Frontiers: Exploring the World of Data | Revolutionizing Sports Betting Through Analytics

Tim Winkler: 00:00:00

Hey listeners, Tim Winkler here, your host of the pair program. We've got exciting news introducing our latest partner series beyond the program. In these special episodes, we're passing the mic to some of our savvy, former guests who are returning as guest hosts. Get ready for unfiltered conversations, exclusive insights, and unexpected twist as our alumni pair up with their chosen guest. Each guest host is a trailblazing expert in a unique technical field. Think data, product management, and engineering, all with a keen focus on startups and career growth. Look out for these bonus episodes dropping every other week, bridging the gaps between our traditional pair program episodes. So buckle up and get ready to venture beyond the program. Enjoy.

Brian Beachkofski: 00:00:51

Hello

Jazmin Furtado: 00:00:53

everyone, and welcome to Frontiers, exploring the world of data. Frontiers dives into how people are using their data science minds to shape organizations and change the landscape outside of big tech. In each episode, we explore the far reaching corners of the world of data. My name is Jasmine and I'm your host for this series. I myself am passionate about empowering people to make data driven decisions, and I'm always amazed at how others do it every day. Today, we're exploring the sea of sports analytics. How advanced analytics can reshape the sports landscape. With that, I want to introduce our guest for today, Brian Beachkovsky. Brian is a leader in innovation who has worked in a variety of industries in both the private and public sectors, but a common thread has been his focus on creating value through software, leveraging data and analytics to empower people. I had the pleasure of working for him while at Kessel Run, an organization building software for the Air Force. And while Kessel Run was only a small part of his career, he made a lasting impact on not just the organization, but also the team he led and the users he supported, which I strongly suspect is a theme for him with all the teams he has worked with. He is currently co founder and CTO of Rhythm, a tool that provides predictive analytics for sports betting, which is our main topic for today. And I'm so curious about how he got into this space, into this... Arena, um, but more of that to come in a bit. So thank you so much for being here, Brian. It is a pleasure to say the least.

Brian Beachkofski: 00:02:33

No, thanks for having me. I look forward to the

Jazmin Furtado: 00:02:34

conversation. It should be fun. So start, uh, speaking of fun, our icebreaker question, I wanted to start off to, uh, get the ball rolling. So the question is, if you were to compete in one sport on the world stage, does it matter whether you're good at it or not you to choose one? Which sport would it be? And why? So I'll start with, I would choose rock climbing because I got into it while I was in college and I really enjoyed it. Uh, and I really liked how it's kind of like a puzzle and you feel kind of like a superhero and a ballerina at the same time while you're doing it. So it's like a really cool mix of like brain strength and grace. So I just think it'd be really cool to just do that on the world stage and not have to worry about whether I'm good at it or not. Just, I think it'd just be

Brian Beachkofski: 00:03:28

really fun. Yeah, that's awesome. I love, I love climbing, but I think I would pick hockey. I, uh, I grew up playing hockey. I'm I love, I love ice hockey. I was a goalie growing up and, uh, uh, there's just no better sport in the world in my eyes. It has everything from. Speed and contact to, uh, uh, strategy and, and all of it mixed together and watching the elite athletes play is, is just an amazing thing to do. And to be able to be out there on the ice with them, even if I was the worst person there, amazing. Are

Jazmin Furtado: 00:04:04

you still

Brian Beachkofski: 00:04:04

playing now? Oh yeah. I was still in men's league. That's one of the things that has one of the highest rates of people playing. And we have a guy who's 65 on our, on our team. So, uh, there's really no upper, upper end of that. You can, you can keep going with the, with everyone far into your later years.

Jazmin Furtado: 00:04:22

Wow. Yeah. I imagine on the East coast, there's like a lot of that, a lot of snow, especially Northeast. So it's a big, big field out there. So, Brian, our main topic for today is to discuss how you've applied, like, your data and analytics mindset to change the playing field in sports, uh, specifically when it comes to sports betting, but you haven't always been in this space. So, uh, I'm curious if we could start off with you speaking 1st about rhythm and speak about, um, what you do and the, the tool and then walk us through and take us back a little bit how you got.

