TL;DR: 5 routes × every departure day in November 2026, cheapest one-way economy fare from Google Flights. Prices were seen on 2 Oct 2026.
- Tuesday was the cheapest day to depart, −5% vs the route's typical price. Monday and Wednesday were close behind (−3%, −2%).
- Friday was the most expensive, +20%. Saturday and Sunday were both about +13%.
- Picking the wrong day cost up to 2.7×: San Francisco → Honolulu was $163 on the cheapest day and $444 on the priciest.
- The Sunday after Thanksgiving (29 Nov) was the most expensive day of the month on New York → LA ($489 vs $184 at best) and on Chicago → Miami ($195 vs $113).
The routes
| Route | Cheapest day | Priciest day | Spread | Google's "price level" on most days |
|---|---|---|---|---|
| New York JFK → Los Angeles | $184 (Fri 6, Mon 16, Thu 19) | $489 (Sun 29) | +166% | typical |
| London LHR → New York JFK | $399 (14 different days from 11 Nov) | $641 (Sun 1) | +61% | low on 21 of 30 days |
| Chicago ORD → Miami | $113 (Sat 14) | $195 (Sun 29) | +73% | typical / high |
| San Francisco → Honolulu | $163 (12 different days, every one a Sun–Wed) | $444 (Fri 20) | +172% | low / typical |
| Los Angeles → Tokyo NRT | $329 (Wed 25) | $823 (Fri 20) | +150% | typical |
How I did it
For each route I searched every departure date from 1 to 30 November: 150 searches in total, one-way, economy, 1 adult, USD, US point of sale. I took the cheapest itinerary per search.
The weekday numbers compare each day's cheapest fare with that route's median over the month, then average across the 5 routes. A $300 route and a $100 route count equally.
Google also labels prices as low, typical or high against its own history. I recorded that label too: London → New York was "low" on 21 of 30 days.
All the data and the analysis script are open: github.com/ISHEMAH/travel-price-studies (CSV, CC BY 4.0). python3 analysis.py reproduces every number in this post.
Caveats
- Five routes and one month, and November includes US Thanksgiving. Read it as a strong hint, not a law.
- Prices were seen on one day (2 Oct). Fares change as departure gets closer.
- Cheapest fares are often basic economy, so check bags and seat rules.
Practical takeaway
- Be flexible by a day or two. Use a date grid, because the difference can be bigger than any coupon.
- Avoid Friday and Sunday departures when you can, especially around holidays.
- Watch Google's price level. When it says low, that's usually your moment.
Reproduce it (or scan your own route)
Disclosure: I built the scraper I used. It's the Google Flights Scraper on Apify. Set scanDepartureUntil and it searches every day in the range, then reports the cheapest day per route in SUMMARY.
{
"routes": ["JFK-LAX", "LHR-JFK", "ORD-MIA", "SFO-HNL", "LAX-NRT"],
"departureDate": "2026-11-01",
"scanDepartureUntil": "2026-11-30",
"maxFlightsPerSearch": 3,
"currency": "USD"
}
# pip install apify-client
from apify_client import ApifyClient
run_input = {"routes": ["JFK-LAX"], "departureDate": "2026-11-01", "scanDepartureUntil": "2026-11-30", "maxFlightsPerSearch": 3}
client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("kuezi/google-flights-scraper").call(run_input=run_input)
summary = client.key_value_store(run.default_key_value_store_id).get_record("SUMMARY")["value"]
for r in summary["cheapestByRoute"]:
print(r["route"], r["cheapestPrice"], "on", r["departureDate"])
Schedule it weekly and you have a fare-drop alert for your own trips.
Same method, hotels: I compared 24 booking sites on 47 hotels. Booking.com, Expedia and Hotels.com were ~26% above the cheapest.
Update: robustness check (thanks to a reader)
A reader pointed out that November has only four Fridays, and two of them sit in the Thanksgiving window. Rerunning the same method on 1–19 Nov only: Mon −5.2%, Tue −2.8%, Wed +2.1%, Thu +10.2%, Fri +13.9%, Sat +7.6%, Sun +5.2%.
- The holiday inflated the headline: Friday falls from +19.7% to +13.9%, and Sunday from +12.7% to +5.2%.
- Monday edges out Tuesday as the cheapest day.
- Friday is still the priciest, but that rests on just 2 Fridays × 5 routes. Treat it as a hint, not a rule. A non-holiday month is the next run.
Top comments (4)
Nice that the CSV and script are public, because one check is quick to run with them. November 2026 has only four Fridays (6, 13, 20, 27), and two of them sit in the Thanksgiving window: the 20th was the priciest day on two routes, and the 27th is Black Friday. With four dates per weekday per route, two holiday-adjacent Fridays can carry most of a +20% average on their own. Rerunning the weekday table on 1-19 November only, or with the 20th-30th dropped, would show whether Friday is expensive in general or just this month.
The same goes for the Sunday result, since the 29th was the most expensive day on two routes. A median across dates instead of the mean would also make the weekday ranking less sensitive to one or two holiday fares. If Tuesday still comes out cheapest on the non-holiday weeks, that's a much stronger version of the headline.
Great check, thank you. I ran it on the public CSV with the same method: each day's fare vs that route's median over the same days, averaged across the 5 routes.
So you're right that the holiday window inflates the headline:
Friday is still the priciest without the holidays, but that now rests on just 2 Fridays × 5 routes (6 and 13 Nov). So treat it as a hint, not a rule. You can see why on 20 Nov: LAX→NRT was $823 against $334 on 6 Nov, and SFO→HNL was $444 against $269.
I'll add this as a robustness note to the post and the repo. A non-holiday month is the obvious next run.
Thanks for running it, that's a clean robustness note. One more thing worth knowing before reading Monday vs Tuesday: in 1-19 Nov each weekday has only two or three dates, so a 2-3 point difference between them is well inside what a single expensive date can do (your 20 Nov example moved one route by more than 100%). Friday at +13.9% is the only gap big enough to take seriously.
For the next month, one design change would help a lot: if all the fares were pulled on the same day, each flight date also has a different booking lead time, so early-month dates are both "a Monday" and "closer to departure". Pulling the same routes on two or three different scrape days lets you compare weekdays at the same lead time, and then the weekday effect is the only thing left in the comparison.
Agreed on both points. With two or three dates per weekday in 1–19 Nov, Monday vs Tuesday is within the noise. Only the Friday gap is large enough to read anything into, and I've worded the update that way.
The lead-time confound is a good catch. All 150 fares were pulled on 2 October, so 1 Nov was 30 days out and 30 Nov was 59 days out. Weekday and lead time are tangled together. I'll re-pull the same 5 routes for the same November dates on 9 and 16 October. Then each date has three lead times, and weekdays can be compared at the same distance from departure. I'll post the result here and add the CSVs to the repo.