1 00:00:02,400 --> 00:00:06,000 Speaker 1: This is the Late Podcast with your host, JJ zachera 2 00:00:06,160 --> 00:00:07,760 Speaker 1: JJ Zachers. 3 00:00:11,880 --> 00:00:12,640 Speaker 2: What's up everyone. 4 00:00:12,640 --> 00:00:15,960 Speaker 1: It's JJ Zachary sin in this episode ten ninety seven 5 00:00:16,000 --> 00:00:18,440 Speaker 1: of the Late Round Fantasy Football Podcast. 6 00:00:20,200 --> 00:00:22,200 Speaker 2: Thanks for tuning in. 7 00:00:22,800 --> 00:00:24,840 Speaker 1: We've got some great questions this week, and we'll get 8 00:00:24,840 --> 00:00:27,120 Speaker 1: to them in just a second. First, remember to check 9 00:00:27,160 --> 00:00:29,400 Speaker 1: out the Late Round Prospect Guide. It's all finished up, 10 00:00:29,520 --> 00:00:32,440 Speaker 1: and it's been finished since the draft ended. There's well 11 00:00:32,440 --> 00:00:35,360 Speaker 1: over one hundred and seventy pages of content. Updated player 12 00:00:35,400 --> 00:00:38,160 Speaker 1: write ups with their draft capital and landing spot in mind, 13 00:00:38,600 --> 00:00:42,280 Speaker 1: updated ZAP scores, rookie rankings, and more. You can get 14 00:00:42,320 --> 00:00:45,880 Speaker 1: it over on lateround dot com. Right now, now, let's 15 00:00:45,880 --> 00:00:51,160 Speaker 1: get to these questions. The first one's from Matt on Patreon. 16 00:00:51,200 --> 00:00:53,440 Speaker 1: It says JJ you touched on a couple specific tight 17 00:00:53,520 --> 00:00:57,920 Speaker 1: end situations in yesterday's episode, sadekon Stours and on past episodes, 18 00:00:58,080 --> 00:01:00,640 Speaker 1: you've talked about the uptick of twelve percent and that 19 00:01:00,680 --> 00:01:04,200 Speaker 1: we should expect to continue this year as offenses respond 20 00:01:04,200 --> 00:01:08,320 Speaker 1: to smaller defenses. For example, you've talked about Terrence Ferguson 21 00:01:08,319 --> 00:01:11,160 Speaker 1: on the RAMS as McVeigh likely being a year ahead 22 00:01:11,160 --> 00:01:13,640 Speaker 1: of the trend. But then The Rams took Max Clare 23 00:01:13,680 --> 00:01:16,399 Speaker 1: in the second round, with ten tight ends taken in 24 00:01:16,440 --> 00:01:18,840 Speaker 1: the first three rounds, which do you take as a 25 00:01:18,920 --> 00:01:22,240 Speaker 1: bad sign for the incumbents on those teams versus teams 26 00:01:22,240 --> 00:01:24,240 Speaker 1: just wanting more tight end depth to be able to 27 00:01:24,319 --> 00:01:28,080 Speaker 1: run more twelve Curious to unpack the situations that these 28 00:01:28,120 --> 00:01:30,760 Speaker 1: guys got drafted to. So I think this is a 29 00:01:30,800 --> 00:01:33,840 Speaker 1: really important question. It's one where each individual team is 30 00:01:33,880 --> 00:01:36,440 Speaker 1: going to have a different answer. But what I'm seeing 31 00:01:36,480 --> 00:01:39,480 Speaker 1: in a high level bird's eye view is teams trying 32 00:01:39,520 --> 00:01:43,280 Speaker 1: to move heavier James Gladstone the Jags GM. He even 33 00:01:43,319 --> 00:01:45,399 Speaker 1: mentioned how heavier tight end sets are a trend in 34 00:01:45,400 --> 00:01:48,520 Speaker 1: the NFL and that led to their rationale for some 35 00:01:48,560 --> 00:01:52,280 Speaker 1: of their selections. So clearly, more and more teams are 36 00:01:52,320 --> 00:01:55,960 Speaker 1: hopping onto this bandwagon. The Jags last year, according to 37 00:01:56,000 --> 00:01:59,000 Speaker 1: Fantasy Life data, they ran out of eleven personnel at 38 00:01:59,000 --> 00:02:01,120 Speaker 1: the six highest rate in the league. 39 00:02:01,280 --> 00:02:02,000 Speaker 2: Even though I think. 40 00:02:01,920 --> 00:02:05,440 Speaker 1: Their current wide receiver room dictates that should continue, their 41 00:02:05,520 --> 00:02:09,000 Speaker 1: draft says that it won't continue at quite the same rate, 42 00:02:09,680 --> 00:02:12,480 Speaker 1: and we know league wide we're seeing more twelve and 43 00:02:12,560 --> 00:02:15,840 Speaker 1: thirteen personnel. Now, I do think this is one of 44 00:02:15,880 --> 00:02:19,080 Speaker 1: those places where reality doesn't have to equate to fantasy football. 45 00:02:19,680 --> 00:02:22,240 Speaker 1: Teams are often doing things that are in the interest 46 00:02:22,280 --> 00:02:24,840 Speaker 1: of their team as a whole, not scoring fantasy points. 47 00:02:25,600 --> 00:02:28,360 Speaker 1: Some tight ends they're gonna be better at blocking, and 48 00:02:28,400 --> 00:02:31,040 Speaker 1: they might not be pure receivers like an Eli Stowers. 49 00:02:31,639 --> 00:02:33,560 Speaker 1: So just because a tight end was picked with decent 50 00:02:33,639 --> 00:02:36,359 Speaker 1: draft capital, it doesn't mean that that tight end has 51 00:02:36,360 --> 00:02:38,880 Speaker 1: more fantasy football upside than one of the tight ends 52 00:02:38,880 --> 00:02:42,040 Speaker 1: already on the team. Like in Jacksonville actually think their 53 00:02:42,040 --> 00:02:44,760 Speaker 1: fifth round tight end has more fantasy football upside than 54 00:02:44,760 --> 00:02:47,600 Speaker 1: their second round one, and the ZAT model actually agrees. 