the Yankees are paying Cano $30 million a year to hit .250 with poor defense, and the Orioles have won the division year in and year out, I’ll be able to look back with pride at my prescience.

Or maybe, the Yankees will just win more World Series with or without Cano, while the Orioles dwell in mediocrity every year.

A man can dream, though….

——————————————————————————————————————

¹For some reason which escapes me, there isn’t an option to sort the leaderboards by solely infielders, even though there’s an outfielder option.

²Hopefully, you would’ve figured that out on your own, but I put it in there just to be safe. Also: All stats are as of Saturday, September 21st, 2013.

³Otherwise, you’ll end up with pieces-of-shit “analysis” like this.

⁴Bartlett also had a DPR of -3.8 in 2006, but he didn’t qualify that season.

⁵That was his ridiculous fluke season–you know, the one that Joe Maddon just gets out of every scrub the Rays find on the street.

⁶You have no idea how long I’ve waited to use that word.


The “Exceptional” Kyle Lohse

After the 2012 season, Kyle Lohse declined the qualifying offer of the St. Louis Cardinals, and hit the free agent market.  Lohse’s 2012 season was exactly what any starter would want in a contract year: a career-best 2.86 ERA over 211 innings.  It completed a comeback from a rough 2010 in which Lohse battled arm trouble, and had one of his worst seasons. 

Many commentators felt that that Lohse’s 2012 campaign was a one-time affair.  Lohse’s ERA benefited from an unusually low .262 Batting Average on Balls in Play (BABIP), and the usually reliable pitching statistic of Fielding Independent Pitching (FIP) dinged him for it, pegging his real performance at 3.51 — almost three quarters of a run higher.  Furthermore, Lohse spent 2012 at Busch Stadium, a pitcher’s park, and got to have his pitches called by Yadier Molina, perhaps the best catcher in the game.  

But was Lohse’s low BABIP in 2012 truly a fluke? 

Let’s start by comparing Lohse to other Cardinals starters with at least 150 IP that year.  Like Lohse, they pitched their home games in the same pitcher’s park, and also took their signs from Yadier Molina:

Name IP BABIP ERA FIP
Kyle Lohse 211 0.262 2.86 3.51
Jake Westbrook 174.2 0.312 3.97 3.8
Adam Wainwright 198.2 0.315 3.94 3.1
Lance Lynn 169 0.316 3.67 3.47

Of all Cardinals starters that year, Kyle Lohse had the best starter BABIP by 50 points, and was the only one below the league BABIP average.  Interesting.  But, one season proves nothing.  So, let’s look at 2011, again for Cardinal starters with at least 150 IP:

Name IP BABIP ERA FIP
Kyle Lohse 188.1 0.269 3.39 3.67
Chris Carpenter 237.1 0.312 3.45 3.06
Jake Westbrook 183.1 0.313 4.66 4.25
Jaime Garcia 194.2 0.318 3.56 3.23

In 2011, Kyle Lohse’s BABIP was a mere seven points higher than his 2012 BABIP, and still absurdly low.  Once again, Lohse’s BABIP was by far better than any other Cardinals starter, and well below league average.  Is this still a fluke?  Does Yadi just save his best calls for his friend Kyle?

Perhaps, the key is to get Lohse away from Molina and Busch Stadium.  Fortunately for our purposes, the Milwaukee Brewers indulged this notion, signing Lohse at the conclusion of 2013 Spring Training.  Miller Park, where the Brewers play, is a hitter’s park where the fly balls go a long way and batters get more hits.  Furthermore, in 2012, the Brewers had one of the worst defenses in baseball.  The stage seemed to be set for a substantial BABIP regression.

The 2013 season is now almost complete for the Brewers.  Yet, as of the time this article was written, here are the statistics for Brewers starters with at least 150 IP in 2013:

Name IP BABIP ERA FIP
Kyle Lohse 184.2 0.284 3.46 4.1
Yovani Gallardo 161.2 0.299 4.18 3.95
Wily Peralta 172.1 0.292 4.49 4.28

Lohse’s BABIP did regress a bit.  Yet, Lohse’s BABIP is not only the lowest of the three qualifying Brewers starters, but still notably below the .294 BABIP average of baseball. 

One last comparison: other NL Central starters play in many of the same stadiums that Kyle Lohse does.  How does his BABIP compare to starters who have also spent the last three years pitching at least 450 innings exclusively for NL Central teams?

