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Just how much better *should* the Giants’ offense be?

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Baseball’s publicly available statistical projections are for entertainment purposes only, but in this case, are they more entertaining than the product we’ve seen on the field?

MLB: San Francisco Giants at New York Mets Andy Marlin-USA TODAY Sports

We’ve done posts before about how the Giants’ hard hit balls suggest they should be performing better offensively and that there’s nothing to worry about with certain players, so here’s what you need to know going into this one:

Weighted On-Base Average (wOBA) combines all the different aspects of hitting into one metric, weighting each of them in proportion to their actual run value. While batting average, on-base percentage, and slugging percentage fall short in accuracy and scope, wOBA measures and captures offensive value more accurately and comprehensively.

It is a context-neutral value (runners on base, etc.) that also doesn’t incorporate park factors. Hitting at Coors Field will give a player a higher wOBA than others, but wOBA at large still gives a better sense of offensive contribution in a single form a la OPS. So, yeah, think of it as a better-measured OPS and you’re all set.

MLB’s Baseball Savant site uses Statcast data (hard hit balls, barreled ball percentages, etc.) in concert with wOBA to generate an xwOBA stat — that is, an expected Weighted On-Base Average. wOBA tells us the value of what happened, xwOBA suggests what should have happened based on the quality of a player’s contact. Baseball Savant goes into more detail:

Expected weighted on-base average (xwOBA) is formulated using exit velocity and launch angle, two metrics measured by Statcast.

In the same way that each batted ball is assigned a Hit Probability, every batted ball has been given a single, double, triple and home run probability based on the results of comparable batted balls -- in terms of exit velocity and launch angle -- since Statcast was implemented Major League wide in 2015.

This can be useful to us because our eyes and feelings can lead us astray. The following 2018 xwOBA leaderboard won’t brighten your day, but it will make the Giants’ post-All Star Break fart smell 1% better.

The standing MLB leaderboard has a minimum threshold of 250 plate appearances. Only 8 Giants qualify for that list, so there are three spots here that are Not Ranked that get on after expanding the minimum to at least 150 plate appearances.

2018 xwOBA leaderboard

Brandon Belt 0.350 0.383 1st 33rd
Andrew McCutchen 0.336 0.365 2nd 67th
Buster Posey 0.328 0.362 3rd 74th
Nick Hundley 0.319 0.350 4th Not Ranked
Joe Panik 0.282 0.338 5th 130th
Evan Longoria 0.299 0.333 6th 144th
Brandon Crawford 0.318 0.327 7th 164th
Pablo Sandoval 0.312 0.317 8th 185th
Gorkys Hernandez 0.304 0.314 9th 192nd
Hunter Pence 0.244 0.253 10th Not Ranked
Alen Hanson 0.324 0.244 11th Not Ranked

That’s a .170 variance overall between the Giants’ xwOBA and actual wOBA and as you can see, their better hitters should be doing even better and fluky fun guy Alen Hanson is getting away with a lot.

For context, the MLB league wOBA is .315. The Giants have six players with at least 150 plate appearances who are hitting above the league average. However, the Giants have 35 players with at least one plate appearance, and they’ve combined for a team wOBA of .299 (26th in MLB). If you convert that wOBA to a runs per game total (the squared value of the team wOBA divided by league wOBA, multiplied by the league’s average runs per game total, which in this case is 4.45), then you get 4.00 runs per game. Which is what the Giants had been averaging until these past 5 games. Their team average heading into tonight’s game is 3.97 runs/game. But that just confirms that wOBA really does work.

I don’t have the ability to average the xwOBA of all 35 Giants who’ve taken a plate appearance so I can’t use the average of that leaderboard to figure out how much better the Giants’ offense could be. There’s zero chance the team is collectively performing 17% worse than their expected value, as 10 of the 11 on the leaderboard are. So, I’m going to take some shots in the dark.

My initial calculation was a team-wide 10% difference between xwOBA and wOBA, which would boost the Giants’ team wOBA to .329 and convert to 4.85 runs per game (which would make them 5th place in MLB and #1 in the National League). That’s way too high and pretty ridiculous. And, yeah, if every team’s offense was 10% better, we’d see an improvement of a whole order of magnitude, league-wide.

Let’s get conservative and boost the Giants’ team wOBA (again, based on the xwOBA leaderboard) by 5%. That moves them from .299 to .314, just a tick under league average. What would a tick under league average Giants offense look like? They’d average 4.42 runs per game, (again, 4.45 runs per game is the MLB average) and 17th overall, ahead of the Pirates, Brewers and Phillies.

Expected stats are meaningless beyond showing what could be possible based on the available data. The Giants constructed a team with players they figured could perform to at least this far-fetched-sounding team .314 wOBA and have faced injuries and setbacks all season long. This doesn’t suggest that they’ll turn it around and go on a run over these final 6 weeks or so, it’s just good to know that the slog could’ve felt a little less sloggy.