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AI in the NFL: Tracking Players, Preventing Injuries, and Changing the Game

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In this episode of Cream City Calculations, Colleen, Frankie, and Sal huddle up to explore how artificial intelligence and data analytics are transforming the NFL and professional sports more broadly. From RFID chips in shoulder pads to Amazon Web Services processing over 500 million data points each season, the team breaks down the cutting-edge tech driving decisions on the field—and in front offices.

They dive into how real-time tracking, next-gen stats, and AI-powered film analysis are improving player performance, reducing injuries, and even changing how teams draft talent and manage contracts. Along the way, they discuss fan engagement, ethical questions about data in gambling and fantasy sports, and what AI might mean for the future of scouting and sports broadcasting.

With real-world examples like the NFL’s Digital Athlete system and a shoutout to Green Bay hosting the upcoming NFL Draft, this episode connects the dots between high-tech and high-impact sports moments. Whether you're a data geek, sports fanatic, or both, this episode offers a lively and insightful play-by-play of AI's growing role in the game.

Colleen:

Welcome to the Cream City Calculations podcast. We're three colleagues and friends that love data and to talk about how data is impacting our lives. I'm Colleen. I'm Frankie. And I'm Sal.

Sal:

Blue 42! Blue 42! Say it! Hike! Well, as you can read, today's topic is around the NFL and the use of AI, within the NFL and in different sports as well. I thought this one is extremely interesting, especially because as you see. All the time, on ESPN, you see all the stats on ESPN, or if you're watching any football games, you're always seeing like next gen stats. So we get to talk about that, talk about a little bit of how they do it, and then what they collect and kind of how it's modernizing, the sport.

Colleen:

and how it's used. Yeah. So I think one of the first things we want to talk about, there's a page on the NFL's website, the National Football League's website about next generation stats. Um, And like all of our episodes, we'll put these, the links to these articles in our show notes. But, this article calls out sort of what their tracking system consists of.

Sal:

Yeah, I think maybe I'm one of our last. Episodes or one of our episodes prior, we talked about, RFIDs, and how they're being used in the NFL at that point. But this is to go a little bit more detail, but one of the main things that they use in the technology they use is R-R-F-I-D chips. and they put'em in their shoulder pads. For football players, referees use them. And honestly, like I've seen a lot of other sports have like, it's almost like a sports bra where they put the sensor in, and a lot of them are using this to track, both in practice and on the field, their players and how they're moving, and how much effort a lot of these players are so that they can manage their rest Performance, to get the most out of each of the athletes.

Colleen:

Yeah, and Sal, I'll never forget. We did an event together once with the Boys and Girls Club of Milwaukee, where you had some of these little chips and let the kids hold on to them and then tracked their data as they moved around. And There was nothing cooler to those kids than to think that they were like their sports heroes and that they could kind of analyze and take a look at where they had been within the space of the room while they were holding them.

Sal:

Yeah, that was really fun and I think it blew their mind

Frankie:

Thank you for

Sal:

they learned wow, this is the art of the possible.

Frankie:

again soon.

Colleen:

it made it very clear to them how that technology works. Yeah. you know, it also helps track things as far as injuries. So if you think of, players being in a certain space on the field, you know, what percentage of the time do people encounter injuries in certain situations? And is there a way then to prevent those injuries by eliminating those scenarios?

Sal:

Absolutely. A couple of other things as well with that is they have the stadium sensors and cameras also, to monitor a location, speed, and acceleration data. with that, there's also a lot of different types of technology that NFL teams are using stadium sensors where they can look at real time location, speed and acceleration, and something closer, to my heart is advanced analytics, using AI powered models to. Generate insights around game strategy, player efficiency, and honestly, their health risks as you talked about.

Colleen:

Yeah. that's really interesting, when you've got information that you can analyze to see, X percent of the time when the quarterback throws the ball to this side of the field or many times is that play completed?

Sal:

Yeah, I think it's almost the amount of data points that they get. It must be overwhelming. They have to be using. Really good, databases to process this information and turn it around quickly so that they can get insights back to the coaches as quickly as possible. So it's quite amazing.

Colleen:

And I think, too, about, you announcers, the Men that are commentating the game, get data, processed and turned around back to them very quickly within the course of the game so that they can comment on it.

