Shortly before Thanksgiving, I wrote an article about how Chris Sale had been hurt last season by lousy receivers. That was an interesting observation from the data, but it wasn’t the only interesting observation from the data. According to Baseball Prospectus, Sale lost the second-most runs from his pitch-framers. Brandon Finnegan, however, pitched to the worst strike zone, his framers costing him an estimated 7.8 runs. Meanwhile, from the same source, Madison Bumgarner pitched to the best strike zone, his framers helping him by an estimated 11.0 runs. That’s a 19-run difference from catchers alone.

Maybe you don’t believe the spread was really that big. It’s easy to believe there was some spread — Bumgarner pitched almost exclusively to Buster Posey, while Finnegan pitched to Tucker Barnhart and Ramon Cabrera. One should also be wary of putting everything on the catchers. Pitchers with better command are easier to receive than pitchers with worse command, and Bumgarner throws with greater accuracy than Finnegan does. So, in part, the zones were the pitchers’ fault. But one thing we know for sure is that, in the end, Bumgarner’s strike zone was more generous. Arguably the most generous. So here is how the Bumgarner and Finnegan called strike zones compare:

Pretty interesting! Here is an alternate view of the same information. Note this is also from the catcher’s perspective. This shows called-strike rates out of all called pitches:

Both pitchers are southpaws. Bumgarner got the far better zone high. He got the far better zone arm-side. He got the far better zone low. Glove-side, it’s about equal, if not in favor of Finnegan. That’s of some note — Finnegan wasn’t losing strikes everywhere. It seems like he frequently tried to target that glove-side edge, but he’d often miss, and his catchers were probably worse at receiving missed locations. So it goes. It’s another example of a point to be debated. Bumgarner got the way more generous strike zone than Finnegan did. Some of this is because Bumgarner hit his spots better than Finnegan did. That reflects well on Bumgarner’s talent! But with an automated strike zone, the gap in performance between the pitchers would’ve been narrowed. Bumgarner’s zone would’ve been worse, and Finnegan’s zone would’ve been better. You either like the way things are, or you don’t. They’ve been this way forever, even if we’ve only recently taken to measuring it.

An estimated gap of 18.8 runs. This compares the two extremes, but there was about the same difference in WAR last year between Max Scherzer and Carlos Martinez. Individual ball and strike calls seldom make a big difference in the moment, but, holy hell, can the differences ever add up.

Below is an analysis of the prospects in the San Francisco Giants farm system. Scouting reports are compiled with information provided by industry sources as well as from my own observations. The KATOH statistical projections, probable-outcome graphs, and (further down) Mahalanobis comps have been provided by Chris Mitchell. For more information on thes 20-80 scouting scale by which all of my prospect content is governed you can click here. For further explanation of the merits and drawbacks of Future Value, read this. -Eric Longenhagen

The KATOH projection system uses minor-league data and Baseball America prospect rankings to forecast future performance in the major leagues. For each player, KATOH produces a WAR forecast for his first six years in the major leagues. There are drawbacks to scouting the stat line, so take these projections with a grain of salt. Due to their purely objective nature, the projections here can be useful in identifying prospects who might be overlooked or overrated. Due to sample-size concerns, only players with at least 200 minor-league plate appearances or batters faced last season have received projections. -Chris Mitchell

Other Lists
NL West (ARI, COL, LAD, SD, SF)
AL Central (CHW, CLE, DET, KC, MIN)
NL Central (CHC, CIN, PIT, MIL, StL)
NL East (ATL, MIA, NYM, PHI, WAS)
AL East (BAL, BOS, NYY, TB, TOR)

Relevant/Interesting Metrics
Slashed .224/.278/.294 at home in 2016, .315/.348/.438 on the road. Worth +11 runs at combination of shortstop and third base this year per Clay Davenport

Scouting Report
Arroyo was viewed as a bit of a reach when he was drafted because he was already very likely to move off of shortstop and quite unlikely to develop prototypical, corner-worthy power. Some scouts wanted to give him a try behind the plate because it was the only place they thought his bat would profile. While scouts were right about Arroyo’s power projection, it may prove less relevant to his future than originally anticipated because his feel to hit compensates so well for it.

