616: Wide Receiver Prospects Who Produced Like Studs

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Speaker 1: This is the Labor Podcast with your host JJ zacher Retha j J. Zacher reson. What's up everyone. It's JJ zachar Eaeson and this is episode six hundred and sixteen of the Late Round Fantasy Football podcast sponsored by FanDuel. Thanks for tuning in. I always like to say that not all productive college wide receivers and running backs are good in the NFL, but almost all good wide receivers and running backs in the NFL were productive in college. Now, of course it doesn't apply to everyone. Outliers like Tyreek Hill can happen, but generally speaking, higher end running backs and wide receivers in the NFL they were productive in college. I go through this exercise each year and it serves as a good primer for draft season, at least in my eyes. But essentially, I look at effective NFL wide receivers and running backs and I see how well they did in college, and then I see if there are players from the incoming draft class who match or exceed those metrics. That way, we could see which players from the current class produced like studs when they were in college, Hence the title of this episode. Now, before I explain things further, Basketball fans. FANDL and TNT are giving you the opportunity to get in on the action with the NBA TNT over under contest for any TNTNBA broadcast. All you have to do is opt in and choose over or under for each prop, lock in your picks before the end of the first quarter, and compete for a chance at five thousand dollars in prizes. Head over to fandle dot com today and participate in the free to play mbatnt over under contest. Eligibility restrictions apply. Go to fandle dot com or download the FANDLE app for more details. Okay, so we're talking wide receivers today and we're trying to see which wide receivers from this year's class produce the way that NFL studs did when they were in college. And this is all fantasy football centric, of course. So here's what I did to make this all clearer. I looked at wide receivers since twenty eleven who have given us multiple seasons with fifteen or more PPR points per game, and in those seasons they had to play at least eight games. Why fifteen or more PPR points per game? Well, this isn't really an exercise about precision. Remember this is more of a primer. We're just getting a general idea here of how productive these players were when they were in college, so we can compare that to the current class. But fifteen or more PPR points per game that generally leads to a top twenty season at the position in fantasy football, So you could think of this group of wide receivers as players who had multiple top twenty seasons over the last eleven years. Now that filter gives us a list of forty three guys, players like Deontay Johnson, DJ Moore, AJ Brown, DK Metcalf, they've only accomplished that feat once in their careers, whether that's because of the games played threshold or in the case of DJ Moore, he literally only hit fifteen or more PPR points per game once in his career. Among those forty three wide receivers, some of them are very old, like Larry Fitzgerald. Fitz was drafted in two thousand and four. My database goes back to two thousand and six. I had to throw some old guys out of this sample, like fits and like Roddy White. I also don't deal with undrafted players who didn't hit the combine, so more names were removed, like Doug Balwin and Adam Thielen. So after removing all of these players, I was left with thirty three studs. Thirty three wide receivers with multiple fifteen plus point per game seasons since twenty eleven. In my wide receiver model, I deal with three main production metrics, best season receptions per game, best season receiving yards per team pass attempt and best season touchdown share. Now, stand alone a metric like receptions per game, it's not the most important thing in the world, but combined with everything in the model, it does get signal. And for the record, I walk through my model in detail in the late Round Prospect Guy that drops on March fourteenth. You can pre order it right now over on Lateround dot com. Now, within that stud sample, we get the following averages. Inside these three production metrics. Of the thirty three wide receivers, the the average best season receptions per game rate was six point eight, the average best season yards per team pass attempt rate was three point one four, and the average touchdown share was just under forty three percent. A ton of you probably have no context to what those numbers even mean, are they even good? Well to give you an idea, I'll do the same exact exercise among players who were invited to this year's combine this year's draft class. Among that group, the average best season receptions per game rate was five point five, so one point three receptions per game lower than our STUD sample. And remember the STUD sample are NFL wide receivers with multiple top twenty seasons. Since twenty eleven, the average best season receiving yards per team pass attempt rate was two point three to five in this year's class. Remember among the STUD sample it was three point one four. And then in terms of touchdown share, the STUD sample had a share of about forty three percent. Among players in this year's class, that number fell to thirty two percent. Now, these numbers shouldn't be surprising. Players who have done well in fantasy football traditionally did very well in college. That's why their averages are significantly higher. The next step is to see which players have concerning numbers within each metric. Now, I gotta say this before I dig into this year's class. My prospect model does not weigh each of these statistics the exact same. By far, the best one is yards per team pass attempt, but receptions per game is not irrelevant in the model, and the same goes with touchdown share, even though there's some variants there. So I'll save yards per team pass attempt for last because it's the most important. And also keep in mind that the prospect model is not just based on production. Again, I outline everything in that guide. I won't get into all of that here, but you should know that there's more in the model than just these three production metrics. But on this show today, we're talking production because it does still matter. So let's start with receptions per game. Again, this metric as a standalone one is not necessarily super valuable because in college there are a wide variety of offenses run Some teams are really run heavy, some are really pass heavy. That's naturally going to change a player's receptions per game rate. But consider this. At least of the wide receivers who scored fifteen or more PPR points per game in just one of their first