1 00:00:03,400 --> 00:00:06,760 Speaker 1: Hey, everyone, welcome to the first ever episode of the 2 00:00:06,800 --> 00:00:10,320 Speaker 1: New York Herb podcast. I'm your host, Olivia Landis. I 3 00:00:10,360 --> 00:00:14,200 Speaker 1: am so incredibly excited to bring you engaging and unique 4 00:00:14,240 --> 00:00:17,320 Speaker 1: content this year, never before done here at the New 5 00:00:17,400 --> 00:00:21,160 Speaker 1: York Jets. I really wanted to create a platform where 6 00:00:21,200 --> 00:00:24,320 Speaker 1: we could highlight women and their roles in the NFL 7 00:00:24,720 --> 00:00:27,880 Speaker 1: while at the same time, of course, talking about Jets football. 8 00:00:28,240 --> 00:00:30,440 Speaker 1: So I'm so excited for this season. Thank you guys 9 00:00:30,440 --> 00:00:33,880 Speaker 1: for tuning in and listening. I cannot wait, and without 10 00:00:33,920 --> 00:00:37,600 Speaker 1: further ado, I am so excited to introduce my very 11 00:00:37,680 --> 00:00:42,760 Speaker 1: first guest, NFL network analytics expert and Jets three sixty 12 00:00:42,880 --> 00:00:46,239 Speaker 1: content contributor, Cynthia Freeland. Cynthia, thank you so much for 13 00:00:46,320 --> 00:00:49,920 Speaker 1: joining me. Congratulations. This is gonna be so much fun. 14 00:00:49,960 --> 00:00:51,840 Speaker 1: I'm really proud of you. I can't wait to see 15 00:00:51,920 --> 00:00:54,120 Speaker 1: what you come up with, and I am so honored 16 00:00:54,120 --> 00:00:55,800 Speaker 1: to be your first guest. Thank you so much for 17 00:00:55,840 --> 00:00:58,000 Speaker 1: having me. Thank you so much, Cynthia, that that really 18 00:00:58,040 --> 00:01:01,120 Speaker 1: means a lot. I think that's that is exactly why 19 00:01:01,120 --> 00:01:03,840 Speaker 1: I'm doing it, because we need as women so much 20 00:01:03,880 --> 00:01:07,880 Speaker 1: support here in the NFL, and there's a lot of us. 21 00:01:07,920 --> 00:01:12,840 Speaker 1: Our numbers are growing, which is so exciting. Absolutely, it's 22 00:01:12,880 --> 00:01:14,880 Speaker 1: a lot of fun to see the growth of you know, 23 00:01:15,080 --> 00:01:18,160 Speaker 1: different roles for women too, So not just one traditional role. 24 00:01:18,200 --> 00:01:20,360 Speaker 1: Now you're doing all of these different things across all 25 00:01:20,400 --> 00:01:23,440 Speaker 1: these different platforms and it's great. So it's just let's 26 00:01:23,440 --> 00:01:26,720 Speaker 1: just keep it rolling, Cindy. I want to dive into 27 00:01:27,400 --> 00:01:30,319 Speaker 1: you as a person and what your exact role is 28 00:01:30,400 --> 00:01:34,240 Speaker 1: in the league NFL analytics expert. A lot of people 29 00:01:34,319 --> 00:01:36,480 Speaker 1: might hear that and not know exactly what that means. 30 00:01:36,760 --> 00:01:39,920 Speaker 1: So can you explain to the general audience what it 31 00:01:40,000 --> 00:01:42,679 Speaker 1: is that you do for the NFL network. So in 32 00:01:42,720 --> 00:01:45,120 Speaker 1: the same way that you have like front office people 33 00:01:45,160 --> 00:01:47,800 Speaker 1: that are going TV, or you have former players that 34 00:01:48,000 --> 00:01:50,960 Speaker 1: go on TV to try to tell stories bringing their perspective, 35 00:01:51,320 --> 00:01:56,400 Speaker 1: I'm representing the perspective of analytics staff, so deep data insights. 36 00:01:56,440 --> 00:01:59,600 Speaker 1: How do you get an edge against your opponents? What 37 00:01:59,640 --> 00:02:03,120 Speaker 1: are your coaches and front office staff looking at in 38 00:02:03,240 --> 00:02:06,160 Speaker 1: order to create the best possible scenario for the Jets. 39 00:02:06,160 --> 00:02:08,360 Speaker 1: The Jets have an analytics department. Every team has an 40 00:02:08,400 --> 00:02:12,600 Speaker 1: analytics department. So I'm showing that view to the public 41 00:02:12,680 --> 00:02:14,840 Speaker 1: so we can see, all right, what is the strategy. 42 00:02:15,120 --> 00:02:17,840 Speaker 1: How do you break down all these deep data science 43 00:02:18,240 --> 00:02:22,359 Speaker 1: things and make sure you're seeing the trends and all 44 00:02:22,440 --> 00:02:25,080 Speaker 1: the different ways to analyze the game, just like a 45 00:02:25,120 --> 00:02:27,440 Speaker 1: coach does, just like the front offices. That's more around 46 00:02:27,480 --> 00:02:29,920 Speaker 1: draft season. But how do you bring in the right players, 47 00:02:30,040 --> 00:02:31,640 Speaker 1: how do you pay them the right amount of money? 48 00:02:31,800 --> 00:02:34,640 Speaker 1: How do you win football games? All that through the 49 00:02:34,639 --> 00:02:36,720 Speaker 1: perspective of math. It's kind of like a scout. A 50 00:02:36,760 --> 00:02:39,040 Speaker 1: scout gives you for the perspective of their eye and 51 00:02:39,080 --> 00:02:41,280 Speaker 1: going to watch them in college games and how they 52 00:02:41,280 --> 00:02:44,280 Speaker 1: can translate to the NFL. Well, this is how do 53 00:02:44,320 --> 00:02:47,079 Speaker 1: you use deep insights about trends to get that edge 54 00:02:47,080 --> 00:02:51,040 Speaker 1: against your opponents. I'm so happy that you you dove 55 00:02:51,040 --> 00:02:54,000 Speaker 1: a little bit into how you're similar to a scout. 56 00:02:54,080 --> 00:02:56,840 Speaker 1: Like you said, you're analyzing all of the data and 57 00:02:56,880 --> 00:03:00,400 Speaker 1: trying to incorporate how exactly teams can use that and 58 00:03:00,440 --> 00:03:03,120 Speaker 1: what it means for them. We actually talked about this 59 00:03:03,600 --> 00:03:07,000 Speaker 1: maybe a few months ago, and you were telling me 60 00:03:07,080 --> 00:03:09,400 Speaker 1: something so interesting that I didn't even know this was 61 00:03:09,480 --> 00:03:12,000 Speaker 1: part of your job, But you said you work very 62 00:03:12,040 --> 00:03:16,040 Speaker 1: closely with scouts and coaches to let them know, Hey, 63 00:03:16,080 --> 00:03:17,760 Speaker 1: these are the things that you need to look out 64 00:03:17,800 --> 00:03:20,040 Speaker 1: for if you want to find success in a bean 65 00:03:20,160 --> 00:03:23,000 Speaker 1: c areas. Can you dive a little bit deeper into 66 00:03:23,240 --> 00:03:26,800 Speaker 1: what exactly that means? Yeah, so a lot of us 67 00:03:26,840 --> 00:03:30,000 Speaker 1: have preconceived notions and biases. We all do, and math 68 00:03:30,120 --> 00:03:32,960 Speaker 1: can help kind of remove some of those. So what 69 00:03:33,040 --> 00:03:36,120 Speaker 1: you can say is, for example, let's say you're deciding 70 00:03:36,160 --> 00:03:38,600 Speaker 1: between three running backs to add in free agency and 71 00:03:38,640 --> 00:03:40,640 Speaker 1: they're all around the same price. Let's just you know, 72 00:03:40,720 --> 00:03:43,080 Speaker 1: these are the prices. You could say, let's do a 73 00:03:43,120 --> 00:03:46,160 Speaker 1: deep dive into these running backs that bit our scheme. Well, 74 00:03:46,200 --> 00:03:48,400 Speaker 1: we like to run outside the tackles a lot, we 75 00:03:48,520 --> 00:03:50,600 Speaker 1: like to do all of these different things. So you 76 00:03:50,640 --> 00:03:53,400 Speaker 1: create sort of a resume, and of those resumes, you 77 00:03:53,440 --> 00:03:57,120 Speaker 1: can look back through the past of the experience that 78 00:03:57,200 --> 00:03:59,600 Speaker 1: certain running backs have in the league and say they've 79 00:03:59,600 --> 00:04:01,760 Speaker 1: been really good at rushing outside attackle they're a better 80 00:04:01,800 --> 00:04:05,480 Speaker 1: fit for the Jets than say someone else, for example. Right, 81 00:04:05,520 --> 00:04:08,880 Speaker 1: So it's really about finding the right attributes of a player. 