Name BABIP ERA FIP
Kyle Lohse 0.271 3.22 3.75
Bronson Arroyo 0.278 4.13 4.63
Mike Leake 0.284 3.87 4.21
Homer Bailey 0.292 3.76 3.67
Yovani Gallardo 0.293 3.79 3.83
Jake Westbrook 0.307 4.23 4.15

There he is again.  The lowest BABIP in the NL Central for starters over the last three years belongs to Kyle Lohse.

What is going on?  Does Kyle Lohse simply possess The Will to Pitch? 

Certainly, many of you might claim Kyle Lohse is the beneficiary of nothing more than good luck.  It is almost an article of faith among observers that BABIP is essentially a random attribute beyond the pitcher’s control, benefiting substantially from defense.  One could also argue I am using arbitrary endpoints.  While Kyle Lohse had a terrific pitching BABIP from 2011–2013, his major league BABIP was .364 in 2010.  Move the goalposts, some would say, and get a different result.  Finally, Derek Carty suggests that BABIP can take as long as 8 years (~3729 batters) to stabilize into a predictable indicator of a pitcher’s ability, which is another way of saying that it never really stabilizes at all, and is therefore indicative of nothing.

As to Kyle Lohse, that view may be correct.  But I suspect it is not.  Rather, I suspect that Kyle Lohse’s career renaissance has actually been driven in part from his ability to limit the damage caused by balls put into play.  To explain why, I’ll first address the arguments I just made in favor of his performance being unsustainable.

First, let’s talk about BABIP.  Although it common to attribute BABIP entirely to luck, it is more complicated than that.  Tom Tango and his colleagues found, for example, that BABIP was 44% luck.  The remainder (majority) of BABIP was attributed to a combination of the pitcher, the park, and fielding.  The pitcher was given 28% of the credit for his BABIP, but that is just an average; many observers suspect that a small class of pitchers has a unique ability to control their BABIP by inducing less effective contact.  Strikeout pitchers are one example. So, while it is common to dismiss good BABIPs as flukes, it is intellectually lazy to do so, particularly if a pitcher is generating low BABIPs on a consistent basis. 

Second, let’s address arbitrary endpoints.  Am I excluding Kyle Lohse’s dreadful 2010 season from my endpoints?  Yes.  Why? A few reasons.  First, because Lohse was injured that year and dealing with arm trouble that he finally was able to resolve.  In fact, the 2010 season was the culmination of a few injury-plagued seasons for Lohse.  But since the 2011 season that followed, Lohse has consistently pitched at least 180 innings per year and also consistently been effective, more so than he was ever was before.  Since 2011, his walk rates have been the best of his career, as have the ratio of his strikeouts to walks, both attributes that everyone agrees are controlled primarily by the pitcher’s ability.  Also, as Russell Carleton has found, a pitcher’s recent BABIP performance tends to be more predictive of their BABIP going forward.  So, what some would call an arbitrary endpoint (the beginning of Lohse’s 2011 season), I would call appropriate, and indicative.    

Finally, there is the issue of sample size.  Although I have no quarrel with the method Derek Carty used to conclude that a pitcher’s BABIP can take 3729 batters to stabilize, Kyle Lohse has faced over 2400 batters in the past three years.  That is not trivial sample, particularly when it spans home stadiums at opposite ends of the park factor spectrum. 

My suspicions about Lohse are further confirmed when you consider the differential between his RA9-WAR and his fWAR.  FanGraphs bases fWAR for pitchers entirely on their FIP.  However, FanGraphs also recognizes that FIP, while effective in evaluating most pitchers, does not properly evaluate pitchers who actually possess the skill to limit the damage on balls put into play.  Rather than toss FIP and fWAR aside, FanGraphs last year began publishing RA9-WAR as an alternative metric to allow a comparison between the number of runs that actually come across the plate while a pitcher is on the mound, versus those that FIP is willing to credit to the pitcher as having personally prevented.  The differential between a pitcher’s RA9-WAR and fWAR tells you how much of that pitcher’s run prevention cannot be explained by the three “true” outcomes of home runs, walks, and strikeouts.  Niftily, FanGraphs also estimates how the other runs were prevented — through BABIP (BIP-Wins) and by runners stranded (LOB-Wins).  Both RA9-WAR and fWAR are also park-adjusted.

Let’s start with the entire time period of 2011-2013.  For starters with 450 IP, Lohse’s RA9-WAR / fWAR differential is one of the top 10% in the game.