55 00:02:48,760 --> 00:02:51,200 Speaker 1: So yes, the amount of tight ends being drafted early 56 00:02:51,240 --> 00:02:54,520 Speaker 1: in how teams spent their capital in the position definitely 57 00:02:54,560 --> 00:02:59,639 Speaker 1: signals an uptick and heavier personnel use. Gladstone even said that, however, 58 00:03:00,000 --> 00:03:02,000 Speaker 1: we shouldn't treat all of these tight ends the same. 59 00:03:02,760 --> 00:03:04,720 Speaker 1: I know, we all know that, but tight end can 60 00:03:04,760 --> 00:03:07,640 Speaker 1: be difficult to evaluate for this very reason, because some 61 00:03:07,680 --> 00:03:10,080 Speaker 1: of these guys are made for blocking while others are 62 00:03:10,120 --> 00:03:13,120 Speaker 1: made for pass catching. And in fantasy football, sure we 63 00:03:13,160 --> 00:03:15,440 Speaker 1: won a well rounded tight end because we won them 64 00:03:15,480 --> 00:03:17,760 Speaker 1: on the field. We want them on the field. A lot, 65 00:03:17,960 --> 00:03:21,280 Speaker 1: but we want the pass catcher. And then on top 66 00:03:21,360 --> 00:03:24,280 Speaker 1: of that, anytime a team makes a draft selection, you 67 00:03:24,440 --> 00:03:27,960 Speaker 1: have to look at their future situation. The Rams taking 68 00:03:28,040 --> 00:03:31,639 Speaker 1: Max Claire accomplishes two things. Number One, they get more 69 00:03:31,639 --> 00:03:33,959 Speaker 1: tight end depth because they are running out of a 70 00:03:34,000 --> 00:03:38,720 Speaker 1: lot of heavier personnel. But number two, smart teams think ahead. 71 00:03:39,280 --> 00:03:42,200 Speaker 1: The Rams historically have not really used their rookies on 72 00:03:42,280 --> 00:03:45,160 Speaker 1: offense under Sean McVay. And look at what's happening in 73 00:03:45,160 --> 00:03:50,000 Speaker 1: twenty twenty seven next year. Colby Parkinson, free agent Davis Allen, 74 00:03:50,320 --> 00:03:53,960 Speaker 1: free agent Tyler Higbee is signed through twenty twenty seven, 75 00:03:54,160 --> 00:03:56,560 Speaker 1: but he's thirty three years old and he's not making 76 00:03:56,600 --> 00:03:59,920 Speaker 1: a ton of money. Max Claire is a good prosper 77 00:04:00,320 --> 00:04:03,080 Speaker 1: who can play a receiving role, but he's also someone 78 00:04:03,120 --> 00:04:05,720 Speaker 1: who profiles more as a twenty twenty seven player than 79 00:04:05,720 --> 00:04:08,160 Speaker 1: a twenty twenty six to one. Now, look, if I'm 80 00:04:08,200 --> 00:04:10,400 Speaker 1: being totally honest here and I'm giving my more amateur 81 00:04:10,400 --> 00:04:13,200 Speaker 1: take on this tight end renaissance, I also would imagine 82 00:04:13,200 --> 00:04:16,560 Speaker 1: that some teams just aren't thinking about this properly. The 83 00:04:16,600 --> 00:04:18,840 Speaker 1: beauty of what the Rams are doing is that they 84 00:04:18,880 --> 00:04:22,440 Speaker 1: have flexibility, having a player like Terrence Ferguson can be 85 00:04:22,560 --> 00:04:26,080 Speaker 1: hypothetically beneficial for them. And I say hypothetically because we 86 00:04:26,200 --> 00:04:29,159 Speaker 1: just don't know right now, but I definitely see the vision. 87 00:04:29,960 --> 00:04:32,400 Speaker 1: Ferguson is more of that tweener type. You can get 88 00:04:32,480 --> 00:04:34,720 Speaker 1: them out there in these heavier packages, but you can 89 00:04:34,760 --> 00:04:37,080 Speaker 1: shift them to the outside and let them play X 90 00:04:37,120 --> 00:04:40,400 Speaker 1: receiver at times. You can get really really creative with 91 00:04:40,480 --> 00:04:43,440 Speaker 1: a tight end like Terrence Ferguson. Now, some teams may 92 00:04:43,480 --> 00:04:46,840 Speaker 1: not have the same offensive genius that Sean McVay does 93 00:04:47,320 --> 00:04:49,840 Speaker 1: to really pull something like this off, like he can 94 00:04:49,920 --> 00:04:53,640 Speaker 1: run tight ends out of eleven personnel effectively. He did 95 00:04:53,680 --> 00:04:56,840 Speaker 1: that last year, which is why it's extra important to 96 00:04:56,839 --> 00:04:59,000 Speaker 1: remember to still focus on the things we no matter 97 00:04:59,080 --> 00:05:01,800 Speaker 1: at tight end for fantasy purposes, which is athleticism and 98 00:05:01,880 --> 00:05:05,159 Speaker 1: receiving production. Some of the tight ends selected earlier in 99 00:05:05,200 --> 00:05:08,080 Speaker 1: this year's draft, they didn't really have those two things. 100 00:05:08,960 --> 00:05:11,599 Speaker 1: Some of them did, like an Eli Rarenan, who I 101 00:05:11,600 --> 00:05:14,760 Speaker 1: still think is really undervalued in rookie drafts right now, 102 00:05:15,320 --> 00:05:18,800 Speaker 1: but it's gonna be extremely case by case, and we 103 00:05:18,920 --> 00:05:21,520 Speaker 1: have to keep in mind through all of this, it's 104 00:05:21,560 --> 00:05:24,640 Speaker 1: difficult for tight ends to make serious fantasy football impacts. 105 00:05:25,120 --> 00:05:27,760 Speaker 1: So if there's a player that you believe is just him, 106 00:05:27,960 --> 00:05:30,159 Speaker 1: that he's got the tools to be a crazy good 107 00:05:30,160 --> 00:05:33,360 Speaker 1: pass catcher, I wouldn't let a crowded tight end room 108 00:05:33,760 --> 00:05:36,560 Speaker 1: get in the way of drafting that player, unless you're 109 00:05:36,560 --> 00:05:39,400 Speaker 1: looking at something at the extremes like Sam Rouch. In Chicago, 110 00:05:40,279 --> 00:05:44,400 Speaker 1: things are extremely crowded. It's really a unique situation. I'm 111 00:05:44,400 --> 00:05:46,760 Speaker 1: more thinking about like the Rams or the Jags when 112 00:05:46,800 --> 00:05:50,279 Speaker 1: I say something like that, where there's no Colston Loveland, 113 00:05:50,960 --> 00:05:54,159 Speaker 1: chances are the best receiving tight end will still emerge 114 00:05:54,440 --> 00:05:56,640 Speaker 1: because he's gonna have opportunity to run routes and he 115 00:05:56,640 --> 00:05:59,280 Speaker 1: can differentiate himself from there. So I don't see it 116 00:05:59,320 --> 00:06:02,480 Speaker 1: as an automatic bad thing for the incumbents. It