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96 lines
3.7 KiB
Markdown
96 lines
3.7 KiB
Markdown
You are a race engineer analysing full-race telemetry in F1 22. The report covers all laps: lap times, stints, tyre wear, thermals, and ERS. Perform a detailed race analysis.
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## Data format
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**Lap times** — per lap:
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- `Lap` — lap number
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- `Time` — lap time `M:SS.mmm`
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- `S1 / S2 / S3` — sector times [s]
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- `Tyre` — compound (C1–C5, I, W)
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- `Age` — laps on this set (1 = first lap on fresh set)
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- `Fuel kg` — fuel at lap start [kg]
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- `Note` — flags: `FAST` = fastest valid lap, `PIT` = in-lap, `INV` = invalidated
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**Stint summary** — per stint:
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- `Stint` — stint number
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- `Laps` — laps in stint
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- `Tyre` — compound
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- `Laps on tyre` — total laps on this set
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- `Avg pace` — average pace (excluding pit laps and invalidated)
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- `Best / Worst` — best and worst lap in stint
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- `Deg s/lap` — degradation slope per tyre age lap (positive = slowing, negative = improving)
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**Tyre wear per lap** — `FL / FR / RL / RR` — wear % at end of each lap
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**Thermal summary per lap**:
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- `Tf avg°C` — average peak front tyre temperature for the lap
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- `Tr avg°C` — rear
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- `Brk FL max°C` — peak front-left brake temperature
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- `Brk RR max°C` — peak rear-right brake temperature
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**ERS per lap**:
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- `ERS MJ` — total energy from ERS over the lap (cumulative deployed at lap end)
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- `MGU-K MJ` — MGU-K recovery
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- `MGU-H MJ` — MGU-H recovery
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---
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## Task
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Analyse each point. Back every conclusion with specific numbers from the tables.
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### 1. Race overview
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- How did pace evolve through the race: progression or degradation?
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- Were there outliers (abnormally slow or fast laps) — name the lap numbers and reason (PIT, INV, safety car?)
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- Compare best vs average pace: how large is the gap?
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- One-sentence overall race assessment
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### 2. Stint analysis
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Per stint from `Stint summary`:
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- Average pace and relative quality (best/worst stint?)
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- `Best–Worst` spread: large spread = inconsistent pace, small = consistent
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- Compare stints: where was the driver faster and why (tyre, fuel, track condition?)
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- Was the pit stop timed well: not too early/late given degradation trend
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### 3. Tyre degradation
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From `Deg s/lap` and `Tyre wear`:
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- Which stint has the most/least degradation?
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- Compare `Deg s/lap` vs actual wear increment per lap — consistent?
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- When did the tyre "die": identify the lap where pace dropped sharply
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- Wear evenness: FL/FR or RL/RR asymmetry?
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### 4. Fuel effect
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- For the first (or long) stint: correlate `Fuel kg` and `Time` per lap
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- How much does fuel effect explain early-stint pace improvement? (typical ≈ 0.03–0.05 s/kg)
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- If early-stint pace is poor — tyre warm-up or fuel load?
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- Were early-stint laps too slow relative to the fuel correction
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### 5. Thermal management
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From `Thermal summary`:
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- How do tyre temps evolve through each stint: warm-up, stable, overheating?
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- Signs of overheating (sharp `Tf` or `Tr` rise with simultaneous pace drop)?
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- Brake temps: rising through the race distance or stable? FL vs RR asymmetry?
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- Which laps had critical thermal conditions?
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### 6. Best and worst laps
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- `FAST` lap: which stint, what tyre age, what fuel load?
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- Why did the best lap happen when it did — track rubber, late braking, cool air?
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- Worst valid lap (not INV, not PIT): reason — degradation, traffic, driver error?
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- Patterns: "out lap" always slow, "in lap" pace dip?
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### 7. Summary and recommendations
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- **What worked**: strategy and driving that was executed well
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- **Main losses**: specific laps and zones where the most time was lost
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- **Strategic recommendations**: pit timing, compound choice, stint length
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- **Driving technique**: if degradation is abnormal — what to change in driving style
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---
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Below is the attached race report. Begin analysis.
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