Advocating For A Different Type of Swing Change by WAR Enthusiast February 8, 2019 When Statcast was launched, we were graced with incredible new stats such as Exit Velocity and Launch Angle, which revolutionized how we evaluate hitting. This new information confirmed obvious things like that Giancarlo Stanton hits missiles, but it also gave us a new breed of hitter. Daniel Murphy, Justin Turner, J.D. Martinez, and others looked at the data and made adjustments that started maximizing their power outputs. The standard evaluation method has become to look at EVs mixed with LAs to determine who is one tweak away from stardom. Hitting is a complex beast, with pitchers throwing 95-plus with nasty hooks to go with shifting defenses. Ultimately, a hitter is looking to produce solid contact regardless of where the ball goes. The goal of this analysis is to identify hitters who have an inefficient spray chart and see how they could optimize their profile by hitting more balls in a different direction to maximize production. Luckily with Statcast, we can now try to find these answers. To do this analysis, I used Baseball Savant to gather 2018 Exit Velocity and xwOBA to Pull Side, Straight Away, and Oppo Side for all hitters with at least 50 plate appearances. I then used FanGraphs to pull the 2018 data for Pull%, Mid%, and Oppo% to discern how often a hitter attacks that field. I used 50 PAs as a filter since this is about where exit velocities become stable and helps weed out pitchers and other noise. This does create gaps in the data because some players didn’t register 50 PAs of a batted-ball direction. This dataset gives us the ability to look at how hard a hitter hits the ball to a field, what was their expected damage (xwOBA) to that field, and how often they went that way. The first category I looked at was players who could use the opposite field more often. To do this, I looked at players who had an above average Oppo Side xwOBA and a below-average Oppo%. I used exit velocities to each field as a proxy to justify the directional swing change. Players Who Could Stand To Go Oppo More Often Player Oppo xwOBA Pull xwOBA Pull – Oppo xwOBA 2018 Pull% 2018 Oppo% Pull Avg EV Oppo Avg EV Tommy Pham 0.519 0.415 -0.104 37.80% 22.40% 92.9 91.4 David Freese 0.507 0.430 -0.077 36.00% 30.30% 88.6 89.1 Shin-Soo Choo 0.492 0.350 -0.142 38.10% 26.80% 88.0 90.3 Trey Mancini 0.485 0.387 -0.098 38.00% 26.30% 89.6 90.6 Wilson Ramos 0.443 0.339 -0.104 40.10% 27.60% 90.7 91.5 Eric Hosmer 0.412 0.342 -0.070 31.80% 29.00% 86.6 89.0 Michael A. Taylor 0.362 0.353 -0.009 39.30% 24.30% 85.9 86.3 Tim Anderson 0.332 0.346 0.014 44.40% 27.20% 84.9 87.4 In this list, we have a variety of guys who do a ton of damage to the opposite field despite going there 30% of the time or less with exit velocities to back it up. In the case of everyone but Pham, they even hit the ball harder the other way than to their pull side. Pham, Choo, Ramos, and Mancini are completely different hitters with 100-point or larger swings in production. If they could shift their Oppo% into the mid-30s or better, we may be discussing these guys as hitters finding a new level and potential All-Stars. Ironically, we find old friend Eric Hosmer, who could now stand for two different swing changes. Michael A. Taylor is an outlier in terms of production, however, as he is a guy who hits the ball hardest to the opposite field and has better production despite only going there 24% of the time. Further investigation shows that his average oppo launch angle is 23 degrees. Not shown is his xwOBA of .307 in the middle of the field with a launch angle of four degrees, so his middle approach attack isn’t without flaws. In 2017, Taylor was worth 3.1 WAR in 385 PAs. With some tweaks in approach and launch angle to the middle of the field, he could regain that form or be even better. Tim Anderson represents another outlier in this group in terms of xwOBA production. Despite pulling the ball more than 40% of the time at 84.9 mph, he hits the ball 2.5 mph harder when he goes to the opposite field. The main culprit would be a 24.8-degree launch angle which, when combined with below-average EV, can result in many easy flyouts. If he could change his angle to somewhere between 10 degrees and 20 degrees and make a concerted effort to go the other way, he could be a piece that pushes the White Sox to the contender they want to be. Next, I ran the same exercise as before, but this time focused on guys who could make gains in the middle of the field. Interestingly, Shohei Ohtani, Christian Yelich, and Khris Davis are guys who already destroy the middle of the field and have optimized their swing as such. They display exit velocities and have the higher Mid% than Pull% and Oppo%. This group could stand to follow their lead. Players Who Could Stand To Hit Up The Middle Player Mid xwOBA Pull xwOBA Pull – Mid xwoba 2018 Pull% 2018 Mid% Pull Avg EV MID Avg EV Daniel Descalso 0.511 0.413 -0.098 49.20% 29.30% 89.9 90.7 Wil Myers 0.485 0.396 -0.089 50.20% 33.30% 88.6 92.6 Kole Calhoun 0.485 0.404 -0.081 42.60% 35.20% 