Sports analytics, baseball metrics, and Shiny apps | Brian Chase | Data Science Hangout
ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Brian Chase, Vice President of Baseball Systems at the Miami Marlins, to chat about the role of baseball systems and data analytics in professional sports, leveraging technology and hardware for player performance and team strategy, the evolution of sports analytics since the "Moneyball" era, and career insights for breaking into niche industries (like baseball!). In this Hangout, we explore how the Miami Marlins leverage advanced technology and data analytics to enhance player performance and team strategy. Brian details the use of innovative hardware like the Trajekt robotic pitching machine that allows batters to practice against simulated versions of opposing pitchers with exact spin rates and velocities. He also discusses the Hawkeye cameras and other systems, which capture real-time player and ball tracking data, including bio-mechanical information, using 12 cameras at LoanDepot Park. This extensive data is then platformed in tools like Snowflake and Google Cloud for analysis. The team also utilizes wearables to track bat speed and other metrics. Insights are delivered to coaches and players through pre-game and post-game reports and dashboards, often built using Posit Connect and Shiny apps, which helps translate complex data into easily digestible stories. Resources mentioned in the video and zoom chat: Miami Marlins Job Opportunities → https://www.teamworkonline.com/baseball-jobs/miamibaseball/miami-marlins Technology & AI Magazine Feature on Miami Marlins → https://technologymagazine.com/brochure/miami-marlins-using-ai-to-accelerate-competitive-sport Trajekt Sports (Trajekt pitching machine) → https://www.trajektsports.com/ MIT Sloan Sports Analytics Conference → https://www.sloansportsconference.com/ Astroball by Ben Reiter → https://www.porchlightbooks.com/products/astroball-ben-reiter-9780525576648 The MVP Machine by Ben Lindbergh and Travis Sawchik → https://www.sandmanbooks.com/book/9781541698925?srsltid=AfmBOorVPNYGMdhNFsiaUfJ2N88em1qiGfEf6Kok5Z3LmaREs-Iv25ac If you didn’t join live, one great discussion you missed from the zoom chat was about mascots! Libby shared her love for mascots, especially the San Antonio Missions "puffy taco". Attendees chimed in, sharing their favorite team mascot pics - you really have to be there live to appreciate all the fun in the chat!! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps: 00:00 Introduction 00:55 "Tell us a little bit about what you do and how you got here?" 09:15 "How did you get your foot in the door for an MLB job?" 14:02 "What coding languages, tools, and systems do your teams work with the most?" 19:35 "What does data analytics have to do with baseball hardware?" 23:51 "What do pre-game and post-game reports look like, and how do you use Shiny?" 26:39 "How do you measure ROI for technologies, and how do you get buy-in?" 30:41 "How has stats analytics evolved since the Moneyball era?" 34:25 "Is qualitative analysis still used alongside quantitative data from scouts?" 35:27 "Who will play you in the future of Moneyball 2?" 36:27 "Will AI benefits plateau if all teams have similar access to tools and data?" 40:22 "Are you measuring off-field factors like sleep, nutrition, and injuries in your models?" 42:57 "What's the thought process behind developing new features and actionable metrics in sports analytics?" 47:53 "What might someone be surprised by when turning a hobby into a job?" 50:40 "Did you ever feel discouraged during your 4-year job search?" 50:50 "Was getting your MBA worth it for your career in sports?"
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Transcript#
This transcript was generated automatically and may contain errors.
Hey there, welcome to the Paws at Data Science Hangout. I'm Libby Herron, and this is a recording of our weekly community call that happens every Thursday at 12 p.m. U.S. Eastern Time. If you are not joining us live, you miss out on the amazing chat that's going on. So find the link in the description where you can add our call to your calendar and come hang out with the most supportive, friendly, and funny data community you'll ever experience.
Can't wait to see you there. With that, I am so excited to introduce our featured leader today. We have Brian Chase, Vice President of Baseball Systems at the Miami Marlins, and I will let him introduce himself and tell you what baseball systems means. Brian, thank you so much for being here with us today. Can you tell us a little bit about what you do and how you got here and something you like to do for fun?
