Hotels are only one leg of tournament travel economics — flights into 16 host cities and ride-hailing around match venues complete the picture, and both are publicly priced, continuously, in scrapable form. Here's what our tournament-window tracking shows so far, and how the methodology works.
| Layer | Method | Coverage |
|---|---|---|
| Flight fares | Route-panel quote capture, daily | 60 routes into host cities (domestic + key international origins), economy/flexible buckets |
| Ride-hailing surge | Fixed route panels every 10 min on match days vs control days | Stadium↔downtown↔airport corridors in 8 host cities (Uber/Lyft US-CA; Uber/Didi MX) |
| Context joins | Match schedule + weather | Surge attribution by event window |
Same quote-capture frameworks as our ride-hailing surge study and hotel parity work — no bookings, only publicly displayed prices.
Event-driven surge is the cleanest natural experiment in dynamic pricing: known demand shock, fixed locations, public prices. Teams use the tournament dataset as a template for concerts, conferences, and sports seasons — the same panels run year-round for corporate travel benchmarking, mobility competitive analysis, and urban-policy research. (Event surges of this shape are exactly what our year-round ride-hailing tracking quantifies.)
Quote-level capture on fixed route panels at fixed intervals — standard price-research methodology; only publicly displayed quotes, no transactions.
Yes — custom route panels for any host city (or any event), with capture frequency to 5-minute resolution for surge studies.
Where transit fares are public and fixed they serve as the control line; the surge analysis is about the dynamic-priced layers on top.
No — independent pricing analytics from publicly visible data; tournament references are descriptive, with no match content or marks.
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