The World Series is over, and though the San Francisco Giants have nailed down their third championship in the last five seasons, there is seemingly no time to savor the accomplishment. Qualifying offers are being extended, the first offseason transactions have begun to trickle across the wire, and hot stove season will soon begin in earnest. This week, however, let’s take one last look at the two World Series participants from a top-sided, macro perspective. How good were the Giants and the Royals, in various aspects of the game? Today, let’s take a look at the champion Giants.

Much has been made of the fact that the 2014 World Series not only pitted two wild card clubs against one another, but two sub-90 win wild cards. On one hand, 162 games is the longest season among the four major sports leagues, and one would think that such an endurance test would weed out potential pretenders to the throne. On the other hand, a whole lot of randomness remains in play, even over the span of 162 games.

Utilizing batted ball data, one can calculate the MLB average AVG and SLG for various exit speeds and angles. Throughout the season, I have attempted to evaluate individual players using such data, taking their actual BIP information and applying it to such averages to see how players might perform in a totally neutral environment. Today, let’s do this on a team-wide basis, and separate context from the Giants’ true offensive, pitching and defensive talent, and see whether their 88-74 record is a fair approximation of their cumulative ability.

First, let’s look at the Giants’ offense. Below is their team plate appearance frequency data:

FREQ – 2014 Giants – HIT % REL K 20.5% 100 BB 7.0% 92 POP 6.2% 81 FLY 30.3% 108 LD 20.5% 98 GB 42.9% 99

First off, I have decided not to include percentile rank data, which I use for individual players, for team-wide analysis. There are hundreds of players, but only 30 clubs, and it just isn’t as valuable. The frequency data doesn’t tell us all that much – we can see that the Giants don’t walk all that much, don’t hit many popups, and have a slight fly ball tendency as a club. We’ll have to rely on their production by BIP type data to learn what makes their offense tick.

PROD – 2014 Giants – HIT AVG OBP SLG REL PRD ADJ PRD FLY 0.278 0.679 97 123 LD 0.683 0.862 103 100 GB 0.248 0.273 103 102 ALL BIP 0.321 0.491 98 105 ALL PA 0.255 0.311 0.388 99 104

There’s some interesting data here, especially in the very first line item. The Giants batted only .278 AVG-.679 SLG on fly balls this season, for an actual REL PRD of 97. MLB hitters batted .275 AVG-.703 SLG on fly balls in 2014. The Giants’ actual fly ball production was just 3% below MLB average despite the fact that they played their home games in AT&T Park, the second most pitcher-friendly park in baseball both overall (84.7 park factor) and with respect to fly balls (67.3 park factor). Adjustment for context boosts the Giants’ ADJ PRD on fly balls way up to 123, in the upper tier of MLB clubs.

The Giants are in the league average range with regard to production from all other BIP types, before and after adjustment for context. After the K’s and BB’s are added back, the Giants go from having a 99 REL PRD – 1% below league average offensive production without contextual adjustment – to 104 ADJ PRD afterward. The 2014 Giants were an above average offensive club, largely due to their ability to hit the ball hard in the air without having the high K and popup rates that usually accompany power in the air.

It makes sense – Buster Posey, especially, and Hunter Pence, Michael Morse and even Pablo Sandoval, to somewhat lesser extents, all hit the ball harder than MLB average in the air, and none are big popup or K guys. When you pile enough of those guys on one club, it begins to take on their characteristics as a whole. Their homer totals ranged from 16 to 22, but would have been substantially higher in most other offensive environments. The Giants have pieced together a relatively low risk, moderate upside offense whose proficiency is obscured by the extremely pitcher-friendly nature of their home park.

How about the pitching? Again, let’s take a peek at their plate appearance frequency and production by BIP type data to get a better feel. First, the frequency info:

FREQ – 2014 Giants – PIT % REL K 20.4% 100 BB 6.5% 86 POP 5.3% 69 FLY 27.8% 100 LD 21.2% 102 GB 45.6% 105

There are a couple of notable items here. The Giants don’t walk people (86 compared to MLB average of 100), don’t induce many popups (69), but have a fairly high ground ball rate (105). While Madison Bumgarner is their clear and unquestioned ace, on the whole the Giants’ staff seemed to take on the personality of their World Series Game 7 starter, Tim Hudson. Next, the production by BIP type info:

PROD – 2014 Giants – PIT AVG OBP SLG REL PRD ADJ PRD ACT ERA CALC ERA TRU ERA FLY 0.286 0.717 99 107 LD 0.656 0.915 103 99 GB 0.214 0.233 78 99 ALL BIP 0.304 0.482 90 97 ALL PA 0.241 0.297 0.382 93 98 3.50 3.48 3.66

The one standout piece of information here is on the ground ball line item. The Giants allowed very meager production on ground balls this season – .214 AVG-.233 SLG compared to an MLB average of .245 AVG-.267 SLG. After adjustment for context, it becomes apparent that this variance is attributable not to the Giants’ pitchers, but to their infield defense. The Giants’ pitchers also benefit to some extent from the fly ball-killing nature of AT&T Park, as their 99 REL PRD on fly balls is boosted to a 107 ADJ PRD after contextual adjustment.

