Last week, we covered hitters who outperformed and underperformed their projected value. We’re doing the same exercise for pitchers, focusing on starters. We won’t discuss all these pitchers below, but here is the list of pitchers who outperformed their expected value by a least $10 this season.
We’re following up on an earlier article about hitters who are outperforming their projected value this season. But there’s also the other side of that coin: 19 hitters among the top 200 picks who are underperforming their projected value by at least $20. Unsurprisingly, the hitters drafted within the top 50 picks who were injured or struggled have been the biggest busts.
The visual below shows hitters who have lost $20 or more this season.
In 12-team leagues, 37 hitters earned $20 or more on the Player Rater. Of those 37 hitters, 24 were drafted within the top 100 picks. Meanwhile, four hitters went undrafted or were past pick 400: Jake Bauers, Jake McCarthy, Jordan Walker, and Liam Hicks. We want to hit on these breakout hitters in the draft or acquire them during the season. These are the waiver pickups that turn into regular starters on our fantasy squads.
The 37 hitters who earned $20 or more are seen below.
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.
If you’re a nineties baby playing in the majors, I think your time has passed. Make way for Gen Z. The 2000’s babies. Even the 2005 babies are starting to make a real impact.
Bye bye Vladimir Guerrero Jr. and hello Junior Caminero. So long Juan Soto and welcome Pete Crow-Armstrong. Sal Stewart, yes you’ll be taking over for Mr. Manny Machado. And Fernando Jr. of Tatis fame? The McGonigle and Wetherholt party will take it from here.
So many of our favorite young stars from the last five years have aged out of rising stardom and seem to be either plateauing or, even worse, skiing down the final hill, and fast.
Here’s a quick collection of players I’d consider consensus picks to become generational stars a few years ago who are currently between 27 and 29 years old. To me, this captures the players many of us would have confidently said were entering the primes of their careers as recently as two years ago. Not yet on the hill, and surely not over it. Read the rest of this entry »
“I think you know my opinion on RP.”
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 recent articles 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.