Sector Intelligence

Sector Advantage Map

We reveal where your opponent is structurally strong — without telemetry access.

Built on QEIv15™ by Neoamorfic™. Sector intelligence decomposes race performance into segment-level structure to identify local competitive strength, asymmetry, and conversion opportunities.

Neoamorfic™QEIv15™ Core Open Championship

Why it matters

Identify opponent strengths

Detect which sectors competitors consistently convert into advantage, even when lap-time differences appear small.

Reveal setup differences

Persistent sector asymmetry between teammates indicates different car setups and structural trade-offs.

Enable overtaking strategy

Identify where competitors are weakest structurally, not just slower, to define optimal overtaking zones.

Australia 2026 — sector leaders

Australia shows split sector leadership inside the leading Mercedes layer. Russell leads S1 and S2 structurally, while Antonelli leads S3. This is useful because it suggests internal asymmetry rather than a single uniform performance profile across the lap.

S1 leader

RUS

Mercedes

Avg field advantage: 0.823
Avg sector rank: 3.49
Best sector laps: 22

S2 leader

RUS

Mercedes

Avg field advantage: 0.482
Avg sector rank: 4.60
Best sector laps: 18

S3 leader

ANT

Mercedes

Avg field advantage: 0.907
Avg sector rank: 3.29
Best sector laps: 24

Why it matters

Split sector leadership is strategically valuable. It indicates that the leading car/team may not be strongest in the same way across the lap, which can imply different balance choices, different tire usage characteristics, or different conversion opportunities from one sector to the next.

Operational reading

In Australia, Mercedes did not express one single dominant shape. Russell controlled the opening and middle parts of the lap more effectively, while Antonelli was structurally strongest in the final sector. That kind of distribution can matter for setup interpretation and overtaking planning.

Australia 2026 — sector conversion case

The battle window between Russell and Leclerc shows how sector structure can explain outcome more clearly than the visible duel alone. Russell holds the stronger conversion profile in S1, but Leclerc gains the decisive structural edge later in the lap — especially in S3.

Sector Conversion Profile — RUS vs LEC

Battle Window • Laps 6–12

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Sector Conversion Map — RUS vs LEC

Windowed sector field advantage profile

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What the conversion profile shows

Russell retains the opening-sector edge, but Leclerc takes over the later phases of the lap. The strongest single-sector advantage in the window appears in S3, where Leclerc’s conversion profile is clearly superior.

Why it matters

This is the kind of signal that becomes operational during a race. It indicates not only who is strong overall, but where and how that strength is expressed across the lap.

China 2026 — sector leaders

China provides another example of sector-level decomposition, showing where structural advantage concentrates across different parts of the lap.

S1 leader

ANT

Mercedes

Avg field advantage: 1.143
Avg sector rank: 2.96
Best sector laps: 22

S2 leader

ANT

Mercedes

Avg field advantage: 1.335
Avg sector rank: 2.20
Best sector laps: 28

S3 leader

ANT

Mercedes

Avg field advantage: 1.152
Avg sector rank: 3.02
Best sector laps: 17