Mariners to Endure Visit From Rivals

MARINERS (27-35) Δ Ms PADRES (20-41) EDGE
HITTING (wOBA) -43.4 (28th) -11.2 -24.3 (25th) Padres
FIELDING (BABIP) 25.6 (2nd) -5.5 -4.0 (19th) Mariners
ROTATION (tRA) 0.8 (17th) 1.3 -5.0 (20th) Mariners
BULLPEN (tRA) -11.1 (28th) 1.2 1.5 (14th) Padres
OVERALL (RAA) -28.1 (21st) -13.6 -31.9 (23rd) MARINERS
Explainer

A series that began in a most memorable way finished in ways that we'd all rather forget. How better to forget then with an emotional defense of our home city against the hated monks from down south?

Last series I had to beg off with a curtailed version of my usual previews. And the Mariners got crushed in the last two games. To make it up to you, the Gods, and my own completion of some of the work I've been doing behind the scenes for months now, here (here = after the jump) is a greatly expanded series preview. I welcome all forms of polite feedback. I'd like to make these as interesting as possible to you, the reader so suggestions welcome.

Essentially, I've added some more detailed looks at the hitting, defense and bullpens where previously I had only the above table. The starting pitchers are currently pushed down between the fielding and relievers so as to match the order of the summary table at the top.

Because I'd find it silly to tally up how hitters and pitchers have done over just three or four day spans, the tables below will include data from the past two weeks (for hitters) or four weeks (for relief pitchers). In a way, this preview is now more like a preview plus a rolling report cart.

What I wanted for the detailed tables was a summary of the stats that I prefer to look at when quickly evaluating each. They aren't going to contain all the data required for a good in-depth study, but that's beyond the requirement here and would be tedious to see twice a week. These capture how well someone is doing at the plate recently with the numbers that I think best represent that.

A quick primer before the chart. The data here is totaled over the past two weeks (14 days). There's a quick glossary below the chart along with the league averages for the rates. The hitters are sorted by a metric not included in the table. If you can correctly guess what it is, you'll win a coveted Matthew Rec™.

Batter PA P/PA Slash line nBB K (sw) 1B/2B/3B/HR Sw% Ct% Qual+
M Saunders* 53 4.0 .420/.453/.660 3 7 (6) 13 / 6 / 0 / 2 55 83 100.3
K Seager* 50 4.1 .326/.420/.651 7 10 (7) 6 / 5 / 0 / 3 42 78 138.5
D Ackley* 47 4.6 .282/.404/.436 8 13 (8) 7 / 3 / 0 / 1 36 81 111.0
J Jaso* 29 3.6 .296/.379/.444 2 5 (4) 6 / 1 / 0 / 1 43 84 100.3
J Smoak^ 55 3.7 .255/.364/.468 8 12 (10) 8 / 1 / 0 / 3 36 78 116.9
J Montero 50 3.5 .298/.380/.426 3 7 (6) 10 / 3 / 0 / 1 56 80 106.2
M Olivo 32 3.8 .219/.219/.469 0 6 (6) 3 / 2 / 0 / 2 50 74 94.5
I Suzuki* 49 3.2 .229/.260/.417 0 3 (2) 8 / 0 / 0 / 3 54 93 120.8
M Carp* 31 3.7 .160/.344/.200 6 8 (7) 3 / 1 / 0 / 0 44 71 75.7
B Ryan 39 4.2 .111/.231/.222 3 10 (9) 1 / 2 / 1 / 0 52 67 39.6

P/PA = pitches per PA [avg~3.8], nBB = uBB + HBP, Sw = swinging [avg~45%], Ct = contact [avg~81%], Qual+ = a measure of quality of batted balls [avg=100, higher is better]

Qual+ is the only column here that I hope is baffling. The rest should be straightforward if you've spent any time around this site before. My aim with Qual+ is to sum up in one number how good the batted balls have been. I didn't want to just break out here's how many ground balls, here's how many line drives, and so on. One, that would make the chart too horizontally large. Two, it's bereft of context. What's a good breakdown? Sure, line drives are good, but how often? Do a lot of line drives make up for a lot of pop flies? Those aren't questions I think many people have an innately comfortable handle on. I don't.

So what I've tried for now — and again, feedback welcome — is to take all the batted balls (home runs included) and add up their expected run values. Based on run expectancy charts, you can find out how many runs the average ground ball or average fly ball or the average whatever generates. Those values are already used in tRA to figure out the expected number of runs a pitcher would have allowed. Here, I take that sum and divide it by the number of batted balls for the hitter (to make it a rate stat) and then divided that by the league average to put it on a relative scale. So think of it like OPS+, but for batted ball types only.

