fig = px.bar(merged_nba_teams, x='general_manager', y='winning_percentage', hover_data=['nickname'], color='winning_percentage', text = 'nickname',
             labels={'win%':'Win %'})

# Updating layout
fig.update_layout(xaxis={'categoryorder':'total descending'})
fig.update_layout(title={
                'text' : 'Win Percentage of Current NBA GMs',
                'x':0.5,
                 'xanchor': 'center'},
                  xaxis_title='General Managers',
                  yaxis_title='Win %')
fig.update_traces(textfont_size = 14)

fig.add_hline(y=np.mean(merged_nba_teams['winning_percentage']), line_dash="dash", line_color="green", annotation_text="avg win % " + 
              str(round(np.mean(merged_nba_teams['winning_percentage']),2)))

fig.show()

fig2 = px.bar(merged_nba_teams, x='general_manager', y='days_of_ownership',  color='days_of_ownership', text = 'nickname',
             labels={'days of ownership':'Days of Ownership'})

# Updating layout
fig2.update_layout(xaxis={'categoryorder':'total descending'})
fig2.update_layout(title={
                'text' : 'NBA GM Ownership Duration',
                'x':0.5,
                 'xanchor': 'center'},
                  xaxis_title='General Managers',
                  yaxis_title='Days of Ownership')
fig2.update_traces(textfont_size = 14)

fig2.add_hline(y=np.mean(merged_nba_teams['days_of_ownership']), line_dash="dash", line_color="green", annotation_text="avg duration = " + 
              str(round(np.mean(merged_nba_teams['days_of_ownership']),2)))

fig2.show()

fig = px.scatter(merged_nba_teams, x='wins_since', y='losses_since', hover_data=['nickname', 'general_manager', 'gm_start_date'])
fig.update_traces(marker_color="rgba(0,0,0,0)")
maxDim = merged_nba_teams[["wins_since", "losses_since"]].max().idxmax()
maxi = merged_nba_teams[maxDim].max()

for i, row in merged_nba_teams.iterrows():
    fig.add_layout_image(
        dict(
            source=list_of_logos[i],
            xref="x",
            yref="y",
            xanchor="center",
            yanchor="middle",
            x=row["wins_since"],
            y=row["losses_since"],
            sizex=150,
            sizey=150,
            sizing="contain",
            layer="above"
        )
    )

fig.update_layout(title={
                'text' : 'Win/Loss Ratio since Current GM took role',
                'x':0.5,
                 'xanchor': 'center'},
                  xaxis_title='Amount of Wins',
                  yaxis_title='Amount of Losses')

fig.update_traces(textfont_size = 14)
fig.update_yaxes(range = [-40,900])
fig.add_hline(y=np.mean(merged_nba_teams['losses_since']), line_dash="dash", line_color="red", annotation_text="avg losses = " + 
              str(round(np.mean(merged_nba_teams['losses_since']),2)))
fig.add_vline(x=np.mean(merged_nba_teams['wins_since']), line_dash="dash", line_color="green", annotation_text="avg wins = " + 
              str(round(np.mean(merged_nba_teams['wins_since']),2)))

# Add trendline
fig.add_trace(go.Scatter(
    x=[0, 900],
    y=[0, 900],
    mode='lines',
    showlegend=False,
    line=dict(color='orange', dash='dash')
))

# Add annotation text
fig.add_annotation(
    x=850,
    y=850,
    text="Linear Trendline",
    showarrow=False,
    font=dict(color="Orange", size=14)
)

fig.show()

from nba_api.stats.endpoints import leaguegamefinder


nets_master = merged_nba_teams.query('nickname == "Nets"')
nets_id = nets_master['id'].iloc[0]


# Query for all regular season games for Nets
gamefinder = leaguegamefinder.LeagueGameFinder(team_id_nullable=nets_id)

# The first DataFrame of those returned is what we want.
nets = gamefinder.get_data_frames()[0]
nets['OPP_TEAM_ABBREVIATION'] = nets['MATCHUP'].str.extract(r'[@vs\.] ?(\w+)$')
nets['LOCATION'] = np.where(nets['MATCHUP'].str.contains('@'), 'Home', 'Away')

print("Number of historical games:", len(nets))
print("Minimum Game Date:", min(nets['GAME_DATE']))
print("Maximum Game Date:", max(nets['GAME_DATE']))

nets.head()

nets['SEASON_ID'].unique()