just 117 00:06:02,520 --> 00:06:05,760 Speaker 1: depends on the player in the situation, as it usually does. 118 00:06:08,320 --> 00:06:11,440 Speaker 1: This A's questions from Twitter's from at Jeff Blaylock. It says, 119 00:06:11,760 --> 00:06:14,760 Speaker 1: out of many head scratching Day two picks to choose from, 120 00:06:15,120 --> 00:06:19,080 Speaker 1: which for you was the most head scratchiest. So people 121 00:06:19,080 --> 00:06:20,799 Speaker 1: are probably expecting me to go off in the forty 122 00:06:20,880 --> 00:06:23,760 Speaker 1: nine ers picks, but design stribbling. He at least was 123 00:06:23,800 --> 00:06:27,120 Speaker 1: gaining steam during the draft process, and Kyle Shanahan has 124 00:06:27,200 --> 00:06:30,400 Speaker 1: done the round three running back reach many times as 125 00:06:30,440 --> 00:06:33,560 Speaker 1: forty nine Ers head coach. The one for me, the 126 00:06:33,720 --> 00:06:36,960 Speaker 1: craziest pick in my opinion was Caleb Douglas. At least 127 00:06:36,960 --> 00:06:39,680 Speaker 1: was Zavian Thomas, who was another big reach. At least 128 00:06:39,680 --> 00:06:41,559 Speaker 1: you can point to him being a gadgety and special 129 00:06:41,600 --> 00:06:44,680 Speaker 1: teams type player, and it's on a team where there's 130 00:06:44,720 --> 00:06:49,040 Speaker 1: a ton of established talent at pass catcher. With Caleb Douglas, 131 00:06:49,240 --> 00:06:51,760 Speaker 1: you have a death chart that needs serious help at 132 00:06:51,800 --> 00:06:56,240 Speaker 1: wide receiver. And Douglas is just not a good prospect analytically, 133 00:06:56,839 --> 00:06:59,400 Speaker 1: and he's one who is projected to go past pick 134 00:06:59,400 --> 00:07:03,279 Speaker 1: two hundred. Douglas had a twenty fifth percentile breakout scorer, 135 00:07:03,560 --> 00:07:06,640 Speaker 1: a twenty eighth percentile career yards perrout run rate, a 136 00:07:06,720 --> 00:07:09,680 Speaker 1: twenty third percentile first downs per rout run rate, and 137 00:07:09,720 --> 00:07:12,320 Speaker 1: he never saw higher than a twenty percent target share 138 00:07:12,560 --> 00:07:16,400 Speaker 1: in a single collegiate season. He has size and he's 139 00:07:16,400 --> 00:07:19,280 Speaker 1: not too old, but man, it's a really, really rough 140 00:07:19,320 --> 00:07:23,880 Speaker 1: profile to be drafted in round three. I expected Stribbling 141 00:07:24,040 --> 00:07:26,360 Speaker 1: to at least go in round three given pre draft 142 00:07:26,440 --> 00:07:29,160 Speaker 1: draft capital and with the running back class being as 143 00:07:29,160 --> 00:07:31,840 Speaker 1: weak as it was, I'm not overly surprised that we 144 00:07:31,880 --> 00:07:34,160 Speaker 1: saw a random player get Day two Draft capital. 145 00:07:34,560 --> 00:07:34,720 Speaker 2: Now. 146 00:07:34,720 --> 00:07:36,640 Speaker 1: Did I think it was gonna be Kaylon Black? No, 147 00:07:37,280 --> 00:07:40,160 Speaker 1: but Caleb Douglas. I hope he proves me wrong. It's 148 00:07:40,200 --> 00:07:43,200 Speaker 1: just a tough profile to get behind, especially when they 149 00:07:43,240 --> 00:07:45,360 Speaker 1: almost definitely could have gotten him with one of their 150 00:07:45,400 --> 00:07:50,240 Speaker 1: fourth round picks. This next question is also from Twitter, 151 00:07:50,280 --> 00:07:52,920 Speaker 1: is from at d Harwit. It says, how would you 152 00:07:52,920 --> 00:07:56,800 Speaker 1: compare Tyler Schuck's success last year with Bryce Young's flashes 153 00:07:56,800 --> 00:07:59,600 Speaker 1: in the prior year? Are there any signals that would 154 00:07:59,600 --> 00:08:02,280 Speaker 1: point him I'm building on that in year two versus 155 00:08:02,320 --> 00:08:05,040 Speaker 1: it being a mirage. I don't even think that you 156 00:08:05,040 --> 00:08:06,480 Speaker 1: have to look at Bryce Young from a couple of 157 00:08:06,520 --> 00:08:09,640 Speaker 1: years ago. You can literally look at Bryce Young last year, 158 00:08:09,720 --> 00:08:12,000 Speaker 1: his third year in the league, which was also his 159 00:08:12,120 --> 00:08:16,160 Speaker 1: best by most measures. And maybe this is controversial. I 160 00:08:16,200 --> 00:08:18,560 Speaker 1: don't know, because I don't think these quarterbacks are compared 161 00:08:18,680 --> 00:08:22,320 Speaker 1: very often, but you could argue very strongly that Schuck 162 00:08:22,440 --> 00:08:25,880 Speaker 1: was the better quarterback last year than Bryce Young as 163 00:08:25,920 --> 00:08:30,480 Speaker 1: a rookie. I would argue that he was completion percentage 164 00:08:30,480 --> 00:08:33,680 Speaker 1: over expected according to next gen stats. It was plus 165 00:08:33,720 --> 00:08:36,760 Speaker 1: point six percent for Shuck. It was minus point three 166 00:08:36,840 --> 00:08:41,000 Speaker 1: percent for Bryce Young. Yards per attempt, seven point three 167 00:08:41,040 --> 00:08:44,920 Speaker 1: for Shuck, six point three for Young, epa per dropback, 168 00:08:45,280 --> 00:08:48,720 Speaker 1: minus point zero five for Shuck, minus point zero eight 169 00:08:48,920 --> 00:08:51,880 Speaker 1: for Bryce Young. PFF passing grade if you're into that 170 00:08:51,960 --> 00:08:54,400 Speaker 1: kind of thing. Schuck was at seventy three point one 171 00:08:54,440 --> 00:08:58,600 Speaker 1: for passing grade, Young sixty eight point two. I said 172 00:08:58,600 --> 00:09:00,920 Speaker 1: it on the show earlier this week, but I love 173 00:09:00,920 --> 00:09:02,800 Speaker 1: what the Saints are doing, or what they did in 174 00:09:02,800 --> 00:09:06,600 Speaker 1: the draft. You surround