CIS 545 Final Project

import pandas as pd import sqlite3 import seaborn as sns import matplotlib.pyplot as plt

db_path = '/work/basketball.sqlite' con = sqlite3.connect(db_path) # create connection object to database print("SQL database connected") table = pd.read_sql_query("SELECT name FROM sqlite_master WHERE type='table'", con) print(table)

game_df = pd.read_sql_query("SELECT * from game;", con) game_df

year_df = pd.DataFrame(pd.DatetimeIndex(pd.to_datetime(game_df["GAME_DATE"])).year) year_df = pd.DataFrame(year_df.GAME_DATE.value_counts()).reset_index(level=0).sort_values('index') year_df.rename(columns={"index": "Year", "GAME_DATE": "Count"}, inplace=True) year_df.plot("Year", "Count").set_title("Number of NBA Season Games Played vs. Year")

merge_df = game_df.copy() merge_df["Year"] = pd.DataFrame(pd.DatetimeIndex(pd.to_datetime(game_df["GAME_DATE"])).year)

years = list(pd.DatetimeIndex(pd.to_datetime(game_df["GAME_DATE"])).year.unique()) teams_df = game_df["TEAM_ABBREVIATION_HOME"].unique() num_teams = [] for year in years: num_teams.append(len(merge_df[merge_df["Year"] == year]["TEAM_ABBREVIATION_HOME"].unique())) pd.DataFrame({"Years": years, "Num Teams": num_teams}).plot("Years", "Num Teams").set_title("Number of NBA Teams vs. Year")

game_df["PF_HOME"].mean()

important_columns = ["GAME_ID", "LAST_GAME_HOME_TEAM_POINTS", "LAST_GAME_VISITOR_TEAM_POINTS", "WL_HOME"] home_columns = ["OREB_HOME", 'OREB_HOME','DREB_HOME', 'REB_HOME', 'AST_HOME', 'STL_HOME', 'BLK_HOME', 'TOV_HOME', 'PF_HOME'] away_columns = [column.replace("HOME", "AWAY") for column in home_columns] important_columns += home_columns + away_columns important_columns

extraction_df = game_df[important_columns]

extraction_df.isna().sum()

extraction_df = extraction_df.dropna() len(extraction_df)

wins = len(extraction_df[extraction_df.WL_HOME == 'W']) losses = len(extraction_df[extraction_df.WL_HOME == 'L']) home_won = (wins)/(wins + losses) home_won

extraction_df.columns

blk_winner_home = extraction_df[extraction_df.WL_HOME == 'W'].BLK_HOME.astype(float).dropna().sum() blk_winner_away = extraction_df[extraction_df.WL_HOME == 'L'].BLK_AWAY.astype(float).dropna().sum() blk_winner = (blk_winner_home + blk_winner_away)/len(extraction_df) print('Winners average blocks:', blk_winner)

blk_loser_home = extraction_df[extraction_df.WL_HOME == 'L'].BLK_HOME.astype(float).dropna().sum() blk_loser_away = extraction_df[extraction_df.WL_HOME == 'W'].BLK_AWAY.astype(float).dropna().sum() blk_loser = (blk_loser_home + blk_loser_away)/len(extraction_df) print('Losers average blocks', blk_loser)

reb_winner_home = extraction_df[extraction_df.WL_HOME == 'W'].REB_HOME.astype(float).dropna().sum() reb_winner_away = extraction_df[extraction_df.WL_HOME == 'L'].REB_AWAY.astype(float).dropna().sum() reb_winner = (reb_winner_home + reb_winner_away)/len(extraction_df) print('Winners average rebounds:', reb_winner)

reb_loser_home = extraction_df[extraction_df.WL_HOME == 'L'].REB_HOME.astype(float).dropna().sum() reb_loser_away = extraction_df[extraction_df.WL_HOME == 'W'].REB_AWAY.astype(float).dropna().sum() reb_loser = (reb_loser_home + reb_loser_away)/len(extraction_df) print('Losers average rebounds:', reb_loser)

tov_winner_home = extraction_df[extraction_df.WL_HOME == 'W'].TOV_HOME.astype(float).dropna().sum() tov_winner_away = extraction_df[extraction_df.WL_HOME == 'L'].TOV_AWAY.astype(float).dropna().sum() tov_winner = (tov_winner_home + tov_winner_away)/len(extraction_df) print('Winners average turnovers:', tov_winner)

tov_loser_home = extraction_df[extraction_df.WL_HOME == 'L'].TOV_HOME.astype(float).dropna().sum() tov_loser_away = extraction_df[extraction_df.WL_HOME == 'W'].TOV_AWAY.astype(float).dropna().sum() tov_loser = (tov_loser_home + tov_loser_away)/len(extraction_df) print('Losers average turnovers:', tov_loser)

stl_winner_home = extraction_df[extraction_df.WL_HOME == 'W'].STL_HOME.astype(float).dropna().sum() stl_winner_away = extraction_df[extraction_df.WL_HOME == 'L'].STL_AWAY.astype(float).dropna().sum() stl_winner = (stl_winner_home + stl_winner_away)/len(extraction_df) print('Winners average steals:', stl_winner)

stl_loser_home = extraction_df[extraction_df.WL_HOME == 'L'].STL_HOME.astype(float).dropna().sum() stl_loser_away = extraction_df[extraction_df.WL_HOME == 'W'].STL_AWAY.astype(float).dropna().sum() stl_loser = (stl_loser_home + stl_loser_away)/len(extraction_df) print('Losers average steals:', stl_loser)

pf_winner_home = extraction_df[extraction_df.WL_HOME == 'W'].PF_HOME.astype(float).dropna().sum() pf_winner_away = extraction_df[extraction_df.WL_HOME == 'L'].PF_AWAY.astype(float).dropna().sum() pf_winner = (pf_winner_home + pf_winner_away)/len(extraction_df) print('Winners average personal fouls:', pf_winner)

pf_loser_home = extraction_df[extraction_df.WL_HOME == 'L'].PF_HOME.astype(float).dropna().sum() pf_loser_away = extraction_df[extraction_df.WL_HOME == 'W'].PF_AWAY.astype(float).dropna().sum() pf_loser = (pf_loser_home + pf_loser_away)/len(extraction_df) print('Losers average personal fouls:', pf_loser)

blk_dif = round((blk_winner - blk_loser), 1) reb_dif = round((reb_winner - reb_loser), 1) tov_dif = round((tov_loser - tov_winner), 1) stl_dif = round((stl_winner - stl_loser), 1) pf_dif = round((pf_loser - pf_winner), 1)

print('The winner averages:', blk_dif, 'more blocks,', reb_dif, 'more rebounds, ', tov_dif, 'fewer turnovers, ', stl_dif, 'more steals, ', pf_dif, 'fewer personal fouls.')