Brian Beachkofski: 00:05:01

Yeah, absolutely. Uh, so, so like you said in the intro, rhythm helps, uh, helps the average person make smarter bets. What we realized is that people have been, uh, betting the same way for an eternity. Uh, they, they go with their gut. They ask their buddy who they like on tonight's game, or they just think someone's due or, uh, have some other hunch based way of doing it. And that just seems out of place in a world where that person probably won't even pick a restaurant based on a hunch, right? They check, they check for reviews and they look at the data and they, they make a data informed decision on, on where to get dinner, but they. They go with their gut when it comes to putting money down on a bet. So that shouldn't be. And like, why, why is there not something out there? And it looked like, uh, it, it hadn't existed because there are a couple of things that made it hard to, to create models for people, um, uh, and to let people have that insight into their own modeling approach. So that's basically our goal is to make, uh, to make modeling easier for people to understand sports betting. Uh, Ultimately trying to get to a no code analytic service for folks to make their own models to play around with things and have access to tools that will enable them to make data informed decisions when they're, when they're doing sports betting. Uh, so the other part of the question is how do, how do I end up there from, uh, all the other things I've done? Um, well. I started at, in the air force research labs doing analysis of jet engine reliability and like, uh, how do we make maintenance decisions better? When can you predict, uh, maintenance will be needed or when there could be a higher probability. Of an incident with a, with an engine, uh, but believe it or not, uh, even within the air force, it's hard to get engine data, uh, the, the, the companies, uh, who manufactured the data on that data, there's usage, right? So I found it was much easier for me to go out and scrape sports data. Like I said, I grew up playing sports. I like sports. Uh, so being able to learn some of the analytical, uh, approaches and apply it on sports data was just a way for me to teach myself and have that internal professional development, uh, while, uh, while trying to apply that within the Air Force. So that was my, my first approach into sports analytics was, was just using that to teach myself the techniques that I was using for my day job. Um, from there, I worked in the Pentagon for a while, doing some budget analysis, doing, uh, uh, uh, more analytics around, um, patent and scientific journal publication data to identify emerging tech, uh, research areas. So that got more into, uh, the language modeling there and in predictive analytics from that side. Uh, and then after that job, I went to private industry and worked at a startup, uh, in the nonprofit sector. Thank you. That was doing data analytics to understand the impact of human service delivery in a local community. So how can we use that community's data to understand if work programs are actually developing people into a career as intended? Are programs for homeless services getting people self sufficient and in stable housing down the road? But still using that to understand, using the data of outcomes to understand. How to make those decisions up front better. So that was like the path I got here. And then I met my co founder, Megan, who said, what if we did something similar, uh, for sports betting? And I said, yeah, I think, you know, at that time I was then doing college basketball analytics as my side project, trying to teach myself stuff. And, uh, she was a division one basketball coach in a former life. So being able to, uh, connect there. And, uh, put what was, you know, a hobby at the time into practice was

Jazmin Furtado: 00:08:58

awesome. And I think when people talk about their careers today, in retrospect, you're like, Oh, these were like the common things are like, you know, it just seems to all piece together when you look back at it. Is that how it was kind of unfolding to you as you were experiencing it, or were there a lot of. Whether it's a be dead ends or a lot of exploration and other fields to see what's stuck, you know, what was the journey like, as you were going through it?

Brian Beachkofski: 00:09:26

Yeah, that's a great question because no, that story did not play out. Like, it does have a common theme in retrospect, but it was not part of a grand strategy. Uh, uh, I. I left the research lab because I was at that point. A lot of us get to, uh, when, when you get around 30 of, am I going to be a deep technical expert or go into a managerial track? And, uh, I realized that I was more interested in making an impact in a way that was possible from the management side of. The, uh, the business. So that's where I went into the Pentagon to get a breadth of experience within the, my air force career to be able to set that up. Uh, and then when I, when I went to third sector capital partners, uh, that, that nonprofit, uh, social impact team, uh, that was. Uh, relationship I had developed at business school that they were looking for someone to expand their quantitative analysis. And I had that experience. So part of that career track was based on my quantitative experience bringing it in, but it was, it was there because it was an interesting problem. Uh, and, and I wanted to solve hard and interesting problems and it was there. And that's, that's a continuing theme too, is just take something that's interesting. And I think your career. Story will write itself in retrospect. If you keep challenging yourself with, uh, interesting and challenging problem, meaningful problems that actually address, uh, some, something someone needs fixed.

