1105: Market Score 2.0

**JJ Zachariason** (0:11)
What's up, everyone? It's JJ Zachariason, and in this episode 11.05 of The Late-Round Fantasy Football Podcast, thanks for tuning in. A few weeks ago, I entered just a new metric to you guys called market score. It's something that Brandon Gadula and I, we've been working on for a couple of months, and it felt like it was in the right place to share with you. As I mentioned on that episode, it was still a work in progress. We still had some things that we wanted to add and change, but we didn't know how major those changes would be. Turns out, we needed some relatively big changes. After that episode was published, I received a lot of messages of confusion from people. Listeners didn't quite get what we were trying to do, and that's a problem. One of the hardest parts of my job is being able to communicate ideas that aren't always easy to communicate, and I want to do a better job with that when it comes to market score. So Brandon and I went back into the lab, and we've emerged with a different type of way to talk about market score. And that's what I'm going to talk about today, and I'll be able to provide more concrete examples of who the metric likes and dislikes here in 2026
Before getting to that though, as a reminder, all of this market score stuff along with game theory talk, strategy talk, rankings and tiers and more, are all in this year's Late-Round Draft Guide. That's available for pre-order right now on the new lateround.com. Or you can become an all-access member on the site, where you also get free access to the draft guide and the prospect guide, as well as just becoming a member. It's all up to you. But to learn more, check out lateround.com. Now, if you didn't listen to the market score episode from a few weeks ago, no worries. You don't need to go back and listen to it. In fact, you may be more confused if you do that. Basically, the idea of market score stems from the ZAP model. As you guys know by now, the ZAP model is my prospect model that looks at different collegiate inputs to determine how well a player is going to perform in fantasy football across the first three years of his NFL career. A central piece of that model is draft capital, or where a player gets selected in the NFL draft. Draft capital captures a lot of things that we should care about for fantasy purposes. And it's pretty predictive when looking at how well a player performs in the NFL. No surprise there. The higher a player gets drafted, generally speaking, the better he's going to perform in fantasy. When I sat back and thought about it, I've realized that I've kind of been doing the exact same thing as the ZAP model, but for season long leagues for years. I just didn't have a model for it. Let me explain. If you've read previous iterations of The Late-Round Draft Guide or even if you've just listened to this podcast, then you've read all of my analysis on average draft position expectation, or how many points we'd expect a player to score at a particular ADP. Essentially, when you do some regression analysis, you can find the number of points per game you'd anticipate a player to score based on where he's drafted. A wide receiver picked 50th overall might be projected to score 13 PPR points per game, all based on history. Someone 70th overall might be at like 11
That's just based on what's happened historically. Right now here in 2026, based on current ADP we can estimate a player's PPR points per game output. It's a way to use the market in order to get a general understanding of how many points per game a player might score.
So we have average draft position for season long leagues, then we have draft capital for prospecting purposes. Those are both market values. For season long leagues, ADP is telling us what the general fantasy football market thinks about a player. For the NFL draft and the NFL itself, draft capital is telling us what the NFL thinks about a player.
Now, as we know, the ZAP model then takes that draft capital input and it finds a score for a prospect based on additional inputs, like adjusted total yards per team play or breakout score. For market score, we're gonna do the exact same thing, but instead of looking at collegiate metrics, we'll be looking at previous season numbers, aside from rookies where we will actually lean on some of those college metrics.