I've seen you search the amazon sports analytics database(or whatever it’s called) to see what time college football kickoff actually occurs. Can you give me list actual kick off times of some of the recent bowl games, this year’s college football playoff games, and also I would like to know what time the kickoff occurred on last nights quarterfinal football game between the Hurricanes and Ole Miss.
so basically, I want a list of what the advertised start time of game of the game was. Versus when the kickoff actually occurred.
But I would like to know that I actually believe the kickoff occurs when the referee flips the coin to me that is the actual exact moment of the start of the game is the coin toss, but naming it a coin toss transit breakdown is funny.
But with that said, I’m assuming that the kickoff occurs within one minute of the coin toss or there must be some kind of time or guideline or rules that the official leagues stipulate on how much time the teams have to get on the field after the coin toss

ChatGPT 5.2 said:
Here’s what I can pull from official CFP recap/gamebook pages (they often publish both the advertised ET start and the actual “Kickoff Time” they recorded after the game). For the Miami vs Ole Miss semifinal, the CFP recap page with “Kickoff Time” doesn’t appear to be publicly indexed yet, so I used CBS for the advertised time and the Ole Miss official box score PDF for the recorded kickoff time.

Advertised start time vs actual kickoff time (CFP 2025–26 postseason)

Sources for the times above (the ones with both “Advertised time” and “Kickoff Time” are easiest because they’re in the same official recap page):

• CFP recaps (advertised + kickoff time recorded): Miami–Ohio State , Oregon–Texas Tech , Indiana–Alabama , Ole Miss–Georgia , Miami–Texas A&M , Tulane–Ole Miss , Alabama–Oklahoma
• Miami–Ole Miss semifinal: advertised time (CBS) ; recorded kickoff time from Ole Miss box score PDF

Coin toss vs kickoff (and the “how long after” question)

You’re not wrong to think “coin toss = true beginning” from a symbolic/astrology standpoint. But in rules/record-keeping, “start time” is generally tied to kickoff (ball kicked) — that’s what most official recaps/gamebooks publish as “Kickoff Time.”

As for the timing guideline: NCAA rules spell out that the coin toss is conducted 3 minutes before the scheduled starting time (the referee brings captains out, gives options, etc.). In other words, by rule, coin toss is designed to be only a few minutes ahead of kickoff — but TV and ceremony timing can push the actual kickoff later than the “advertised” time you see on schedules.

Practical takeaway for your charts:

• If you can get an official coin toss timestamp, that’s a clean “true start” moment for astrology.
• If you can’t, the published kickoff time is the most consistently available “actual start” timestamp—and you can see from these CFP games it’s usually ~10–16 minutes after the advertised time.

If you want, tell me which additional bowls you care about (non-CFP bowls too), and I’ll pull the same advertised-vs-actual kickoff pairs for those as well.

You said:
Thanks, your information was useful. And I would like to use this information to determine an average college football kickoff time for future astro charts we generate.

ChatGPT said:

Perfect — you can turn that table into a simple “kickoff delay model” that’s good enough for pre-game chart generation, then tighten it over time as you collect more samples.

What our sample says (from the CFP games we listed)

Delays (actual kickoff minus advertised): 16, 11, 11, 12, 11, 10, 10, 10 minutes

• Average (mean): 11.4 minutes
• Median (best “default”): 11 minutes
• Typical spread: about ±2 minutes (most games landed 10–12)

Recommendation for your default setting:
✅ Estimated kickoff = advertised time + 11 minutes (use median)

That avoids getting pulled by the occasional longer TV delay (like the +16).

A practical kickoff estimate you can bake into ChartGen

When you’re generating charts ahead of time:

• Primary estimated kickoff: advertised + 11 min
• Confidence window: advertised + 9 to + 13 min
• If you want one extra “TV-heavy” fallback: advertised + 15 min

Why this matters for astrology

A difference of ~11 minutes is not small astrologically (angles move fast). So the “+11” estimate is great for automation, but you’ll still get cleaner hits when you later swap in the official kickoff time.

How to refine the model (without overcomplicating it)

If you keep logging just two columns per game (advertised, actual kickoff), you can maintain:

1. Overall median delay (your default)
2. Time-slot medians, because delays cluster by broadcast pacing:

• Noon / early games: often slightly tighter
• Prime time / NY6 / CFP: often a bit longer

Even a simple split like:

• Day games default: +10 or +11
• Prime time default: +12
  …will usually beat a one-size-fits-all.

