Archive for the ‘Stat of the Week’ Category

Who Are This Year’s Potential Breakout Players?

Sunday, April 8th, 2012

by John Dewan

Spring Training statistics have been the subject of many debates in baseball circles. On one hand, teams are making roster decisions based on how players have performed this spring. On the other hand, hitters are working into shape and working on their swings while pitchers are building up strength and refining new pitches. Especially early in March, raw minor league prospects play as much as major league veterans, so the quality of competition is hardly consistent.

However, our past research has shown that in certain cases Spring Training stats can predict breakout seasons. We found that players who beat their career slugging percentage by more than 200 points in Spring Training have over a 60 percent chance at beating their career slugging percentage. The most recent example was Jose Bautista, who topped our 2010 list with a phenomenal spring and followed up with 97 homers over two seasons. Melky Cabrera and Alex Gordon were two names on last year’s list who had career years in 2011.

Here’s the list of 2012 breakout candidates (minimum 200 regular season at-bats and 40 spring training at-bats through April 1, 2012):

Slugging Percentages of Top Breakout Candidates

Hitter, Team

Spring

Career

Difference

Andre Ethier, Dodgers

.896

.479

.417

Cody Ross, Red Sox

.860

.456

.404

Carlos Ruiz, Phillies

.795

.393

.402

Alex Gonzalez, Brewers

.780

.399

.381

Jonathan Lucroy, Brewers

.729

.366

.363

Chris Johnson, Astros

.737

.417

.320

Joe Mather, Cubs

.694

.384

.310

Darwin Barney, Cubs

.647

.345

.302

Chris Young, Diamondbacks

.737

.437

.300

Luke Hughes, Twins

.644

.347

.297

Delmon Young, Tigers

.714

.428

.286

Ryan Raburn, Tigers

.735

.456

.279

Howard Kendrick, Angels

.698

.434

.264

Ian Kinsler, Rangers

.729

.469

.260

Alfonso Soriano, Cubs

.765

.506

.259

Albert Pujols, Angels

.870

.617

.253

Eric Hosmer, Royals

.714

.465

.249

Jeremy Hermida, Padres

.660

.415

.245

Tyler Colvin, Rockies

.661

.422

.239

A.J. Pierzynski, White Sox

.660

.422

.239

Ryan Zimmerman, Nationals

.711

.479

.232

Jemile Weeks, Athletics

.636

.415

.221

Melky Cabrera, Giants

.617

.398

.219

Curtis Granderson, Yankees

.711

.493

.218

Billy Butler, Royals

.672

.458

.214

Alex Gordon, Royals

.646

.434

.212

Chris Snyder, Astros

.600

.394

.206

Austin Kearns, Marlins

.622

.417

.205

Travis Snider, Blue Jays

.625

.423

.202

Used with permission from John Dewan’s Stat of the Week®, www.statoftheweek.com.

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To Shift or Not To Shift

Sunday, April 1st, 2012

by John Dewan

That is the question.  In our book, The Fielding Bible—Volume III, Ben Jedlovec and I provide some research that suggests that a shift, in particular, the Ted Williams Shift with three infielders to the right of second base, reduces the batting average on groundballs, short liners and bunts by the top-shifted hitters by 40 to 60 points. In a recent article on Bill James Online, Bill James states that he doesn’t believe that the shift helps a team defensively, and that there are some flaws in the research.

This essay is a little longer than our usual Stat of the Week, but I think it is worth it.  I will address Bill’s comments, but before I do that, I’d like to provide some further information about The Shift.

Tampa Bay Rays

The Tampa Bay Rays were the best defensive team in baseball last year.   Here is a list of the top teams from 2011:

2011 Runs Saved Leaders

Team Runs Saved
Tampa Bay Rays

85

Arizona Diamondbacks

54

San Diego Padres

46

Cincinnati Reds

44

Colorado Rockies

34

Just to put that in perspective, the Rays won 91 games in 2011.  Using the rule of thumb that 10 runs is a win, if the Rays had an average defense (zero Defensive Runs Saved), they would have won 8 or 9 fewer games.    Instead of 91 wins, that comes out to 82 or 83 wins, just barely over .500.  That incredible last day of the season with the Rays overtaking the Red Sox would not have occurred without Tampa Bay’s excellent defense all year.