Name RA9-WAR BIP-Wins LOB-Wins FDP-Wins RAR WAR RA9 / fWAR Differential
Jered Weaver 17 6.1 -0.1 6 102 10.9 6.1
Jeremy Hellickson 9 4.6 0.6 5.2 37.2 3.8 5.2
Hiroki Kuroda 14 1.7 2.6 4.3 90.4 9.7 4.3
Clayton Kershaw 21.9 5.6 -1.5 4.1 152.9 17.8 4.1
Bronson Arroyo 6.6 2.3 1.7 3.9 23.3 2.6 4
Kyle Lohse 11 3.6 0.2 3.8 66.1 7.2 3.8
Ervin Santana 8.2 4.6 -0.9 3.7 41.9 4.5 3.7
R.A. Dickey 11.8 3.2 0 3.2 80 8.6 3.2
James Shields 15.5 2 1 3 117.3 12.5 3

Lohse’s differential has intensified in 2012-2013.  Over the last two years, among those with 300 IP pitched, only one starter in baseball had a larger RA9-WAR / fWAR differential (last column) than Kyle Lohse:

Name RA9-WAR BIP-Wins LOB-Wins FDP-Wins fWAR RA9-WAR minus fWAR
Clayton Kershaw 14.6 4.3 -0.9 3.4 11.2 3.4
Kyle Lohse 8.3 2.4 0.9 3.3 5 3.3
Hiroki Kuroda 10.3 1.6 1.1 2.7 7.6 2.7
Bronson Arroyo 6.7 1.3 1.1 2.5 4.2 2.5
Jarrod Parker 7.2 2.1 0.2 2.3 5 2.2
Jordan Zimmermann 8.3 1.1 0.8 2 6.4 1.9
Ervin Santana 3.5 3.4 -1.6 1.9 1.7 1.8
R.A. Dickey 8.2 2.3 -0.4 1.9 6.4 1.8
Chris Sale 11.3 0.8 0.8 1.6 9.7 1.6

That guy’s name is Clayton Kershaw, and he is pretty good.  In fact, Kershaw and Lohse have beat their FIP by basically the same amount over the past two years.  Unlike Kershaw, Lohse has pitched one of those seasons at home in Miller Park.

Overall, it is safe to say Lohse is showing a strong and consistent ability to beat his FIP, and over the last few years, is doing so better than almost any starter in baseball.  He is doing so by generating balls in play that are uniquely unsuccessful at becoming hits, and which his defense seems unusually capable of being able to field for outs.

How is he doing this?  It certainly is not his strikeout rate.  Lohse is not anybody’s idea of a strikeout pitcher.

What Lohse does do, however, is control the count, minimize walks, and consistently pitch from ahead.  This quality makes Lohse an extremely enjoyable pitcher to watch: despite topping out at 90 mph, he pounds the zone and challenges hitters.  His BB/9 over the last three years has ranged from 1.62 to 2.01.  During that same time frame, only Cliff Lee is more likely than Kyle Lohse to throw a first-pitch strike, which Lohse did 67.5% of the time.  The fact that Lohse is throwing first-pitch strikes against 2/3 of the batters he faces without getting killed suggests that he is putting those strikes in locations where batters want no part of them.  In short, Lohse has terrific control and consistently finds himself in counts where he and his catcher have the luxury of choosing their pitch.

Does Lohse’s control affect the quality of the ball being put into play against him?  It very well may.  Although his sample size could have been larger, Russell Carleton found that pitcher BABIPs correlated with the pitch counts the hitters were facing when they put the bat on the ball.  The more favorable the count to the pitcher, the less likely the hitter will get on base from his hit.  Kyle Lohse’s three best counts for limiting batter wOBA this year?  Why, those would be 0-2, 1-2, and 0-1.  And the three counts Kyle Lohse faces far less than any others?  Those would be 3-0, 3-1, and 3-2. 

The bottom line is that Kyle Lohse is an exception among aging starters: a pitcher who has gained effectiveness in his mid-thirties through terrific control that not only forces hitters to beat him, but also apparently limits the damage even when batters do hit the ball.  Should the Brewers make Lohse available at the trade deadline next year, contenders would be foolish not to give him a close look, particularly with Lohse under control through 2015.  When the difference between collecting a pennant and going home can be a batted ball just out of reach, it makes sense to have a pitcher with a demonstrated knack for putting the ball in the defender’s glove.  