Overall, the Giants’ pitching, with an actual ERA of 3.50, appears to well better than MLB average. After adjustment for context, their “tru” ERA of 3.66 is much closer to MLB average. Yes, despite the presence of one of the best, most durable starting pitchers in the game in Bumgarner, the Giants’ pitching is very close to MLB average overall. This perhaps isn’t that surprising, considering that they were hard-pressed to get even three credible innings from any of their other starters as the postseason came to a close. Their bullpen was strong in the front – Yusmeiro Petit – and in the back – Santiago Casilla, Jeremy Affeldt, Sergio Romo – but had some holes in between. Pitching is where the Giants need to focus their disposable dollars this offseason.

How about the defense? Again utilizing granular batted ball data, I have established a method to evaluate team defense, from a big-picture macro perspective, rather than the play-by-play micro perspective that methods such as DRS and UZR utilize. Simply compare each team’s offensive and defensive actual and projected AVG and SLG – what each team “should” have hit/allowed based on the speed/exit angle mix of all balls in play (excluding home runs), and convert those actual and projected events to run values. You are basically comparing each team’s defense to that of their opponents over 162 games. If a team’s defense was exactly as good as their opponents’ over 162 games, their team defensive multiplier would be 100. Better than average defenses have scores under 100, below average team defenses have scores over 100. How did the Giants fare in 2014?

IN-PLAY SF BAT ACT AVG 0.300 BAT ACT SLG 0.381 BAT PRJ AVG 0.290 BAT PRJ SLG 0.376 PIT ACT AVG 0.282 PIT ACT SLG 0.370 PIT PRJ AVG 0.290 PIT PRJ SLG 0.371 DEF MULTIPLY 95.7 —————- ———- FLY MULTIPLY 100.6 LD MULTIPLY 99.9 GB MULTIPLY 88.2

First of all, the Giants’ 95.7 overall defensive multiplier places them safely among the top quarter of MLB defenses. Most importantly, their 88.2 ground ball multiplier is exceptional – this is what enabled their grounder-generating staff to hold opponents to well below MLB average production on all of those grounders, saving a bunch of runs on the whole. While UZR, DRS and other advanced defensive metrics do a very good job of evaluating individual defense, a top-sided method such as this does a good job of grading team defense. The Giants overall team defense surged in the second half, as the injuries to Michael Morse and Angel Pagan turned a subpar outfield defense into an above average one, and the promotion of Joe Panik turned a good infield defense into an extremely good one.

Putting it all together, you have a solidly above average offensive and defensive club, with very slightly above MLB average pitching. Not too many clubs are above average in all of the major phases of the game. Convert everything in the above tables to run values, apply their defensive multiplier, and do some final Pythagorean magic, and the 2014 Giants are a 90-win club, just a tad better than their actual 88-74 mark. Calculating a team’s projected record using this method actually yields very similar results to the Pythag approach, with only a couple of clubs per season breaking materially out of their projected win total range. More on one such club later this week.

What does this method tell us about the Giants going forward? Well, they probably shouldn’t break the bank on Pablo Sandoval if it’s going to drain dollars away from their greatest need – starting pitching. Of course, winning titles increases revenue streams, so it’s possible they can keep their incumbent third baseman while still going hard after pitching upgrades. Still, they might be able to get 80-90% of Sandoval’s value while spending 50-60% of the annual salary and 25-30% of the long-term salary guarantee by pursuing a different 3B target, enabling them to get one of the bigger pitching fish on the market.

At this point, however, I’m inclined to give the Giants the benefit of the doubt on all fronts. They have put together a winning core with a strong cumulative risk/reward ratio, and have made quality adjustments on the fly. Brian Sabean, Bruce Bochy and their entire staff deserve much credit. 2015 is an odd-numbered year, so recent history suggests a temporary step backward, but this club’s combination of talent, cash and organizational consistency from the top down suggests that they will make the decisions necessary to keep them in the postseason discussion for the foreseeable future.