🏠 Vault / School / H2 Math / MIT_Project / F1_Tyre_Strategy_Replacement_Brief.md

MIT Replacement Brief: F1 Wet-to-Slick Tyre Strategy

<#>MIT Replacement Brief: F1 Wet-to-Slick Tyre Strategy

<##>Verdict after checking the uploaded 2026 MIT documents This is a viable and probably stronger direction than the current HDB + MLP project, as long as it is framed as a mathematical investigation rather than a racing/engineering documentary.

Best framing: > When should an F1 driver switch from intermediate/wet tyres to slick tyres during a drying race to minimise total race time?

Even better title: > The Crossover Lap: Using Regression and Optimisation to Decide When F1 Drivers Should Switch from Wet Tyres to Slicks

Singapore-facing title: > Rain at Marina Bay: Modelling the Optimal Tyre-Switch Lap in the Singapore Grand Prix

<##>Actual MIT briefing requirements extracted From the uploaded files:

2026_MIT_Student_Briefing_FINAL.pdf
2026_MIT_Instructions_Guide_FINAL.pdf

Key requirements:

Work in pairs or threes.
Choose an authentic real-world context.
Submit a final product in any creative format: video, model/simulation, app, infographic, game, report, etc.
Product must demonstrate authentic, original use of mathematics and be the group’s own work.
Choose an investigation theme. The 17 SDGs and Singapore Budget are given as example prompts, not as a strict requirement that every project must be SDG 11 or Budget.
Use at least one A-Level H2 Mathematics topic. Secondary-level concepts can be included, but at least one H2 topic must be present.
Include citations and an annex: datasets, software, libraries, group roles, platforms used, URLs/access dates, software versions.
If AI is used, include an AI Use Declaration with exact prompts and outputs incorporated.
A maximum two-page mathematical report is required only if the product itself does not clearly show the mathematical concepts.
Teachers may conduct a short interview to verify understanding/authenticity.
Deadline: 25 May 2026, Monday, 23:59.

<##>Why this project fits the MIT brief The guide says MIT should involve authentic, real-world mathematical modelling where students identify a situation, pose a question, model it, justify assumptions, and communicate clearly.

This F1 topic fits because:

It has a real decision problem: when to pit for slicks.
It naturally involves trade-offs: tyre pace vs pit-stop time loss vs track drying.
It can be modelled with H2 Math rather than advanced ML.
It can become a concrete product: simulator/dashboard/video/interactive graph.
It is original and engaging, avoiding a generic regression project.

<##>Recommended H2 Math core Keep the maths simple and visible.

Let:

I(l) = predicted lap time on intermediate/wet tyres on lap l
S(l) = predicted lap time on slick tyres on lap l
P = pit stop time loss
N = number of laps considered

Fit simple regression models, for example:

I(l) = a_i + b_i l
S(l) = a_s + b_s l

The basic crossover lap solves:

I(l) = S(l)

A fuller race-strategy model:

T(k) = sum from l=1 to k-1 of I(l) + P + sum from l=k to N of S(l)

where k is the lap where the driver switches tyres.

Choose the switch lap k that minimises T(k).

Decision rule:

Switch when the future time saved by slicks exceeds the pit-stop loss:
sum from l=k to N of [I(l) - S(l)] > P

<##>H2 syllabus links Main concepts:

Correlation and linear regression

- Fit lap-time trends under different tyre/weather conditions. - Use scatter plots and residuals to evaluate model quality.

Optimisation

- Minimise total predicted race time T(k). - Compare different pit-stop timings.

Sequences and summation

- Total race time is cumulative lap-by-lap time.

Optional extension:

Probability / expected value

- Include probability of further rain. - Expected time = p(time if rain returns) + (1-p)(time if track dries).

Recommendation: make regression + optimisation the main focus. Probability should be an extension only.

<##>Product format Best product: > A small interactive simulator/dashboard showing the optimal tyre-switch lap.

Could be built as:

Streamlit app;
Jupyter notebook with graphs;
Desmos-style interactive graph;
explanatory video with plotted graphs and formulas.

Minimum features:

choose pit-stop loss P;
show predicted intermediate/wet lap-time curve;
show predicted slick lap-time curve;
show crossover lap;
plot total race time T(k) against switch lap k;
output the recommended switch lap.

<##>Data sources Best source:

FastF1 Python package

- lap times; - tyre compound; - tyre age/stint; - pit-in/pit-out data; - weather data: rainfall flag, track temperature, air temperature, humidity, wind.

Good case studies:

2022 Singapore GP

- Wet start/drying track. - Strongest local/Singapore framing.

2023 Dutch GP

- Early rain and rapid switches between slicks and intermediates.

2021 Russian GP

- Late rain strategy decision; useful for wet-weather decision-making.

Secondary data source:

RaceFans interactive lap charts for lap times and tyres.

<##>Theme framing Since the uploaded guide treats SDGs/Budget as prompts, not a strict requirement, the F1 theme is acceptable if we make the real-world application clear.

Possible theme: > Data-driven decision-making under changing weather conditions

Stronger local framing: > Rain at the Singapore Grand Prix: using mathematics to optimise decisions in a weather-sensitive urban event

If you want to explicitly connect to SDGs:

SDG 9: Industry, innovation and infrastructure — data-driven optimisation.
SDG 11: Sustainable cities and communities — major urban events and weather resilience.
SDG 13: Climate action — decision-making under changing weather patterns.

Do not over-force the SDG link. The guide mainly wants an authentic mathematical application.

<##>Risks and mitigations

Data sparsity

- Risk: not many laps where both tyre types are used under identical track conditions. - Mitigation: use a case-study model over a drying/wetting window, not a universal F1 tyre model.

No direct track-wetness variable

- Risk: FastF1 gives weather/rainfall but not exact water depth or grip. - Mitigation: use lap number/time since rain stopped as a drying proxy, and state this assumption.

Too much physics

- Risk: tyre thermodynamics/grip modelling becomes outside H2 Math. - Mitigation: keep the core as regression + optimisation.

Authenticity/interview risk

- Risk: if the group cannot explain the model, teachers may question ownership. - Mitigation: use formulas you can derive and explain by hand.

<##>Recommended final direction Proceed with the pivot.

The project should be: > A mathematical model/simulator for finding the optimal F1 wet-to-slick tyre-switch lap, using regression and optimisation on real race lap data.

This is cleaner than the old project because the mathematical question is obvious, the output is visual, and the H2 Math concepts are easier to defend.