def corr(result, result2, result3):
    """
    Computes the correlation between the three regression methods by taking squared differences from averages in each category and then averaging those results. 
    Interpret this as a smaller # meaning a better score / agreement. 

    Parameters 
    ----------
    result: pandas dataframe 
        linear regression results 
    result2: pandas dataframe
        the neural network results 
    result3: pandas dataframe 
        the random forest regression results 

    Returns 
    ----------
    correlation: pandas.core.series.Series
        the correlation value for the given player 
    
    """
    avpts = (result['Pts'] + result2['Pts'] + result3['Pts']) / 3
    diffs_pts = (avpts - result['Pts'])**2 + (avpts - result2['Pts'])**2 + (avpts - result3['Pts'])**2

    avast = (result['Ast'] + result2['Ast'] + result3['Ast']) / 3
    diffs_ast = (avast - result['Ast'])**2 + (avast - result2['Ast'])**2 + (avast - result3['Ast'])**2

    avreb = (result['Reb'] + result2['Reb'] + result3['Reb']) / 3
    diffs_reb = (avreb - result['Reb'])**2 + (avreb - result2['Reb'])**2 + (avreb - result3['Reb'])**2

    correlation = (diffs_reb + diffs_ast + diffs_pts) / 3

    return correlation