The Worst Playoff Bunts from 2002-2012

I’m generally opposed to the sacrifice bunt, except in the rarest of circumstances. This less than optimal strategy is utilized even more in the playoffs. Derek Jeter, the all-time leader in playoff sacrifice bunts with 9, bunts almost twice as frequently in the playoffs as the regular season. That in itself should tell you that managers tend to go bunt-happy in the postseason since Jeter is a career .308/.374/.465 playoff hitter. I used Win Probability Added (WPA) and Run Expectancy (RE) in my calculations. For the record, the sum of Jeter’s sacrifices is -0.13 WPA and -1.88 RE. Anyways, here’s the list of the five worst playoff sacrifice bunts since 2002. Data is provided by Baseball Reference’s Play Index.

5. Daniel Descalso 2012, NLDS, Game 1. The Cardinals were losing to the Nationals 3-2 in the 8th when Descalso came to the plate with Adron Chambers on first and Tyler Clippard on the mound. Descalso laid down a bunt, sending Chambers to second. WPA: -0.04 RE: -0.19. Pete Kozma and Matt Carpenter would be retired, and the Nationals would go on to take Game 1. Descalso would hit two home runs in the series.

4. Eric Bruntlett 2004, NLCS, Game 6. Down 4-3 in the 9th, the Astros pinch-hitter faced Cardinals closer Jason Isringhausen with Morgan Ensberg on first and no outs. Bruntlett had 4 home runs and a 111 wRC+ in 61 regular-season PA, but a go-ahead home run was not on manager Phil Garner’s mind. Bruntlett bunted Ensberg to second. WPA: -0.05 RE: -0.21. After Craig Biggio flew out, Jeff Bagwell would deliver a game-tying single, but the Cardinals would eventually win it in the 12th. Though I’m not a fan of judging decisions based on results rather than process, you could say that this decision “worked.”

3. Brad Ausmus 2005, WS, Game 4. The Astros were trailing 1-0 when Jason Lane led off the bottom of the 9th with a single off White Sox closer Bobby Jenks. The 36 year-old catcher had posted a .351 OBP in 2005, one of the best marks of his career. Nevertheless, he sacrificed on the first pitch he saw, moving Lane to second and decreasing the Astros’ chance of scoring. WPA: -0.05 RE: -0.21. Pinch hitters Chris Burke and Orlando Palmeiro would be retired, and the White Sox took game 4 on their way to winning the series.

2. Elvis Andrus, 2010 ALCS, Game 1. The Rangers shortstop came to the plate against Mariano Rivera in the bottom of the 9th inning, with the Rangers trailing 6-5 and Mitch Moreland on first with no outs. With the count at 1-2, Andrus got down a bunt, sending Moreland to second. WPA: -0.06 RE: -0.22. Rivera would strike out Michael Young and get Josh Hamilton to ground out, ending the game. This bunt is even worse than the numbers because of the 1-2 count on Andrus and the fact that there was little to no risk of grounding into a double play, as the speedy Andrus had just 6 GDP in almost 700 PA. I should add that noted lover of bunting Ron Washington was managing the Rangers, who have had the most sacrifice bunts in the AL during his tenure.

1. Danny Espinosa, 2012 NLDS, Game 1. The Nationals were trailing the Cardinals 2-1 in the top of the 8th. With Ian Desmond on first and Michael Morse on third and no outs, Espinosa came to the plate, facing Cardinals reliever Mitchell Boggs. Espinosa was 0-3 on the day with 3 strikeouts. He still had some pop though, as he had 17 home runs on the season. For whatever reason, on an 0-1 count, Espinosa tapped a bunt to Boggs, advancing Desmond to second. WPA: -0.09 RE: -0.44. The next hitter, Kurt Suzuki, would strike out. Fortunately for Espinosa and the Nationals, pinch hitter Tyler Moore would come through with a two-run single, and the Nationals would win the game 3-2.

The sacrifice bunt by a position player is almost universally a negative play, but even in the age when statistical information is readily available and most teams are employing an army of nerds, the tactic refuses to die. Perhaps it’s because “that’s the way the game was played” when many of these managers were players. Or maybe it’s the conservative nature of managers. The players usually get saddled with the blame if an opportunity with runners in scoring position is squandered after a sacrifice bunt. But if a player grounds into a double play when he could have bunted, the manager might be taking the heat. Whatever the case, expect managers to keep ordering the bunt come October.


Probabilistic Pitch Framing (part 1)

Let’s take a look at some recent pitches and assess the framing job by the catchers.

Exhibit A: pitch #4 in this sequence from Freddy Garcia to Lucas Duda, as framed by Gerald Laird.


