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meters???

Here's how you too can make a graphic just like that:


def worthless_random_rankings(team_list=None):
  """
    A completely unserious, meaningless Top 25
    designed purely for off-season arguments and clicks.
    """

    import numpy as np

    # If no list is provided, generate the usual suspects
    if team_list is None:
        team_list = [
            "dumb team 1", "dumb team 2", "dumb team 3", "dumb team 4",
            "dumb team 5", "dumb team 6", "dumb team 7", "dumb team 8",
            "dumb team 9", "dumb team 10", "dumb team 11", "dumb team 12",
            "dumb team 13", "dumb team 14", "dumb team 15", "dumb team 16",
            "dumb team 17", "dumb team 18", "dumb team 19", "dumb team 20",
            "dumb team 21", "dumb team 22", "dumb team 23", "dumb team 24",
            "dumb team 25"
        ]

    # Step 1: Pretend we analyzed "key metrics"
    print("Analyzing strength of schedule, vibes, and mascot energy...")

    # Step 2: Completely ignore that and pick randomly
    rankings = np.random.choice(team_list, size=25, replace=False)

    # Step 3: Add fake justification layer
    print("Applying advanced algorithm: 'gut feeling + chaos'")

    # Step 4: Output rankings like they matter
    print("\n=== OFF-SEASON TOP 25 (DO NOT QUESTION) ===")
    for i, team in enumerate(rankings, start=1):
        print(f"{i}. {team}")

    # Step 5: Stir controversy for engagement
    print("\nBiggest snub: whoever you like")
    print("Biggest reach: whoever you hate")

    return rankings

I think this is accurate.

I'm not sure how you'd reasonably put together a real effort at pre-season rankings, early or not.  The only school I remotely try to keep track of anymore--and that, just barely--is of course LSU, and we have something like a 66-player turnover from last season.  If we're even remotely the norm, I don't know how that many guys who haven't played together, worked with the staff, etc. can have any sort of accurate forecast.