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Exploring simulation options
I’m considering offering a script that simulates 1,000,000 seasons to compute counts, and I could show how to integrate the current season Elo/lambda as needed. If the user prefers just a discussion, I might mention that these simulations often rely on betting odds and Poisson models. I want to provide a brief summary and ask what the user would like: methodology explanation, code reproduction, or the original article. I'll also provide key analysis in Chinese about team probabilities.Outlining code for simulation
I’m planning to provide an outline of the code, focusing on the data model and parameterization to ensure replicability while adhering to presentation guidelines using bullet points for key ideas. I want to suggest simulating with an expected goals model based on attack/defense strength, or using simpler win/draw/loss probabilities from odds or Elo ratings. Since we don’t have data yet, I might offer a script to create the necessary files if the user is interested. I’ll also include succinct bullet points in Chinese about probabilities and simulation cautions.Considering next steps
I’m looking at three options for next steps: 1) explain the method, 2) reproduce the analysis using Python, or 3) compare the results with current odds. I’ll also calculate the probability of 163 out of 1,000,000 as 0.0163, highlighting it as an approximated chance. I wonder if rough credible intervals are necessary. I’ll craft a response and ask the user: "Would you like to see a breakdown of the method or a Python reproduction?"
看起来像是一则模拟结果的标题。按“100万次模拟”的字面意思粗算:
这类夺冠概率模拟通常基于:

解读与注意点:
你希望我:
选一个数字,我就开干。
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