Thanks, Libby. Thanks, Rachel, for having me. Really excited to be here on the Data Science Hangout today. I'm Brian Chase. I'm the Vice President of Baseball Systems here at the Miami Marlins. I'm here at the ballpark in Miami today. There's a lot of co-workers from the Marlins that are on, so just wanted to give them a special shout out too. Originally from New York City, lived in New York City my entire life until February when I moved down to Miami for this job with the Marlins.
My background is in computer science and media entertainment and technology MBA from NYU Stern School of Business back in 2015. Really excited to be here today. Some hobbies and things that I like to do. A lot of people are musicians. I see a lot of people with guitars and instruments in the background. I am an avid guitar and bass player myself. I have five guitars at my apartment here in Miami, so that's big for me. Love sports, obviously. Really love baseball. Love the Marlins. Really excited to be here.
At the Marlins, as VP of Baseball Systems, I oversee software engineering and product development. I oversee data engineering, data analytics, machine learning, baseball hardware, and infrastructure. Think of my teams here at the Marlins as doing a little bit of everything to help our team both get good players onto the team and help get those players better. I'm really excited to be here. The Marlins are having a really good season so far relative to expectations. We're having a lot of fun here in South Florida and really excited to take your questions. Thanks, Libby.
I'm certainly enjoying the Florida weather. Love traveling, going to Argentina and Brazil in a couple of months, so really like traveling. Big poker player. Love going and playing poker with friends. And then one of the things that we do here at the Marlins for fun during the games is one of our assistant general managers, he used to be a professional baseball player. And at the first inning of all home games, he will do a daily workout. And lots of people come to the clubhouse in the weight room to do a workout with former Major League Baseball player. And so I've enjoyed doing those as well.
Background and path into sports
Brian, I wanted to ask you a couple of things about your background because you said you came from computer science. I think I remember you saying that you went into finance, though, not sports, even though you were a sports kid and loved sports and like played some college basketball, maybe.
So, yeah, I played a year of college basketball. I went to a Division 3 tech school called Rensselaer Polytechnic Institute in upstate New York, outside of Albany. You may have heard of that. If you did, say hi in the chat.
For me, coming out of school, I had done an internship at IBM. I lived in North Carolina for a year and was just like everyone, just trying to break into the industry, trying to get my first job, trying to pay off my student loans. There was a company that had a lot of alumni from RPI where they did a lot of recruiting on campus. They were called Faxet. They're still a very big company. And I got my start there as an entry level data engineer way back when.
I won't age myself too much, but I spent 13 years in finance where I did a lot of database engineering, data development. Eventually, that was the early days of cloud computing. So we did a lot of client services and Google Cloud and AWS and Microsoft Azure. And that's where I got my start. But I always wanted to get into sports. And eventually, as I mentioned, I went back to school for my MBA, which I went to NYU Business School. I got my MBA in essentially sports management and sports information and looked to parlay that into a job in sports.
But it took a little bit of time. I eventually spent 13 years at Faxet. But in 2019, I joined the MLB League office as a principal engineer in test for MLB, in which I worked on really cool baseball infrastructure. And that's how I got my start in sports and was happy to be there. I worked on the StatCast platform. If you're an MLB fan, when you watch MLB games, it says StatCast powered by Google Cloud. That's what I worked on when I was at MLB and worked on a lot of really fun things. Spent three seasons at the league office in New York.
And then COVID happened and there wasn't really baseball for a little bit. I eventually got a job working in sports betting. I was at FanDuel for around four years where I led the technology groups for brick and mortar sports betting. So if you've ever gone to Las Vegas and gone to the sports book, then on sports, we had a number of those venues that were FanDuel owned and operated across the U.S. And eventually worked on that for a while. And now I'm back in baseball again with the Marlins here in Miami. That's kind of my journey over the last two decades.
Breaking into sports: networking and persistence
I wanted to talk a little bit before we hop into questions about your sort of journey into getting into the MLB job, because it took you at least like four years. Right. And there are a lot of people who are like, I'm trying to get into this. I'm trying to move into data. I'm trying to, you know, switch careers or pivot industries and are disheartened at how long it takes. I was wondering if you could talk a little bit about what you did in that four years and how you sort of networked and got your foot in the door.
