The Ones We Missed: Javier Baez & Trevor Story by Jeff Zimmerman October 9, 2018 In the #2earlymock drafts run by our own Justin Mason, Javier Baez is going 17th and Trevor Story is going 20th among all hitters. The picks are quite high considering Baez was the 58th hitter taken, and Story was 65th in NFBC drafts last year. The pair didn’t have must-draft preseason hype and their suspect plate discipline limited their perceived value. Both exceeded all expectations as they came in at 6th and 7th overall this year. This was a huge miss by the industry and I’m going to see if some traits point to why some low plate discipline players break out and others don’t. For every Baez and Story, other bad plate discipline hitters failed like Byron Buxton (.383 OPS), Chris Davis (.539 OPS), Miguel Sano (.679 OPS) and Jonathan Schoop (.682 OPS). No obvious difference stood out. While Chris Davis is old, Buxton, Sano, and Schoop should be in their primes. To find out who may break out, I decided to start with the 2018 Bad Plate Discipline Class. I utilized the 2018 Steamer projections and limited the sample to hitters projected for at least 300 PA and a K%-BB% of 15% or more. In all, 63 hitters made the cut. 2018 Hitters with Bad Plate Discipline Name PA K%-BB% BABIP OPS PA K%-BB% BABIP OPS Projected Actual Matt Davidson 508 27.5% .284 .673 496 22.8% .313 .738 Jorge Alfaro 363.9 26.7% .312 .663 377 31.8% .406 .731 Mike Zunino 433.5 23.8% .279 .747 405 31.1% .268 .669 Randal Grichuk 480.9 23.7% .292 .774 462 20.6% .282 .803 Adam Engel 431.1 23.1% .287 .607 463 24.0% .322 .614 Michael Taylor 457.6 22.9% .326 .718 385 22.6% .320 .644 Nick Williams 413.8 22.7% .318 .736 448 17.6% .312 .749 Carlos Gomez 406.6 22.1% .310 .705 408 19.1% .266 .634 Chad Pinder 400.7 22.0% .313 .699 333 18.3% .325 .769 Chris Davis 523.6 21.7% .296 .808 522 28.9% .237 .539 Tim Anderson 571.1 21.5% .331 .692 606 19.6% .289 .687 Bradley Zimmer 519.9 21.2% .331 .718 114 32.5% .367 .611 Tim Beckham 614.8 21.2% .326 .726 402 18.2% .282 .661 Trevor Story 551.1 21.1% .326 .796 656 18.5% .345 .914 Miguel Sano 476.5 21.0% .334 .860 299 28.1% .286 .679 Joey Gallo 569 20.9% .285 .849 577 23.1% .249 .810 Adam Duvall 495.2 20.7% .273 .734 427 18.7% .237 .639 Colby Rasmus 313.4 20.6% .302 .785 49 32.7% .200 .426 Byron Buxton 570.4 20.4% .329 .758 94 26.6% .226 .383 Paul DeJong 513.1 19.7% .311 .769 490 17.8% .288 .746 Matt Chapman 565.6 19.7% .276 .747 616 14.3% .338 .864 Welington Castillo 399.2 19.6% .304 .736 181 20.4% .322 .710 Khris Davis 599 19.4% .287 .819 654 17.7% .261 .874 Steven Souza Jr. 344.4 19.2% .322 .779 272 17.3% .298 .678 Austin Hedges 415.3 19.1% .270 .684 326 21.2% .280 .711 Javier Baez 509.7 19.1% .313 .775 645 21.4% .347 .881 Justin Upton 614.3 18.7% .309 .803 613 18.3% .321 .808 Ronald Acuna 432.7 18.5% .348 .779 487 16.0% .352 .917 Martin Maldonado 343.7 18.4% .273 .660 404 20.3% .280 .627 Ryon Healy 438.6 18.4% .304 .725 524 16.4% .257 .688 Paulo Orlando 311.8 18.3% .328 .676 93 23.7% .231 .394 Ian Happ 516.7 18.3% .304 .783 462 21.0% .362 .761 Mikie Mahtook 379.2 18.1% .319 .727 250 18.0% .238 .635 Yoan Moncada 583.4 17.9% .317 .719 650 23.1% .344 .714 Jason Castro 338.5 17.9% .307 .712 74 23.0% .216 .495 James McCann 411.4 17.8% .304 .698 457 19.7% .282 .581 Jorge Soler 516.4 17.8% .310 .765 257 16.0% .340 .820 Tyler Flowers 340.6 17.6% .325 .759 296 13.9% .292 .700 Jonathan Villar 494.9 17.6% .327 .721 515 18.8% .339 .709 Jorge Bonifacio 346.2 17.5% .309 .728 270 15.6% .301 .672 J.D. Martinez 538.1 17.5% .342 .909 649 11.9% .375 1.031 Trey Mancini 566.1 17.4% .329 .782 636 17.1% .285 .715 Mark Trumbo 370.5 17.3% .285 .787 358 17.6% .303 .764 Ian Desmond 535.7 17.1% .346 .804 619 15.0% .279 .729 Alex Avila 348.6 16.8% .332 .741 234 22.7% .253 .603 Robinson Chirinos 313.7 16.8% .278 .746 426 22.3% .304 .757 Aaron Altherr 449.9 16.7% .303 .773 285 19.3% .247 .628 Leury Garcia 307.5 16.6% .314 .686 275 21.8% .355 .679 Corey Dickerson 461.9 16.6% .313 .791 533 11.1% .333 .804 Jonathan Schoop 616.2 16.3% .307 .805 501 19.2% .261 .682 Domingo Santana 572.7 16.3% .328 .821 235 24.3% .386 .740 Kyle Schwarber 498.6 16.3% .290 .830 510 12.2% .288 .823 Amed Rosario 535.8 16.1% .312 .670 592 15.2% .310 .676 Scott Schebler 464.9 16.0% .280 .776 430 14.0% .301 .777 C.J. Cron 431.2 15.9% .288 .754 560 19.3% .293 .816 Aaron Judge 614 15.9% .318 .886 498 15.3% .368 .919 Rougned Odor 569.2 15.7% .278 .782 535 15.7% .305 .751 Eric Thames 507.6 15.7% .304 .834 278 24.5% .284 .783 Alex Gordon 510.2 15.6% .299 .712 568 13.0% .299 .694 Nick Castellanos 596.3 15.4% .318 .807 678 15.0% .361 .854 Scooter Gennett 489.8 15.3% .302 .736 638 13.0% .358 .847 Yolmer Sanchez 473.7 15.3% .299 .682 662 13.4% .300 .678 Salvador Perez 506.3 15.1% .283 .763 544 16.7% .245 .713 Eddie Rosario 572.2 15.0% .313 .784 592 12.5% .316 .803 Second, I divided the players up into three groups. One was those who improved by 50 points of OPS, those