MASTER NOTES: The Common Denominator, Part 3

The first two weeks of Master Notes, I talked about pitcher outcomes as a percentage of their Total Batters Faced. The idea was that by standardizing the denominator across our various pitcher metrics, we could add up some “good” batted-ball outcomes, like strikeouts and medium-hit fly balls, and some “bad” outcomes like line drives and hard-hit fly balls. The result of that effort was to find some pitchers who might be overvalued or undervalued at draft because their fantasy scoring results in 2016 were out of line with their batted-ball outcomes in 2016.

This week, we’ll turn the exercise around and look at hitter outcomes, again to see if there are any hitters who might be improperly valued, bad on their good and bad outcomes, using plate appearances (PA) as the consistent denominator, and setting a 100-PA minimum.

For hitters, the “bad” outcomes were pretty much the pitchers’ good ones, and vice-versa. The good outcomes started with hard-hit GB (HHGB) and hard-hit FB (HHFB), both of which consistently have hit rates around 50 per cent (and hard-hit fly balls are the source of a lot of extra-base hits, especially HR). Good outcomes also included line drives, which consistently have hit rates from about 65% to 75%. Finally, good batter outcomes included HBP and walks, because both of those have 100% on-base rates.

The bad outcomes likewise tracked the pitchers’ good outcomes: strikeouts, soft- and medium-hit GB and FB. And infield flyballs, which are the equivalent of strikeouts for the offense in that they create no baserunner movement in addition to being easy outs. The Baseball Info Solutions data we used didn’t include grounding into double-plays.

Finally, we again subtracted the bad outcomes from the good, to find hitters who were most successful at having productive plate appearances.

The Good Outcomes leaders look much like what we would expect. The list was led by Joey Votto, who notoriously never hits infield fly balls, and had 53% good outcomes in 2016. Six other hitters were above the 50-per cent mark, including David Ortiz, Mike Trout and Daniel Murphy. But that premium cohort also included a few somewhat surprising names, who might be worth bumping up a round or a buck or two: Matt Carpenter, Brandon Belt and Freddie Freeman.

Moving slightly down the list, the top 10 percent of good-outcomes hitters were the Paul Goldschmidts, Jose Altuves, and Nolan Arenados we’d expect, but that list also included such names as Shin-Soo Choo, Carlos Ruiz, Stephen Drew (!), Nick Markakis, Joe Mauer, and Daniel Descalso. The bottom of the list is mostly made up of guys who won’t be on fantasy rosters or will be tail-end picks and endgame $1 buys. But you might want to adjust expectations for such names as Dee Gordon, Byron Buxton, Billy Hamilton and Javier Baez.

The highest rates of bad outcomes belonged mostly to the same hitters, but also included Yasiel Puig and Brett Lawrie. And the best bad-outcome rates included a few names not already seen in the highest good outcomes, including Austin Jackson, Dexter Fowler and Yadier Molina.

The best metric in the study was Good-Bad (G-B), as it was with pitchers. As noted earlier, very few hitters were net positive in G-B, because for hitters, the game is mostly about failing. No surprise that the G-B list peaks at +7 percentage points (Votto), and a hitter could be Top-10% with a G-B net of -12 points.

That said, it might be useful to consider that a few top good-outcome guys had high-enough bad-outcome rates to knock them out of the Top 10 per cent of net G-B. Those hitters include Arenado, Yoenis Cespedes and Adrian Beltre. They’re still terrific hitters, but ...

The lowest guys for G-B had differences of at least -36 percentage points, usually because of very high bad-outcome rates. The worst of the worst were Billy Burns and Mikie Mahtook, both at -47 points on the scale. SS Ketel Marte, who has drawn a bit of interest with his trade from SEA to ARI, is also among the worst net G-B hitters, at -37 points.

Again, it’s important to remember that while these metrics do show some stickiness from season-to-season, we haven’t really tested their predictive value. But it does seem that if you can roster a guy who has a lot more good outcomes than another, or fewer bad, you might give him a little added consideration.

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One other topic that came up in the pitchers discussion was HR per flyball rates. I argued that calculating this percentage using all fly balls makes no sense, because there is no way a soft- or medium-hit fly ball can get into the seats, at least in fair territory. The HR/FB percentage should count only the hard-hit flies, which of course are the only ones that have any chance of becoming taters.

The HR/HHFB rate across baseball has been climbing slowly and was at 33% last season. Among hitters with 500+ PA, some players appeared to get more HR than they might have earned, at least in percentage terms. That list is headed by last year’s phenom, Gary Sanchez, at 63%—nearly double the prevailing rate. Other power sources with unusually high HR/HHFB rates included Evan Gattis (60%), Todd Frazier (59%) and Khris Davis (56%).

Among the potential power guys who appeared not to get full HR benefit from their hard-hit flies were Brandon Belt, Jose Ramirez, Buster Posey and DJ LeMahieu, all at or under 20% HR/HHFB. If any of these hitters gets a little more outbound luck, there could be a power bump waiting to get cashed.

The jury is out on this metric, though, because the median three-year range for batters with rates in 2014-2016 was about 14 percentage points, with most hitters in the range of 20 percentage points. Is that “persistent”? We might need to take a closer look at how regressive we should expect this overperformance (or any underperformance) to be. Gattis’ 60% rate in 2016 was preceded by a 41% in 2015 and a 38% in 2014, a range of 22 points. Frazier bounced from 42% in 2015 to 37% in 2015 to 59% last year. Davis went 30%-45%-56%. Park effects might be an issue as well.

I like these outcomes metrics but there’s more work to do. But it will have to wait for another day, because that’s my thousand words for this edition.


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  For more information about the terms used in this article, see our Glossary Primer.