# %% [code]
# -*- coding: utf-8 -*-
"""advanced-stats.ipynb
"""

import numpy as np 
import pandas as pd 
import matplotlib.pyplot as plt
from math import pi
import os
#import xlsxwriter

def create_advanced_stats():

    df = pd.read_csv('../input/march-madness-analytics-2020/MDataFiles_Stage2/MRegularSeasonDetailedResults.csv')

    #Points Winning/Losing Team
    df['WPts'] = df.WScore
    df['LPts'] = df.LScore


    #Points Winning/Losing Team
    #df['WPts'] = df.apply(lambda row: 2*row.WFGM + 3*row.WFGM3 + row.WFTM, axis=1)
    #df['LPts'] = df.apply(lambda row: 2*row.LFGM + 3*row.LFGM3 + row.LFTM, axis=1)

    #Calculate Winning/losing Team Possesion Feature
    wPos = df.apply(lambda row: 0.96*(row.WFGA + row.WTO + 0.44*row.WFTA - row.WOR), axis=1)
    lPos = df.apply(lambda row: 0.96*(row.LFGA + row.LTO + 0.44*row.LFTA - row.LOR), axis=1)
    #two teams use almost the same number of possessions in a game
    #(plus/minus one or two - depending on how quarters end)
    #so let's just take the average
    df['Pos'] = (wPos+lPos)/2
    df['WPos'] = wPos
    df['LPos'] = lPos

    """### Advanced Metrics"""

    #Offensive efficiency (OffRtg) = 100 x (Points / Possessions)
    df['WOffRtg'] = df.apply(lambda row: 100 * (row.WPts / row.WPos), axis=1)
    df['LOffRtg'] = df.apply(lambda row: 100 * (row.LPts / row.LPos), axis=1)
    #Defensive efficiency (DefRtg) = 100 x (Opponent points / Opponent possessions)
    df['WDefRtg'] = df.LOffRtg
    df['LDefRtg'] = df.WOffRtg
    #Net Rating = Off.Rtg - Def.Rtg
    df['WNetRtg'] = df.apply(lambda row:(row.WOffRtg - row.WDefRtg), axis=1)
    df['LNetRtg'] = df.apply(lambda row:(row.LOffRtg - row.LDefRtg), axis=1)

    #Assist Ratio : Percentage of team possessions that end in assists
    df['WAstR'] = df.apply(lambda row: 100 * row.WAst / (row.WFGA + 0.44*row.WFTA + row.WAst + row.WTO), axis=1)
    df['LAstR'] = df.apply(lambda row: 100 * row.LAst / (row.LFGA + 0.44*row.LFTA + row.LAst + row.LTO), axis=1)
    #Turnover Ratio: Number of turnovers of a team per 100 possessions used.
    #(TO * 100) / (FGA + (FTA * 0.44) + AST + TO)
    df['WTOR'] = df.apply(lambda row: 100 * row.WTO / (row.WFGA + 0.44*row.WFTA + row.WAst + row.WTO), axis=1)
    df['LTOR'] = df.apply(lambda row: 100 * row.LTO / (row.LFGA + 0.44*row.LFTA + row.LAst + row.LTO), axis=1)

    #The Shooting Percentage : Measure of Shooting Efficiency (FGA/FGA3, FTA)
    df['WTSP'] = df.apply(lambda row: 100 * row.WPts / (2 * (row.WFGA + 0.44 * row.WFTA)), axis=1)
    df['LTSP'] = df.apply(lambda row: 100 * row.LPts / (2 * (row.LFGA + 0.44 * row.LFTA)), axis=1)
    #eFG% : Effective Field Goal Percentage adjusting for the fact that 3pt shots are more valuable 
    df['WeFGP'] = df.apply(lambda row:(row.WFGM + 0.5 * row.WFGM3) / row.WFGA, axis=1)      
    df['LeFGP'] = df.apply(lambda row:(row.LFGM + 0.5 * row.LFGM3) / row.LFGA, axis=1)   
    #FTA Rate : How good a team is at drawing fouls.
    df['WFTAR'] = df.apply(lambda row: row.WFTA / row.WFGA, axis=1)
    df['LFTAR'] = df.apply(lambda row: row.LFTA / row.LFGA, axis=1)

    #OREB% : Percentage of team offensive rebounds
    df['WORP'] = df.apply(lambda row: row.WOR / (row.WOR + row.LDR), axis=1)
    df['LORP'] = df.apply(lambda row: row.LOR / (row.LOR + row.WDR), axis=1)
    #DREB% : Percentage of team defensive rebounds
    df['WDRP'] = df.apply(lambda row: row.WDR / (row.WDR + row.LOR), axis=1)
    df['LDRP'] = df.apply(lambda row: row.LDR / (row.LDR + row.WOR), axis=1)                                      
    #REB% : Percentage of team total rebounds
    df['WRP'] = df.apply(lambda row: (row.WDR + row.WOR) / (row.WDR + row.WOR + row.LDR + row.LOR), axis=1)
    df['LRP'] = df.apply(lambda row: (row.LDR + row.LOR) / (row.WDR + row.WOR + row.LDR + row.LOR), axis=1)

    """#### PIE : Measure of a team's performance

    > *A high PIE % is highly correlated to winning. In fact, a team’s PIE rating and a team’s winning percentage correlate at an R square of .908 which indicates a "strong" correlation*

    from the official site of the [NBA](https://stats.nba.com/help/glossary/)
    """

    wtmp = df.apply(lambda row: row.WPts + row.WFGM + row.WFTM - row.WFGA - row.WFTA + row.WDR + 0.5*row.WOR + row.WAst +row.WStl + 0.5*row.WBlk - row.WPF - row.WTO, axis=1)
    ltmp = df.apply(lambda row: row.LPts + row.LFGM + row.LFTM - row.LFGA - row.LFTA + row.LDR + 0.5*row.LOR + row.LAst +row.LStl + 0.5*row.LBlk - row.LPF - row.LTO, axis=1) 
    df['WPIE'] = wtmp/(wtmp + ltmp)
    df['LPIE'] = ltmp/(wtmp + ltmp)


    #df.to_csv('../input/march-madness-analytics-2020/MDataFiles_Stage2/NCAASeasonDetailedResultsEnriched.csv', index=False)
    #print(df)
    return df