88.7 92.8 Chris Taylor 0.484 0.416 -0.068 37.90% 34.30% 88.2 90.9 Max Stassi 0.482 0.322 -0.160 45.60% 37.40% 88.2 93.4 Nomar Mazara 0.477 0.356 -0.121 38.00% 36.70% 90.8 92.1 Willson Contreras 0.413 0.302 -0.111 42.00% 31.40% 85.8 90.3 In this next group, we find two notable catchers, an annual breakout candidate, a Swiss Army Knife, the improved Daniel Descalso, and two veteran outfielders at career crossroads. Like the Oppo group, the Mid group also displays higher EVs in the middle of the field than the pull side. This group is less extreme in terms of batted-ball frequency, as each hitter has at least 30% of their hits going to the middle of the field. Stassi, Contreras, and Mazara stand to gain the most by using the middle of the field more. Stassi and Contreras hit the ball nearly 5 mph harder to the middle than the pull side. If these hitters could push their Mid% into the 40s by trading away some Oppo% and Pull%, they too could be breakout hitters and be the most optimal and productive versions of themselves. The last part of this analysis keys in on guys who could stand to pull the ball more with more frequency. To identify this group of hitters, I used the completely made up and straightforward Points Lost by Not Pulling (Pull xwOBA – Oppo xwOBA + Pull xwOBA – Mid xwOBA). Then, I looked at Pull%, Mid%, and Oppo% to identify hitters with an inefficient batted-ball mix to see who could get more from the pull side. My definition of inefficient for this exercise is a hitter whose directional exit velocity and xwOBA production were not maximally aligned with their spray percentages. Once again, I used directional exit velocity as a proxy to control for noise. I included an additional chart with exit velocities because some of the differences from pull side are stark. Players Who Could Stand To Pull More Player Points Lost Not Pulling Pull xwOBA Mid xwOBA Oppo xwOBA 2018 Pull% 2018 Mid% 2018 Oppo% Tyler Austin 0.523 0.660 0.437 0.360 38.20% 34.20% 27.60% Adalberto Mondesi 0.421 0.518 0.427 0.188 41.60% 40.60% 17.80% Joey Rickard 0.334 0.410 0.280 0.206 41.80% 34.20% 24.10% Yasiel Puig 0.337 0.500 0.413 0.250 41.90% 35.00% 23.10% Pablo Sandoval 0.290 0.442 0.293 0.301 33.50% 36.90% 29.60% Corey Dickerson 0.227 0.456 0.371 0.314 31.10% 36.20% 32.70% Ketel Marte 0.159 0.379 0.364 0.235 36.70% 39.20% 24.10% Ryan Zimmerman 0.146 0.464 0.419 0.363 31.50% 42.60% 26.00% Ian Desmond 0.144 0.410 0.353 0.323 30.00% 40.80% 29.20% Players Who Could Stand To Pull More Player Pull Avg EV Mid Avg EV Oppo Avg EV Tyler Austin 92.7 87.4 87.0 Adalberto Mondesi 88.2 86.8 87.3 Joey Rickard 90.3 82.9 78.6 Yasiel Puig 92.5 91.9 81.8 Pablo Sandoval 90.5 90.8 87.9 Corey Dickerson 89.9 87.9 85.6 Ketel Marte 89.9 90.2 84.4 Ryan Zimmerman 95.0 92.1 91.5 Ian Desmond 89.4 90.9 89.2 The members of this group either distribute too many hits to the middle of the field (Sandoval, Dickerson, Marte, Zimmerman, Desmond) or could stand to further increase their Pull% (Austin, Mondesi, Rickard, Puig). Ultimately, this group has better EVs and production to the pull side and hits in any other direction are suboptimal. Starting with Tyler Austin, he already favors the pull side but displays the highest variance in production when not pulling the ball. Mix that with an EV 5 mph greater to the pull side and you have the ingredients of a power hitter who could display more than the 103 wRC+ he contributed in 2018. Newly minted Red Yasiel Puig ran a .500 xwOBA while pulling at 41.9% and is moving to a hitter-friendly park that should only encourage adding Pull%. Mondesi fits in the same category as Puig in that his pull side xwOBA and EV warrant additional balls to the pull side. This adjustment could cement Mondesi as a building block for the next competitive Royals team. Dickerson and Zimmerman are most productive to their pull side but they direct their hits through the middle of the field. In addition to being their most productive direction, their EVs to the pull side are also highest (89.9 and 95.0 respectively), meaning that a pull-heavy approach makes justifiable sense. We have seen pitchers such as Lance McCullers and Rich Hill ramp up usage of their best pitches and teams using data to optimize pitcher repertoires. Since hitters must compete with these pitchers and persistent shifting, shouldn’t they too try to optimize their batted-ball profile to their specific strengths? We have already seen the dramatic production increases for the original swing changers. This analysis has shown that there are hitters with suboptimal batted-ball profiles who too could change their approach, swing towards their strengths, and produce more offense. This change could allow teams to reap additional production and push them towards contention or potentially into a trade chip depending on where the teams are in their competitive cycle. We know some players aren’t receptive of data or swing overhauls, considering they already got to The Show by being the best at what they do, however there may be another level to reach that will pay dividends on the field and in their wallets.