349: Wide Receiver Prospects Who Produced Like Studs

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Speaker 1: This is the Label Podcast with your host JJ zacher Retha J J zacher.

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Speaker 2: Reson What's up everyone.

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Speaker 1: It's JJ Zacharyesen, the editor in chief at fandel and at number five dot com. And this is episode three hundred and forty nine of the Late Round podcast on many shows that are part of the FanDuel podcast network.

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Speaker 2: Thanks for tuning in.

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Speaker 1: You hear me talk about my prospect models all the time on this show, so today we're gonna talk about that a little bit, specifically with wide receivers. I had a fairly bad year in twenty nineteen with evaluating wide receivers. I like some of the right guys like AJ Brown, but I was higher than the consensus on Andy Isabella and JJ R Sega Whiteside, and that looks like a bad call. I got a question closer to the end of the season that I answered on a mailbag show that essentially asked, is it time to change your approach since you messed up this past year? And the short answer was no. You can't let one season and a few misses change the way that you approach things as long as your approach is solid. And the approach overall had led me to some really strong results. I mean, the prospect model is why I named Kenny Galladay Baby Tron right after he was drafted by the Lions.

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Speaker 2: But every year I try to.

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Speaker 1: Refine the approach and I did that once again this year, and in an effort to be more transparent about what goes into these models, I wanted to walk through some of the production related factors that the models look at in order to explain why I'm higher and lower on certain players. Before doing that, though, I wanted to let you guys know that we're getting closer to golf's first major. So Fandl's rewarding fans who make the cut by giving them a chance to compete for their share of fifteen thousand dollars. Just finishing the top fifty percent of users in the weekly Streak eligible contest, and your make the Cut streak continues. Best of all, you don't need to worry about missing a week. Your streak stays alive until you fail to finish in the top fifty percent. For more details, visit fandle dot com or download the FANDLE app today. Eligibility restrictions apply, So I want to start off at a high level here and explain the approach that I take with these models. Again, I want to be transparent and maybe even motivate some of you listening to build something of your own, because it's fun and fantasy football is all about entertainment. My wide receiver database goes back to two thousand and six and it includes every relevant season. But the sample that I work with includes wide outs who are either drafted or who got invited to combine. Then look at how all of those players perform during their first three years in the NFL. So, for example, if a player went undrafted and he didn't go to the combine, but he somehow did really well during the first three seasons, he's not included in the data set, you have to draw the line somewhere. The goal of the model was really to beat draft capital, meaning if my prospect scores can predict first three year success way better than draft capital can or where players get drafted, then that's a big win, and it does that by a very large margin. The model itself does include draft capital, though, so think of it this way. And this goes for both the wider s receiver and the running back model. When you compare the metrics used for the model versus where a player got drafted. There's not a whole lot of differentiation in how predictive things are. The metrics used without draft capital isn't necessarily more predictive than draft capital itself. That's not super surprising because high draft capital for a player not only means that he'll likely get an immediate shot to play, but there's talent evaluation within draft capital. A team liked a player to spend a high pick on them, so we should assume that that player isn't bad at football. The thing is, when you factor in draft capital into the model, things get sweet. The model becomes way more useful at predicting the output during a player's first three seasons versus looking strictly a draft capital And I'm not ashamed of using capital either. I'm trying to win in fantasy football. My goal is in too predict where a player should be drafted, and since the majority of rookie drafts and redraft league drafts happen after the NFL draft, it only makes sense to use that information in order to be more precise. Now, every one of these players has a prospect score that gets calculated by looking at a number of different factors. Today, I'm going to look at the production factors that go into that score, but keep in mind that things like breakout age, BMI, strength of competition, there are a lot of things that are factored into this prospect score.

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Speaker 2: The three main production.

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Speaker 1: Related categories for the wide receiver model, though, are receptions per game, yards per team attempt, and touchdown share. That's it, and those are all final season numbers. If a player didn't play that much during their final year, then his most recent relevant season is used.

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Speaker 2: I should add that return yards are a nice boost if there's sort.

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Speaker 1: Of a WTF type player like Deontay Johnson who gets drafted earlier than expected, but that's not factored into the model. It's sort of an after the fact look. So I want to talk through those three main statistical categories today. We'll look at all wide receivers who are going to the Combine this year and will compare their statistical profiles to stud wide receivers in the NFL. That way, you can get an idea of which players produced like studs. Hence the TI of today's show, Because generally speaking, players who were productive in the NFL were also productive in college. That means successful NFL wide receivers. Successful fantasy receivers were really good within the production metrics that I just talked through. If they weren't, then the model wouldn't work. But what does being successful at the NFL even mean for purposes of this podcast and to talk through things to define that term, I just looked at the thirty four wide receivers since twenty eleven who played D one ball, who also finished with multiple top twenty seasons, and I filtered out Jerryatrix like Larry Fitzgerald, since his college data went further back than two thousand and six.