The Rays were also the most aggressive team with shifts.  In 2011 there were 216 times when a ball was put in play while the Rays employed some type of shift.  Here are the teams with the most shifts in 2011:

2011 Shift Leaders

Team Shifts
Tampa Bay Rays 216
Milwaukee Brewers 170
Cleveland Indians 148
Toronto Blue Jays 117
Three Teams With 75

The Rays had the most (216).  It drops off quite a bit to the Brewers with 170.  Another large drop to the Indians at 148, then again to the Blue Jays at 117.  And then yet another good size fall-off in total shifts to the three teams tied with 75.

Is it a coincidence that the team with the best defense in 2011 was also the team that shifted the most?

Milwaukee Brewers

The Brewers entered the 2011 season with this infield in place:

Prince Fielder, 1b  – he cost the Brewers 17 runs defensively the previous year, 2010.  Out of the 35 players we rank at each position each year he ranked 35.

Rickie Weeks, 2b – he cost Milwaukee 16 runs defensively in 2010. That ranked 34 out of 35.

Yuniesky Betancourt, ss – he lost 27 runs with his defense in 2010.  Rank: 35 out of 35.

Casey McGehee, 3b – 14 runs lost in 2010.  Rank: 31 out of 35.

Could you have put together a worse defensive infield if you tried?  The only player who could make this infield worse defensively that I can think of would have been Mark Reynolds at third base.  Or still worse, Miguel Cabrera.

So what did Ron Roenicke, the new manager of Milwaukee, do?  He took the Brewers from being the least aggressive team regarding shifting in 2010 (only 22 shifts all year) to the second-most aggressive in 2011 with 170.  And he did it in a little different way than the Rays.

Baseball Info Solutions categorizes shifts into two types.  The Ted Williams Shift, with three infielders on one side of the bag, and Other Shifts, where players are clearly shifted well out of the normal infield alignment but short of three fielders on one side of the bag.   There are a handful of hitters that almost always get shifted by most teams, and then most other players almost never get shifted.  The way the Rays were more aggressive was by shifting against these other players.  They shifted 110 times against the top ten shifted hitters, and equally as much against the rest (106 times).  Ron Roenicke and the Brewers were even more extreme.  They shifted 45 times against the top-ten, and almost three times as often against other players (125 times).

Did this shifting work for Roenicke?

Here are the results comparing the non-shifting season (2010) with the shifting season (2011).

Milwaukee Brewers Infield Runs Saved 2010-2011

Player 2010 Runs Saved 2011 Runs Saved
Prince Fielder

-17

-9

Rickie Weeks

-16

-5

Yuniesky Betancourt

-27

-7

Casey McGehee

-14

3

Total

-74

-18

This data is striking.  These Brewer infielders improved by 56 runs in 2011.  They were still below average with 18 defensive runs lost overall, but that 56 run improvement means five or six wins added to their win total.
Is it a coincidence that as the Brewers became the second most shifting team in baseball their infielders improved so dramatically?

I think not.

However, this information about Tampa Bay and Milwaukee is not direct evidence that shifting works.  Both are anecdotal pieces of evidence.  They are strong pieces of evidence, but anecdotal.  In the article in The Fielding Bible—Volume III called “The Ted Williams Shift,” we tried to provide some direct evidence.

Comments and Thoughts from Bill James

Bill James wrote an article on Bill James Online called “John, Ben, David and the Ted Williams Shift.”  The John and Ben in the title refer to Ben Jedlovec, my co-author on The Fielding Bible—Volume III, and me.  David refers to David Ortiz.  First of all, I want to thank Bill for his kind comments about the book.   Getting a good review from Bill is high praise indeed, but Bill does point out that he and I have been friends for quite some time.  Almost 30 years now.

Nevertheless, Bill and I stand in disagreement about The Shift.  Having said that, Bill has thoughts and suggestions about some things in the research that we could have done better.  Let me address those.

In the book Ben and I studied the top-ten shifted hitters.  Here’s a list of who they are:

Top 10 Shifted Hitters 2010-11

Hitter

All Shifts 2010-11

David Ortiz

486

Ryan Howard

461

Carlos Pena

341

Adam Dunn

305

Prince Fielder

253

Jim Thome

223

Adrian Gonzalez

205

Mark Teixeira

180

Brian McCann

118

Jack Cust

115

Baseball Info Solutions tracked every shift on batted balls in the last two years, but for the most part, the data is mostly about these ten guys.  Other than the Rays and Brewers, most teams only shift against these guys.  As a result, we focused our research on these ten hitters because that’s where we had data.    In the book, we excluded Carlos Pena and Brian McCann from our look at shift effectiveness because they have been successful beating the shift with the bunt.  We found that the batting averages of the other eight guys dropped by 51 points on grounders, short liners and bunts when the Ted Williams Shift was employed.