# Count the number of 'W' and 'L' within each season
win_loss_counts = nets[nets['PLAY_TYPE'] == 2].groupby(['SEASON', 'WL']).size().unstack(fill_value=0).reset_index()
win_loss_counts['W_L_PERC'] = win_loss_counts['W'] / (win_loss_counts['W'] + win_loss_counts['L'])
win_loss_counts


# Create a line plot using Plotly
fig = px.line(win_loss_counts, x='SEASON', y='W_L_PERC', markers=True,
              labels={'value': 'Percentage', 'SEASON': 'Season'})


# Update layout to position the title in the middle bottom
fig.update_layout(
    title={
        'text': 'Nets W-L% over Regular Seasons since 1990',
        'y': 0.02,  # Adjust this value to position the title vertically (0.02 is near the bottom)
        'x': 0.5,   # Center the title horizontally
        'xanchor': 'center',
        'yanchor': 'bottom'
    }
)


# Update layout for better visualization
fig.update_layout(
    xaxis_title='Season',
    yaxis_title='Percentage',
    hovermode='x unified',
    template='plotly_dark'
)

# Customize hover template to show percentage rounded to two decimals
fig.update_traces(
    hovertemplate='<b>Season</b>: %{x}<br>' +
                  '<b>Percentage</b>: %{y:.2f}<extra></extra>'
)

# Add a vertical line at the year 2016
fig.add_shape(dict(type='line', x0=2016, y0=0, x1=2016, y1=1, xref='x', yref='paper', line=dict(color='red', width=2)))

# Add a horizontal line at percentage .5 to indicate a "good" regular season
# where they win at least half of their games
fig.add_shape(dict(type='line', y0=0.5, y1=0.5, x0=0, x1=1, xref='paper', yref='y', line=dict(color='green', width=1)))

# Add annotation to the red line
fig.add_annotation(dict(x=2016, y=1, xref='x', yref='paper', text='Sean Marks', showarrow=True, arrowhead=2, ax=0, ay=-40, font=dict(color='white')))

# Add a vertical line at the year 2010
fig.add_shape(dict(type='line', x0=2010, y0=0, x1=2010, y1=1, xref='x', yref='paper', line=dict(color='red', width=2)))

# Add annotation to the red line
fig.add_annotation(dict(x=2010, y=1, xref='x', yref='paper', text='Billy King', showarrow=True, arrowhead=2, ax=0, ay=-40, font=dict(color='white')))

# Add a vertical line at the year 2000
fig.add_shape(dict(type='line', x0=2000, y0=0, x1=2000, y1=1, xref='x', yref='paper', line=dict(color='red', width=2)))

# Add annotation to the red line
fig.add_annotation(dict(x=2000, y=1, xref='x', yref='paper', text='Rod Thorn', showarrow=True, arrowhead=2, ax=0, ay=-40, font=dict(color='white')))

# Add a vertical line at the year 2008
fig.add_shape(dict(type='line', x0=2008, y0=0, x1=2008, y1=1, xref='x', yref='paper', line=dict(color='red', width=2)))

# Add annotation to the red line
fig.add_annotation(dict(x=2008, y=1, xref='x', yref='paper', text='Kiki Vandeweghe', showarrow=True, arrowhead=2, ax=-35, ay=-40, font=dict(color='white')))

# Add a vertical line at the year 1990
fig.add_shape(dict(type='line', x0=1990, y0=0, x1=1990, y1=1, xref='x', yref='paper', line=dict(color='red', width=2)))

# Add annotation to the red line
fig.add_annotation(dict(x=1990, y=1, xref='x', yref='paper', text='Willis Reed', showarrow=True, arrowhead=2, ax=-35, ay=-40, font=dict(color='white')))

# Add a vertical line at the year 2008
fig.add_shape(dict(type='line', x0=1996, y0=0, x1=1996, y1=1, xref='x', yref='paper', line=dict(color='red', width=2)))

# Add annotation to the red line
fig.add_annotation(dict(x=1996, y=1, xref='x', yref='paper', text='John Nash', showarrow=True, arrowhead=2, ax=-35, ay=-40, font=dict(color='white')))


fig.show()

