How a Sports Agent Uses Data Science (Ian Greengross) - KNN Ep. 104
Updated: Jul 8, 2022
Today, I had the pleasure of interviewing Ian Greengross. Ian is my friend, a business partner, and a sports agent with over 25 years of experience. He represents: NFL players, football coaches, hockey coaches, and now data scientists. In this episode, we learn about how Ian uses data in his work as a sports agent, and how even late in his career he is finding joy in programming. Special thanks to Nick Wan for the introduction.
[00:00:00] Ian: And so I bought a Python book first, right, cause I, you know, I'm old. Again, I used to learning, you know, just like I did for, you know, the Apple stuff. So, you know, and I started this and then I said, you know what, there's gotta be something maybe people around or something. And so I looked around and sure enough, there's the Chicago Python User Group.
And, you know, you couldn't have asked for a nicer bunch of people. And once, you know, they have many meetings a month, you know, usually once a week on Thursdays. But the meeting I started attending was essentially what's called Project night, and Project night was in two parts. It fit, you know, geographically speaking.
So there was a big space at a tech company here and, you know, one side of a big kind of open meeting area. And then on the back side of that wall was actually the lunch room. And so, you know, they order in pizza and, you know, everybody eats and then they say, all right, you have two choices, you know.
I mean, obviously it's not like they're enforcing the rules with a, you know, security guard. But I mean, basically the whole night is based around two options. Option A is go on the lunchroom, stay on the lunchroom side, after we're done eating and you can work on your own laptop, do your own thing.
And you know, there'll be people around and you know, maybe you tell people what you're working on and they want to help you, or you help them, or, you know, vice versa. Essentially, it's kinda. Just open forum, you know, for anyone who brings their computer to do whatever they want. And then the Project side, hence Project night is you rate yourself, you know, then they use a little Python script to scramble everybody up in the groups of four.
So, you know, a 10 gets put with a one and a seven and a three. So the whole group kind of averages out. And so that way, you know, you have some people who are just learning and some people have experience. And, you know, even though they made me feel welcome from day one or from meeting one or each month, I much preferred those first few months to at least go on the, do it yourself side and continue, you know, and I had my book next to me and, you know, sure enough people would come up and be like, Oh, you know, what are you learning? Or why are you trying to learn?
[00:02:06] Ken: This episode of Ken's Nearest Neighbors is powered by Z by HP. HP's high compute, workstation-grade line of products and solutions. Today, I had the pleasure of interviewing Ian Greengross. So Ian is a friend, he's also a business partner, and finally, he's a sports agent with over 25 years of experience. He represents NFL players, football coaches, hockey coaches, and now data scientists.
In this episode, we learn about how Ian uses data in his work as a sports agent, and also how even late in his career, he's been finding a lot of joy in learning Python programming. Special thanks to Nick... for the introduction. And I really hope that you enjoy this episode. I know I had a great time speaking with Ian.
So Ian, thank you so much for coming on the Ken's Nearest Neighbors Podcast. Obviously we've worked together now for a while. You have an incredibly interesting background as a sports agent and you've also taken it, you know, on yourself to integrate data with your work as well. I think that that's incredibly fascinating and I'm excited to hear about, you know, the origins of that and where you're expecting it to go in the future.
[00:03:18] Ian: Alright, so in terms of the origins, so I'm probably older than most of your guests and crowd. So I got into computers probably around sixth grade, which for me would've been right around 1980-1981 Apple IIe and Apple II Plus were released really, you know a lot of schools bought them. And so my school was fortunate enough to have an after school computers program.
And, you know, while I was an athlete and still somewhat am I was always very good at math science. You know, I had very much had a math science brain. I am very much my father's child. And when the computers came along and, you know, they showed us, you know, what they could do and, you know, especially, you know, programming is kind of, you know.
There's back then it was kind of ordinal and, you know, you had processes and flows and you know, it could do math and it could do all these kinds of things. And so I said, Hmm, let me try it. And there were this, there were these great set of books and they, you know, cuz obviously this is pre-internet so you couldn't watch a YouTuber like Ken Jee and learn things.
So you had to, you know, do things out of a book and there were these great workbooks. Really geared towards teaching, you know, school, age, middle school kids, how to program in Apple BASIC. And basically I went through them like, you know, a fish through water. I mean, it just, it seemed natural to me. I enjoyed it.
It worked for me, it clicked into place. And before you know it, you know, I was really doing well at writing stuff in Apple BASIC. I mean, obviously we're talking about 64 K of RAM and like eight colors, but nonetheless, you know, I was doing stuff. And so by the time I got to eighth grade about, you know, year and a half, two years later from starting all of this couple things, number one, you know, they allowed eighth graders to spend as much time in the computer lab, as long as, you know, someone was there as we wanted.
And B I was able to, you know, convince my parents that this is a wonderful tool that not only can I use it, but, you know, there were word processors back then as well. And so, you know, I could write my papers on it for school and, you know, if they had a memo to write or something, you know, they could use it in, you know, my old dot matrix printer.
And, you know, so yes, we got an Apple IIe for our house, and that really jumped me forward. And by the end of my eighth grade year, I took it on myself to do something fun-ish. And for me, that was making a math game of all things where you were a frog. And you started out at the bottom of a set of stairs.
And I don't even know how I thought of this. Right. And math problems would pop up on the screen and, you know, you had time to answer them. And as the timer counted down, you had to keep answering questions. And every time you answered a question, you'd get enough power to jump up a stair. And at the top of the stairs was a door.
And, you know, you were a goal was to make it out the door because if you didn't make it out the door in time, eventually some snakes would be released and the snakes would slither along and climb up the stairs. And eventually they would bite and poison the frog and the frog would die. I don't know if that's brilliant or morbid for an eighth grader, but nonetheless, that was the game I wrote.
[00:06:29] Ken: Sounds par for the course for ninth grade.
[00:06:31] Ian: You know, and then, you know, again, I kept, you know, as a high schooler, I still stayed even though IBM started to crack the market with DOS PCs or MS-DOS PCs, rather than Apple DOS. You know, I stayed pretty much loyal with my Apple two. All the way through high school and, you know, in high school I kept, you know, expanding my knowledge of computers.
I learned Pascal, which right again, no one seems to even know exists anymore, but it did. And I learned it. And then, you know, just again, knowing how well I knew this, my junior of high school, our high school in its eternal wisdom decided that people should become computer literate. And so they had a mandatory learn computers class.
It was wasn't for a grade or anything. It was more like, we need to teach you this, so you'll learn it. And so I show up the first day and, you know, I see what the curriculum is. And I said to the instructor, I said, is there a final exam for this thing? And he said, Sure. He said, If you can write a program that plays craps you're done.
I said, Okay. Sat down about 20 minutes later, I said, I'm done. He's like, You're done. I said, I'm done. Comes over and I'm like, Go ahead and play. And he played it. He's like, Yeah, you're done. And that was so, you know, I really kept, you know, and I kept, it was fun. I even taught it to do my, you know, and again, being a math science person, this'll be no surprise to anyone.
My senior year of high school, I took the two hardest math classes available in my high school. At the same time. I was the first person in the history of my high school to do it. And so you know, some of it, you know, you had to do some calculus graphing and so on and so forth. And I taught my Apple because, you know, back then, you know, you didn't really think about like zero, zero as it kind of still is the upper corner. Right.
So I had to reset zero, zero to the middle of the screen in order to graph it correctly and so on. And I came to school with it and my math teacher was like, Well, if you could teach the computer to do it, then you're fine. So, you know, I really kind of kept up with it and then. You know, I got to college and I was in the school of business because the school of business is where, you know?
Yeah. I think there probably was a computer science major. I'm not entirely certain, but you know, I also wanted some business side of it too, cuz I was fortunate given my computer experience. I had had a couple of quote unquote real summer jobs where I worked for accounting firms on their consulting side.
And you know, I converted Book binders, like, you know, a company that actually bound books. I converted their sales and invoicing system, which was written in IBM, you know, general basic to back then the big database programs were dBase IV and Paradox. And each of them had their own scripting language, almost like a light version of SQL in a certain sense, but directly related to the databases themselves, cuz they were relational databases.
So you could query from one table to another rather than a flat file database. And so, you know, I had had a couple of summer jobs and I realized, you know, look that was probably going to be my focus in life. So I was in the school of business and I was what was called back then and information sciences major.