82 00:04:09,040 --> 00:04:11,720 Speaker 1: So you take into consideration the situations that they've been 83 00:04:11,760 --> 00:04:14,360 Speaker 1: in and kind of the context of the score. That 84 00:04:14,600 --> 00:04:15,960 Speaker 1: is it late in the game, is early in the 85 00:04:16,000 --> 00:04:18,280 Speaker 1: game do they run on first down? For example? And 86 00:04:18,320 --> 00:04:20,880 Speaker 1: you put all those things together, you get all these insights, 87 00:04:20,920 --> 00:04:23,800 Speaker 1: all of these snaps together to fit a profile. So 88 00:04:23,960 --> 00:04:26,800 Speaker 1: one fit player does fit better than others. And in 89 00:04:26,839 --> 00:04:28,440 Speaker 1: just the same way as scout looks at it through 90 00:04:28,480 --> 00:04:31,040 Speaker 1: their eyes, you can use math to help you kind 91 00:04:31,040 --> 00:04:33,240 Speaker 1: of you know, figure out, oh, is this one a 92 00:04:33,279 --> 00:04:35,440 Speaker 1: little better than this one, and which one really fits 93 00:04:35,440 --> 00:04:37,599 Speaker 1: our scheme and what we'd like to execute the most. 94 00:04:37,760 --> 00:04:39,800 Speaker 1: And the better the fit, the better the opportunity to 95 00:04:39,880 --> 00:04:43,039 Speaker 1: execute those plays that the coach wants to run. I 96 00:04:43,120 --> 00:04:46,800 Speaker 1: have to ask, because the coaches really listen to you, 97 00:04:46,920 --> 00:04:50,000 Speaker 1: and you have all of this analysis, analytics, all of 98 00:04:50,000 --> 00:04:53,800 Speaker 1: this data that helps them. How satisfying is it when 99 00:04:54,160 --> 00:04:56,160 Speaker 1: some of the coaches take your advice and take the 100 00:04:56,240 --> 00:04:58,839 Speaker 1: data that you've provided them and you see it play 101 00:04:58,880 --> 00:05:02,560 Speaker 1: out on the field success fully. How how gratifying is that? 102 00:05:03,160 --> 00:05:06,119 Speaker 1: You know, it's incredibly gratifying. It's one of those things 103 00:05:06,120 --> 00:05:08,039 Speaker 1: that look, at the end of the day, you just 104 00:05:08,080 --> 00:05:10,039 Speaker 1: want to be useful and helpful, and you want to, 105 00:05:10,120 --> 00:05:13,400 Speaker 1: you know, move the ball forward literally and figuratively, so 106 00:05:13,520 --> 00:05:16,320 Speaker 1: being able to help contribute to that, you feel like 107 00:05:16,360 --> 00:05:18,640 Speaker 1: the small team. It can be this tiny of a part, 108 00:05:18,720 --> 00:05:21,680 Speaker 1: but you feel like this small thing you've done actually 109 00:05:21,839 --> 00:05:23,640 Speaker 1: has made a difference and you get to see it 110 00:05:23,800 --> 00:05:27,240 Speaker 1: and that's really amazing. So it is incredibly gratifying, and 111 00:05:27,279 --> 00:05:28,960 Speaker 1: then it makes you want to do more. I use 112 00:05:29,040 --> 00:05:30,240 Speaker 1: I use a lot of like I read a lot 113 00:05:30,240 --> 00:05:34,480 Speaker 1: of computer code, which is very incredibly nerdy, Like I 114 00:05:34,920 --> 00:05:38,239 Speaker 1: will own my nerdiness. But it's interesting to see how 115 00:05:38,400 --> 00:05:40,160 Speaker 1: some of the things that you know you've mapped out 116 00:05:40,200 --> 00:05:43,640 Speaker 1: on computer vision actually work out in real life. So 117 00:05:43,760 --> 00:05:46,560 Speaker 1: it's it's very very cool, very cool. How is this 118 00:05:46,680 --> 00:05:50,400 Speaker 1: role going to develop within the league? Because please correct 119 00:05:50,440 --> 00:05:53,440 Speaker 1: me if I'm wrong, but it's fairly new. Correct this 120 00:05:54,440 --> 00:05:59,560 Speaker 1: implementing all of this analytics into coaches decision making? How 121 00:05:59,600 --> 00:06:02,039 Speaker 1: do you this developing with it being fairly new, but 122 00:06:02,080 --> 00:06:05,520 Speaker 1: it's taking off. People are really using this. So the 123 00:06:05,680 --> 00:06:08,800 Speaker 1: a word analytics because people won't claim that it is 124 00:06:08,839 --> 00:06:12,520 Speaker 1: that it's been hidden in quality control or in research 125 00:06:12,640 --> 00:06:14,520 Speaker 1: or you know, they've had different names for it for 126 00:06:14,560 --> 00:06:17,760 Speaker 1: a long time. The really the big difference is the 127 00:06:17,880 --> 00:06:21,320 Speaker 1: advent of using lots of big data and looking at 128 00:06:21,320 --> 00:06:23,760 Speaker 1: snaps kind of in a in a very long, Like 129 00:06:23,800 --> 00:06:26,080 Speaker 1: I can look at ten years worth of season data 130 00:06:26,240 --> 00:06:29,680 Speaker 1: very quickly. And that's really what's made a big difference, right, 131 00:06:29,720 --> 00:06:32,320 Speaker 1: It's the ability to use all this code and the 132 00:06:32,440 --> 00:06:35,760 Speaker 1: power that computers and computing has in order to give 133 00:06:35,800 --> 00:06:39,640 Speaker 1: you a lot more information. So your sample size is larger. 134 00:06:39,920 --> 00:06:42,040 Speaker 1: In football, we only play sixteen games this season or 135 00:06:42,200 --> 00:06:44,559 Speaker 1: you know, before the postseason, and of those two hundred 136 00:06:44,600 --> 00:06:47,280 Speaker 1: fifties six games, you're not getting a ton of information. 137 00:06:47,320 --> 00:06:50,120 Speaker 1: Whereas basketball, baseball, they play way more games, so you 138 00:06:50,240 --> 00:06:53,240 Speaker 1: just get way more snaps, right, you get way more pitches, 139 00:06:53,320 --> 00:06:57,680 Speaker 1: you get way more possessions in basketball. And because of that, 140 00:06:58,240 --> 00:07:01,159 Speaker 1: being able to use years in years worth of football 141 00:07:01,240 --> 00:07:03,919 Speaker 1: data and be able to track okay, it's like which 142 00:07:04,000 --> 00:07:06,480 Speaker 1: this coach worked under this coach, what kind of scheme, 143 00:07:06,520 --> 00:07:09,280 Speaker 1: like who's their mentor being able to create those deep 144 00:07:09,320 --> 00:07:12,160 Speaker 1: insights gives you a bigger edge. But you can't do 145 00:07:12,200 --> 00:07:14,160 Speaker 1: it just from watching field because it would take you 146 00:07:14,480 --> 00:07:17,680 Speaker 1: a very long time to watch all of those games. 147 00:07:17,840 --> 00:07:20,640 Speaker 1: So the computing gives you that horsepower. That's really what's 148 00:07:20,640 --> 00:07:26,080 Speaker 1: supercharged it. That's so incredible. I hope everybody listening just 149 00:07:26,200 --> 00:07:29,600 Speaker 1: took all of that information in and realizes how critical 150 00:07:29,880 --> 00:07:33,480 Speaker 1: your role is in the league. Make your kids learn 151 00:07:33,560 --> 00:07:37,560 Speaker 1: how to write computer code. That's what it's the future. 152 00:07:38,320 --> 00:07:41,680 Speaker 1: It sounds like it sounds like it. How long did 153 00:07:41,680 --> 00:07:46,040 Speaker 1: it take you to develop in computer code? I mean, 154 00:07:46,080 --> 00:07:47,640 Speaker 1: I know you said you used to work on Wall 155 00:07:47,720 --> 00:07:50,600 Speaker 1: Street and you've always dealt with finances and math. How 156 00:07:50,640 --> 00:07:52,680 Speaker 1: long did it take you to learn computer code? Because 157 00:07:52,680 --> 00:07:56,440 Speaker 1: that's not the average easy thing to do. I keep 158 00:07:56,520 --> 00:07:58,840 Speaker 1: learning every single day. You have to keep learning, and 159 00:07:59,000 --> 00:08:02,200 Speaker 1: technology of volves. There's always something new, there's always a 160 00:08:02,240 --> 00:08:04,840 Speaker 1: new language, there's always something more you can do, and 161 00:08:04,880 --> 00:08:06,560 Speaker 1: so you've got to keep at it, and keep at 162 00:08:06,560 --> 00:08:08,960 Speaker 1: it and keep at it. But that also means that 163 00:08:09,040 --> 00:08:11,480 Speaker 1: if you start right now, you can get ahead to right. 164 00:08:11,520 --> 00:08:13,200 Speaker 1: You don't have to go back and work years and 165 00:08:13,280 --> 00:08:15,120 Speaker 1: years and years. You just have to start putting in 166 00:08:15,120 --> 00:08:17,200 Speaker 1: the time, putting in the reps. It took me a 167 00:08:17,240 --> 00:08:19,720 Speaker 1: long time. And I'm evolving and I'm not the best coder. 168 00:08:20,000 --> 00:08:22,560 Speaker 1: All I know is enough to be dangerous with football, 169 00:08:22,640 --> 00:08:26,320 Speaker 1: right Like I couldn't I couldn't go write you code 170 00:08:26,320 --> 00:08:28,960 Speaker 1: for creating an app for the Apps Store to play 171 00:08:29,000 --> 00:08:31,160 Speaker 1: a game. But I can write you some computer vision 172 00:08:31,280 --> 00:08:33,000 Speaker 1: to tell you, you know what's going on with your 173 00:08:33,040 --> 00:08:36,680 Speaker 1: running back. A dangerous woman when it comes to coding football, 174 00:08:36,960 --> 00:08:43,439 Speaker 1: I love that the codes. That actually is a perfect 175 00:08:43,480 --> 00:08:47,160 Speaker 1: segue into my next question for you, being a woman 176 00:08:47,600 --> 00:08:51,720 Speaker 1: in this role. How difficult at times is it? Because 177 00:08:51,800 --> 00:08:54,480 Speaker 1: I mean, please share any of this information if you know. 178 00:08:54,520 --> 00:08:56,280 Speaker 1: But I don't know a lot of other women who 179 00:08:56,320 --> 00:08:59,079 Speaker 1: are in the world that you are in in this league. 