Left: IMAGN IMAGES via Reuters Connect; Right: USA TODAY Network via Reuters Connect
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.
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.
Earlier this week when I detailed what is sure to be the most important human versus machine debate our society will face, I found that the projection robots very narrowly edge the ADP market when it comes to predicting fantasy performance. That analysis, though, was based on a half-season of 2026 data with a hint that there might be more to come.
This is that follow-up. My goal here was to conduct a broader analysis on more data to see if the conclusions held up, and it turns out that they did.
While writing my recentarticles about how to use projections to find undervalued players based on market ADPs, I was struck by a follow-up question: when ranking players, which is better – ADP, or projections?
As we strive to value players more objectively, probing this question is valuable. It will help calibrate how much weight to assign consensus rankings versus your favorite projection systems. If we’re lucky, it might point us to some things that the market or the robots identify particularly well relative to the other. When we can marry the strengths of objective, data-driven processes with the advantages of human behavior and judgment, we are better off for it.
And this is a meaty question; theoretically each method has its strengths. Average draft position is a ranking derived from the wisdom of thousands of fantasy owners. It should be valuable in this respect. But we fantasy owners are subject to our own pernicious little human biases when it comes to player evaluation. Are we better off just letting the robots do the work for us?
I left you earlier in the week with a promise to dive deeper into the bargain leaderboards created by comparing projected dollar value against the expected dollar value for a pick in that draft slot. Please accept my sincerest apologies if, left without more context, you found yourself mired in the digital equivalent of a TJ Maxx aisle, paralyzed with indecision while selecting which 10 articles of clothing you want to bring to the dressing rooms.
Ignore Kohl’s in the distance. That’s out of our price range.
Part 1 of this series earlier in the month laid out some groundwork and provided a few basic examples of the benefits of striving to value fantasy assets more rigorously. Part 2 illustrated some of the differences in player value across different types of league settings, and taught you how to construct that analysis for yourself. In this article, I aim to build on those concepts to show you how to hunt for bargains in the draft.