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Speaker 2: So, for example, those.

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Speaker 1: Thirty four stud wide receivers when they were in college, they averaged six point six receptions per game during their final collegiate season. Unsurprisingly, among the fifty five wide outs who previte into the combine, their receptions per game was significantly lower at four point seven. That's a near two receptions per game difference. When looking at yards per team attempt, the STUD wide receiver sample averaged almost three yards per team at ten atempt, the twenty twenty class average fewer than two. And then the STUD wide receivers had a touchdown share during their final season of forty one point four percent. This year's class, it was closer to twenty seven percent. So the stud wide outs did way better than the list of combine invites. That's not shocking. The question is which players in this year's class then failed to perform in those three statistical categories. Which ones didn't even come close to performing like a stud during their final collegiate season. Now, I should mention that these three metrics likely work well together because yards per team attempt can often favor big play receivers, whereas receptions per game can be higher for players with lower average up to targets and pass heavy offenses. So when you combine the two, it helps create a strong profile. That's at least the logic that I'm using as to why these things seem to work. Regardless, two players really stood out as underperforming when looking at these metrics. One is Jalen Rager and the other is Henry Ruggs. Raggers on the top of some people's draft boards at wide receiver. If you look at some website rankings like ESPN's prospect rankings, he's ranked a lot lower, so he's a polarizing player. He does have a lot of positives to his profile. He had an elite breakout age, he's entering the draft as a junior, and he was a returner too. Because he didn't perform well statistically doesn't mean that he's a bad prospect. There are some statistical red flags, though. People seem to be brushing off his twenty nineteen season because TCU's offense was trash, and that's fair to a degree. The fact that TCU is pretty run heavy doesn't help his receptions per game average either, but this is also why we look at market share numbers or yards per team attempt, which is in the context of a player's specific offense.

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Speaker 2: Regor was below average there.

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Speaker 1: Now if you look at his twenty eighteen season, things look a lot better. That's really why I still like Regor despite his poor final season numbers. We've seen situations like this before, but I can't deny that final season numbers are the most predictive, and his were lacking. It's a red flag and something to keep in mind. Otherwise, he's a really good prospect, and if he gets strong draft capital to back everything up, then I'll likely care even less about those final season numbers. Henry Ruggs is a different story, though ESPN's prospect rankings have Rugs as the third best wide out. Walter Football has him as the third best wide out. Draft scout dot com has him as the third best wide out. It's usually Jerry Judy, Ceedee Lamb, and then Henry Ruggs. My model doesn't see it. Ruggs averaged three point three receptions per game last year, that was below average for the twenty twenty class. He had a yards per team attempt of one point eight four, which was also below average. His fourteen percent touchdown share with seven percentage points lower than the average as well, which means all of those numbers were way below the NFL stud average. Ruggs's aggregate stat score, which weighs those three statistics appropriately, it rings thirty eighth of the fifty five wide receivers who are invited to combine this year, and if you look at her sample of studs, none of them had a stat score that's as low as Rugs from twenty nineteen. The one player who's closest is Mike Waller, which is interesting because they're both players who can really stretch the field. The big piece here that I haven't talked about is that Ruggs played with Jerry Judy, who's at the top of some draft boards, and DeVante Smith, who's going back to Alabama for his senior season. Two things about that, though. The model does look at what I call a teammate factor, but it's not weighed very heavily, meaning it doesn't matter that much. Generally speaking, good players see good numbers because they're good. We saw that with AJ Brown and DK Metcalf a year ago when we were looking at this stuff, So I tend to see that more as an excuse than anything else. And it's not like Ruggs is close to having good numbers. He's still pretty far away. And then on top of that, I couldn't find any meaningful way to use combine numbers with the wide receiver model. The combine matters a lot less at the position than it does it say, running back and tight end, even though it's probably overrated there as well. So if Ruggs has a crazy forty time, it really shouldn't change the way that we feel about my overall assessment on Rugs based of all of this is at number one, he'll still look okay enough of my model if he has good draft capital but I'll likely not view him as a top notch high end fantasy asset because he doesn't profile to one. To me, he might just be a better real life player than a fantasy one. Reger and Ruggs are two players who stood out the most just given how much people are talking about them. They were the two players I wanted to talk about most today. On the negative side, there really weren't huge discrepancies elsewhere. But let's look on the flip side, which players looked really good statistically? Which players hit on all three averages from our stud wide receiver sample. There were actually two receivers who hit on all three marks. The first was Omar Bayless from Arkansas State. You may have no idea who this guy is since he's a smaller school guy, but he averaged over seven receptions per game, a yards per team attempt of over three and a half, and a touchdown share of nearly forty six percent. Those are Studley numbers, but they also, like I said, came against weak competition, and Bayless actually didn't do a whole lot until his final season, which is not a great sign. And we can't expect draft capital to be on his side. So let's push Baylis aside from now and look at the other player who hit on these metrics, which is Tyler Johnson. Johnson has an elite breakout age, and his final season receptions per game, yards per team attempt, and touchdown share were all above the NFL stud average, especially his yards per team attempt, which is the stat that gets the most weight in my model. The biggest downside with Johnson right now is that he's not locked in as an elite prospect in this class, so you may not get drafted super high. And he also didn't declare early, which is somewhat of an issue. But statistically he's amazing. I'm sure you guys want to know who else did well within the stats score in my model. Devin Duvernet popped up in third, but the rest of his profile isn't super strong. He only had one strong year of productions who had a late breakout age, and he didn't declare early either. Those aren't great signs, and it's doubtful that he goes super high in the draft, so the model's just not going to like him that much overall when it's all said and done. Isaiah Hodgens is another guy that the stat scorer liked, and he's more interesting than Duvernet in my opinion. He's in early declare. He was well above average in all three stat categories, and he has a sub twenty year old breakout age. He's summoned to watch as we move through the process. And I guess that I should talk about Jerry Judy and CD Lamb since they're generally the consensus top wideouts in the class.

