{
    "data_id": "43653",
    "name": "NBA-PLAYERS--2016-2019",
    "exact_name": "NBA-PLAYERS--2016-2019",
    "version": 1,
    "version_label": "v1.0",
    "description": "Context\nThis Dataset was created for an University project in Milan. The goal of the project was to create a Robust Model to predict the All Star Game score for each player. \nThe score is calculated by dividing the players by conference and position held in the field (external or internal),\nthe athletes are ranked in descending order depending on the number of votes taken for each category\nvoter, so as to obtain three different rankings. From these for each player the rank is calculated (1st\nPosition involves Rank = 1, 2nd Position Rank = 2 etc. . . ). With the values obtained then an average is calculated\nweighed, with weight 0.50 to that of the votes of the fans and 0.25 to the two remaining.\nDoing this work we have merged some datasets from kaggle (https:\/\/www.kaggle.com\/noahgift\/social-power-nba),  basketball-reference.com  and hoopshype.com.\nObviously for our work we didn't use all of the variables and for a problem of indipendent observations we took only the last seasons' observation for each player.\nOther analysis could be performed using salary as a target but also a cluster analysis for players or a PCA.\nWe wouldn't be here without the help of others. Thank you Riccardo,Alfredo and Daniel.\nSome variables:\nPOS1= Main position (some players have a second position called POS2)\nG= Games played\nGS= Games started\nMP= Minutes played\nFG =Field Goals Per Game\nFGA=Field Goal Attempts Per Game\nFG.= Field Goal Percentage\nX3P= 3-Point Field Goals Per Game\nX3PA= 3-Point Field Goal Attempts Per Game\nX3P.= FG on 3-Pt FGAs.\nX2P =2-Point Field Goals Per Game\nX2PA =2-Point Field Goal Attempts Per Game\nX2P.= FG on 2-Pt FGAs.\neFG. = Effective Field Goal Percentage\nFT=Free Throws Per Game\nFTA = Free Throw Attempts Per Game\nFT.= Free Throw Percentage\nORB = Offensive Rebounds Per Game\nDRB = Defensive Rebounds Per Game\nTRB = Total Rebounds Per Game\nAST = Assists Per Game\nSTL= Steals Per Game\nBLK = Blocks Per Game\nTOV = Turnovers Per Game\nPF = Personal Fouls Per Game\nPTS =Points Per Game\nMEAN_VIEWS= Daily views on wikipedia\nPLAY= If the player played in the all star game",
    "format": "arff",
    "uploader": "Dustin Carrion",
    "uploader_id": 30123,
    "visibility": "public",
    "creator": null,
    "contributor": null,
    "date": "2022-03-24 01:01:51",
    "update_comment": null,
    "last_update": "2022-03-24 01:01:51",
    "licence": "CC0: Public Domain",
    "status": "active",
    "error_message": null,
    "url": "https:\/\/www.openml.org\/data\/download\/22102478\/dataset",
    "default_target_attribute": null,
    "row_id_attribute": null,
    "ignore_attribute": null,
    "runs": 0,
    "suggest": {
        "input": [
            "NBA-PLAYERS--2016-2019",
            "Context This Dataset was created for an University project in Milan. The goal of the project was to create a Robust Model to predict the All Star Game score for each player. The score is calculated by dividing the players by conference and position held in the field (external or internal), the athletes are ranked in descending order depending on the number of votes taken for each category voter, so as to obtain three different rankings. From these for each player the rank is calculated (1st Posi "
        ],
        "weight": 5
    },
    "qualities": {
        "NumberOfInstances": 1408,
        "NumberOfFeatures": 45,
        "NumberOfClasses": null,
        "NumberOfMissingValues": 2891,
        "NumberOfInstancesWithMissingValues": 1398,
        "NumberOfNumericFeatures": 36,
        "NumberOfSymbolicFeatures": 0,
        "Dimensionality": 0.03196022727272727,
        "PercentageOfNumericFeatures": 80,
        "MajorityClassPercentage": null,
        "PercentageOfSymbolicFeatures": 0,
        "MajorityClassSize": null,
        "MinorityClassPercentage": null,
        "MinorityClassSize": null,
        "NumberOfBinaryFeatures": 0,
        "PercentageOfBinaryFeatures": 0,
        "PercentageOfInstancesWithMissingValues": 99.28977272727273,
        "AutoCorrelation": null,
        "PercentageOfMissingValues": 4.562815656565657
    },
    "tags": [
        {
            "uploader": "38960",
            "tag": "Computer Systems"
        },
        {
            "uploader": "38960",
            "tag": "Machine Learning"
        }
    ],
    "features": [
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        },
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