The next step of our research was one with which Bill had a problem.  In retrospect, I have to agree.  We removed three more players from the group of eight based on the fact that our Defensive Positioning software no longer suggests a shift for them.  One of them was David Ortiz.  The remaining five players had their averages drop by 61 points with the Ted Williams Shift on.
I should have known that excluding David Ortiz would raise a big red flag for Bill.  He’s been telling me for years that he’s not sure the shift is working against Big Papi.  In his article Bill points out that shifting on Ortiz has probably caused more problems for teams than it has solved.  And in fact, Ortiz has hit slightly better against the Ted Williams Shift than not (.245 vs. 232.) in the last two years.  However, Ortiz is the most-shifted hitter in the history of the game, Bill contends, and pulling him out of the study for whatever reason is not appropriate.  In retrospect, I agree.

In our defense, before Bill’s article came out, Ben and I had already switched our focus in all of our presentations (at the SABR Analytics conference and with MLB teams) on the part of the study that includes Ortiz.

A second area that Bill focuses on in his article is some anecdotal evidence of his own.  He points out several incidents where The Shift has backfired with Ortiz batting.  Most of these are with runners on base when Ortiz is batting.  Part of the research that Ben and I presented showed that the batting average dropped for the eight shifted hitters (excluding Pena and McCann) pretty much equally when there were runners on as when the bases were empty.  But what Bill points out is that a lot of things can go wrong when there are runners on base with players being out of position.  I agree with this as well.  We just measured the batting average, but we didn’t measure the run values.  That is, because of players out of position, runners can take extra bases against The Shift that they might not get against a standard defense.  How much is this worth?  I don’t know.  Is it worth something?  Yes.  But we haven’t measured this yet.  If I were to re-write what Ben and I wrote I would say something like: While we feel confident that we have evidence that suggest that using The Ted Williams Shift is effective with no runners on base, some caution and judgement needs to be exercised when there are runners on base, despite the evidence here that batting average also drops when using The Shift with runners on base.

Based on what I know and what the data suggests, I would still shift with men on base, but maybe not as often and maybe not as aggressively.

Finally, there is one statement that Bill makes that I want to point out that I disagree with.  Bill wrote, “John wants to focus on groundballs and short line drives, which, again, is a legitimate and constructive step toward understanding the problem, even though I think it is being used to create an exaggerated estimate of the shift’s effects.”  I totally disagree with the part starting with “even though”.  We are not trying to create an exaggerated estimate.  We are presenting the facts that we have.  Right now Baseball Info Solutions is undergoing an extensive video review effort to record every plate appearance and every batted ball where a Ted Williams Shift occurred in the last two years.  It’s a massive effort.  Our data currently splits our Shift info between Ted Williams Shifts and Other Shifts for grounders and short liners only.  We did these first because it would lead to the quickest initial significant results.  Now we are going back to review all plate appearances, not just the grounders and short liners, to split our shift data between these two types of shifts.  This has nothing to do with trying to exaggerate the data and everything to do with trying to develop useful research.  I think most people would agree that a Ted Williams Shift is more likely to affect grounders, short liners and bunts than it would affect a player’s flyballs to the outfield, how often he strikes out or walks, or even how often he hits a pop-up that gets out of reach of a fielder playing out of position.

Conclusion

Unlike the anecdotal evidence showing how shifting seems to be working for the Rays and Brewers, we consider the 40- 50 point drop in batting average on grounders, short liners and bunts against the Ted Williams Shift to be direct evidence in favor of The Shift.  Is it conclusive?  No.  Is it comprehensive?  Not really.  It’s only two years and doesn’t include all plate appearances.  Is there more research to be done?  Yes.  We are working on this as we speak.  In particular, we are working at looking at all plate appearances, not just grounders and short liners.  Nevertheless, there is good preliminary evidence that The Ted Williams Shift appears to be working, especially with the bases empty.

Bill suggests a number of additional items to research regarding The Shift.  We would be silly to ignore his excellent suggestions and we plan to follow through on them as best we can.

