1
00:00:00,120 --> 00:00:03,320
Speaker 1: Everyone gets their cravings while watching the games, and no

2
00:00:03,360 --> 00:00:06,440
Speaker 1: one wants to be the one to miss the big play. Well,

3
00:00:06,480 --> 00:00:10,240
Speaker 1: now Grubhub's got you covered. From the extras to the essentials.

4
00:00:10,400 --> 00:00:13,600
Speaker 1: Grubhub now delivers all your go to convenience items all

5
00:00:13,680 --> 00:00:17,040
Speaker 1: day long. Whether it's that late night snack craving or

6
00:00:17,079 --> 00:00:19,520
Speaker 1: you forgot to get the paper towels from the grocery store.

7
00:00:19,920 --> 00:00:23,000
Speaker 1: Enjoy convenience delivered right to your door, just in the

8
00:00:23,079 --> 00:00:25,880
Speaker 1: nick of time, and you'll never have to leave the house.

9
00:00:26,440 --> 00:00:28,960
Speaker 1: Order your convenience items through the grub Hub app or

10
00:00:29,040 --> 00:00:32,440
Speaker 1: online today. Welcome in to the Giants Huddle podcast, brought

11
00:00:32,520 --> 00:00:34,960
Speaker 1: to you by a WS, the proud partner of the

12
00:00:34,960 --> 00:00:38,560
Speaker 1: New York Football Giants. I'm Madelinburg, joined today by NFL

13
00:00:38,640 --> 00:00:42,440
Speaker 1: Networks analytics expert Cynthia Freeland. I almost want with analytics

14
00:00:42,479 --> 00:00:44,960
Speaker 1: analysts because I was just trying to flex on that.

15
00:00:44,960 --> 00:00:51,000
Speaker 1: That was a big flex times fast. Thanks, actual time. Actually,

16
00:00:52,640 --> 00:00:54,360
Speaker 1: thanks you're taking the time with us today. We're out

17
00:00:54,360 --> 00:00:57,240
Speaker 1: here at the NFL Combine, taken in a lot of data.

18
00:00:57,280 --> 00:00:59,240
Speaker 1: I mean, analytics is a word that it seems like

19
00:00:59,280 --> 00:01:03,160
Speaker 1: people think a new wave of things, but really gathering

20
00:01:03,240 --> 00:01:06,120
Speaker 1: data and information has always been around, So how do

21
00:01:06,160 --> 00:01:08,520
Speaker 1: you explain the way that the collection of it all

22
00:01:08,600 --> 00:01:10,840
Speaker 1: is evolved? I mean, basically it's a tool for the

23
00:01:10,840 --> 00:01:13,800
Speaker 1: tool kid. And the interesting part about how it's evolved

24
00:01:13,840 --> 00:01:16,800
Speaker 1: is that technology has really enabled us to look at

25
00:01:16,880 --> 00:01:19,360
Speaker 1: new data sets. So next you stats of course, which

26
00:01:19,400 --> 00:01:23,319
Speaker 1: really started in you get more GPS based data, but

27
00:01:23,360 --> 00:01:25,960
Speaker 1: then different technologies like computer vision which allows you to

28
00:01:26,000 --> 00:01:29,080
Speaker 1: measure things back from before. You know, like I gotta

29
00:01:29,120 --> 00:01:31,600
Speaker 1: measure Mathias Knuka. That's like my college best friend right there.

30
00:01:31,640 --> 00:01:35,440
Speaker 1: So how to throw that into you know, love it um.

31
00:01:35,520 --> 00:01:38,759
Speaker 1: So long story short, it's really the technology that's enabled

32
00:01:38,800 --> 00:01:40,840
Speaker 1: us to look at things in a different way and

33
00:01:40,880 --> 00:01:43,840
Speaker 1: then work with coaches hand and glove to say what

34
00:01:43,920 --> 00:01:47,360
Speaker 1: am I looking at here? Does the data support this conclusion?