Hey, great framing job, Gerald Laird! That pitch was clearly a rulebook ball and you got a strike called for your pitcher. 1 point for you.

Exhibit B: pitch #3 in this sequence from Jeff Samardzija to Joey Votto, as framed by Dioner Navarro.


Boo, terrible framing job, Dioner Navarro! You just cost your pitcher what was clearly a rulebook strike! -1 points for you.

To the best of my knowledge, this is how most pitch-framing calculations currently work.  We check to see if the pitch was in the zone, and give the catcher a positive or negative credit for pitches that were called differently from how they “should” have been called.  But is that really answering the right question?

Consider the two (extreme, cherry-picked) examples above.  In example A, a pitch was called a strike that was just off the outside corner of the plate to a left-handed hitter on a 3-0 count.  It is almost certainly the case that no one in the ballpark was surprised at the result of that pitch.  After all, we know that the strike zone as it is called to left-handed hitters extends a bit off the corner, and that on 3-0 counts the umpire tends to extend the strike zone a bit anyway.  So should Gerald Laird get full credit for getting that pitch called a strike?

Exhibit B is the exact opposite case in many ways.  We had an 0-2 count on a left-handed hitter, and the pitch was near the top of the strike zone.  Given that the strike zone as it is called shrinks somewhat in an 0-2 count, and that it is shifted away to a left-handed hitter, the catcher was unlikely to get that call.  So should Dioner Navarro get a full demerit for that pitch being called a ball?

Let’s do some crude calculations.  The pitch to Duda was 0.974 feet from the center of the plate, and 2.01 feet off the ground.  Since the start of the 2012 season, there have been (according to the best data I can find) 203 pitches to left-handed hitters in a 3-0 count that fell between 0.9 and 1.2 feet from the center of home plate (in the right-handed batter’s box) and ended up between 1.6 and 2.4 feet off the ground.  Over 77% of those pitches (157/203) were called strikes.  Laird shouldn’t get much credit at all for that frame job, right?

Similarly, let’s explore exhibit B.  The pitch to Votto was 0.671 feet from the center of the plate and 3.341 feet off the ground.  I can find 89 pitches that fell between 0.47 and 0.83 feet from the center of the plate (inside to a lefty, of course) and ended up between 3 feet and the top of the strike zone to left-handed hitters in an 0-2 count.  Of these, 50 (56%) were called balls.  So should we really be penalizing Dioner Navarro all that much for that frame job?

As I hinted above, we have been answering the wrong question.  We shouldn’t be comparing what a catcher did to the rulebook strike zone.  We should be comparing what a catcher did to the probability that the call would have gone the way it did anyway.  It doesn’t matter what the actual strike zone is; all that matters is how the umpires are calling it.  This turns the calculation from a binary one (like the calculation of fielding percentage) to a probabilistic one (like the calculation of plus/minus).  Under this system, Laird would have received a credit of 0.23 for his frame, and Navarro a demerit of 0.44 for his framing.

In part 2 of this series, we will actually go about constructing the formal system to do this so we don’t have to do crude approximations like the ones above (spoiler: it will look a lot like the excellent work Matthew did here).  There will be math, yes, but there will also be lots of pretty pictures and maybe even an animated gif!  In part 3, we will actually apply this system to see which catchers have done the best frame jobs since the start of 2012 (assuming I can associate catcher data to my pitch f/x data by then).

Huge thanks to MLB for making the pitch f/x data freely available (seriously, how awesome is that?), Mike Fast for teaching me how to make a pitch f/x database, and Brooks Baseball for making the images in this post.  Also, thanks to you for reading this post and adding helpful, insightful comments below.


Victor Martinez: The Best Fielding First Baseman in the Majors (No, Really)

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

It’s been one crazy season for Victor Martinez. In the first half, he was one of the worst players in baseball, with an 88 wRC+ and -0.6 WAR in 392 plate appearances; however, this was largely due to a .269 BABIP, and when his BABIP increased (to .372), his wRC+ and WAR (140 and 1.1, respectively, in 223 plate appearances) increased with it. This, though, is not the focus of my writing today. I chose, instead, to focus on one of the oddest statistics of the 2013 season, and one that truly proves that this blog is aptly named.

