if you care to read, I’ve written a few pieces below on why I started, why I’m stepping away, and what I learned in between. Just know I’ll always be grateful for everyone who was a part of this chapter, and I’d love to be a resource to anyone who wants to go deeper on building data-driven applications that aren’t AI slop, building custom data feeds (they don’t have to be sports related), or just wants to pick my brain on what worked and what didn’t.
readings below:
If you would like to support me as I build another project that cultivates wisdom and emphasizes what it means to critically think in the age of AI, please donate here. You can also reach me at bryan.cash@programITcash.com for any inquiries.
A kid from Queens, NY. The Mets fan who got teased for rooting for the little brother. A child who went to every Subway World Series game in 2000 and had to be reminded daily why the Yankees were the superior team, because championship culture. The kid who rooted for the Knicks when Channing Frye was supposed to be our savior. My childhood sports love was rooted in heartbreak.
What I did have were my Pittsburgh Steelers and the next superstar of the NBA sharing a birthday with me. Part of my child personality was proving LeBron would be the greatest. I studied the profiles of current and past NBA players, looked for the correlations that showed when Jordan struggled versus dominated, all to build talking points against anyone who came at the narrative. When the Knicks and Mets were the reason for disappointment, I wanted to be loud about something. That’s the whole origin of the statistics geek. I was doing data engineering and data science before I knew those were careers.
Fast forward to middle school. I was in a strategy class built around Texas Hold’em, chess, fantasy baseball, March Madness brackets. I’ve always been competitive, and it found its outlet fast: four hours a day crunching statistics to set a daily fantasy lineup. Then MLB Beat The Streak. My day was school, basketball practice, then the rest of it inside sports statistics. I won my first fantasy baseball league, and shortly after I was in four baseball leagues, five football, four basketball. I actually managed all of them. I also predicted a perfect Final Four my first March Madness (all #1 seeds went.)
In high school, when everyone had to start answering what they wanted to be, my answer was actor and basketball player. I knew I had a gift with numbers but had no idea what the career behind it was. Then it clicked, watching the quirky analytics ESPN would put on screen mid-broadcast. I wanted to be the analyst behind those graphics.
I studied Computer Science at Syracuse. I graduated wanting to work for ESPN, but ended up doing everything but sports. I worked in equity crowdfunding, mass tort litigation, publishing and circulation, federal health reporting, insurance/reinsurance, and beyond. All meaningful work but none of it rooted in what brought me to Computer Science.
This project was my full circle moment. I wanted my own startup, and before building one, I wanted to learn the ins and outs of taking something from zero with no help. What works, what fails, on something I actually cared about.
We all know compulsive gambling is bad. When you’re not running a sports page, it’s easy to keep discipline and treat gambling as occasional fun. When you are running one, specifically one titled ‘sportbetdata.ai,’ gambling stops being occasional and becomes habitual, and the wagers have to keep getting bigger, or nobody cares what you have to say. Put your money where your mouth is. I agree with that in principle. But even when I was up, the thing that gets attention was never the disciplined bet. It’s turning $1 into $50K. The bets almost certain to lose are the ones that get you noticed.
That’s not the lifestyle I signed up for. Statistics and my love for sports are why I built this page, and I did not understand going in how much gambling I would have to do to keep it alive.
The second reason is related. I’m all about authentic intelligence, not artificial. Most sports pages I see now have outsourced their entire workflow to AI, which is the opposite of why I started. I’m all for AI as a booster to the authentic knowledge of an individual, not as a full level replacement. Unfortunately, the sports betting community in my opinion rewards volume and reckless action, not the person who actually knows the sport. I’d rather stay sharp as a statistician than trade my own computing power for a few extra subscribers.
I started this project as a data engineer and data scientist who had always had a team behind me for infrastructure and front-end. This project forced me into full-stack, and I came out able to own every layer of a build myself.
I’ve landed a CTO position since, and I’ve been in rooms I couldn’t have imagined a few years ago, on the strength of what I learned here as a developer and as a thinker. My ROI came out positive, and it came from my own decisions on my own platform rather than from massive subscription money.
I’m building enterprise applications now that go well past anything I was doing here, and none of it happens without the foundation this project gave me. Thank you all.