Frankie:

circling back to what the next gen stats are from, from the NFL. That's a partnership with Technologies, Wilson Sporting Goods, all this is run entirely on Amazon Web Services infrastructure. And so the whole goal of this was to provide. NFL clubs with data to analyze the trends and player performance while enhancing fan experience and on online. And then also during the telecast. And I also just want to call out the RFID stands for radio frequency identification. And those tags are installed in the player's shoulder pads, but they also tested a bunch of other different spots of where to put those.

Sal:

Thanks for calling that out. One of the things that I always like to see is max speed. I like to see when an athlete can see who's the fastest and they track it with the line. So, as a fan of the sport, I love this.

Colleen:

Yeah, so there's another interesting stat in an article from, Vertisant. says the NFL's partnership with Amazon Web Services processes 500 million data points per season.

Sal:

That is, I think even less than I thought,

Colleen:

Yeah. I mean,

Sal:

I was thinking like billions and billions of data points. It's crazy that they can build models to do that and process through that, really fast.

Colleen:

Right.

Sal:

A couple of other things in that article they talk about is AI is now taking. So one of the biggest things that coaches and athletes do is watch video, right? Watch film. AI is helping curate that a little bit better. And so they're using AI to analyze game film, how athletes are moving so that these athletes are not spending so much time to get the insights that they are. now these AI, is telling them those stats before having to go through all that.

Colleen:

I feel like it's a really great example of the speed of evolution, I guess, the word I was looking for, in a industry that is so fast moving and so fast paced. I feel like it's really kind of like the sensors to those kids, right? Like, it paints a very good picture of how quickly, things can change and innovate. Right.

Sal:

Absolutely. one kind of the, it's such a fast growing, industry or broadcast, I guess you can call it. AI is driven, insights having boosted NFL's viewership with the NFL games accounting for 93 percent of all the most watched US. TV broadcast in 2020 in this article 2023, which you think of 93 percent of all of the biggest views Super Bowl is all NFL games. And to have AI help drive that is quite amazing

Colleen:

Right. And the same article kind of calls out the differences in generations, like there are far fewer millennials watching sports than Gen Xers. using data in this way or AI in this way can certainly help to try and get some of that market share back from younger generations.

Frankie:

Yeah, that's interesting. Why do you think that is?

Colleen:

You know, I think I see a lot of trends happening that way. If we talked about, in our beer episode about, the younger generations aren't consuming as much alcohol as the older generation. millennials and Gen Z, as the Gen Zers sort of come into adulthood, they're buying fewer homes, fewer cars. we could comment on all sorts of different socio economical reasons why those trends occur maybe this is more of that.

Frankie:

I wonder too, like how they track their viewers and how they get to that number. Because I think about all the people that go to like a bar to watch a game. and how, how would they be able to recognize something like that? And I also think like that's more the, the Gen X. and even somewhat millennial generations where they're going somewhere else to watch a game.

Colleen:

And you're

Frankie:

be

Colleen:

suggesting like the younger generations are doing that less.

Sal:

or streaming it on their phone and watching it in a different game on their TV or watching it in multiple different formats, I think that's,

Colleen:

You'd

Sal:

I think

Colleen:

evolved with the times and that now it's like streaming and amount of, of, you know, where the streams are coming from. Like, I don't know how you would know what generation the, you know, the person streaming the game would be from. there may be something to be said for, you know, people streaming and watching the sports in a way that traditionally wasn't done.

Frankie:

Yeah, and I'm sure they have something there to account for that. But how, yeah, like, how do they know many people are watching that screen? If there's only one screen, could they be undershooting it? Or are they making estimates and models to estimate, like, based upon the square footage of the building? because that's all public information, you can look at that. then the location of the streaming, are they estimating how many viewers there are,

Colleen:

Right.

Frankie:

which could be overshooting it, too?

Sal:

now with a lot of the tracking, and the cookies that people have, it's probably a lot easier than you would imagine to get what released from your phone, your computer, probably your smart TV. and knowing who's tracking that, or knowing what they're watching based on the cookies,

Colleen:

also remember the use of cookies is a very American thing.

Sal:

Yes.

Colleen:

are talking about American sports here, but

Sal:

Yeah.

Colleen:

you would. if anywhere, you'd be able to track that, it would be here. You're right.

Sal:

That's why we don't know the premier league.

Colleen:

The premiere, right? That's why we don't know who's watching football. The non American football.

Frankie:

really interesting point, though. Like, are they including, is it just S. viewers, or are they including international viewers, too? Because I would imagine that we've seen an uptick, in international viewers for sports like American football. but maybe the downtick is actually for US viewers, because I did see that soccer increased.