Jazmin Furtado: 00:11:01

And it, but with sports betting or with sports generally, you've always had an interest. It's something that has been something that's kind of been on the outskirts and then you've been able to like tap into, um, as you follow your more professional or like formal career, I guess, path, um, for lack of better terms. Uh, The sports interest, though, became more of a reality later on in your career in terms of an actual career move or pivot. What was it about the timing of that or was there like, what other factors at play that made you realize, okay, this is a good time to actually get into this space and actually start to capitalize on these interests of mine. Outside of just something that's more on the side of more of a hobby. Yeah,

Brian Beachkofski: 00:11:44

it's a, it's an interesting story because the timing was sort of. Uh, fortuitous, I guess. So it started out with me and Megan, uh, talking about, uh, is this possible? And it was a hobby. So on nights and weekends wrote some Python code and, uh, uh, was making predictions, uh, for games and, and tracking out how they were actually. Working in a spreadsheet, and we did that enough and said, actually, it looks like this is working pretty well and outsourced a very rough first iteration of the web app to go out there. And we actually spent about 2 years. Just collecting data with about 50 friends and family users that were tracking things on there, uh, and got about 2000 games of predictions and showed that there was, that it was working and Megan was in business school at the time and wrote it up as a, as a project plan for one of, uh, one of her classes to, to build out a, uh, a pitch deck and business plan and through a friend that got into, uh, uh, Um, A VC seed company, uh, who wanted to take a look and got interested and made an offer and the timing worked out pretty well that it was right at the time my command tour at Kessel Run was coming to an end too. So, uh, it was actually a little earlier than we were planning it to be, uh, but that's how, you know, if, if you have a hobby and you're developing your skills and the data is there and someone else is making a data driven. Decision, it can, uh, it can, it can drive your time.

Jazmin Furtado: 00:13:23

Were there other, uh, like more macro influences at play in terms of like the landscape of sports betting or the landscape of technology that really enabled or jumpstarted the development or the fruition of this tool?

Brian Beachkofski: 00:13:39

Yeah, there was a Supreme Court decision with PASPA a few years ago, which was the one that took away the federal prohibition against states, uh, making sports betting legal. Uh, so that, that happened a few years ago. And so you saw more states coming on and in passing legalization of sports betting. Um, and I think that drove a lot more investment into this area of tools and, and how to foster that what people knew was going to be a growing sector. Uh, so they're making investments there. So that was definitely part of the timing as well. Yeah, I can

Jazmin Furtado: 00:14:19

imagine that with all of the, the increased focus or the ability to be able to, um, put more out into the market and the space that there's a lot of competition is probably like a lot of people that are trying to, you know, it's like a little bit of a new frontier. Um, I was wondering what. You found and how you came upon your value proposition and your niche, like, how to differentiate yourself in such a growing field.

Brian Beachkofski: 00:14:52

Yeah, no. So, so this is where focusing on the user problem is something that you hinted. We probably get to, uh, was really important. Um, because, uh, the, the vast majority of companies out there that are providing, uh, pick support are just telling you what to pick and are not transparent at all about how they got to that side. Um, so people follow them until they don't win and then they move on and you really don't know. And there's, there's quite a bit of fraud, right? Right. Famously, a lot of pick services in the past would call and tell, uh, let's say there was a game on tonight, they would, the first person to call, they'd say, uh, take team a, the second person to call, they'd say, take team B and they know that half their customers would win, right? So, like, that is, uh, uh, fraud. I think is a good way to say it, but when somebody wins their first bet, they're more likely to pay for your service. And, uh, but, but you could do that when there's nothing behind it, right? Like when there's no transparency in what your record is. So like part of this is what is, what is the core user problem? One, the user doesn't have any idea of whether or not it's working or it's not working. Um, and, and they are, are still not solving the core problem, which is. I know when people who bet sports are usually interested in sports and have an opinion, but they're calling a pick service because they don't have a way to put that opinion into their own model. Right? So if they think, I think offense is very important, or I think high tempo is very important. They. The average person doesn't have the time or the skills to make an analytical model that would turn that, that, uh, theory into, into practice. That's what we're trying to solve is how do you take someone who's interested in sports, has a theory about sports, but doesn't have the modeling skills or the time to maintain their, their data feeds and to, to have their server up and running and constantly doing the analysis on it. If we offload that and just make it very easy for someone to say, here's what I think is important. And then get that. What does that mean in terms of my expected value on all these plays? That's what we're trying to solve. And that was actually, uh, there's not, there's not anyone out there in the market that is truly focused on that problem of how do you provide no code analytical model development for users?

Jazmin Furtado: 00:17:13

How do you find that sweet spot when it comes to transparency? Versus accessibility, or, like, understanding, you know, when it comes to these models, they get very complicated. And. I imagine when you're, you know, trying to tell people what your model is doing, you have to figure out how much to be able to, you know, what's, what are the important things to get across? So how did you figure that out? And then what, where did you find that sweet spot?