And so I, you know, again, I still kept programming. You know, I wrote essentially a leads tracking system for a local pizza place where, you know, again, they could take down the phone number, enter it and start building a database. Cuz why send someone who orders a pizza a week, a discount for 50%, they're already ordering, send 'em a discount for 20% or maybe build even a customer loyalty thing.
And I wrote that in Paradox, you know, and I just, I kind of kept at it and they made a certain COBOL by the way. So, you know, when all the banks had to convert, I probably could have cleaned up there if I had really paid attention. But COBOL to me was so basic compared to even what dBase IV and Paradox offered as relational databases.
I let's just say I learned COBOL, but if you asked me even one command right now, I probably couldn't recall it. Whereas if you asked me to break out an Apple IIe emulator, I could probably rewrite the frog game in, you know, a couple days. So I stuck with it. And then as I got to graduation, you know, back then all of the, and it's funny, cuz I do love programming, but all the jobs and back then you really didn't do a whole lot of customer facing stuff the first couple years.
And I had in a sense paid my dues by working those summers and a couple of 'em for some major firms. And I said, you know, I just don't know if this is. Really what I want to do, you know, I just, I think I may need to look at something else and, you know, and going to law school, it always kind of fascinated me.
And, you know, as, as you've come to know me and your audience probably will by me going on this 10 minute diatribe, which isn't even halfway over yet, I'm not exactly a shy human being. Right. So, you know, I knew that I could, you know, be a lawyer, whether it was someone, you know, a litigator or someone in the courtroom, I, you know, I'm not shy or, you know, even if it was doing business deals or maybe even computer law or something, you know, cuz again, we're talking, this is 1992.
So I went to law school and funny enough, I didn't even think about, so you've mentioned I'm a sports agent and you know, we're gonna delve into that a little more soon, but I didn't even think about being a sports agent. When I first went to law school to tell you the. You know, I had played hockey. So as an undergrad, I mentioned maybe I went to Boston university.
And so when I was there, I mentioned I was a little bit of an athlete. I was like the fifth string goalie for the varsity hockey team. Basically my biggest contribution to the hockey team was my grade point average. But nonetheless, you know, I skated you know, every day at practice or at least, you know, for two of the four years, at least I went to practice and, you know, I played with guys who, you know, are almost hall of Famers and, you know, some of them scored 500 goals in the NHL and so on.
But, you know, I knew my athletic career was coming to an end. The moment college was coming to an end. So again, you know, I thought about a lot of these things and you know, here I am at law school and I go out to dinner one night at law school with a friend of mine who, you know, went somewhere else. And one of his, and he is a couple years older.
And so one of his friends had come in and, you know, they've been working for a couple years and this friend of my friends, his job was actually working for a basketball agent. Helping him recruit new clients in the business. It's called a runner. And so, you know, as we're at dinner, I've never met him before, you know, and we're all just talking and so on.
And my friend mentions how I played hockey and da, da, da, da. And he said, well, wait, Boston university don't they put out a lot of pro hockey players. And I said, They do. And he said, Oh, so you're in law school to represent him. And I'm like, Well, I am now. So you know, I kind of started thinking about, wait, this is something I could do.
This is before the movie Jerry McGuire came out. So I wasn't writing the coattails of Jerry McGuire. This is before that. And so I looked around I live in Chicago here in Chicago. I looked around and there was an agent here in Chicago, you know, and been around for a little while. And you know, he. He kind of liked what I had to offer in terms of being a connection to the east coast and you know, that I was in law school and the whole thing.
And so he allowed me to start working for him and for those next two years or so I did, while I was in law school, I worked for him. I would go out to Boston and, you know, try and bring in players who I knew or were around or, you know, some of the coaches who coached me had moved on to other schools as well.
And so they were helpful too. And yeah, after those two years, I, you know, I had like three or four minor league guys, no one in the NHL yet, but you know, I was moving along. And then the guy who I was working for was a very successful trial attorney on top of being a sports agent. And so he was part of a group that I was asked to bid on being the owners of the expansion minor league team.
That's now here in Chicago, the Chicago Wolves and his group was successful in that bid. Well, there's a, I teach sports law now at my old law school, from the professor who taught it to me. And this will also be mentioned in a minute, but there's a case in my sports law class about conflict of interest for agents.
And one of them is you can't own a team in professional sports in at least in that same industry and represent players in that same industry. So once my mentor, you know, who I was learning the business from once he. Bought into the minor league team. He couldn't represent players anymore. So I had to find a new job.
And, you know, at that time I was taking sports law at law school. And my professor was someone who was mostly a lawyer, but had helped out a very famous and big sports agent here in Chicago. Someone who represented almost half the bears when they won the super bowl when I was 15 years old. So I knew like all his clients and I knew who he was and so on.
And so this law professor, you know, again, I'm not shy. I was very much a participant in class and so on. And you know, he had come to know me a little bit. And so based on the recommendation of my sports law professor and my the hockey agent I had worked for, I got an interview with this very big football agent, because he was looking for someone to add, because, and again, another lucky break here the salary cap had just come to football.
And so, you know, it's not that he couldn't do the math himself. He's a very brilliant man and, you know, but he was always, you know, more of a big picture guy and so on. And, you know, he wanted someone to really work more on the details of some of the stuff on the big contracts on the salary cap. And again, it was brand new and think of new and creative ways and so on.
And so between my law degree and, you know, my experience and being able to, you know, work back then Excel, right? Didn't learn Python just yet. So, you know, whip things around in Excel and so on, you know, it really worked out. And so I was very fortunate. I got the job. And so for the first couple years I was helping the football agent who was, you know, this successful in football and, you know, first round draft picks and so on.
And, you know, I was trying to build hockey where, you know, I'm starting from down here and it was very tough for me to continue to recruit hockey players as clients, because when I would go out and try and compete to get them, you know, I was competing against other agents who maybe even were here, but had hockey players, my only hockey players were here.
And I mean, they were great guys, but I mean, in terms of the level they were playing at versus in football, you know, the guy I'm working for is, has guys like this. So I started working on football players and after about two years, I pretty much became a full-time football agent. And so I worked for him for about seven years.
And then, you know, he understood and we remained friends until the day, you know, he passed, it's almost four years now. But you know, I started my own thing and you know, for a while there, you know, slowly built it and so on and so forth. And so I've. A sports agent for football players and football coaches for 27 years.
I've had hockey coaches for about the last four or five years. So now fast forward, how is all this relevant to me sitting in front of you with data? So as you know or, and maybe you're outta some of your audience, don't because they're maybe not sports fans, both foot, all the big sports here in America, football, baseball, basketball, hockey, soccer, all of them or football, depending on where you're from have all over the last, you know, to varying degrees and varying start times, but at least over the last 10 years or so, very much moved into using what they call analytics or data science, really to help evaluate everything they do to help them win games.
Whether it's figuring out the best time to use your timeouts in certain situations to play a performance, to trying to predict, you know, who should we draft based on their performance, because, you know, there's no standardized level that everyone's competing at, whether it's college or in hockey, whether it's Europe and you know, the Canadian leagues and the American leagues.
So, I mean, there's a lot of variants there. So they started, you know, very much using a lot of these things and, you know, Twitter can be both good and bad, but the good part for me was, you know, I could see very publicly, a lot of people on Twitter would publish their stuff, not from the teams directly, but you know, people who were also much, you know, who were data, scientists, people as smart as you, and you know, who have as much experience as you would very much publicly publish a lot of their stuff.
You know, I understood it. I mean, it wasn't so much that I was at least at that moment thinking about trying to do it, which will lead into the whole Python thing in a minute. But it was very much where I was like, yeah, I know what they're talking about here. I see, you know, I get expected value or I understand, you know, this, or I understand, you know, this analysis they did through regression and so on and so forth.
And I'm like, Hmm. So about God, it's almost probably going on call it a good four years now. So about four years ago, I said, all right, somewhere way in the back here are all those programming skills I acquired. You know, when I was starting, you know, 10-11 years old and you know, the muscles haven't gone completely soft.