Your “coin toss = true start” idea

Totally valid as a separate astrology anchor.

A good workflow is:

• Pre-game forecast charts: use advertised + 11 (with a ±2 min window)
• Post-game validation charts: use official kickoff (and if you later find coin-toss time, log it too)

If you want, I can format a tiny “Kickoff Estimator” block you can paste into your SportsAspects methodology page (and a simple rule-of-thumb you can keep consistent across NFL + CFB).

You said:
Yes, I would like you to format a tiny kickoff estimator. We still need to find an average true kickoff time for NFL games as well, so, I guess you have to put together that data, before we proceed further on this subject

ChatGPT said:

Alright Captain — here’s a tiny kickoff estimator you can drop into your workflow, using real “Kickoff/Start Time” stamps from official gamebooks/recaps as the “true kickoff,” and comparing them to the advertised/scheduled start time.

Recent “advertised vs true kickoff” samples (CFP / bowls)

CFB/CFP delay average from the sample above: ~ +11.1 minutes (7 games).
That’s a very usable default for future astro chart timing.

NFL “advertised vs true kickoff” samples (so we can start an NFL average)

NFL delay average from this small sample: ~ +3.7 minutes (3 games).
That’s enough to start an estimator, but we’ll want a bigger sample to lock it in.

Tiny Kickoff Estimator (v0.1)

Defaults you can use right now

• CFB / bowls / CFP: true_kickoff ≈ advertised + 11 minutes (good general default)
  • If it’s a mega-game (CFP semifinal / title vibe): use +14 to +16 (Fiesta sample was +16).
• NFL: true_kickoff ≈ advertised + 4 minutes (starter average from sample)
  • Early Sunday window tends to be tighter (+2 in our sample).

If you want “coin toss moment” instead of kickoff

Since you want the “start” to be the coin toss, just define:

• coin_toss_time ≈ estimated_true_kickoff - 4 minutes
  (That “-4” is a practical working offset; we can refine later if you decide you want -3 or -5 as your standard.)

Drop-in config + tiny function (for ChartGen logic)

from datetime import datetime, timedelta

DEFAULT_DELAYS_MIN = {
    "cfb": 11,  # bowls/CFP baseline from sample
    "nfl": 4,   # starter baseline from sample
}

# Optional “big game” override (CFP semis/title style)
BIG_GAME_DELAYS_MIN = {
    "cfb_big": 16,  # observed in Fiesta semifinal sample
}

def estimate_true_kickoff(advertised: datetime, league: str, big_game: bool = False) -> datetime:
    if league == "cfb" and big_game:
        delay = BIG_GAME_DELAYS_MIN["cfb_big"]
    else:
        delay = DEFAULT_DELAYS_MIN[league]
    return advertised + timedelta(minutes=delay)

def estimate_coin_toss_from_kickoff(true_kickoff: datetime, minutes_before: int = 4) -> datetime:
    return true_kickoff - timedelta(minutes=minutes_before)

What I’d standardize for SportsAspects (so we stop guessing)

• CFB charts: generate at advertised + 11 min by default; tag semis/title as big_game=True.
• NFL charts: generate at advertised + 4 min (until we expand the NFL dataset).

If you want, next step is simple: we expand NFL sampling by grabbing a bunch of official NFL gamebooks across the main TV windows (1:00 / 4:05 / 4:25 / 8:15 / 8:20) and compute per-slot averages. That’ll give you a rock-solid estimator instead of a global “+4”.

You said:
this is a good start. but I would like a larger dataset. And perhaps see if there is a common delay among the NFL games that occur on: Sunday Early, Sunday Late, Sunday Night Football, Monday Night Football, Thursday Night Football. Saturday. And international NFL games. Take your time compiling such data logic

ChatGPT said:

Captain — here’s what your “kickoff vs. coin toss” hunch looks like once you anchor it to real NFL gamebook data and split it by TV window.

Coin toss vs “true kickoff” (what the rules actually say)

In both NFL and NCAA, the coin toss is scheduled for ~3 minutes before the scheduled start time (not “within a minute of kickoff”).

So in practice:

• Coin toss time ≈ scheduled start − 3 minutes
• True kickoff ≈ scheduled start + (window delay)

That means coin toss → kickoff is usually ~4–6 minutes, and sometimes more in special broadcast setups.