Brian Beachkofski: 00:17:41

Yeah, so this is, this is a real importance of having a great team. So I am a quantitative background kind of person. So my default position is actually to over explain and to get into too much detail. Megan comes from a different background that hasn't had that quantitative, uh, uh, experience. So she's always the tension between us is, is, is how do you get it so that you are explaining what's happening without getting so technical that you lose people and actually having, uh, two folks with very different perspectives on the same problem, trying to communicate it to people is incredibly helpful. So that's been really good and how we've balanced that what we don't have that tension around us. We always want the UI to be as simple as possible. A lot of a lot of stats pages out there give the error of give the error of being, um, Technical by making the UI, like actually terrible, they overwhelm you with numbers and graphs and plots and things to make it feel like something's going on there. But actually pulling information out is hard when they just dump raw numbers at you. So that's that's the key for us is how do you make the UI? Simple, sleek, easy to understand, while also having the technical level of support be really high while not confusing people by overwhelming them with detail. So that, the tension is where do you, how do you explain it in a way that doesn't overwhelm people, uh, while keeping that UI very clean, very straightforward and simplified.

Jazmin Furtado: 00:19:23

So there's, I can see there's, there's really a level of creativity when it comes to like. What visually does this need to look like, but I imagine there's also a level of creativity on the back end in terms of how everything's put together. Um, how do you like translate user inputs into something that's actually valuable to the model? And then, you know, what do you wait most important with, you know, that stuff going on in the background. So how did you come up, come to what were the important things to consider? What, or the, what, what do you need to get from your users versus where do you need to get from else more automated sources? So can you talk a little bit more about how you come to the backside, the

Brian Beachkofski: 00:20:05

backend? This was a really complicated problem as well. And I think this is one where if, if there's core proprietary information that we have, it is in that, how do we simplify it while still having a complex model? So, because, um, so in the basic sense, what we we've done is for every sport, we do the feature engineering as a whole, uh, and do a lot of the testing to make sure that the baseline. Models are predictive, uh, and then to make, which would be great, but now you only have one model, right? So now how do you get personalization when you've done all of the feature engineering to, to meet the baseline problem? That's that actually reduces the flexibility in the model. So how do you reintroduce the flexibility? We got our inspiration from another data scientist I was talking to about these issues, and he said, well, for computer vision, they can change this sensitivity for cancer screenings. If you take a biopsy and you have a slide of the biopsy, the way they train it to be sensitive to the cancer cells is in the training set, they overrepresent Thank you. Thank you. The cancerous cells so that it becomes more sensitive to those pieces. Now we took that as inspiration. And so we take our baseline model and we have the sensitivity for the classification of every, of every game in history, uh, that in our dataset to each of those factors. So now if the user says, I think offense is more important. We take the games where offense was more determinative of how that game was classified by the model, and over represent games like that in the training set. So we make a bespoke training set for every individual user, where the model dynamics underneath it are the same, but the differences in training make it more sensitive to different features. Per the per the users, um, intuition

Jazmin Furtado: 00:22:02

that we're, um, you're talking about features and I get very excited whenever we're talking about features because I think it's just. The hard part and a lot of data science applications is, like, finding the data and, like, getting good features. Do you have that issue when it comes to sports and for sports betting? Do you have a problem with, like, finding good data or finding enough data? Uh, where are your challenges if any, when it comes to. Obtaining the database.

Brian Beachkofski: 00:22:31

So, so the good part in sports, there are a lot of, uh, good data providers out there. So we actually work with a data provider company, uh, who gives us historical stats. Uh, and that's all been, um. Curated and so we don't have to do a lot of the time consuming of data cleansing and keeping your scraping feeds and things open. So that that has been very helpful for us. Um, we do we do do a little bit of scraping here and there. And that takes a little bit of time. For example, like we know what we know. It's very important to get how far a a road team travel to get to a game, uh, both in terms of miles traveled, but elevation change is really A factor, uh, having gone to the Air Force Academy, which is 7, 258 feet above sea level, I can tell you, uh, altitude or elevation matters. So, uh, we have that. It's like

Jazmin Furtado: 00:23:34

you memorized that or something.

Brian Beachkofski: 00:23:35

Yeah, yeah. Uh, so they, uh, so we have to scrape some of that and use, uh, some other API interfaces to turn that long into an elevation. Um, and, and those can be a bit tricky. But basically, we were able to procure the information we need from data providers, uh, and then we also, um, are able to make our own proprietary, um, uh, stats internally. So, um, we use a Bayesian ranking process to be able to take, uh, the results of games plus, uh, uh, Initial estimates of team rankings and then take the initial estimates as a prior, you, you put the actual game results in, uh, as observed, and you can get a Bayesian ranking formula out of that and how we, what, how we actually use those parameters is how we make our own ranking system. But so we do that as well, where we take those source inputs that we get from our data provider and then build our own derived statistics off of, off of those as well.