Hopefully let me see if I can, you know, maybe find a way to do some of this myself, cuz I'm not gonna keep up with an NFL team or a hockey team. You know, they have a whole staff and they've got much, you know, a lot more money to spend on resources just to do. Because that's what they do. Right. And I mean, certainly I could spend the money, but I'm not going to, so I said, you know what, at least let me be at a point where I can maybe take some things and do some things that are relevant just to me on much smaller scale.
And so I said, all right, let's do this. And so I bought a Python book first. Right. Cause I, you know, I'm old again. I used to learning, you know, just like I did for, you know, the Apple stuff. So, you know, and I started this and then I said, you know what, there's gotta be something maybe people around or something.
And so I looked around and sure enough, there's the Chicago Python User Group. And you know, you couldn't have asked for a nicer bunch of people. And once, you know, they have many meetings a month, you know, usually once a week on Thursdays. But the meeting I started attending was essentially what's called project night.
And project night was in two parts. It fit, you know, geographically speaking. So there was a big space at a tech company here and, you know, one side of a big kind of open meeting area. And then on the backside of that wall was actually the lunch room. And so, you know, they order in pizza and, you know, everybody eats and then they say, All right, you have two choices.
You know, I mean, obviously it's not like they're enforcing the rules with a, you know, security guard. But I mean, basically the whole night is based around two options. Option A is go on the lunchroom, stay on the lunchroom side, after we're done eating and you can work on your own laptop, do your own thing.
And you know, there'll be people around and you know, maybe you tell people what you're working on and they want to help you, or you help them, or, you know, vice versa, essentially. It's kind of like just open forum, you know, for anyone who brings their computer to do whatever they want. And then the project side, hence project night is you rate yourself.
Then they use a little Python script to scramble everybody up into groups of four. So, you know, a 10 gets put with a one and a seven and a three. So the whole group kind of averages out. And so that way, you know, you have some people who are just learning and some people have experience. And, you know, even though they made me feel welcome from day one or from meeting one each month, I much preferred those first few months to at least go on the, do it yourself side and continue, you know, and I had my book next to me and, you know, sure enough people would come up and be like, Oh, you know, what are you learning?
Or why are you trying to learn? And not that I'd give them this 19 minute spiel that I'm already up to, but I, you know, I generally told them, look, I'm a sports agent and you know, I'm trying to, you know, I have some old school Apple, you know, grew up on Apple toes, Apple basic. I mean, I've got it in here.
I'm so I'm figure I can learn Python. And they're like, Oh, great. You know, why don't you try this? Or, you know, why, and slowly but surely, you know, they would give a little tips here and little tips there. And, you know, so I would say those first six to eight meetings, you know, over that first six to eight months, I definitely stayed on the, you know, do it yourself side to build up something more than just print "Hello, world".
And so, you know, when I finally felt, I was at least, you know, enough to not slow anyone down and again, you know, they would never have said anything and they're very welcoming, but nonetheless, I wanted to be more than just a one I wanted to put in at least a three, you know, by the time I went over to project night.
So I did so after like six or eight months, I went over to project night and the project night side. And, you know, I started putting down a three and, you know, Generally speaking the same group of five or six nines and tens, you know, in Python. Generally speaking, they were the ones always to do project night cuz you know, for them it was fun, you know, to like teach someone new.
But also some of the projects, you know, they were very interesting. And so you know, my greatest asset as a programmer and everyone kind of does this now with stack overflow, but nonetheless, my greatest asset as a programmer is being able to mimic what someone else does right. Or which we all do now through Stack Overflow.
But you know, I could watch these, you know, men and women who. Were tens at Python on project night and it would be do this, or, you know, we would figure out that and I could watch them. And, you know, because I had at least a, the understanding of how programming works, which really hasn't changed since my Apple days in a certain sense.
And because now I had at least a little bit of a layer of Python, I could follow along and say, Oh, I see what you're doing here. And then I would go home. and of course, you know, they would send an email at the end and they would email us the script so we could see it at home, but kind of almost like my own personalized stack overflow.
I could then take what we did that night and be like, all right, let me recreate it myself. And maybe just put a little twist on it to see if I truly understand it and sure enough. And so I did that for about another year. So heading into about two years of it you know, by then I'd known everybody, you know, and, you know, really, I mean, again, a great crowd and, you know, become friends with a couple of 'em.
And one of 'em worked on a little bit of a hockey project with me just to do some stuff and so on. And so. They every six months they offer what they call the mentorship program, where, you know, now you really get signed with someone one on one, you have a project idea and they, you know, they don't write it for you but much like project night, they're like, Well, wait, you're trying to accomplish this.
Did you think about this thing here that works like this and does this thing try that? You know, and so I submitted a project which was finally gonna be something relevant to what I do. I wanted to use Python to predict, you know, a contract for a player who's been in the league for a few years.
So someone who has three or four years of playing at the professional level is going to be, do a new contract because their contract is coming to an end. And so therefore, based on their accumulated statistics across. Many many so features now, right? Across many features, you know, which ones are predictive and so on and so forth.
And so, you know, this really begins my, my Python journey to where I am now. So, you know, first thing I had to do was, you know, ... the data, clean it. And so on which funny enough, that was something I already kind of had down from my days of, you know, doing Paradox and dBase right. I mean, you still had to build your databases.
Still had to have good data. I mean, just cuz you know, now we're in this much more technologically advanced stage, data's still data, right? I mean, if you have bad data, doesn't work, whether it's 1987 or, you know, 2022. So that was kind of before we...
[00:26:13] Ken: Before we get into the specifics of the project, I definitely wanna highlight something real quick is the sort of community aspect of the Python meetup that you're describing. I think so many people they're looking in the wrong places for mentorship, you know, they're reaching out to people on LinkedIn they're talking to people all over the place and those people, maybe aren't in a position to be mentors. You know, maybe someone at meta, someone at Google, they're working really hard.
They don't have time to be able to mentor in a specific way, but if you're going to meetups that have a built in mentorship component, all of the people there who are those nines and tens in Python that you described, they're looking to be mentors. And it's a really good kind of synergistic thing for both the person who's interested in learning as well.
The people who are teaching because they are, they are interested in doing that. I think going out and necessarily finding the person that you think is the perfect mentor for you might not be the best approach because they might not have the time or the specific know how or the interest in mentoring you specifically. So I really liked that construct. I also really liked how you went about finding it. You were like, Okay, I found the speed up. This is what we're going forward. And obviously. Seems to have worked out for you.
[00:27:32] Ian: Yeah. Well, well, right. I mean, I'll finish that in a minute, but yeah, but here, you know what, as you said it, and you spoke the words, right?
You said it in a much more eloquent way than I did in a sense those nine and tens were staying on project night. And again, it's not because you know, they're staying there. Yes. The project is fun, but they also know that right. They're gonna be teaching people. I mean, like, you know, funny enough, there were two guys named Ray, there's a guy named Zacks.
I mean, you know, all of these, they stayed on the project side. Sure. They wanted to do the project, but also because they were dying to help me. I mean, it was a great point you just made, right. I mean, and so yes, through, through meet up enough, I know there's one in funny enough in San Francisco, of course, cuz somehow I accidentally clicked on that one once.
And so I still get the meetup notices for that one, but yeah. But yeah, you know, right. Someone on LinkedIn. I wouldn't have even thought of that to tell you the truth. But yeah, I mean, no, I just, well, I think that that's again, different era. I would never think of. I mean, I reach out to people on LinkedIn all the time to get stuff done.
I mean, you've come to know me. I'm not shy. And again, I'll reach out to anyone to help a client. But in the sense of like, I would never have thought like, Hey, I wanna learn Python. You are the lead programmer at Meda. Come teach me Python. I just wouldn't have thought of that.
[00:28:45] Ken: Yeah, exactly. I also think that there's a very neat element of the community that is open source. That's collaborative, but you have to go to the specific areas of the world or of the internet to access that, you know, someone on stack overflow. For example, if you ask a question, you might get absolutely flamed by people because it, maybe someone asked it before or whatever it might be. On the other hand, if you're in a more beginner focused community, you're, you're in an area where it's expected that people don't have that much knowledge.
That might be a really great teaching moment or really good question that people can expand upon. And I think so many people are just looking in the wrong places for specific resources or interacting in the wrong places for specific resources that if they just tweaked that a little bit, maybe they were going to a beginner Python night or a place where there's these massive ranges of ability. They would have a lot more success rather than being discouraged by being shot down in a couple specific, not as well welcoming communities.