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Eric Longenhagen is publishing brief, informal notes from his looks at the prospects of the Arizona Fall League and, for the moment, the Fall Instructional League. Find all editions here.

Braves 2B Travis Demeritte has looked tremendous at second base this fall. Not only has he made several acrobatic plays but he’s handled some bad hops and sucked up errant throws on steal attempts as well. While his hands remain somewhat rough, Demeritte’s range and athleticism have forced me to reckon with the idea of plus-plus defense at second base — as well as to remember if I’ve ever put a 7 on a second baseman’s glove before. I don’t think I have, and I suppose it’s worth asking if such a thing even exists, as one might wonder why a 70 or 80 glove at second base couldn’t play shortstop in some capacity. I think the right concoction of skills (chiefly, great range and actions but a poor arm) can churn out a plus-plus defender there. I’d cite Ian Kinsler, Brandon Phillips and Dustin Pedroia, and Chase Utley as examples from the last eight or 10 years. It’d be aggressive to put a future 7 on Demeritte’s glove right now because his hands and arm accuracy are too inconsistent, but those are things that could be polished up with time.

Tigers RHP Spencer Turnbull was up to 94 and mixed in five different pitches last night. Nothing was plus and Turnbull doesn’t have especially good command but I liked how he and Brewers C Jake Nottingham sequenced hitters and how to and that Turnbull was willing to pitch backwards and give hitters different looks each at-bat. He and Rays RHP Brent Honeywell have the deepest repertoires I’ve seen so far in Fall League.

Giants righty Chris Stratton sat 89-92 last night with an average mid-80s slider that is good enough to miss bats if he locates it, and last night he did. I think the changeup is average, as well, while Stratton’s curveball is a tick below but a useful change of pace early in counts. He looks like a back-end starter.

Quite a few defenders got to air it out last night. Here are some grades I put on guys’ arms:

Dawel Lugo, 3B, ARI: 6

Miguel Andujar, 3B, NYY: 6

Pat Valaika, INF, COL: 5

Gavin Cecchini, INF, NYM: 45

Christin Stewart, OF, DET: 4

Angels CF Michael Hermosillo, who was committed to Illinois to play running back before signing with Anaheim after the 2013 draft, displayed tremendous range in center field last night. He looks erratic at the plate but he hit well at Burlington and Inland Empire this year and is an obvious late-bloomer follow as a two-sport prospect from a cold weather state.

The playoffs roll on, with subplots galore, most of them involving pitching-staff usage patterns that are long overdue. Meanwhile, let’s conclude our two-part series examining macro team BIP data for the 10 playoff teams, broken down by exit speed and launch angles. (Read the Part 1 here.) We’ll examine what made these teams tick during the regular season and allowed them to play meaningful October baseball. It’s more or less a DNA analysis of the clubs that made it to the game’s second season.

First, some ground rules. For each club, all offensive and defensive batted balls were broken down (first) by type and (second) by exit speed. Not all batted balls generated exit speed and/or launch angle data; just over 14% were unread, most of them weakly hit balls at very high or low launch angles. How do we know this? Well, hitters batted .161 AVG-.213 SLG on them, a pretty strong clue.

BIP types do not strictly match up with FanGraphs classifications. For purposes of this exercise, any batted ball with a launch angle of over 50 degrees is considered a pop up, between 20 and 50 degrees is a fly ball, between 5 and 20 degrees is a line drive, and below 5 degrees is a ground ball. For background purposes, here are the outcomes by major-league hitters for each of those BIP types: .019 AVG-.027 SLG on pop ups (5.7% of measured BIP), .326 AVG-.887 SLG on fly balls (30.9%), .658 AVG-.870 SLG on liners (24.4%) and .238 AVG-.260 SLG on grounders (39.1%).