Jazmin Furtado: 00:24:37

Yeah, when you can offload that, like, data wrangling and data cleaning, it's amazing how much free time that frees up so you're able to actually, like, really focus on, like, the meat and potatoes of the work.

Brian Beachkofski: 00:24:50

Well, and it's still like, as they famously say, 90% of your time is in the data cleansing, right? And the treating of it. And then what we found is the other, still, even when that's taken care of, 90% of our time is still in those edge cases. So like in football, wacky things happen all the time where like, uh, this, this play didn't end right, or this happened. So there's always weird things that happen in sports that you still are like, Oh, I got to take, I got to figure that out. I got to solve for this edge case. And you go, why, why does that not look right? And then you still have to go back. So we still spend about 90% of our time looking at the edge cases, even when we, when we get clean data that come in.

Jazmin Furtado: 00:25:32

Interesting, I'm wondering what your observations have been when it comes to sports and sports data and the sports analytics. So, what have you seen as unique to this field that you haven't seen in the other work that you've done?

Brian Beachkofski: 00:25:47

Yeah, so I'll say. The thing that has been most different is the rate of uptake of insights from data. Um, and what I mean by that, I can give a couple examples. One is, uh, when you find someone who's doing very interesting analytics work, and oftentimes it's like on Twitter, you just find people who put interesting results out there on Twitter. Um, Some of them get picked up by the pro teams to be on their analytics team. So, uh, uh, Namita was a great follow and she's now on the Kraken staff, uh, in the NHL. So like you, you see, um, you see a quick adoption of, if somebody has something that is actually providing real value to an organization, or it could be useful to an organization, they go after it very quickly. And you see that. Um, from the beginning of, uh, uh, Moneyball all through today, I was listening to a podcast before where they said, you know, um, An advantage in draft order now persists a year or two. So if you're an organization and you find some little metric that gives you an advantage to be able to get someone of higher quality than you should be getting. You can maintain that advantage for a year, maybe two. And then other people start asking, why would they have picked that person and can actually back into what you were doing and they copy each other. So that speed of adoption is incredibly different than it was. Like when I was working with state and local governments and they said, Hey, can you, can you get us an AI algorithm to predict what's the best practice to be able to, uh, to help children, uh, children in our community? And I say, how do you use data today? And they go, well. We have one person with a spreadsheet in the back office. I'll go again. So we can't, we can't take you there overnight, but that's like the difference is, um, the desires there, but the actual adoption rate is so much faster in sports than it was in other areas I've worked.

Jazmin Furtado: 00:27:45

Yeah, it really requires you to stay on your toes. Yeah, does that mean that the competition is just really cutthroat, like, it's hard to, like, maintain a competitive advantage, um, because the, your algorithm is only as good as its last run.

Brian Beachkofski: 00:28:01

Yeah, that's right. It's it's a classic Red Queen problem where you have to run faster to stay in place, right? So that's what we do all the time. We're always looking at our model predictions and making sure that there's still predictive that things are still working. So this is we've had over 60, 000 track bets within the rhythm platform, and we have a positive almost 1% ROI across all track bets, which is great when you compare it to you. The minus 5% that a naive better usually gets so that, uh, we, we track that and we want to make sure that we're still offering massive value to our, uh, to our users, but that's, you can't just say I had a good model and then next season, assume it's going to work every, before every season, we have to go back in and retune and tweak and see if there's any learnings that we can incorporate in because you have to make those advantages just to keep the same position you've had.

Jazmin Furtado: 00:28:56

Yeah, you have to really stay on top of. On the latest and greatest, like, outside of just like, the, the, you can't just look at your model to see how it performs. You really have to keep a not awareness of the whole industry in terms of how that affects your, your team makeup and the people that need to come together to build this. Do you need to have a diversity in, you know. Backgrounds, or do you find that people really are just like clumps? You know, you have a lot of you are able to get a lot of diversity and like, you know, few core individuals. What does your team makeup have to do to reflect the challenges of the field?

Brian Beachkofski: 00:29:32

Yeah, it's, it's interesting because we, uh, we talked a lot about this up front and, and I think, I mean, I, I've even brought it up here. The diversity of viewpoints, even between the CEO and me has made our product better. And we definitely are, are very interested in getting great people who have a great Uh, skill set on the team and having that domain knowledge is kind of secondary because people can learn a new domain. It's having the things that are most important is, is being curious. It is having a right breadth of experience to be able to handle new problems because when. When you do have to reinvent every season to be able to maintain that advantage, you need someone on your team who is okay with having to scrap the old and start over and, and not know exactly what it's going to look like when you're done, because there is no rinse repeat here. It is always pushing the frontier. So that's what has been the thing that's been most important for us. A group of people who are a curious problem solvers that are okay working in ambiguity, uh, rather than people who know the ins and outs of sports, uh, or betting.

