Juiced Baseball: Hitters to Target
After doing an overview of the juiced ball and a focus on pitchers, I started down a path to find out which hitters would be most affected by the juiced ball. I didn’t know it was going to be overrun with thorn bushes and misleading signs. The process was nearly impossible for the simple fact that the league’s hitting profile changed. Besides even considering the ball, batters were hitting more flyball, hitting the ball harder, and pulling it over short corner fences. I tried to find one answer but ended finding another.
One key to this analysis is that I wanted to keep it simple. I didn’t want to pump the data into some neural network for a more “correct” answer where I’d not sure of the factors in play and how each one was weighted. I wanted some clarity.
One set of factors I initially used was the StatCast information but I didn’t use it for the final analysis because it didn’t add any accuracy. Groundball rate is almost a perfect proxy for Launch Angle. Home run per batted ball is basically a Barrel. Also, StatCast data has only been available since 2015 when the juiced ball started. There is no baseline data for the deadened ball period.
Also, I tried adding in plate discipline and age with both being dead ends. In the end, here are the stats I used and their values from the last 10 years.
| Season | GB% | Pull% | Hard% | ISO | HR/9 | HR/FB | HR/BIP |
|---|---|---|---|---|---|---|---|
| 2010 | 44.3% | 39.2% | 30.1% | .145 | 0.96 | 9.4% | 3.4% |
| 2011 | 44.4% | 39.8% | 24.3% | .144 | 0.94 | 9.7% | 3.4% |
| 2012 | 45.1% | 39.4% | 28.5% | .151 | 1.02 | 11.3% | 3.8% |
| 2013 | 44.5% | 38.8% | 29.9% | .143 | 0.96 | 10.5% | 3.5% |
| 2014 | 44.8% | 40.2% | 29.1% | .135 | 0.86 | 9.5% | 3.2% |
| 2015 | 45.3% | 39.1% | 28.8% | .150 | 1.02 | 11.4% | 3.8% |
| 2016 | 44.7% | 39.7% | 31.4% | .162 | 1.17 | 12.8% | 4.4% |
| 2017 | 44.2% | 39.8% | 31.8% | .171 | 1.27 | 13.7% | 4.8% |
| 2018 | 43.2% | 40.3% | 35.3% | .161 | 1.16 | 12.7% | 4.4% |
| 2019 | 42.9% | 40.7% | 38.0% | .183 | 1.40 | 15.3% | 5.4% |
Since, 2014, all the metrics point to more home runs with a small power drop from 2017 to 2018. Isolated power is up almost 50 points and home run per batted ball is up 68% over the entire time frame.
My initial goal was to correct for all the variables, but too much was going on. Instead, I decided to embrace all the changes. Most of the hitters who saw the biggest jumps in home runs as the ball has got juicer-and-juicer also changed their approach. Selecting the 2018 hitters who saw a 2% point jump in HR/BIP from 2017, here is how their other stats changed (min 200 PA in each season).
| Season | GB% | Hard% | Pull% | ISO | HR/FB | HR/BIP |
|---|---|---|---|---|---|---|
| 2019 | -1.7% | +4.8% | +2.6% | +0.076 | +8.9% | +3.6% |
What I found is that these players had room to improve. They were at or below league average in all the categories. They could put more balls in the air more. They could pull it more. They could hit it harder (Note: Contact rate did drop showing an intention for more power). There was an exception, they needed to pass a base power threshold for the above-average power jump. No ball or approach change is going to make Dee Gordon a power threat. Some men you just can’t reach.
For now, I’m using the following variables to find hitters who may see a power (home run) spike in the next season.