Sal:

I think that'd be a really interesting future podcast is how they track, Ratings,

Colleen:

hmm.

Sal:

but in this case, I think it's just us based according to this art.

Colleen:

I do think, we have seen more, interest in American football, if we're to believe, you based on scheduling of NFL games I mean, you're seeing more and more every season being played in Germany or, in the UK. And you'd have to think that there was some demand for that there, or they wouldn't try to make it a thing.

Sal:

Oh, absolutely.

Frankie:

I'd love to see, too, how the AWS stats compare for the games in a different country versus the games at home for these teams.

Colleen:

Yeah. So if you're out there and you you work in that industry, we'd love to talk to you.

Sal:

Yeah, I would imagine that, like, not every stadium is the same. Not every. Like, system is the same. how it's kind of overall Changing the landscape of, different applications or different things that are bringing into the sport how people are using this type of data for gambling or for kind of fantasy football, I would love to hear your take on that should they provide some kind of licensing that this can't be used for that? Or yeah, this is great. This is actually adding to the ability to gamble or to do fantasy with fun.

Colleen:

was going to ask about some of the rules changes that we've seen in the NFL in recent years. know now we've got more data. We can know more about these injuries or about certain types of plays and the outcomes that you like, has that led them to change some of the officiating rules? think that's a really interesting, like, should it be allowed to use that in gambling? why would that not be fair game?

Sal:

but you just have to be able to collect all of it,

Colleen:

what if you could like pay for a subscription to get the data from all these sensors and you could analyze it yourself? Is that a fair advantage to have if you are a gambler?

Sal:

wonder if you can actually buy this data publicly.

Colleen:

I mean, it's not going to be publicly available data, but at the same time, it could be a revenue stream for the NFL should they need another one.

Sal:

Yeah.

Colleen:

And I also

Sal:

You probably would love it.

Colleen:

In case you missed that episode, we interviewed a friend of all of ours and also local to the Milwaukee area who does a lot of sports betting. And so we have an episode about that and about the ways he's used data to make his bets. yeah, I feel like just having this amount of data available has opened up all sorts of for discussion or for uses. For that data, one of these articles here that I'm looking at, you talks about fan engagement and how could you use this data to make sure that your fans are engaged with the game and want to go to games? how do you make sure that they want to the game or to go to a bar and watch the game? the officiating, just drafting players think about how much more, competitive it must be these days versus 20 years ago when all this data wasn't available.

Sal:

Yeah, and honestly, like having, it almost makes a combine not needable If they're doing this in college, like, don't we know they're 40 times based on what they can run.

Colleen:

right,

Sal:

Yeah.

Colleen:

worth of data to back that up,

Sal:

I wonder, if we have anybody out there that does this, feel free to hit us up and we will love to have you on our podcast.

Colleen:

Cause we can be super nerdy and talk about all the data with you.

Sal:

Absolutely.

Colleen:

Yeah,

Frankie:

Do we want to focus in on injury for a few

Colleen:

we could do that. I

Frankie:

So the NFL has developed something called the Digital Athlete System, and so this is That they created, that was really just taking strides in player safety and injury prevention. The injury rates were pretty significant for a while there, and they've been decreasing. I think I saw a stat in one of these articles that said, That in 2022 to 2023, they saw a reduction of injuries 700 fewer missed games due to injuries in 2023 compared to 2022.

Colleen:

that's a great improvement, but what would the numbers start out as, you know,

Frankie:

And,

Colleen:

a small percentage of the injuries that we saw in previous years?

Sal:

I like it. Cause it talks about like AI looking at stress patterns, on lower limbs. So like knees and analyzing the movement. Of a player in practice and, in a game and making sure that they're adjusting the training workload and kind of stresses on that, and probably coaching them into different kind of movements so that they can not have season ending injuries. The 1 thing I do notice is like when people get hurt. And this is probably something for our healthcare system or whatever. If they get their back way quicker than they used to be. And then, I wonder if a lot of it is like. Understanding like, all right, these are the stress points. And if we kind of support those muscles around the stress points, how do we, how can we kind of better perform better counter injuries?

