Circuit Comparison — 2024 · 2025 · 2026
Chinese Grand Prix
Shanghai International Circuit — side-by-side chaos metrics across seasons at this circuit.
Race Snapshots by Year
Key metrics for each year at this circuit, side by side.
Season
2024
Lap inversions
1325
Volatility score
1.183
Start → finish
19
DNFs
3
Winner
VER
Biggest mover
HAM +9
Season
2025
Lap inversions
1685
Volatility score
1.5045
Start → finish
28
DNFs
4
Winner
PIA
Biggest mover
BEA +9
Season
2026
Lap inversions
1160
Volatility score
0.9416
Start → finish
13
DNFs
7
Winner
ANT
Biggest mover
SAI +8
Race Vitals at a Glance
Side-by-side key facts from each year's race at this circuit.
2024
2025
2026
Volatility Score
1.183
1.5045
0.9416
Lap Inversions
1,325
1,685
1,160
On-Track Overtakes
28
17
52
SC / VSC Events
9 SC · 3 VSC
None
4 SC · 0 VSC
DNFs
3
4
7
Winner
VER
PIA
ANT
Winner Started
—
—
—
Biggest Mover
HAM +9
BEA +9
SAI +8
Podium Comparison
Who stood on the podium at Chinese Grand Prix across the years?
Grid start positions shown in brackets — green = gained places to podium.
Year-over-Year Changes
How much did volatility shift between seasons?
Green = increased chaos, red = decreased.
The total row spans the full 2024–2026 range.
2024 → 2025
Inversion delta
+360
lap inversions
Volatility delta
+0.3215
score change
Change
+27%
more chaos
2025 → 2026
Inversion delta
-525
lap inversions
Volatility delta
-0.5629
score change
Change
-31%
more chaos
2024 → 2026
Full Range
Inversion delta
-165
lap inversions
Volatility delta
-0.2414
score change
Change
-12%
more chaos
Inversions Per Lap — All Years
2024 vs 2025 vs 2026 — hover for exact values. Higher peaks = more overtaking activity.
Cumulative Inversions — Race Chaos Over Time
Steeper slope = more chaotic phase. The gap between curves shows the effect of regulation changes.
Driver Position Changes by Year
Positions gained (green) or lost (red) from grid start to finish.
DNF drivers shown in grey.
2024 Race
2025 Race
2026 Race
Methodology
Inversions are counted using a Fenwick Tree (BIT) per lap, measuring each lap's position array against the original grid order. Start→finish totals use a MergeSort-based counter. DNF drivers are excluded from inversion counts but shown in movement charts. Data via FastF1.
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