Hitting by Position, 2019

The first obvious thing to look at is the positional totals for 2018, with the data coming from Baseball-Reference.com. "MLB” is the overall total for MLB, which is not the same as the sum of all the positions here, as pinch-hitters and runners are not included in those. “POS” is the MLB totals minus the pitcher totals, yielding the composite performance by non-pitchers. “PADJ” is the position adjustment, which is the position RG divided by the total for all positions, including pitchers (but excluding pinch hitters). “LPADJ” is the long-term positional adjustment that I am now using, based on 2010-2019 data (see more below). The rows “79” and “3D” are the combined corner outfield and 1B/DH totals, respectively:



There’s nothing too surprising here, although third basemen continue to hit above their historical norm and corner outfielders outhit 1B/DH ever so slightly.

All team figures from this point forward in the post are park-adjusted. The RAA figures for each position are baselined against the overall major league average RG for the position, except for left field and right field which are pooled. NL pitching staffs by RAA (note that the runs created formula I use doesn’t account for sacrifice hits, which matters more when looking at pitcher’s offensive performance than any other breakout you can imagine):



This range is a tad narrower than the norm which is around +/- 20 runs; no teams cost themselves a full win at the plate. This is the second year in a row in which this is the case; of course as innings pitched by starters decline, the number of plate appearances for pitchers does as well.

The teams with the highest RAA at each position were:

C—SEA, 1B—NYN, 2B—MIL, 3B—WAS, SS—HOU, LF—WAS, CF—LAA, RF—MIL

Usually the leaders are pretty self-explanatory, although I did a double-take on Seattle catchers (led by Omar Narvaez with a quietly excellent 260 plate appearances from Tom Murphy) and Milwaukee second basemen (combination of Keston Hiura and Mike Moustakas). I always find the list of positional trailers more interesting (the player listed is the one who started the most games at that position; they usually are not solely to blame for the debacle ):



Four teams hogged eight spots to themselves, kindly leaving one leftover for another AL Central bottom feeder. Moustakas featured prominently in Milwaukee’s successful second base and dreadful third base performances, but unfortunately Travis Shaw was much more responsible for the latter (503 OPS in 66 games! against Moustakas’ solid 815 in 101 games). Also deserving of a special shoutout for his contributions to the two moribund White Sox positions is Daniel Palka, who was only 0-7 as a DH but had a 421 OPS in 78 PA as a right fielder. His total line for the season was 372 OPS in 93 PA; attending a September White Sox/Indians series, it was hard to take one’s eyes off his batting line of the scoreboard (his pre-September line was .022/.135/.022 in 52 PA).

The next table shows the correlation (r) between each team’s RG for each position (excluding pitchers) and the long-term position adjustment (using pooled 1B/DH and LF/RF). A high correlation indicates that a team’s offense tended to come from positions that you would expect it to:




The following tables, broken out by division, display RAA for each position, with teams sorted by the sum of positional RAA. Positions with negative RAA are in red, and positions that are +/-20 RAA are bolded:





A few observations:

* The Tigers were below-average at every position; much could (and has) been written about Detroit’s historic lack of even average offensive players, but a positional best of -9 kind of sums it up

* The Indians had only one average offensive position, which was surprising to me as I would have thought that even while not having his best season, Franciso Lindor would have salvaged shortstop (he had 17 RAA personally). Non-Lindor Indian shortstops had only 92 PA, but they hit .123/.259/.173 (unadjusted).

* That -30 at third base for the Angels, wonder what they’ll do to address that

* Houston had 109 infield RAA, the next closest team was the Dodgers with 73. The Dodgers had the best outfield RAA with 77; the Astros were fifth with 46.

Finally, I alluded to an update to the long-term positional adjustments I use above. You can see my end of season stats post for some discussion about why I use offensive positional adjustments in my RAR estimates. A quick summary of my thinking:

* There are a lot of different RAR/WAR estimates available now. If I can offer a somewhat valid but unique perspective, I think that adds more value than a watered down version of the same thing everyone else is publishing.

* When possible, I like to publish metrics that I have had some role in developing (please note, I’m not saying that any of them are my own ideas, just that it’s nice to be able to develop your own version of a pre-existing concept). I don’t publish my own defensive metrics and while defensive positional adjustments are based on more than simply player’s comparative performance across positions using fielding metrics, they are basic starting point for that type of analysis.

* While I do not claim that the relationship is or should be perfect, at the level of talent filtering that exists to select major leaguers, there should be an inverse relationship between offensive performance by position and the defensive responsibilities of the position. Not a perfect one, but a relationship nonetheless. An offensive positional adjustment than allows for a more objective approach to setting a position adjustment. Again, I have to clarify that I don’t think subjectivity in metric design is a bad thing - any metric, unless it’s simply expressing some fundamental baseball quantity or rate (e.g. “home runs” or “on base average”) is going to involve some subjectivity in design (e.g linear or multiplicative run estimator, any myriad of different ways to design park factors, whether to include a category like sacrifice flies that is more teammate-dependent)

I use the latest ten years of data for the majors (2010 – 2019), which should smooth out some of the randomness in positional performance. Than I simply calculate RG for each position and divide by the league average of positional performance (i.e. excluding pinch-hitters and pinch-runners). I then pool 1B/DH and LF/RF. Only looking at positional performance is necessary because the goal is not to express the position average relative to the league, but rather to the other positions for the purpose of determining their relative performance. If pinch-hitters perform worse than position players, I don’t want them to bring down the league average and thus raise the offensive positional adjustment, because pinch-hitters will not be subject to the offensive positional adjustment when calculating their RAR. (I suppose if you were so inclined, you could include them, and use that as your backdoor way of accounting for the pinch-hitting penalty in a metric, but I assign each player to a primary position (or some weighted average of their defensive positions) and so this wouldn’t really make sense, and would result in positional adjustments that are too high when they are applied to the league average RG.

For 2010-2019, the resulting PADJs are: