Author Archive: Jonathan Vander Lugt

Continuing my recent theme of diving deeper into topics I’ve covered previously, I wanted to revisit my analysis looking at the impact of debut k-rate on the probability of fantasy viability.

Back in June, I found that, unsurprisingly, k-rate had a marked impact on whether or not a player would reach various measures of viability. This was particularly stark (again, unsurprisingly) at higher k-rates:

That’s basically where that analysis ended though. I paid some brief lip service to the notion of back-testing by looking roughly at incidence rates of viability from 2020+ debutants, but it wasn’t a particularly robust exercise and it didn’t say anything about what traits eventually lead to viability despite a penchant for strikeouts nor about the individual prospects of recent high-k debutants.

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Back in August when I published my research on fantasy specific aging curves, I noticed something unique: catchers were dominating the lists of “chalkiest” careers. I found this odd at the time and noted it as a potential topic for future research. Now that we’re in full (fantasy) off-season swing, now is the time to look into these curios. So, do catchers age differently from other players, with respect to fantasy performance? Turns out the answer is yes. But also, no. Read on to find out what I mean.

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To recap where we last left off in our trip down the reliever rabbit hole: long-term trends on overall reliever dollar value do not seem to be changing that much over time, based both on the number of relievers registering valuable seasons and the proportion of “one-hit wonders” in the modern era.

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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.

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Like a moth drawn to a flame, I find myself returning to the strange comforts of relief pitcher analysis.

In last week’s article, I looked at whether or not there seems to be big-picture movements in relief pitcher value and found that generally, despite the overall vibe shift and changing usage patterns, the proportion of good reliever seasons and distribution among the players accruing these seasons is not materially changing.

But – as one commenter noted – what about SVHD (saves + holds) leagues? It’s a fair question and one to which I am personally sympathetic. Despite all of my digital hullabaloo about standard 5×5 roto settings, I don’t actually play in a league where holds are not counted. As more and more home leagues shift away from saves dominance it might be worth a closer look.

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Back again to the bailiwick which is perhaps my least compelling at cocktail parties (and trust me, this is saying something): relief pitcher fantasy performance.

A while ago, I looked at whether or not drafting relievers was worthwhile and found, ultimately, that yes, it is worth devoting targeted draft capital to relief pitchers. But one of the outstanding questions from that analysis was whether the composition of reliever performance is changing over time, so that’s what I decided to look into next.

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Earlier in the week I covered some of the Dynasty Model’s biggest risers. Here, I’ll move on to presenting some of the model’s spicier takes. As if I were serving up wings in Hot Ones, I’ll go in order from least to most spicy. Hopefully by the end you aren’t left in tears like many of the guests on that show.

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With the number 1 pick in the 2026 fantasy draft, the Phlavertown Phantatics select…

…  

Jahmyr Gibbs!

…

Wait, sorry, last week I said I was messing with the draft but that’s not what I meant. You could be forgiven though, for having your head turned by the novelty of the recently commenced NFL season. If you’re sitting here reading this though I assume you are a dynasty/keeper sicko and if that’s the case you’re in luck because I’ve got the September 2026 RotoGraphs Dynasty Rankings hot off the press.

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After a detour writing a couple articles with cheeky little bits and also a much more extended effort to develop a model for dynasty rankings, I wanted to return more explicitly to my bread and butter: the Asset Valuation series. After my first teaser article, my next three in this series showed how scoring settings can affect player value, and how you can exploit knowledge about these dynamics for your gain.

Those analyses, though, held one thing constant: the value of draft picks. To get those points across, I assumed a standard 12-team redraft setting, and flexed the value of players based on scoring scheme. Here, I’m doing the opposite: I’m messing with the timeline draft pool while keeping scoring scheme constant to show you how the value of a draft pick itself can change based on league size, keepers, and whether you use projections or actual realized dollar value.

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I’m pleased with how the first release of the RotoGraphs Dynasty Model went, both in the results it produced as well as the reception here on the site. I’m excited for its future use as well, because the methodology is flexible meaning there is no reason it couldn’t be expanded to reflect a deeper league or a different settings scheme. For now, though, I’m satisfied with the 12-team, 5×5 proof of concept that this run provided.

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