Jazmin Furtado: 00:30:46

Yeah, I can, I could, I could see how, how many new things you can pull and learn while you're actually just like building the model in of itself. Things that you insights that maybe even didn't know before that you found out the model showed. I think it's like, both ways. You need to, you learn and you provide input and it's just like, there's like, positive feedback loop. Talking about feedback loops, actually, I'm curious how in your. Your experiences to date. Being at rhythm or being in sports, what are the skills that you've had to pull on the most in this field at this point in your career?

Brian Beachkofski: 00:31:27

Yeah, great question. So I would say the, the, the ones that matter the most right now have been. Kind of that team building and problem solving because like any startup, uh, you're, you're solving problems more than you are implementing the pristine plan you had ahead of time. So having, uh, having an ability to, um, understand. What's most important and stay focused on the thing that matters most is critically important. Um, and bringing the team together so that you can have, uh, diverse. Ways of solving problems, but still be a cohesive team. That's one that like, uh, cause there are plenty of stories out there. People who have people with fundamentally different views on how to solve a problem that can't work together because they can't. See themselves on the same team. So that teamwork and problem solving number one. The second is having that, um, technical curiosity. So like you said, the exploring and finding insights that you didn't know, uh, when you're building the models, there's, you know, two phases. The first is exploratory where you're just in a Python notebook and you're looking at the data and trying to figure out, okay, is this predictive is that near exploring and making combinations and being able to find those insights and just being. Continuously curious about what's there and then saying, why would this happen? Is it spurious? Is it just a random correlation or is there truly an insight there? And then being able to talk through that without it becoming, I'm always in exploration and never actually moving it to prod because that you can run into that instance as well, where, and there's a lot of technical people who. Always say, but I could make this better and this better and this better, and then it never actually gets the prod. Right? So having that balance is another one. That's really important of saying yes, but right now we're good enough and there can be another iteration.

Jazmin Furtado: 00:33:28

Yeah, it's it's I think that. Is always a tough bridge to cross when you get people that are. Just they're used to maybe building something in a lab. Or does it just continuing on the research? Oh, I just want to make it a little bit more better a little bit better when it comes to actually points thing out into production. You need to. Find something that's going to, you have all these other things to consider in terms of like the timeliness and the how much, how much overhead that provides that gives you and you're creating and you're deploying the app. There's there's not just the algorithm itself. The algorithm performance itself is not the only factor. So,

Brian Beachkofski: 00:34:10

uh, it's not providing user value if it's not in the user's hands.

Jazmin Furtado: 00:34:15

Right, right. Uh, the last question I wanted to post to you was, um, talking about the future of your field. Um, where do you see sports betting going? Obviously, you're talking about, you know, the just such a fast pace of iteration and advancement. Where do you see it heading in the next, you know, in the near future or more long term? And then what advice do you have for folks that are interested in this field? And what advice do you have to get them into it?