35
00:01:47,520 --> 00:01:49,960
Speaker 1: Or the coaches can say, hey, can you go kind

36
00:01:50,000 --> 00:01:52,840
Speaker 1: of research this because we're seeing this trend of like

37
00:01:52,920 --> 00:01:54,640
Speaker 1: if you look at some of the quarterbacks that have

38
00:01:54,680 --> 00:01:58,440
Speaker 1: been coming out of recent years, Joe Burrow, Patrick Mahomes

39
00:01:58,480 --> 00:02:01,200
Speaker 1: have this data where you know, is there something different

40
00:02:01,360 --> 00:02:03,200
Speaker 1: I'm seeing on the field that like when we play

41
00:02:03,200 --> 00:02:05,240
Speaker 1: a single high safety we get burnt, but when we

42
00:02:05,280 --> 00:02:07,560
Speaker 1: do to safety shells. Something is different, and then the

43
00:02:07,640 --> 00:02:09,280
Speaker 1: numbers can go back and support it and be like,

44
00:02:09,320 --> 00:02:12,000
Speaker 1: you're absolutely right, this is because of this, and it

45
00:02:12,360 --> 00:02:15,760
Speaker 1: just helps you deepen and enrich those conversations about strategy

46
00:02:15,960 --> 00:02:18,320
Speaker 1: so you can ultimately win more games, pick the right

47
00:02:18,400 --> 00:02:21,799
Speaker 1: draft prospects, figure out the right blend of people in

48
00:02:21,880 --> 00:02:24,600
Speaker 1: free agencies to acquire or pay or not, and kind

49
00:02:24,639 --> 00:02:26,440
Speaker 1: of put that out together. So what you're putting on

50
00:02:26,480 --> 00:02:29,600
Speaker 1: the field is absolutely your best strategic attempt to beat

51
00:02:29,639 --> 00:02:32,720
Speaker 1: your opponents, right, And so analytics isn't anything new. It's

52
00:02:32,760 --> 00:02:36,360
Speaker 1: just more information and it's just a new or a

53
00:02:36,400 --> 00:02:39,119
Speaker 1: different way of approaching. And you know, you've got your

54
00:02:39,160 --> 00:02:41,720
Speaker 1: model and your math equations that you plug in and

55
00:02:41,800 --> 00:02:44,400
Speaker 1: some of that. I still am just like how like

56
00:02:44,919 --> 00:02:47,960
Speaker 1: I can barely run an Excel spreadsheet. So how do

57
00:02:48,000 --> 00:02:50,320
Speaker 1: you kind of start Where do you start with these things? Well?

58
00:02:50,360 --> 00:02:52,720
Speaker 1: I started in the League office in two thousand and eight,

59
00:02:52,760 --> 00:02:54,920
Speaker 1: and I was working in finance, and I have a

60
00:02:54,960 --> 00:02:57,280
Speaker 1: finance background. When you obviously in New York, lots of

61
00:02:57,280 --> 00:02:59,800
Speaker 1: people have finance backgrounds. But I got a chance to

62
00:02:59,800 --> 00:03:02,640
Speaker 1: work in the League office on some some projects about

63
00:03:03,000 --> 00:03:05,480
Speaker 1: you know, figuring out, Okay, what's the right number of

64
00:03:05,600 --> 00:03:07,760
Speaker 1: games to put the best product on the field. Is

65
00:03:07,800 --> 00:03:11,360
Speaker 1: it sixteen regular season games and four pre seventeen and three.

66
00:03:11,560 --> 00:03:13,680
Speaker 1: You obviously see where we net it out, But in

67
00:03:13,720 --> 00:03:16,000
Speaker 1: doing so, you have to work with the competition committee.

68
00:03:16,040 --> 00:03:19,360
Speaker 1: So these awesome coaches who teach you what you're watching

69
00:03:19,400 --> 00:03:22,320
Speaker 1: on film and why teams win and why teams don't win,

70
00:03:22,440 --> 00:03:24,799
Speaker 1: or what's a high probability low probably what is the

71
00:03:25,080 --> 00:03:29,040
Speaker 1: philosophy behind more competitive games? Because ultimately, what you want

72
00:03:29,080 --> 00:03:31,280
Speaker 1: on the field is a game where you think you

73
00:03:31,320 --> 00:03:33,600
Speaker 1: can win any single game and you don't feel like

74
00:03:33,639 --> 00:03:36,520
Speaker 1: it's David versus Goliath in any game, and you want

75
00:03:36,840 --> 00:03:41,040
Speaker 1: more teams to be more playoff eligible games to happen longer.

