1083: Wide Receiver Prospects Who Produced Like Studs

**JJ Zachariason** (0:02)
This is The Late-Round Podcast with your host, JJ Zachariason.
What's up, everyone? It's JJ Zachariason. In this episode 1083 of The Late-Round Fantasy Football Podcast, thanks for tuning in. Before I get to the topic today, I just want to send another reminder that The Late-Round Prospect Guide is currently available for pre-order, and it's better than ever. This year's guide includes an outline of what goes into the models, prospect profiles for every running back, wide receiver, and tight end who's going to be at the combine, over 30 year 2 profile write-ups, rankings, and more. It'll launch in mid-March, so pre-order now for $14.99. It's going to rise to $19.99 in just a few weeks. You can learn more over on lateround.com.
Last week on the show, we talked through running backs in this year's draft class who produce like studs. Because there's some back story to all of that, if you haven't listened to that episode, I'd pause this one, go to that episode, listen to it, and then come back here. We're going to go through that exact same exercise today, but with wide receivers. We're going to look at successful NFL wide receivers, see what kind of production traits they shared in college, and then we'll see which players from this year's class, the guys invited to the combine, produce like studs. To find our stud sample at Running Back, I looked at all running backs since 2011, who had multiple seasons with 14 or more PPR points per game. I then had to filter out running backs who weren't in the ZAP model, because I don't have data for them. Like I said, if you didn't listen to the Running Backs episode, you probably are missing some context, but I think most would appreciate if I didn't repeat everything that I said last week. Anyway, I'll do the same thing at wide receiver, but looking at 15 PPR point per game seasons. So look at how many ZAP model wide receivers since 2011 had 15 or more PPR points per game in multiple seasons. Doing so gives me a stud sample of 35 wide receivers. They range from Pukinakua to T. Higgins to Randall Cobb to Tyler Lockett to Julio Jones to Cooper Cup. It's a little bit of everything. Now, the wide receiver ZAP model has a ton of inputs, but the main production input is a metric that I call breakout score. It's predictive, it's tested, and it's all driven off of a player's receiving yards per team pass attempt rate, which is a metric that's intuitive. It's just a player's receiving yards divided by how many pass attempts his team had. Essentially, breakout score looks at every season the player played in. It looks at the receiving yards per team pass attempt rate that a player had in a given season. It then prorates that for games missed. For a season to count, the wide out had to play at least six games. And then from there, it adjusts for strength of schedule. A player will get some multiplier, whether good or bad, based on his team's strength of schedule. Lastly, there's an age layer to it. A wide receiver who produces really well in schedule-adjusted receiving yards per team pass attempt who's young, he's going to look better than someone who does the exact same thing when they're old. The degree in which that breakout happens, that's important too. You guys have probably seen the metric breakout age out there, and while there is some predictiveness to it, to me, it's just outdated. It's a binary way of looking at a breakout, and the metric that's being used in that metric in breakout age is dominator rating. Based on my research, that's not the best metric that you can use for age-adjusted production. Breakout score is on a scale from to 100 50 isn't exactly an average score or like a 50th percentile score, but just know that anything above that is pretty good. Anything in like the 80, 90 range, it's excellent. And for the record, the scaling for breakout score changed this year. The raw input in the model is the exact same, but I started to scale it a little bit differently for anyone who followed along last year. Now on today's show, I'll look at both breakout score and best season receiving yards per team pass attempt. Even though receiving yards per team pass attempt goes into breakout score, we'll see how these guys did in their best season in college. Unadjusted for age, unadjusted for strength of schedule. Breakout score covers a lot of ground from a production standpoint within the ZAP model, but there's also an input for best season age and schedule-adjusted fantasy points per game. That's just fantasy points per game adjusted for both strength of schedule and age. And that's in a season with a minimum six games played. We'll look at that metric too, even though it's not nearly as important in the model as breakout score. Within our stud sample of 35 wide receivers, the average breakout score was 68.1. The average best season receiving yards per team pass attempt was 3.03, and the average best season adjusted fantasy points per game, 25.1. Meanwhile, there were 46 wide receivers invited to the combine this year. Naturally, because all of those players aren't studs, their breakout score average is 31, significantly lower than the NFL stud sample. Their average best season yards per team pass attempt is 2.24. The stud sample was at 3.03, and their fantasy points per game, 16.2. Again, the stud sample over 25 Each number is a whole lot lower than that stud average, but that's not a surprise. Now I could just say, okay, here's who produced like a stud. Here's who hit all of the stud sample averages. But I think going through each metric and shouting out some players who do have some red flags within those metrics, that's helpful. That's useful. It also allows me to talk through this class as sort of a primer. So let's start with breakout score, the most important metric in the ZAP model by far. The vast majority of this year's class is below that 68 mark from the NFL stud sample. But let's look at some names that are well below that, and who currently project as fairly high draft picks, according to nflmockdraftdatabase.com.