Colleen:

like how can you recover more quickly by resolving, the cause. I think about the concussion protocols too. I think that's a big one. You sort of called this out earlier, Sal, but, they don't mess around with that anymore. And there have been changes to. both the helmets and to the game to try and lessen the amount of concussions. But they also, you, if you're under concussion protocol, like you're out of the game, like you have to meet certain criteria before you're going to come back in. think that goes a long way toward preventing other future injuries as well.

Frankie:

And just for reference that digital athlete system has only been around for two full seasons.

Colleen:

Okay.

Frankie:

So

Colleen:

Interesting.

Frankie:

And it's using real time data and then it's creating simulations. So that coaches can also utilize those simulations to see, if they play a player for X amount of games, what's the likelihood of them getting an injury?

Colleen:

Yeah.

Sal:

it actually kind of brings up a debate in MBA. Right? Again, this is you're talking to NFL here, but, 1 is like game management, and whether an athlete should be play every game. if especially if they have a chance to win, maybe, the NBA championship. and so. It's in the NFL. I wonder if this might lead into more player management of like, oh, okay, we're not going to play every one of our receivers or we're going to do specific routes that put less strain on them as in a game managed Game, I guess, so that an athlete, maybe a top tier athlete that you need deep in the playoffs, is actually doing less rigorous routes early in the season or at certain points, where they think they, they could get it. They can get that advantage.

Colleen:

Yeah, now that's interesting. playing your best guy, you every single game will get you to the playoffs, but can that player persist through all of the playoffs? Probably not.

Frankie:

it would be interesting to see, for the most part in the NFL, it's like, you play your first string if they can play,

Colleen:

Yeah.

Frankie:

occasionally your second string when they need a break. And I'd love to see, how could that be different? If they started to utilize more of the AI capabilities that are available and they could predict, like, would the second string person be able to beat out the other team's first string and give that person a rest? And if they got a rest, how would that impact their score or their performance stats.

Sal:

Yeah. Like it's something that comes to mind and get my brain thinking, is like. How hockey rotates

Frankie:

Yeah.

Sal:

I wonder if they're going to have more of like a line or especially around what I think is like offense and defensive lines, where they're rotating people in and out more frequently. I bet you that you can already see that happening.

Colleen:

Yeah.

Sal:

It's rotating them in and out so that they might have less reps overall, but they're not getting beat up on plays that, hey, this play is going to the right? And I find my left tackle. Maybe I don't need to be in on that play. I can save myself a little bit on that play.

Colleen:

really comes down, I think, to being smarter, just like you said, rotating the players in and out, but also rotating the types of plays that you run, so as to sort of balance that impact on your various players, and I wonder, too, like you suggested that maybe we could already see this happening already. I bet you happens so slowly that if you compared The rotation of players in 2024. That was our most recent season, compared to five years before that. Can you notice a in that five year span? willing to bet it'll become even more pronounced as we as we go forward with that, because it like a smart way to retain your talent and not only, throughout the course of one season, but player's entire career,

Sal:

Yeah, no, absolutely. They absolutely will start doing that. one of the things that come in mind is do you think they're going to use AI to counter that? from a perspective of like, Hey, they're rotating this player out. We know typically if they rotate. they're doing this type of play.

Colleen:

aren't rotating this player out.

Sal:

yeah.

Colleen:

know, like, if, if You could probably make all sorts of implications off of whether people do or do not do the things that they typically do.

Frankie:

Right, or even just analyzing the best matchup if they rotate a player out,

Colleen:

I'm sure, Sal. I'm sure that's all part of it. If it isn't already, it will be. Interesting. You know, one of the other things that, like, connections that my brain makes is we talk about, you know, kind of, rotating players in and out as to sort of lessen the impact on their bodies. you think that's going to affect player stats? Whereas you've got X number of players that may have hit a thousand rushing yards in a game in their career. think if they're going to be sort of played less or, maybe split sort of that starter role to a certain extent with other, I don't know, let's just say running back, do you think that you'd have players that are disappointed by that, practice, given that they might not be able to hit these milestones?

Sal:

Can't say that, that the Detroit Lions did that, but based on how they rotated their, I think it was Montgomery and Gibbs. Yeah, the two running backs, like they rotated him out and they both had astronomical numbers this year because they were probably more fresh. But then on the flip side, you have like, shake on Barkley, who is. He, I don't think he came out.

Colleen:

Yeah.

