📅 Published on: 2024-11-27 23:40:10

⏱ Duration: 00:27:59 (1679 seconds)

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📝 Video Description:

Career Journey and Baseball Analytics Insights with Justin Newman, Head of Player Analysis for the Pittsburgh Pirates.

In the very first episode of the Sport Analytics Podcast, our host Amrit Vignesh sat down with Justin Newman, the newly appointed Head of Player Analysis for the Pittsburgh Pirates. Justin takes us on a fascinating journey through his career in Major League Baseball, from his early days as a Baseball Informatics Intern to managing the Pirates’ Research and Development team to his current role as Head of Player Analysis.Justin shares exclusive insights into the day-to-day responsibilities of his various roles, the challenges of transitioning into leadership, and his vision for the future of baseball analytics. He also dives deep into the technical side, discussing the predictive models his team relies on, the programming languages and tools they use, and standout projects that shaped his career.Whether you’re an aspiring sports analyst, a baseball fan, or simply curious about how data shapes decisions in professional sports, this episode is packed with valuable advice and inspiration. Learn what it takes to break into the field, how to build a standout portfolio, and which skills Justin looks for when hiring new analysts.If you’re passionate about sports analytics or looking to kickstart your career in the industry, this is an episode you won’t want to miss!

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🎵 Music Credit: Intro and outro music for this episode is “Nomu” by Good Kid.

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🕵️‍♂️ Transcript:

[Music] welcome to the first episode of the sport analytics podcast today we’re joined with Justin Newman who is the head of player analysis at the Pittsburgh pirat Justin how are you doing I’m doing great thanks for taking time to chat with me today yeah it’s an honor to have you on this first episode of this podcast and we would love to go through your journey so I would like to first ask like if you can walk us through your career Journey with the Pirates like you obviously started as a baseball informatics inter you’ve been with the organization for around eight years now um so how did your responsibilities like evolve with each promotion leading up to your current role as head of player analysis sure yeah I think um starting out as an intern you’re working in a little bit more of a support role you um were you know this cool opportunity with that had the opportunity both to support um others in their work helping with queries and doing smaller analyses but then also had the independence to be able to um to um do my own work and share that with others in the the group so that’s kind of how it started out was um as an intern um just being able to to um help any of our different analysts with queries and other support work um but then getting to to do that other work in my more in like my spare time um but then as time evolved um you know a lot of the work I was doing was more directly demanded by um other groups within baseball operations so after my internship I went on to um to become a draft analyst that’s where I spent most of my time here um and I think there’s a couple main things right more direct interactions with amateur scouting um and then secondarily um there’s a lot more ownership um over the whole process starting from the code all the way down to um to like how you’re communicating with end users so um that was kind of the first big jump on responsibility is is being responsible for everything from the um you know producing the information to presenting it um and then over the last year or so I’ve been in a little bit more of a a management leadership type of role um so now I an awesome group of people behind me who can help um help with a lot of this work and I I give them some guidance and guard rails to work within and and they’re able to kind of really run within those guard rails so it’s a little bit of a different seat that I’m in now yeah that sounds really interesting and obviously you’ve been with a lot of different roles within the organization so I wanted to ask what were some of the biggest challenges you faced when transitioning from one role to the next you know obviously starting as like a quantitative analyst role and moving more towards a managerial position now yeah I mean I think um first I’ll talk about like going from like an intern to to full-time um I was figuring out a lot of that on the Fly you know like I was um pretty young and I was a year or two out of college and I was then in the draft room as to um asked to present information on um on players to inform our draft decisions um so I made a lot of mistakes right I figured out a lot of it as I went along um and um but yeah and then talking more about the like individual to contributor to management role um I think there’s kind of no playbook for um transitioning from from an more of like an IC role to management role so um a lot of a lot of that was also figuring out as you go and just relying kind of on your your instincts and your gut and some whatever your core principles might be to to help people grow grow and hopefully impact your decisions across across the Pirates yeah yeah that sounds really interesting and it’s great to see you know the upper upward Mobility that you had you know throughout the organization you know working up to a really high role um I want to ask like could you describe like a typical day in each of your previous roles and how your focus may be changed over time uh particularly now as the major of research and development yeah so when I was a draft analyst um a lot of my time was spent coding if it was kind of the off season um so just trying to develop new metrics tools models um so you know a lot of the focus of an offseason might be trying to improve our draft model whether that’s adding additional features or trying to do more with the information that we do have um and then um then more inseason right that the day to day is a little bit more focused on um taking the results of those models and trying to translate them to um to Scouts so um so that could be you know targeting um you know sending out Target list to Scouts of here’s some players who are interesting who we don’t yet have reports on um or you know here are some guys we’ve scouted um a little bit but we want to see some more so a lot of the dayto day was either in like decoding or or doing you know short player writeups um nowadays um I spent a lot of my day on meetings um and um you know basically just trying to um work together with with my colleagues and um build some of the larger structures of here’s how we should structure a player right up um and then helping them you know with with different problems that they’re facing whether that’s you know they get crossed up by a c certain difficult profile of a play um or if that’s they’re trying to um figure out a way to improve improve an existing model or build a new one and just trying to to help support them in any way that I that I can and um lucky that I have a cool group where I can learn a lot from them and hopefully um help them a bit too in in the process yeah it’s really interesting to hear like the journey that you had to go through um and now I want to talk about your new role as head of player analysis so looking ahead to your new new role what are your like primary goals for the department and like what type of Innovations do you hope to implement yeah so I think a lot of what we’re um trying to do is to um take our insights and make decision making easier for anyone whether that’s you know a coach a coordinator Scout or the GM right um we’re trying to Target our um Target our analysis and reports in a way that um is geared towards easy decision making um so there probably only so much I can share there for some um proprietary reasons but that’s kind of some of the overarching mission that we’re we’re trying to achieve in the the player analysis group and then the other thing that we’re working on is trying to you know we have analysts that work on Player Development the draft Pro evaluation International and we’re trying to make it um so that those evaluations look as similar as possible and they’re all guided from some of the same core principles yeah it’s really interesting I wish the best of luck to you um in your new role um also want to ask to the extent which you can answer this you know what types of predictive models do you and your team build most often and how do they influence decisions within the organization yeah there’s there’s quite a range of models that people are building from you know things like like computer vision models um all the way to you know simple linear regressions to more um sophisticated machine laundering models like neural networks to um statistical models such as you know mixed models or or basian models um so the way our team is structured a lot of those models um are a lot of those more involved longer term models are built in our data science team um and then our player analysis team is responsible for um collaborating with them to help you know guide some of the baseball principles behind those models and then taking those models and applying them um and using them for writeups and communicating with our our end users um so yeah so our group is doing some shorter