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To automate the statistical analysis of sports data, we need to gather relevant data from reliable sources, such as APIs, CSV files, or web scraping. Once we have the data,we can use data processing libraries like Pandas in Python to clean, manipulate, and structure it for analysis. We can then apply statistical models using libraries like SciPy or statsmodels to perform analyses like player performance, team efficiency, or historical trends. For automation, we can set up a scheduled script (using cron jobs or task schedulers) to pull and analyze new data regularly, and generate reports or visualizations with libraries like Matplotlib or Seaborn. If you want more advanced analytics, machine learning models using scikit-learn or TensorFlow could be used to predict future performance or trends based on historical data.
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