Just before Thanksgiving, the Blue Jays signed Dylan Cease to a seven-year, $210 million contract. He now returns to the American League, where he spent five seasons with the White Sox before joining the Padres for two seasons. Coming off the second worst ERA over a full season in his career, how might his new park affect his results? Let’s consult the park factors to find out.
Last Tuesday, the Red Sox acquired Sonny Gray from the Cardinals, allowing him to don his sixth uniform in 10 seasons. It’s pretty surprising to see a guy with a career ERA- of 87 to be constantly packing his bags to join a new team, but perhaps the positive spin is that so many organizations want him! After spending two seasons calling Busch Stadium home, let’s dive into the park factors to find out how the move to Fenway Park might impacts his results.
A couple of weeks ago, I reviewed the early starting pitcher Stuff+ risers and fallers. Unfortunately, comparing 2025 Stuff+ to 2024 Stuff+ meant that I was missing a whole group of potentially interesting pitchers to evaluate — both injury returnees and rookies. So I dedicated a post to reviewing these two groups’ Stuff+, but without any comparisons to the previous season. Let’s find out how they finished the season.
In late April, after about a month of the season was in the books, I compared starting pitcher Stuff+ grades to their marks from the 2024 season, identifying the risers and fallers. Just like with any other metric, could pitcher go on a stuff hot streak or suffer through a slump? Or does such changes early on tend to stick? Let’s review the names to see how they finished the season in both Stuff+ and strikeout rate, which correlates strongly with the former.
The preseason and early season reviews continue! Today, I’m going to review the early starting pitcher velocity changes to find out if the gainers held onto their gains and if the decliners suffered their losses all year. Since we know that fastball velocity positively correlates with strikeout rate, I’ve included the change from 2024 to 2025 as well to see if both moved in the same direction.
At the beginning of April, I took a super early look at hitter maxEV gainers. Obviously, a hitter had all season to record that maximum exit velocity, so a lower maxEV than the previous year at the time was not a cause for concern. However, if a hitter had already posted a meaningfully higher maxEV than the previous season, did that turn out to be an early indicator of a power breakout? Let’s review the names and find out.
At the end of March, I dove into the hitter average bat speed risers and fallers. I realized afterward that although the metric stabilizes rather quickly, it was still simply too early to conclude anything. So I then discussed risers and fallers again with two posts back-to-back in mid-May. Let’s find out if these hitters maintained their mid-May levels, and if so, if it impacted their performance in the same direction as the bat speed change.
Yesterday, I reviewed my $14 NFBC offense, filled with 14 hitters with an average auction value of $1 in late February when I published my imaginary roster. Today, let’s now flip over to reviewing my fake $9 pitching staff. I would imagine it’s easier to hit on some big wins from the crop of $1 pitchers than $1 hitters, so let’s see if that was the case this year. As a reminder, I rostered three true hits on the hitting side, composing just over 21% of my team. Did I do better on the pitching side?
One of the most enjoyable things I’ve been doing over the last couple of years is creating imaginary NFBC teams based on their average auction values (AAV). It’s a fun way to identify potentially undervalued players since I’m using actual market values and not just speculating. Today, let’s review my $14 NFBC offense, which is comprised of, you guessed it, only players with an AAV of $1.
In early February, I compared 2024 actual wOBA to xwOBA to identify players who might be in for a wOBA surge or decline in 2025, based on the gap between to the two metrics. Let’s now review these lists to find out how the players actually performed. Did the potential surgers actually surge and did the decliners actually suffer declines?