As you might expect, there are massive differences in production within BIP types based on relative exit speed. If you hit a fly ball over 100 mph, you’re golden, batting .766 AVG-2.739 SLG. If you drag that category’s lower boundary down just 5 mph, however, you get to the top of the donut hole, where fly balls go to die. Hitters batted just .114 AVG-.209 SLG on fly balls between 75-95 mph. All other fly balls — yes, even including those hit under 75 mph — fared much better, generating .387 AVG-.786 production.

Line drives tend to be base hits at almost all exit speeds. All the way down to 75 mph, hitters bat over .600 on batted balls in the line-drive launch-angle ranges; down to 65 mph, hitters still bat around .400 range in each velocity bucket. At 65 mph and higher, a liner generates an average .673 AVG-.889 SLG line. Under 65 mph, liners tend to land in infielders’ gloves; hitters batted just .170 AVG-.194 SLG on those. On the ground, hitters batted a strong .423 AVG-.456 SLG on grounders hit at 100 mph or higher. Under 85 mph, however, the hits dry up almost totally, with hitters producing a .107 AVG and .117 SLG. Between 85-100 mph, hitters bat closer to the overall grounder norm, at .267 AVG-.294 SLG.

With that as a backdrop, let’s conclude our look at each playoff club’s offensive and defensive BIP profiles. Last time, we profiled the Orioles, Red Sox, Cubs, Indians and Dodgers; today, we’ll look at the other five, in alphabetical order:

New York Mets
Two of the 10 playoff teams played well over their true talent this season, at least based on my BIP-centric method of team evaluation. Both will be covered today. First, the Mets hit significantly more pop ups than their opponents (+69), not including untracked ones in that 14% “null” group. On the positive side, the Mets hit 160 more fly balls than their opponents; they were a whopping +86 vis-à-vis their opponents in the 95-105 mph buckets. This explains why they hit 66 more homers than their opponents.

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This probably has to be said up front; Bruce Bochy has historically done a masterful job of running his pitching staff in the postseason. It’s one of the main reasons — well, along with Madison Bumgarner anyway — that he has three world series championships, and is almost certainly going into the Hall of Fame someday. Over the long run, I don’t think bullpen management has been a weakness of Bochy’s Giants.

But there’s one thing about this Giants second half bullpen meltdown that I’ve never really been able to understand, or seen explained with solid reasoning. And this thing was only magnified during the season-ending bullpen meltdown in the ninth inning of the NLDS; why doesn’t Bochy trust Will Smith?

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The 2015 season represented the worst of John Lackey’s career in terms of facing left-handed batters. He rectified that split this year, which is probably just because the balls bounced differently. But it’s also notable that the Cubs’ Game 4 starter changed his approach against lefties this season. He’s mimicking a strategy he last used in 2011, the worst year of his career. Strangely, it’s working.

This lefty problem has always been a thing for Lackey — he’s just better against righties (.309 wOBA career) than lefties (.325) because of platoon splits and also because his best secondary weapon is his slider — but last year, the problem was worse than usual. He recorded a 4.84 FIP against lefties and a 2.69 FIP against righties in 2015. It was also the year he threw the most fastballs, the fewest curves and changeups.

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Here on FanGraphs, we host live, interactive win-expectancy graphs for every game, and they usually don’t look like this:

It’s rare for any one of these graphs to stretch 13 innings. It’s rare for the team in complete control for innings six through eight to wind up losing the game. It’s rare for the biggest play in regulation to read “J Arrieta Home Run.” It’s rare for there to be such a large and sudden spike at the end. I’m not sure I’ve ever seen that parabolic shape in the middle before.

Game 2 of the National League Division Series between the Giants and Cubs was bonkers. Let’s address all 10 times it looked like it might be over.