76
00:03:41,520 --> 00:03:43,200
Speaker 1: And so that's what we tried to figure out, and

77
00:03:43,240 --> 00:03:45,680
Speaker 1: by kind of mapping together what happened on the field

78
00:03:45,920 --> 00:03:48,480
Speaker 1: to finance, that's where we came up with. That's where

79
00:03:48,520 --> 00:03:50,680
Speaker 1: I started writing those things. So it's all it's all finance.

80
00:03:50,720 --> 00:03:52,640
Speaker 1: It's all finance. It's the same things like Wall Street

81
00:03:52,640 --> 00:03:55,360
Speaker 1: trading stuff. For me, some people are more statistically inclined.

82
00:03:55,600 --> 00:04:01,320
Speaker 1: I'm a trader. There you go, that's is there anything

83
00:04:01,360 --> 00:04:03,280
Speaker 1: more annoying than having to run to the store and

84
00:04:03,320 --> 00:04:06,000
Speaker 1: freezing cold weather when all you want to do is

85
00:04:06,040 --> 00:04:09,760
Speaker 1: stream endlessly from the comfort of your couch, Or realizing

86
00:04:09,800 --> 00:04:13,240
Speaker 1: after just going to three different grocery stores that you

87
00:04:13,360 --> 00:04:16,920
Speaker 1: forgot the toilet paper and refuse to enter yet another

88
00:04:17,000 --> 00:04:19,960
Speaker 1: parking lot. Wouldn't it be nice to have someone appear

89
00:04:20,000 --> 00:04:23,120
Speaker 1: with the items you're missing right to your door. Well, now,

90
00:04:23,200 --> 00:04:26,520
Speaker 1: grub Hub's got you covered. Grubhub now delivers all your

91
00:04:26,520 --> 00:04:29,640
Speaker 1: go to convenience items all day long. Whether it's a

92
00:04:29,760 --> 00:04:32,560
Speaker 1: craving for something sweet during a commercial break, or you

93
00:04:32,680 --> 00:04:36,440
Speaker 1: forgot those bathroom essentials. Grubhub will deliver anything from the

94
00:04:36,440 --> 00:04:39,520
Speaker 1: convenience store right to your door and you'll never have

95
00:04:39,560 --> 00:04:42,200
Speaker 1: to leave the house. Order your convenience items through the

96
00:04:42,200 --> 00:04:46,560
Speaker 1: Grubhub app or online today. When you get to this

97
00:04:46,640 --> 00:04:49,080
Speaker 1: point of the season two though, when you're evaluating talent

98
00:04:49,120 --> 00:04:52,520
Speaker 1: and and also converting from college to pro. I mean,

99
00:04:52,600 --> 00:04:55,159
Speaker 1: it's it's football, but it's not really the same game.

100
00:04:55,680 --> 00:04:57,960
Speaker 1: How do you kind of take that data from college

101
00:04:58,320 --> 00:05:02,360
Speaker 1: and apply the necessary variables to show how it would translate?

102
00:05:02,440 --> 00:05:05,600
Speaker 1: Or can you you can um the way that Again,

103
00:05:05,640 --> 00:05:07,200
Speaker 1: another tool in the tool about just the way that

104
00:05:07,240 --> 00:05:10,039
Speaker 1: scouts go and say this one looks like this guy

105
00:05:10,160 --> 00:05:13,719
Speaker 1: reminds you hear comps. All of it's comp season. Oh

106
00:05:13,760 --> 00:05:16,560
Speaker 1: he's the next so and so. Right, But some of

107
00:05:16,600 --> 00:05:19,880
Speaker 1: that mathematically does work out. And people think about moneyball,

108
00:05:19,920 --> 00:05:21,560
Speaker 1: and they think about baseball, and there are guys who

109
00:05:21,600 --> 00:05:23,640
Speaker 1: are sluggers and home run hitters, and then there are

110
00:05:23,640 --> 00:05:26,159
Speaker 1: guys who are hit singles and doubles. And they're not

111
00:05:26,240 --> 00:05:28,880
Speaker 1: the same body type. They're not the same expectations of them.