Sal:

So yeah, I think it's depending on the athlete and how they're built and how fatigued they get and injury prone they might be I absolutely think that they're already starting to do that. there's absolute game management and stat management. What's going to be a big impact is. Your contracts, a lot of your contracts are based on stats. Are athletes going to be willing to say, Hey, I know that the analytics is telling me to sit out more plays. Or take rests on these types of plays, but my contract says I need to get 1500 yards. if I sit out 20 percent of my typical runs throughout the season, I'm not going to hit that number. And so how do I adjust that contract to get even deeper,

Colleen:

the other side of that coin, though, too, is that if they do this sort of rotation based on potential injury, could players have longer seasons?

Sal:

longer careers. for sure.

Colleen:

Longer careers. maybe that'll be reflected, hopefully reflected in their contracts, instead of saying I need to get 1500 yards this season, maybe it's 1000 yards this season, but then it's a three year contract or a five year contract that says you need to meet that for five years.

Sal:

Absolutely.

Frankie:

Yeah, I bet that their contracting is going to be different to like, just as you're kind of saying, but also thinking about if you have around a professional sports player, knowing their injury history, knowing their likelihood of how long it'll be until they're injured again, that would probably impact how long they would sign contracts with a person.

Colleen:

Absolutely.

Frankie:

I've seen some enormous astronomical contracts come through I'm just like, I don't get it. I don't get why they sign these players for that many years, knowing they're probably going to be hurt.

Colleen:

then too, what about analysis of the contracts themselves? Like it'd be interesting to hear out of, let's say there's been a hundred contracts written for a long period of time. Let's say it's five years. How many of those players were able to fulfill that contract for that length of time? I bet you there's all sorts of analysis done around that side of the business as well.

Sal:

Absolutely.

Colleen:

Like, is it worth the risk of writing a contract in those terms? Are we going to be able to get the results we're hoping for?

Sal:

Colleen, you mentioned something around, the draft and how athletes are going to start, or how they're using AI to start, like, doing some of it. I'm really excited about the Packers holding, the draft at, Lambeau Field, or I guess the NFL holding the draft at Lambeau Field. I am really curious on how much AI is going to be used around the draft picks, where people are going to be. I think now more than ever, as AI, has jumped tenfold in the last couple of years, I'm curious on how much more it will be used in this draft versus the last one,

Colleen:

think that's really timely and close to home because again, as you mentioned, it's, draft this year will be held just a few hours north of where we are, up in Green Bay. Do you have any background? Info on that sale. I

Sal:

I mean,

Colleen:

was trying to give you a really good intro there to talk about something.

Sal:

overall, I do think Like historically in the evolution of sports in general, there was the scout that was the computer, right? that scout would come and tell the coaches, Hey, I think this player is great. They'd have a collective of, of, of scouts that would tell the coaches and then they, the coach or the GM would make the decision. I think this is going to shift a little bit out of the scouting hands. I think a scout is going to be. Part of it, but I think it's going to be heavily weighted this year, at least in the analytics, and I think in the last couple of years, but even more than ever, it's going to be shifted towards the analytics and how, how athletes are going to perform under. Under this, the overall metrics and the KPIs that come from that, versus the other way, like the old way.

Colleen:

In what ways do you think predictive analytics maybe plays a role in something like the draft? I think about, you comment we made just a few minutes ago about, combine and about how we would have, potentially for a college player, four years worth of historical data about their performance in any particular role. Do you have any thoughts on, how that might be used in something like the draft?

Sal:

Yeah, I don't think it's going to be majorly insightful on the first round, right. because those athletes have been seen and. Used a lot, right?

Colleen:

like they've had their time on ESPN already.

Sal:

Yeah. And these coaches and GMs and scouts all know these athletes and they perform and they can see kind of see how they would perform, right? Obviously, they're going to take that into some of the analytics into account and AI and use AI for that. But I think where they're going to use the prediction is your fringe players. So who is that? the 249th pick of the NFL draft, who I'm thinking of is like Purdy, right? He was the last pick, and he was an NFL starting quarterback and no one knew that he was as good as he was, right? and he performed way outside of what his scouts would have said. I'm curious on if now he is A predictor or like that kind of attribute or what kind of attributes go into that unknown player that can perform for you?

Colleen:

What can we learn from the Brock Purdy experiment, is what you're asking.

Sal:

Exactly. And honestly, the defensive player of the year, Brawn, He was a linebacker, everybody kind of wrote him off. I don't think he went in the second or third round or something lower. And he was now one of the best NFL linebackers out there. And it's because he has these traits that give him more drive or he's faster and certain things, or he can read a defense faster than the typical person that gives him an edge.