So, yeah, I knew I always wanted to get into sports and I felt like going back to school for a business degree was helping in my personal journey and development. But back in those days, there weren't a lot of avenues to get into sports at all. There weren't job boards. And so one of the things that I tried to do was go to conferences around the U.S. that focused on sports and sports analytics. Many of you maybe have heard about the MIT Sloan Sports Analytics Conference. I used to be a frequent conference goer to that conference and got the opportunity to do networking and put myself out there.
I was unsure if I wanted to stay in technology and sports or actually traditional front office roles in finance or marketing, accounting, those types of things. And one of the things that I had realized was a lot of these jobs are few and far between and they're very competitive. And for me, one of the things that I tried to do to help myself, kind of two things. One was put myself out there, did a lot of networking, cold introductions, did my own projects on the side, told people about what I was doing. And then also, it's just when you're trying to get into some of these niche industries, it's very much a apply to a lot of places and get rejected and and being able to handle rejection. If you're applying for a role and there's 500 applicants for the role, only one of 500 people get the job.
So being comfortable with handling rejection, finding ways for you to stand out, whether that's in your resume, your cover letter, were all things that I did. But eventually, the way I got into MLB was through the networking at NYU and through these conferences, had friends of friends who just happened to be working at MLB, put in a really good work for me with the senior director of engineering at MLB, who reached out to me and then got an in-person interview. And when you do get those in-person opportunities, you have to take advantage of it. So you have to do a lot of preparation, a lot of mock interviewing.
And a lot of times these roles are few and far between. So when you get those opportunities, you have to put your best foot forward and try to take advantage. And a lot of, and Libby, to the point, I think a lot of it can sometimes be luck. And I just happened to be in the right place at the right time with my career.
Same thing happened here at the Marlins, where I saw the role come up on a Monday morning, asked a former colleague if they knew anyone at the Marlins. And they happened to know someone and put in a good work for me. And the rest was history. So there's a lot of things that are in your control to put your best foot forward. But it's also a lot of luck involved and being in the right place at the right time. So I would be remiss if I said I didn't get lucky a few times in my career as well.
So I would be remiss if I said I didn't get lucky a few times in my career as well.
Tools, technologies, and the Traject machine
And it is what kind of coding languages, what tools, what systems, models, etc. Do you and your teams work with the most? And can you talk about some example projects?
And one of the things is Libby and I were preparing for this session. The Marlins have more than 40 different vendors that we work with in a technology standpoint. So definitely can't go into all of them. But as I've come to the Marlins, one of the things that we've tried to do is sort of act like a traditional software engineering group. So we're using standardized technologies that are used in many Fortune 500 companies. We do a lot of data and infrastructure on the cloud. Google Cloud is a huge partner for us.
We're building a lot of our data engineering infrastructure using common technologies. We're building our DAGs and our ETL processes in Python. We use Airflow. We use Flyway for our database schema management. We're working in open source database tools such as Postgres. We have some Microsoft SQL Server that we have on-premises as well. And then when we're doing a lot of our software development, we're building things in React and React Native for the web and mobile. We're building traditional RESTful APIs in Python and Java Spring. And from our data science and machine learning capabilities, building a lot of R and Python models and libraries.
So for me, it's a lot of traditional approaches to just problems in sports and problems in baseball. So there's a lot of overlap. And we find that it's really important to try to utilize technologies that have widespread appeal because people want to work in sports. And we love hiring good people. And one of the best ways to do that is to be working in technologies that are very approachable and are getting taught in college curriculums and things like that.
I could talk a little bit about some cool work that we're doing at the Marlins that I think would be fun for the audience. So one of the things that we have thought about, and one of our core competencies at the Marlins is just trying to be innovative and trying to think outside the box because we're competing with 29 other teams. And it's our goal to beat those teams. And what are the things that we can do to prepare better than our opponents to give ourselves a leg up?
And we have adopted this hardware that's really cool. It's called the Traject Machine. And if you know anything about this, it's essentially a pitching simulator where the batters can take batting practice before the game. What's really cool about this technology is we get to program the Traject Machine using all the attributes for the pitchers that we're facing. And so you see their video on the Traject Machine. You see them throw just like they would throw on the mound. It's the same height. It's the same release point of the baseball. And it's the same pitches that they throw during the game. It has the exact spin rate and velocity that they normally throw at.