[00:29:50] Ian: Yeah. I mean, there's certainly for sure that, I mean, you know, look, I know I was extreme. I've always kind of gotten that right break, whether it's the career or everything else. And I mean, I feel very fortunate that there was this kind of community here in Chicago because yeah. I mean, would I have learned it out of the book? Maybe I'm not saying I absolutely would've, but you know, having that people and just like again, seeing like, so, you know, there's the one Ray who actually, you know, I became closest to, and kind of did a little bit of a hockey thing with me, you know?
I mean, just again, he just sits there and he'll like, I could message him on slack and be like, Hey, wait, how do I do this? And he'd be like, just, I mean, and I know on the other end, he's smiling while he's typing it, rather than being like Ian, come on, buddy. You know, dear Lord, you know, he just, you just got that feel and it just, yeah, I mean, that encouragement definitely helped me get to, and again, I'm probably still only a five, but nonetheless it definitely made that leap from three to five, you know, much more encouraging and certainly kept me going to make sure I made it from three to five.
And so getting back to the project then, so circling back. So I had a mentor and he was great. And, you know, again, he didn't spoon feed it to me. He made me think about it now, you know, certainly he pointed out things that there was no way I knew they existed in a certain sense kind of like, you know, and your audience will appreciate this.
Like you pointed out streamlet the other day and how I've kind of started my path down streamline. I didn't know it existed, cuz again, I'm not really searching for streamline. So you know, he, he would suggest, so first, you know, I got the data and I cleaned it and then he is like, all right, well, what methods do you know?
And I'm like, obviously I know linear regression and a few things, but you know, I haven't really done any, you know, big models, you know, whether, you know, from matte plot lib or, you know, whether it's seaborne or any of that stuff. I mean, you know, I've never used any of it really. And so he is like, Well, now's your time?
So, you know, he said, well, you know, look, what do you know, start with what, you know, so of course I did some linear aggression and you know, there's just, it's not linear, you know, it's, I mean, it's. When I tell you what it finally comes to, which of course will be very, you already know, which will make it very nice tie into this whole thing in a moment.
But, you know, but he said, all right, well, why don't you do some principle component analysis? And I'm like principle component analysis and he's like, look it up. And I said, Okay, I'll look it up. And I, you know, again, and he just, he nudged me in the right direction. So I looked up principle component analysis, figured out how to do that.
Okay. So now maybe these features are more important and so on and so forth. And, you know, eventually we went through, you know, a whole bunch of different stuff. You know, I didn't get too far in terms of like XGBoost or Random forest. You know, that was a little bit above my pay grade at the time.
[00:32:28] Ken: This episode is brought to you by Z by HP. HP's high compute, workstation-grade line of products and solutions. Z is specifically made for high performance data science solutions, and I personally use the ZBook Studio and the Z4 Workstation. I really love that Z workstation can come standard with Linux, and they can be configured with the data science software stack. With the software stack, you can get right to work doing data science on day 1 without the overhead of having to completely reconfigure your new machine now back to our show.
[00:32:58] Ian: But as it turns out, what do I do in the real world? Cuz I'll do the big reveal. I'll bring out. As I say, in my math, in my sports law class, I'll bring out jaws in a minute. Won't get to the third act just yet. So in the real world, when you're negotiating for a veteran player for his new contract, what do you do?
Well, you compare the players who are most similar to that player, right? And you do it on a few different data points. Hmm. What does that generally sound like? It sounds like KNN, and sure enough, KNN was the most. Predictive in terms of the result of, you know, from the data we already had, clearly I've got years of data of players whose contract have expired and have gotten their new deals some more recent than others.
So we held back the most recent ones to see if it kind of kept up with the ... in a sense. And so right. Ran it through, did the test data then ran through and, you know, sent it through and sure enough KNN was fairly predictive. and I'm like, of course it is, I should have thought of this right off the tip, because that's exactly how you do it in the real world.
My player kind of has these statistics over here and this kind of player messages over here on the graph and yeah. You know, you sort, and of course that's exactly what you do in the real world. I just never thought of it as KNN until I thought of it as KNN. And so, yeah. So, and then once I did that, I was kind of off and running.
I mean, now I know Pandas, I know, you know, all of these things. My weakest point once I was done in the world shutdown was, you know, doing things, you know, that were browser ready, whether it was through, you know, Jengo or flask or anything like that. I re and I, and again, with the world shut down and everything else, you know, no meetings, I didn't really push myself too far on Jengo or flask, because, you know, at that point I was, I don't wanna say self-satisfied, but I've at least covered.
Like now, if I wanted to take running back statistics or, you know, even some of the more esoteric things that people are putting out there, I could now go back to Twitter, see some of these things and be like, Okay, not only do I understand it, but I can take the data myself and put my own twist on it.
What's important for me and my clients, because now I can do exactly what you did, you know? And so therefore I was at the point at least, then to make myself in my business, sorry, to not make my it's... I was at the point where I could do things for my business and not have to learn flask or jingle or anything else.
Until about three weeks ago, this wonderful guy named Ken G points out streamlet. And now I'm like a streamlet addict trying to build, you know, dashboards and stuff and so on and so forth. And so that is pretty much, sorry. I was just gonna finish that pretty much sums up my Python journey. And as long as we've mentioned, the Chicago Python User Group, that is the Chicago flag and instead of stars, it's Python.
[00:35:53] Ken: Alright, so if anyone is in Chicago, definitely check out that group. I don't know if they're doing live meetings...
[00:35:58] Ian: Yeah, the last time I checked and not to interrupt the last time I checked, cuz I wanna go back to the live meetings. The last time I checked was about three months ago and I don't think they were live yet still. I haven't, I have been remiss a little bit. These last few months. I have not checked probably in the last three months, but as of three months ago they were not.
[00:36:14] Ken: Well as people find out you've been very busy the last three months.
[00:36:17] Ian: Yeah. Well we're about to get to that, right?
[00:36:20] Ken: Yeah. I mean, one thing I just really wanted to highlight was the quality of mentorship that you were given? Oh, it was unbelievable. I think that that's, that's a really important thing is making people work for themselves or providing feedback. A lot of people, when they hear mentorship, they're just expecting someone to tell them what to do, to give a roadmap, to, to do whatever that might be.
And that's exactly how bad mentorship works. And that's exactly how mentorship shouldn't work. Mentorship should be you coming to the other person with, well, well thought out ideas and bouncing them off the other person or the, just as you described the other person coming to you with something that was outside of your sphere of reference and allowing you to experiment with it on your own. I mean the stream load example, like I'm not your mentor in any way, shape or form, but...
[00:37:08] Ian: Well, I have watched the first 30 minutes of your video. I'm still stuck at 30 minutes.
[00:37:13] Ken: But, I think the idea that is the exact same though, is that I shared to you, this platform that I really enjoy, that I think is very useful and you took it on yourself to go and explore that. Right.
And that's, again, how I believe that mentorship equation should work is that this person who I'm working with, maybe I come to them and I say, Hey, this is what I've built. These are the two directions in which I'm planning to go, or I'm thinking about going, how would you approach it? How, how do do you think I could improve on that thought process?
And they say, Okay, you know, based on the feedback and all the work you've done, this is what I would approach, or this is what I would try. And then you can go ahead and implement a lot of these things on your own. I think that that's so important that there's no, nobody knows all the correct answers.
Even these people that you describe as tens and Python, they're still with coding a variety of different ways to do the same one thing. Even with, if a data scientists even more and. Just giving people feedback and like iterative feedback enough to maybe unstuck them just a little bit is what I see the role of the mentor being rather than the other way around.
Cause if you're thinking about anyone you'd want to be your mentor, I would expect they have time commitments. Right? They have things that they're doing the way that I just described. Mentorship is something that is reasonable for both parties. I mean, in another version where they're doing everything for you, that's completely invasive on their, their own time as well. I love just the case study that you gave there and how that worked for you. I think that that can work really well for other people.
[00:38:50] Ian: Yeah. Well, I mean, you know, look in my mind and again, I'm no expert by any means, but you know, what we do with programming is we're solving puzzles, you know, or a fancy way of saving, solving problems. Right. I mean, so I didn't want him to give me the answer in that sense of the word. I mean, sure. Look, if I was a hundred percent stuck and had no clue then sure. I mean, you know, at least give me something to go on, but I mean, if he could just say, look, here's what you need to do. And I'm like, all right, I'll figure out how to do it.
