Following up on excellent recent pieces by Travis Sawchik and Jeff Sullivan, I had a hypothesis: If there is truly a swing-path revolution underway in MLB, perhaps the best hitters by wOBA and wRC+ showed more marked FB+LD%’s (Air%) tendencies in 2015-2016 than in years past? If not them, then perhaps there is a trend among the middle and/or lower classes of hitters?
The hypothesis was wrong, but the investigation still gave some interesting context to the 2016 power spike and the profiles of recent successful/unsuccessful MLB hitters in general.
Here’s a plot of the average FB%+LD% (Air%) for each year, 2009-2016, for all qualifying MLB hitters per FanGraphs leaderboards, divided into three roughly even buckets of 40-50 players by wRC+ (<100wRC+ left, 100-120wRC+ center, >120 wRC+ right):
Here’s a plot of the average FB%+LD% (Air%) for each year, 2009-2016, for all qualifying MLB hitters per FanGraphs leaderboards, divided into three roughly even buckets of 40-50 players by wOBA ( <.320 left, .320-.350 center, >.350 right):
The consistency of these numbers is remarkable. The writing has been on the wall for some time with regards to the benefits of hitting it in the air.
Perhaps plenty of hitters are (and always have been) trying to hit it in the air more often and are either failing to make the change stick, or not finding success quickly enough to stick with the change / stay in the league?
We aren’t seeing across-the-board nor player-class-specific changes that stand out beyond random variation by this method (yet).
There could be an equilibrium point here where given the best pools of pitching and hitting talent available (regardless of how they arrived at said status), the outcomes will be pretty similar at a macro level, save for major fundamental changes to how the game is played.
This does not mean that individual players cannot aspire to find more optimal approaches. Surely there have always been hitters finding success via these means, and only recently have we been focusing on batted-ball data and focusing on these traits of the transformations.
Preach on, Josh Donaldson: Ground balls? They call those outs up here.
This article was inspired by the phenomenal work on 2013 shift data at THT by Jeff Zimmerman:
Brian McCann’s 5 year 85MM signing by the Yankees has been noted as a pretty good deal as far as Free Agent contracts go. I do not necessarily disagree since he brings leadership and not wholly quantifiable defensive contributions as a marquee catcher. He posted an ISO above the .200 mark in 2013 for the first time since 2009 and reached 20HR for the 7th time in 8 seasons despite only playing in 102 games due to injury. His generally above average OBP rebounded from a career low .300 in 2012 to .336. His heinous .234 babip from 2012 regressed upward somewhat back to .261. While there are many outward signs that his 2013 bounce back re-established him as a premier offensive contributor (122 wRC+) there are some other numbers that give me pause about his future in New York.
I found Jeff Zimmerman’s 2013 infield shift data article fascinating in so many different ways but one of the major takeaways that I got from it was the disparity of shifting frequency across MLB divisions. Granted, a division with more extreme ground ball pulling shift candidates may lead to more shifts. However, the league leading Orioles had 470 shifts implemented on ball in play events compared to just 473 shifts in the ENTIRE NL EAST in 2013 (108 of those 473 NL East shifts were implemented by the Braves). Overall there were 1800 ball in play shift events in the AL East in 2013 compared to 473 shifts in the NL East. 11 of the top 15 shifting teams in 2013 MLB were AL clubs. (AL East teams are #’s 1,2,6,8,16 overall in # of 2013 shifts)
This is where Mr. McCann and his offensive future comes in: Brian McCann hit into 123 shifts out of 402 PA (30% of PA) in 2013. He hit .179 on balls in play against the shift and .299 when the shift was not on. For comparison David Ortiz hit into 338 shifts in 2013 in 600 PA (56% of PA). Obviously there are smaller than ideal samples in this data and we all know babip fluctuates wildly. That being said the shift deflated McCann’s babip to some degree unquestionably last season and probably has been doing so for a while (I’d love to see this data for 2012, 2011 etc. broken out by batter).
If generally shift-conservative NL East teams were exploiting this aspect of McCann’s game then you can bet he’ll see even more shifts in the shift-happy AL East and across the AL in general. McCann’s GB/FB distribution has stayed slanted toward FB throughout his career around a 0.88 ratio. He has seen his babip decline like most MLB veterans do post-peak. There’s a good chance that his babip will continue to decline and perhaps quite precipitously upon his move to the AL East.
I’ll end this article with an intentionally scary and possibly not totally fair comparison since it’s a strictly left handed hitter compared to a switch hitter: McCann’s career line is .277/.350/.473 with a .289 babip and 0.88 GB/FB ratio. Mark Teixeira’s Left Handed Hitting career line is .267/.359/.518 with a .277 babip and 0.87 GB/FB ratio. If McCann’s batting average/babip were to decline at a similarly faster than normal rate like Teixeira’s I’d blame those shifty AL rivals. The short porch in New York may create some extra HRs but the AL East defensive environment could take those gains away and then some on balls in play.
It will be interesting to compare the 2014 shift data to the 2013 season and see which teams decided to implement the shift more and less frequently. The caveat must also be mentioned that not all shifts are created equal and some teams were much more effective at converting shift balls in play into outs than others. Does that have to do with superior personnel/positioning?
Thanks again to Jeff Zimmerman for the inspired shift research that made this piece possible.