your quarterback with as much talent 175 00:09:06,679 --> 00:09:09,680 Speaker 1: as you possibly can find. That way, there are no 176 00:09:09,880 --> 00:09:13,880 Speaker 1: question marks about how good that quarterback is. But Shuck 177 00:09:13,960 --> 00:09:17,080 Speaker 1: actually showed some solid signs last year, and he's got 178 00:09:17,080 --> 00:09:20,200 Speaker 1: more rushing juice than Bryce Young has two. I don't 179 00:09:20,200 --> 00:09:22,320 Speaker 1: think this means Shuck is a sure thing, but I 180 00:09:22,320 --> 00:09:24,320 Speaker 1: do love him as a late round quarterback this year. 181 00:09:24,840 --> 00:09:27,800 Speaker 1: We should always look for year two quarterbacks with mobility, 182 00:09:28,040 --> 00:09:31,120 Speaker 1: and he has that all while getting serious upgrades of 183 00:09:31,120 --> 00:09:34,120 Speaker 1: pass catcher and at running back. There's some promise to 184 00:09:34,160 --> 00:09:39,960 Speaker 1: his overall profile. This best question is from at FPL Mahomes. 185 00:09:40,000 --> 00:09:42,760 Speaker 1: It says, there are so many people creating models in 186 00:09:42,760 --> 00:09:45,600 Speaker 1: the fantasy space today, each think that theirs is great. 187 00:09:45,920 --> 00:09:48,240 Speaker 1: It's hard for people to know what to listen to. 188 00:09:48,760 --> 00:09:51,520 Speaker 1: Would love to hear more in depth about what makes 189 00:09:51,559 --> 00:09:54,120 Speaker 1: a good model in your eyes, the concept of overfitting, 190 00:09:54,440 --> 00:09:57,640 Speaker 1: or how we can evaluate them better, etc. I had 191 00:09:57,640 --> 00:10:00,480 Speaker 1: someone else who said that they echoed this question, So sure, 192 00:10:00,559 --> 00:10:03,240 Speaker 1: I'll get to this question. Let's talk about it first 193 00:10:03,240 --> 00:10:05,800 Speaker 1: and foremost. I'm not a data scientist. I didn't go 194 00:10:05,840 --> 00:10:08,920 Speaker 1: to school for mathematics or statistics. I was always really 195 00:10:08,920 --> 00:10:10,640 Speaker 1: good at math. I was a couple of years ahead 196 00:10:10,679 --> 00:10:13,280 Speaker 1: than the average student in high school humblebrag. And I 197 00:10:13,320 --> 00:10:15,800 Speaker 1: was able to take a few statistics courses across high 198 00:10:15,800 --> 00:10:18,600 Speaker 1: school and college. But like, I've just gotten better at 199 00:10:18,600 --> 00:10:21,680 Speaker 1: this through repetition and learning. When I started my fantasy 200 00:10:21,720 --> 00:10:25,040 Speaker 1: football analysis journey back in twenty eleven twenty twelve, that timeframe, 201 00:10:25,440 --> 00:10:28,080 Speaker 1: my statistical analysis was absolutely trash. 202 00:10:28,160 --> 00:10:28,880 Speaker 2: It was horrible. 203 00:10:29,760 --> 00:10:33,720 Speaker 1: I look back and I cringe. There were so many flaws, 204 00:10:34,160 --> 00:10:36,360 Speaker 1: and I even find flaws to what I did a 205 00:10:36,440 --> 00:10:39,400 Speaker 1: year or two ago. That's gonna happen. In my opinion, though, 206 00:10:39,400 --> 00:10:42,199 Speaker 1: this is about learning and getting better with more data 207 00:10:42,440 --> 00:10:46,160 Speaker 1: and more information. I wanted to preface with that though, 208 00:10:46,280 --> 00:10:49,800 Speaker 1: because my perspective might be different than someone else's perspective 209 00:10:49,840 --> 00:10:53,080 Speaker 1: on a topic like this. With that being said, we've 210 00:10:53,120 --> 00:10:55,839 Speaker 1: seen more and more prospect models pop up over the 211 00:10:55,920 --> 00:10:58,160 Speaker 1: last couple of years, and I think that's going to 212 00:10:58,280 --> 00:11:01,840 Speaker 1: keep exponentially growing with A and that's a really good 213 00:11:01,840 --> 00:11:04,559 Speaker 1: thing from the perspective of getting people to critically think 214 00:11:04,800 --> 00:11:08,240 Speaker 1: and formulate these things. Where it's bad is some of 215 00:11:08,280 --> 00:11:10,400 Speaker 1: the things that you mentioned in this question, like overfitting 216 00:11:10,720 --> 00:11:14,400 Speaker 1: and not really understanding what these models are saying and doing. 217 00:11:15,320 --> 00:11:17,880 Speaker 1: In math, overfitting is when a formula or a model 218 00:11:17,960 --> 00:11:20,760 Speaker 1: fits too closely to its training data and it results 219 00:11:20,800 --> 00:11:24,160 Speaker 1: in a model that just can't actually forecast properly. So, 220 00:11:24,240 --> 00:11:26,960 Speaker 1: for example, we know that pookin Akua was a really 221 00:11:27,000 --> 00:11:30,280 Speaker 1: big Day three hit, some model will use his data. 222 00:11:30,520 --> 00:11:33,880 Speaker 1: It'll use that information when testing it, and it might say, oh, man, 223 00:11:34,040 --> 00:11:36,360 Speaker 1: we've got to find as many pookin akuas as we 224 00:11:36,480 --> 00:11:40,480 Speaker 1: possibly can find. And what that does is focused too 225 00:11:40,559 --> 00:11:44,200 Speaker 1: heavily on the wrong variables without realizing that Nakua is 226 00:11:44,280 --> 00:11:46,960 Speaker 1: probably just a one of one. You can find some 227 00:11:47,000 --> 00:11:49,160 Speaker 1: things that this profile had, like an elite yards pro 228 00:11:49,240 --> 00:11:52,760 Speaker 1: out run rate. But it's very easy for models to 229 00:11:52,800 --> 00:11:58,640 Speaker 1: focus too heavily on those variables, especially when those models 230 00:11:58,640 --> 00:12:02,840 Speaker 1: have smaller sample sizes, like in a hypothetical imagine a 231 00:12:02,920 --> 00:12:05,559 Speaker 1: model had just ten wide receivers that it was analyzing. 