{
    "data_id": "43655",
    "name": "Football---Expected-Goals-Match-Statistics",
    "exact_name": "Football---Expected-Goals-Match-Statistics",
    "version": 1,
    "version_label": "v1.0",
    "description": "Context\nIn recent years statisticians and data scientists alike have been trying to come up with new ways to evaluate team performance in Football. Sometimes a result is not a fair reflection on a teams performance, and this is where expected goals come in. \nExpected goals is a relatively new football metric, using quality of passing and goalscoring opportunities to rank a teams performance. Understat.com provides these statistics by using neural networks to approximate this data and I have therefore scraped statistics for matches played between the 2014-15 and 2019-2020 seasons to provide the following dataset.\nThe Leagues included in this representation are:\n\nEnglish Premier League\nLa Liga\nBundesliga\nSerie A\nLigue 1\nRussian Football Premier League\n\nContent\nThe dataset contains 22 columns, a lot of which will be self explanatory such as date, home team etc. Some of the less common features will be outlined below:\nChance  - the percentage prediction of an outcome based on expected goals.\nExpected Goals - the number of goals a team is expected to score based on performance.\nDeep - number of passes completed within an estimated 20 yards from goal.\nPPDA - number of passes allowed per defensive action in the opposition half.\nExpected Points - number of points a team is expected to achieve in this game.\nInspiration\nIs the expected goals feature an accurate representation of a teams performance?\nHow can this feature be improved?\nCan we predict the outcome of future games based on previous games?",
    "format": "arff",
    "uploader": "Dustin Carrion",
    "uploader_id": 30123,
    "visibility": "public",
    "creator": null,
    "contributor": null,
    "date": "2022-03-24 01:02:12",
    "update_comment": null,
    "last_update": "2022-03-24 01:02:12",
    "licence": "CC0: Public Domain",
    "status": "active",
    "error_message": null,
    "url": "https:\/\/www.openml.org\/data\/download\/22102480\/dataset",
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    "suggest": {
        "input": [
            "Football---Expected-Goals-Match-Statistics",
            "Context In recent years statisticians and data scientists alike have been trying to come up with new ways to evaluate team performance in Football. Sometimes a result is not a fair reflection on a teams performance, and this is where expected goals come in. Expected goals is a relatively new football metric, using quality of passing and goalscoring opportunities to rank a teams performance. Understat.com provides these statistics by using neural networks to approximate this data and I have there "
        ],
        "weight": 5
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    "tags": [
        {
            "uploader": "38960",
            "tag": "Computer Systems"
        },
        {
            "uploader": "38960",
            "tag": "Machine Learning"
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