I have a fairly small portfolio of fantasy teams – prior to joining the staff here at RotoGraphs I had pared down over the years because managing daily teams on different platforms is a bit of a slog. Going into next year, I might explore joining some leagues with weekly adds. But in any case, I’d rate my season as successful overall.
I’ve written a couple of late-season articles on overperformers and underperformers based on projected versus earned value. As someone who spends plenty of time examining pitchers, my fantasy teams admittedly struggled in pitching points. My team’s pitching ratios exploded due to blowup outings from Jack Flaherty, Luis Castillo, Logan Webb, and others.
On the hitter side, I rostered plenty of veteran players that might be seen as boring ones. Common hitters on my roster included Trea Turner, Jose Ramirez, Dansby Swanson, Ian Happ, Bryan Reynolds, and Seiya Suzuki. In one 12-team league, I started with Ronald Acuña Jr., Garrett Crochet, Manny Machado, Bryce Harper, and Geraldo Perdomo.
On another 12-teamer, I went with Shohei Ohtani, Treat Turner, Ketel Marte, William Contreras, Hunter Brown, Suzuki, Carlos Estevez, Corey Seager, Alex Bregman, and Spencer Strider with my first 10 picks. Unsurprisingly, that team struggled across the board.
I’ll walk through a few lessons from my draft and in-season strategy from the 2026 fantasy baseball season.
Lon Horwedel-Imagn Images
With the dust finally settling, I ended the 2026 season with $3K in profit over 12 leagues. Here are my league results and some of overarching thoughts on the season and some leagues. Read the rest of this entry »
THOMAS CORDY/PALM BEACH POST / USA TODAY NETWORK via Imagn Images
I thought I’d had a smart idea. I wanted to see how “dead” affected a league’s standings as the season drew to a close. With managers quitting their teams for various reasons, I wanted to see how the rate stats for underperforming teams changed. The counting stats needed to move up or down the standings are easier to calculate for each league, but rate stat movement is a bit of a mystery. I expected the bottom teams to go after counting stats and ignore their ratio categories. They did not. Instead, I verified regression remains unbeaten … probably.
Jayne Kamin-Oncea-Imagn Images
Over the years, I’ve gone to the cutting edge of stats to find advantages. I can go into a deep analysis on any player’s fantasy value, but there isn’t always time to analyze each player’s profile. For a shortcut, I use OPS to evaluate hitters and xFIP and botERA for pitchers. They give me a good predictive look, especially in a small sample. Besides those stats, I might make a cursory look at BABIP and Contact% for hitters and K%-BB% for pitchers.
Note: Recent plate appearances could be the #1 stat to follow for hitters, but I’m going to focus on production stats.
Syndication: Tuscaloosa News
With several websites hosting fantasy baseball leagues and analysts reporting rates from different sites, it’s tough to know how a rostership rate compares to the league each person is in. I hope to clarify some of that today by comparing the rostership rates at various websites.
Previously, some shmuck compared rostership rates at various hosting, but that was over eight years ago. A few things have changed since then. I chose to go to 500 players to get a feeling for the rates in 15-team, 30-man rosters. These leagues have 450 players on their active rosters, but there is disagreement on the end guys, so I included an additional 50 players. Read the rest of this entry »
Once considered anomalies, cutters and sinkers have moved to the forefront of pitchers’ arsenals in 2026. As pitchers develop and pitching staffs look to push the boundaries, increased horizontal and vertical movement on fastball variants has given pitchers yet another edge over hitters. While these pitches have always existed, rather than being utilized as a primary pitch in isolation they are now being used more and more often in relationship to one another. This juxtaposition is what makes them such a challenge for hitters and increasingly popular as an arsenal tool.
So the real question for hitters has become: which fastball is coming? They are being used increasingly interchangeably, thrown from similar release points, with almost identical velocities, which compounds the challenge with each at-bat. From a fantasy perspective, this trend toward a certain profile or archetype of multi-fastball pitcher is one worth monitoring as we consider draft strategy — particularly in the later rounds — or waiver additions, as the never-ending search for value continues.
Nathan Ray Seebeck-Imagn Images
Earlier this week on the CBS Fantasy Baseball Today podcast, Chris Towers noted that Shane McClanahan was struggling more than he would have expected, even with his STUPH model grades down. Chris wondered if a pitcher’s STUPH drops to a new level, does the pitcher perform worse than those who were already at that level? Pitchers need time to adjust to their new talent level. Well, I decided to give the question a quick look.
When McClanahan last threw back in 2023, he had a Pitching+ of 115, and now has a Pitching+ of 90. While he has the same number of strikeouts (9 K/9), the rest of his results have been horrible (5.00 ERA, 1.33 WHIP) as his walks have ballooned (5.5 BB/9). The biggest issue with analyzing this change is that it is extreme, with a two-year gap between throwing. I needed to simplify the search.