One last thing: Bill felt we left out some specific info about David Ortiz.  For his benefit, here it is:

The Ted Williams Shift Against David Ortiz
Grounders, Short Liners and Bunts – 2010 and 2011

Ted Williams Shift On

 

AB

H

Avg

Overall

237

58

.245

Bases Empty

139

30

.216

Runners On

98

28

.286

No Ted Williams Shift

 

AB

H

Avg

Overall

125

29

.232

Bases Empty

34

8

.235

Runners On

91

21

.231

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The Best Players of 2011 Based on Total Runs

Sunday, March 25th, 2012

by John Dewan

One of the byproducts of our work developing a system to measure runs for defensive play (Defensive Runs Saved) is that we can combine it with runs for offensive play and runs for pitching.  We do this in the book, The Fielding Bible – Volume III, and call it Total Runs.  The goal for Total Runs is to capture a player’s entire contribution to his team in the currency of the game – runs.  Here is the top 10 leaderboard from the book for the 2011 season.  This is a list of the best overall players in baseball in 2011 based on all aspects of the game, as best we can measure them with our Total Runs system.

2011 Total Runs Leaders

Player

Runs
Created

Baserunning
Runs

Pitching
Runs
Created

Runs
Saved

Positional
Adjustment

Total
Runs

Jacoby Ellsbury

131

4

0

7

27

169

Dustin Pedroia

116

-2

0

18

31

163

Ian Kinsler

106

9

0

18

28

161

Matt Kemp

131

4

0

-5

28

158

Ben Zobrist

98

2

0

29

28

157

Jose Bautista

134

5

0

-2

18

155

Alex Gordon

112

6

0

19

17

154

Justin Verlander

0

0

143

5

3

151

Ryan Braun

127

2

0

3

16

148

Adrian Gonzalez

127

-5

0

12

12

14

Both the reigning American League and National League MVPs, Justin Verlander and Ryan Braun, had impressive seasons in 2011, but using Total Runs we find that there were more valuable players in each league.  In the National League, Matt Kemp produced 158 Total Runs despite costing his team five runs in the field.  Kemp was one home run shy of joining the 40/40 club and led the senior circuit in home runs, RBI, and runs scored in 2011.  Jacoby Ellsbury had a tremendous year with the bat en route to 131 Runs Created.  Ellsbury also had positive contributions on the basepaths and in the field.  He led all players with 169 Total Runs in 2011.

Total Runs uses a few different measures of a player’s ability.  We measure offense using Bill James’ Runs Created system.  His system measures stolen base runs, but excludes activity on the basepaths other than that.  We add in Baserunnning Runs to complete the offensive part of the equation.  For pitching, we have an article in the book that describes how we developed our new Pitching Runs Created system so that we can measure a pitcher’s contribution compared with a hitter.  The last part is the Positional Adjustment.  This is a technique we developed three years ago in The Fielding Bible Volume II to take into account, for example, that a shortstop has more defensive value than a first baseman.  Our Defensive Runs Saved system doesn’t reflect the relative defensive importance of one defensive position compared to another, which makes the Positional Adjustment necessary.

Used with permission from John Dewan’s Stat of the Week®, www.statoftheweek.com
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The Impact of Good Plays and Misplays

Sunday, March 18th, 2012

by John Dewan

There are 54 separate Defensive Misplays and 28 separate Good Fielding Plays that Baseball Info Solutions has “scouted” going back to 2004.  One of our biggest undertakings in the last three years has been to convert Good Fielding Plays and Defensive Misplays into Runs Saved.  Today we’re going to walk through an example that shows the magnitude of what we now refer to as Good Play/Misplay Runs Saved.