180 00:08:59,120 --> 00:09:01,240 Speaker 1: So what are some of the challenges of being a 181 00:09:01,280 --> 00:09:04,760 Speaker 1: female in that role? There are a few, and we're 182 00:09:04,800 --> 00:09:07,319 Speaker 1: getting There's more and more happening every day, which is good. 183 00:09:07,640 --> 00:09:10,680 Speaker 1: And if anyone is interested, but again, you gotta you 184 00:09:10,679 --> 00:09:12,760 Speaker 1: can contact me, hit me up and I will. I 185 00:09:12,760 --> 00:09:14,679 Speaker 1: will help as best I can, especially if you know 186 00:09:14,720 --> 00:09:16,400 Speaker 1: how to code and you do the work. People who 187 00:09:16,400 --> 00:09:18,680 Speaker 1: do the work. I'm just like you know, Olivia. If 188 00:09:18,679 --> 00:09:20,480 Speaker 1: someone does the work, you know, I'm happy to help them. 189 00:09:20,760 --> 00:09:24,760 Speaker 1: So ultimately, the thing that the thing that's really interesting 190 00:09:24,800 --> 00:09:27,080 Speaker 1: about being a woman here is you have an opportunity. 191 00:09:27,400 --> 00:09:29,880 Speaker 1: You can look at it as a disadvantage or you 192 00:09:29,880 --> 00:09:32,160 Speaker 1: can look at it as an advantage. Because there is 193 00:09:32,240 --> 00:09:34,760 Speaker 1: nothing about my role that means that I needed to 194 00:09:34,760 --> 00:09:36,960 Speaker 1: have played football in my past. I don't get an 195 00:09:37,080 --> 00:09:41,840 Speaker 1: edge being I clearly didn't play right. I'm not very tough. 196 00:09:42,120 --> 00:09:45,120 Speaker 1: So ultimately you can use it as an advantage when 197 00:09:45,120 --> 00:09:47,320 Speaker 1: a coach I talked about biases. When I asked a 198 00:09:47,360 --> 00:09:50,240 Speaker 1: coach a question, if it's as if it's a reasonably 199 00:09:50,240 --> 00:09:52,960 Speaker 1: intelligent question, it seems like it's a very intelligent question, 200 00:09:53,000 --> 00:09:55,360 Speaker 1: because how would I know where the safety is supposed 201 00:09:55,360 --> 00:09:57,920 Speaker 1: to go on that play? Because I never played safety, right, 202 00:09:58,000 --> 00:10:00,360 Speaker 1: So it can be an advantage as well. So you 203 00:10:00,400 --> 00:10:03,320 Speaker 1: have an opportunity to view it as a disadvantage or 204 00:10:03,400 --> 00:10:05,360 Speaker 1: view it as an advantage. That is, it is all 205 00:10:05,480 --> 00:10:08,080 Speaker 1: on you to view or an obstacle as a roadblock 206 00:10:08,200 --> 00:10:10,280 Speaker 1: or an obstacle as something to help prop you up 207 00:10:10,320 --> 00:10:12,679 Speaker 1: so you can jump even higher. That's and you've got 208 00:10:12,679 --> 00:10:15,079 Speaker 1: to just keep doing it just like that. But if 209 00:10:15,080 --> 00:10:17,480 Speaker 1: there is nothing about this role that needs that, you 210 00:10:17,520 --> 00:10:19,720 Speaker 1: need to have played football before. And that's the thing 211 00:10:19,960 --> 00:10:23,400 Speaker 1: people need to to to really understand about analytics and 212 00:10:23,440 --> 00:10:26,200 Speaker 1: all this stuff. So in what other ways do you 213 00:10:26,280 --> 00:10:30,520 Speaker 1: think women can place themselves to be successful in this 214 00:10:30,640 --> 00:10:33,240 Speaker 1: specific role, Because, like you said, you could look at 215 00:10:33,280 --> 00:10:37,120 Speaker 1: it as a disadvantage. Or an advantage because oftentimes people 216 00:10:37,160 --> 00:10:39,840 Speaker 1: say you need to have played the sport to understand it, 217 00:10:39,880 --> 00:10:42,839 Speaker 1: which just is not true at all. There are advantages 218 00:10:42,880 --> 00:10:45,880 Speaker 1: to playing the sport of course, However, like you said, 219 00:10:45,920 --> 00:10:48,880 Speaker 1: there are roles where you never need to even play 220 00:10:48,920 --> 00:10:52,120 Speaker 1: the sport to begin with in order to understand. So 221 00:10:52,160 --> 00:10:55,360 Speaker 1: how can women successfully set themselves up to be in 222 00:10:55,400 --> 00:11:00,520 Speaker 1: positions like this with data analytics? So, as with everything, 223 00:11:00,679 --> 00:11:02,960 Speaker 1: you have to love it. If you don't love it, 224 00:11:02,960 --> 00:11:05,680 Speaker 1: it doesn't. This is this is every job everywhere. Find 225 00:11:05,720 --> 00:11:07,680 Speaker 1: what you really love, find what you're good at, find 226 00:11:07,720 --> 00:11:09,920 Speaker 1: where you can where you're working for hours and hours. 227 00:11:09,960 --> 00:11:11,480 Speaker 1: You know what, it doesn't even really feel like you're 228 00:11:11,480 --> 00:11:13,960 Speaker 1: working for hours and hours. So once you found that, 229 00:11:14,000 --> 00:11:16,200 Speaker 1: if if this is where that is, then the way 230 00:11:16,240 --> 00:11:19,400 Speaker 1: you can set yourself apart is to think creatively use 231 00:11:19,480 --> 00:11:22,480 Speaker 1: the fact that you didn't play. Go ask a player, 232 00:11:23,040 --> 00:11:25,480 Speaker 1: listen to what they say. And when you listen, the 233 00:11:25,559 --> 00:11:28,400 Speaker 1: more empathy you have for their position, the better off 234 00:11:28,440 --> 00:11:30,640 Speaker 1: you are up coding it. What I was really lucky. 235 00:11:30,640 --> 00:11:33,080 Speaker 1: I worked in the league office about a million years ago, 236 00:11:33,320 --> 00:11:37,040 Speaker 1: two thousand, twelve years ago, and I listened, I listened 237 00:11:37,360 --> 00:11:39,920 Speaker 1: to what the coaches who are on the competition committee, 238 00:11:40,040 --> 00:11:42,360 Speaker 1: we're telling me, and I just modeled it out. I 239 00:11:42,360 --> 00:11:45,160 Speaker 1: didn't I didn't inject my own thoughts. I didn't inject 240 00:11:45,200 --> 00:11:47,199 Speaker 1: any of it. And I modeled out what they were saying, 241 00:11:47,240 --> 00:11:49,000 Speaker 1: and then that allowed me to look for something else. 242 00:11:49,080 --> 00:11:52,400 Speaker 1: Then I asked another person, and then I got really curious, 243 00:11:52,440 --> 00:11:55,720 Speaker 1: and I dug really deep. And by being super curious 244 00:11:56,000 --> 00:11:58,320 Speaker 1: and adding something that I was good at, which was 245 00:11:58,400 --> 00:12:00,920 Speaker 1: creating models out of like things that were not normally 246 00:12:01,000 --> 00:12:04,040 Speaker 1: modeled in a in a way that people weren't really 247 00:12:04,240 --> 00:12:06,360 Speaker 1: talking about. They were doing it, but they weren't really 248 00:12:06,400 --> 00:12:09,880 Speaker 1: talking about it, and then communicating it back. That's really 249 00:12:09,920 --> 00:12:12,040 Speaker 1: the key empathy. You gotta listen and then you gotta 250 00:12:12,040 --> 00:12:14,120 Speaker 1: be able to communicate it back. And the but the 251 00:12:14,160 --> 00:12:16,320 Speaker 1: empathy to me is it's just like anything else. Like 252 00:12:16,559 --> 00:12:19,720 Speaker 1: if you are into data analytics, don't get in your 253 00:12:19,720 --> 00:12:21,800 Speaker 1: own don't go down your own rabbit hole. Sometimes people 254 00:12:22,000 --> 00:12:24,840 Speaker 1: they think they're only they're the right answer, right math 255 00:12:24,920 --> 00:12:27,680 Speaker 1: people were kind of introverted, so we kind of stay 256 00:12:27,679 --> 00:12:30,160 Speaker 1: in our own little link. But listen, go out there, listen, 257 00:12:30,200 --> 00:12:32,360 Speaker 1: go out there, have empathy, Go solve a problem that 258 00:12:32,440 --> 00:12:35,120 Speaker 1: somebody needs to have solved. That's how you get that's 259 00:12:35,120 --> 00:12:37,439 Speaker 1: for anyone in any job, right go solve the problem 260 00:12:37,559 --> 00:12:39,959 Speaker 1: needs to be solved and be able to communicate it. Well. 261 00:12:40,480 --> 00:12:46,000 Speaker 1: That's fantastic advice, not even just for your positions specifically, 262 00:12:46,000 --> 00:12:49,520 Speaker 1: but in any professional career, especially in the sports world, 263 00:12:49,880 --> 00:12:53,280 Speaker 1: because I think it's especially for women. It's always changing, 264 00:12:53,440 --> 00:12:56,800 Speaker 1: always needing to learn, always needing to communicate. That's one 265 00:12:56,800 --> 00:12:59,160 Speaker 1: of the biggest things, in my personal opinion, is really 266 00:12:59,200 --> 00:13:01,960 Speaker 1: good communication. And but another key word you said that 267 00:13:02,000 --> 00:13:06,200 Speaker 1: I loved was empathy. That's so huge. When you're genuine 268 00:13:06,840 --> 00:13:10,199 Speaker 1: and empathetic towards people and their jobs and what they 269 00:13:10,240 --> 00:13:12,960 Speaker 1: have to do, people are so much more open to 270 00:13:13,000 --> 00:13:17,000 Speaker 1: being communicative with you and sharing their insight in order 271 00:13:17,000 --> 00:13:20,600 Speaker 1: to help you with your job make your life easier. 