232 00:12:06,120 --> 00:12:08,840 Speaker 1: With the result of a Nakua, that model is gonna 233 00:12:08,840 --> 00:12:11,920 Speaker 1: think that every hit needs to look just like him. 234 00:12:12,400 --> 00:12:14,160 Speaker 2: He's playing a huge role. 235 00:12:14,080 --> 00:12:17,200 Speaker 1: In how that model is going to forecast, and it's 236 00:12:17,200 --> 00:12:19,760 Speaker 1: probably going to downplay the role that draft capital has 237 00:12:19,920 --> 00:12:24,920 Speaker 1: when forecasting wide receiver production. That's overfitting. So how do 238 00:12:24,920 --> 00:12:27,480 Speaker 1: you know if a model's overfitting? You don't for sure 239 00:12:28,120 --> 00:12:29,920 Speaker 1: you're not the one who created that model, and you 240 00:12:29,960 --> 00:12:32,320 Speaker 1: probably don't have the formula staring right back at you, 241 00:12:32,679 --> 00:12:34,160 Speaker 1: and you don't have the test set, you don't have 242 00:12:34,200 --> 00:12:37,480 Speaker 1: the data. I'd say one very quick thing to do 243 00:12:37,720 --> 00:12:40,400 Speaker 1: to look at though, is the combination of knowing what 244 00:12:40,400 --> 00:12:43,280 Speaker 1: the model is measuring against versus how far it strays 245 00:12:43,320 --> 00:12:46,440 Speaker 1: away from draft capital. Now, look, every model has a 246 00:12:46,440 --> 00:12:49,280 Speaker 1: different goal. For the ZAP model, I measure against B 247 00:12:49,360 --> 00:12:51,640 Speaker 1: two s or how well a player performed across his 248 00:12:51,679 --> 00:12:54,960 Speaker 1: first three seasons averaging his best two PPR point per 249 00:12:54,960 --> 00:12:58,800 Speaker 1: game seasons some models look at top twelve, top twenty 250 00:12:58,840 --> 00:13:02,240 Speaker 1: four seasons. There's no single way to do it. It's 251 00:13:02,320 --> 00:13:05,599 Speaker 1: just important for a model to have some goal in mind. 252 00:13:06,120 --> 00:13:10,160 Speaker 1: If someone's just eyeballing things, that's a huge problem. But 253 00:13:10,320 --> 00:13:13,040 Speaker 1: generally speaking, you can take that goal and you can 254 00:13:13,080 --> 00:13:15,640 Speaker 1: combine it with how far these players are shifting against 255 00:13:15,720 --> 00:13:19,160 Speaker 1: draft capital. Because we know, for instance, that Day three 256 00:13:19,200 --> 00:13:22,079 Speaker 1: wide receivers there haven't been a lot of hits historically, 257 00:13:22,679 --> 00:13:25,480 Speaker 1: so if a model is training off of history, it's 258 00:13:25,559 --> 00:13:27,640 Speaker 1: more than likely not gonna see a ton. 259 00:13:27,520 --> 00:13:28,440 Speaker 2: Of Day three hits. 260 00:13:29,200 --> 00:13:31,360 Speaker 1: Now, some models out there, they're gonna just look at 261 00:13:31,360 --> 00:13:34,080 Speaker 1: bus let's say, and it might say to just fade 262 00:13:34,080 --> 00:13:36,640 Speaker 1: a player, even if that player has really good draft 263 00:13:36,640 --> 00:13:39,880 Speaker 1: capital because all of his metrics are just totally egregious. 264 00:13:40,280 --> 00:13:42,120 Speaker 1: That's not the way that I would do things, because 265 00:13:42,120 --> 00:13:43,760 Speaker 1: I want to keep more of an open mind about 266 00:13:43,800 --> 00:13:47,679 Speaker 1: being wrong. But okay, whatever, Some people might fade an 267 00:13:47,720 --> 00:13:50,200 Speaker 1: early second round pick for a Day three wide receiver, 268 00:13:50,640 --> 00:13:53,199 Speaker 1: even though we know that Day three wide receivers hit 269 00:13:53,200 --> 00:13:54,480 Speaker 1: at a way worse rate. 270 00:13:54,440 --> 00:13:55,680 Speaker 2: Than early Day two picks. 271 00:13:56,559 --> 00:13:58,839 Speaker 1: What I'm really talking about here is a bigger problem 272 00:13:58,880 --> 00:14:01,000 Speaker 1: when you see a Day three wideide receiver with maybe 273 00:14:01,120 --> 00:14:03,960 Speaker 1: slightly better numbers overall compared to like a Round one 274 00:14:03,960 --> 00:14:07,040 Speaker 1: wide receiver, and that day three wide out is ranked 275 00:14:07,040 --> 00:14:10,840 Speaker 1: above a Round one wide out, especially when people are 276 00:14:10,840 --> 00:14:14,520 Speaker 1: looking at things in hindsight, not forecasting. But when you 277 00:14:14,559 --> 00:14:17,040 Speaker 1: look at someone sharing the results of their model and 278 00:14:17,080 --> 00:14:20,000 Speaker 1: you see all these day three wide receivers ranked ahead 279 00:14:20,000 --> 00:14:24,360 Speaker 1: of what are admittedly Round one bus at the very least, 280 00:14:24,560 --> 00:14:27,440 Speaker 1: I would message that person and want to learn more, 281 00:14:27,840 --> 00:14:30,560 Speaker 1: because that's a really interesting result. And when you get 282 00:14:30,600 --> 00:14:33,920 Speaker 1: really really interesting results that's stray that far away from 283 00:14:33,920 --> 00:14:35,080 Speaker 1: what the NFL is doing. 