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.
With the number 1 pick in the 2026 fantasy draft, the Phlavertown Phantatics select…
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Jahmyr Gibbs!
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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.
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.
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.
All models are wrong, but some are useful.
After extending my stay at Prospect Station, I have arrived at the next leg of my travels: Aging Curve Junction. I acknowledge that this metaphor is somewhat tortured, but I do think it gestures toward something genuine. A junction is a place where two paths of travel meet, and a dynasty ranking system requires merging the hypothetical of prospects and the real-life production of current big-leaguers over a period of future years. For our purposes, the bridge between those links is the aging curve.
On Tuesday, I followed up my study from last week on how prospect lists translate to fantasy auction value. Last week’s research primarily identified key stratifications in career and peak value based on list placement and whether the player is primarily a pitcher or a hitter. This week, I found that the predictiveness of ranking lists relative to fantasy auction value doesn’t seem to be changing much over time.
But before crossing “prospect lists” off of my dynasty ranking model checklist, I wanted to combine these two analyses to look at how or if the ranking buckets hold when broken into more granular year groupings. This is important to my final rankings because I want whatever bucket groupings I use to be representative enough to capture a reasonable picture of the forward-looking value of a given prospect.
So, how do the buckets shake out? Well, read on.
Last week, I studied how well Baseball America’s prospect rankings translated into eventual fantasy performance.
The results were fairly conclusive; after adjusting to floor negative-value seasons, hitters performed better than pitchers, and there was clearer stratification in hitter value across different ranking buckets than their counterparts.
That analysis included a significant amount of data going back to BA’s original lists in 1990, which has its pluses and minuses. On the plus side, the more the merrier. Sample sizes are bigger and we can more confidently assert statistical significance with the right tests. But on the other hand, 1990 was a long time ago (a phrase typed with millennial tears hitting the keyboard). Macro trends in prospect evaluation and baseball writ large have changed immensely.
You may have noticed over the years that RotoGraphs has not typically published dynasty rankings. This is likely for any number of reasons. There is no lack of dynasty content or rankings on other sites and, absent a ranking model that adds particular value relative to the rest of the industry, there isn’t much incentive to, as my editor put it, “vibe out a list of rankings.”
I wanted to change that, though. The full rankings model is not ready to hit the press yet, but I wanted to walk you through some of the steps and methodology that are going into my rankings because I both value transparency and think that there are useful insights to glean along the way. First up is the key differentiator in any dynasty ranking: prospects.
“I think you know my opinion on RP.”