Sector Intelligence

Sector Advantage Map

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

Built on QEIv18™ by NeoAmorfic™. Sector intelligence decomposes race performance into segment-level structure to identify local competitive strength, asymmetry, and conversion opportunities.

NeoAmorfic™QEIv18™ 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 sector leader

RUS

Mercedes

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

S2 sector leader

RUS

Mercedes

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

S3 sector 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.

Competitive 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.

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 sector leader

ANT

Mercedes

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

S2 sector leader

ANT

Mercedes

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

S3 sector leader

ANT

Mercedes

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

Japan 2026 — sector leaders

Japan sector analysis shows where the decisive competitive edge was created across the lap. QEIv18™ separates the circuit into structural segments and identifies where control, stability, and conversion were strongest.

S1 sector leader

PIA

McLaren

Avg field advantage: 0.646
Avg sector rank: 4.23
Best sector laps: 10

S2 sector leader

LEC

Ferrari

Avg field advantage: 1.150
Avg sector rank: 3.04
Best sector laps: 20

S3 sector leader

ANT

Mercedes

Avg field advantage: 0.923
Avg sector rank: 4.34
Best sector laps: 20

Why it matters

Sector decomposition helps distinguish visible result from underlying performance. A driver may win or lose through strategy, traffic, or timing, but the sector map reveals where the strongest structural pace and control were actually expressed.

Competitive reading

Japan tests whether the final race narrative is supported by the lap structure itself. The sector leaders identify where the front-running hierarchy was built and whether the result reflects sustained competitive strength rather than isolated circumstance.

Japan 2026 — front-group sector maps

Sector structure in Japan shows divergence between frequency and magnitude of performance. In S1, PIA leads by average rank, indicating more frequent sector-level leadership. However, ANT records stronger mean field-relative advantage, indicating greater intensity when the sector edge is expressed. This distinction separates consistency of position from magnitude of control.

Sector Advantage Intensity — Front Group

ANT · RUS · PIA · LEC

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Sector Advantage Intensity — Front Group

Sector Order Stability — Front Group

ANT · RUS · PIA · LEC

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Japan 2026 — per-sector stability layer

The instability layer measures how much each driver’s sector ranking fluctuates lap to lap inside the front group. Lower variability indicates a more repeatable structural profile, while higher variability suggests less stable sector control.

S1 sector stability leader

LEC

Ferrari

Rank variability: 4.418
Evaluated laps: 52

S2 sector stability leader

LEC

Ferrari

Rank variability: 3.437
Evaluated laps: 53

S3 sector stability leader

RUS

Mercedes

Rank variability: 3.924
Evaluated laps: 53

Why it matters

A driver can show strong average pace while still being structurally volatile. The stability layer separates raw sector performance from repeatability, which is often what determines whether an apparent edge can be sustained over race distance.

Competitive reading

Taken together, the Japan sector layers distinguish three different properties: magnitude of advantage, frequency of top-sector ordering, and stability of that ordering lap to lap. That combination is substantially richer than standard sector timing summaries.

Miami 2026 — sector leaders

Miami adds a different sector structure from Japan. Norris leads S1 and S3, while Antonelli dominates S2. This creates a split race-state picture: McLaren controls the entry and closing phases, while Mercedes holds the strongest middle-sector control signal.

S1 sector leader

NOR

McLaren

Avg field advantage: 0.795
Avg sector rank: 3.84
Best sector laps: 19

S2 sector leader

ANT

Mercedes

Avg field advantage: 1.285
Avg sector rank: 2.35
Best sector laps: 36

S3 sector leader

NOR

McLaren

Avg field advantage: 0.964
Avg sector rank: 3.18
Best sector laps: 26

Miami 2026 — front-group sector maps

The Miami sector maps show the race-state split directly. Norris leads the race index and controls S1/S3 structure. Antonelli retains the strongest local control phase in S2. Piastri confirms McLaren depth by ranking strongly in both the race index and the sector maps.

Sector Advantage Intensity — Miami Front Group

NOR · PIA · ANT · RUS · HAM · LEC · VER · COL

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Sector Advantage Intensity — Miami Front Group

Sector Ordering Map — Miami Front Group

Average sector rank, lower is stronger

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Structural Sector Leadership Frequency — Miami

Count of laps where QEIv18™ ranks a driver strongest in that sector

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Structural Sector Leadership Frequency — Miami

Miami 2026 — upper-group stability layer

The instability layer measures lap-to-lap variability in sector rank inside the evaluated upper group. Lower variability indicates a more repeatable sector-ordering profile, but it is interpreted only alongside field advantage and sector intensity.

S1 sector stability leader

HAM

Ferrari

Rank variability: 3.529
Evaluated laps: 56

S2 sector stability leader

NOR

McLaren

Rank variability: 2.763
Evaluated laps: 57

S3 sector stability leader

PIA

McLaren

Rank variability: 3.418
Evaluated laps: 57

McLaren structure

Norris leads S1 and S3, while Piastri supports McLaren’s upper-band sector profile. This separates race-level control from sector-specific control and shows how the realised field structure changes from event to event.

Mercedes structure

Antonelli’s S2 dominance is the strongest local control signal in Miami. That matters because it separates a race-level McLaren advantage from a specific Mercedes control phase.

Canada 2026 — sector leaders

Canada shows why sector intelligence is essential. Hamilton and Verstappen convert the strongest event-score layer, while Antonelli remains one of the strongest raw sector-control references. The sector maps separate event conversion, local control, and repeatability.

S1 sector leader

VER

Red Bull Racing

Avg field advantage: 0.665
Avg sector rank: 3.32
Best sector laps: 17

S2 sector leader

HAM

Ferrari

Avg field advantage: 0.735
Avg sector rank: 2.89
Best sector laps: 21

S3 sector leader

HAM

Ferrari

Avg field advantage: 0.743
Avg sector rank: 3.03
Best sector laps: 16

Canada 2026 — competitive sector maps

The Canada sector maps follow the Miami presentation principle: first show sector advantage intensity, then sector ordering, then structural sector leadership frequency.

Sector Advantage Intensity — Canada

Average field advantage by sector · higher = stronger

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Sector Ordering Map — Canada

Average sector rank · 1 = strongest; top = stronger

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Structural Sector Leadership Frequency — Canada

Count of laps where QEIv18™ ranks a driver strongest in that sector

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Structural Sector Leadership Frequency — Canada

Canada 2026 — upper-group sector stability layer

The stability layer measures lap-to-lap sector-rank variability inside the Canada upper group. Lower variability means the sector profile is more repeatable; it should be interpreted alongside advantage intensity.

S1 sector stability leader

COL

Alpine

Rank variability: 2.381
Evaluated laps: 66

S2 sector stability leader

COL

Alpine

Rank variability: 2.089
Evaluated laps: 66

S3 sector stability leader

HAM

Ferrari

Rank variability: 2.380
Evaluated laps: 66

Control versus conversion

Canada shows a premium QEIv18™ distinction: Antonelli can appear stronger in raw control layers while Hamilton and Verstappen convert the event score more cleanly.

Team-facing value

Sector intelligence shows where advantage is created, whether it repeats, and whether it converts into event-level structure.