Take, for example, Alfonso Soriano of the Chicago Cubs.  He’s now 36 years old and a below-average outfielder, according to Good Plays and Misplays.  In fact, his 13 runs lost on Good Plays and Misplays in the last three years is the worst among all outfielders. He had 22 Good Plays, 73 Misplays and 25 Errors. That’s 76 more misplays and errors than good plays. The next worst left fielder is exactly half as bad. Logan Morrison had 38 more misplays and errors than good plays. The best left fielder in GPF/DME runs saved is Jason Bay. He had 73 good plays and only 47 misplays and errors in the three years, a net of 26. Compared to Soriano’s net of -76. Here is how they compare in runs saved:

Good Play/Misplay Run Impact Chart 2009-11

Good Play/Misplay Type

Bay
Runs Saved

Soriano
Runs Saved

Mishandling ball after safe hit

3

-4

Outfield assist after hit or error

2

-1

Holds to single

1

-2

Wasted throw after hit/error

1

0

Cutting off runner at home

0

1

Giving up on a play

0

-1

Hesitating before throwing

0

-1

Slow to recover

0

-1

Robs home run

0

-1

Missing the cutoff man

0

-1

Overrunning the play

0

-1

Slipping

0

-1

Total

7

-13

Soriano has cost his team 13 runs with his poor play in the field since 2009 on Good Plays and Misplays alone, while Bay has saved his team 7 runs in that time.  That’s a difference of 20 runs, or roughly two wins.  That’s huge.

What we can see from the chart is that Soriano struggles in a number of areas.  In fact, the only areas where he rates as average or better are “Wasted throw after hit/error” and “Cutting off runner at home.”   In these types of plays, Soriano performs at least how we’d expect an average fielder to perform, in the same opportunities as Soriano.  In every other way, Soriano rates below-average.  The biggest problem Soriano has, according to Good Plays and Misplays, is “Mishandling the ball after a safe hit”, where he cost his team four runs since 2009.  Jason Bay, on the other hand, excelled in that department, saving his ream three runs by having far fewer Misplays for “Mishandling the ball after a safe hit” than an average fielder would have in the same number of opportunities as Bay.  Bay is also slightly above-average in three other categories: “Outfield assist after hit or error”, “Holds to single” and “Wasted throw after hit/error.”

For more on Good Play/Misplay Runs Saved, check out The Fielding Bible – Volume III, available now.

 

Used with permission from John Dewan’s Stat of the Week®, www.statoftheweek.com

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Who Will Be the Best Defenders in 2012?

Sunday, March 4th, 2012

by John Dewan

One of the new features of The Fielding Bible – Volume III (arrived at the publisher today!) is a section on defensive projections.  The calculation is simple: prorate each player’s three-year Defensive Runs Saved over the number of innings we forecast them to play at each position in 2012.  In this week’s Stat of the Week, we’ll take a look at the projected leaders at each position and the top-projected defensive teams for 2012.

The projected 2012 Runs Saved leaders:

Position Player

Projected 2012
Runs Saved

P Mark Buehrle, Marlins

4

C Matt Wieters, Orioles

8

1B Albert Pujols, Angels

10

2B Ben Zobrist, Rays

16

3B Evan Longoria, Rays

15

SS Brendan Ryan, Mariners

16

LF Brett Gardner, Yankees

20

CF Austin Jackson, Tigers

15

RF Jason Heyward, Braves

11

Even though Mark Buerhle is taking his talents to South Beach, we fully expect him to continue his fielding dominance in the National League, as a member of the Marlins.  We also expect Buehrle’s fellow-reigning Fielding Bible Award winners Matt Wieters, Albert Pujols, Brett Gardner, and Austin Jackson to maintain their high level of play in 2012.  Two members of Florida’s other team, the Rays, are projected to be the top players at their positions.  The gloves of Evan Longoria and Ben Zobrist were a big part of the reason why the Rays led baseball with 85 Defensive Runs Saved in 2011.  We expect the Rays to duplicate their fielding excellence in 2012 and they are the top-projected team.  Here are the top defensive teams for 2012.

Team

Projected 2012 Runs Saved

Rays

42

Mariners

32

Reds

29

Rangers

26

Angels

22

A full season of Franklin Gutierrez in center field should elevate the defense of the Mariners, who finished with just one Run Saved as a team in 2011.  In the National League, the Reds will be bolstered by their defense at shortstop.  Paul Janish and Zack Cozart, who we expect to split time at shortstop for the Reds in 2012, are projected to save nine runs for the Reds defensively.

You can find a complete overview of each team’s projected defense in The Fielding Bible – Volume III, available now.

“Used with permission from John Dewan’s Stat of the Week®, www.statoftheweek.com.”

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How Well Do Advanced Defensive Statistics Correlate?

