The 2022 Pod Projections are now available and include nearly 550 player lines! As usual in my Pod Projection posts, I’ll dive into my projection methodology (detailed in Projecting X 2.0) by sharing my process on several hitters and pitchers.
2022 Pod Projection Index
It’s always a challenge to value previous season breakouts, so it’s no surprise that Logan Webb has exhibited one of the larger pick ranges in NFBC leagues during February. He has been picked as early as 44 and as late as 122, while being taken as the 68th player off the board on average. Webb didn’t show a whole lot during his first 90+ innings in the Majors in 2019 and 2020, as his ERAs sat over 5.00, his SIERA marks were well above 4.00, and his strikeout rates stood below the league average.
Then spring training came along, where his tiny sample results was the first potential sign that a performance uptick may be on the way. He struck out 22 of the 61 batters he faced, for a 36% strikeout rate. Again, the smallest of samples, but it’s a huge leap for a pitcher who notched just a 19.8% strikeout rate heading into the 2021 season. That performance ended up correctly foreshadowing the season to come, as his strikeout rate did end up spiking, the rest of his skill metrics improved, and he posted a 3.03 ERA, backed by a 3.13 SIERA. What does he do for an encore? Let’s try to figure that out.
Games Started | IP: 30 | 168
Webb started 27 games last year, averaging about 5.7 innings per start, and made one relief appearance. I’m figuring three additional starts as he moves into the team’s “ace” role heading the rotation, which should guarantee he makes the most starts on the team if he remains healthy all year. On the flipside, I’m projecting a slight dip to 5.6 innings per start as some performance regression might knock him out of the game a tad earlier.
Webb’s swinging strike rate surged last year and was a big driver of his breakout. Three factors fueled that spike — a jump in his sinker velocity and both increased usage of his curveball/slider (slurve) plus a massive increase in its SwStk%. It’s anyone’s guess whether the higher velocity will stick, while his slurve was graded as his best pitch in his last published scouting report. The strikeout rate jump feels mostly real and was validated by my xK% equation, but anytime a pitcher posts such a spike, it’s usually smart to project some regression the following year.
As a reminder, I don’t manually project strikeout rate, but rather the components of my xK% equation, and then that equation calculates the projected strikeout rate. I have the ability to change that output though if the pitcher has proven to consistently overperform or underperform his xK% mark. Webb doesn’t have a long enough track record to determine whether he’ll fall into any of those camps.
The breakout wasn’t only fueled by an increase in swings and misses, but also improved control. Webb’s strike rate surged, while he also avoided 3-0 counts at a much better rate. My xBB% equation wasn’t completely buying that his control improved this much though, but still did agree it was the best control he showed in his short career. I’m projecting some regression in his improved strike percentage, but his forecasted walk rate would still rank as the second best of his short career, and second best compared to his historical xBB% marks.
Like my projected strikeout rate, I don’t project walk rate manually either. It uses the same variables I projected to get to projected strikeout rate, with the addition of strike percentage.
GB%/LD%/FB%: 55.5% / 21.5% / 23.0%
Webb’s ground ball rate jumped for the second straight season, but by a larger degree than it did from 2019 to 2020. It’s pretty clear what drove the increase — his sinker usage went from the mid-teens to 38.2%, as he replaced most of his four-seam usage. Naturally, his sinker is his best ground ball pitch, generating a 68% rate over his career, a significant increase from the 41.3% mark his four-seamer has generated. As a result, I’m projecting half his GB% gains to be maintained, but accounting for the possibility that he moves back to his four-seamer a bit more this year.
Webb’s career HR/FB rate sits at 13.1%, versus a 13.4% xHR/FB rate (same variables as my hitter xHR/FB rate equation. Pitching half his games at homer-suppressing Oracle Park helps and should allow him to continue posting better than league average marks.
Despite the strong skills everywhere else, Webb still hasn’t figured out how to suppress hits on balls in play. His BABIP sat significantly above the league average during his small sample 2019 and 2020 seasons, and while it dramatically improved in 2021, it was still far worse than average. The issues here are pretty clear by just checking his batted ball distribution profile — he’s an extreme ground ball pitcher, a batted ball type that goes for a hit more often than fly balls, and his pop-up rate is extremely low, not just because he doesn’t allow that many fly balls, but even as a percentage of his total fly balls. So he simply induces a low rate of easier outs, which means unless his batted ball profile changes dramatically, he’ll continue posting higher than league average BABIP marks.
His Statcast xBABIP marks validate his struggles. I’m projecting a better BABIP than his .322 career average, but essentially flat compared to his 2021. While Oracle Park is pitcher friendly for home runs, the park’s dimensions result in favorable conditions for singles, doubles, and triples. That all adds up to higher BABIP marks as well.
Below is my final projected pitching line, along with the other systems for comparison:
Logan Webb Projections
Yellow = most optimistic
Red = most pessimistic
Aside from ZiPS’ extreme optimism, the ERA and WHIP projections are relatively close, while the wins projections (typically a crapshoot) are within just one win. The BABIP projections are amazingly close, which is a bit surprising considering how much luck fuels that metric.
The differences lie in the strikeout and walk rates. I explained why I believe that most of Webb’s strikeout rate gains are for real, but still need to account for the possibility of regression, as the changes that drove that spike might not be fully repeated. I can do that because I’m human. The computer systems may be using less data and are more pessimistic about Webb’s strikeout rate as a result.
On the other hand, I’m most pessimistic on Webb’s walk rate, as I’m likely using a very different methodology than the rest of the systems, by using strike percentage and a variety of other pitch-related variables.
The bottom line is that I, and the other systems, expect Webb to be quite good again, but not nearly as good as last year. As usual, I think his earliest pick of 44 is far too early, while his latest pick of 122 is far too late.