No Respect: Adjustments, Correlation, & Breakouts

A couple of weeks ago, I examined hitters who were getting No Respect from the opposing defense. The pitchers were throwing these batters slow fastballs right across the plate. Additionally, the fielders moved up because they didn’t expect the guy to hit the ball with authority. After getting some hints from the comments and some changes I listed in the original article, I decided to implement a some with the goal to find inexperienced major league hitters who might not have shown all their power and have another gear. The MLB teams know of this power since they have access to minor league Trackman information.

Changes

Initially, I used four inputs for the original No Respect value: fastball velocity, fastball percentage, zone percentage, and center fielder distance. After, mucking around to get the list to match my perceived reality, I removed the fastball percentage and added the average of the distance the left and right fielders play. The fastball percentage helps the rankings look better at the “respected” end. Some of these guys (e.g. Javy Baez) are only thrown non-fastballs because they have problems making contact with pitches that move.

As for the additional distance measure, I found some heavy pull power hitters were getting rated as being disrespected but this additional information puts them more in line with reality. The reason I combined the two corner values is that I didn’t want the outfield alignment to have more inputs than form the pitchers. One option I considered was taking the larger of the two corner values.

With the new values, it’s time for an idiot check on the least and most respected hitters and a full list is here (min 100 PA).

The hitters aren’t perfect on the high end, but better. The key aspect is met with the low values being weak hitters.

Correlation to other stats

The next step was to find the stats that correlate best to the “No Respect” metric. The key is to find what information teams might use against hitters.

The one stat that sticks out is the Hard Hit% (>95 mph) with xISO and Barrel% right behind. These rankings are far from perfect but I’m surprised the correlation was so high with 30 teams being involved.

Disagreements

So, I created a simple expected hard hit rate by using the No Respect value. With that value, here are some young hitters who are underperforming a 40% xHardHit% by 10% points or more.

Kyle Lewis: 32% HardHit%, 45% xHardHit%, 12% Diff

Lewis is a couple of years removed from winning the Rookie of the Year award. He’s dealt with injuries since then limiting him to about 200 major plate appearances.

While he’s had two call-ups this season (May and July), he’s struggled in the majors with a .529 OPS because of a .147 BABIP. In AAA, he has a .945 OPS with 8 HR in 110 PA. Teams see him as a power hitter, but he has not shown the ability to make contact (30% K%) might limit how much of that power he can unlock.

Drew Waters: 25% HardHit%, 42% xHardHit%, 17% Diff

I wish Waters didn’t meet the cutoffs. While the 23-year-old has struggled a bit in the majors (.669 OPS), he has shown enough power to not be written off, but not enough to garner a ton of interest He can be especially valuable in points leagues and on-base leagues because of his walks (14% BB%).

With Kansas City in AAA, he had 7 HR and 13 SB in just 143 PA. Even with a 31% K%, the Royals have started in 13 of the last 15 games. If he can just get the strikeouts under control, he could be a nice break-out candidate.

Ezequiel Duran: 31% HardHit%, 41% xHardHit%, 10% Diff

I didn’t expect Duran to make this list. His .643 OPS is disappointing after having OPS’s over .900 in AA and AAA this season. Across all levels, He has 18 HR and 14 SB in just over 500 PA. That’s not horrible.

The big disconnect with his major league power is a 51% GB% after having a 41% GB% in AAA. He has improved with a 62% GB% in the first half and a 46% GB% in the second half. If a person squints, there is 20/20 upside and will likely be ignored with Josh Jung’s promotion and the middle of Texas’s infield set.

Akil Baddoo: 30% HardHit%, 41% xHardHit%, 17% Diff

Really? The guy with a .029 ISO (.047 xISO)? He hasn’t had a major league extra-base hit since March. He’s been a complete mess this season with a .468 OPS in 149 PA. He was better in the minors with a .905 OPS and .200 ISO (131 PA).

Teams seem to believe in the power he showed last season and this season in the minors (16 HR in ~500 PA). Maybe he should not be completely written off and could have a chance to turn around his young career like Jorge Mateo.

Bubba Thompson: 27% HardHit%, 40% xHardHit%, 13% Diff

I was most excited to see Thompson on the list. He is best known for his speed (49 SB in AAA, 10 in MLB) but his value could jump if he can hit for some power. Right now, none is expected from him.

The biggest step the 24-year-old could do is to put the ball in the air a bit (55% GB%). Out of his 26 hits, only two have gone for extra bases. With his speed, he should be able to leg out a few double and triples.

He has hit home runs in the past with 16 in AAA in 2021 and 13 in AAA this season.

Lewin Díaz: 29% HardHit%, 40% xHardHit%, 12% Diff

Power has always been part of Diaz’s game and he’s not been able to get to much this season (.071 ISO). I’d not be surprised if his ISO wasn’t the highest of this group over the rest of the season.

Summary

After going through some potential power breakouts, I still see three areas for improvement.

1. Players in Colorado are causing some issues since defenders have to naturally play deep in the huge stadium. Possibly I have to use road stats with them.
2. I’d like to go back through previous seasons and look into the predictability of the “No Respect” measure. I’m likely to dive into this approach in the offseason.
3. As I noted in the previous article, I would like to have month-to-month information to see if teams change how they approach certain hitters.

Overall, I like where it is heading. It’s a power projection without actually measuring a hitter’s power. I’m just working my way through the values.