No. 1 – First inning

If you’re an extremist, this thing might’ve looked over before it started. Sure, the Cubs were up two games to none in the series and had clearly looked like the better team thus far, but this is Madison Bumgarner we’re talking about, whose soul exits the body and watches over its human figure pitching from above during the postseason. I don’t know why that little box is showing regular-season stats over on the right. The more compelling graphic would’ve been the postseason version, which just consists of “as many as there are” in the games and innings columns and a bunch of zeros in the rest.

Actual win expectancy: 54%, Giants

Perceived win expectancy: EvenYearBumgarner%, Giants
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The appeal of lists and rankings, whatever its cause, is very real. That thing you like? Sure, it’s good, but is it better than this other thing?! We’ve seen this carry over into baseball presumably since the sport began. Williams or DiMaggio? Aaron or Mays? Garciaparra or Jeter or Rodriguez? We’ve even clung to “Trout or Harper?” for as long as we possibly can. Whether this urge to create a clear hierarchy is good, that’s not for me to say, but it’s a tendency into which I’ve found myself constantly falling when thinking about one particular playoff team: the San Francisco Giants.

It goes without saying that the Giants are not in an enviable position. They’re down two games to none to the Cubs in the Division Series and their opponent is widely regarded as the best team in baseball on paper. But the Giants have been in a similar position before and come out alright, so it would be disingenuous to say they’re hopeless. Perhaps the biggest reason to maintain even a shred of hope that the Giants will fight back in the series is related to this fact: by at least one metric, the two best games pitched by a starter so far this postseason have been by Giants pitchers Madison Bumgarner and Johnny Cueto.

Having two elite starting pitchers doesn’t guarantee postseason success for any team – one only needs look at the Texas Rangers for confirmation of that fact – but it’s also unequivocally beneficial. It may or may not be enough to help the Giants claw their way back in this series, especially considering Bumgarner and Cueto can only start two of the remaining three wins the Giants need. But it’s a situation that lends itself to an intriguing debate that I personally am incapable of avoiding — namely, the question of who’s better, Bumgarner or Cueto?

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Episode 688
Lead prospect analyst Eric Longenhagen is the guest on this edition of the pod, during which he discusses recent prep work on his horrible burden — namely, the forthcoming organizational prospect lists, which will begin with NL West clubs. By way of preview, Longenhangen discusses one prospect of note from each the five western teams: Jazz Chisholm (Arizona), Joan Gregorio (San Francisco), Michel Miliano (San Diego), Riley Pint (Colorado), and Jordan Sheffield (Los Angeles).

This episode of the program either is or isn’t sponsored by SeatGeek, which site removes both the work and also the hassle from the process of shopping for tickets.

Don’t hesitate to direct pod-related correspondence to @cistulli on Twitter.

You can subscribe to the podcast via iTunes or other feeder things.

Audio after the jump. (Approximately 1 hr 16 min play time.)

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Come playoff time, you tend to see a lot of team-to-team comparisons. And when you see team-to-team comparisons, the people doing the comparing frequently lean on regular-season statistics. And, you know, in theory that makes plenty of sense. Those numbers are readily available all over the place, and, isn’t the regular season a hell of a sample? Doesn’t the regular season pretty adequately reflect the level of talent on a given roster?

I’m not going to argue that regular-season numbers are or aren’t more important than, say, postseason numbers. The regular season obviously has the biggest and therefore the most meaningful sample. But as should go without saying, things change come October. Rosters are optimized, and usage patterns shift. For example, during the year, Rangers hitters had a 98 wRC+. Rangers hitters on the roster today averaged a weighted 106 wRC+. During the year, Rangers relievers had a 100 ERA-. Rangers relievers expected to relieve in the playoffs averaged a weighted 75 ERA-. The Rangers aren’t what they were for six months. No team is, entirely. So what do we have now? What does the actual, weighted playoff landscape look like?

Time for some tables of numbers. That’s almost as fun as actual baseball!

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