284 00:14:35,480 --> 00:14:36,400 Speaker 2: I want to learn more. 285 00:14:37,160 --> 00:14:39,400 Speaker 1: It's not necessarily that they're wrong, it's just that you 286 00:14:39,400 --> 00:14:43,000 Speaker 1: should investigate more. And that leads me to this next part. 287 00:14:43,120 --> 00:14:46,160 Speaker 1: I think whoever is publishing the model, whoever's talking about it, 288 00:14:46,560 --> 00:14:48,960 Speaker 1: if they're talking about it without humility, that's a big 289 00:14:49,000 --> 00:14:52,720 Speaker 1: red flag. Anyone with experience with this stuff knows that 290 00:14:52,720 --> 00:14:55,560 Speaker 1: they're going to be humbled really quickly. This stuff is 291 00:14:55,600 --> 00:15:00,080 Speaker 1: really hard to forecast. Perfection is impossible. And if someone 292 00:15:00,280 --> 00:15:03,960 Speaker 1: out there constantly touting how good their model is, how 293 00:15:04,000 --> 00:15:07,600 Speaker 1: accurate it is, and they're only showing partial results and 294 00:15:07,640 --> 00:15:10,280 Speaker 1: they're not giving you any glimpse at all as to 295 00:15:10,320 --> 00:15:13,280 Speaker 1: how it's calculated or what the inputs are. That is 296 00:15:13,360 --> 00:15:17,200 Speaker 1: a red flag. My overall sentiment, though, is that it's 297 00:15:17,280 --> 00:15:20,800 Speaker 1: awesome to see more models in the fantasy space. I've 298 00:15:20,880 --> 00:15:23,160 Speaker 1: learned a lot from other people who are also building 299 00:15:23,200 --> 00:15:26,120 Speaker 1: prospect models. I mean, just listen to last week's Perspectives 300 00:15:26,120 --> 00:15:30,000 Speaker 1: episode with Addison Hayes. He's got a totally different methodology 301 00:15:30,160 --> 00:15:33,440 Speaker 1: than how I approach things, but it's a thoughtful methodology 302 00:15:33,720 --> 00:15:36,640 Speaker 1: and that's what's most important. So do your best to 303 00:15:36,640 --> 00:15:40,880 Speaker 1: sniff out the snake oil salesman, because unfortunately those people 304 00:15:40,960 --> 00:15:46,000 Speaker 1: do exist in this space. The last question this week 305 00:15:46,080 --> 00:15:48,840 Speaker 1: is from Patreon. It's from Norm. It says, when you're 306 00:15:48,880 --> 00:15:52,040 Speaker 1: doing your rookie rankings, what does your methodology look like 307 00:15:52,240 --> 00:15:55,680 Speaker 1: When a player you like slides quite a bit, Bell 308 00:15:55,800 --> 00:15:59,240 Speaker 1: from Miami as an example, Everyone calls it a great value, 309 00:15:59,480 --> 00:16:01,280 Speaker 1: but I can't shake the voice in my head that 310 00:16:01,360 --> 00:16:04,800 Speaker 1: said every single team passed on him three, four, and 311 00:16:04,840 --> 00:16:09,600 Speaker 1: sometimes five times before he was selected. So to reiterate 312 00:16:09,680 --> 00:16:11,320 Speaker 1: some of the stuff that I talked about this week, 313 00:16:11,560 --> 00:16:13,480 Speaker 1: just so we're all on the same page. In the 314 00:16:13,560 --> 00:16:17,160 Speaker 1: ZAP Model database. I have players projected draft capital, which 315 00:16:17,200 --> 00:16:19,560 Speaker 1: is from NFL mock Draft database, and then I have 316 00:16:19,680 --> 00:16:23,320 Speaker 1: his actual draft capital. I then use Chase Stewart's NFL 317 00:16:23,400 --> 00:16:26,480 Speaker 1: Draft pick value chart, and I say, okay, at pick fifty, 318 00:16:26,560 --> 00:16:29,160 Speaker 1: let's say this is the value of that pick. At 319 00:16:29,160 --> 00:16:32,640 Speaker 1: pick sixty, this is the value. That way, I can 320 00:16:32,680 --> 00:16:36,760 Speaker 1: subtract actual real value from one pick to another when 321 00:16:36,800 --> 00:16:41,120 Speaker 1: comparing projected draft capital to actual draft capital, and that'll 322 00:16:41,120 --> 00:16:43,640 Speaker 1: tell me how big of a reach that player was 323 00:16:43,680 --> 00:16:45,960 Speaker 1: for that team. And I do it this way because 324 00:16:46,000 --> 00:16:48,840 Speaker 1: looking strictly at raw picks doesn't tell us that much. 325 00:16:49,240 --> 00:16:51,600 Speaker 1: If a player's drafted tenth overall and he was expected 326 00:16:51,640 --> 00:16:54,560 Speaker 1: to go third overall, that's a seven pick difference. If 327 00:16:54,560 --> 00:16:56,920 Speaker 1: he's drafted one hundred and fiftieth overall but he was 328 00:16:56,960 --> 00:16:59,360 Speaker 1: expected to go one hundred and fortieth overall, that's a 329 00:16:59,400 --> 00:17:03,240 Speaker 1: ten pick diff difference. But clearly the player going tenth overall, 330 00:17:03,520 --> 00:17:06,439 Speaker 1: that's the bigger value, just given how much those picks 331 00:17:06,440 --> 00:17:10,600 Speaker 1: mean from a value perspective, how much they're worth. So 332 00:17:10,760 --> 00:17:13,800 Speaker 1: as we've learned the biggest reaches by using this methodology, 333 00:17:14,000 --> 00:17:16,359 Speaker 1: they've not been great for fantasy Football. I talked about 334 00:17:16,359 --> 00:17:20,520 Speaker 1: that earlier this week. I'm measuring this via B two s, which, 335 00:17:20,600 --> 00:17:23,240 Speaker 1: like I said earlier, it's a player's best two seasons 336 00:17:23,240 --> 00:17:26,159 Speaker 1: and PPR points per game across his first three years. 337 00:17:26,800 --> 00:17:30,000 Speaker 1: That's what the ZAP model is trying to project from 338 00:17:30,040 --> 00:17:32,399 Speaker 1: twenty sixteen, which is when this data dates back to. 339 00:17:32,760 --> 00:17:35,159 Speaker 1: But from twenty sixteen through twenty twenty three, we've had 340 00:17:35,200 --> 00:17:38,080 Speaker 1: fifteen wide receivers who saw a loss of four points 341 00:17:38,160 --> 00:17:40,680 Speaker 1: or more in draft capital value. That's according to the 342 00:17:40,760 --> 00:17:45,240 Speaker 1: Chase Stewart chart. It's just projected draft capital minus actual 343 00:17:45,320 --> 00:17:48,960 Speaker 1: draft capital. In this case, these are the fifteen biggest 344 00:17:48,960 --> 00:17:52,960 Speaker 1: reaches of the position. Only three of those players provided 345 00:17:53,000 --> 00:17:55,120 Speaker 1: a point or more and B two s over expected. 