Sunday, February 26th, 2012

by John Dewan

We’ve put a lot of effort into improving defensive metrics in recent years, but how much progress have we really made? In the introduction to The Fielding Bible—Volume III, I said:

“For hitters, we might be at the 85-90 percent mark of being able to measure offense. We have a lot of good tools like OPS (on-base plus slugging), Runs Created, Wins Above Replacement. For pitchers, we are not quite as far along. Maybe we’re at the 75 percent level of understanding pitcher effectiveness with our numerical tools like ERA, Batting Average on Balls in Play, and Opponent OPS. For defense, ten years ago we were probably around the 10th percentile. Now with three volumes of The Fielding Bible under our belts, plus the work of many other excellent sabermetricians, we are probably in the 60-70 percent range.”

In our book, The Fielding Bible—Volume III, we put our newest defensive analytics to the test. If our statistics are measuring something meaningful, we would expect them to correlate well from year to year. In other words, since Evan Longoria topped all third basemen with 20 Defensive Runs Saved in 2010, we would expect him to remain one of the league’s top defenders at the position in subsequent seasons. (Longoria saved an estimated 22 runs in the field in 2011, also a league-leading total.)

To measure the consistency of our Defensive Runs Saved numbers, we calculated what we’ll call Even/Odd Year Correlations. We added each fielder’s Runs Saved totals from 2006, 2008, and 2010 and compared to the subtotal from 2007, 2009, and 2011, with the requirement that the fielder have amassed at least 667 innings in both subsets. We would expect the players with higher totals in even years to also have high totals in odd years, while players with low totals in even years should also tend to have low totals in odd years.

By calculating the correlation coefficient of the even and odd year totals, we can measure just how consistent our statistics are. Correlation coefficients range from -1.0 to 1.0 and show relationships between two sets of numbers. A correlation coefficient of 1.0 represents a perfectly predictable relationship. For instance, if every fielder had the same number of Runs Saved in both even and odd seasons, that would produce a correlation of 1.0. On the other hand, a correlation coefficient of zero means that there is no measurable relationship, while a correlation coefficient of -1.0 signifies an inverse relationship between the sets of numbers.

Defensive Runs Saved produced an Even/Odd Year Correlation of .59. This high, positive correlation value indicates a strong relationship between even and odd season totals and a good consistency in measuring fielders’ value. But, how does this compare to traditional hitting and pitching statistics?

Even/Odd Year Correlation Coefficients for Commonly Cited Statistics

Statistic

Correlation

Batting Average

.56

ERA

.51

Defensive Runs Saved

.59

As you can see, both batting average and ERA also produce high positive Even/Odd Year correlations, though Defensive Runs Saved correlates better than both. (We used a minimum of 150 innings or 500 at bats in both subtotals for pitching and hitting statistics, respectively, although the correlations didn’t change much when we adjusted the minimum cutoffs in either direction.)

Comparing our defensive analytics to batting average and ERA, which have been the staples of analytics in baseball for the first 100 years of its existence, we find that our Defensive Runs Saved system is a better way to measure defense than are batting average to measure offense and ERA to measure pitching.

Of course, we now have more advanced measures of hitting and pitching performance. Let’s see how well a few other statistics correlate between even and odd seasons.

Even/Odd Year Correlation Coefficients for Additional Statistics

Statistic

Correlation

Home Runs

.83

OPS

.69

Pitcher Strikeouts per 9 Innings

.88

Pitcher Walks per 9 Innings

.79

Opponent OPS

.61

Home runs correlate at .83, indicating a very strong correlation between even and odd seasons. OPS correlates at .69, and Opponent OPS, which for me is the most important pitching statistic, correlates at .61.

We are at the point where our defensive analytics are nearly as reliable as offensive and pitching analytics. Just looking at the single best statistic in each: OPS is .69, Opponent OPS is .61, Defensive Runs Saved is .59. We’ve come a long way.

Used with permission from John Dewan’s Stat of the Week®, www.statoftheweek.com

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Diamondbacks Sign Kubel – I Don’t Get It

Saturday, December 24th, 2011

by John Dewan

Maybe they have some other plans, but it sure seems to me the Arizona Diamondbacks just threw $15 million out the window.  Why sign a 30-year-old outfielder coming off a season cut short by injury to come in and take the place of a 25-year-old outfielder who just won a Gold Glove?

The D’Backs’ signing of Jason Kubel a couple of days ago to a reported two-year $15 million contract is a puzzler.  Yes, he hit 20+ homers three years in a row before last year, but that’s about all you can say that he has over the man he is rumored to be replacing, Gerardo Parra, as the everyday left fielder for Arizona.