So for us, it's really cool because you're able to take real life, almost simulated batting practice off the pitchers that you're about to face. So when you're starting the game, it's not like you're seeing them for the first time. It's like you're seeing them for the fourth time or the fifth time. And that's really helped us in the first inning, second inning, third inning of games where we're doing a really good job of scoring runs really early because of that preparation. So I really like nerdy baseball stuff like that.
Shiny apps and pregame reporting
And I know I was talking with Rachel and she said that there are pregame and postgame reports that happen. So I feel like that's another aspect of the behind the scenes tools that get used. And so I was wondering how those happen and what you use for them. And I think that you guys use Shiny for stuff. So I'm curious how you use Shiny. I know a lot of us here use Shiny in either R or Python. But what does the reporting look like? How does that happen?
Yeah. And this is where I'll give Posit the shout. We've really enjoyed working with Posit. And we're big Posit Connect users here at the Marlins. And we take advantage of the Shiny platform. And using the Shiny platform has allowed our solutions and research analysts to create really interesting reports and dashboards on top of all this data. And we use it in a few ways. One is pregame and postgame reporting. So they are creating reports on each player, the advanced materials of like the pitchers that we're facing and the batters that we're facing, as well as just instructions and guides for the players ahead of that game. And then postgame, we have similar reports to show how they did that game. Were there opportunities that we missed? Did they take advantage of those opportunities? And we're platforming all of that in Shiny apps and reports that we're able to give our coaches and players access to.
So Posit has really been key for us in platforming all those data and those insights in really cool ways. And my team, at the end of the day, we're not building those reports ourselves, but we are building the infrastructure and the platforms to enable other Marlins teams to do that work in easy ways. So we've abstracted away a lot of the infrastructure and the boilerplate code that they need to do to get up and running. So they can really take advantage of their core competencies, which are doing analysis and instructing coaches.
And one of the key things is, how do you bring all that data and all that analytics to coaches and players and have them understand it in an easy to consume way? Because it could be overwhelming. And you always hear in baseball, there's kind of this divide of, well, the old school baseball feel versus the analytical generation. But here at the Marlins, we're doing both. And it's really important in terms of the storytelling of using visualizations, showcasing data in a way that helps tell your story, whether that's baseball or finance or business or whatever your industry is. It's one thing to have a lot of data and a lot of insights. But if you can't tell stories around that data to your audience, it's not going to have the impact that you may think it would.
It's one thing to have a lot of data and a lot of insights. But if you can't tell stories around that data to your audience, it's not going to have the impact that you may think it would.
ROI, winning, and buy-in
There's an anonymous question in Slido about this as well. It says, how do you measure ROI for the technologies that you're using? And a follow on to that might be like, how do you convince people to actually use that? If you're handing coaches or players reports afterwards, how do you get them to trust it?
The joke that I like to make, and if you're on the Marlins and you're on this call, you probably heard this before. The baseball operations side of the Marlins, our goal is to spend money. We're just spending money. The business side of the Marlins, their goal is to make money. So the truest ROI, I think of all the work that we're doing is, are we putting a competitive team on the field? Are people coming to the games and enjoying themselves and the product that's on the field? So that's very easy, sort of holistically, right? Is, are we making more money than we're spending? Yes, we are.
But two, I think another way to measure ROI is, well, are we winning more games? And there's not an exact science to this, but the Marlins ahead of the season, just coming from the sports betting background, we were projected to essentially go 62 wins and 100 losses. And that's not very good. But to date, I think someone in the chat can correct me. I think our record is 54 wins and 55 losses. So we're projected to win around 78 or 79 games. So that'd be a 16 game improvement.
So it's really easy to get buy-in from coaches and players when we're winning, they're seeing success on the field. Our coaching staff is doing a great job of working with players each and every day. And they see the results, they're getting better. We're winning more games. So there's a lot of really good buy-in. And it starts from the top, from our president of baseball operations. He's my boss, his name is Peter Bendix. It starts from the top down in terms of the culture of what we want to set. And at the Marlins, we are using technology, we're using data, we're leaning into that. But we're not dismissing kind of the expertise of our coaching staff and our player development who has a lot of traditional baseball experience. So all in all, it's a team effort and we think we're on the right track here.
