THE RAMSEY-MASTERSON DEAL

> The Cardinals traded minor league outfielder James Ramsey for Indians starter Justin Masterson today.  Let’s break it down.

Ramsey has been an okay hitter in double-A, posting a 123 WRC+ over 416 plate appearances last season with a higher mark and higher BABIP this year.  123’s good at the MLB level, but in double-A it’s not that impressive.  Nothing’s guaranteed but I would not expect him to be even average offensively once he reaches the Majors.  Still, his defense is apparently really good and I could see him becoming a solid, cheap piece in Cleveland for years to come.

As for Masterson, I find it hard to doubt the Cardinals when they bring in a pitcher regardless of who he is.  With so many successful reclamation projects under their belts, it’s hard to base future expectations on past performance.  That said, the simple fact that Masterson, tenth in groundballs per batter faced since the beginning of last year, will be going from an infield of Nick Swisher (career -0.1 defensive WAR per 150 innings at first base), Jason Kipnis (-0.6), Asdrubal Cabrera (-1.4) and Lonnie Chisenhall (-1.2) to an infield of Matt Adams (+0.2), Kolten Wong (-0.0), Jhonny Peralta (+1.1 last four years) and Matt Carpenter (+0.7) is clearly a good thing and will help to iron out his freakishly high .355 BABIP (.421 with runners in scoring position).

Pitching was the need the Cardinals had to address, with weak links Joe Kelly and Shelby Miller comprising 40% of the starting rotation and the somewhat unknown commodity Carlos Martinez pitching in place of the MIA Michael Wacha.  These are gaping holes that can be massively improved even by an average starter, while for the position players there are no obviously weak positions to fill that are not occupied by someone with a reasonable expectation for success.  (Except catcher, which they opted not to fill with the possibility of an early Yadier Molina return.)

Overall I like the upside of Masterson.  Again it wouldn’t shock me if Ramsey goes on to be a solid contributor in his career, but he is the weakest of the Cardinals’ outfield prospect group that included him, Oscar Taveras, Steven Piscotty and Randal Grichuk.  This was a relatively conservative trade to move an unneeded piece in order to get the pitching they need to hang with the other four-to-six teams in their current playoff race.  That’s the right way to approach the modern trade deadline when there are several teams in contention and variance in a small sample can hijack everything, even the addition of someone like Jon Lester.

ADJUSTED ATTENDANCE LEADERS

> Did this about a year ago but made a few adjustments this time.

This is the leaderboard for attendance per game once adjustments for population and gross income per capita are made.  Shared markets had their populations split in half and if a team’s expected attendance exceeded stadium capacity as was the case in New York and Washington DC, their expectation was reduced to stadium capacity since they can’t be expected to sell more tickets than they have.

Income and population stats apparently aren’t kept for Toronto so I had to leave them off.

Actual Expected Diff
01. Brewers 33,295 10,931 305%
02. Cardinals 43,399 18,879 230%
03. Reds 31,064 14,284 217%
04. Pirates 28,430 14,899 191%
05. Rockies 34,027 19,307 176%
06. Royals 23,032 13,841 166%
07. Orioles 29,394 22,616 130%
08. Indians 18,019 13,910 130%
09. Padres 26,849 21,086 127%
10. Dodgers 46,488 40,301 115%
11. Tigers 35,006 31,036 113%
12. Twins 28,267 25,681 110%
13. Giants 41,604 39,338 106%
14. Red Sox 36,384 35,955 101%
15. Rays 17,274 18,047 96%
16. Angels 38,304 40,301 95%
17. Cubs 32,794 34,930 94%
18. Mariners 24,344 25,954 94%
19. D-Backs 25,602 27,321 94%
20. Yankees 42,026 49,642 85%
21. Rangers 35,299 44,863 79%
22. Nationals 31,591 41,418 76%
23. Braves 29,321 39,356 75%
24. Phillies 30,322 42,237 72%
25. Mets 26,637 41,922 64%
26. Marlins 21,633 34,099 63%
27. A’s 24,107 39,338 61%
28. Astros 22,616 38,028 59%
29. White Sox 20,692 34,930 59%

AROLDIS CHAPMAN IS AWESOME

> A fun little stat.