272 00:13:20,760 --> 00:13:23,880 Speaker 1: I'm a hacker in always, I'm hacker with computer code. 273 00:13:24,000 --> 00:13:25,840 Speaker 1: I do what I need to. But also, if you 274 00:13:25,920 --> 00:13:28,480 Speaker 1: if someone is going to tell me their experience, I'm 275 00:13:28,520 --> 00:13:30,600 Speaker 1: never going to have more. I'm never going to have 276 00:13:30,640 --> 00:13:33,360 Speaker 1: more play calling experience than Adam Gays, I'm never going 277 00:13:33,400 --> 00:13:37,800 Speaker 1: to have more roster ability experience than Joe Douglas. But 278 00:13:37,880 --> 00:13:39,840 Speaker 1: what I can do is I can listen to them 279 00:13:39,880 --> 00:13:42,400 Speaker 1: and I can understand where they're coming from and trying 280 00:13:42,400 --> 00:13:45,200 Speaker 1: to ask questions to be like, Okay, what does this mean? 281 00:13:45,640 --> 00:13:48,320 Speaker 1: Let's clarify this, and then I can help them in 282 00:13:48,440 --> 00:13:51,600 Speaker 1: terms of figuring out how to contribute something that's meaningful 283 00:13:51,800 --> 00:13:54,480 Speaker 1: for the things that they want to achieve. Any coach, 284 00:13:54,600 --> 00:13:57,040 Speaker 1: any GM, it's all the same. I have one more 285 00:13:57,080 --> 00:13:59,800 Speaker 1: question for you about your career before we move on 286 00:13:59,840 --> 00:14:04,200 Speaker 1: to some JETS topics. Is that challenging to have to 287 00:14:04,240 --> 00:14:07,720 Speaker 1: talk to so many different coaches, so many different players 288 00:14:07,720 --> 00:14:09,599 Speaker 1: and gm s. I feel like you're gonna have to 289 00:14:09,679 --> 00:14:13,240 Speaker 1: keep a laundry list of what everybody is saying. You know, 290 00:14:13,840 --> 00:14:16,800 Speaker 1: It's luckily it's not a huge world, right there's there 291 00:14:16,800 --> 00:14:19,520 Speaker 1: are not many people, and they do stay a long time. 292 00:14:19,840 --> 00:14:22,440 Speaker 1: The thing that the thing that's I would think probably 293 00:14:22,520 --> 00:14:25,120 Speaker 1: the most the most interesting part of this is to 294 00:14:26,000 --> 00:14:28,320 Speaker 1: the thing I really try because I cover all of 295 00:14:28,360 --> 00:14:30,160 Speaker 1: the teams, right, I get a chance to speak to 296 00:14:30,480 --> 00:14:34,040 Speaker 1: a lot of different people, which is hugely, hugely helpful, 297 00:14:34,080 --> 00:14:36,560 Speaker 1: and I learned so much. The thing that's that's the 298 00:14:36,600 --> 00:14:40,600 Speaker 1: trickiest is probably figuring out, Okay, how do I because 299 00:14:40,640 --> 00:14:42,720 Speaker 1: some people are just nicer than other people, and some 300 00:14:42,720 --> 00:14:44,880 Speaker 1: people are more generous without their time than others. Right, 301 00:14:45,120 --> 00:14:47,760 Speaker 1: how do I keep it all unbiased? So, and all 302 00:14:47,800 --> 00:14:50,400 Speaker 1: of my models, all of the teams are labeled things 303 00:14:50,400 --> 00:14:51,800 Speaker 1: that are not teams, so that I don't look in 304 00:14:51,880 --> 00:14:53,240 Speaker 1: and be like, oh, I like the Jets. Maybe I 305 00:14:53,280 --> 00:14:55,120 Speaker 1: want them to win, right, Like, I try to keep 306 00:14:55,160 --> 00:14:58,000 Speaker 1: it all like really really like it is. All the 307 00:14:58,000 --> 00:15:00,880 Speaker 1: fidelity of my data is untouched. They're just named things 308 00:15:00,880 --> 00:15:03,760 Speaker 1: that are mostly useless to me, right, like things I 309 00:15:03,840 --> 00:15:06,440 Speaker 1: like equally. So that's the hardest part is to not like, 310 00:15:06,680 --> 00:15:09,000 Speaker 1: oh but I like this person, right, I don't want 311 00:15:09,000 --> 00:15:12,560 Speaker 1: to get that involved. That's that's fantastic, though it's difficult. 312 00:15:12,720 --> 00:15:15,960 Speaker 1: It's difficult, like you said, because we're it's a good 313 00:15:16,000 --> 00:15:18,160 Speaker 1: problem to have a right Let's move on to some 314 00:15:18,280 --> 00:15:21,080 Speaker 1: Jets talk. As we all know, they are taking on 315 00:15:21,200 --> 00:15:24,520 Speaker 1: the Buffalo Bills this week in Buffalo for week one. Now, 316 00:15:24,520 --> 00:15:26,200 Speaker 1: you're numbers woman, so I want to I want to 317 00:15:26,240 --> 00:15:28,280 Speaker 1: throw some numbers at you, if you're cool with that. 318 00:15:29,280 --> 00:15:34,200 Speaker 1: Sam Donald against this bill's defense, Cynthia. The Bills finished 319 00:15:34,240 --> 00:15:37,480 Speaker 1: number three in the NFL in total defense last year. 320 00:15:37,520 --> 00:15:44,280 Speaker 1: In number four against the past. Now, the Bills actually 321 00:15:44,560 --> 00:15:49,840 Speaker 1: lead the league in returning snaps defensively and offensively. They 322 00:15:49,880 --> 00:15:52,680 Speaker 1: have eighty point four percent of their defensive snaps returning. 323 00:15:53,000 --> 00:15:55,840 Speaker 1: So how much of a challenge does quarterback Sam Donald 324 00:15:55,880 --> 00:16:00,920 Speaker 1: have against this very talented pass defense. Yeah, it's a challenge, 325 00:16:01,000 --> 00:16:03,080 Speaker 1: I mean, but you know, the good news and the 326 00:16:03,120 --> 00:16:05,600 Speaker 1: bad news is that you can look at preseason and 327 00:16:05,640 --> 00:16:08,440 Speaker 1: the lack of preseason and you can say, all right, um, 328 00:16:08,440 --> 00:16:10,920 Speaker 1: well that stinks for us, or you can say it 329 00:16:11,000 --> 00:16:13,920 Speaker 1: also stinks for them. They didn't have those snaps together 330 00:16:13,920 --> 00:16:16,480 Speaker 1: in the preseason either, so we're all kind of coming 331 00:16:16,520 --> 00:16:19,280 Speaker 1: from the same spot and maybe you could you can 332 00:16:19,360 --> 00:16:21,920 Speaker 1: kind of imagine that, you know what Adam Gaze has 333 00:16:22,040 --> 00:16:24,400 Speaker 1: been cooking up for the past month, we've seen none 334 00:16:24,400 --> 00:16:26,280 Speaker 1: of it. We haven't seen anything from the Bills either, 335 00:16:26,480 --> 00:16:28,880 Speaker 1: But in terms of figuring out those passes and kind 336 00:16:28,880 --> 00:16:31,080 Speaker 1: of keeping that trajectory, if you look to see how 337 00:16:31,120 --> 00:16:34,120 Speaker 1: Sam Donald improved towards the end of the season in 338 00:16:34,160 --> 00:16:37,040 Speaker 1: the deep passing game, Obviously, let's go away from Trude 339 00:16:37,040 --> 00:16:41,480 Speaker 1: Avious White because let's just stay stay from him. He's 340 00:16:41,480 --> 00:16:44,200 Speaker 1: really good. Let's stay away from him and against the Blitz. 341 00:16:44,240 --> 00:16:45,720 Speaker 1: So the you know, if you look to see the 342 00:16:45,800 --> 00:16:49,400 Speaker 1: last season, the Bills blitz the ninth most often and 343 00:16:49,440 --> 00:16:52,040 Speaker 1: they had the fifth best passer rating allowed against blitz 344 00:16:52,040 --> 00:16:54,920 Speaker 1: being the fifth lowest. And that to me is you 345 00:16:54,960 --> 00:16:57,160 Speaker 1: know who else knows that, Adam Gaze? You know who 346 00:16:57,200 --> 00:16:59,560 Speaker 1: else knows that Sam Donald. So they're gonna try to 347 00:16:59,600 --> 00:17:03,480 Speaker 1: figure out a way to create the right opportunities away 348 00:17:03,560 --> 00:17:06,880 Speaker 1: from the biggest threats. Obviously, the Bills safeties and dagous 349 00:17:06,920 --> 00:17:09,760 Speaker 1: Way are their biggest threats. So why not just you know, 350 00:17:09,840 --> 00:17:12,520 Speaker 1: play the hits, figure out where they have the best 351 00:17:12,520 --> 00:17:15,639 Speaker 1: success in their playbook and and move towards that. And 352 00:17:15,680 --> 00:17:17,720 Speaker 1: we saw Sam Donald approve against the Blitz towards the 353 00:17:17,800 --> 00:17:20,920 Speaker 1: end of the season significantly. We saw Sam Donald improve 354 00:17:20,960 --> 00:17:24,000 Speaker 1: with the deep passes towards the end of the season significantly, 355 00:17:24,080 --> 00:17:27,000 Speaker 1: And both of those things also Adam Gaze knows. So 356 00:17:27,240 --> 00:17:29,800 Speaker 1: these are areas where I think you're gonna see Sam 357 00:17:29,880 --> 00:17:32,600 Speaker 1: Donald kind of returned to form. Last season was a 358 00:17:32,600 --> 00:17:36,400 Speaker 1: weird anomaly for him moving forward. Let's go with all 359 00:17:36,440 --> 00:17:39,720 Speaker 1: that being said, could you possibly see Adam Gaze having 360 00:17:40,200 --> 00:17:43,560 Speaker 1: We're implementing a heavy run game because of all of 361 00:17:43,600 --> 00:17:47,240 Speaker 1: the factors we just talked about. Sure, I mean, a 362 00:17:47,560 --> 00:17:50,639 Speaker 1: healthy run game and play calling balance is one of, 363 00:17:50,720 --> 00:17:53,240 Speaker 1: if not the best ways for a team to succeed 364 00:17:53,280 --> 00:17:55,959 Speaker 1: in the NFL. Just that's that is the data bears 365 00:17:56,000 --> 00:17:58,960 Speaker 1: that out. You've seen. I say it kind of one 366 00:17:59,000 --> 00:18:02,520 Speaker 1: of the ways because sometimes short passes can be substituted 367 00:18:02,560 --> 00:18:05,680 Speaker 1: as running plays, right. So those short passes we saw 368 00:18:05,720 --> 00:18:07,719 Speaker 1: it to Leave Bell for example when he was a Steeler, 369 00:18:07,920 --> 00:18:09,919 Speaker 1: Those are the types of things that are their runs, 370 00:18:10,000 --> 00:18:13,080 Speaker 1: but they're technically passes, right, So those are the types 371 00:18:13,119 --> 00:18:16,240 Speaker 1: of things we're on early downs. The success on early 372 00:18:16,320 --> 00:18:19,040 Speaker 1: downs just to get those five yards four yards to 373 00:18:19,119 --> 00:18:22,879 Speaker 1: become more favorable second and third downs, that's where you're 374 00:18:22,920 --> 00:18:24,600 Speaker 1: gonna see. That's where this game will be one or 375 00:18:24,640 --> 00:18:27,399 Speaker 1: lost on both sides. Is are you going to be 376 00:18:27,440 --> 00:18:30,720 Speaker 1: good on early downs? Yes? Or no? The the other 377 00:18:30,760 --> 00:18:33,960 Speaker 1: stuff can keep keep third down manageable, and that's the 378 00:18:34,000 --> 00:18:36,960 Speaker 1: team that went. I just have to quickly say what 379 00:18:37,119 --> 00:18:40,200 Speaker 1: I absolutely love about so many women just in sports 380 00:18:40,200 --> 00:18:43,320 Speaker 1: in general, but just listening to you talk it just 381 00:18:43,359 --> 00:18:46,000 Speaker 1: makes me so I just love it. I get so 382 00:18:46,119 --> 00:18:50,640 Speaker 1: excited because you're so intelligent you. You just I learned 383 00:18:50,680 --> 00:18:52,960 Speaker 1: so much from me listening to some of the stats 384 00:18:53,000 --> 00:18:55,359 Speaker 1: that you talk about, how you implement it into the 385 00:18:55,400 --> 00:18:58,480 Speaker 1: way you think about the game. It's incredible. Just side note, 386 00:18:58,480 --> 00:19:00,679 Speaker 1: I had to say that they you. I very much 387 00:19:00,680 --> 00:19:03,239 Speaker 1: appreciate that. And you know, the best part about being 388 00:19:03,240 --> 00:19:05,080 Speaker 1: a woman in football, if if you're going to do it, 389 00:19:05,119 --> 00:19:08,240 Speaker 1: is we all love it. So you love it. I like, 390 00:19:08,280 --> 00:19:10,480 Speaker 1: I love listening to ut like I learned more about 391 00:19:10,480 --> 00:19:12,760 Speaker 1: the Jets from you. I learned in the bills for example. 392 00:19:12,800 --> 00:19:14,960 Speaker 1: You know and whoever their opponent is. And it's a 393 00:19:15,000 --> 00:19:18,880 Speaker 1: good thing because sometimes we're asked to know more. Definitely 394 00:19:19,480 --> 00:19:22,159 Speaker 1: are not allowed to make any as many mistakes, about 395 00:19:22,240 --> 00:19:23,960 Speaker 1: half the amount of mistakes and you've got to know 396 00:19:24,280 --> 00:19:27,040 Speaker 1: three times them out. But right, as long as we 397 00:19:27,160 --> 00:19:29,479 Speaker 1: know it, so all right, second question here for you, 398 00:19:29,560 --> 00:19:34,160 Speaker 1: Cynthia josh Allen. Right, he is a very mobile quarterback. 399 00:19:34,200 --> 00:19:36,640 Speaker 1: Everybody knows it, like you said, Adam Gaze defensive cornery 400 00:19:36,680 --> 00:19:39,879 Speaker 1: and Greg Williams knows it. Very mobile quarterback was third 401 00:19:39,960 --> 00:19:43,240 Speaker 1: in the league among all quarterbacks in rushing yards with 402 00:19:43,400 --> 00:19:46,960 Speaker 1: five d and ten rushing yards, behind Kyler Murray and 403 00:19:47,119 --> 00:19:51,320 Speaker 1: of course Lamar Jackson. What exactly does Greg Williams defense 404 00:19:51,440 --> 00:19:56,040 Speaker 1: need to do to contain Josh Allen inside that pocket. 405 00:19:57,359 --> 00:19:59,720 Speaker 1: I mean, look, there's two things that really need to happen. 406 00:19:59,760 --> 00:20:01,800 Speaker 1: John H. Allen is a bit of a wild card. 407 00:20:02,040 --> 00:20:05,919 Speaker 1: Those rushing yards aren't the same as Lamar jackson rushing yards, 408 00:20:06,080 --> 00:20:07,879 Speaker 1: and they're not even really the same as Kyler Murray's 409 00:20:07,920 --> 00:20:10,240 Speaker 1: rushing yards. Kyler Murray had some designed runs in there, 410 00:20:10,440 --> 00:20:14,080 Speaker 1: and Lamar Jackson most certainly had designed rushes in there. 411 00:20:14,280 --> 00:20:18,080 Speaker 1: So the difference is is Josh Allen scrambling has been great, 412 00:20:18,480 --> 00:20:20,719 Speaker 1: and he does have more deep threats this season. They 413 00:20:20,720 --> 00:20:24,240 Speaker 1: add as Stefon Diggs obviously, But the the interesting part 414 00:20:24,280 --> 00:20:27,120 Speaker 1: here is you gotta get him in those weird, unmanageable 415 00:20:27,160 --> 00:20:30,159 Speaker 1: third down situations, which Greg Williams is a great opportunity 416 00:20:30,160 --> 00:20:33,200 Speaker 1: to do given the last season, looking at first down, 417 00:20:33,320 --> 00:20:35,879 Speaker 1: Jets allowed the fewest yards per play. That was a 418 00:20:35,920 --> 00:20:39,160 Speaker 1: function of stopping the run primarily being very good against 419 00:20:39,200 --> 00:20:41,920 Speaker 1: the run, especially on first down, and then that leads 420 00:20:41,960 --> 00:20:45,320 Speaker 1: to more unmanageable third downs because we've seen Josh Allen 421 00:20:45,640 --> 00:20:49,479 Speaker 1: make mistakes when he's had too much time in a 422 00:20:49,480 --> 00:20:52,119 Speaker 1: weird amount of time. I say weird because sometimes it 423 00:20:52,160 --> 00:20:54,040 Speaker 1: was a quick one that just went the wrong spot, right. 424 00:20:54,080 --> 00:20:57,840 Speaker 1: But so it's really about keeping Josh Allen. He's Josh 425 00:20:57,880 --> 00:21:01,000 Speaker 1: Allen off balance mentally, so that he has to think 426 00:21:01,040 --> 00:21:03,800 Speaker 1: about what's going on and read his progression tree because 427 00:21:04,000 --> 00:21:05,800 Speaker 1: that's where you're going to see the lack of preseason 428 00:21:05,840 --> 00:21:08,720 Speaker 1: come into play. Right. They don't have that chemistry yet, 429 00:21:08,760 --> 00:21:11,959 Speaker 1: he doesn't. He sure he's been working with Stefon Diggs, 430 00:21:11,960 --> 00:21:14,880 Speaker 1: but they haven't had game speed yet, so that chemistry 431 00:21:14,960 --> 00:21:17,800 Speaker 1: is going to take some time before him. Something interesting 432 00:21:17,840 --> 00:21:20,240 Speaker 1: that you said that, to be quite honest, I didn't 433 00:21:20,240 --> 00:21:23,960 Speaker 1: even necessarily think about is the designed run part, because 434 00:21:24,359 --> 00:21:26,800 Speaker 1: you're absolutely right when it comes to Lamar Jackson, even 435 00:21:26,840 --> 00:21:29,000 Speaker 1: Kyler Murray, I didn't. I didn't even think about it 436 00:21:29,040 --> 00:21:32,360 Speaker 1: in that way with Kyler Murray specifically. But they're more 437 00:21:32,400 --> 00:21:36,720 Speaker 1: designed runs for the former, right, as opposed to Josh Allen, 438 00:21:36,720 --> 00:21:39,000 Speaker 1: who a lot of the times was just scrambling out 439 00:21:39,000 --> 00:21:44,760 Speaker 1: of the pocket because he was forced out of there. Yeah, okay, 440 00:21:44,760 --> 00:21:48,200 Speaker 1: final question about the Jets heading to the Bills. Heading 441 00:21:48,200 --> 00:21:51,600 Speaker 1: to Buffalo to take on the Bills, brand new offensive line, 442 00:21:52,000 --> 00:21:55,560 Speaker 1: four new starters along the offensive line, one returning and 443 00:21:55,640 --> 00:21:58,520 Speaker 1: left and left guard Alex Lewis. But you look at 444 00:21:58,520 --> 00:22:01,600 Speaker 1: the rookie left tackle McKay beck in from left to right, 445 00:22:02,080 --> 00:22:04,680 Speaker 1: How are they going to develop chemistry so quickly, because, 446 00:22:04,720 --> 00:22:07,520 Speaker 1: like you said, there's been absolutely no game speed. Now 447 00:22:07,560 --> 00:22:10,600 Speaker 1: good thing, there's been no game speed for anybody around 448 00:22:10,640 --> 00:22:13,280 Speaker 1: the league. So that's a that's a positive note. But 449 00:22:13,480 --> 00:22:15,080 Speaker 1: what kind of chemistry do you expect to see on 450 00:22:15,080 --> 00:22:18,120 Speaker 1: the field between the offensive line. So the interesting part 451 00:22:18,160 --> 00:22:20,960 Speaker 1: I got a chance to really look at McKay Beckton's 452 00:22:20,960 --> 00:22:23,320 Speaker 1: Louisville tape and he didn't play a lot of the 453 00:22:23,520 --> 00:22:26,600 Speaker 1: passing steps are different from the NFL. The interesting part 454 00:22:26,720 --> 00:22:29,960 Speaker 1: is he's super athletic, which means his ability to spring 455 00:22:30,040 --> 00:22:33,760 Speaker 1: forward quickly is more like like an Aaron Donald super fast, right. 