Hardware, tracking, and the Moneyball era
One of the really cool things about working in baseball is there's a lot of really fun technology and hardware in the baseball space. So one of the things that we have, I'm here at Lone Depot Park in Miami, and we have the main piece of equipment that we use is something called Hawkeye cameras. Hawkeye is a subsidiary of Sony. If you ever watch tennis and you are watching tennis and there's a replay and it's like, is this ball in or is this out? There's camera tracking that is used to track exactly where that ball goes. That's the same system.
Here at Lone Depot Park, we have 12 cameras set up all across the concourse, essentially. And those cameras capture everything that's happening on the field from the pitcher throwing the ball to the bat, to the players on the field, even to the umpires. And so we are getting those packets of data in real time from the hardware. We are serializing that through APIs and services. And then we use all that data to do really cool analysis of what's happening on the field.
So when I talk about hardware, we talk a lot about player tracking and ball tracking systems. So you may hear, there's three main companies that we work with in the space. Hawkeye, as I mentioned, Kinetrax, which is another Sony company now. They capture 300 frames per second of biomechanical data. Everything from where people are to their joint positions. And that all is XYZ coordinates. And we get all that data. We're platforming it in Snowflake and Google Cloud. And we're able to do really good analysis on that.
So there's the tracking aspect of the hardware. There's just traditional infrastructure like Google Cloud and building servers and databases. But then all the other cool baseball technology that we have here at the Marlins, we're doing a lot of things with wearables. So you can have a sensor on the bat to kind of track how fast the bat is going and the angle of the bat when you're swinging. Or you can wear, I don't know, a heart rate monitor or things of that nature. So there's a lot of really cool hardware that we try to use to help us get better at the game of baseball. And the more data that we have, the more inferences that we can make, the better projections that we can have on performance. And the more signal that we can have on performance, too.
You know, obviously, for those that don't know, Moneyball was a book by Michael Lewis on the Oakland A's and how they were able to use data and analytics to field really good teams with essentially like payroll implications, right? And I think we can talk about that here too in Miami. And Miami is a pretty big metro area. We have the lowest payroll in MLB. And so the question and the challenge given that is how do you maximize the development of our players on the field, given the costs, right?
Like Juan Soto, if you're familiar, went to the New York Mets, signed a very huge deal. We have teams like the Yankees and the Dodgers and the Mets spending lots on payroll. And so can we get more value out of our players per dollar than other teams? And if we are able to do that, generally we're going to be successful.
But to answer your question, I think the biggest change from the Moneyball era until now is there's just a lot more data that's being generated through the hardware that we had been talking about. So all the tracking technology, all the video, that wasn't there 10 years ago. You had some companies that were in that space, but MLB has done a really good job of democratizing the use of the tracking technology throughout all 30 ballparks. So there's just a lot more data than there ever was before.
So a lot of data, tracking data at the major league level. We have these same systems at the minor league levels too. So if you're a minor league player for the Marlins, you're seeing the same technology footprint when you enter the minor leagues at our low A facility in Jupiter, Florida, all the way to the major leagues in Miami. So there's a consistent coaching and player development approach all the way from the minor leagues all the way up to the major leagues.
So the biggest thing here is just that there's just a proliferation of more data, lots of problems in big data, but there's a lot more data being captured even for college players, for high school players, for international players that you just didn't have before. You're just used to traditional scouting approaches where people would travel all over the US and internationally looking at players and just kind of grading their talents on look and feel. But now we have data on all these players and we have models and projection systems that can be used. And I think that's the biggest change from previous Moneyball era.
Yeah, do you feel like the qualitative is still in there though? Like now it's a blend of qualitative and quantitative, like is the look and feel still there from scouts?
Certainly. And one of the things that we've been able to do here at the Marlins is use AI tooling to get sentiment analysis and evaluate kind of the traditional qualitative approach by our scouts. And our scouts are in the room when we are making decisions around who we draft and they're taking stands on certain players. And all of that goes into our projections and our models. So inevitably we take all those qualitative and quantitative inputs and that produces our overall Marlins sentiment on certain players.