I don't intend this to be any kind of precise statistic. My only aim is to be able to see at quick glance if a batter has been roughly average, above or below and get a fuzzy sense of by how much. It's like a sanity check on the triple slash line; a hopefully improved version of the "look at line drive rate then compare to BABIP" method. So I wouldn't put too much weight on them. My Qual takeaways from the above chart would include:

-Michael Saunders had probably been a little lucky to have hit .420 the past fortnight.
-Kyle Seager is a boss.
-Brendan Ryan has been crap.

None of those are surprising.

Beyond the confusing batted balls, I took a warming to Dustin Ackley's line. I hadn't felt that he was doing well, partly because his overall line is still bad and partly because I'm continuously confused how he doesn't hit .350 all the time, but I guess I would settle for a .280/.400/.430 line, in Seattle*, with adequate second base defense. Additionally, it would be less than I dreamed for, but if Justin Smoak settled in as a .255/.365/.470 bat would you be satisfied? Disappointed? Content?

*which reminds me; perhaps I should park-adjust those triple-slash lines. Or would that confuse people too much?

Batter PA P/PA Slash line nBB SO (sw) 1B/2B/3B/HR Sw% Ct% Qual+
C Quentin 40 3.4 .429/.500/.971 5 4 (4) 6 / 4 / 0 / 5 54 79 222.4
C Headley^ 55 4.2 .383/.473/.617 8 10 (9) 11 / 5 / 0 / 2 45 72 142.1
L Forsythe 31 4.3 .346/.452/.654 5 7 (4) 5 / 1 / 2 / 1 37 90 160.1
E Cabrera^ 47 4.5 .325/.447/.625 7 10 (6) 5 / 6 / 0 / 2 39 84 113.1
W Venable* 29 3.0 .321/.345/.714 1 4 (4) 3 / 3 / 1 / 2 53 81 147.4
C Denorfia 40 3.6 .316/.350/.579 2 4 (3) 6 / 3 / 2 / 1 44 84 121.3
C Maybin 33 3.2 .226/.303/.516 2 5 (3) 3 / 1 / 1 / 2 50 78 145.7
J Baker* 25 4.0 .208/.320/.208 1 6 (5) 5 / 0 / 0 / 0 50 76 70.8
N Hundley 25 3.6 .130/.160/.217 1 6 (6) 1 / 2 / 0 / 0 58 71 73.0
Y Alonso* 49 3.5 .109/.204/.109 3 12 (7) 5 / 0 / 0 / 0 47 80 86.9

So Carlos Quentin has been mashing the ball. And wow, Chase Headley, Logan Forsythe, Everth Cabrera, Wil Venable, all of them have been bruising. The Padres have launched many a home run in the last two weeks, but overall have not been plating all that many runs. Fifty-four over those 12 games (4.5 per) is a high mark for the Padres, but not one that strains credibility. Hopefully the cool Seattle climate will slow down these hard-hitting friars!

Next comes fielding and though I do distrust individual fielding statistics, the way I compute mine are based on performance against batted ball types (see the link at bottom of table). Since those are fairly easily divided between the primary responsibility of infielders (grounders, bunts, half of the pop flies) and outfielders (fly balls, line drives and the other half), I might as well offer that breakdown, right?

MARINERS Δ Ms PADRES EDGE
INFIELD 15.0 (4th) 0.0 1.4 (14th) Mariners
OUTFIELD 10.6 (9th) 0.0 -5.4 (18th) Mariners
RBBIP 0.285 (2nd) 0.0 0.313 (19th) Mariners
OVERALL 25.6 (2nd) 0.0 -4.0 (19th) MARINERS
Explainer

A RBBIP* in the bottom half of the league gets represented in red. I thought I'd have more to say about this, but nope. Brendan Ryan is really good at defense, I bet.