Stat: Value
- GB%: > 40%
- Pull%: < 43%
- HR/FB: > 7% and < 12%
- Hard% > 25%
It’s simple query and are the results of these players’ performance compared to the league average.
| From | To | GB% | Pull% | Hard% | ISO | HR/FB | HR/BIP |
|---|---|---|---|---|---|---|---|
| 2014 | 2015 | -0.9% | 1.6% | 0.2% | -.008 | -0.6% | -0.1% |
| 2015 | 2016 | 0.1% | 1.0% | -0.3% | .001 | 0.9% | 0.3% |
| 2016 | 2017 | -1.3% | 1.9% | 0.2% | .008 | 0.8% | 0.4% |
| 2017 | 2018 | -1.6% | 0.9% | -0.9% | .007 | 0.7% | 0.4% |
| 2018 | 2019 | -0.6% | 2.5% | 1.1% | .022 | 2.1% | 0.8% |
The big ‘Oh Shit’ moment was looking at the 2017 to 2018 data when the overall league power drop. The ball was less lively but these players showed a comparable power increase. And when MLB used the Happy Fun Ball in 2019, this group saw their stats skyrocket up. In this sample of years, this player group seems to have enough room to grow even if the ball gets dejuiced.
Just because the average hitters outperformed the league over this time frame, it doesn’t mean each one will in the future or even the entire group. Some part of the metagame could change. The results provide a nice tie-breaker when the talent curve flattens out where many hitters seem the same. While looking for any small edge, these players might provide one.
So, I’ll finally get to the players who have room to increase their power in 2020 even if some juice is let out of the ball.
| Name | Age | PA | GB% | Pull% | Hard% | ISO | HR/FB | HR/BIP |
|---|---|---|---|---|---|---|---|---|
| Alex Gordon | 35 | 633 | 46.2% | 40.6% | 38.9% | 0.129 | 9.2% | 2.8% |
| Amed Rosario | 23 | 655 | 48.3% | 30.4% | 33.4% | 0.144 | 10.3% | 3.0% |
| Austin Barnes | 29 | 242 | 41.4% | 38.0% | 33.5% | 0.137 | 7.6% | 3.2% |
| Ben Gamel | 27 | 356 | 44.5% | 30.6% | 37.8% | 0.125 | 11.5% | 3.4% |
| Brandon Crawford | 32 | 560 | 48.2% | 38.8% | 39.3% | 0.122 | 10.1% | 2.8% |
| Buster Posey | 32 | 445 | 49.0% | 34.8% | 35.7% | 0.111 | 7.4% | 2.1% |
| Cesar Hernandez | 29 | 667 | 48.6% | 38.0% | 32.8% | 0.129 | 9.5% | 2.7% |
| Cheslor Cuthbert | 26 | 330 | 42.6% | 42.6% | 37.2% | 0.133 | 9.5% | 3.7% |
| Colin Moran | 26 | 503 | 43.8% | 42.8% | 34.3% | 0.152 | 11.5% | 3.7% |
| Dawel Lugo | 24 | 288 | 48.4% | 32.3% | 33.2% | 0.136 | 9.8% | 2.8% |
| Ehire Adrianza | 29 | 236 | 40.9% | 35.7% | 32.7% | 0.144 | 9.4% | 2.9% |
| Elvis Andrus | 30 | 648 | 51.0% | 37.0% | 38.5% | 0.118 | 8.5% | 2.3% |
| Ender Inciarte | 28 | 230 | 48.4% | 41.5% | 32.7% | 0.151 | 11.6% | 3.1% |
| Garrett Hampson | 24 | 327 | 43.3% | 34.0% | 30.2% | 0.137 | 10.5% | 3.7% |
| Grayson Greiner | 26 | 224 | 52.5% | 35.0% | 39.3% | 0.106 | 11.1% | 3.6% |
| Greg Garcia | 29 | 372 | 55.1% | 27.3% | 32.0% | 0.106 | 7.7% | 1.7% |
| Harold Castro | 25 | 369 | 52.4% | 29.3% | 39.6% | 0.093 | 8.3% | 1.8% |
| Isan Diaz | 23 | 201 | 41.2% | 41.3% | 38.8% | 0.134 | 9.3% | 4.1% |
| J.P. Crawford | 24 | 396 | 44.7% | 38.4% | 28.4% | 0.145 | 7.6% | 2.6% |
| Jarrod Dyson | 34 | 452 | 49.2% | 38.5% | 29.3% | 0.090 | 7.2% | 2.2% |
| Jean Segura | 29 | 618 | 51.9% | 34.5% | 33.1% | 0.141 | 8.8% | 2.4% |
