Entelect GP Solver
Python hackathon solver for generating validated race strategies across multi-level Grand Prix constraints.
- My role
- Hackathon Developer
- Timeframe
- 2026
- Project status
- Public repository
The problem.
The Entelect Grand Prix challenge required a program that could generate valid race strategies under changing constraints such as braking distance, fuel, weather, tyre wear and pit stops.
Entelect GP Solver is a Python hackathon project that reads level data, generates a race strategy and validates the output against the challenge rules. The work focused on deterministic problem solving, simulation, scoring and packaging submission-ready artefacts.
My contribution.
Built a deterministic solver, validation pipeline, scoring helpers and pre-submit packaging flow for the hackathon problem.
- Reads level data and generates a race strategy for the defined constraints
- Validates generated output against the challenge rules before submission
- Packages repeatable submission artefacts through a pre-submit flow
The implementation.
A Python constraint-solving project combining simulation, scoring, validation and deterministic CLI-oriented tooling.
- Handled braking, speed, fuel, weather, tyre-wear and pit-stop decisions in the strategy model
- Added validation and scoring helpers so generated strategies could be checked locally
- Kept the generated strategy deterministic so optimisation and debugging could be repeated locally
Python · Algorithm Design · Simulation · Unit Testing · CLI Tooling
Project identity.
The project mark identifies the work. The implementation evidence is described above and linked where available.
What was delivered.
- Built a deterministic strategy generator for braking, speed, tyre and pit-stop decisions
- Implemented validation and scoring helpers for checking generated submissions against the race rules
- Added a pre-submit flow that regenerates outputs, checks determinism and packages submission artefacts
- Verified the local quality pipeline with 6/6 unittest checks on 07 September 2026
What I took forward.
- Constraint-heavy problems need a validator as much as they need a solver
- Deterministic output makes optimisation and debugging easier under hackathon time pressure
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