* reached base on ball in play: BABIP + errors

12 JUN 19:10

FELIX HERNANDEZ CLAYTON RICHARD*
var data = new google.visualization.DataTable(); data.addColumn('string','Pitch'); data.addColumn('number','SI 29% 93');data.addColumn('number','CH 22% 89');data.addColumn('number','FA 20% 94');data.addColumn('number','CV 13% 82');data.addColumn('number','SL 12% 86');data.addColumn('number','Overall');data.addRows(3); data.setCell(0,0,'Contact%'); data.setCell(1,0,'Strike%'); data.setCell(2,0,'GB Rate'); data.setCell(0,1,50);data.setCell(1,1,75);data.setCell(2,1,50);data.setCell(0,2,60);data.setCell(1,2,70);data.setCell(2,2,75);data.setCell(0,3,60);data.setCell(1,3,45);data.setCell(2,3,60);data.setCell(0,4,60);data.setCell(1,4,55);data.setCell(2,4,65);data.setCell(0,5,55);data.setCell(1,5,40);data.setCell(2,5,80);data.setCell(0,6,65);data.setCell(1,6,60);data.setCell(2,6,70); var options = { width:270, height:125, backgroundColor:'#F2F2F2', legend: {position:'top', textStyle: {color: 'black', fontSize: 9}}, vAxis: {viewWindowMode:'explicit',viewWindow:{max:80,min:18},gridlines: {count:7}, textStyle: {color: 'black', fontSize: 10}}, hAxis: {textStyle: {color: 'black', fontSize: 11}}, chartArea:{left:20,top:20,width:250,height:90}, series: [ {color:'#7E22AA'}, {color:'#0131CB'}, {color:'#F20000'}, {color:'#0A800A'}, {color:'#FFE606'}, {color:'black', visibleInLegend: false}]}; var data = new google.visualization.DataTable(); data.addColumn('string','Pitch'); data.addColumn('number','SI 44% 91');data.addColumn('number','FA 17% 91');data.addColumn('number','CH 17% 84');data.addColumn('number','SL 14% 84');data.addColumn('number','Overall');data.addRows(3); data.setCell(0,0,'Contact%'); data.setCell(1,0,'Strike%'); data.setCell(2,0,'GB Rate'); data.setCell(0,1,65);data.setCell(1,1,70);data.setCell(2,1,55);data.setCell(0,2,65);data.setCell(1,2,55);data.setCell(2,2,60);data.setCell(0,3,30);data.setCell(1,3,40);data.setCell(2,3,60);data.setCell(0,4,40);data.setCell(1,4,50);data.setCell(2,4,55);data.setCell(0,5,45);data.setCell(1,5,55);data.setCell(2,5,60); var options = { width:270, height:125, backgroundColor:'#F2F2F2', legend: {position:'top', textStyle: {color: 'black', fontSize: 9}}, vAxis: {viewWindowMode:'explicit',viewWindow:{max:80,min:18},gridlines: {count:7}, textStyle: {color: 'black', fontSize: 10}}, hAxis: {textStyle: {color: 'black', fontSize: 11}}, chartArea:{left:20,top:20,width:250,height:90}, series: [ {color:'#7E22AA'}, {color:'#F20000'}, {color:'#0131CB'}, {color:'#FFE606'}, {color:'black', visibleInLegend: false}]};

The pitcher graphs should be familiar or else you've never read one of these before, but I have made them over as well. They are no longer powered by me doing them by hand, but rather by The Google and my database. So they're a bit more interactive than mere still images. Frankly I don't care much about that, but it's kind of neat.

The biggest benefit I feel is that I was now able to finally enforce consistent colors. So fastball grades are always the red bars, changeups the blue, sinkers are purple, curveballs are green, sliders are yellow, cutters are grey and knuckleballs will be pink. Suggestions are welcome on color changes as well. I freely admit to having no eye for design. So by suggestions, I mean, spell out the RGB or Hex color codes you think would be better.

13 JUN 19:10

HECTOR NOESI JASON MARQUIS
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Hey, Hector, you know sinkers are supposed to, you know, sink, right? They are not supposed to spin in the top of the strike zone. Try aiming for the knees?

14 JUN 19:10

TBD EDINSON VOLQUEZ
var data = new google.visualization.DataTable(); data.addColumn('string','Pitch'); data.addColumn('number','Overall');data.addRows(3); data.setCell(0,0,'Contact%'); data.setCell(1,0,'Strike%'); data.setCell(2,0,'GB Rate'); data.setCell(0,1,20);data.setCell(1,1,20);data.setCell(2,1,20); var options = { width:270, height:125, backgroundColor:'#F2F2F2', legend: {position:'top', textStyle: {color: 'black', fontSize: 9}}, vAxis: {viewWindowMode:'explicit',viewWindow:{max:80,min:18},gridlines: {count:7}, textStyle: {color: 'black', fontSize: 10}}, hAxis: {textStyle: {color: 'black', fontSize: 11}}, chartArea:{left:20,top:20,width:250,height:90}, series: [ {color:'black', visibleInLegend: false}]}; var data = new google.visualization.DataTable(); data.addColumn('string','Pitch'); data.addColumn('number','FA 35% 93');data.addColumn('number','CH 26% 83');data.addColumn('number','CV 21% 78');data.addColumn('number','SI 18% 94');data.addColumn('number','Overall');data.addRows(3); data.setCell(0,0,'Contact%'); data.setCell(1,0,'Strike%'); data.setCell(2,0,'GB Rate'); data.setCell(0,1,70);data.setCell(1,1,40);data.setCell(2,1,65);data.setCell(0,2,80);data.setCell(1,2,50);data.setCell(2,2,65);data.setCell(0,3,75);data.setCell(1,3,35);data.setCell(2,3,70);data.setCell(0,4,65);data.setCell(1,4,40);data.setCell(2,4,55);data.setCell(0,5,80);data.setCell(1,5,35);data.setCell(2,5,70); var options = { width:270, height:125, backgroundColor:'#F2F2F2', legend: {position:'top', textStyle: {color: 'black', fontSize: 9}}, vAxis: {viewWindowMode:'explicit',viewWindow:{max:80,min:18},gridlines: {count:7}, textStyle: {color: 'black', fontSize: 10}}, hAxis: {textStyle: {color: 'black', fontSize: 11}}, chartArea:{left:20,top:20,width:250,height:90}, series: [ {color:'#F20000'}, {color:'#0131CB'}, {color:'#0A800A'}, {color:'#7E22AA'}, {color:'black', visibleInLegend: false}]};