Last year Kubel, a lefty, hit .273 with 12 homers and a .766 OPS in about 400 plate appearances.  Parra, also a lefty, hit .292 with 8 homers and a .784 OPS in just under 500 plate appearances.  Parra created 71 runs to 59 for Kubel.  Given the fewer plate appearances for Kubel, you can say offensively the two players were pretty even.  But it’s defense that made Parra a much better player than Kubel in 2011.  Parra saved an estimated 12 runs for Arizona last year. He won a Gold Glove in recognition for his superlative play in the field.  Kubel cost his team about 3 runs defensively.  That 15-run difference is huge.

Not to mention that Parra is five years younger (Kubel turns 30 and Parra turns 25 in May).

Let’s give Kubel the benefit of the doubt and think of 2011 as simply a down year.  The best way to assess these players going forward is to look at their projections for 2012.  The projections from The Bill James Handbook 2012 take into account the entire career of each player to this point to estimate what they’ll do in 2012.  Here’s what the projections show:

  AB HR RBI AVG OBP SLG OPS Runs Created
Kubel

485

20

84

.274

.343

.466

.809

77

Parra

518

9

58

.293

.352

.427

.779

78

The most interesting number is the projected Runs Created, the Bill James statistic that measures total offensive contribution.  Kubel has 77 projected runs created while Parra has 78.  Parra has a few more at-bats, but I think you can easily say that these two players are pretty close offensively.

But not defensively.  In the last three seasons Parra has saved 33 runs defensively while Kubel has cost his team a total of 3.  That’s 36 runs better for Parra, and it makes him a better overall player than Kubel.  Factoring offense and defense, you can estimate that with similar regular playing time, Parra will produce about 85-90 runs when you add in his defense compared to 75-80 runs for Kubel.

Not to mention that Parra is five years younger.  (Did I mention that yet?)

It’s possible that the Diamondbacks know something that we don’t know.  Maybe they have another deal in the works.  Maybe there’s something wrong with Parra.  Maybe they can project players better than we can.  But whatever it is, I don’t get it.

Happy Holidays!

“Used with permission from John Dewan’s Stat of the Week®, www.statoftheweek.com.”

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Bunting for a Hit

Saturday, December 17th, 2011

by John Dewan

One of the projects we are working on here at Baseball Info Solutions for The Fielding Bible—Volume III is evaluating the effectiveness of defenders on bunt plays.  We currently have a method that does this, but we are developing a new method that takes into account the location of each bunt.  As every baseball fan knows, the key to an effective bunt is its location.  A bunt right back to the pitcher is pretty useless, whereas a bunt right on the third base line is excellent.  What we can do now is quantify how effective various bunt locations are.

We’ve broken the field into six zones.  We drew a line from home plate through the pitcher’s mound and through second base.  We have three zones to the left of that line and three zones to the right, broken up into equal sizes.  Think of them as pie slices with the center of the pie located at home plate. Zone 1 has all bunts that are along the first base line.  Zone 2 is in the middle of the area between the line we drew through the pitcher’s mound and the first base line, and Zone 3 is the area closest to the pitcher on the first base side.  Zones 4, 5 and 6 are to the left of the pitcher’s mound.  Zone 4 is closest to the pitcher. Zone 5 is between the pitcher and the third base line.  Zone 6 is along the third base line.

Here is a graphical depiction of the zones:

What are the batting averages on bunt attempts in each of these zones?

Before we do that, we have to take one more step.  We have to break this into two different situations, one where the defense is expecting the bunt (sacrifice situations) and one where the defense is not.  When a sacrifice situation was in effect last year (a bunt with men on base and less than two outs) there were 2,285 bunts put into play.  232 resulted in a hit for a .102 “batting average.”  On the other hand, there were 850 bunts put into play in a non-sacrifice situation last year, with 372 going for hits, making for a .438 batting average.

We’ve pointed this out before: bunting for a hit in non-sacrifice situations has been an effective strategy for many players since we started tracking this in the early 1990s.  The best bunters hit well over .500 when bunting for a hit.

As in real estate, bunting for a hit is all about location, location, location.  Here are the bunt batting averages in sacrifice situations by zone.