Innovation, differentiation, and feature engineering
I think there are things that we're doing better than other teams and things that other teams are probably doing better than us. One of the things that I'm a part of at MLB is a central technology working group where MLB is democratizing certain technologies and platforms that all teams have access to because they're not necessarily competitive advantages relative. So all teams have access to generally the same data and infrastructure at just like a base level. And one of the reasons why that's really important is you don't want it to be an arms race where the teams that have the most money get access to the most data and the most insights. So there needs to be some standards and governance around that.
Teams are building their own projection systems and their own models based on a lot of this data. But I think one of the differentiating factors for us and probably other teams where we can get more ingrained and more nuanced with our analytics is in the practice space actually and the non-game events. So our ability to simulate game-like conditions in practice and to track that using the same technology gets us a lot more data and insight. So we're really investing heavily in the wearables as I mentioned, having more game-like conditions in practice.
And I think at the end of the day, coaching and player development is going to be one of those things that provides the differentiation. Players buying into what we're trying to do culturally is also going to be really important because if players aren't bought in, they're not going to want to try to implement the strategies. So there's a cultural aspect. There's a practice aspect. But at the end of the day, two teams are competing on roughly the same field with the same conditions and trying to beat the other team. And we're just trying to make the best decisions that we can to put the best product in the field. But I'm not the one hitting the baseball. You don't want me hitting the baseball. So people that are much better at this than me are doing that. And all that preparation, all that practice is going to be our differentiator.
Yeah, one of the things that we've been really taking a look at here at the Marlins in a broad sense are what are the data points that best tell the stories about player performance? And there's all these stats, right? There's all these things that happen on the field and off the field. But eventually we build all these models and there's all these outputs, but effectively we just come to very sort of core numbers.
And in baseball, it's effectively a couple of things. One, you've probably heard about this a lot. It's called wins above replacement and it's effectively MLB's calculation of how good is a player relative to just someone coming up from the minor leagues essentially. And so our ability to project that value into the future successfully gives us really good insights into how a player is going to perform.
The other is a pretty widely adopted standard, but at the Marlins, we call it surplus value. And that is based on their projections and their contract and all those obligations. How much value does that player have to the team? Like just for like dollars and cents. So we just had the trade deadline last week and we made a few trades and we didn't make a few trades, but the key thing for us is, okay, if this player we think is worth, I don't know, 20 million dollars of surplus value, we essentially want to get players back that equal or surpass our surplus value calculations. And teams all calculate this differently. So there are market inefficiencies and information asymmetry that can mean that both sides can feel like they're ripping the other team off. But in actuality, both teams like the other team's players better.
So there's a lot of nuance, but at the end of the day, we have come and sort of gotten consensus on what we feel the key data items are. And a lot of our R&D efforts are working towards how can we get as accurate as we can with those 10 to 20 data items. And that really guides the story for everything. And when we're building applications, it's how do we platform that data in a way that looks and feels right. And we're focusing a lot on data visualization and data design so that we can tell those stories with data really easily.
And the last thing we want to do is waste time, just as an example from the trade deadline, if I feel like one player is worth $20 million and Libby thinks that player's worth $25 million and Rachel feels like that player's worth $15 million, now we're sort of going back and forth on what we think the true value of the player is rather than this is what the Marlins think this player is worth. And that's what we're going to use as the basis because we know what has gone into those calculations that we all agree in the methodology that went into those calculations. And that was really important for us. This trade deadline was having consensus on what we thought the methodology was and trusting the modeling and the analytical results and using that as our source of truth.
Career advice and turning a hobby into a job
Well, as someone who turned a hobby into a job, I'd say it's been great so far. So yeah, I think there's that adage of like, oh, if you're doing something for fun and now you're doing something for a job, is it still going to be fun? And the idea is, well, what are the things that we can do to keep it fun?