Most pitches thrown this season at or above 101 miles per hour:

1. Aroldis Chapman (104)
2. Kelvin Herrera (1)
3. Nobody

Most pitches thrown this season at or above 100 miles per hour:

1. Aroldis Chapman (209)
2. Kelvin Herrera (24)
3. Yordano Ventura (23)

Most pitches thrown this season at or above 99 miles per hour:

1. Aroldis Chapman (298)
2. Yordano Ventura (106)
3. Kelvin Herrera (92)

Most pitches thrown this season at or above 98 miles per hour:

1. Aroldis Chapman (340)
2. Yordano Ventura (289)
3. Kelvin Herrera (227)

Most pitches thrown this season at or above 98 miles per hour:

1. Yordano Ventura (555)
2. Kelvin Herrera (374)
3. Aroldis Chapman (353)

Aroldis Chapman missed the Reds’ first 35 games.

IMPACT OF EACH HOMEPLATE UMPIRE ON THE OUTCOME OF A GAME

> MLB umpires are really bad at calling balls and strikes.  It’s hard to blame them since it’s a job human beings cannot do, but nevertheless the impact is real.  Below are the performances of umpires this year, how many call-able pitches they missed and the estimated number of runs they give to pitchers/take away from hitters per game.  (Per 75 since that’s the average number of call-able pitches in a game.)

Totals are at the bottom, and they are bad.

