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I've updated the magic weights, and I too can get the result I want:

    WEIGHTS = {
      'w_xg': 0.09,
      'w_goals': -0.07,
      'w_star': 0.018,
      'w_value': 0.18,
      'w_rank': 0.4,
      'w_def': -0.12,
      'xga_share': 0.85,
      'w_gk': 0.0042
    }

    $ python3 worldcup_model.py --sims 100000

    2026 FIFA World Cup -- championship probabilities (100,000 simulations, from Round of 32)

     1. England                 11.7% *
     2. France                  10.0% *
     3. Spain                    9.3% *
     4. Argentina                8.4%
     5. Germany                  8.1%
It's coming home!


Not with tuchel Ball


Not with Tucheliban


Maybe in an alternate reality. Jokes aside, discovering the right weights to match the current fixture, along with the limited data available, were the key challenges in forecasting at such an early stage.




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