112
00:05:29,040 --> 00:05:30,800
Speaker 1: They don't have the same you know, tools, and their

113
00:05:30,839 --> 00:05:33,320
Speaker 1: tool about from an athletic standpoint, is the same thing

114
00:05:33,400 --> 00:05:35,679
Speaker 1: for us. So you are having a supply. I'm looking

115
00:05:35,680 --> 00:05:37,400
Speaker 1: at some of these people who are I guess I'm

116
00:05:37,400 --> 00:05:39,360
Speaker 1: not only the only media now that the players are

117
00:05:39,400 --> 00:05:41,000
Speaker 1: now gone, but I was looking at them, and you

118
00:05:41,040 --> 00:05:43,600
Speaker 1: see different body types for wide receivers. Right, Some are

119
00:05:43,600 --> 00:05:47,440
Speaker 1: now like very tall and very good at you know, balls,

120
00:05:47,480 --> 00:05:49,440
Speaker 1: and more like you know, maybe like they're like a

121
00:05:49,480 --> 00:05:52,640
Speaker 1: forward in in in basketball. And some are more shifty

122
00:05:52,680 --> 00:05:55,279
Speaker 1: and small and they create more separation and they're speedier

123
00:05:55,320 --> 00:05:57,560
Speaker 1: in their first three yards off the line of scrimmage,

124
00:05:57,560 --> 00:06:00,400
Speaker 1: are really fast. So It's really about what what am

125
00:06:00,440 --> 00:06:02,520
Speaker 1: I looking at? What are the things that this thing

126
00:06:02,600 --> 00:06:04,400
Speaker 1: can and can't do? And I say thing because we

127
00:06:04,560 --> 00:06:07,839
Speaker 1: were every single position right, And then you map it

128
00:06:07,880 --> 00:06:10,440
Speaker 1: together with what's currently on your roster and where you

129
00:06:10,480 --> 00:06:13,159
Speaker 1: have needs and what your coach strategically thinks is going

130
00:06:13,200 --> 00:06:17,680
Speaker 1: to make you be the Eagles, for example, or the Cowboys. Cowboys?

131
00:06:19,120 --> 00:06:21,679
Speaker 1: Um Now, I mean when you look at the combine,

132
00:06:21,720 --> 00:06:23,880
Speaker 1: this is a big data gathering event, right. There are

133
00:06:23,880 --> 00:06:26,520
Speaker 1: a lot of events some guys choose to participate in all.

134
00:06:26,560 --> 00:06:28,280
Speaker 1: Some guys choose to participate in some. But are there

135
00:06:28,400 --> 00:06:31,360
Speaker 1: some that you think are more valuable or more predictive

136
00:06:31,600 --> 00:06:34,520
Speaker 1: for certain positions. I'm going to tell you something, and

137
00:06:34,560 --> 00:06:36,720
Speaker 1: all my analytics people are going to lose their minds.

138
00:06:37,200 --> 00:06:39,880
Speaker 1: This doesn't really matter. As long as people file into

139
00:06:39,920 --> 00:06:44,440
Speaker 1: a range that's acceptable. There are diminishing returns for these

140
00:06:44,440 --> 00:06:46,640
Speaker 1: guys who run blazing fast forwards. I mean it's fun.

141
00:06:47,160 --> 00:06:49,480
Speaker 1: I love fun. Sure, it's great for fun, But when

142
00:06:49,520 --> 00:06:51,280
Speaker 1: it comes to what happens on the field, like when

143
00:06:51,320 --> 00:06:53,320
Speaker 1: you put pads on and you're running a crossing rap,

144
00:06:53,480 --> 00:06:55,200
Speaker 1: it doesn't matter that you had four or three speed.

145
00:06:55,400 --> 00:06:57,240
Speaker 1: I mean it would matter if you had five three

146
00:06:57,600 --> 00:07:01,800
Speaker 1: not speed, the opposite of speeds five three slow whatever. Um,

147
00:07:01,839 --> 00:07:04,560
Speaker 1: but it would matter if that But between you know,

148
00:07:04,600 --> 00:07:07,000
Speaker 1: if you're falling in these acceptable ranges, none of that

149
00:07:07,080 --> 00:07:09,440
Speaker 1: stuff matters. But what you're really doing is you're giving

150
00:07:09,440 --> 00:07:13,120
Speaker 1: evaluators here a chance one to interview too. They go

151
00:07:13,160 --> 00:07:14,920
Speaker 1: through the process. It's like everything. It's like when we

152
00:07:14,960 --> 00:07:16,240
Speaker 1: went to college you had to take the SA T.