I mean, you know, it was much more self satisfying to figure out how to do it myself, cuz two parts. I did it. And then the other part is I learned it, right? I mean, sure. You could again be spoon fed as you've well pointed out, but I think you just, you know, if you've learned it for yourself, you're just gonna be that much further ahead.
Now here, I had a whole different thing that was much more, not in even necessarily business related, but I was, you know, working with some data and I was trying to figure out how to do something where I could iterate across a certain thing and the way it was structured. And I went to Zacks who is Zacks is kind of the head of the Chicago Python group, him and the other way, not the Ray I became closest to, but I mean, I'm friendly with all of them.
And so I said, you know what? I'm kind of stumped on this, cuz this was outside of the mentorship project. This was something else entirely. And he said, the way this is structured, turn it this way. So instead of your table being, you know, this, you could do it like this and that's all he didn't needed to tell me.
I mean, he didn't need to, you know, and sure it solved my whole. You know, again, he kind of spoon fed me the answer in a certain sense, but I mean, I was done. I mean, I had nowhere to go anymore, but yeah. I mean, certainly the way I learned from going to a three to a five, it was much more self satisfying to be like, yes, PCA, I'll figure it out. And if I couldn't, then he certainly would've, you know, again, done kind of more of the Zacks thing and been like, Well, here's what you're missing. Why don't you try it this way?
[00:40:48] Ken: So you mentioned self-satisfied. I thought that was a really interesting concept. I mean, from the time when you were a kid, it seems like you were very interested in solving problems and it's always been fun for you. And that concept has been fascinating to me.
So a long time ago I read Thomas Jefferson's biography. And when you look back in those times, there was nothing for people to do. Right. They would read a lot, they would learn a bunch of languages. It was fascinating for them. Like the most interesting things they had to do at the time were learning and reading, because that was some form of stimulation. I mean, now when was the crossing and boring? That's a good question. I don't know. When was it?
[00:41:32] Ian: I know, I don't know, cuz I mean, here I do the cross, but I mean, we talk like here, I'm certainly my father's child, you know, he and not to interrupt your flow on Thomas Jefferson, you know, he would do the crossword puzzle every morning.
I mean, again, I'm old enough for printed newspapers, so, you know, you know, and again, I saw him do it. I do it. I mean, I still do the crossword now. Obviously I do it on a tablet, but I mean, you know, I still do the crossword every day and you know, I mean solving puzzles, I mean, yes, I, you know, I was kind of, one of my nicknames was kinda like the shell answer, man. Because like, if I didn't know it, I loved to look it up. I always wanted to have the answer. I loved solving puzzles.
[00:42:07] Ken: Yeah. Well, I mean that, that's the thing is that if we look back in history, You know, learning a new language, writing 50 pages of documents. So those were the puzzles of the time. Now we have all of this technology.
I mean, why would a kid ever wanna, you know, sit down and read a book when they can play a video game? Just the level of stimulation that we have is so much greater. You look at what, you know, when you were a kid first learning, programming that to you is comparable to a puzzle, right? That's the next most fun thing when video games don't exist and it was in exhilarating, right?
And we we've gotten to this point, I think where we have such good options to occupy our time, that the things that used to be very fun generationally, they're just boring in comparison to what we have. But that suggests to me that we can somehow convince ourselves or make any of these activities.
Very fun if we change our mindset a little bit, I mean, that's been something for me in order to, to learn coding or learn any of these things. You know, if I'm not someone that plays video games, if I was someone that plays video games, I'd probably think coding was pretty boring. Right. Or if I use different parts of my brain or whatever it might be, I think it's, it's really yeah.
As I've grown, I just look at the compar comparables that we have. And I keep thinking about how could anyone be so excited about reading or something like that when they could watch it, or, you know, and we kind of creep up this chain of command. How do you still keep it fun when you have such really exciting alternatives to, to coding and learning and doing whatever, right?
[00:43:44] Ian: So as you know, but now I'll tell the world. I have six year old twin boys and they love to invade my office, cuz again, being a sports agent, as you can see, there's lots of fun stuff in here. And of course there's a TV right off screen here. So I can watch my clients, you know, whether it's football on Sundays, not so much in my office, on football, on Sundays, but you know, hockey during the week or a Thursday night football game or whatever, I might be in my office.
And, you know, they started invading my office. And so funny enough, you talk about video games. I did have an in television set which was Atari's competitor. I got that before I got my Apple too. So I did have video games before I started programming, nevertheless eight bit games, but nonetheless Plugged into that TV to keep my sons occupied in case I need to do some real work and something important is a retro in television. Right.
And of course they love playing video games. They wanna play games on my phone. They wanna play games on in television, but when I've told them, Hey, you know what, let's do a little bit of coding. And you know, they've gotten a little bit of this at school, but not like Python or anything, but I have, I've sat them on my lap.
And I said, Alright, this is Python. You know, and obviously they're not exactly ready to, you know, write a whole stream lit dashboard, but you know, it's funny to them when they can write, you know daddy likes, poop and tell the computer to print it 10 times. They think it's hilarious. And so now they want to do more programming.
And so, you know, I mean, you know, is it the same as playing video games? Of course not. I mean, let's not be silly, but I mean, you can start them at a young enough age. And even with all the technology we have, and I have started them at a young age to keep it fun where, you know, they want to tell the computer to do things.
I mean, here, like they clearly know YouTube even before you and I started working together and which again will cover soon. I'm sure. But you know, they knew about YouTube anyway, cuz they've watched, you know, like the dinosaur videos or there's the one, Oh, that's the, there's a monkey and he, Oh, it does all these dinosaur videos.
And anyway, I can't remember his name, you know, but, but nonetheless, you know, even with all that stuff, I'm like, Okay, well, do you want to tell the computer how, how to do these things? And so it isn't so much that they're learning boring computer stuff it's oh wait, they made these videos. I can tell the computer how to make these videos.
Like, you know, I use the turtle function or, you know, the turtle package in Python to, you know, use it to draw dinosaurs and lions form 'em and so on. And they're like, Oh, well we wanna draw it. I'm like, Okay, here's how you tell the computer to do that. So, I mean, I've, I've tried to make it for them where it's almost like playing a game, but the game is actually learning some code.
[00:46:24] Ken: That's awesome. I think that there's a fun element of open woundedness and the idea that you can build on anything with code that at least for me, is what made it the most exciting, right? Is that there are these things that I'd like to do. And I have no means of doing them before coding. And if I learn the skillset, I learned this skill set, I can have access to these things that I found so that I was just conceiving of before, rather than being able to implement that to me is the most rewarding thing.
And I think that that's a really important point is you can make all these things fun for yourself, even fun beyond outside stimulation or video games or whatever it is, if you attach it to things that, that you enjoy. And that is sort of the ultimate thing is that with programming, it could be related to anything that you have interest in, right. You know, it could be related to YouTube videos or video games or any of these things. And I love that concept that very clearly articulated.
[00:47:22] Ian: Yeah. I mean, so here, so talking about, so here in my quest to learn Streamlit, right? Some of the first things I've done are like, you know, header size fonts and dropdowns and so on and so forth.
So, you know, they they've, I said, do you want to help daddy? And so, you know, they write, you know, again, daddy likes poop. And then when I run stream lit, as you know, it looks like a website. So to them, they're like, Oh, we wanna, we wanna do our website, you know, every day it's like, can we, can we work on our website?
Can we work on our website? Cause to them, that's how they view the world. I mean, obviously they don't think of it as just a dashboard. They not, you know, locally hosted on my computer, but to them it's, Oh, we have a website, you know, and it says Ronan and Aiden love daddy or whatever, or daddy loves poop or, you know, all that kind of stuff, you know?
And so to them, they're excited to add in, you know, now obviously they don't understand all of the commands and everything, you know, I don't expect them to become streamlined experts before I do, but nonetheless you know, again, it's part of their learning right now. So at least hopefully it will stick with them.
Now, do I expect them to become sports agents, programmers or anything else? No. If one, one of 'em is very creative. If he becomes an artist, that's perfectly fine, but you know, at least in a sense he's gotten the exposure of all these things and knows what he can do with them. And you talk about even reading.