Umpire Call-able Pitches Outside Strikes Inside Balls Total Missed Runs/75
Brian O’Nora 2555 19% 10% 17% 1.2
Tim Welke 2927 18% 8% 15% 1.1
Doug Eddings 2926 17% 6% 14% 1.1
Angel Hernandez 3098 17% 8% 15% 1.1
Fieldin Culbreth 2834 17% 9% 15% 1.1
Ron Kulpa 2974 17% 8% 15% 1.1
Bill Miller 3058 18% 7% 15% 1.1
Phil Cuzzi 2641 16% 5% 13% 1.1
Mike Everitt 2010 16% 7% 14% 1.1
Jerry Layne 2367 17% 11% 16% 1.1
Will Little 3051 16% 7% 14% 1.0
Hunter Wendelstedt 2278 16% 7% 14% 1.0
Jim Wolf 2215 17% 9% 15% 1.0
Sean Barber 2475 17% 9% 15% 1.0
Clint Fagan 3191 17% 8% 14% 1.0
Kerwin Danley 2595 17% 9% 15% 1.0
Gary Cederstrom 2895 17% 10% 15% 1.0
Paul Nauert 2881 18% 12% 16% 1.0
Marvin Hudson 3200 16% 9% 15% 1.0
Paul Emmel 2843 16% 9% 14% 1.0
Tim Timmons 3116 16% 11% 15% 1.0
Bob Davidson 2878 17% 11% 15% 1.0
Mark Ripperger 3157 16% 8% 14% 1.0
Joe West 2982 17% 12% 16% 1.0
Victor Carapazza 2965 15% 7% 13% 1.0
Brian Gorman 2717 17% 10% 15% 1.0
Mike Estabrook 2697 16% 9% 14% 1.0
Eric Cooper 2733 15% 6% 12% 0.9
Chris Guccione 2507 15% 8% 14% 0.9
Quinn Wolcott 3346 15% 7% 13% 0.9
Cory Blaser 3159 15% 9% 14% 0.9
Scott Barry 2786 16% 9% 14% 0.9
Ed Hickox 2472 16% 9% 14% 0.9
Marty Foster 3116 16% 10% 15% 0.9
Jeff Nelson 3220 16% 10% 14% 0.9
Jeff Kellogg 2470 15% 11% 14% 0.9
John Tumpane 3123 15% 9% 13% 0.9
Jim Reynolds 2627 15% 10% 14% 0.9
Tom Hallion 2655 15% 10% 14% 0.9
John Hirschbeck 278 17% 16% 17% 0.9
Chad Fairchild 1403 16% 10% 14% 0.9
CB Bucknor 3035 14% 8% 13% 0.9
Alfonso Marquez 1515 16% 11% 14% 0.9
Jordan Baker 2513 15% 10% 14% 0.9
Lance Barrett 2720 15% 11% 14% 0.9
Dan Bellino 2785 15% 9% 13% 0.9
Brian Knight 3051 14% 8% 13% 0.9
James Hoye 2769 15% 9% 13% 0.9
Adrian Johnson 2457 15% 11% 14% 0.9
Rob Drake 3033 16% 12% 15% 0.9
Larry Vanover 3081 15% 11% 14% 0.9
Tom Woodring 1806 15% 11% 14% 0.8
Dan Iassogna 2954 15% 11% 14% 0.8
Laz Diaz 3067 16% 13% 15% 0.8
Todd Tichenor 3287 15% 10% 13% 0.8
Tripp Gibson III 3301 14% 8% 13% 0.8
Jerry Meals 2736 15% 11% 14% 0.8
Mike DiMuro 2738 15% 12% 14% 0.8
Gabe Morales 3511 15% 10% 13% 0.8
Marcus Pattillo 1740 14% 10% 13% 0.8
Jim Joyce 1523 15% 10% 14% 0.8
Ted Barrett 3096 15% 10% 14% 0.8
Angel Campos 1171 14% 9% 13% 0.8
Adam Hamari 2760 14% 8% 12% 0.8
David Rackley 2586 15% 12% 14% 0.8
Dale Scott 2793 15% 12% 15% 0.8
Bill Welke 2796 15% 11% 14% 0.8
Gerry Davis 2725 14% 8% 13% 0.8
D.J. Reyburn 2697 14% 8% 13% 0.8
Mark Wegner 2924 14% 11% 14% 0.8
Chris Conroy 2307 14% 8% 12% 0.8
Al Porter 3047 15% 11% 14% 0.8
Lance Barksdale 2921 13% 9% 12% 0.8
Mike Muchlinski 2542 15% 12% 14% 0.8
Manny Gonzalez 2614 14% 10% 13% 0.8
Pat Hoberg 3282 14% 11% 13% 0.8
Mark Carlson 2849 15% 12% 14% 0.8
Dana DeMuth 1544 15% 12% 14% 0.8
Stu Scheurwater 419 15% 13% 14% 0.8
Chris Segal 2597 14% 10% 13% 0.8
Greg Gibson 2650 14% 10% 13% 0.7
Mike Winters 1927 14% 12% 14% 0.7
Tony Randazzo 1460 13% 9% 12% 0.7
Toby Basner 2295 14% 11% 13% 0.7
Seth Buckminster 2279 13% 11% 12% 0.7
Paul Schrieber 3115 12% 11% 12% 0.7
Andy Fletcher 2924 13% 13% 13% 0.6
Jeff Gosney 171 6% 11% 7% 0.1
Jon Byrne 133 8% 16% 11% 0.0
Total 229667 15% 10% 14% 0.9

ESTIMATING THE VALUE OF WEARING OUT PITCHERS

> We’ve looked before at the effect of bad umpires on the value of a hitter.  Today I think I’ve got another supplemental hitting stat that tries to measure how much seeing more pitches helps a player’s team win.

This season, a run has scored after every 35.4 pitches thrown.  So I took every hitter’s total pitches seen, compared that to what would be expected if he saw an average number of pitches, and divided the result by 35.4.  The resulting number is in theory applicable to his total WAR.

Here are the season leaders in pitches seen above average and their added value.  The listings are in runs, and 10 runs is equal to one win above replacement.