Bunt Batting Averages by Zone, 2011
Sacrifice Situations Only

Zone 1 .149
Zone 2 .094
Zone 3 .032
Zone 4 .026
Zone 5 .134
Zone 6 .291
Overall .102

As we would expect, a bunt down the third base line is best with a .291 batting average.  Bunting back towards the two zones closest to the pitcher get you .032 and .026 batting averages.

Here are the bunt batting averages in non-sacrifice situations by zone.

Batting Average by Zone, 2011
Non-Sacrifice Situations

Zone 1 .246
Zone 2 .412
Zone 3 .164
Zone 4 .139
Zone 5 .520
Zone 6 .720
Overall .438

Again, the third base line is most effective with a .720 batting average.  At a distant second is the middle zone between the pitcher and the third base line at .520.  The next best zone is interesting.  Pushing a bunt towards the second base position nets a .412 batting average.

In the chart above for sacrifice situations, we are counting all bunt attempts in the “batting average”. What if we consider a successful sacrifice as no at-bat, just like we do when we compute a normal batting average?  Here are the bunt batting averages by zone in this situation:

Batting Average by Zone, 2011
Sacrifice Situations, SH is not an AB

Zone 1 .591
Zone 2 .437
Zone 3 .140
Zone 4 .075
Zone 5 .482
Zone 6 .743
Overall .375

These numbers are now very similar to bunting for a hit in non-sacrifice situations, except along the first base line where the batting average becomes more than twice what it is in non-sacrifice situations.

“Used with permission from John Dewan’s Stat of the Week®, www.statoftheweek.com.”

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Yu Darvish

Saturday, December 10th, 2011

by John Dewan

According to his agent, Don Nomura, Yu Darvish was posted yesterday (Thursday, December 8) for a move to MLB from Nippon Professional Baseball (NPB), the top Japanese professional baseball league.  This is a process whereby major-league teams bid in a silent auction for the exclusive rights to negotiate with Darvish.  The auction is four days long.

Darvish is the latest superstar Japanese player to make the move across the Pacific, and MLB teams have been waiting for him to become available ever since he recorded the final out of the 2009 World Baseball Classic to clinch Japan’s second WBC title.  And now that the big names like Mark Buehrle and C.J. Wilson are off the board, Darvish becomes one of the best remaining free-agent starting pitchers available.

Each year in The Bill James Handbook we include the career stats of players that are most likely to leave the Japanese leagues to come over and play in the United States.  This year, Darvish is obviously the most high-profile such player.

Here are Darvish’s career numbers from Japan, playing for the Hokkaido Nippon-Ham Fighters.

Season

Age

Wins

Losses

ERA

IP

SO

2005

18

5

5

3.53

94.1

52

2006

19

12

5

2.89

149.2

115

2007

20

15

5

1.82

207.2

210

2008

21

16

4

1.88

200.2

208

2009

22

15

5

1.73

182.0

167

2010

23

12

8

1.78

202.0

222

2011

24

18

6

1.44

232.0

276

Career

-

93

38

1.99

1268.1

1250

If you are curious how that compares to the last highly-touted young pitcher that helped Japan win a World Baseball Classic title (MVP of the 2006 tournament) before deciding to join MLB the following year, here are Daisuke Matsuzaka’s career numbers playing for the Seibu Lions.

Season

Age

Wins

Losses

ERA

IP

SO

1999

18

16

5

2.60

180.0

151

2000

19

14

7

3.97

167.2

144

2001

20

15

15

3.60

240.1

214

2002

21

6

2

3.68

73.1

78

2003

22

16

7

2.83

194.0

215

2004

23

10

6

2.90

146.0

127

2005

24

14

13

2.30

215.0

226

2006

25

17

5

2.13

186.1

200

Career

-

108

60

2.95

1402.2

1355

It will be interesting to see what kind of posting fee and contract Darvish gets.  Dice-K pitched a bit more at a young age, but Darvish has been more consistently dominant than Dice-K was.  Darvish has had an ERA under 2.00 for five years running, and threw more than 200 innings in four of those five years.  Will that lead to a similar $100 million outlay, like Dice-K got ($51 million posting fee plus $52 million 6-year contract), or will teams spend more cautiously after seeing the up-and-down performance of Dice-K since he entered MLB?

You can find more statistics on Japanese players that are likely to sign MLB contracts this year in The Bill James Handbook 2012.

“Used with permission from John Dewan’s Stat of the Week®, www.statoftheweek.com.”

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