three seasons in the league since twenty eleven, only thirteen percent of them had a best season receptions per game rate below four receptions. In our sample of stud wide receivers, only Tyreek Hill, who's the outlier of all outliers. Only Tyreek Hill and Damarius Thomas, who played in an incredibly run heavy Georgia Tech offense, had a receptions per game rate in their top season in that statistic under four. The remaining thirty one had a best season receptions per game rate over four. We've got a lot of players under the four receptions per game mark in this year's draft class, eight of them, but I think the one that stands out the most is Christian Watson. Watson is a huge wide out who should test well at the Combine, and he evidently did really well at the Senior Bowl. But his best season at North Dakota State saw him with three point six receptions per game at the very least within this exercise, that's some concern. Now. Like a lot of players who are about to be drafted with a low receptions per game mark, the reason is usually because the offense that they were in. That's the case for Watson. North Dakota State did not throw the ball a lot, and fortunately for Watson, he's got enough potential in other areas to the point where this isn't that big of a deal, but is the obvious call out among this group. Now, if you look at the stud sample that I've been referencing, just four of the thirty three were actually under five receptions per game. Almost half of the forty wide receivers going to the Combine this season were under that mark. That's not great. The two biggest names in this group are George Pickens and Justin Ross. Both of these players have gone through some wild collegiate journeys. Both guys had really good freshman seasons and that gave them really good breakout ages, but then injuries derailed what could have been. Ross's injury was a bigger deal than pickens Acl tear, and it's probably gonna cost them some draft capital. They're both bigger dudes with some length. Honestly, analytically, like from a spreadsheet standpoint, they're not amazingly different. Draft capital is more than likely going to separate them. But it's just kind of interesting. But both players were well below that five receptions per game mark. I think we can forgive them to some degree given their age adjusted production. But across my model, Pickens and Ross don't look great because it's just hard for them to look great given the amount of time that they spent in college and when they got hurt. The model doesn't like despise them, but with projected draft capital in there, it does see them as slightly overrated. I think they still present a ton of upside, though, because the model isn't going to see what happened with their injuries. They were once really highly sot after wide receivers. Basically, what I'm saying here with Pickens and Ross is that there are flaws with their production profiles, so the receptions per game thing doesn't really matter that much because you're already doing more subjective projecting with them. Anyway, You're likely having to rely on your eyes a little bit more with those players than other players, and hopefully their rookie draft ADP doesn't rise a whole lot and you're able to get them in a range where you're just shooting strictly for potential. Anyway, ADP dictates whether or not I'm going to be into most players, but that's even more so for players with different types of journeys like Pickens and Ross. Now let's move on to touchdown share, or the percentage of receiving touchdowns that a player has in his offense. To remind you, the average in our NFL stud sample for a best season touchdown share was forty three percent. Of the forty wide receivers in this year's class who were invited to combine, thirty one were under that mark, So it's a tough baseline to meet. If a player is under twenty percent with his best season touchdown share, it's really not good news. Going back to the wide receivers who hit at least fifteen PPR points per game in one of their first three years since twenty eleven, fewer than eight percent of them had a touchdown share under the twenty percent mark during their best season in college. In this year's class, the player who stands out most with that low of a share is John Mechi. Yes, Mechi played for a program with lots of competition at pass catcher. Yes, maybe he gets over the twenty percent hump if he doesn't tear his ACL in December. Maybe, but we can make excuses for a lot of players. The fact is Mechi doesn't have the most complete profile with a best season sixteen point seven percent touchdown share, and even though I haven't gotten to yards per team pass attempt yet, he looks pretty bad. There too. His draft capital should keep him afloat to some degree in my model. Right now, I have him projected at seventy six overall thanks to NFL Mock Draft database dot com. But with that type of capital, he's a seventy seven percent tile wide receiver in my model. I try not to it's super excited about players under the eightieth percentile mark. We've definitely seen worse prospects than Mechi, but his production profile is lacking. Let's go to receiving yards per team pass attempt now. This statistic has been floated around the dynasty space for some time, and it's been the main production metric in my model for years now. Just as a reminder of the NFL stud sample had an average yards per team pass attempt rate during their best season of three point one four and that was a good bit lower with this year's draft class at two point three to five. Now, in our stud sample, of the thirty three wide receivers, six of them had yards per team pass attempt rates under two and a half. So eighteen percent of the sample had a yards per team pass attempt rate under two point five in this year's draft class. Just to give you an idea of why this metric is important. That percentage jumps to about fifty eight percent. So which higher end players should you be concerned about. Well, let's talk about someone who's arguably the best wide receiver in this year's draft class, and that Drake London. London's best season yards per team pass attempt rate in college was just two point one point seven. That's not great, especially for a player who's supposed to be one of the best in the class. But again, these numbers aren't everything. They're guiding us. Context is your friend London only played eight of twelve games this past year, his final year in college. That's traditionally when we see best seasons for college wide receivers. If you look strictly at games played, his yards per team pass attempt rate jumps to three point twenty six. And by the way, some of you are gonna ask me why I don't use individual games instead of full season statistics, and it's because it doesn't