What the 2025-season NFL gamebooks show (sample-based delays)

I sampled “Start Time” from official NFL gamebook PDFs (they record the actual start to the minute), then compared against the advertised window start (1:00 / 4:05 / 4:25 / 8:15 / 8:20 / 9:30, etc.). Example gamebooks:

Window-delay summary (minutes after advertised start)

Important nuance:

• “Sunday Late” looks like +0 in this sample (gamebooks show 4:05/4:25 on the dot). That might mean the true kickoff is very close to the slot start or you’re seeing minute-rounding behavior that hides 30–90 seconds. Either way: for charting, +0 is fine.
• Saturday has the widest spread (one game was +10), so treat Saturday as “use the slot-specific rule if you know it; otherwise default +3.”

Quick examples per category (so you can sanity-check)

TNF

• Cowboys @ Eagles (season opener slot 8:20) start time recorded 8:23 PM EDT → +3.
• Steelers @ Bengals (8:15 slot) start time 8:16 PM EDT → +1.
• Jets @ Patriots (8:15 slot) start time 8:16 PM EST → +1.

SNF

• Packers @ Steelers (8:20 slot) start time 8:22 PM EDT → +2.
• Lions @ Eagles (8:20 slot) start time 8:22 PM → +2.

MNF (slot matters)

• Standard MNF (8:15 slot) example start 8:16 → +1.
• Late MNF (10:00 ET slot) example start 10:05 ET → +5 (this is a real thing on some doubleheaders).
• Early MNF (7:15 ET slot) Bills/Falcons was scheduled 7:15 ET, and the gamebook start matched that minute.

International (9:30 ET slot)

All three samples I pulled landed at +2 (9:32 ET equivalent).

Saturday (varies)

• Eagles @ Commanders start 5:03 PM ET → likely +3 on a 5:00 slot.
• Ravens @ Packers start 7:10 PM CT → scheduled sources list 7:00 CT, so +10 (outlier but real).

Tiny Kickoff Estimator (NFL) — the version you can actually use

Use median offsets so one weird broadcast doesn’t poison the model.

Recommended defaults (minutes):

• Sunday Early (1:00 ET): +2
• Sunday Late (4:05 / 4:25 ET): +0
• TNF (8:15 ET): +1 (opener 8:20 ET: +3)
• SNF (8:20 ET): +2
• MNF (8:15 ET): +1
• MNF early doubleheader (7:15 ET): +0
• MNF late doubleheader (10:00 ET): +5
• International (9:30 ET): +2
• Saturday (unknown slot): +3 (but prefer slot-specific if you know it)

Here’s a clean mapping you can drop into ChartGen logic:

NFL_KICKOFF_DELAY_MIN = {
  "SUN_EARLY": 2,        # 1:00 ET
  "SUN_LATE": 0,         # 4:05 / 4:25 ET
  "TNF": 1,              # 8:15 ET
  "TNF_OPENER": 3,       # 8:20 ET (season opener style)
  "SNF": 2,              # 8:20 ET
  "MNF": 1,              # 8:15 ET
  "MNF_EARLY": 0,        # 7:15 ET
  "MNF_LATE": 5,         # 10:00 ET
  "INTL": 2,             # 9:30 ET
  "SAT_UNKNOWN": 3       # fallback
}

def estimate_true_kickoff(advertised_dt, window_key):
    return advertised_dt + timedelta(minutes=NFL_KICKOFF_DELAY_MIN[window_key])

And if you want a Coin Toss Transit Breakdown time that’s rule-aligned:

def estimate_coin_toss(advertised_dt):
    return advertised_dt - timedelta(minutes=3)

That “−3 minutes” is explicitly in the NFL/NCAA start-of-game rules.

What I’d do next (to make this bulletproof for SportsAspects)

1. Keep a rolling table in your project like: game_id, league, window, advertised_time, actual_start_time, delay_minutes, source_url.
2. Recompute rolling medians per window weekly (median beats mean for TV weirdness).
3. For college, do the same but split by network + event type (regular ESPN vs CBS vs FOX vs bowls/CFP). College delays are usually larger and less uniform than NFL.

If you want, paste one week of your NFL slate (with the advertised ET times you’re using in ChartGen) and I’ll map each game to the correct window key automatically so the estimator can run hands-free.