Brian Beachkofski: 00:34:49

Yeah, so I think we're at a really fun and interesting part in this space, right? We, we. We're betting the same way we have for 150 years at least. I think I said earlier in eternity, but in Casey at the bat, the poem, they say, I put up even money with Casey at the bat. So somebody is, is, uh, wants to live bet that that guy's going to get a hit at a baseball game. Like this could be what my buddies are doing this weekend is, is betting on a hunch that this guy's going to get a hit this weekend. Right? So things are going to change. Uh, and how do you give people the tools to make that happen? And, and. The space, the, the, the, the sports betting space has been relatively resistant to real fundamental change, like it changes with now it's on an app, but, or we use more parlays or we, it changes on the margins, but at its core, it's largely the same. And I think we're going to see that continue to evolve. So there, there's going to be more, uh, more diversity in what people can play on, but I think more importantly. Like I said before, no one chooses where to have dinner now without consulting reviews and, and no one picks out what, uh, what thing they're going to buy online without seeing the reviews and the ratings. We're going to see more of that in sports betting, where you use tools to make picks because it just seems backwards to go with your gut or to just use what somebody flashed up in front of you. And in what we've seen are there two big. Different groups, so 1 is the person who is is just want some support the equivalent of of of a rating and say, I trust this thing. I'm going to go do it. So that's 1 group. And they're going to they're they're want to model. They want some sort of reason for doing it, but they don't want to go deep. But there are a lot of people who really want to go that extra level. And that's what's going to be most exciting for where we're going is having. This premium experience. That's a no code modeling. So rather than us do all the feature engineering, let people go in and see here are the base level stats. How can I group them together in a way that makes sense to me, make my own features, get get feedback on how, how, uh, effective they are in and build their own models, change different, uh Uh, modeling approaches switch from a linear model to a tree based model. And maybe some people, like I said, don't want to go into that depth, but there's a lot of people who are very interested in doing that, but just don't have the time or training to do it. And if we can reduce that barrier, I think it's going to be amazing. The number of people that will have the tools and be able to make their own insights and be more engaged, not only with sports, but within analytics itself and get excited about it. The potential like Saber metrics is huge with baseball analytics and people doing that and imagine if that same kind of engagement can happen, but with with without having to have your own coding solution there, you can just. Go in and pick what you want and get those results. So I think the future is amazing. I think we're at a point where everyone will be using some sort of, uh, uh, analytical tool to help them get engaged in sports betting and in the amount of engagements people are going to have with sports betting in general is going to grow because it's such a great fan engagement tool for the leagues and the teams themselves. So how would someone get into this? I, I would do it just kind of like I did is the great thing about sports data is there's so much out there that's free. Um, you can go to baseball reference dot com and scrape their data. And I think most sports now have a football reference, soccer reference, like the basketball reference. They're all out there and you can scrape the data and, and just like how that's where I started because it was easier to get sports data than it was engine data. Um, I was, I was just talking to a recent grad today who was asking for, for mentorship advice and. And that's what I said to him is just go find problems. If you listen to sports talk radio, there's 50 great research topics said every hour about like, Oh, what's the value of this, uh, draft pick? Oh, this guy's going to be injured. What's that going to cost us in terms of playoff position? People throw out these questions and then move on. But if you're an analyst, you're like, man, that's awesome. I should go figure that out. And in all the data you need is publicly available. So that's what I would do. I'd spend a half hour listening to sports talk, hear the, hear the guys spout off questions they never really want to answer, but then go in and get the actual analytical solution to those questions and, and, uh, put it to

Jazmin Furtado: 00:39:38

practice. Yeah, it's such a low barrier to entry, uh, to just start getting interested and start tinkering in the space. And I think that, uh, is a breath of fresh air for people that have trouble just trying to get in, get information. What you were talking about earlier with, uh, people being interested in the space and the future of this field kind of just exploding. It's just so interesting that you're looking at the user. In the future, you know, people you're expecting for people to be more digitally competent. You're expecting for people to expect. Certain features, be able to dive a little bit deeper under the hood of these algorithms, because people are just have more of that knowledge in this digital age that they're in. So it's. Great have to look forward, looking not just at the industry, but also the users who's going to be like, what do they expect from your tools? And that really brings back at the end of the day. It's the user. It's what. You know, it's you don't have a product if it's not valuable to them, you can have the coolest thing under the hood. But at the end of the day, they want to be able to. People interact with it and use it as useful for them where their skill levels at and everything needs to be centered around there. So I think it. That is just very exciting. It's so fast paced. That is also very different. It's like a great arena where you have technology that you're able to rapidly iterate on, but then also rapidly show to users just rapidly iterate the whole loop. So that's just such a cool, cool space that you're in. I love it. Thank you for doing that. No, thank you for having me. It was great. So, for the last part of this episode, I wanted to close with this game I'm calling Fact or Fiction. So, I have a few statements, uh, here about sports, and I'm not expecting you to know the answers. I was hoping to make it so hard so that you wouldn't know the answers, uh, and I want you to tell me if you think that they are fact or fiction. So, are you ready? I am ready. All right, so the first one. There are over 8, 000 sports played around the world. That's not the, that's not the, that's not the one in question. But about 4 of them, 4% of them are represented in the Olympics. Is that fact or fiction?

Brian Beachkofski: 00:42:07

Uh, well, 4% of 8, 000? Is, uh, what? 320. So I'm going to say that seems fact.

Jazmin Furtado: 00:42:21

That is fiction. There, there are 33 sports played at the Olympics. In 2021, there were 33 sports.

Brian Beachkofski: 00:42:32

Okay. Yeah. Was leading events, not sports.

Jazmin Furtado: 00:42:36

So less than 0.4% of sports are represented in the Olympics. Yes.

Brian Beachkofski: 00:42:43

That makes sense. Yeah. I was thinking like, uh, like track, uh, like running would be one. Not like the 100, 200, 400, 800. Yeah. Yeah.

Jazmin Furtado: 00:42:52

Okay. I need to find some machine to recalculate this for advance. Alright. A second. About 20% of people in the U. S. participate in sports on a daily basis, or like, on any given day.

