2026 Calcutta — Round-by-Round Matchup Analysis

Generated by valuation/matchups/matchup_model.py. Monte-Carlo N=40,000 sims per pairing (15 pairings x 20 flights = 300 matches simulated), dedicated fixed seed. Player scoring distributions and the 9-hole net better-ball machinery are imported read-only from value_model.py.

This is a companion to the aggregate flight sim, not a replacement. The aggregate sim answers who wins the flight; here we open up the 6x6 head-to-head matrix behind it to see where each team's points come from, which matches actually decide the flight, and which favorites are fragile (lead hinges on coin-flips) vs robust (win across the board). The suggested_adjustment column is an interpretive lean to apply on top of the sim's P(win) — explicitly NOT a new probability.

How to read this

Headline finding first: 9-hole NET better-ball off the LOW player in the foursome is a genuinely high-variance, near-coin-flip format. Across the whole field ~62% of head-to-head matchups land inside 45–55%, only ~4% reach 60/40, and the single most lopsided matchup anywhere is ~78/22. Handicaps off the low player plus only 9 holes to separate teams compress nearly everything toward 50/50. So 'fragile favorite' is the NORM here, and the bands below are calibrated to this format's realized (tight) dispersion, not a wider sport.

• p_A_beats_B: P(team A wins the 9-hole match) + 0.5·P(match halved), so it pairs exactly with the points split and p_A_beats_B + p_B_beats_A = 1.
• Strong win for A: p_A_beats_B ≥ 57% (≈ top quartile of matchups in this field). Likely loss: ≤ 43%. Swing: in between. A tighter coin-flip band (45%–55%) is used for fragility.
• robustness_score (0–1): wide head-to-head margins AND few coin-flips ⇒ high. A team can lead on expected points yet score LOW here if that lead is built on 50/50s.
• suggested_adjustment: capped at ±6%. UP = the sim may under-rate this team (favorable matchup spread / strong H2H vs the specific top rivals it must beat). DOWN = the sim may over-rate it (nominal lead leans on winning multiple coin-flips). Interpretive, not a P(win).

Inherited assumptions (from value_model.py)

Same per-hole construction as the aggregate sim: 9 holes, integer gross-vs-par clipped to [-2,+7], net better-ball off the LOW player in the foursome, 10 match points (+1/hole, +0.5/halve, +1 to the match winner). Variance layers RHO_FIELD=0.15, RHO_PARTNERS=0.35, HOLE_NOISE_FRAC=0.65. The flight-wide FIELD shock is shared by all four players in a match (it nearly cancels head-to-head, matching how value_model treats a single match); the PARTNER shock is per team and does not cancel. Full player-distribution assumptions are documented in value_model.py/flight_report.md.

Favorites at a glance — fragile vs robust

UP/DOWN adjustment calls (interpretive overlay)

These are leans on top of the aggregate sim's P(win), capped at ±6%. They flag where the matchup structure suggests the sim may be mis-rating a team. Not a replacement for the sim.

▲ UP — the sim may UNDER-rate these (favorable matchup spread):

▼ DOWN — the sim may OVER-rate these (lead leans on coin-flips):

Per-flight detail

Flight 1

Matchup matrix — P(row beats column):

Where the points come from:

Swing matches that most decide the flight (closeness × contention):

Flight 2

Matchup matrix — P(row beats column):

Where the points come from:

Swing matches that most decide the flight (closeness × contention):

Flight 3

Matchup matrix — P(row beats column):

Where the points come from:

Swing matches that most decide the flight (closeness × contention):

Flight 4

Matchup matrix — P(row beats column):

Where the points come from:

Swing matches that most decide the flight (closeness × contention):

Flight 5

Matchup matrix — P(row beats column):

Where the points come from:

Swing matches that most decide the flight (closeness × contention):

Flight 6

Matchup matrix — P(row beats column):

Where the points come from:

Swing matches that most decide the flight (closeness × contention):

Flight 7

Matchup matrix — P(row beats column):

Where the points come from:

Swing matches that most decide the flight (closeness × contention):

Flight 8

Matchup matrix — P(row beats column):

Where the points come from:

Swing matches that most decide the flight (closeness × contention):

Flight 9

Matchup matrix — P(row beats column):

Where the points come from:

Swing matches that most decide the flight (closeness × contention):

Flight 10

Matchup matrix — P(row beats column):

Where the points come from:

Swing matches that most decide the flight (closeness × contention):

Flight 11

Matchup matrix — P(row beats column):

Where the points come from:

Swing matches that most decide the flight (closeness × contention):

Flight 12

Matchup matrix — P(row beats column):

Where the points come from:

Swing matches that most decide the flight (closeness × contention):

Flight 13

Matchup matrix — P(row beats column):

Where the points come from:

Swing matches that most decide the flight (closeness × contention):

Flight 14

Matchup matrix — P(row beats column):

Where the points come from:

Swing matches that most decide the flight (closeness × contention):

Flight 15

Matchup matrix — P(row beats column):

Where the points come from:

Swing matches that most decide the flight (closeness × contention):

Flight 16

Matchup matrix — P(row beats column):

Where the points come from:

Swing matches that most decide the flight (closeness × contention):

Flight 17

Matchup matrix — P(row beats column):

Where the points come from:

Swing matches that most decide the flight (closeness × contention):

Flight 18

Matchup matrix — P(row beats column):

Where the points come from:

Swing matches that most decide the flight (closeness × contention):

Flight 19

Matchup matrix — P(row beats column):

Where the points come from:

Swing matches that most decide the flight (closeness × contention):

Flight 20

Matchup matrix — P(row beats column):

Where the points come from:

Swing matches that most decide the flight (closeness × contention):