456 00:22:33,880 --> 00:22:36,359 Speaker 1: He doesn't need that extra minute to figure it out. 457 00:22:36,440 --> 00:22:39,119 Speaker 1: He just reacts. He's got good instincts. That's going to 458 00:22:39,200 --> 00:22:42,679 Speaker 1: be very helpful. Obviously, it will take some time for 459 00:22:42,800 --> 00:22:44,360 Speaker 1: the old line to develop. I think in the month 460 00:22:44,359 --> 00:22:46,680 Speaker 1: of September across the league, you're going to see more 461 00:22:46,720 --> 00:22:49,400 Speaker 1: false starts. You're just see more penalties along the old 462 00:22:49,440 --> 00:22:51,880 Speaker 1: line than even if then we've seen in years past 463 00:22:51,960 --> 00:22:54,320 Speaker 1: and and there's always an uptick in September just just 464 00:22:54,720 --> 00:22:56,639 Speaker 1: by virtue of the fact that we're getting back to 465 00:22:56,920 --> 00:23:00,000 Speaker 1: back to football. So sure there will be some miss 466 00:23:00,040 --> 00:23:02,480 Speaker 1: steps in that regard, So don't get mad Jets fans 467 00:23:02,480 --> 00:23:04,640 Speaker 1: when you see me maybe a fall start right. So, 468 00:23:05,760 --> 00:23:07,919 Speaker 1: but what I what I think is really interesting is 469 00:23:08,080 --> 00:23:12,160 Speaker 1: the athleticism and the fact that you can't teach that. 470 00:23:12,359 --> 00:23:14,960 Speaker 1: You can teach someone shifts, you can teach them how 471 00:23:15,000 --> 00:23:17,000 Speaker 1: to block for a run, you can teach them how 472 00:23:17,000 --> 00:23:19,600 Speaker 1: to block for a past, you can't teach them to 473 00:23:19,720 --> 00:23:23,280 Speaker 1: be an instinctual athlete, and that's what McKay Beckton really has. 474 00:23:23,520 --> 00:23:25,440 Speaker 1: Then you have a lot of depth along the rest 475 00:23:25,440 --> 00:23:28,200 Speaker 1: of the O line, and that in trading out is 476 00:23:28,200 --> 00:23:31,080 Speaker 1: going to be they'll find that right rotation, they'll find 477 00:23:31,119 --> 00:23:33,800 Speaker 1: that right combination of who the right guys are in 478 00:23:33,800 --> 00:23:37,840 Speaker 1: the right situations. Maybe not Snap one against the Bills, 479 00:23:37,880 --> 00:23:42,000 Speaker 1: but it'll as the game project game one week one, 480 00:23:42,480 --> 00:23:45,399 Speaker 1: especially this year more than ever, is a game of adjustments, 481 00:23:45,520 --> 00:23:48,200 Speaker 1: and all those adjustments you'll keep seeing it. So don't 482 00:23:48,200 --> 00:23:50,439 Speaker 1: be surprised if you see a lot of rotation, a 483 00:23:50,480 --> 00:23:52,639 Speaker 1: lot of subbing in and out and changing spots. So 484 00:23:52,760 --> 00:23:55,080 Speaker 1: you gotta get it just right because now you have 485 00:23:55,280 --> 00:23:57,240 Speaker 1: lead Bell needs to perform this year, you have Frank 486 00:23:57,280 --> 00:23:59,480 Speaker 1: Gore back there. The strong run game will help the 487 00:23:59,520 --> 00:24:02,040 Speaker 1: pass and it will also help the old line. Those 488 00:24:02,240 --> 00:24:04,280 Speaker 1: that that is going to be the key to not 489 00:24:04,400 --> 00:24:06,560 Speaker 1: just this game, but all of the games, because you 490 00:24:06,600 --> 00:24:09,199 Speaker 1: know after the Bills that you got the Niners, that 491 00:24:09,240 --> 00:24:11,360 Speaker 1: one's gonna be tough too. So yeah, it's it's really 492 00:24:11,400 --> 00:24:16,240 Speaker 1: adapting to that. Absolutely incredible insight, especially on the offensive 493 00:24:16,240 --> 00:24:18,680 Speaker 1: line and McKay peckton. That's encouraging. Jets fans should be 494 00:24:18,720 --> 00:24:21,520 Speaker 1: encouraged by some of those stats. So definitely, yeah, definitely, 495 00:24:21,680 --> 00:24:24,040 Speaker 1: thank you so much. We're gonna move into our final 496 00:24:24,080 --> 00:24:27,159 Speaker 1: segment of the podcast right now, and it's just a 497 00:24:27,200 --> 00:24:28,760 Speaker 1: little fun. We're gonna have a little fun with it. 498 00:24:29,000 --> 00:24:31,040 Speaker 1: We asked we went to Twitter and we asked some 499 00:24:31,119 --> 00:24:33,439 Speaker 1: of you Jets fans to ask Cynthia and I some 500 00:24:33,520 --> 00:24:36,040 Speaker 1: questions that we can answer. I have a couple of 501 00:24:36,040 --> 00:24:38,200 Speaker 1: them pulled up. Now let's go ahead and go into 502 00:24:38,240 --> 00:24:40,280 Speaker 1: the first one. We're gonna read it right here off 503 00:24:40,280 --> 00:24:43,960 Speaker 1: the phone. This one is from Stephanie Miller and I 504 00:24:44,000 --> 00:24:47,000 Speaker 1: actually love this question, Cynthia. We we talked about this 505 00:24:47,080 --> 00:24:49,680 Speaker 1: right before we went on air. How can women from 506 00:24:49,800 --> 00:24:53,919 Speaker 1: underprivileged communities get more involved in the NFL? Talks to 507 00:24:53,920 --> 00:24:56,000 Speaker 1: you first, Cynthia, But that's a great question. It's a 508 00:24:56,000 --> 00:24:59,119 Speaker 1: great question totally. The good part about the NFL is 509 00:24:59,160 --> 00:25:01,879 Speaker 1: that it's every where. You have so many opportunities in 510 00:25:01,960 --> 00:25:04,359 Speaker 1: so many different cities, and so many ways to contribute. 511 00:25:04,600 --> 00:25:07,639 Speaker 1: So keep an eye on Honestly, it sounds really simple, 512 00:25:07,760 --> 00:25:09,600 Speaker 1: but keep an eye on the website for jobs that 513 00:25:09,640 --> 00:25:12,200 Speaker 1: pop up, keep an eye on each team. They all 514 00:25:12,280 --> 00:25:14,960 Speaker 1: they come up at weird times sometimes, but just keep 515 00:25:15,000 --> 00:25:17,360 Speaker 1: an eye out for it because they're always there. Now 516 00:25:17,359 --> 00:25:20,480 Speaker 1: if you want to specifically, look to things like analytics. 517 00:25:20,480 --> 00:25:23,280 Speaker 1: Just where I know there are some really great organizations 518 00:25:23,280 --> 00:25:26,200 Speaker 1: like Girls in Tech, and that it doesn't matter where 519 00:25:26,280 --> 00:25:28,080 Speaker 1: you live. You can join Girls in Tech and you 520 00:25:28,080 --> 00:25:31,119 Speaker 1: can learn a lot more about how women can get 521 00:25:31,200 --> 00:25:34,040 Speaker 1: jobs in technology fields. And and tech doesn't just mean 522 00:25:34,119 --> 00:25:36,399 Speaker 1: like you know, big tech in Silicon Valley, it means 523 00:25:36,440 --> 00:25:40,040 Speaker 1: football tech, It means tech for everything. So I would say, 524 00:25:40,080 --> 00:25:43,400 Speaker 1: just you know, look into the organization, see who I 525 00:25:43,440 --> 00:25:46,680 Speaker 1: follow if you're if you're interested in in data analytics, right, 526 00:25:46,720 --> 00:25:49,080 Speaker 1: so see where it is and and look to education. 527 00:25:49,119 --> 00:25:51,560 Speaker 1: There are a bunch of great programs and they don't 528 00:25:51,560 --> 00:25:53,840 Speaker 1: even need to necessarily be You can go on Coursera. 