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        "author": "Angus Williams <anguswilliams91@gmail.com>",
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        "description": "# bpl\n\n[![Build Status](https://travis-ci.org/anguswilliams91/bpl.svg?branch=master)](https://travis-ci.org/anguswilliams91/bpl)\n[![codecov](https://codecov.io/gh/anguswilliams91/bpl/branch/master/graph/badge.svg)](https://codecov.io/gh/anguswilliams91/bpl)\n\n`bpl` is a python 3 library for fitting Bayesian versions of the Dixon \\& Coles (1997) model to data.\nIt uses the `stan` library to fit models to data.  \n\n ## Installation\n\nYou will need a working C++ compiler.\nIf you are using anaconda, you can install gcc with  \n\n```bash\nconda install gcc\n``` \n\nYou can then install with `pip`:\n\n```bash\npip install bpl\n```\nThis may take a little while, as two stan models are compiled as part of the build.\n\n## Usage\n\n`bpl` provides a class `BPLModel` that can be used to forecast the outcome of football matches.\nData should be provided to the model as a `pandas` dataframe, with columns `home_team`, `away_team`, `home_goals` and `away_goals`.\nYou can also optionally provide a set of numerical covariates for each team (e.g. their ratings on FIFA) and these will be used in the fit.\nExample usage:\n```python\nimport bpl\nimport pandas as pd\n\ndf_train = pd.read_csv(\"<path-to-training-data>\")\ndf_X = pd.read_csv(\"<path-to-team-level-covariates>\")\nforecaster = bpl.BPLModel(data=df_train, X=df_X)\nforecaster.fit(seed=42)\n\n# calculate the probability that team 1 beats team 2 3-0 at home:\nforecaster.score_probability(\"Team 1\", \"Team 2\", 3, 0)\n\n# calculate the probabilities of a home win, an away win and a draw:\nforecaster.overall_probabilities(\"Team 1\", \"Team 2\")\n\n# compute home win, away win and draw probabilities for a collection of matches:\ndf_test = pd.read_csv(\"<path-to-test-data>\") # must have columns \"home_team\" and \"away_team\"\nforecaster.predict_future_matches(df_test)\n\n# add a new, previously unseen team to the model by sampling from the prior\nX_3 = np.array([0.1, -0.5, 3.0]) # the covariates for the new team\nforecaster.add_new_team(\"Team 3\", X=X_3, seed=43)\n```",
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