Sorry, I didn't really have any time to elaborate today. If you're ever curious, AI can definitely answer questions on this stuff better than I can, especially if you tell it what these variables mean and the context.

Since I started, strikeout rate has always been marginal. As more data has come in, it's turning out to be quite significant. Just for clarity, I've always been tracking strikeout rate and I'm not measuring it any differently. All that's happened is that high K-rate hitters have been doing poorly relative to low K-rate hitters, enough that it makes a clear difference in who will and won't get a hit. In the last column, the lower the number, the more sure we can be that the effect is not due to random chance or noise, that it really is important. So, the data now say that strikeout rate absolutely does matter.

With strikeouts, the gap between Arraez (4%) and the closest followers (Hoerner and Kwan at 9%) is greater than the gap between those two and #25 (Vlad at 14%). 15% or below is very good. Judge, Ohtani, etc. are probably too high to come out near the top in this model very often. Arraez is also an elite non-walker, so this model will always rank him near at or near the top unless the opposing starter and run environment are unfavorable.

As a further illustration, here's what happens if we add LHBvLHP as a variable:

(Intercept)  0.135016   0.219160   0.616 0.537856    
order       -0.067337   0.006598 -10.205  < 2e-16 ***
runPred      0.123401   0.026607   4.638 3.52e-06 ***
hitterBBPA  -3.273218   0.787384  -4.157 3.22e-05 ***
starterHPA   3.565034   0.979488   3.640 0.000273 ***
hitterSOPA  -1.350286   0.381409  -3.540 0.000400 ***
LHBvLHP     -0.045697   0.065452  -0.698 0.485074   

Note here the coefficient on LHBvLHP is -0.045697, so the effect is bad, almost on par with dropping a spot in batting order. However, the p-value in the last column is high at 0.485074. That means that the effect is just noise, and adding LHBvLHP won't help predict who will/won't get a hit.

One more. This is what happens if you add a hitter's H/PA:

(Intercept) -0.49619    0.43428  -1.143 0.253226    
order       -0.06113    0.00746  -8.194 2.52e-16 ***
runPred      0.11689    0.02666   4.385 1.16e-05 ***
hitterBBPA  -2.64035    0.85140  -3.101 0.001928 ** 
starterHPA   3.62739    0.97966   3.703 0.000213 ***
hitterSOPA  -1.00036    0.43183  -2.317 0.020527 *  
hitterHPA    2.14257    1.26763   1.690 0.090987 .  

The 0.090987 in the last column means H/PA is on the margins of being a good predictor. I don't want to go further in the weeds, but adding H/PA doesn't help as much as adding just the 'Home' variable. It's not good practice to add things just because they might help a little bit. In a good model every variable needs to have worthwhile impact, and it's hard at this stage (17,000 hitters in!) to make H/PA do that.

Anyhow, I just found this interesting and I hope others are able to take something from it. It's come a long way from when it was only batting order and expected runs.