term research some smaller studies that might inform some of the data science work but a lot of that data science work um is done under a slightly separate umbrella which is a part of our r large R&D group so I’m I’m not as directly responsible for um managing some of those longer term um research projects but I think myself and our entire play analysis group are really key um really key stakeholders and collaborators with our data scientist in developing those models yeah I think it’s really essential to have that collaboration between those different departments who sure you know it benefits the team as a whole um want to ask like which programming languages and tools do you think are most integ integral to your team’s workflow and are there any like emerging technologies that maybe excite you yeah it’s a great question um so I think like the the for data science or analysis work right r on python are are kind of the the standards we have some people use R some use Python some use both um for us if you’re you know good in one that’s that’s great you don’t have to be an expert in both necessarily um we also use all the SQL um then on the emerging Technologies front I would say um chat GPT is a pretty exciting um exciting tool I I’ve just been playing around with the the newest 01 version and kind of impressed of what it’s able to to output and how it can help guide your search on not just taking you code this for me but um here’s how you can think through this conceptually and here’s where there might be pitfalls um just a little you know little bit that I’ve scratched the surface with it has been been really impressive yeah that’s really interesting um I want to ask you know throughout your time with the Pirates is there like maybe a particular project or analysis that stands out as a favorite or a game changer for the team to the extent you know you can reveal yeah yeah um probably my favorite thing that I work on is our drop model um and so that’s basic what that is is basically taking um all of the um inputs in our scouting and you know draft process so that could be anything from the player’s age to some performance information to um batter ball track matter we get and then our scouting reports and um you know valuations that SC put on a player and trying to combine those things um to ultimately predict um some some form of major leue value for a player um so I think some of the cool thing about that is you not just the data science um and Analysis work itself but actually getting to see how um our decision making evolved over time be more centered around our our draft model and we’re now at the point our you our draft model is that sets our board and we have a a very specific process for um moving players off of that draft model and I I really enjoy um both the process of building the model and then trying to figure out where might be the gaps or holes that would allow us to to bet against the model or or take the over under player yeah and it’s been really inspiring to hear you know the journey that you gone um through with the Pittsburgh Pirates you know if you’ve been there for a great amount of time so it’s really inspiring um would like to move to a little bit of a diff different realm um what advice would you give to aspiring analysts who want to break into Major League Baseball or professional sports in general Yeah couple things jump out here um one I think something that’s different now than when I I was trying to break into baseball many years ago um is that there’s a lot of different ways you can skin a cat a lot of different ways that you can um add value to a team so when I started departments consisted of five people so you had to be able to do the database work build a model analyze a player and communicate with the coach or Scout right and that was fun and then think a great learning experience but now um but you know I think that that led to more Jack of all trades kind of requirements where now you don’t necessarily need to be an expert on the baseball side if you’re willing to learn and you can build an awesome model um or on the other side of it right if you’re really good on the baseball side and the applied side you might not be able to be the one that generates all the code and and all the models or database code um behind the information you’re presenting so um basically what I’m getting at is I think there’s a lot more room for specialization um there’s you know skill sets from computer vision to physics to biomechanics to you know the more traditional analyst statistical modeling skill sets um that can all add value to a major league team so kind of the the broader takeway is figure out what it is that um that if you’re kind of superpower or your special skill and and really lean into that and you don’t have to try and be everything to everyone you don’t have to know our python sequ shiny Tableau right um just figure out what it is that you can um do that that other people can’t do and and really wrun with that yeah I think that’s really inspiring advice for students like me trying to you know enter the industry at one point um when hiring for your department you know what types of projects or portfolio pieces have you know maybe caught your attention the most or maybe in general you know anything that maybe stands out to you yeah that’s a great question um let me think for a second on that one if there’s anything specific um I would say in general like any any kind of published research is a a huge plus if we’re looking more in the data science side um on the analyst side um looking for creativity and how you um think about the game um so we’ve had people have you know run their own baseball analytics fla um and and just um seeing someone think creatively about ingame strategy or prior evaluation or anything that um makes you think like I I quite thought of it that way um I think that’s that’s what we’re looking for something that that can really separate chemists yeah it’s really valuable advice uh for someone just starting out which areas of analytics or programming would you recommend they focus on mastering first yeah it’s a great question um I’m kind of a big believer in that um getting a good foundation in programming not even any specific language but just in like an intro to computer science type of class so the one I took was in Java and I haven’t written a line of java since I I left college but um I got all the fundamental concepts of iteration and iic and things like that which is I think a really good foundation to to build on um and then I think on the statistic side um I think there’s a lot of interesting things in econometrics and and some like the um you know more apped quantitative social sciences um I think there’s a lot of similarities and an overlap between um the thinking and the metrics and thinking in baseball so we um are constantly dealing with compounding variables but we’re trying to understand the relationship between um you know velocity and er right but um but often times this is confounded by something else where the players of higher velocity tend to have worse command when you’re looking at Major League players and players who thow 85 to 87 miles per hour um almost exclusively have great command otherwise they wouldn’t be in the major league so um I think there’s a lot of the um the same kind of thinking and like an econometrics um type of course that that really applies pretty well to baseball yeah um that’s really valuable advice I’m sure for a lot of our viewers um so here at sport an Lakes our platform you know focuses on educating and certifying aspiring a analyst um from your perspective what skills or topics would you like to see emphasized in like certification courses it’s a