I mean, you know, every night we make sure we, you know, and I know we've strayed far away from kind of can, but you know, E even every night we make sure, you know, there's 15 minutes of reading every night before. You know, I mean, yeah, we are in a very much modern, you know, you know, they have my believe me, Minecraft is on here.
And if I turn my back for two minutes, they'll steal my phone and play Minecraft, no question about it. But, you know, we make sure there are all these other things going on and, you know, and just kind of circle it back to what we're talking about now, again, you know, any of this, anyone who's gonna watch this, anyone who does any of this, it's always about like learning and solving something.
And in a sense, so is Minecraft, right? I mean, to kind of, you know, bridge this gap here, playing a video game, right? I mean, whether it's shark shark on, in television, where you're this little fish and you have to eat smaller fish and keep getting bigger and bigger. Again, solving that one of my sons, I mean, his high scores, like triple mine, cuz all he does is play it, but he's solved it in a certain sense.
I mean all of this in a sense is solving problems. Whether it's just solving a video game and how to win that solving, you know, how to build in Minecraft and not die or, you know, figuring out on Streamlit how to present the data.
[00:49:48] Ken: I love that and you know, talking about solving problems. So we met through a mutual friend, Nick Wan so you worked on a hockey project with him.
I'm really interested into what your exposure into maybe more formal data science was like, and you know, how you were able to solve a program. I a problem, I think you were working on it's the big data cup. I believe it is with him. And I definitely want to hear about that experience because I think it all ties everything together pretty well.
[00:50:19] Ian: Yeah. Right. So, you know, I've mentioned how I followed some of these analytics people on Twitter. And one of them is named Ethan Douglas and Ethan covered the chiefs for a while. And I had, was fortunate enough to have their starting running back. When they won the super bowl, he scored two touchdowns in the Superbowl, ran for over a hundred yards again, named Damien Williams.
And so through my representation of Damien, Ethan and I over Twitter had become friendly. And every year in football for the last four, going on five years, maybe there's a thing called the big data bowl. And that is the NFL releases a data set from its tracking. So in NFL football players have trackers right here on their shoulders.
So they, you know, know orientation, they know speed, they know all of that and the ball can be tracked, excuse me, as well. And so the NFL releases a data set around a specific. Type of play pass plays over 20 yards, or it was punting plays or so on and so forth. And, you know, they release it on Kaggle and there's competitions to see who can come up with the most predictive you know, measurements and analysis based on the dataset they've released and the winners from the like three or four winning teams every year, a lot of them get hired to NFL teams or to companies that provide data or do analysis and so on.
And so in hockey, they just had their second one. So we were part of the first one. So two years ago Ethan. Well, I guess technically 15 months ago, Ethan Douglas came to me and said, Hey I know you've got some Python skills. Do you want to enter the big data cup? Hence the Stanley cup, big data cup.
So I said, sure. I said, but remember my Python's about, you know, it's not here anymore. It's probably about, you know, here and we're gonna be competing against people who are here. And he said, not to worry, I'm gonna add some people. So he added two people. One is a man named Sean Clement. Sean is in the military, but you know, he's allowed to work part-time outside and he is very good at data science.
He is here and he works for some NFL teams on a consulting basis. So he was eligible to do the hockey data contest. And then as you mentioned, Nick Wan so the last person, the fourth person added to our group was Nick Wan so two things about that. Number one, Nick, as you know, does a Twitch stream where he'll hop on Twitch, pull up a Jupyter notebook and just start programming and Python analyzing generally sports-related data.
Cuz Nick does work for the Cincinnati Reds. So he doesn't do baseball data, but he's done even like league of legends, like, you know, the professional league of legends or the Overwatch league he'll analyze that or sometimes he'll do basketball or whatever it is.
And I have funny enough had watched Nick's stream because of his relation within the sports community. I had seen him on Twitter and I'm like, Oh, let me check out his stream. And so the four of us started together on a paper for, and again, Nick is also up here on data science and Python. So the four, the three of them are tens and or nines and tens at least that they would consider themselves.
And you know, here I am a four and a half maybe at this point. And so, you know, Again, it was all remote, cuz Sean was in Florida at that point. Nixon's in Cincinnati. Ethan's in Kansas City and you know, I'm in Chicago. And so you talk about, you know, what did I learn and what was my experience? You know, first we had to download the data and see what was there.
And so then we had to decide, you know, what were we going to try and do with the data? And our set of data was women's hockey data. And so what we took from the women's hockey data was we wanted to see if we could make an all encompassing metric to evaluate how good a player is, you know, goals are great, ... are great, you know, and again, the hockey analytics communities come a long way, but we wanted to see if we could make one all encompassing metric. So in terms of with the data...
[00:54:33] Ken: Like a similar thing would be in baseball, you have like, wins above replacement and football. You have, I'm not as good at football basketball. You'd have like player efficiency rating, right.
[00:54:43] Ian: Something along those lines. And so, you know, we looked at it and you know, we looked at the data and I said, all right, let me, you know, start doing a few things that I can do. And, you know, I mean, I've become pretty good at plotting, you know, you know, iterating through and sending a Pan's database through to plot out, you know, the rows of data.
And, you know, so like I plotted out passes that lead to shots that lead to goals and so on, you know, and things to kind of see, like, are we down the right track? Whereas, you know, the rest of the conversation. So out of like an hour, that would take like five minutes and our rest of our hour zoom meetings, you know, that we would have every few days Nick and Ethan and Sean would be like, all right, should we use Random forests or XGBoost?
And I would be like, Yeah, this is pretty much where my knowledge ends, you know? I mean, so, you know, we kind of circle back to what I learned in a sense through the mentorship program. This was also like having in a certain sense of mentorship program because at least I had the domain knowledge for hockey.
I could kind of see where this was going. Even if I didn't know how to do XGBoost or how to do Random forests or any of that other stuff, I could at least follow along and read their code and say, Oh, I see what you did. I see what it spit out. I kind of get how this works, you know, and so on. And, you know, look, I didn't wanna take too much of this to be Ian learns how to do what the, you know, Nick and Sean and Ethan know how to do so.
I did that more on my own and where we had our meetings. I provided much more of the domain knowledge. I'm like, you know, are, is this meshing up to what I would. Expect to see as just a hockey person. And then does it mesh up to what we, people quote, unquote, agree who are the best players? So, you know, my biggest contributions, you know, again, kind of joking back to when I was in, you know, college, my GPA for the hockey team, you know, here it was my hockey, you know, instead.
So instead of my GPA and my hockey was less here, my hockey was more and my, you know, knowledge was less, but, you know, look, I certainly, you know, they carried me across the finish line. There's no question about that. In terms of the data science portion, we would not have been an honorable mention paper if it was just me and Ethan, cuz you know, Ethan wouldn't have had the time to carry me.
Like, you know, three of them did at once. Did I contribute some stuff? Absolutely. You know, they'll admit that to at least, you know, if they're under oath, but you know, it was much more of for me almost a learning experience again, but with domain knowledge, you know, kind of like it was one thing to learn.
Very slowly, you know, over a five month period in a certain sense at my own pace for me, for myself in the mentorship program. Whereas here we had a couple months, we had a deadline, we were submitting for other people, you know, I didn't want to get in the way and you know, not, not that they would have ever said I was, but you know, I let them do their thing.
And so, you know, Nick would come up with some great stuff, shoot it out and like, here's the data set. And then I would be like, all right, let me see if there's something I can make out of this. But in terms of like running it back through and, you know, do we have log loss? And I mean, and you know, I know that doesn't really apply, but I mean, I'm just saying, you know, whatever it was, they did the heavy lifting on that part.
But my two biggest contributions did make it in though. So we were Nick, Ian, Sean, and Ethan, NISE. So I decided we would be team. Nice, nice baby. And our metric to follow along with the vanilla ice reference, our metric was V ice. So nice hockey, ice. I like that. Right. So nonetheless, but you know, you talk about, again, learning by, you know, having other people just being around almost like a project night.
This was a two month project night, you know, with the same set of mentors in a certain sense. So once again, by taking where I, you know, and again, I was certainly a three and a half for maybe even a four and a half, you know, I could watch what they did and continue to boost my learning by watching people much smarter than me.