153
00:07:16,360 --> 00:07:17,960
Speaker 1: It's the same thing. You just have to go through

154
00:07:18,000 --> 00:07:20,560
Speaker 1: these things that gather the data doesn't mean anything at all.

155
00:07:21,000 --> 00:07:23,280
Speaker 1: And then ultimately, if you're falling out a range and

156
00:07:23,440 --> 00:07:26,160
Speaker 1: it makes them go a second look, either in a

157
00:07:26,200 --> 00:07:28,480
Speaker 1: good way or a second look in a bad way,

158
00:07:28,720 --> 00:07:30,760
Speaker 1: and you just don't want to do the bad one.

159
00:07:30,880 --> 00:07:32,760
Speaker 1: So these are all just really benchmarks that you have

160
00:07:32,840 --> 00:07:36,240
Speaker 1: to hit. And that's fair. And I think it maybe

161
00:07:36,280 --> 00:07:38,280
Speaker 1: don't tell my bosses that I said that. I won't. Well,

162
00:07:38,320 --> 00:07:41,000
Speaker 1: just only broadcasting figure it out. I'm sure they're listening

163
00:07:41,000 --> 00:07:43,680
Speaker 1: live right now, so thank you. UM. But I think

164
00:07:43,680 --> 00:07:46,120
Speaker 1: it's interesting too because you see some of these guys

165
00:07:46,160 --> 00:07:47,880
Speaker 1: that really put their name on a map, at least

166
00:07:47,880 --> 00:07:52,120
Speaker 1: in the public forum, and by these impressive performances, and

167
00:07:52,240 --> 00:07:54,320
Speaker 1: some of them it translates to success, and some of

168
00:07:54,320 --> 00:07:56,480
Speaker 1: them you know, like not so much. I mean we

169
00:07:56,520 --> 00:07:59,720
Speaker 1: saw Giants receiver John Ross, I mean his combine performance

170
00:07:59,720 --> 00:08:05,120
Speaker 1: was in leg legendary forty um. How much though, I mean,

171
00:08:05,360 --> 00:08:10,120
Speaker 1: especially the newer evolution and availability of GPS information and

172
00:08:10,160 --> 00:08:12,960
Speaker 1: the speed and data. How much is that data being

173
00:08:13,080 --> 00:08:16,560
Speaker 1: used by NFL teams or is it available the smart teams?

174
00:08:16,640 --> 00:08:18,920
Speaker 1: And I will say I know for a fact now

175
00:08:18,960 --> 00:08:21,120
Speaker 1: your new GM and your new coach, because I had

176
00:08:21,160 --> 00:08:23,240
Speaker 1: spent some time with the Bills last preseason, maybe you

177
00:08:23,240 --> 00:08:25,920
Speaker 1: guys can have me this preseason because well we'll work

178
00:08:25,960 --> 00:08:28,000
Speaker 1: it out. Well, it will be a team. No, that'd

179
00:08:28,000 --> 00:08:31,600
Speaker 1: be fun anyway. So is there anything more annoying than

180
00:08:31,640 --> 00:08:33,720
Speaker 1: having to run to the store and freezing cold weather

181
00:08:34,160 --> 00:08:36,520
Speaker 1: when all you want to do is stream endlessly from

182
00:08:36,520 --> 00:08:40,120
Speaker 1: the comfort of your couch, Or realizing after just going

183
00:08:40,120 --> 00:08:43,720
Speaker 1: to three different grocery stores that you forgot the toilet

184
00:08:43,720 --> 00:08:48,079
Speaker 1: paper and refuse to enter yet another parking lot. Wouldn't

185
00:08:48,080 --> 00:08:50,080
Speaker 1: it be nice to have somewhat appear with the items

186
00:08:50,080 --> 00:08:53,280
Speaker 1: you're missing right to your door. Well, now grub Hub's

