The Giants aren’t a bad team. They just made the playoffs, and they signed a closer in Mark Melancon who (hopefully) won’t make the citizens of San Francisco tear their hair out. Hunter Pence should be healthy! That makes things fun. Fun baseball is good baseball, and the Giants are locked in to a pretty fun team at this point. Every position is accounted for, for the most part. Only left field offers a little room for finding something to write about pondering, so let’s ponder, shall we?

Currently, it looks like the Giants are going to deploy a platoon of Jarrett Parker and Mac Williamson there. Surprisingly enough, no, Parker and Williamson are not tertiary characters from It’s Always Sunny in Philadelphia, but actual baseball players. They’ve both seen some playing time since 2015 in fits and starts as depth players.

The Parker/Williamson combo package could, in theory, be fine. The Giants likely aren’t expecting more than league-average production here, after all, and they don’t necessarily need more than that. Parker also has some serious pop in his bat, and frankly, there’s always room for some highlight-reel bombs.

That’ll do! That kind of power works in San Francisco, and if he can meet his ZiPS WAR projection of 1.4 as the big side of the platoon, maybe they don’t need to go get Saunders after all. Parker is also out of options, and may have a hard time making it through waivers to Triple-A.

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What type of contract would Mike Trout have commanded this offseason had he been a free agent? Coming off an MVP-award-winning campaign in which he compiled 9.4 WAR and about to enter just his age-25 season, Trout would have easily been one of the most sought after players ever to hit the open market. And given the state of this year’s historically weak free-agent class, the bidding for Trout may very likely have ended up in the $400-500 million range over eight to ten years.

Considering that Trout signed a six-year, $144.5 million contract extension back in 2014 – an agreement that runs through 2020 – this is just an interesting, but hypothetical, thought experiment, right?

Not necessarily. A relatively obscure provision under California law — specifically, Section 2855 of the California Labor Code — limits all personal services contracts (i.e., employment contracts) in the state to a maximum length of seven years. In other words, this means that if an individual were to sign an employment contract in California lasting eight or more years, then at the conclusion of the seventh year the employee would be free to choose to either continue to honor the agreement, or else opt out and seek employment elsewhere.

Although the California legislature has previously considered eliminating this protection for certain professional athletes – including Major League Baseball players – no such amendment has passed to date. Consequently, Section 2855 would presumptively apply to any player employed by one of the five major-league teams residing in California.

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While the 2016 campaign is over and the flurry of moves after the season has come to a halt for the moment, a whole year’s worth of data remains to be examined. Today’s post is an easy one and a fun one. Let’s find the best pitches that were thrown regularly last year.

Before we begin: the word “results” appears in the headline, but I’m not going to use results judged by things like singles and doubles and the like. The samples gets pretty small if you chop up the ball-in-play numbers on a single pitch, and defense exerts too much of an influence on those numbers. So “results” here denotes not hit types, but rather whiffs and grounders.

I’ve grouped all the pitches thrown last year, minimum 75 for non-fastballs, 100 for fastballs. I combined knuckle and regular curves, and put split-fingers in with the changeups. So the sample per pitch type is generally around 300 — a lot less for cutters (89) and a bunch more for four seamers (500) — but generally around 300 pitches qualified in each category. Then I found the z-scores for the whiff and ground-ball rates on those pitches. I multiplied the whiff rate z-score by two before adding it to the ground-ball rate because I generally found correlations that were twice as strong between whiff rates and overall numbers like ERA and SIERA than they were for ground-ball rates.

The caveats are obvious. Pitches work in tandem, so you may get a whiff on your changeup because your fastball is so devastating. This doesn’t reward called strikes as much as swinging strikes, so it’s not a great measure for command. On the other hand, there isn’t a great measure for command. By using ground-ball rate instead of launch-angle allowed, we’re using some ball-in-play data and maybe not the best ball-in-play data.

But average-launch-angle allowed is problematic in its own way, and ground-ball rate is actually one of the best ball-in-play stats we have — it’s very sticky year to year and becomes meaningful very quickly. Whiff rates are super sexy, since a swing and a miss represents a clear victory for the pitchers over the batter — and also because there’s no room for scorer error or bias in the numbers. And while the precise way in which pitches work in tandem remains obscure in pitching analysis, we can still learn something from splitting the pitches up into their own buckets.

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After having typically appeared in the very famous pages of Baseball Think Factory, Dan Szymborski’s ZiPS projections have been released at FanGraphs the past few years. The exercise continues this offseason. Below are the projections for the San Francisco Giants. Szymborski can be found at ESPN and on Twitter at @DSzymborski.

Other Projections: Arizona / Atlanta / Boston / Chicago NL / Cleveland / Detroit / Houston / Los Angeles AL / San Diego / Tampa Bay / Toronto / Washington.

Batters
A cursory examination of the club’s field players reveals a theme: almost all of them receive both a better-than-averge (a) strikeout and (b) fielding-runs projection. Nor should this be very surprising: Giants batters produced the second-lowest strikeout rate in the majors last year and the second-most defensive runs. Buster Posey (512 PA, 5.1 zWAR) is well acquitted by both measures. Brandon Crawford (575, 4.3) receives something closer to a league-average strikeout-rate projection; on the defensive side, however, the combination of his fielding mark (+9) and hypothetical positional adjustment (something like +7, probably) produce about 1.5 wins, rendering him league-average player almost without any consideration of his offensive skills.

Overall, the position-player side of things appears well suited to avoiding the awful. If an area of weakness remains, it’s in left field, where Jarrett Parker (449, 1.4) and Mac Williamson (389, 0.6) are expected to form a platoon. Even that combination, though, appears capable of providing wins at a league-average rate.

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When your favorite team signs a new player, you don’t want to hear about the downside. You don’t want to hear about other players whom your team could have signed, or how your player will age, or if the deal constitutes an overpay. You want to hear about how awesome that player is and how much better he’ll make your team. I’m here for you. I’ll tell you those things about Mark Melancon, who has reportedly signed with the San Francisco Giants for what is likely to be something like four years and $62 million. And then… then comes some cold water. Just warning you.

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Sort this year’s free-agent corner outfielders by last year’s production, and Angel Pagan’s name appears right at the top of the list. Sort that same list by projected production, however, and Pagan falls to seventh best, right behind the recently signed Matt Joyce. We all know how projections work: at the most basic level, they’re the product of past performance and age. For most veteran players, those two variables conspire to create a pretty dependable vision of the future.

Pagan has proven to be a difficult case for projection systems, however. He’s been particularly volatile over the course of his career — specifically with regard to his offensive production. If we could identify the causes of that volatility, perhaps we could improve upon the vision of Pagan’s future provided by the projections. And along the way, we might find him the right team.

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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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