[00:58:52] Ken: Yeah. Well, I love the project concept and you said much smarter than you. I don't necessarily, I obviously don't think that's true.
[00:59:01] Ian: I appreciate that. I mean, look, I'm paying them compliments in that sense. I mean, do, I don't think that I'm dumb by any stretch of the imagination, but I mean, you know, in the certain, in here for what we were doing, they were far ahead of what I was doing.
[00:59:13] Ken: Well, I think that that's an important thing to note is that you can work on a project with people that are more advanced and there are different types of value you can offer. For sure. So an example is your subject area, domain, your subject area expertise within hockey. That's something that they have very, they very likely did not have as much experience.
[00:59:32] Ian: They did not.
[00:59:34] Ken: Yeah. Another thing is time and effort, right? If I am working on a project, I'd much rather be doing the modeling, the stuff that I'm good at rather than the data cleaning. And so if you're someone who's interested in working with other people, you can say, Hey, in order to be a part of this in order to see how you, other people do things I'm willing to take on some of these other tasks where you're still gonna learn stuff, you still gotta be part of the process.
You still gotta ask other people and have them be resources to you, but you're providing value to them as well. And I I've really liked how in your story. I think that you've been a mentee or you've been a part of things in exactly the right way. Where you're creating value. You are in the right places where other people want to help you learn.
And that's, in my opinion, maybe there was some luck involved, but a lot of that is by design is that you're taking the roles where, you know, you could help and you're meeting the right people who want to help you and you become friends with them and whatever that might be. And to me, that is, that is half the battle, right?
The battle is not like, Oh, I have to go out and search for the right person. It's I have to put myself in the right positions and do the right things so that these people come into my lives or these opportunities come into my lives or I'm finding the right things. and that can be done through communities. It can be done through whatever it might be, but I really like how that's transpired for you because I think it takes a really thoughtful approach on your side, even though it might not have been a hundred percent completely intended every one step of the way.
[01:01:06] Ian: No. Well, but ..., and first of all, thank you. But second of all, you know, you're right here. As I constantly joke, I'm not shy, but, but that always leads to stuff, right. I mean, I'm not saying to be obnoxious, right. You know, don't send that person on LinkedIn, 15 requests, every five minutes to be your mentor, you know, you're just gonna get yourself blocked. But I mean, you know, yes. You know, and part of it also is the trade off, right?
Even representing athletes where, you know, the people bend over backwards for them and so on. I never want something for nothing for my athletes as well. You know, not certainly personally, right. I'm not famous. I'm not anything, I never want something for nothing, but in that sense, but like, I always want the person, whether it's, you know, a car dealership giving my client a loaner car or whatever, I always want that person on the other end of the transaction.
Not that these are transactions, these mentorships and so on, but in a sense. They are transactions and that in one sense, you know, I never want them to be like, Man, I never want to deal with him again. In fact, the emotion I wanna strike with them is, wow, that was great. I'd do that all over again. So you know, whether, again, like, let's say it's a car dealership giving a loaner, you know, I'm like, look, I'm gonna tell my client, bring a Jersey, you know, and if they're not the most famous person on their team, you know, bring a, bring a second Jersey from the quarterback or whatever, I'll find up from the dealership.
If they have a favorite player, you know, bring some footballs, give 'em some tickets, you know? So again, you're getting a loaner car that essentially is like $10,000 worth of value. You could spend a thousand dollars to make, to get $10,000 worth of value. And you know, what's gonna happen. They're gonna wanna do the deal again next year because you've given them all this stuff.
They're gonna think this is the greatest thing. They're gonna take a picture with you that goes on the wall. The sign jerseys are gonna go on the wall. The footballs are gonna go into the case. They're gonna bring it home to their kid or whatever. They're gonna think it's the greatest deal ever, you know?
And they loaned you a, all they did was loan you a car. Yeah. So it's kind of the thing, you know, so you talk about right making yourself, you didn't say it in these words, but making yourself useful, right. If you can't do the, you know, the data crunching or whatever, then clean the data. Right. I mean, you know, there are other ways to, to be part of it.
And, you know, even going back to project night when I was a three, you know, part of what they do to force you to do these things is you get to use out of the four people. You get to use two laptops, right? One is to do the actual script writing, and one is to look stuff up and they make you rotate every 15 minutes.
So that everybody's contributing. And now it's not just me watching Ray, write something, getting the email later, going home and just rewriting what Ray wrote. Again, they force your hand in order for you to contribute. I mean, I would've done it anyway, but, you know, yes. Contributing takes on many forms and right.
I mean, the reason Ray and Ray and Zacks are willing to help me is because again, I never made it feel like, Oh, you're only giving to me. I wanted to give back to them as much as possible.
[01:03:53] Ken: I love that. I think that, again, that sort of embodies a lot of what the broader data or Python communities are about is this collaboration, the giving without pure intention of having something come in return. And it usually pays itself off, you know, far more than, than two or three acts.
[01:04:16] Ian: I'm living proof of that.
[01:04:18] Ken: Yeah. Well, you know, I think that that also leads us into how we got connected. So I guess not, I haven't been very public about it, but you, myself and Tina Hong have created a. I guess it's an agency or a cohort for content creators.
So how that works is that if companies come to us, we can offer sponsorship opportunities to a bunch of other creators and the companies. They only have to reach out to one point of content, which point of content, which is us. And then we can put together this consolidated campaign that frankly saves money in time on both people's parts.
It also lets us to do a really good job of vetting and making sure that the sponsors that we're working with are bright for our creators and making sure that the creators do a really good job for the actual sponsors themselves. But this is in my mind, another one of these really unique opportunities where, you know, you're creating a lot of value.
I mean, from the legal perspective, from a negotiation perspective and Tina and I have this very different value creation that we have, you know, I have a really good relationship with. Most of the creators Tina does as well. And she's also bringing in a lot of deal flow for us. You know, can you speak about your experience with that?
What, you know, what have you learned about maybe this different domain of, of not athletes, more like domain specific educators and some of the things maybe that surprised you on that front?
[01:05:50] Ian: Yeah. All right. So first we will tell the story of how we got together. So we talk about Nick Wan and so, you know, obviously we submit a paper and, you know, you talk to someone, you know, three nights a week for an hour over two months every week you get to know.
So, you know, me and Nick and Sean and Ethan have all stayed friends since the paper. So we submitted the paper last March, and then in August, Nick called me up and said, Crazy thought there's a company that wants to sponsor me on Twitch. Will you be my agent slash attorney? I said, of course I will. And so, you know, I look over the deal and let's just say, because they are our manufacturer.
If they're giving 'em product, it costs 'em about 10 cents on the dollar. And I said, this is criminally low. As Nick recalls the conversation. I said, you need to ask for about like three times what they're giving you and you need to get clarify about 16 paragraphs. This thing is written like, you know, I mean, clearly their lawyer wrote it for them without any consideration for the fact of what you're going to do.
And I mean, no offense to them, but, you know, they didn't, they didn't take the time to be like, Oh, well maybe there are a few things we should throw in there. And he is like, wait, you're sure I should send him all of this. And I'm like, yes, trust me on. Copy and paste, send him exactly this. He's like, all right.
And so, you know, like two days later he called me back. I'm like, am I fired, said, Nope, you were right. They agreed to pretty much everything. And they changed, you know how much they're gonna gimme. They changed this. They changed that said, thank you very much. He said, look again, you carried me across the line.
I'm more than happy to do it for, you know, and again, Allstate friends and da, da, da, da, and then in early December of 2021, just last year, Nick calls me up and says, all right, crazy idea. Number two. I said, all right, shoot. And he said, I have a good friend, Ken Jee. And he said, Ken has this great idea of there's this whole space of educators, content, creator, educative, or educational content creators.
Can't even say my own business. Right? Educational content creators that are out there that, you know, they do all these videos and teach people Python and SQL and R and so on and data science. And he said, you know, they get offers all the time. Kind of like I did. Can, you know, he wants to kind of put it together where, you know, he can help all of them.
Was that something you're interested in? I said, absolutely. So then this guy named Ken Jee whoever he is reaches out to me about a few days later and we have a phone call or over zoom, and sure enough, as you know, you were an athlete, you appreciate athletics. So we bonded over a couple things. We bonded over the fact that, you know, we both love sports and there are certain things about athletics that we both appreciate.