187
00:08:53,320 --> 00:08:56,320
Speaker 1: got you covered. Grubhub now delivers all your go to

188
00:08:56,440 --> 00:08:59,720
Speaker 1: convenience items all day long. Whether it's a craving for

189
00:08:59,760 --> 00:09:02,719
Speaker 1: something sweet during a commercial break, or you forgot those

190
00:09:02,760 --> 00:09:06,680
Speaker 1: bathroom essentials, grubhub will deliver anything from the convenience store

191
00:09:06,760 --> 00:09:09,360
Speaker 1: right to your door and you'll never have to leave

192
00:09:09,360 --> 00:09:12,160
Speaker 1: the house or are your convenience items to the grubhub

193
00:09:12,200 --> 00:09:15,480
Speaker 1: app or online today. Spend some time with them last year,

194
00:09:15,520 --> 00:09:18,400
Speaker 1: and I know they're very thoughtful about, you know, speed,

195
00:09:18,720 --> 00:09:21,559
Speaker 1: Like these metrics in a in a in a vacuum

196
00:09:21,600 --> 00:09:24,600
Speaker 1: don't matter, but when you look to see okay, you

197
00:09:24,640 --> 00:09:27,280
Speaker 1: know is For example, one of the things I find

198
00:09:27,320 --> 00:09:29,960
Speaker 1: most fascinating is the erosion in speed from the fourth

199
00:09:30,040 --> 00:09:32,600
Speaker 1: quarter to the first quarter. So if you're getting tired

200
00:09:33,040 --> 00:09:36,400
Speaker 1: and you're running significantly slower, maybe then that's a thing. Hey,

201
00:09:36,400 --> 00:09:38,080
Speaker 1: we gotta get our strength and conditioning guy in here,

202
00:09:38,120 --> 00:09:41,080
Speaker 1: because are we gotta make our asset we gotta keep

203
00:09:41,120 --> 00:09:43,640
Speaker 1: we gotta keep this guy healthier or figure out something different,

204
00:09:43,720 --> 00:09:45,920
Speaker 1: or you know, it's a it's a flag for ways

205
00:09:45,960 --> 00:09:48,080
Speaker 1: that you can be better. And the teams that do

206
00:09:48,120 --> 00:09:51,680
Speaker 1: it right use the information to enhance the things that

207
00:09:51,760 --> 00:09:54,080
Speaker 1: they have. And I know that I know your head

208
00:09:54,080 --> 00:09:56,160
Speaker 1: coach and I know your GM are both people who

209
00:09:56,480 --> 00:10:00,360
Speaker 1: really understand that and really know how to Like again,

210
00:10:00,360 --> 00:10:02,120
Speaker 1: it's being it's cool to be like, hey, you know

211
00:10:02,120 --> 00:10:04,079
Speaker 1: who the rand the fastest this week. But by the way,

212
00:10:04,280 --> 00:10:06,240
Speaker 1: often when a guy runs the fastest, it's like a

213
00:10:06,440 --> 00:10:10,440
Speaker 1: go route and that like a busted coverage. Like that's great, cool,

214
00:10:10,720 --> 00:10:15,480
Speaker 1: but also not predict exactly. And when you have so

215
00:10:15,559 --> 00:10:18,160
Speaker 1: much data being gathered in the college level between the

216
00:10:18,160 --> 00:10:22,560
Speaker 1: film the GPS data, does it in a way make

217
00:10:22,640 --> 00:10:25,640
Speaker 1: some of this stuff at the combine obsolete. I think

218
00:10:25,640 --> 00:10:27,920
Speaker 1: maybe the count was a little obsolete before, but I

219
00:10:27,960 --> 00:10:30,560
Speaker 1: think what it really does is it adds context to

220
00:10:31,160 --> 00:10:33,760
Speaker 1: like part of what these guys are being judged on

221
00:10:34,000 --> 00:10:38,120
Speaker 1: is their ability to go through this process and to prepare.