529 00:25:53,960 --> 00:25:56,200 Speaker 1: You can do it from home. You don't necessarily even 530 00:25:56,200 --> 00:25:58,800 Speaker 1: need to go to I want to Northwestern. You don't 531 00:25:58,800 --> 00:26:02,879 Speaker 1: even need necessarily have a Masters of Predictive Analytics like 532 00:26:02,880 --> 00:26:05,000 Speaker 1: I have. You don't need that. You can you can 533 00:26:05,000 --> 00:26:06,760 Speaker 1: go look on Coursera and see if you want to 534 00:26:06,760 --> 00:26:11,240 Speaker 1: get into it in the data analytics field. Great answer, 535 00:26:11,640 --> 00:26:13,480 Speaker 1: and I'm probably just going to reiterate a lot of 536 00:26:13,520 --> 00:26:16,160 Speaker 1: the things that you said. I think it's really important, 537 00:26:16,240 --> 00:26:19,919 Speaker 1: especially as women, to reach out to two women in 538 00:26:20,080 --> 00:26:23,480 Speaker 1: roles that you see you might be interested in. Like 539 00:26:23,560 --> 00:26:26,920 Speaker 1: you you gave an example, anyone that's interested in analytics 540 00:26:26,960 --> 00:26:29,399 Speaker 1: something like that. Follow some of the women on social 541 00:26:29,400 --> 00:26:31,600 Speaker 1: media that you see in those roles and see that 542 00:26:31,640 --> 00:26:35,359 Speaker 1: are successful within the NFL, reach out to them. I 543 00:26:35,359 --> 00:26:37,919 Speaker 1: would say that's my piece of advice because when I 544 00:26:37,960 --> 00:26:40,760 Speaker 1: was a college student, I was that annoying college student 545 00:26:40,840 --> 00:26:44,160 Speaker 1: that would message people on social media. I would bother 546 00:26:44,320 --> 00:26:47,160 Speaker 1: some of the female reporters after the games asking them, 547 00:26:47,280 --> 00:26:48,960 Speaker 1: can you please take a look at my real can 548 00:26:49,000 --> 00:26:52,080 Speaker 1: you please give me any piece of advice? Honestly, the 549 00:26:52,119 --> 00:26:54,960 Speaker 1: squeaky will gets the oil. That's that's the old saying, 550 00:26:54,960 --> 00:26:57,000 Speaker 1: and I think it's true. Don't ever be afraid to 551 00:26:57,080 --> 00:27:00,520 Speaker 1: be too annoying or to persistent because a lot of 552 00:27:00,560 --> 00:27:03,719 Speaker 1: times I can't speak for you, Cynthia, but you know, 553 00:27:03,760 --> 00:27:08,160 Speaker 1: being a professional, you get so many emails, so many 554 00:27:08,200 --> 00:27:10,919 Speaker 1: tasks that you have to do every single day, So 555 00:27:10,960 --> 00:27:13,719 Speaker 1: a lot of times some things can slip through the cracks. 556 00:27:13,840 --> 00:27:17,200 Speaker 1: So if you're persistent and you constantly are finding ways, 557 00:27:17,280 --> 00:27:20,080 Speaker 1: creative ways to to reach out to women in roles 558 00:27:20,119 --> 00:27:23,040 Speaker 1: that you see fit for yourself, I'm gonna say nine 559 00:27:23,040 --> 00:27:26,479 Speaker 1: times at attend women are very especially in the NFL, 560 00:27:26,680 --> 00:27:29,080 Speaker 1: women are very supportive of other women. I wouldn't be 561 00:27:29,119 --> 00:27:31,960 Speaker 1: here today without some of the women who were in 562 00:27:32,040 --> 00:27:35,600 Speaker 1: professional roles at the time didn't help me and reached out, 563 00:27:35,720 --> 00:27:37,439 Speaker 1: you know, they reached out to me, They helped me, 564 00:27:37,520 --> 00:27:39,840 Speaker 1: looked over my resume, gave me tips. I would not 565 00:27:39,920 --> 00:27:42,119 Speaker 1: be where I'm at today without those women. So I 566 00:27:42,119 --> 00:27:45,000 Speaker 1: have to pass along the favor to other women, absolutely, 567 00:27:45,040 --> 00:27:47,080 Speaker 1: and I would I would amend that just a little bit, 568 00:27:47,440 --> 00:27:52,120 Speaker 1: make it a conversation. Sometimes I get some emails and 569 00:27:52,359 --> 00:27:55,879 Speaker 1: it I'm happy to help wherever I can. But if 570 00:27:55,920 --> 00:27:58,600 Speaker 1: you email someone and say, Hi, my name is so 571 00:27:58,680 --> 00:28:02,160 Speaker 1: and so and I graduated from blah blah blah yesterday 572 00:28:02,160 --> 00:28:05,119 Speaker 1: and I need an internship tomorrow, can you get me one? Like, 573 00:28:05,680 --> 00:28:09,560 Speaker 1: let's like, ask something that is achievable. Do you have 574 00:28:09,640 --> 00:28:11,600 Speaker 1: fifteen minutes to talk to me? I want to know 575 00:28:11,680 --> 00:28:13,760 Speaker 1: your path because I want to know what it would 576 00:28:13,800 --> 00:28:16,479 Speaker 1: take to become what you did? What did you do? 577 00:28:16,680 --> 00:28:19,120 Speaker 1: How did you get here? Right? Like, ask a real 578 00:28:19,200 --> 00:28:22,400 Speaker 1: question because there's no I can't do it for you 579 00:28:22,480 --> 00:28:24,520 Speaker 1: and you can't do it for them, Like I'm I 580 00:28:24,560 --> 00:28:27,440 Speaker 1: will do everything I can to ignite and lift up, 581 00:28:27,680 --> 00:28:31,600 Speaker 1: but you gotta be ask the question that I can answer. Right. 582 00:28:32,400 --> 00:28:34,760 Speaker 1: Of course, I've got fifteen minutes for you. Get you 583 00:28:34,800 --> 00:28:40,120 Speaker 1: an intership tomorrow. I don't have an internship to give you. Yeah. 584 00:28:40,560 --> 00:28:42,239 Speaker 1: I also think it kind of goes back to what 585 00:28:42,280 --> 00:28:45,360 Speaker 1: we said earlier about just being genuine, if you're genuine 586 00:28:45,440 --> 00:28:48,960 Speaker 1: with people and not just interested in an end goal, like, oh, 587 00:28:49,000 --> 00:28:51,040 Speaker 1: I need a job, so let me reach out to 588 00:28:51,080 --> 00:28:53,920 Speaker 1: this person. No, no, no. If you're genuine and you 589 00:28:53,960 --> 00:28:56,600 Speaker 1: truly want to know somebody's path and are curious about 590 00:28:56,600 --> 00:28:58,840 Speaker 1: advice that they can give you, you can always pick 591 00:28:58,880 --> 00:29:01,800 Speaker 1: up on stuff like that. I all right, let's go 592 00:29:01,800 --> 00:29:03,640 Speaker 1: into the next tweet. I'm gonna read this second one. 593 00:29:04,400 --> 00:29:08,280 Speaker 1: This one is from Joe de Prospero. Hopefully I pronounced 594 00:29:08,280 --> 00:29:10,360 Speaker 1: your name right, Joe. It says, this is a fun 595 00:29:10,400 --> 00:29:13,080 Speaker 1: one of all the players on the Jets roster, who's 596 00:29:13,120 --> 00:29:15,320 Speaker 1: the most fun to work with? Cynthia, Have you had 597 00:29:16,000 --> 00:29:18,080 Speaker 1: enough experience with some of the Jets players to go 598 00:29:18,120 --> 00:29:21,400 Speaker 1: off this one? So not this year's roster, obviously, because 599 00:29:21,440 --> 00:29:23,880 Speaker 1: I don't know if anyone. Yeah, that's true, it's all virtual. 600 00:29:24,880 --> 00:29:28,600 Speaker 1: But last year I was. I did some work on preseason, 601 00:29:28,960 --> 00:29:31,280 Speaker 1: and preseason was fun because first of all, everyone's in 602 00:29:31,280 --> 00:29:34,360 Speaker 1: a great mood because everyone's got a winning record, and 603 00:29:34,560 --> 00:29:36,280 Speaker 1: you know, it's just a little bit more casual. So 604 00:29:36,600 --> 00:29:38,560 Speaker 1: I feel like I had a pretty good I got 605 00:29:38,680 --> 00:29:41,000 Speaker 1: a pretty good chance to you know, I talked to 606 00:29:41,040 --> 00:29:42,960 Speaker 1: a few people, and I feel like I feel like 607 00:29:43,000 --> 00:29:46,160 Speaker 1: I can I can make an accurate assessment of who 608 00:29:46,200 --> 00:29:48,560 Speaker 1: I think is the most fun, one of the most 609 00:29:48,600 --> 00:29:51,600 Speaker 1: fun players on the team. You ready for it? I'm ready. 610 00:29:51,680 --> 00:29:53,440 Speaker 1: I'm ready. I was a big I was a big 611 00:29:53,440 --> 00:29:57,800 Speaker 1: Brian Pool fan. Really okay, yep, Like just I like, 612 00:29:57,960 --> 00:30:02,760 Speaker 1: you know, a little smart, quiet focused I was. That's 613 00:30:02,760 --> 00:30:05,120 Speaker 1: actually very true, very true about Brian Pool. He is 614 00:30:05,360 --> 00:30:08,040 Speaker 1: I would say a little bit more reserved with what 615 00:30:08,080 --> 00:30:12,920 Speaker 1: I've experienced from him anyways, especially on camera. Yeah, yeah, 616 00:30:12,960 --> 00:30:15,440 Speaker 1: he's a little bit more reserved, very nice, always willing 617 00:30:15,480 --> 00:30:18,480 Speaker 1: to help out. I like that answer. I think for me, 618 00:30:19,760 --> 00:30:23,040 Speaker 1: this is tough because honestly, people are gonna say, oh, 619 00:30:23,040 --> 00:30:25,480 Speaker 1: you're biased, but truly, there are a lot of great 620 00:30:25,520 --> 00:30:28,920 Speaker 1: guys inside that locker room. But I'm gonna have to 621 00:30:29,000 --> 00:30:33,920 Speaker 1: say I'm not have to say full Aronzo Fadokasi, he 622 00:30:34,160 --> 00:30:37,640 Speaker 1: is just, honestly, Cynthia probably one of the most genuine, 623 00:30:38,400 --> 00:30:40,760 Speaker 1: kind human beings I've ever met. A lot of people 624 00:30:40,880 --> 00:30:42,840 Speaker 1: can back that up to. He's just We did a 625 00:30:42,840 --> 00:30:45,800 Speaker 1: segment with him the other day for Jet Life and 626 00:30:45,920 --> 00:30:48,600 Speaker 1: it was a fun segment. It was it's called Jeopardy, guys, 627 00:30:48,600 --> 00:30:52,280 Speaker 1: gotta watch it out shameless plug Jet Life, Jeopardy, super 628 00:30:52,400 --> 00:30:56,760 Speaker 1: super fun. He was energetic, he was interactive, just