Finding a large enough sample set was a pain. The set needs to include starting pitchers (I stayed away from combining starters and relievers) with a STUPH decline into a range with a large enough sample of other pitchers already pitching in that range. And all this occurs in the few seasons that STUPH models have existed. In the end, I went with Pitching+ as my metric because I wanted the pitcher’s overall talent. Also, I wanted a quick snapshot, not a full-blown study. Future studies could use several factors.
For the sample, I took all starters with at least 40 IP in back-to-back seasons. I subdivided that group into pitchers who saw their Pitching+ drop between five and 10 points into the 95 to 105 Pitching+ range (n=26). Then I created another group of pitchers with a 97.5 to 102.5 Pitching+ range in subsequent seasons (n=37).
Here are the average values from the two samples.
| STUPH Model | Previous Pitching + | Pitching+ | Stuff+ | Location+ | IP | ERA | FIP | xFIP | SIERA | K/9 | BB/9 | WHIP |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Pitching+ Decline to Range | 106.8 | 100.2 | 96.7 | 102.8 | 108 | 4.50 | 4.51 | 4.26 | 4.34 | 8.70 | 3.39 | 1.35 |
| Pitching + Stable in Range | 100.0 | 100.1 | 97.5 | 102.1 | 125 | 3.86 | 3.97 | 4.07 | 4.15 | 8.45 | 2.84 | 1.24 |
Even though Pitching+ was the factor controlled for, both sets’ Stuff+ and Location+ values ended up similar. And that’s about it for any similarities. The declining pitchers threw fewer innings (possible injury?) with an ERA that’s 0.64 higher and a WHIP that’s 0.11 higher. These pitchers seemed to struggle at the new talent level compared to pitchers who had time to adjust to it.
The effects seen by this one small subset mean someone should dive in to verify the results. The different ways to cut up the data could be endless. Different change sizes? Include TheBot Values? Do changes in Stuff values lead to changes in Location values? After a season with worse STUPH, do the pitchers’ results improve? As long as the sample sizes remain reasonable, the combinations are endless. I’m afraid Chris opened a can of worms.
Dan Hamilton-Imagn Images
In this week’s FAAB and Waiver Wire Report, I mentioned that José Soriano’s fastballs were a matched pair with a short description of the concept. I wrote the idea up in this year’s edition of The Process, but here is another description of it (so I can link it instead of writing it up every time I mention it). Besides the matched fastballs, I discuss having a unique drop rate compared to the other pitches, which leads to a plus pitch. Read the rest of this entry »
David Frerker-Imagn Images
In a recent article, I wanted to show the average fastball velocity increase from Spring Training to the regular season. I went to Mike Fast’s classic article, “Spinning Yarn: Do Spring Speeds Matter?” at Baseball Prospectus, and noticed it was 15 years old. In the article, he found a 0.6 mph increase from Spring Training to the regular season. As much as I trust Mike’s work, it’s time for an update. After looking at the numbers, the velocity difference has shrunk to almost zero.
To find the change, I took the available Spring Training fastball velocities from the past three seasons for both sinkers and four-seamers. Then I calculated the average and median differences, along with the standard deviation. Additionally, it seems like relievers are down more than starters (>=50% GS/G in regular season) in Spring Training, so I split them up.
That’s pretty much it, so here are the results.
| Pitch (Role) | Average | Median | SD | 1 SD (68% chance) | 2 SD (95%) | 3 SD (99%) |
|---|---|---|---|---|---|---|
| FF (All) | 0.26 | 0.22 | 0.94 | -0.7 to 1.2 | -1.4 to 2.4 | -2.0 ti 3.6 |
| FF (SP) | 0.08 | 0.07 | 0.87 | -0.8 to 0.9 | -1.6 to 1.9 | -2.4 to 2.8 |
| FF (RP) | 0.37 | 0.34 | 0.96 | -0.6 to 1.3 | -1.2 to 2.7 | -1.8 to 4.0 |
| SI (All) | 0.18 | 0.13 | 0.89 | -0.7 to 1.1 | -1.4 to 2.1 | -2.1 to 3.2 |
| SI (SP) | 0.01 | 0.01 | 0.81 | -0.8 to 0.8 | -1.6 to 1.7 | -2.4 to 2.5 |
| SI (RP) | 0.29 | 0.24 | 0.92 | -0.6 to 1.2 | -1.3 to 2.4 | -1.9 to 3.6 |
The overall increase is cut in half from the original study, with starters seeing almost no increase … on average. All the standard deviations approach 1 mph, so there can be some major differences from one pitcher to the next. I included the velocity ranges for different standard deviations. In the best-case scenarios (3 SD), starters gain about 2.5 mph while relievers are adding 4 mph.
With that knowledge, feel free to navigate our player pages to see who is up and who is down. And for me, it’s back to Mining the News.