great question um so I there’s I think a lot of different ways that you could um take it um I think there’s a million different methodologies that you could teach but I think the most important thing is teaching um as you’re teaching the methodologies making sure to teach some of the problem solving and understanding some of the the pitfalls of of um how you can the data you might be dealing with right so I think what separates um really good Junior candidates from um from the average ones is that they’re able to um really understand the data they’re working with and um not just thr a bunch of things into a model but um make really well motivated decisions behind um why they’re doing what they’re doing um and make sure that they’re um you know if if there’s any limitations of their analysis be able to communicate that so it’s a bit of a bit of a scatter answer but hopefully something in there that that helps you guys out a bit Yeah I think that’s definitely helpful um are there any like common skill gaps that you notice in candidates applying for analytics positions you know and how do you think our programs can like ours can help address those yeah I think um I think some of the most common ones that we see this is more in like data science hiring is that um there’s people who are you know really really bright and way more gifted technically than um myself or many other people um but then they lack some of the um motivation and you know baseball or Sports understanding to be able to connect their methodology to the work that they’re doing um so that’s one and then um on the anal side I think I think this we we tend to get more people who have you know really strong baseball backgrounds and then then it’s more about trying to get their their technical skills up to right level um so I think you know in general like strong programming Foundation ability to write models and R python um are things that can help help people really get to speed yeah um that’s really interesting and obviously you have probably experienced you know baseball in Lees on you know its expansion and you know how much it’s changed since you you entered the Pirates so how have you seen the field the baseball analytics evolved since you started and where do you think it’s headed in the next five years yeah that’s a great question um so to even go further back right the first people hired in baseball analytics were the ones who were just most able to convince um a decision maker that um we should value walks as an example right um this was like something that was pretty well known if you were reading um the early days of baseball prospectus and other blogs in the internet right in the year 2000 but no one in front offices is actually using that information to make decisions so back then to be ble to convince people that just hey walks are valuable or you know pitchers don’t control bad at Ball results right and kind of the second way of people that were hired were more when Pitch up ecstatic came out in 2008 209 um then there’s this huge um influx of that on every single pitch measuring not just the result of it but this spin rate velocity allocation movement Etc um and the skill set that required was more of like a databased data engineering skill set where you need someone who could actually process this information um and you could gain a competitive Advantage just by you know loading this information and um describing the players who have the best spin rates or best vertical movement um and you might be able to be an edge over our teams um but then I think the next the next wave was was more um taking those insights um and being able to build a model that um that not just says hey this is a high vertical movement pitch or high spin rate pitch here’s actually what this means for um the expected value of a pitch right so that kind of um necessitates more of a data science or um you know modeling research type of skill set um going forward I have have a couple thoughts I think there’s a huge amount of um potential insights to be gain from the biomechanics information um that’s that you know ml has put out there um also takes you know a huge amount of data science and and uh data engineering to be able to even work with that data um but I think we’re going to continue to get a level deeper in an understanding um you know maybe how you might generate a certain vertical movement or spin rate right from a from a biomechanical perspective so that’s that’s the biggest one the other one that I thr out there is just um whether teams start bringing in um internal um people would actually start building new technology to to collect um different information than um than the team than other teams can get so you know all 30 teams have access to the same you know fat ball biomechanic tracking data um but I can imagine a world you know five or 10 years on the road where you’re you know bringing engineers and he to try and build technology that will allow you to collect different data than other teams are are collecting yeah I think the field seems to have a lot of potential you know to grow you know in many ways um having worked your way up what lessons about leadership and teamwork have you learned that You’ share with is looking to grow in this field that’s a great question um I think one of the biggest things that jumps out to me is just be yourself care about people um I think it’s there’s a lot you know in baseball and in other Industries there’s a lot of like type A people who really want to be ambitious and and move up and I think that you know sometimes can serve people on the short term but but um in the longer term I think the the biggest thing that um that I’ve seen have success is is just wanting to invest in people uh wanting the best of people and that you know can take a number of different forms of investing in their their growth and putting the right resources in front of them to help them grow to making sure that they have you know a healthy work life balance and and don’t get burned out so um I think those the biggest takeaways I I have yeah yeah thanks a lot for all you know all that information and I just want to close this by asking you know do you have any final words or wisdom for aspiring analysts or advice for us as we launch this podcast and continue to develop our platform yeah I would say for um aspiring analysts um the biggest thing I can say is just um get your work out there and and do do independent research or do your own analysis right so there’s um tons and tons of students nowadays who have the requisit programming skills and have some experience working with the team at their college right um so you’re not separating yourself anymore by just being able to program or or having some baseball knowledge the way to separate yourself I think is to um to be able to um show hey here’s an analysis on a player um and make some of the team say I had thought of that before or this is an interesting angle on on this player or I like the way they approach the um biome mechanics part of this write off or you know if you want to be more on like the longer term research data side and side then um you know find some public data and and build a model and and do a short write up on it or build an app or something to visualize it and and share that model because I think ultimately um that’s the best way to kind of separate yourself from from the back yeah thanks a lot Justin you know your advice and your experience has been really valuable to us and I’m sure a lot of our viewers will use it to good use um thanks a lot everyone for tuning in to our first episode of our sport analytics podcast and we will see you next time [Music]

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