We bonded over the fact that I, at least not just a lawyer and an agent, but as we've now well discussed, I'm a lawyer and an agent who knows this much Python. And so you said to me, do you really wanna do this? And I said, Because, you know, whether I'm, you know, a sports agent or whether I still play hockey myself, there's that still part of me, that's close to my heart of, you know, that kid who got the Apple IIe and the bug bit him and has been there ever since.
And so you know, you said let's do it. And you know, here we are five months later. So what have I learned in those five going on six months now, I guess really? Cause we started just after the first. So I've learned in those six months that it's not just people who make these videos and, Oh, here's a video about R and here's a video about Python.
And so on what I've learned is, you know, there's different strategies and different types of content creation to do these videos. There are people who are really good at doing what we now have learned called tutorials, right. Where they can say, all right, kind of like yours on streamlet. We're gonna take this package called streamlet.
I'm gonna run you through a whole thing. And by the end of this video, you're gonna be pretty good on streamlined. And there are people who do that really, really well. And then on the flip side, there are people who don't, but there are people who then, you know, in terms of, you know, doing the things that they do, where they're much more, they do demonstrations or they do integrations, or they do all kinds of things.
And, you know, I never, first of all, I didn't even really think about this world. I mean, again, way I grew up, I never thought of like going to YouTube and watching a video, even though I've done it for like small mechanical things, cuz I'm a little bit handy around the house, but I never really thought about doing it for, you know, education on data science or all the other topics that, you know, our creators cover never really thought about it, first of all, even in the first place, but you know, now having come to learn it, I see that, you know, it's not so easy, you know, you don't just sit on the computer, you don't just say, Oh, I'm gonna do this project, film yourself, doing it.
And that's the end of it. You know, I have a great saying kind of like for my clients, but it also applies here. People, you know, only see the stakes. They have no idea how you have to slaughter the cows. Right? I mean, there's so much that goes into making these videos and I'm not talking about, Oh, you got a sponsor and you need to include them.
And so on the thought and the creativity that goes into this, it's almost like many Hollywood productions, you know, in terms of how these creators come up with, Okay, I need to say this, or I need to model this, or, you know what? I kind of need to, you know, there's been, I don't wanna say so much action shots, but there's been, it's not always just someone sitting in a computer and the level of creativity, you know, when you're talking about programming, you'd think, Oh, well these are just, you know, print, "Hello, world", you know, load Pandas into, you know, it's not boring.
I mean, it's not just sit there and like, Oh my God, I'm dying over here. It's these things are well thought out. Well, reasoned well produced. Well, I mean, it's just, there's so much that goes on that I never would've even thought about. Just, and again, it's like a mini Hollywood production. I'm not, yes. It's not exactly, you know, Sylvester Stallone blockbuster, but I mean, it's not just some guy hooked his camera up to his computer, wrote a few lines of code, put it out there in the world for everyone to learn.
And so, you know, the talents of our creators across the board, whether, and again, it's not always YouTubers. I mean, we have LinkedIn people and so on and, you know, the way they write their posts and so on and just all of the creativity behind it, you know, it's funny, you'd think, you know, the stereotype of the computer persons, right?
Some bookish person sitting at a computer typing away and, you know, never sees sunlight, but I mean, our creatives are very vibrant people who are, or who are creative. I mean, you know, if they weren't doing program, they might be doing something that related much more to like, you know, a film or type of industry like that.
If they weren't doing programming. And that was, I don't wanna say a surprise, but very much a revelation that I never really even thought about. So, you know, it's really two parts it's really. The creativity of our creators is just, again, something that I just never even thought about or knew of. But on top of that, just their levels in terms of who is good at what is also something that I had never really thought about.
[01:12:45] Ken: I love that. And so something interesting.
[01:12:49] Ian: Are you almost done? Dinner needs to be cooked relatively soon. We have some time still apparently this is my, you know, like warning life. Are you almost done? Yes. What's up bud saying hi to Mr. Ken. Hey, Ken Jee. What is the all right boys, a few more minutes, and then I will cook dinner.
I promise. What time minutes it's here. Just take the phone and go what here goes the Minecraft that we talked about earlier. No, this is for people to see this one's for people to see.
[01:13:18] Ken: We can, we can edit it if we need to, or I can leave it in.
[01:13:21] Ian: No, absolutely, leave it in.
[01:13:23] Ken: Okay. So I guess my last question is, you know, from working with athletes to working with content creators, what's the difference?
[01:13:30] Ian: Oh, well, I mean, look. As I kind of hinted at, or not hinted, but I kind of explicitly said a few minutes ago, you know, the world bends over for athletes. You know, there was a show called Arla, which I actually is probably too old for most of you to remember in this audience, but it was a show on HBO about a sports agent, you know, and funny enough, like one of the opening, like phrases over the opening credits was, you know, athletes are our last warriors.
You know, we don't have people going to the arena and fight lions anymore, but you know, athletes are our last warriors and, you know, athletes are put up on a pedestal. I mean, you know, everyone wants to, and I'm not everyone, not everyone's a sports fan, but I mean the vast majority of am of Americans follow some sports.
So, you know, in terms of athletes, when I call a company about an athlete, there's a lot of things that I get to do or be a part of because I represent the athletes. So in terms of behind the scenes of my representing, they have a lot more. I'm a lot more involved in their lives. I mean, this is, you know, something new where, you know, the creators aren't looking to me to, you know, Okay, well they're ... and we would do this for them, absolutely.
But no one yet that we represent on the, you know, creator side has said, Alrighty, and I just got a new job and I'm moving. Can you find me a car shipper where that's just the most natural thing every season for my athletes, because you know, they live made their home maybe in one place and they play on a team in another place.
And so I'm much more involved in the daily aspects of my athletes and coaches' lives because that's the way their lives are structured. Whereas here so far, and again, we're only a few months into it. It's much more of a business part. I'm the lawyer, I'm the negotiator, I'm this. So, you know, in terms of that, my daily life for my athletes is much more involved in taking care of so many things for them. Whereas with the creators so far, the only thing I take care of them is getting them paid, but Hey, that's pretty good too.
[01:15:18] Ken: Amazing stuff. Well, thank you so much, Ian, for coming. I'm glad we gotta meet your kids as well. Any final thoughts? Any, anything you wanna before we ship out?
[01:15:26] Ian: No, I mean, look here, I certainly have been very lucky in all aspects, right? Whether it was, you know, playing hockey, which led, you know, and then not taking a job out of college, which led to law school, which led to being a sports agent, which, you know, then knowing computers led, I mean, so. My path is just one, but I mean, the, you know, and you've talked about this and, you know, I'll reiterate and again, you know, I call it not being shy and okay.
You don't necessarily have to be me. You don't have to be the, you know, the kind of person who walks in an elevator and has three friends by the time you get off. But by the same token, you know, and kind of what you said, it's certainly not necessarily link reaching out on LinkedIn, but it's looking around and seeing what opportunities exist for you.
Right. And there's certainly, and you know, if you live in a smaller town and there's not a Python users group find something, but you know, if you don't push it for yourself, no one else is gonna do it for you. That's for sure. Unless you have an agent, of course, then they can push it for you. But, you know, look, if you're really out there and you know, you want to get to some place, you know, just putting yourself out.
I mean, I can't tell you, and this is the other side of the agent business, and this is a whole other podcast, but you know, when I start out every year, trying to add new clients to represent. I start with a list like this. And do you know how many actually become clients at that end of that yearly cycle?
You know how much I hear No. Yes. I'm fortunate to be successful. I do have clients. I've had clients who've won the super bowl and the Stanley cup and so on. And yes, I mean, there's a roof over my head and food on my table, but nonetheless, the amount of times I've heard, no, I don't think Python counts that high.
But the whole point is though, is that eventually though, out of all these nos, there are a few yeses and that's all it takes. So it's not so much whether or not you have access to it directly, even if you don't. And again, I'm not saying, you know, again, let's be real. You have to also be somewhat realistic.
You know, if you send in LinkedIn request to the chief programmer at meta, you're probably not gonna get a response, but there, if you look around and find the right places, you will find someone to continue helping you on this journey. That I do know.
[01:17:39] Ken: I love it. Thank you so much, Ian. I'm sure we'll talk like sometime next week, but it is awesome to have you in and until next time, go get dinner with your family.