And for me, one of the things that I've tried to do is, I've taken advantage of AI actually a lot in this space using tools like ChatGPT to help me with, quote unquote, the not fun things about the job, right? Creating documentation or, I don't know, doing budgeting or random things that are just like not fun baseball stuff. And if I can get really good at harnessing the power of AI to do a lot of those things, now it gives me the opportunity to do all the fun stuff. So it allows me to travel to our minor league teams and see how they're doing or work with a vendor on some cool new tech and go downstairs into the clubhouse where indoor batting cages are and play around with the Traject machine, as I'd mentioned.
So for me, I think if you're someone that has hobbies and you're like, this is really fun, I think it's better to have a job where you are trying to have fun than one that feels like a job and you dread going to. So I would certainly try to join companies and jobs where you're passionate about what the company does, you're passionate about what you'd be working on. You only have one life, so you might as well have fun doing it. And at the Marlins, I think we're doing a good job of, we have a lot of really smart, talented people. They are very passionate about what they do. They care a lot about what they do and they take it seriously. But this is baseball, it should be fun. And that's something that we try to do every day is make sure that we're having a good time.
You only have one life, so you might as well have fun doing it.
Yeah, well, I think on the MBA front, I definitely feel like it was worth it to me, just the connections that I made, the education that I got, and the opportunities that going to a school like NYU opened up for me was worth it. Is it worth it for everyone? It's hard for me to answer that, but for me it was. So I think it's really in the eye of the beholder.
Remind me of the other question, Libby? The other question was, did you ever get discouraged in that four years and think about like, oh, maybe I should go a different direction?
That's a no. I think one of the things that was really in my favor is I had the luxury of working at a company in finance that I actually really liked and I liked what I was doing. So it was four years, but I had the luxury of not having to take the first thing necessarily that popped into my inbox. Not everyone has that luxury, I understand. But for me, I had very clear goals of what I wanted to do and I wasn't, you know, when I went to MLB, I sort of took a leap of faith and I was doing something that I wasn't like, I would say the most qualified to do, but it was really good to get in the door. I grinded a lot, I learned a lot, and that opened up doors and other avenues.
So was I discouraged? Probably at times, right? Being rejected in anything is not necessarily fun, but you learn to have thick skin, you learn to know sort of what your worth is. And a lot of times the rejections have nothing to do with you as an individual. Maybe just, you know, luck of the draw in some cases or networking or like, there's so many variables. I wouldn't take it personally. Just understand that in anything, it's a business and a lot of times these are business decisions and it's never personal.
Brian, thank you so much for joining us today. We are somehow at the top of our hour. It happens every time, I don't know how. We have so many questions that did not get answered. If your question did not get answered, I still read all of them. And I actually like reach out to people and try to get them answered. If Brian has time, maybe he can give me a couple of answers. I'll reach out to you. If you put your name on them, I will reach out to you on LinkedIn. If there's anonymous, I can't. But thank you so much, Brian, for hanging out with us. I hope you had a good time.
I had a great time. Thanks for having me, Libby. We have lots of job openings here at the Marlins. So I know you're going to send out the site, the job posting site. So I encourage people to look at that. Put it back in the chat right now. Apply for roles. If you apply for roles in the comments of the job, just say that you found the job through the data science hangout. And thank you again, Libby. Thanks, Rachel, for having me.
I also wanted to give one more shout out to this feature in the technology and AI magazine that features Brian and the Miami Marlins. It's a really cool spotlight. I'm working on getting it up to the Posit site as well. But you can access the magazine through that link I just shared.
Yes, thank you so much, Brian. Thanks, everybody. If you would like to save the chat, maybe there's resources in there that you want to hang on to. There's three dots at the top right. You can click that and then save chat. That will let you keep all of those. But I also put them up with the YouTube video in the description. So if you are watching later or listening later, hopefully you can see all of them too. Thank you, everybody, for coming out and hanging out with us this Thursday. Next week, we have Keegan Rice, senior statistician at NORC at University of Chicago doing consulting. If anybody remembers her, I think it was a Posit conference talk Keegan gave that was called Wait, This is Shiny, which was so fun. It was one of my favorite talks. I'm so excited to have Keegan chat with us about Shiny apps and all the stuff that she does. So come hang out with us next week. I can't wait to see you. Have a fantastic Thursday, Friday, and rest of your week. Bye, everybody.