Colleen:

again?

Sal:

To a model and then doing a prediction on that and saying, how likely are these fringe players going to outperform them?

Colleen:

Yeah. you know, sometimes too, I think you could have outliers. You do have those anomalies that are outside of the curve.

Sal:

you want to identify those because you can take advantage. That's where you're going to get one. That may be a little bit of leg, both from a contracting perspective and the money perspective, right? You get a cheaper athlete that is performing at a higher rate. That's great for an NFL team. the other thing around that is, is you're going to identify maybe a way to like play a player in a specific role based on these analytics, and say, Hey, this guy has the potential to maybe be a better kick returner for us or to be a better slot receiver in these types of plays. I think that's going to be pretty impacted.

Colleen:

Absolutely.

Frankie:

Yeah, another thing to bring up, too, is there aren't as many scouts needed anymore because of the technology that's been developed. like, for example, Brigham Young University, they took this example and found that, so many coaches, so many scouts are spending a ton of time analyzing footage, watching games, and really this is something that could be analyzed through AI. And so this is a task that usually takes A ton of time to together like you're, you know, You're traveling to a game. but the solution was an AI algorithm that automated player detection and automated formation analysis. And so if all the stats that you need on a person, and you have the footage analyzed through AI. You can make, judgments or at least rule people out a little bit quicker and focus on who you need to see.

Colleen:

Yeah, I do, like to argue still to keep the human element and things. But to your point, keep it simple and work smarter, not harder type of thing, where you can maybe you still need a scout, I I think there is something to be said for a person's grit and determination and their attitude on the field. But to your point, I lot of the work can be done by those algorithms.

Sal:

That actually brought me a good point. So one of the things that I think these models might do that are kind of thinking outside the box is thinking about player fit. Not just like fitness, but fit. how do they, understand if they're going to be a headcase in the locker room? are they going to do, will they fit with our, our ready? If you're taking all these. Metrics, especially if you're bringing in social metrics to, this can NFL teams start to understand, all right, this is going to be a leader within our organization. And we actually see a lot of potential based on, using AI models to say, Hey, this guy has a high leader score, or this guy has a bad locker room score That we might not want to put him in our locker room because he actually might be toxic for our team, because I think that's a huge part.

Colleen:

Yeah, or his personal life is a mess and what the activities he engages in outside of the football field make him not a good fit for this team.

Sal:

Exactly.

Frankie:

Okay.

Sal:

So I think that the NF l's gonna start using it, which is, it is funny'cause I would never think, I don't know why in my head like the NFL is so I didn't think was so cutting edge, but they are

Colleen:

But think

Sal:

we

Colleen:

any industry that's got that much money to spend and invest

Sal:

Exactly.

Colleen:

its players. I think it just goes without saying that they would then have the money to invest in these technologies.

Sal:

But the fact that they can move quickly, I think that's more impressive.

Frankie:

Sure.

Sal:

all worked in data in, in. The financial industry and healthcare industry, like in legal industry, it doesn't move fast in corporate world, but here, some reason they can move light years faster than us

Colleen:

to bet reason is like million dollar

Sal:

ownership. Yeah, I think it's ownership.

Colleen:

Yeah.

Sal:

and so versus all the different corporations,

Frankie:

And Colleen, to your point before too, there was a really good line in one of these articles that says, AI provides valuable insights, but the critical decisions remain in the capable hands of the human experts. I don't foresee AI taking over and making decisions. I think they're always just going to be that tool in the toolbox to help the coaches and the owners and GMs make good decisions.

Colleen:

Yeah. AI is a great tool.

Sal:

yeah, I think that can bring us to the end of our podcast and say AI is going to be a compliment to human judgment. it's going to add insights, but it's still up to the human So When you're watching the NFL and getting mad that the GM, picked this person or extended their contract and you don't like them, know that there is some analytics in there that they're taking additional information in

Colleen:

And I think too, like if you do work in the data space, you use the NFL as an example, like in what ways can you replicate what they're doing to make sense for the industry that you work in?

Sal:

Absolutely

Frankie:

Maybe we need to do an analysis on the bears why their analytics are such a failure to them.

Colleen:

That would be interesting.

Frankie:

That's a wrap on today's episode. If you loved today's episode, make sure to subscribe to stay up to date on other topics related to data. Thank you for listening to Cream City Calculations. Until next time, keep calculating.