Archive for the ‘Stat of the Week’ Category

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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Is the Ted Williams Shift Effective?

Saturday, December 3rd, 2011

by John Dewan

The short answer: Absolutely.

But only if the bases are empty.

For the past two years Baseball Info Solutions has been tracking every play during which the defensive team employs a “Ted Williams” type shift where three infielders are playing to the right of second base. Based on our preliminary study of this data, The Shift works when the bases are empty.

There are five players who faced the shift more than 200 times in 2010 and 2011.  They are David Ortiz, Ryan Howard, Carlos Pena, Adam Dunn and Prince Fielder.   When looking at groundballs and short liners that they hit (balls that can be handled by infielders), every one of them did worse when facing The Shift with no one on base.  Here are the results:

Batting Average, 2010-2011
Groundballs and Short Liners Only, Bases Empty
  Shift On No Shift
David Ortiz .208 .259
Ryan Howard .174 .273
Carlos Pena .183 .213
Adam Dunn .207 .263
Prince Fielder .208 .248

On average, that’s 55 points of batting average lost to The Shift.

Based on a smaller sample size (because managers employ The Shift less often with men on base), the data is only showing a 3-point batting average drop when using The Shift with runners on.

These are our preliminary findings.  We will study this in greater detail in The Fielding Bible—Volume III coming out in the spring.

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

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Stats To Be Thankful For

Saturday, November 26th, 2011

by John Dewan

I’d like to wish all my readers a very Happy Thanksgiving!

In keeping with the theme of thankfulness, here are some numbers in the baseball world to which this sentiment applies.

21 – That is the number of consecutive years of labor peace that baseball is guaranteed with MLB and the MLBPA having agreed on a new five-year collective bargaining agreement.  As contentious as baseball’s labor history has been, the general state of harmony that has existed since the last players’ strike ended in early 1995 represents the longest such stretch since the MLBPA was formed in 1953.  In that time the NHL has lost a full season, the NBA lost part of the 1998-1999 season and has already canceled games for this season, and the NFL went through an extended lockout this year before coming to an agreement just before the season started.  Life is good for baseball fans right now.

2,728 – That is the number of career wins for future Hall of Fame manager Tony LaRussa.  Tony goes out on top, having led the St. Louis Cardinals to an astonishing World Series victory after prevailing in an equally thrilling National League Wild Card race on the last day of the regular season.  That gives him three World Series titles to go along with six pennants.

160,000,000 – That is the total dollar value of Matt Kemp’s new contract extension to stay with the Los Angeles Dodgers.  It is great to see that one of the league’s premier franchises is beginning to move past the prolonged financial troubles and legal battles that have been hanging over the club.  Kemp and 2011 Cy Young Award winner Clayton Kershaw are two of the bright young stars of the game, and this signing shows that the Dodgers may yet have a bright future ahead of them.

24 – That is the number of different teams, out of 30 total MLB franchises, that have reached the playoffs in the last 10 years dating back to 2002.  Furthermore, there have been eight different World Series champions in those 10 years.  While there may be some degree of luck involved in getting through the playoffs and winning the World Series, it is an impressive accomplishment to sustain success over the 162-game regular season to make the playoffs.  That level of parity is a reason that every fan should feel hopeful that their team could very easily become the next great contender.  Even Cubs fans have reason to hope!

Infinity – That’s the number of thank yous I’d like to give my staff for all their help in bringing you Stat of the Week.  My name is on this feature, but they do more than their share of the heavy lifting.  Thank you to Rob Burckhard, Charles Fiore, Ben Jedlovec, Amanda Modelski and Joe Rosales.  You guys do great work!

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

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