Brian Beachkofski: 00:43:08

Oh, that's, that's too high. I'm saying false.

Jazmin Furtado: 00:43:13

That is fact! Sports, as in sports, exercise, and recreation. So, based off the Bureau of Labor Statistics. So, about 20%. Doesn't mean that 20% of people exercise daily. It's on any given day, about 20% of folks do. All right, you're 0 for 2. Or 2 for 2, it depends. Or 2 for 2. Number 3. Basketball has the highest estimated rate of sports related injuries among individuals above the age of 25.

Brian Beachkofski: 00:43:48

I would say true. I've seen so many angles roll. I'm going to say true. That

Jazmin Furtado: 00:43:55

is... False. I'm just, I'm like, so proud of myself. Um, cycling, bicycling has the highest rate of sports related injuries. Then basketball is next.

Brian Beachkofski: 00:44:09

Basketball's number two. It's true. I, uh, I had a cycling injury myself. I was going downhill, hit a pothole, flew over my handlebars, broke my helmet on, uh, right, right next to, uh, a telephone pole. That was, uh, it was a bad day. That

Jazmin Furtado: 00:44:25

was, was that in Boston? It was in DC. Oh, in DC. Oh, that's interesting. Oh, I'm glad you're okay. No, those cycling accidents are, they're, they're real. And, um, apparently the biggest sports related injury is in the knees. So like knees are like the most popular, I guess. Most most injuries are in the knees. All right. 4th 1 in the U. S. crime rates can drop during sporting events, but up to 25%. But that's not the fact in question. However, in the capital of the Philippines, when boxer, many fights, the crime rate can drop up to 100%. Or reach 0%. I say true. Yes, that is true. Benny Pacquiao is a hero to many in the Philippines. And so, I found that very interesting. I was like, really? Crime rate was zero. At least reported crime rates were zero. For the whole 7 hours that he was playing. Which is interesting because that's... In the capital, which is so dense, that's so many people, but yeah, yeah, the correlation to sporting games and crime, everyone should just go see more sporting events for the betterment of humanity. All right, so you got 1. All right, so last 1, let's see if we make that 2. MMA is the fastest growing sport in America, as it has consistently grown about 20% annually for the past 3 years.

Brian Beachkofski: 00:46:04

Is this in viewership?

Jazmin Furtado: 00:46:07

This is in just like the Or participation. The dollar value of the industry. It's not participation, it's just like of the whole

Brian Beachkofski: 00:46:15

industry. Okay. Then I'll say yes.

Jazmin Furtado: 00:46:19

That is fiction. Pickleball. Oh, that's right.

Brian Beachkofski: 00:46:23

I forgot pickleball. Of course it is. Of course it's pickleball.

Jazmin Furtado: 00:46:29

Pickleball is the fastest growing sport in America. I have done, my parents introduced me to pickleball, and it's fun! It's addicting. I don't know if you've tried it yourself. I haven't, I haven't tried it. You need to get you may get on a court. There's like, I think 10, 000 courts in the U. S. I know all these things now because I've been on the depths of the Internet for the past a couple of days.

Brian Beachkofski: 00:46:56

Well, maybe we'll get coverage and rhythm here pretty soon.

Jazmin Furtado: 00:47:01

Yeah, you should look into it. I don't know what the stats are behind it or like how much data is being captured on it yet. But, you know, I'm sure it's only going to grow. Yeah. Thank you for playing and for entertaining that game. I know it's like really tough, but hopefully you're able to learn a new a couple new facts. Oh, of course. Thank you as well for being my guest and a fellow data explorer in this world of data we're in. I know firsthand how you've inspired people. All around you and all the teams that you've been a part of, and your focus on people has been really reflected in every step of your journey. And I know that your story really resonates with folks who feel like there's such a breadth of opportunities out there, but haven't seen someone or seen too many people that have really gone out and explored and ventured all those opportunities in real life. So. Thank you for being that example. Well,

Brian Beachkofski: 00:47:54

thank you. I know you've been a great example to many people, too, and congratulations on your post service career and where that takes you. It's going to be amazing.

Jazmin Furtado: 00:48:05

Thank you so much. That's the one thing I love about tech is that you're just constantly learning. So, uh, I'm excited just to explore more of the world and just be enveloped in it even more. Uh, I also wanted to thank Hatch IT for sponsoring this episode and allowing me to host this series. And as always, I'd like to thank you, the listener for tuning into this episode and exploring the world of data with us. Thanks everyone. Take care.

Tim Winkler: 00:48:50

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