1. Mike Trout (8.2)
2. Brett Gardner (7.7)
3. Carlos Santana (7.1)
4. Matt Carpenter (6.6)
5. Mike Napoli (6.2)
6. Adam Dunn (5.3)
7. Brian Dozier, Christian Yelich (4.3)
9. Paul Goldschmidt (3.9)
X. Andrew McCutchen (3.9)

These are the worst.

1. Jose Altuve (-7.9)
2. Andrelton Simmons (-5.9)
3. Adeiny Hechavarria (-5.7)
4. Alexei Ramirez (-5.7)
5. Erick Aybar (-5.6)
6. Salvador Perez (-5.0)
7. Robinson Cano (-4.9)
8. Wilin Rosario (-4.9)
9. Torii Hunter (-4.3)
X. Jean Segura (-4.2)

ARE TAVERAS AND BOURJOS REALLY HURT BY INCONSISTENT PLAYING TIME?

> I think Oscar Taveras and Peter Bourjos should be playing way more than they have been.  Seemingly most Cards fans feel the same way about one or both of them, and one popular line of reasoning is that they will atrophy on the bench if they don’t get regular playing time, and as a result will perform worse when they do get a chance.

There’s not a great way to measure if this is true, but it’s such a widely-held belief that we should at least attempt to find out.  I took every game each of them has played in the Majors and minors and looked at their stats when playing on consecutive days (and two-game doubleheaders) as compared to playing after one or more days off.  The following is what I came up with.

Oscar Taveras
No days off: 1263 PA, .324/.372/.527, .379 BABIP, .203 ISO
 1+ days off:   333 PA, .327/.365/.495, .376 BABIP, .168 ISO

We’re working with about almost a 4:1 plate appearance difference with some variance across levels of the minor leagues, so such a similarity in stats would appear to mean Oscar hasn’t been that negatively affected by inconsistent playing time.  His OPS has only been 4% worse in said situations, and given that the sample is a small 333 PA, it’s fair to assume the difference is just baseball’s randomness at work.

Peter Bourjos
No days off: 2882 PA, .279/.333/.439, .358 BABIP, .160 ISO
 1+ days off:   666 PA, .247/.299/.386, .326 BABIP, .139 ISO

A 13% OPS difference for Bourjos, which seems pretty drastic.  But again, a 4:1 PA comparison over different leagues and in this case we’ve got a big BABIP difference, so the actual effect (if there is one) might not actually be as drastic 13%.

On-base ability seems to stay relatively the same in both cases, though there is a noticeable, possibly relevant power decrease.  In the case of Bourjos we’re talking the equivalent of just one season’s worth of plate appearances with multiple days’ rest and with Taveras a half season’s worth.  The reader can decide if this is enough of a sample to warrant alarm, but I’m personally skeptical.

MARGIN OF ERROR FOR WAR

> Probably won’t break this out again until MVP time, but I’ve noticed the citing of season-to-date WAR has gotten pretty out of control as the stat becomes more popular.  It seems to be blindly accepted as gospel even by some of the biggest analysts in the Game, though if you know how WAR or baseball in general works over small periods of time (which four months is), you know the numbers need much more time to reveal themselves.

To be realistic, though, season-to-date WAR is not going away.  So as a next-best option I’ve devised a rough margin of error for season WAR.  Extra-base hits, unintentional walks, hit-by-pitches and baserunning remains the same since they tend to hold pretty steady.  But from there I created two numbers: the floor WAR and ceiling WAR.  To find the floor, I regressed the player’s defensive WAR and singles-on-balls-in-play rate half way (-50%) to league average for position players since these things are what tend to fluctuate most in a short time period.  To get the ceiling, I increased the defensive WAR and singles-on-balls-in-play rate by the same amount that they were decreased by (+50%).

Let’s use Alex Gordon (3.8 WAR) and Yoenis Cespedes (1.2) as examples.