he 629 00:30:56,800 --> 00:31:01,880 Speaker 1: was like just truly himself, just very fun, happy, love 630 00:31:01,920 --> 00:31:04,440 Speaker 1: that love that about him. All right, let's let's move 631 00:31:04,480 --> 00:31:06,680 Speaker 1: on to our third one, our third and final one. 632 00:31:06,880 --> 00:31:10,280 Speaker 1: Here's Cynthia. This one is from Nat Allard. He says, 633 00:31:10,800 --> 00:31:14,640 Speaker 1: what concrete steps can the NFL, parentheses and other leagues 634 00:31:15,000 --> 00:31:18,000 Speaker 1: take to increase the number of women in front office 635 00:31:18,080 --> 00:31:21,760 Speaker 1: leadership roles and also in the coaching ranks. That's really 636 00:31:21,800 --> 00:31:24,080 Speaker 1: good question. I think the NFL is making steps in 637 00:31:24,120 --> 00:31:26,560 Speaker 1: that way, though, Synday, you want to go first. I 638 00:31:26,600 --> 00:31:29,040 Speaker 1: think part of it is the pipeline. Right, you don't 639 00:31:29,040 --> 00:31:31,160 Speaker 1: get a you don't get a good coaching job or 640 00:31:31,160 --> 00:31:33,680 Speaker 1: a front office job if you don't have experienced, like 641 00:31:33,720 --> 00:31:36,440 Speaker 1: it's very hard to get, you know, in any job. 642 00:31:36,440 --> 00:31:39,120 Speaker 1: It's very hard to get the CEO job if you've 643 00:31:39,120 --> 00:31:42,640 Speaker 1: never worked in the industry that that you're maybe getting 644 00:31:42,680 --> 00:31:44,960 Speaker 1: to be the CEO of. So it's all about the 645 00:31:45,000 --> 00:31:50,080 Speaker 1: pipeline and encouraging younger, more entry level women to start 646 00:31:50,120 --> 00:31:52,960 Speaker 1: in those ranks and then grow them because there is 647 00:31:53,000 --> 00:31:57,160 Speaker 1: an aspect of football and basketball and baseball that's mentorship. 648 00:31:57,480 --> 00:31:59,480 Speaker 1: It's not it's very hard to come in from the 649 00:31:59,520 --> 00:32:02,320 Speaker 1: outside once you get past a certain level, it's very 650 00:32:02,360 --> 00:32:04,160 Speaker 1: hard to come in from the outside and be a 651 00:32:04,160 --> 00:32:07,520 Speaker 1: really big contributor because there's so many nuances. I mean, 652 00:32:07,640 --> 00:32:10,520 Speaker 1: the revenue system alone in the NFL is just so 653 00:32:10,720 --> 00:32:13,480 Speaker 1: unique and very it's very nuanced. So if you don't 654 00:32:13,520 --> 00:32:15,320 Speaker 1: have experience in it be very hard to come in 655 00:32:15,680 --> 00:32:19,600 Speaker 1: at a level that's high. Right, So it's establishing and 656 00:32:19,680 --> 00:32:23,000 Speaker 1: increasing the pipeline of younger women that are getting hired 657 00:32:23,000 --> 00:32:26,600 Speaker 1: to rules that are a bit more entry level. Incredible 658 00:32:26,640 --> 00:32:29,760 Speaker 1: answer again, like, and I think you're very correct in 659 00:32:29,760 --> 00:32:32,600 Speaker 1: that way, in order to just like any other business, 660 00:32:32,640 --> 00:32:34,280 Speaker 1: because at the end of the day, sports still are 661 00:32:34,320 --> 00:32:38,440 Speaker 1: a business. So in order to make steps and you know, 662 00:32:38,560 --> 00:32:41,600 Speaker 1: become promoted being those leadership roles, like you said, you 663 00:32:41,640 --> 00:32:44,280 Speaker 1: have to have more women, which I think there are now. 664 00:32:44,360 --> 00:32:47,880 Speaker 1: I think many more women are encouraged in wanting to 665 00:32:47,920 --> 00:32:51,040 Speaker 1: do entry level roles getting in sports, and sports are 666 00:32:51,080 --> 00:32:55,160 Speaker 1: becoming better and better about that on implementing women into 667 00:32:55,200 --> 00:32:58,160 Speaker 1: the leaks. So I think you're really going to see 668 00:32:58,160 --> 00:33:01,360 Speaker 1: a lot more women in leadership possibly even coaching roles 669 00:33:01,760 --> 00:33:05,160 Speaker 1: within the next years to come, because since women have 670 00:33:05,200 --> 00:33:07,960 Speaker 1: already started getting a foot in the door in sports, 671 00:33:08,240 --> 00:33:11,000 Speaker 1: now you're seeing them rise ranks. Like you said, Now 672 00:33:11,040 --> 00:33:13,440 Speaker 1: you see these women who have been in the league 673 00:33:13,480 --> 00:33:16,320 Speaker 1: for years and years and have learned so many different things. 674 00:33:16,640 --> 00:33:19,000 Speaker 1: So I think it's gonna it could change a lot 675 00:33:19,080 --> 00:33:20,840 Speaker 1: within the future, and I think it will just like 676 00:33:20,880 --> 00:33:24,000 Speaker 1: it has in many other sports. Yeah, I'm really encouraged 677 00:33:24,040 --> 00:33:26,640 Speaker 1: because it's so Yeah, I always get that thing where 678 00:33:26,760 --> 00:33:28,840 Speaker 1: because I don't I don't work in like marketing or 679 00:33:28,880 --> 00:33:32,000 Speaker 1: you know, some some certain rules are more conducive to 680 00:33:32,040 --> 00:33:35,480 Speaker 1: women being hired than others. Right, And I'm seeing changed 681 00:33:35,560 --> 00:33:39,680 Speaker 1: and I'm seeing more females being hired two more finance roles, 682 00:33:39,760 --> 00:33:42,320 Speaker 1: and more rules like mine with analytics like this is 683 00:33:42,760 --> 00:33:45,400 Speaker 1: it's a growing field in general, but you know, women 684 00:33:45,440 --> 00:33:47,640 Speaker 1: are also being a part of that growth. And part 685 00:33:47,680 --> 00:33:50,320 Speaker 1: of it is just knowing that the jobs are available. 686 00:33:50,560 --> 00:33:52,840 Speaker 1: So go out there and pound the pavement. Don't be 687 00:33:52,880 --> 00:33:56,000 Speaker 1: intimidated by the fact that you know, it seems like 688 00:33:56,200 --> 00:33:58,320 Speaker 1: a dream job to go work for the Jets. You 689 00:33:58,360 --> 00:34:00,480 Speaker 1: can go get that job. You just got to keep 690 00:34:01,000 --> 00:34:04,160 Speaker 1: working and keep at it and not give up. And 691 00:34:04,200 --> 00:34:06,440 Speaker 1: that's and that's the same thing. If you want you know, 692 00:34:06,520 --> 00:34:10,160 Speaker 1: they're just are more jobs at banks right, there are 693 00:34:10,200 --> 00:34:13,360 Speaker 1: more banks, you know, like, so these are harder jobs 694 00:34:13,360 --> 00:34:15,560 Speaker 1: to get just in general, so you have to keep 695 00:34:15,600 --> 00:34:18,719 Speaker 1: looking even harder because they're very competitive jobs. So just 696 00:34:18,960 --> 00:34:22,200 Speaker 1: keep going. Yeah, I will say that once you do 697 00:34:22,280 --> 00:34:24,680 Speaker 1: get your foot in the door, it's a lot easier 698 00:34:24,719 --> 00:34:27,080 Speaker 1: to stay. It's all about getting your door in the door. 699 00:34:27,120 --> 00:34:29,520 Speaker 1: But once once you're in, it's easier to stay as 700 00:34:29,520 --> 00:34:31,960 Speaker 1: long as you work your butt off it really, it's true, right, 701 00:34:32,280 --> 00:34:37,440 Speaker 1: that's right, Cynthia Freeland. I am so happy you successful 702 00:34:37,480 --> 00:34:40,640 Speaker 1: first guest for the my new podcast, The New Yorker. 703 00:34:40,760 --> 00:34:43,040 Speaker 1: Thank you so much. I cannot thank you enough. You 704 00:34:43,080 --> 00:34:46,960 Speaker 1: are incredibly intelligent, You're gracious with your time, You're always 705 00:34:47,000 --> 00:34:50,120 Speaker 1: so kind and have wonderful advice. Thank you so so 706 00:34:50,200 --> 00:34:52,640 Speaker 1: much for being the first guest on The New Yorker podcast. 707 00:34:53,400 --> 00:34:56,400 Speaker 1: I am so I feel so grateful. Thank you for 708 00:34:56,520 --> 00:34:59,560 Speaker 1: being allowing me to be your first guest. And I 709 00:35:00,000 --> 00:35:01,480 Speaker 1: and that wait to see what you do with this. 710 00:35:01,680 --> 00:35:03,960 Speaker 1: I know it's going to be great. I'm cheering for 711 00:35:04,120 --> 00:35:06,040 Speaker 1: you all along. Anything you need, you know I've got 712 00:35:06,080 --> 00:35:08,440 Speaker 1: your back. Thank you so much, Cynthia. Everybody, thank you 713 00:35:08,480 --> 00:35:11,279 Speaker 1: for tuning in. This has been an incredible start to 714 00:35:11,360 --> 00:35:14,400 Speaker 1: the New Yorker podcast. I hope you guys listen every 715 00:35:14,400 --> 00:35:17,600 Speaker 1: week whenever, whenever it comes out. This was just a 716 00:35:17,680 --> 00:35:19,520 Speaker 1: start to the season. We're gonna have a lot of 717 00:35:19,520 --> 00:35:22,560 Speaker 1: fun content for you throughout the entire year. So thank 718 00:35:22,600 --> 00:35:24,160 Speaker 1: you and I'll see you guys next week