Archive for the ‘Stat of the Week’ Category

Happy Thanksgiving!

Thursday, November 22nd, 2012

by John Dewan

Life is short and we should always be thankful for all that we have. Those of us who work on Stat of the Week are thankful for all of you, our loyal readers. Here are some numbers in the baseball world that we are also thankful for.

42,990 – That is the number of games played in Major League Baseball since the last labor dispute ended in 1995. MLB is enjoying its longest stretch without a work stoppage since the MLBPA formed in 1953.

6,200,000 – That is what perennial backup catcher David Ross will earn with his new two-year contract with the Boston Red Sox. In four seasons with Atlanta, Ross accumulated close to a full season of at-bats and produced well offensively. His .816 OPS is comparable to some of the better hitting catchers including Carlos Santana and Miguel Montero, albeit in a third of the plate appearances. However, it is defensively where Ross stands out. Ross has saved the Braves 11 runs with his defense, buoyed by throwing out 47 of 127 potential basestealers. His 37.0 caught stealing percentage barely trails five-time Fielding Bible Award winner Yadier Molina, who threw out 37.6 percent of runners over the same time period. Hopefully, his new contract is an indication of an increase in playing time. Ross definitely deserves it.

45 – That is the number of years it had been since a batter had won a Triple Crown before Miguel Cabrera managed the feat in 2012. From 1922 to 1967, also 45 years, there were 11 Triple Crown seasons turned in by nine different players: Rogers Hornsby (twice), Chuck Klein, Jimmy Foxx, Lou Gehrig, Joe Medwick, Ted Williams (twice), Mickey Mantle, Frank Robinson, and Carl Yastrzemski. Cabrera is between a couple of his Triple Crown predecessors, Mickey Mantle and Frank Robinson, with 3,177 total bases before age 30, which is eighth-best all-time. With continued health and production, Cabrera is on track to be one of the best hitters in baseball history.

1 – That is where I rank the team that helps bring you Stat of the Week. These articles may have my name on them, but they would not be possible without all of their hard work. Thank you Charles Fiore, Ben Jedlovec, Amanda Modelski and Scott Spratt. You guys are fantastic!

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

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The Flat Bat Award 2012

Thursday, October 25th, 2012

With the regular season behind us, it is time once again to hand out the Flat Bat Award for the best bunter in baseball in 2012. In 2011, Emilio Bonifacio beat out the two-time defending champion Erick Aybar. However, Bonifacio only played in 64 games this season because of a series of injuries. Will it be Aybar who reclaims the title or someone else?

To decide, we will first look at which players were most successful on bunt hit attempts. These players had the best batting averages on bunt hit attempts with a minimum of 10 attempts:

2012 Bunt Hit Leaders

Name

Bunt Hit Results

Batting Average

Alcides Escobar, KC

11 out of 13

.846

Denard Span, Min

8 out of 10

.800

Alejandro De Aza, CWS

8 out of 11

.727

Emilio Bonifacio, Mia

9 out of 13

.692

Will Venable, SD

8 out of 12

.667

Danny Espinosa, Was

6 out of 10

.600

Ben Revere, Min

9 out of 16

.563

Erick Aybar, LAA

15 out of 27

.556

Juan Pierre, Phi

10 out of 18

.556

Jose Reyes, Mia

8 out of 15

.533

Erick Aybar set the pace for bunt hits with 15, which were four more than Alcides Escobar, the nearest bunter to him. However, Aybar also attempted 27 bunt hits, which were far and away the most in baseball. The result was a .556 average on bunt hit attempts, which is a excellent but still well behind the leaders. Escobar led baseball with a .846 average, and Denard Span and Alejandro De Aza were close behind.

Next, we will consider the most successful sacrifice bunters with a minimum of 10 attempts:

2012 Sacrifice Bunt Leaders

Name

Sacrifice Hit Results

Percentage

Elvis Andrus, Tex

17 for 17

100%

Clayton Kershaw, LAD

14 for 14

100%

Chris Capuano, LAD

13 for 13

100%

Marco Scutaro, 2 Tms

10 for 10

100%

Juan Pierre, Phi

17 for 18

94%

Johnny Cueto, Cin

17 for 18

94%

Bobby Wilson, LAA

13 for 14

93%

R.A. Dickey, NYM

10 for 11

91%

Barry Zito, SF

10 for 11

91%

Ian Kennedy, Ari

10 for 11

91%

Elvis Andrus was an impressive 17 for 17 in sacrifice bunt attempts. He led the American League in sacrifices and did not fail once. Juan Pierre and Johnny Cueto put down 17 successful sacrifices in the National League. Each had just one failed attempt.

Pierre is the one player who appeared on both lists. He bunted 36 times, which was the most in baseball. That is nothing new for Pierre, who led baseball with an incredible 61 total bunt attempts in 2011. The major change for Pierre was with his success rate. A year ago, Pierre batted .438 on his bunt hit attempts and had an 86 percent success rate on sacrifice bunt attempts. This year, Pierre increased his average to .556 on bunt hit attempts and his success rate on sacrifice attempts to 94 percent.

With those improvements, we were tempted to call Pierre the winner. However, Pierre’s edge comes from his sacrifice bunts, which are not nearly as valuable as bunts for hits. In aggregate, players were successful in their sacrifice attempts 85 percent of the time in 2012. Pierre attempted a sacrifice 18 times and was successful 17 of them. In a similar number of attempts, an average bunter would have succeeded between 15 and 16 times. The difference in their run expectancies is only a third of a run.

In contrast, even with his high volume of bunt-for-hit attempts, Erick Aybar had four more successes than anyone else (five more than Pierre), and those successes each create nearly half a run in value, on average. Despite the fewer sacrifice attempts and the more-frequent failed bunt-for-hit attempts, Aybar still outpaced the field by two runs of expected value. That is why Erick Aybar is the 2012 Flat Bat Award winner.

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

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Kings of the Comebackers

Thursday, May 24th, 2012

by John Dewan

Mike Murphy, my radio partner for over twenty years on our Stat of the Week segment here in Chicago, is one of the original Bleacher Bums.  Needless to say, he remains a huge Cubs fan.  He sent me an email last week suggesting a Stat of the Week.

Who is the leader in hitting the ball directly back to the pitcher?

He also guessed the answer, stealing a page from his old “I Predict” radio spot:  “I Predict”…Tony Campana!

Campana has become the regular center fielder for the Cubs this season.  He is an exceptionally speedy slap-hitter with no power whatsoever.  But he gets the bat on the ball with regularity.  Sometimes right back to the pitcher.  And sure enough, Murph is right.  Campana, along with Emilio Bonifacio of the Marlins, are the Kings of the Comebackers:

Most Batted Balls Hit Back to the Pitcher for an Out — 2012

Player

Total
Comebackers

Tony Campana, Cubs

14

Emilio Bonifacio, Marlins

14

Alcides Escobar, Royals

12

Denard Span, Twins

12

Eight tied with

10

Another Cub, Darwin Barney, is one of the eight players tied with 10.  We can see why this play is driving Murph nuts!

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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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