| Johan Camargo | 25 | 248 | 43.9% | 41.1% | 32.6% | 0.151 | 10.3% | 3.7% |
| Jonathan Lucroy | 33 | 328 | 45.3% | 40.0% | 35.5% | 0.123 | 9.6% | 3.3% |
| Jose Iglesias | 29 | 530 | 52.2% | 38.2% | 30.7% | 0.119 | 10.5% | 2.5% |
| Kevin Newman | 25 | 531 | 49.4% | 37.3% | 26.7% | 0.138 | 9.8% | 2.8% |
| Kolten Wong | 28 | 549 | 44.0% | 37.9% | 34.0% | 0.138 | 8.1% | 2.7% |
| Leury Garcia | 28 | 618 | 54.9% | 33.2% | 28.5% | 0.099 | 7.8% | 1.8% |
| Lorenzo Cain | 33 | 623 | 50.2% | 31.5% | 36.5% | 0.112 | 9.9% | 2.4% |
| Luis Rengifo | 22 | 406 | 47.7% | 40.7% | 32.5% | 0.126 | 8.6% | 2.6% |
| Luis Urias | 22 | 249 | 49.0% | 34.6% | 36.5% | 0.102 | 8.3% | 2.5% |
| Melky Cabrera | 34 | 397 | 47.3% | 38.8% | 31.1% | 0.119 | 7.1% | 2.1% |
| Miguel Cabrera | 36 | 549 | 43.8% | 31.3% | 44.1% | 0.116 | 9.7% | 3.1% |
| Neil Walker | 33 | 381 | 41.8% | 39.1% | 37.5% | 0.134 | 9.9% | 3.1% |
| Nick Markakis | 35 | 469 | 47.9% | 34.3% | 41.0% | 0.135 | 9.2% | 2.5% |
| Raimel Tapia | 25 | 447 | 51.7% | 35.0% | 30.1% | 0.141 | 10.7% | 2.8% |
| Steven Duggar | 25 | 281 | 50.0% | 31.7% | 32.8% | 0.107 | 7.7% | 2.2% |
Not many sexy names are on the list but this list isn’t going to be sexy. These players showed below-average skills but have room to improve. Fantasy owners can pay for the below-average projected production and hopefully hit gold if they improve.
Initially, I headed down a path hoping for one answer, couldn’t find it, but may have come up with some hitters who could improve no matter what ball MLB uses next season. I’m still a little worried about those who may see their power numbers drop the most. For them, I keep coming back to the players who would (and did) from 2018 to 2019.
All the questions don’t have to be answered today. I have months to dwell on the questions but for now, it’s time to move on from talking about the ball and its effects. I feel others and I will come back to this topic several times. It’s too big to be ignored.
Jeff, one of the authors of the fantasy baseball guide,The Process, writes for RotoGraphs, The Hardball Times, Rotowire, Baseball America, and BaseballHQ. He has been nominated for two SABR Analytics Research Award for Contemporary Analysis and won it in 2013 in tandem with Bill Petti. He has won four FSWA Awards including on for his Mining the News series. He's won Tout Wars three times, LABR twice, and got his first NFBC Main Event win in 2021. Follow him on Twitter @jeffwzimmerman.
Always great stuff, that makes us readers think.You give stuff we can’t get anywhere else. That is why I finally decided to financially support fan graphs. $20 is a bargain. I do have a question regarding this article. if you used the same criteria following the 2018 season, what under performers would you have listed for 2019?
You get the same content for free, but I am happy that you are happy with your support. There is actually a lot of great analysis on the Internet.
They are the players listed in this tweet: https://twitter.com/jeffwzimmerman/status/1179502674112319488
Thanks for the compliment.