actually change the prospect model. And in situations like this, I can give things context, but usually there are other metrics within the model that make up for a bad touchdown share or yards per team pass attempt rate. That's a little bit lower because a player didn't play a full slate of games or a full season of games. We also tend to make excuses. Sometimes we do a little bit too much projecting. Sometimes we make a lot of assumptions when we don't have a full data set. So there are pros and cons to using full season data and individual game data. My main point to drive home here is that I'm always looking at context. It's incredibly important with this process. Now that that's out of the way, I need to just say that I'm not worried at all about Drake London. There's actually a decent chance he's my wide receiver one in this class. His age adjusted production is absolutely insane, and he did it while competing in college with legitimate NFL wide receivers in Michael Pittman and Aman Ross Saint Brown. Drake London is a stud. Now let's look at David Bell. He's someone with a pretty complete profile. He had some really good production at a young age. But despite having amazing numbers and receptions per game and touchdown share, his best season yards per team pass attempt rate is a little lacking at two point thirty nine. That may have been exceeded had he not missed two games this past year, But he wasn't reaching the NFL study average more than likely, and it's somewhat of a concern. I think Bell is a very solid prospect right now, but I will say that his comparables are a little all over the place. One of them is Chris Godwin. That's great, that's obviously a high end outcome, but we have to remember that Godwin won the athleticism battle. Bell is one of those players who I want to see test well before I have a strong lean one way or the other. So given all this, you may be curious which players do seem like studs. Which guys hit every benchmark laid out by the STUD wide receiver sample. Well, if I filter players out in this class by the averages that the STUD wide receiver sample had, so six point eight receptions per game, three point one four yards per team pass attempt, and a forty three percent touchdown chair. When I filter things out by that, three names emerge. One of them is Jalen Tolbert. Right now, my model is working off of projected draft capital, so things are far from complete. I don't have proper weight and height info for every wide receiver either because the combine hasn't happened. But if Tolbert has better than expected draft capital, he'll have a shot to hit that eightieth percentile mark in my wide receiver model. That usually comes with a higher hit rate. Regardless, he is the second best yards per team pass attempt rate in this class, and clearly his other numbers are really good too. He's a perimeter player with a lot of length, though I'm curious to see where his weight lands at the combine. Now a knock on him would be competition. He played at South Alabama, so if he's going to be a pro, he should have really good numbers at a school like that. His production profile, though, is still incredibly strong given that context, but with smaller school players we should bump up some thresholds. And by the way, my model does look at the programs that players are coming from, so it's factored into his score at the end of the day, and it's part of the reason why he may not reach that eightieth percentile spot, and it's why my model may not like him that much more than where draft cap will have him despite this great production profile. So Jalen Tolbert overall I like him. The other two players who hit the stud sample averages are sky Moore and Khalil Shakir, and I really really like both of them. I could see More picking up some steam as we get closer to the draft, but right now most mocks have him going past pick one hundred. But his production profile is really really solid, and unlike Tolbert Moore who went to Western Michigan, he's a non power five early declare. We don't see that very often, but early declare status is something that my model cares about, and it'll look at More a little bit more favorably because of it. More is like a seventy third percentile prospect in my model right now, with the assumption that he gets picked one hundred and twenty first overall. That's according to NFL mock Draft database dot com. But let's pretend that he goes like eightieth overall. That could bump him into the right spot to where his hit rate increases a whole lot. Regardless, myself and my model will be into sky Moore this draft season. More than likely. His top comp right now in my model is Jarvis Landry, and then with Shakir, I tweeted about this last week, But Shakier's top comp in my model get ready for this Cooper Cup. I'll talk about this more in the Late Round Prospect Guy that comes out in a few weeks. But Cup's best season numbers really do align with Shakir. It's just that there was a little bit more substance the Cup's profile, and no one has a reasonable ceiling right now of Cooper Cup. Comps aren't there to say that a player will definitely go in one direction or another. The point to comparables is to show that we've seen successes with similar profiles before, and Shakir, like Moore and Tolbert, didn't go to some huge program. He's a Boise State guy. But his numbers across the board they look great and he had a decent breakout age two. So Jalen Tolbert, Sky Moore, and Khalil Shakir are the three players who check all the boxes production wise. That doesn't mean they're the best players in this class. It just means that they have at least that portion of their prospect profile going for them. Now, if you'd rather look only at yards per team pass attempt, because like I said, it's the most important metric in my model from a production standpoint, if you want to look at that metric and players who are over the three point one four mark, which is the stud sample average, you add Treylon Burks, Garrett Wilson, Chris Olave, and Wandel Robinson to that list as well. So those names which are higher end names, those are all players to watch and all players that you should probably be in due to some degree. Now, as I've been saying, more goes into my model than just production inputs. But this is at least a good start to thinking about this year's wide receiver class. Next week I'll get into running backs. That's going to do it for today's show, though. Thanks to all of you for listening. If you get subscribed to the Late Round Fantasy Football podcast, make sure you are by starving for it pretty much anywhere podcast can be found, and don't forget to follow me on Twitter at Late Round QB. Thanks everyone. I'll be back in your ears on Friday with the weekly Mailbag show.