Programs

LLM tools for sport.

Data scientists who want to build, evaluate, and ship LLM-powered tools rather than prompt them.

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Opens later16 weeks · Foundations to Retrieval and evaluation to Capstone

No job is guaranteed. We do not promise placement, an interview, or an offer.

Is this you

Three things people say before they start.

If none of these are familiar, this is probably not the right program, and the call will tell you that faster than the page will.

  • You have wired up an API call and you do not know whether the output is any good.
  • You can demo it. You could not defend it.
  • Somebody wants a number for whether it is working, and you do not have one.
The IDE, compiling a Stan programA Stan model compiling on baseball data.

This is a real session, not a promotional cut. LLM Tools runs live alongside the program.

The plan

16 weeks, week by week.

Each phase names the skills you work on, what exists at the end of it, and what you can do by then.

Languages and tools across the program
PythonR
Weeks 1 to 4

Foundations

The data and modeling layer underneath the part everyone talks about.

 In these weeks
  • Working fluency in Python and R
  • Reproducible pipelines and project structure
  • End-to-end git workflow: branches, review, and QA gates before anything lands
What exists at the end
A reproducible pipeline
By now you can

Build a data pipeline that runs the same way twice.

Weeks 5 to 10

Retrieval and evaluation

Where this stops being a demo: measuring what the system does instead of asserting it.

 In these weeks
  • Retrieval pipelines built to be inspected
  • Evaluation harnesses, and the eval set that makes them mean something
  • Model validation gates before anything ships
  • Simulated technical interviews, recorded and reviewed
What exists at the end
A retrieval pipelineAn evaluation harness
By now you can

Say how well your system works and show the number that says so.

Weeks 11 to 16

Capstone

The build, the orchestration around it, and the rooms it has to survive.

 In these weeks
  • Capstone tool with its evaluation harness
  • Deployed and monitored, not just running locally
  • AI orchestration training: planning work for delegation, delegated builds, browser QA
  • Front-office presentation practice: the recommendation, and the pushback
  • Simulated technical interviews, recorded and reviewed
What exists at the end
The capstone toolAn eval reportA portfolio writeup
By now you can

Explain to a non-technical room what your system can and cannot be trusted to do.

Not sure which phase you would start from? That is the first thing the call works out.

Ask about this program
Inside the program

The 5 sessions and courses it is built from.

Each one is on sale on its own, as a recording you buy once, on its own page. The program is the order, the weekly work around them and the capstone at the end.

Sessions 3

Async masterclass · 75 min ASK THE BOX SCORE Ask the Box Score Anything Nic CranePhD · Consultant @ NC Data Labs Available now After this you can point a chat interface at your own sports data and answer questions about it in plain English.
Async masterclass · 75 min KICKOFF TO PRODUCTION Kickoff to Production in Minutes Stefan LinnerFounder & R Developer @ LinneRProgramming Available now After this you can take a sports analytics Shiny app from a blank folder to production with Claude Code doing the plumbing.

Courses 2

Course · 99 lessons MODEL IT. PROMPT IT. SHIP IT. SynergiZing ML & LLMs in R Nic CranePhD · Consultant @ NC Data LabsChristoph ScheuchPhD · Associate Researcher @ Humboldt-Universität zu Berlin Available now After this you can ship an evaluated retrieval pipeline and chat interface inside a Shiny app.
Course · 43 lessons PREDICT THE WIN. Python MachineZ: ML in Sports Patrick McFarlaneDirector of Predictive Modeling @ Philadelphia Phillies Available now After this you can build and honestly evaluate the machine learning models teams use to project performance.
The Coaching Room

Practice with somebody on the other side of the table.

AI coaching is built into the practice sessions. The coach takes the stakeholder's part, and you work the problem in whatever medium the question actually calls for.

AI as copilot, not autopilot. You learn to direct the machine and defend the output. The scarce part is not producing an answer; it is knowing whether the answer is right and being able to say why in a room that will push back.

Simulated practice

A clock, a real task, and a room that pushes back.

How it actually runs, and the kind of question it runs on.

Practice is timed. You get a real task and a clock, not a quiz. The session is recorded, and the review afterwards is about the reasoning you showed rather than whether you reached the expected answer.

Scenarios are written the way the question actually arrives
  • A stakeholder asks: can we trust this thing? Show me how you would answer that with a number.
  • The system gave a confidently wrong answer in a demo. Walk me through how you find out why, and what gate would have caught it.
  • We want this in front of users next month. What would you refuse to ship without?
Questions

The ones people ask first.

Will I get a job at the end?

No job is guaranteed. We do not promise placement, an interview, or an offer. What the program promises is the work: a capstone you can defend, a portfolio with real builds in it, and interview practice that is recorded and reviewed.

What does it cost?

There is no public price and no checkout on this page. Cost, dates and where you would start are settled on a short call, and the honest answer is sometimes that a single session or course is the better start.

Which weeks would I start from?

That is the first thing the call works out. The plan on this page is the whole route; a person who already has the foundations starts further along it.

Not into sports?

Then take the method and leave the sport. Retrieval, evaluation and knowing what a system can be trusted to do are the methods. The same harness, pointed at other documents, is the work in these fields:

  • Legal tech Retrieval over documents where a wrong citation is a real problem
  • Customer intelligence Turning unstructured conversation into something measurable
  • Enterprise search The same retrieval problem at organizational scale
  • Regulated sectors Where evaluation harnesses are the difference between shipping and not
What runs alongside the taught weeks?

Simulated technical interviews, recorded and reviewed. Front-office presentation practice, where a recommendation meets a room that pushes back. AI orchestration: planning work for delegation, delegated builds, and the checks that catch what a model got wrong.

LLM Tools is the live track running alongside the program, so the weekly sessions and the plan pull in the same direction.

Next step

Ask.

There is no public price and no checkout on this page. A short call settles cost, dates and where you would start, and if a single session is the better start we will say so.

No job is guaranteed. We do not promise placement, an interview, or an offer.

Ask about this program
Keep up

One email when something new arrives.

First name and email. One message when a session recording, a skill, a cheat sheet or a posting worth your time arrives here, and no other mail. Replies reach a person.