AI-assisted tournament scheduler
By Gautam Chowdhry
This project helps youth sports operators create tournament schedules far faster by combining LLM-based natural-language constraint capture with deterministic optimization.
View projectThis project helps youth sports operators create tournament schedules far faster by combining LLM-based natural-language constraint capture with deterministic optimization.
This project helps youth sports operators create tournament schedules far faster by combining LLM-based natural-language constraint capture with deterministic optimization. Sonnet translates operator inputs into typed constraints, OR-Tools generates the schedule, and lighter-weight models explain decisions and validate edits in plain English. The workflow preserves human control through a wizard-style interface rather than a black-box chatbot.
The problem
Youth sports operators run their tournaments off spreadsheets. After registration closes, a matchup generator spits out 170+ games with no times or fields, and a single tournament director hand-places every one across divisions competing for the same fields — holding 10-15 rules in their head the whole way.
The result is a 15-20 hour, multi-day grind. The mental load is brutal once multiple divisions fight over shared fields, nice-to-haves like even rest and good slots for marquee matchups quietly fall off, and fixing one conflict often breaks another. There's no second set of eyes, and the cost of a published mistake is high.
The solution
The AI-Assisted Tournament Scheduler pairs natural-language rule capture with deterministic optimization, cutting first-draft scheduling from a full day to under 60 minutes. Operators answer six plain-English questions; the system turns each answer into structured constraints, solves the schedule, and explains what it did.
Crucially, the scheduling itself is not an AI job — that's a math problem handled by a solver. The design keeps the operator in control through a step-by-step wizard rather than a black-box chatbot, directly answering the roughly 90% of operators who want their hands on the wheel. It's built to live inside LeagueApps, which already holds the teams, coaches, fields, and divisions.
How it works
The workflow is a hybrid: the LLM handles language at both ends and a real solver does the placing in the middle. Sonnet reads each operator answer and extracts a fixed set of typed constraints — time windows, rest, field changeover, slot preferences, prime slots — each tagged hard or soft with a weight, returned as clean structured data through a tool rather than free text.
OR-Tools then places every game across all divisions in seconds, because hard rules must be satisfied every time. Haiku writes a plain-English summary and flags items to check, and validates edits in real time as the operator moves games on a visual calendar. Physics-level rules — a coach can't be in two places, a game can't go on a wrong-sized field — are always enforced and never shown to the model as a choice. Constraint intake was tuned across four rounds from 9/27 to 27/27 exact match, with precision and recall reaching 1.00.
Who it's for
The buyers are club directors, league admins, and tournament operators — the decision-makers running youth sports organizations on a platform like LeagueApps. The primary user is the tournament director or scheduler who owns getting games onto the calendar.
The most valuable users are big multi-program operators and tournament hosts, where payments volume and retention concentrate. It's a B2B and B2B2C model: operators buy it, and they in turn serve the families, players, and coaches who depend on a clean schedule.
Why it matters
Youth sports is a $40B business that keeps growing, with operators finally moving off spreadsheets onto real platforms. The scheduling gap between a matchup generator and a published calendar is exactly where a full day of manual work disappears.
Collapsing that to under an hour of structured work drives stickiness and retention, and closes a competitive gap on the tournament side against tools like TourneyMachine and Exposure Events. Rollout is staged — re-running past tournaments first, then a small pilot with trusted, tech-savvy tournament directors — with every operator action logged as the foundation for a system that learns each operator's preferences over time.