This would be Kevin Millwood's spot in the rotation. I am still waiting on word over whether he will make this start, miss it and move everyone up again or, possibly, miss it and domino a roster move.

The final bit is a detailed look at the bullpens. These are similar to the batting breakdowns but obviously geared toward what I fancy in evaluating pitchers. Qual makes a repeat appearance but is now Qual- instead of Qual+. It is calculated exactly the same way but the minus sign is used to differentiate that, for pitchers, lower numbers are better than higher.

Leverage index pokes its furry little nose in at the end. It is the average of all plate appearances by the reliever, not the average of the leverage when the reliever entered. It's another rough barometer statistic. You can use it to see a hazy outline of bullpen roles and usage. Since relievers are less used than hitters and I didn't want super small samples to make this pure noise, the data is over four weeks (28 days) rather than the two weeks for hitters. Also there's a cutoff of 20 batters faced.

Reliever BF Str% nBB Ct% K(sw) GB% HR Qual- LI
H Iwakuma 61 60.6 10 78.2 9 (7) 63.4 1 94.0 0.6
S Kelley 59 64.4 4 79.8 13 (12) 22.0 0 84.9 1.0
B League 53 62.7 5 80.4 10 (10) 42.1 1 133.1 1.7
T Wilhelmsen 48 73.1 2 73.9 15 (11) 45.2 1 77.4 1.4
C Furbush* 39 69.3 1 73.8 13 (12) 56.5 0 64.5 0.7
S Delabar 34 59.4 4 73.6 10 (10) 55.0 2 172.9 0.7
L Luetge* 21 54.8 4 77.5 3 (3) 35.7 0 50.5 0.7

Str% = strike rate [avg~63%], Ct% = contact rate [avg~78%], GB% = groundball rate [avg~45%], Qual- = a measure of quality of batted balls [avg=100, lower is better], LI = leverage [avg~1.2]

None of the Quals should be surprising. Wow, Steve Delabar and Brandon League were getting slapped around? I had no idea! You might state, sarcastically, to those around you who are frankly tired of all your sarcasm. Try just being a genuine person for one entire day, will you?

Hisashi Iwakuma has faced more batters than anyone in the pen the last month. Does that surprise you? It surprised me. What didn't surprise me was that he's walked ten and struck out only nine. That is a big reason why I'm not too keen on the Iwakuma-to-rotation bandwagon. I looked at that bandwagon, but boy, I don't know. That rear axle looks mighty shaky and they ain't carrying a spare.

The Furbush-should-get-another-chance wagon though, now there's a solid vehicle.

Reliever BF Str% nBB Ct% K(sw) GB% HR Qual- LI
B Brach 48 63.2 5 72.8 15 (13) 40.7 3 150.4 1.0
L Gregerson 46 60.0 7 65.0 8 (8) 48.4 3 196.1 1.1
A Hinshaw* 45 61.2 6 81.5 10 (6) 44.8 2 148.0 0.6
M Mikolas 42 62.7 3 79.5 10 (9) 69.0 0 55.8 0.7
A Cashner 41 68.5 2 68.4 15 (13) 33.3 1 137.7 1.6
D Thayer 38 66.7 1 83.3 7 (3) 37.9 3 172.2 1.3
J Thatcher* 35 70.2 2 79.7 10 (9) 39.1 0 43.4 1.0
R Ohlendorf 24 64.0 2 70.5 5 (4) 13.3 0 98.5 1.0

The Padres' hitters have thrown (with their bats) quite a few balls over the fence of late. But the Padres' relievers have also thrown (with their arms, via other people's bats) quite a few balls over the fence.

Luke Gregerson is coming dangerously close to having more walks than strikeouts which in the Padres' bullpen I believe qualifies you for automatic death by being forced to consume 20 gallons of Trappist beer (monk joke). I don't always agree with the San Diego culture, but I do respect it.

Series Beer(s): Delirium Tremens
I think this was my first Belgian beer. It holds a nostalgic place in my brain neurons that represent memory. Also, it's really good. Others agree.

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