346 00:17:55,720 --> 00:17:57,720 Speaker 1: I know this can be kind of confusing, but essentially 347 00:17:57,760 --> 00:18:00,919 Speaker 1: every draft slot has some sort of expense B two s. 348 00:18:01,359 --> 00:18:04,520 Speaker 1: If a player's fiftieth overall, they'd be expected to score X, 349 00:18:04,560 --> 00:18:07,080 Speaker 1: and B two s if they're seventieth overall, that's X 350 00:18:07,160 --> 00:18:11,280 Speaker 1: minus y. It's less because they're drafted later. Only three 351 00:18:11,320 --> 00:18:15,320 Speaker 1: of the fifteen biggest reaches since twenty sixteen have truly 352 00:18:15,480 --> 00:18:16,880 Speaker 1: outperformed B two s. 353 00:18:17,280 --> 00:18:18,560 Speaker 2: That's a horrible hit rate. 354 00:18:19,400 --> 00:18:22,120 Speaker 1: Now, when you look at the opposite direction, the fifteen 355 00:18:22,160 --> 00:18:25,760 Speaker 1: biggest values, you see six out of the fifteen, giving 356 00:18:25,760 --> 00:18:27,960 Speaker 1: you a point or more over expected, and B two 357 00:18:28,080 --> 00:18:31,600 Speaker 1: s expectation. The median outcome for this group is one 358 00:18:31,640 --> 00:18:34,040 Speaker 1: point one to three PPR points per game over expected. 359 00:18:34,520 --> 00:18:37,040 Speaker 1: The median outcome for the reaches is like minus a 360 00:18:37,080 --> 00:18:40,200 Speaker 1: point or so. This doesn't mean that we should really 361 00:18:40,240 --> 00:18:42,840 Speaker 1: really draft all of the values that we can get, though. 362 00:18:43,320 --> 00:18:45,479 Speaker 1: We need to keep in mind that we're looking at 363 00:18:45,480 --> 00:18:49,640 Speaker 1: this versus expectation. When a player is a value, that 364 00:18:49,680 --> 00:18:51,960 Speaker 1: player is more likely to be a Day three pick, 365 00:18:52,840 --> 00:18:55,920 Speaker 1: and from that perspective, it's easier to outperform expectation. It 366 00:18:55,960 --> 00:18:59,000 Speaker 1: doesn't take that much. So instead of looking at all 367 00:18:59,000 --> 00:19:01,560 Speaker 1: the wide receivers in the model, let's just look at 368 00:19:01,560 --> 00:19:04,400 Speaker 1: top one hundred picks. If we look at just top 369 00:19:04,440 --> 00:19:06,919 Speaker 1: one hundred picks, players with top one hundred draft capital, 370 00:19:07,200 --> 00:19:09,879 Speaker 1: the correlation between the value gained by a team with 371 00:19:09,920 --> 00:19:13,080 Speaker 1: a pick versus how will those picks outperform expectation in 372 00:19:13,080 --> 00:19:18,159 Speaker 1: fantasy football, there's very very minor correlation. I'm talking like 373 00:19:18,200 --> 00:19:20,280 Speaker 1: in our value of point one five, not even our 374 00:19:20,359 --> 00:19:24,760 Speaker 1: squared it's not super predictive, but it does tell us 375 00:19:24,960 --> 00:19:29,120 Speaker 1: that in general, at a really small rate, values are 376 00:19:29,160 --> 00:19:33,480 Speaker 1: better than reaches. But it's minuscule, and what's really doing 377 00:19:33,480 --> 00:19:37,360 Speaker 1: the heavy lifting here is the reaches, not the huge values. 378 00:19:38,280 --> 00:19:40,840 Speaker 1: I put a thread together on Thursday on Twitter about this. 379 00:19:41,320 --> 00:19:44,080 Speaker 1: But there's another way to look at all this. I'm 380 00:19:44,119 --> 00:19:46,480 Speaker 1: looking at one hundred and six top one hundred wide 381 00:19:46,480 --> 00:19:49,520 Speaker 1: receivers in this sample. Let's split those one hundred and 382 00:19:49,520 --> 00:19:53,000 Speaker 1: six wide receivers into four groups. Group one is the 383 00:19:53,040 --> 00:19:57,000 Speaker 1: biggest values and it includes twenty six wide receivers. Group 384 00:19:57,040 --> 00:20:00,440 Speaker 1: two that's the second best values. It includes the twenty 385 00:20:00,520 --> 00:20:04,320 Speaker 1: seven wide receivers. Group three is next that has twenty 386 00:20:04,320 --> 00:20:07,240 Speaker 1: seven wide receivers, and then Group four is last the 387 00:20:07,280 --> 00:20:11,879 Speaker 1: biggest reaches, which also includes twenty six wide receivers. We 388 00:20:11,960 --> 00:20:17,120 Speaker 1: can call these buckets huge values, minor values, minor reaches, 389 00:20:17,440 --> 00:20:21,160 Speaker 1: and huge reaches. Let's look at the percentage of wide 390 00:20:21,160 --> 00:20:24,720 Speaker 1: receivers who played above expected versus their expected B two 391 00:20:24,920 --> 00:20:28,680 Speaker 1: s and let's start with a huge values group. Fifty 392 00:20:28,720 --> 00:20:33,440 Speaker 1: four percent of them outperform their expectation. The next two groups, though, 393 00:20:33,600 --> 00:20:37,240 Speaker 1: were at sixty three percent, which means when players were 394 00:20:37,240 --> 00:20:41,239 Speaker 1: either minor values or minor reaches, they did better than 395 00:20:41,280 --> 00:20:44,879 Speaker 1: the players who were huge values, and then with the 396 00:20:44,960 --> 00:20:48,000 Speaker 1: huge reaches the final group, just forty two percent of 397 00:20:48,040 --> 00:20:51,640 Speaker 1: them played above expected by far the worst of the subset. 