V-Mart has never been regarded as a good fielding catcher, and the stats confirm this–since he entered the league in 2002, he’s third-last among catchers in DRS and fifth-last in stolen base runs saved. He is, however, a (comparatively) much better fielding first baseman, with a career UZR/150 of 2.3¹ that would rank 12th out of 19 first baseman this year if he qualified. Throughout this season, Martinez has been mainly a DH², with 128 games started there, and 17 started in the field; of those 17, 11 have been at the 3-spot. He has played 97 innings at first base, which comes out to a little less than 9 innings per start there. So, obviously, we’re dealing with a very small sample size here; and yet, the larger point remains:

Victor Martinez has the highest UZR/150 among first baseman with at least 90 innings.

Surprised? Well, you probably shouldn’t be, as you read the title of this article before perusing the text that lies beneath it, so you probably should’ve seen this coming. In a larger sense, though, you probably are surprised, as this isn’t exactly Albert Pujols we’re talking about here. As I outlined above, Martinez isn’t a particularly bad fielding first baseman⁴, and this is obviously a ridiculously minuscule sample size⁵, but he’s certainly not this good. What, then, has changed? 

First, let’s look at his non-UZR advanced fielding stats. He has had 19 balls hit to his defensive zone (officially, Balls In Zone, or BIZ), and has made a play on 13 of them (just Plays–I guess they ran out of anagrams), for a Revised Zone Rating (RZR) of .684. That figure, if he had enough innings to qualify, would be the worst in the majors by a long shot–the lowest right now is Lyle Overbay, with a .766 RZR–and is also the worst figure of his career.

One thing he is doing, however, is making a lot of Out-Of-Zone plays, or OOZ. Although it isn’t included in UZR, OOZ is still an interesting statistic: it measures the amount of plays a fielder has successfully made when out of his defensive “zone”. Martinez has five OOZs in 97 innings this year; if he were to have played, say, ten times that amount, or 970 innings (about 110 games), he would have 50 OOZs, far more than the current leader, Anthony Rizzo, who has 41. In this regard, though, Martinez’s performance isn’t that different from his career as a whole, as he has 49 career OOZs in 1299.1 career innings (in 163 games) at first.

It’s when we look at the stats that go into UZR that we start to see some key differences. In case you need a refresher (or are simply unedumacated), UZR is composed of four parts: Double Play runs (DPR), Outfield Arm runs (ARM), Range runs (RngR) and Error runs (ErrR). Martinez doesn’t have any DPRs, as he hasn’t initiated any double plays, and because he has yet to play in the outfield⁶, he has no ARMs (his career values for these two are 0 and -0.2, respectively).

It then comes down to the other two components: RngR and ErrR. For his career, he has values of 1.9 and 0.4, respectively, for these stats; in 2013, however, he has values of 1.2 and 0.3, respectively. Again, if we spread these out over ten times his current playing time at first (to get 970 innings, or ~110 games), we get a 12 RngR and a 3 ErrR. While the latter figure is rather formidable–it would lead the league this year–it is the former that truly sets him apart. An⁷ RngR of 12 as a first baseman would be the fifth-highest ever; yes, UZR only goes back to 2002, but that’s still saying something. The only better seasons would be Pujols in 2007 (21.0)(!), Adrian Gonzalez last year (14.6), Travis Lee in 2003 (13.4), and Justin Morneau in 2005 (12.2).

Obviously, this whole exercise should be taken with a grain of salt. 97 innings of defense is an incredibly small sample size, and Martinez’s track record suggests this is almost definitely a fluke. What, then, does this mean? Fluke or not, the Tigers continue to start the ironically-named Prince Fielder and his -4.9 UZR (-4.8 UZR/150) at first base; this point was brought up earlier this year. Despite the welldocumented historical awesomeness of their rotation (to say nothing of that guy over at the hot corner), the Tigers would only get the 3rd seed if the season was to end today, and their defense at first base is a big reason why. While his health concerns would make a full-time move to first unfeasible, playing him there a little more often (at least more than 11 times) certainly couldn’t hurt.

Overall, though, what do I take away from this? Well, as I said earlier

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¹It should also be noted that his career UZR (not adjusted for playing time) is 2.1, as he has played roughly a full season’s worth of games (163, to be exact) at first base over the course of his career.

²Though you wouldn’t know it from looking at his FanGraphs page (which identifies him as a catcher, despite him having caught all of 15 innings this year).

³Really, SpellCheck? “Should’ve” isn’t a word? You know, I don’t recall aksing for your opinion, SpellCheck.

⁴Although to be fair, he did do this.

⁵Especially since this is a defensive stat, for which a sample size of three years is recommended for the best analysis.

⁶Don’t tell Leyland I said that–he might take it as advice.

⁷A or An? I suppose it depends on if you say the anagram or the full name.