222
00:10:38,520 --> 00:10:40,760
Speaker 1: This is their own off season. They did not have

223
00:10:40,800 --> 00:10:42,680
Speaker 1: any They don't work for a teams, so they don't

224
00:10:42,679 --> 00:10:44,800
Speaker 1: have a team giving them their strength and conditioning and

225
00:10:44,840 --> 00:10:47,400
Speaker 1: their So this is what are you gonna It's it's

226
00:10:47,440 --> 00:10:49,719
Speaker 1: your it's your resume, it's it's are you coming with?

227
00:10:49,720 --> 00:10:52,200
Speaker 1: Like this resume that's like formatted beautifully and like have

228
00:10:52,320 --> 00:10:54,360
Speaker 1: you put the time, work and effort and energy into

229
00:10:54,440 --> 00:10:56,960
Speaker 1: it or have you like not like, have you been

230
00:10:57,000 --> 00:10:58,760
Speaker 1: distracted by some of the things that you could be

231
00:10:58,800 --> 00:11:01,640
Speaker 1: distracted by. So it's not making it obsolete, it's just

232
00:11:01,679 --> 00:11:05,120
Speaker 1: more making it transparent that it's about what it's about.

233
00:11:05,160 --> 00:11:08,080
Speaker 1: Your films already on. Scouts already know, they already know.

234
00:11:08,640 --> 00:11:11,560
Speaker 1: Just keep you know you. You HR brought you and

235
00:11:11,559 --> 00:11:13,760
Speaker 1: I in for our interviews and then we nailed it, right,

236
00:11:13,960 --> 00:11:15,839
Speaker 1: you got the jobs. But but if but if you

237
00:11:15,880 --> 00:11:20,600
Speaker 1: hadn't prepared and you would showed up create like all disheveled. Okay,

238
00:11:20,600 --> 00:11:23,560
Speaker 1: maybe she just wanted us things like that. Well, you know,

239
00:11:23,640 --> 00:11:25,880
Speaker 1: you look at this, it's all a job interview for

240
00:11:25,920 --> 00:11:28,920
Speaker 1: these guys. But these coaches, these scouts have been watching

241
00:11:28,960 --> 00:11:31,760
Speaker 1: these players for some time now, and you get all

242
00:11:31,760 --> 00:11:35,000
Speaker 1: this data and in some way is it human nature

243
00:11:35,040 --> 00:11:38,280
Speaker 1: to use it as like confirmation by it or like,

244
00:11:38,320 --> 00:11:40,440
Speaker 1: oh see, I can use these numbers to verify what

245
00:11:40,480 --> 00:11:43,880
Speaker 1: I already think in a way. How do you kind

246
00:11:43,920 --> 00:11:47,920
Speaker 1: of separate the fact from the feeling in that way? Yeah,

247
00:11:47,960 --> 00:11:51,079
Speaker 1: So I guess the best part about analytics is making

248
00:11:51,160 --> 00:11:54,000
Speaker 1: neutral decisions, meaning you don't want to be we never

249
00:11:54,040 --> 00:11:55,679
Speaker 1: want to, you never want to you and I don't

250
00:11:55,720 --> 00:11:58,439
Speaker 1: want to make a decision if I'm emotionally charged, super

251
00:11:58,440 --> 00:12:01,480
Speaker 1: happy or super not happy. Right, So the idea is

252
00:12:01,520 --> 00:12:04,240
Speaker 1: to say, we've got regional scouts that cut like your

253
00:12:04,440 --> 00:12:06,319
Speaker 1: your scouting partm is awesome, by the way, I love

254
00:12:06,320 --> 00:12:09,400
Speaker 1: your scouts, and Jeremy Bright, he's awesome. Long story short,

255
00:12:10,080 --> 00:12:13,480
Speaker 1: they all, but he doesn't see everyone that a North

256
00:12:13,520 --> 00:12:16,440
Speaker 1: East Scouts seas or whatever. However, your break gets down, right,

257
00:12:16,480 --> 00:12:19,160
Speaker 1: So it's trying to put everyone on the same page.

258
00:12:19,480 --> 00:12:22,480
Speaker 1: And the data can be like a benchmark for that, right,

259
00:12:22,520 --> 00:12:24,440
Speaker 1: And so if you're saying that you know this guy

260
00:12:24,640 --> 00:12:27,120
Speaker 1: is going to perform at this level, here's our benchmark.