# Here's a timeline of the General Managers (GMs) of the Brooklyn Nets:
# According to https://www.retroseasons.com/teams/brooklyn-nets/history/general-managers/
# 1967-1970: Max Zaslofsky
# 1970-1972: Herb Brown
# 1972-1975: Lou Carnesecca
# 1975-1987: Bob MacKinnon
# 1987-1990: Harry Weltman
# 1990-1996: Willis Reed
# 1996-2000: John Nash
# 2000-2008: Rod Thorn
# 2008-2010: Kiki Vandeweghe
# 2010-2016: Billy King
# 2016-present: Sean Marks

# 1990-1996: Willis Reed
# 1996-2000: John Nash
# 2000-2008: Rod Thorn
# 2008-2010: Kiki Vandeweghe
# 2010-2016: Billy King
# 2016-present: Sean Marks

w_reed = range(1990, 1996)
j_nash = range(1996, 2000)
r_thorn = range(2000, 2008)
k_van = range(2008, 2010)
b_king = range(2010, 2016)
s_marks = range(2016, 2025)


print("Willis Reed, GM from 1990-96:", round(win_loss_counts[win_loss_counts['SEASON'].isin(w_reed)]['W_L_PERC'].mean(),2))
print("John Nash, GM from 1996-00:", round(win_loss_counts[win_loss_counts['SEASON'].isin(j_nash)]['W_L_PERC'].mean(),2))
print("Rod Thorn, GM from 2000-08:", round(win_loss_counts[win_loss_counts['SEASON'].isin(r_thorn)]['W_L_PERC'].mean(),2))
print("Kiki Vandeweghe, GM from 2008-10:", round(win_loss_counts[win_loss_counts['SEASON'].isin(k_van)]['W_L_PERC'].mean(),2))
print("Billy King, GM from 2010-16:", round(win_loss_counts[win_loss_counts['SEASON'].isin(b_king)]['W_L_PERC'].mean(),2))
print("Sean Marks, GM from 2016-Present:", round(win_loss_counts[win_loss_counts['SEASON'].isin(s_marks)]['W_L_PERC'].mean(),2))

# Plotting
plt.figure(figsize=(20, 10))

# Plot TS% means
plt.plot(nets_post_sm.groupby('SEASON')['TS_PERC'].mean(), marker='o', color = "black", linestyle='--', label='TS%')
plt.plot(nets_pre_sm.groupby('SEASON')['TS_PERC'].mean(), marker='o',  color = "black", linestyle='--')


# Plot AST% means
plt.plot(nets_post_sm.groupby('SEASON')['AST_PERC'].mean(), marker='o', linestyle='--', color = "red", label = 'AST%')
plt.plot(nets_pre_sm.groupby('SEASON')['AST_PERC'].mean(), marker='o', linestyle='--', color = "red")

# Plot TOV% means
plt.plot(nets_post_sm.groupby('SEASON')['TOV_PERC'].mean(), marker='o', linestyle='--', color = "blue", label ='TOV%')
plt.plot(nets_pre_sm.groupby('SEASON')['TOV_PERC'].mean(), marker='o', linestyle='--',  color = "blue")


# Adding labels and title
plt.xlabel('Season')
plt.ylabel('Percentage')
plt.title('Nets AVG TS%, AST%, TOV% over Regular Seasons')
plt.legend()
plt.grid(True)
plt.show()

playoff_nets = nets[nets['PLAY_TYPE'] == 4]

# Group by season, WL, and MATCHUP, and count the occurrences
grouped_df = playoff_nets.groupby(['SEASON', 'WL', 'OPP_TEAM_ABBREVIATION']).size().reset_index(name='count')

# Pivot the table to get the desired format
pivot_df = grouped_df.pivot_table(index=['SEASON', 'OPP_TEAM_ABBREVIATION'], columns='WL', values='count', fill_value=0).reset_index()

# Rename the columns for clarity
pivot_df.columns.name = None  # Remove the categories name
pivot_df = pivot_df.rename(columns={'W': 'Wins', 'L': 'Losses'})

# Display the resulting dataframe
pivot_df = pivot_df[['SEASON', 'OPP_TEAM_ABBREVIATION', 'Wins', 'Losses']]

pivot_df