Alex Gordon’s defensive WAR (2.0 wins) is off the charts this year.  Regressing this and his singles 50% closer to the mean, his floor becomes 2.8 and regressing upwards by that same 50% puts him at 4.8.  Given the uncertain nature of defensive stats and the fact that most of Gordon’s value comes from defense, he has a wide margin of error of about one full win either way.

Cespedes, however, is much easier to peg.  He is league-average defensively (0.0), so his WAR, ceiling and floor are all the same when rounded to one digit: 1.2.  His margin of error is not hurt or helped.

I put all qualified position players into a spreadsheet which you can check out here.  If you’re ever curious about a certain player or players, let me know here or on Twitter.

WE’RE IN A GOLDEN AGE OF CATCHERS

> Going by Baseball-Reference WAR, there were more 2+ win (17) and 4+ win (9) catchers last season than at any point in baseball history.  At the all-star break this year we already have nine two-win catchers (again going by B-R, so not including framing): Evan Gattis, Yan Gomes, Jonathan Lucroy, Russell Martin, Devin Mesoraco, Yadier Molina, Derek Norris, Salvador Perez and Kurt Suzuki.

Here’s a graph of good catchers per season.

And this is total WAR produced by catchers, per Fangraphs.

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NO, A LEBRON JAMES BASEBALL EQUIVALENT WOULD NOT BE WORTH 42 WINS

> Fangraphs put out this thing the other day claiming that the baseball equivalent of LeBron James in Baseball would be either a 23-win player or a 42-win player, depending on how you did the math.

That sentence alone should make you think, “That is wrong.”  And it is; though that didn’t stop the post from being spread everywhere.

Their argument was that since LeBron was worth 2.3% of the league’s total WAR this year, if you applied that same number to Baseball he’d be worth 23 wins or 42 if he were a pitcher who hit for himself.  But the concept is all wrong.  The Heat were able to pass the ball to James whenever they wanted to.  In baseball, he’d have to wait his turn both in the lineup and in the field.

James played an average of 37.7 minutes in 77 of the Heats’ 82 48-minute games.  That puts him on the field for roughly 3/4 of the action.  And during that time, he’s always doing something.  (Or at least is supposed to be.)  A baseball player, however, only plays roughly 1/9 of the time, dividing the action somewhat evenly among his teammates.  This of course depends on position, but in the interest of simplicity we’ll say Baseball LeBron was involved in 1/9 of all his team’s plays knowing our final total won’t be exact, just more practical than the impossible 3/4 Fangraphs is using.

1/9 minus 3/4 is -64%.  Applied to LeBron’s 20.1 WAR (according to ESPN, which is what Fangraphs used in its project), that would put our approximate estimate of Baseball LeBron at 7.2 wins above replacement on the season.  Not bad; that’ll win you an MVP as long as you’re not in the same league as Mike Trout.  But it’s not 42.  That’s ridiculous.

LEBRON JAMES’ EFFECT ON HEAT AND CAVS FANBASES

> LeBron James returned to the Cleveland Cavaliers yesterday.  When he first hit free agency I took down the number of Facebook likes and Twitter followers of every NBA team to see how big an impact adding the best player in the Game would have on the size of his hypothetical new team’s fanbase and also how it would affect his old team’s.

Since James hit free agency, no team has lost followers or likes.  Team pages tend to just keep growing, and since James hit free agency the average NBA Facebook page has grown 1.8% and the average Twitter page has grown 2.2%.  So under normal circumstances the Cavs would have 1,712,845 Facebook likes and 334,026 Twitter followers right now (24 hours after the announcement).  But because of James they have 60,319 (+3.5%) more likes and 36,285 (+10.9%) more followers than projected.

James’ old team, the Miami Heat, would have 14,800,084 likes and 2,682,064 followers.  Their Facebook page has outperformed the projection by 111,162 (+0.8%), but their Twitter page has 9,655 (-0.4%) fewer followers than expected.

James himself has gained 325,611 (2.5%) Twitter followers.  All numbers continue to rise so I may check back on this later.

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