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A lot is happening at Athlyticz.

Follow along here: athlyticz.com/membership

#datascience #rstats #python #llm

For the visual I grabbed open-source SVG state paths, wrote some custom animation code on top, and built a live counter that burns through all 9.2 quintillion brackets in real time.

Happy March Madness!

#MarchMadness #DataScience #SportsAnalytics

So no, you can't brute-force March Madness. You can't brute-force any prediction problem when the search space is this large. What you CAN do is be systematically less wrong across 63 decisions. That's the whole game.

And even then you'd just guarantee a perfect bracket exists somewhere in the pile. You still wouldn't know which one until the final buzzer.

9.2 quintillion is that big.

Take the entire U.S. population, 342.5 million. Every single person fills out brackets nonstop from Selection Sunday to tip-off. No sleep.

Each person would need to complete ~82.9 MILLION unique brackets. Per second.

For almost 4 days!

That's 10x faster than a supercomputer.

🏀 9,223,372,036,854,775,808.

That's how many possible March Madness brackets exist. 9.2 quintillion.

Selection Sunday was yesterday. First Round of 64 tip-off: Thursday, 12:15 PM ET.

That's 324,900 seconds to fill out your bracket.

But what if we wanted to guarantee a perfect one?

Congrats to AthlyticZ student, Jared Markowitz on winning the MIT @sloansportsconf.bsky.social Hackathon (Open Division). Details on Jared's big win in the link below. A special congrats to his Hackathon partner Jonas Dixon as well! #ssac #SportsAnalytics

www.linkedin.com/posts/jared-...

There's never been a better time to get your hands dirty. The tools are accessible. The data is out there. The only thing stopping most people is starting.

Twenty years of Sloan. Still learning. Still building. #SSAC #RSTATS #PYTHON #SPORTSANALYTICS

And if you're teaching this stuff (at any level) I'll say what I always say: craft end-to-end projects that students actually care about. Not toy datasets. Not disconnected exercises. Real questions, real data, real outputs they can point to and say "I built that and I can tell you why it works."

Anyone can copy code now with AI. The analysts who get hired are the ones who can explain the decisions behind it, storytell with data, and interact with stakeholders.

Build a model. Build an app. Write about it. Teach it to someone. You never know who's paying attention. One thing you put out into the world, that's how opportunities find you. Careers in this space aren't built on credentials alone. They're built on proof that you can do the work.

What stuck with me most was meeting the students. Sharp. Curious. Already building things. The future of this field is in good hands.

My message to them was simple:
Build.

But the highlight for me? I was invited by the National High School Sports Analytics Association to run an interactive session on building sports analytics apps with AI. I had some big names to follow on that agenda, people I've looked up to in this space for years, and I don't take that lightly.

Talked shop with professors whose opinions our team takes seriously, making sure we're building with actual academic rigor, not just marketing it.

Finally met in person with some folks I've done consulting work for, always hits different when you can shake hands. Had real conversations about university partnerships and team collaborations that I'm genuinely excited about. Met with instructors who teach on our platform.

Headed up to Boston this weekend for the 20th MIT Sloan Sports Analytics Conference.

Twenty years. Still one of the best weekends on the calendar.

I caught up with friends and former colleagues scattered across teams and league offices.

We are so excited to have @veerle.hypebright.nl on board to help lead the way!

Appreciate the conversations with @christophsax.bsky.social and @davidgranjon.bsky.social to get this done. In the coming months, you’ll hear more about our vision for using blockr in sports, followed by workshop offerings directly from Cynkra on the Athlyticz platform.
Onward. 🚀

A few things that caught our attention:
∙ Build data apps in minutes with drag-and-drop blocks
∙ Each block handles one step: reading, transforming, visualizing
∙ Fully extensible, if you can code, you can build custom blocks

Read more about the project here: www.cynkra.com/blockr/

Think of it as visual programming powered by R, accessible to analysts who want to wrangle data and build dashboards without writing scripts. It’s funded by @bms-news.bsky.social , battle-tested in pharma, and now coming to sports.

Cynkra will be leading workshops on blockr, their open-source framework for building data pipelines using a visual, point-and-click interface. No code required.

Excited to share that @cynkra.bsky.social has officially signed on as a Preferred Partner with Athlyticz. 🤝

What does this mean?
Our goal is to bring the strongest teams and individuals to our students, people at the forefront of data science tools that we believe can be game-changers in sports.

More physics-constrained Bayesian models lead to another interactive mobile app with my students.

Writeup coming soon

The Strangest Bottleneck in Modern LLMs | Towards Data Science Why insanely fast GPUs still can’t make LLMs feel instant

Why is AI so slow? Bottlenecks aren't compute; it's memory.
towardsdatascience.com/the-stranges...

TiDAR = 6x speedup. ⚡ Move from prompts to Infrastructure Eng.

#AI #LLM #DataScience #Engineering #Athlyticz

Can anyone guess what we are building for our students?!

#data #datascience #sportsanalytics #rstats #python

For students & early-career analysts applying to front offices: the model is the core of the work, that's where the rigor lives. But what puts you over the top is showing you can translate that model into expert storytelling, especially in an interview when you're walking someone through your work.

This is also season-long and context-neutral, no L/R splits. A matchup-level application would be a different beast entirely (automated daily pipelines, game-day lineup optimization, etc.).

Projections here are from an actual Bayesian framework that's been jittered/shifted to mask the information (so don't look too far into the values, i.e,. Judge projected 60 HR). This is a tooling demo. In production it pulls from an internal model stored in a database.

It ranks players against their position, flags credible interval overlap, & frames the output the way a front office would: uncertainty bands, replacement value, and roster construction implications. The goal is consistent, repeatable reports that a decision-maker can trust, not a chatbot summary.

It computes positional averages from the dataset itself, classifies hitter archetypes from K%/BB%/ISO combinations (elite contact-and-discipline, boom-or-bust power, patient on-base-driven, etc.), and adjusts its analysis based on defensive position.