398 00:20:52,400 --> 00:20:53,960 Speaker 1: You can look at this from the perspective of these 399 00:20:53,960 --> 00:20:57,159 Speaker 1: groups outperforming expectation by one or two points and B 400 00:20:57,240 --> 00:21:00,200 Speaker 1: two s as well. Overall, it all stays the same. 401 00:21:00,800 --> 00:21:03,479 Speaker 1: The huge values are better than the huge reaches, but 402 00:21:03,520 --> 00:21:05,959 Speaker 1: the players in the middle they're the ones who are 403 00:21:05,960 --> 00:21:09,720 Speaker 1: winning out most. The big values give us a median 404 00:21:09,840 --> 00:21:12,360 Speaker 1: value of plus point five to one and B two s, 405 00:21:12,800 --> 00:21:15,879 Speaker 1: so they on average outperform B two S expectation by 406 00:21:15,880 --> 00:21:18,679 Speaker 1: about half a point. The next two groups, though, the 407 00:21:18,680 --> 00:21:22,320 Speaker 1: middle ones were at two point nine and three point zero. 408 00:21:22,840 --> 00:21:26,560 Speaker 1: They were awesome against expectation, and then the huge reach 409 00:21:26,600 --> 00:21:31,119 Speaker 1: group they were at minus one point. Now, those numbers 410 00:21:31,119 --> 00:21:32,919 Speaker 1: don't add up to zero for a lot of reasons, 411 00:21:32,920 --> 00:21:34,960 Speaker 1: but one of them is because the formula is looking 412 00:21:35,000 --> 00:21:37,840 Speaker 1: at the entire draft, not just top one hundred picks. 413 00:21:38,400 --> 00:21:42,119 Speaker 1: But this is all still relative. What this data tells 414 00:21:42,200 --> 00:21:45,159 Speaker 1: us is that reaches are the worst. The guys who 415 00:21:45,200 --> 00:21:49,560 Speaker 1: are reached for at an extreme rate but huge values, 416 00:21:49,720 --> 00:21:54,280 Speaker 1: they're actually second worst. The best players versus expectation have 417 00:21:54,400 --> 00:21:57,200 Speaker 1: been the ones who were minor values are minor reaches. 418 00:21:58,160 --> 00:22:01,159 Speaker 1: And this makes so much sense. Obviously, when a player's 419 00:22:01,200 --> 00:22:03,159 Speaker 1: a reach, a team is going against the wisdom of 420 00:22:03,200 --> 00:22:06,600 Speaker 1: the crowds. That player is probably less talented, so he's 421 00:22:06,640 --> 00:22:10,399 Speaker 1: not great at performing to expectation. But when a player 422 00:22:10,480 --> 00:22:12,480 Speaker 1: is a huge value, like you asked in this question, 423 00:22:12,880 --> 00:22:15,600 Speaker 1: that means the player is passed on by team after 424 00:22:15,680 --> 00:22:19,240 Speaker 1: team after team, over and over again. That's not necessarily 425 00:22:19,280 --> 00:22:22,960 Speaker 1: a good thing. Something must be up, and generally speaking, 426 00:22:23,320 --> 00:22:25,640 Speaker 1: those players have technically been worse than the guys who 427 00:22:25,640 --> 00:22:27,640 Speaker 1: are just slight reaches and slight values. 428 00:22:28,640 --> 00:22:30,960 Speaker 2: Chris Bell falls in that huge value bucket. 429 00:22:31,600 --> 00:22:33,439 Speaker 1: I don't think it means that you avoid him, but 430 00:22:33,480 --> 00:22:35,719 Speaker 1: I don't think that him being a huge NFL value 431 00:22:35,880 --> 00:22:39,159 Speaker 1: should be considered a good thing. From my perspective with 432 00:22:39,200 --> 00:22:41,720 Speaker 1: Chris Bell, I don't mind him. I see the ceiling vision, 433 00:22:42,240 --> 00:22:44,479 Speaker 1: but I think people need to recognize that seriously a 434 00:22:44,560 --> 00:22:47,920 Speaker 1: highly volatile profile. I'll take that on in rookie drafts, 435 00:22:48,000 --> 00:22:51,760 Speaker 1: especially this year. Rookie drafts are weak, and I'll chase ceiling, 436 00:22:52,560 --> 00:22:55,239 Speaker 1: but Bell had a completely neutral draft capital delta. His 437 00:22:55,280 --> 00:22:58,320 Speaker 1: breakout score wasn't terrible, and honestly, that's not super easy 438 00:22:58,359 --> 00:23:00,639 Speaker 1: to find in this year's class, and I would imagine 439 00:23:00,680 --> 00:23:03,040 Speaker 1: his draft capital would have been higher had he not 440 00:23:03,080 --> 00:23:06,400 Speaker 1: torn his ACL. But it's a high variance profile, even 441 00:23:06,440 --> 00:23:08,440 Speaker 1: if it's a good profile compared to a lot of 442 00:23:08,440 --> 00:23:11,320 Speaker 1: the wide receivers in this class. So I think your 443 00:23:11,359 --> 00:23:14,239 Speaker 1: fear is very valid. When a team decides to not 444 00:23:14,280 --> 00:23:16,600 Speaker 1: pick a wide receiver as high as consensus has him, 445 00:23:16,840 --> 00:23:19,000 Speaker 1: it doesn't mean he's bound to perform well. It does 446 00:23:19,040 --> 00:23:21,879 Speaker 1: not mean that he's a great pick. It means there 447 00:23:21,960 --> 00:23:24,280 Speaker 1: might be something wrong to his profile that the NFL 448 00:23:24,359 --> 00:23:27,520 Speaker 1: knows and that we don't. That's why those players, while 449 00:23:27,520 --> 00:23:30,240 Speaker 1: they're not as bad as the huge reaches, there's still 450 00:23:30,320 --> 00:23:33,280 Speaker 1: scarier bets than the players who are just minor values 451 00:23:33,440 --> 00:23:36,640 Speaker 1: and minor reaches. That's it for today's show, though, thanks 452 00:23:36,640 --> 00:23:38,640 Speaker 1: to all of you for listening. If you had subscribed 453 00:23:38,640 --> 00:23:41,040 Speaker 1: to the Late Round Fantasy Football podcast, make sure you 454 00:23:41,080 --> 00:23:43,320 Speaker 1: are by starching for it pretty much anywhere podcast can 455 00:23:43,359 --> 00:23:45,359 Speaker 1: be found, and to follow me on Twitter and on 456 00:23:45,400 --> 00:23:48,720 Speaker 1: Blue Sky at Late Round QB. Thanks for listening, this 457 00:23:48,760 --> 00:23:52,000 Speaker 1: week everyone, I greatly appreciate it, and I greatly appreciate 458 00:23:52,080 --> 00:23:54,000 Speaker 1: all the support with the Late Round Prospect Guide. It 459 00:23:54,040 --> 00:23:57,119 Speaker 1: seriously means so much. I hope you crush your rookie drafts. 460 00:23:57,200 --> 00:23:58,919 Speaker 1: I hope you enjoy that prospect Guide. 461 00:23:59,040 --> 00:24:01,120 Speaker 2: Have a great weekend. I'll catch you next week.