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Speaker 2: My model likes them both, and with draft capital.

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Speaker 1: They'll likely be the top two wide receivers in this class according to the model. Now that may sound surprising for those of you who have looked at Judy's numbers. According to playerprofiler dot com, He's got just a thirty six percentile dominator rating that doesn't scream success, But he actually had pretty strong final season marks and receptions per game and yards per team attempt. His stat score is lower than i'd like, but there are a couple of wide receivers from our studs group who had stat scorers as low as Judy's. And then when you in then he played with other pros, and then he has a really strong breakout age. Judy's really not a bad prospect. Analytically. Saying he's a bad prospect analytically is a tough sell.

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Speaker 2: In my opinion, Lamb is just as good of a prospect.

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Speaker 1: My worry with both guys is that they have smaller BMIs than you'd like to see from top tier wide receivers. But I'm also not using official height and weight measurements right now because the combine hasn't happened, so take that with a grain of salt. I'd probably put Lamb ahead of Judy, but I do think that they're one in two in this class right now, and then one more guy before I let you guys go. I know I'm sort of rambling through things here, but I figured that you want to hear some takes on these guys since this is the first show where I'm really talking through things. Brian Edwards is an analytical darling. People who prospect through data love Brian Edwards. He's got one of the best breakout ages that you could possibly find. He had similar numbers as Deebo Samuel last year at South Carolina. His dominator rating is in the ninety fourth percentile. The dude looks great on paper, aside from the fact that he wasn't in early declare. Edwards also scored pretty poorly in yards per team pass attempt. A lot of that is due to the fact, according to Pro Football Focus, that he only had four deep receptions last year that ranked tied for one hundred and ninety seventh.

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Speaker 2: He caught a ton of screens that basically tells.

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Speaker 1: Us that we don't know exactly how his game is going to translate. He's a fun prospect given all the measurables, but I do think that there are some question marks about his profile. Now we're going to learn more about these guys of the combine, but I can tell you that I'm not all that concerned about how they perform athletically unless they're outliers. I'm more concerned about how teams may be starting to view them, the momentum that they gain as a result, and to be honest, they're straight up height and weight because that's an input that actually matters in my model. That's going to do it for today's show, though, thanks to all of you for listening. If you've got subscribed to the Late Round podcast, make sure you are by searching for it pretty much anywhere podcasts can be found and don't forget to follow me on Twitter at late Round QB. And remember with the off season mailbags, which will be on Friday, free to email me those questions at jj at number fire dot com. Thanks everyone, I'll talk to you later in the week.