261
00:12:27,200 --> 00:12:28,880
Speaker 1: Let's get all on the same page, and the data

262
00:12:28,920 --> 00:12:30,440
Speaker 1: can help you get on the same page. Is never

263
00:12:30,480 --> 00:12:32,960
Speaker 1: going to make decisions. The refinement needs to come from

264
00:12:33,000 --> 00:12:35,520
Speaker 1: the humans. But like, let's help each other get to

265
00:12:35,640 --> 00:12:38,240
Speaker 1: like this level here, so we can have a conversation

266
00:12:38,400 --> 00:12:41,400
Speaker 1: about like So then you're choosing between one A and

267
00:12:41,520 --> 00:12:44,079
Speaker 1: one B as opposed to like one and seven. Right,

268
00:12:44,160 --> 00:12:46,839
Speaker 1: so you're really just level setting to make things. We

269
00:12:46,880 --> 00:12:49,080
Speaker 1: can all speak the same language. Then you can get

270
00:12:49,120 --> 00:12:50,800
Speaker 1: on the same page with your free agents. How much

271
00:12:50,800 --> 00:12:52,800
Speaker 1: should we pay someone this is what the market says.

272
00:12:52,840 --> 00:12:54,959
Speaker 1: A fifteen million dollar wide receiver looks like. This is

273
00:12:55,000 --> 00:12:57,000
Speaker 1: the type of production. Is this how we want to

274
00:12:57,120 --> 00:13:00,000
Speaker 1: construct our team. Here's what's available in the draft. Here's

275
00:13:00,000 --> 00:13:01,880
Speaker 1: the numbers, Here's what's a bail of and free agency.

276
00:13:01,920 --> 00:13:04,480
Speaker 1: Here's the numbers. Here's our strategy. It's getting everyone on

277
00:13:04,640 --> 00:13:07,720
Speaker 1: one page and kind of math is the universal language

278
00:13:07,720 --> 00:13:10,439
Speaker 1: in that way. So that's a benefit unfortunately, or fortunately,

279
00:13:10,520 --> 00:13:13,760
Speaker 1: I think unfortunately for you because I could listen to

280
00:13:13,760 --> 00:13:16,040
Speaker 1: you talk about this all day. Cynthia Freeland, you can

281
00:13:16,080 --> 00:13:20,360
Speaker 1: find her on NFL Network Analytics Expert and I can't

282
00:13:20,400 --> 00:13:23,640
Speaker 1: say any analytics analystics really hard to say analytics and

283
00:13:23,720 --> 00:13:26,520
Speaker 1: a list and follow her on Twitter at s Freeland

284
00:13:26,760 --> 00:13:28,320
Speaker 1: and also as a reminder, you can find the Giants

285
00:13:28,400 --> 00:13:32,439
Speaker 1: Huddle podcast on the Giants app and all podcast platforms

286
00:13:32,760 --> 00:13:36,520
Speaker 1: for continue coverage of the NFL combine. Thanks for stopping

287
00:13:36,600 --> 00:13:38,920
Speaker 1: by and Giants Hutle podcast of course, brought to you

288
00:13:39,080 --> 00:13:43,320
Speaker 1: by AWS. Everyone gets their cravings while watching the games,

289
00:13:43,679 --> 00:13:45,760
Speaker 1: and no one wants to be the one to miss

290
00:13:45,800 --> 00:13:49,400
Speaker 1: the big play. Well now Grubhub's got you covered, from

291
00:13:49,440 --> 00:13:52,640
Speaker 1: the extras to the essentials. Grubhub now delivers all your

292
00:13:52,720 --> 00:13:56,040
Speaker 1: go to convenience items all day long, whether it's that

293
00:13:56,200 --> 00:13:58,559
Speaker 1: late night snap craving or you forgot to get the

294
00:13:58,600 --> 00:14:02,360
Speaker 1: paper towels from the groceries door. Enjoy convenience delivered right

295
00:14:02,400 --> 00:14:04,920
Speaker 1: to your door, just in the nick of time, and

296
00:14:05,200 --> 00:14:08,120
Speaker 1: you'll never have to leave the house. Order your convenience

297
00:14:08,160 --> 00:14:10,560
Speaker 1: items through the grub Hub app or online today