who worsened by 50 points, and those in-between. Then, I cut-and-diced the data and in the end, I found the following variables the most important: Average Differences After Grouping Hitters by OPS Changes Category K% Diff BB% Diff BABIP Diff Age Improve -1.0% 0.6% .029 26.3 Same 0.1% 0.2% .010 26.8 Worsened 2.6% -0.4% -.043 28.4 The first and easiest factor to check is the age. I started with the above age values and split the age groups by 27 and younger and over 27 I got a drop of .008 for the young hitters and the older hitters down by .016. After adjusting the values, I found 30-years-old to be the key age. The hitters who are 30 and younger had their production drop by only 3 OPS points while it dropped 38 points for those over 30. The statement is true about teaching an old dog new tricks. Additionally, these old dog’s value drop because their projected BABIP dropped from .299 to an actual value of .287. Age may be a smoking gun. Going back to the 2018 trends table, it’s time to see if the BABIP and plate discipline numbers stick. BABIP goes first. BABIP Grouped by OPS Changes OPS Change Proj Y1 BABIP Y1 BABIP Y2 BABIP Improved .303 .327 .301 Same .300 .301 .293 Worsened .305 .273 .298 The previous season’s projected BABIP does a great job of estimating that year’s BABIP. Believe the projection on BABIP instead of the previous season’s values. One player group to target may be these bad plate discipline hitters with underperforming BABIPs. Bad previous season results may be anchoring their value down going into drafts. Now for the plate discipline. Plate Discipline Grouped by OPS Changes OPS Proj Y1 K%-BB% Y1 K%-BB% Y2 K%-BB% Improved .175 .167 .175 Same .180 .188 .190 Worsened .175 .198 .208 For the hitters who improved, they saw their plate discipline stick while those who saw a drop normally saw the drop continue. While the plate discipline and BABIP bounced back to the previous level, the overall OPS did not completely. Plate Discipline Grouped by OPS Changes OPS Proj Y1 OPS Y1 OPS Y2 OPS Improved .740 .844 .768 Same .727 .730 .718 Worsened .738 .620 .685 Since BABIP wasn’t providing the rebound, I need to find out why the change stuck. The answer was power. Plate Discipline Grouped by OPS Changes OPS Proj Y1 ISO Y1 ISO Y2 ISO Improved .180 .229 .199 Same .176 .179 .176 Worsened .178 .129 .166 The preceding ramblings point to three factors which may lead a poor plate discipline hitter breaking out: age, power increase, and plate discipline improvement. To test these variables, I took three regular seasons of data and one projected season of data. With the projected season (Y3), I found those hitters who with a K%-BB% projected for over 15% and age. Then I looked at the two previous seasons to see if the hitter’s plate discipline and/or power improved. The key was to find if any breakouts or breakdowns could be predicted before the season. Here are the differences in actual and projected OPS in Y3. Variables OPS Change Plate and Age -.004 Power & Age -.008 All three -.009 Age -.012 Plate -.015 Power -.016 One of three -.018 Power & Plate -.021 None of three -.045 It might sound cruel (or obvious) but just stay away from over 30-year-old hitters with declining power and plate discipline. Additionally, not one subgroup improves. They just decline less. With strikeout rates jumping from 18.5% when the projections started to 22.3% this past season, hitters with poor plate discipline will struggle more. While power is up also, it’s not able to offset the increase strikeouts from these hack happy free swingers. If an owner must roster someone from this group, ensure they are the younger hitters who at least saw an improvement in their power or plate discipline the previous season. Now, back to the two breakouts, Trevor Story and Javier Baez. With Story, nothing stood out but his age with his previous season ISO dropping from .296 to .219 and his K%-BB% going from 39.7% to 43.3%. Baez also met the age requirement, but he saw his ISO jump from .150 to .264 while his plate discipline took a hit from 27.3% to 34.2%. He at least met two of the categories. Looking forward to next season, both meet the improved power and plate discipline requirements while still being under 30 but still having some plate discipline issues compared to the rest of the league. They should keep some of the improvements but understand their BABIP will likely regress back to their 2018 projections of .313 for Baez and .326 for Story. I’ll come back and examine some hitters who the preceding study applies to once projections become available. The findings aren’t 100% clear and trustworthy but they provide some framework for finding the potential breakouts but even more, they help to find those hitters to stay away from. I feel like I may be missing some key factor tying everything together, but I’ll have to mull on it for a while. Next, I’m off to find the keys for the Max Muncy breakouts.