Archive for Dynasty

On June 27, I had a team sitting in second place in a 4×4 league, 6.5 points out of first. This particular league, an Ottoneu league called the Food and Travel League, pays out prizes in the form of food and/or beverages. It is one of my favorite leagues, both for the prizes and the competition, and finishing top three in that league is a goal every year – it’s much more fun to receive food than to figure out how to ship it.

I had never won this league and while the gap to first made me wonder if this was the right year to push in my chips, I also saw a clear path to finishing second or third (fourth place was 7.5 points behind me). But I was behind the pace on innings pitched and needed arms. I was third in all of the pitching rate stats (HR/9, ERA, WHIP), but was ninth in IP and, as a result, tied for eighth in strikeouts. Gaining points in Ks while maintaining my rates was an obvious path forward.

So I made a trade. I ended up winning the league, but the trade was costly and it is worth wondering if the trade is why I won the league, or if I could have won without the deal.

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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…

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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.

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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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All models are wrong, but some are useful.

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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.

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Series Overview

This series aims to help readers conduct their routine due diligence in their dynasty leagues. It makes use of up-to-date OOPSY Peak projections and my own arbitrary whims to discuss a handful of players, both prospects and major leaguers, who may have seen their perceived value shift recently. Any reference to how a player projects using the FanGraphs auction calculator uses these settings (15-team 5×5 SV 2 C) with OOPSY DC (ROS).

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Intro

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.

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We expect several familiar names returning to Major League rotations in the next month. Let’s dive into how they are progressing, when they might be activated, and what to expect from them for the rest of this season.

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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.

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