Spaces:
Running
Running
James McCool
commited on
Commit
·
6947c31
1
Parent(s):
8bf3c08
maybe removing .loc helps since it's index based?
Browse files
app.py
CHANGED
@@ -274,7 +274,7 @@ with tab4:
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df.replace("", 0, inplace=True)
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-
player_var = df
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player_var = player_var.reset_index()
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if prop_type_var == 'points':
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@@ -334,8 +334,8 @@ with tab4:
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final_outcomes = players_only[['Player', '10%', 'Mean_Outcome', '90%', 'implied_odds', 'beat_prop']]
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final_outcomes['Bet?'] = np.where(final_outcomes['beat_prop'] - final_outcomes['implied_odds'] >= .10, "Bet", "No Bet")
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-
final_outcomes = final_outcomes
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-
player_outcomes = player_outcomes
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player_outcomes = player_outcomes.drop(columns=['Player']).transpose()
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player_outcomes = player_outcomes.reset_index()
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player_outcomes.columns = ['Instance', 'Outcome']
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@@ -425,10 +425,10 @@ with tab5:
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for prop in sim_vars:
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st.write(prop)
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st.table(prop_df_raw)
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-
prop_df = prop_df_raw
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for books in book_selections:
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prop_df = prop_df
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prop_df = prop_df[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
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prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
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prop_df['Over'] = 1 / prop_df['over_line']
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@@ -511,7 +511,7 @@ with tab5:
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players_only['Book'] = players_only['Player'].map(book_dict)
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players_only['Prop_avg'] = players_only['Prop'].mean() / 100
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players_only['prop_threshold'] = .10
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-
players_only = players_only
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players_only['Over_diff'] = players_only['Over%'] - players_only['Imp Over']
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players_only['Under_diff'] = players_only['Under%'] - players_only['Imp Under']
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players_only['Bet_check'] = np.where(players_only['Over_diff'] > players_only['Under_diff'], players_only['Over_diff'] , players_only['Under_diff'])
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@@ -537,40 +537,40 @@ with tab5:
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prop_df_raw = prop_df_raw.rename(columns={"Full_name": "Player"})
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for books in book_selections:
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-
prop_df = prop_df_raw
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if prop_type_var == "NBA_GAME_PLAYER_POINTS":
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-
prop_df = prop_df
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elif prop_type_var == "Points":
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prop_df = prop_df
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elif prop_type_var == "NBA_GAME_PLAYER_REBOUNDS":
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prop_df = prop_df
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elif prop_type_var == "Rebounds":
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prop_df = prop_df
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elif prop_type_var == "NBA_GAME_PLAYER_ASSISTS":
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-
prop_df = prop_df
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elif prop_type_var == "Assists":
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prop_df = prop_df
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elif prop_type_var == "NBA_GAME_PLAYER_3_POINTERS_MADE":
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prop_df = prop_df
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elif prop_type_var == "3-Pointers Made":
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prop_df = prop_df
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elif prop_type_var == "NBA_GAME_PLAYER_POINTS_REBOUNDS_ASSISTS":
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prop_df = prop_df
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elif prop_type_var == "Points + Assists + Rebounds":
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prop_df = prop_df
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elif prop_type_var == "NBA_GAME_PLAYER_POINTS_REBOUNDS":
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prop_df = prop_df
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elif prop_type_var == "Points + Rebounds":
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prop_df = prop_df
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elif prop_type_var == "NBA_GAME_PLAYER_POINTS_ASSISTS":
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prop_df = prop_df
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elif prop_type_var == "Points + Assists":
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-
prop_df = prop_df
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elif prop_type_var == "NBA_GAME_PLAYER_REBOUNDS_ASSISTS":
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-
prop_df = prop_df
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elif prop_type_var == "Assists + Rebounds":
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-
prop_df = prop_df
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prop_df = prop_df[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
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prop_df = prop_df.rename(columns={"over_prop": "Prop"})
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@@ -650,7 +650,7 @@ with tab5:
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players_only['Prop'] = players_only['Player'].map(prop_dict)
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players_only['Prop_avg'] = players_only['Prop'].mean() / 100
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players_only['prop_threshold'] = .10
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-
players_only = players_only
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players_only['Over_diff'] = players_only['Over%'] - players_only['Imp Over']
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players_only['Under_diff'] = players_only['Under%'] - players_only['Imp Under']
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players_only['Bet_check'] = np.where(players_only['Over_diff'] > players_only['Under_diff'], players_only['Over_diff'] , players_only['Under_diff'])
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df.replace("", 0, inplace=True)
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+
player_var = df[df['Player'] == player_check]
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player_var = player_var.reset_index()
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if prop_type_var == 'points':
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final_outcomes = players_only[['Player', '10%', 'Mean_Outcome', '90%', 'implied_odds', 'beat_prop']]
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final_outcomes['Bet?'] = np.where(final_outcomes['beat_prop'] - final_outcomes['implied_odds'] >= .10, "Bet", "No Bet")
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final_outcomes = final_outcomes[final_outcomes['Player'] == player_check]
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+
player_outcomes = player_outcomes[player_outcomes['Player'] == player_check]
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player_outcomes = player_outcomes.drop(columns=['Player']).transpose()
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player_outcomes = player_outcomes.reset_index()
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player_outcomes.columns = ['Instance', 'Outcome']
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for prop in sim_vars:
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st.write(prop)
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st.table(prop_df_raw)
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prop_df = prop_df_raw[prop_df_raw['prop_type'] == prop]
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for books in book_selections:
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prop_df = prop_df[prop_df['book'] == books]
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prop_df = prop_df[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
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prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)
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prop_df['Over'] = 1 / prop_df['over_line']
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players_only['Book'] = players_only['Player'].map(book_dict)
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players_only['Prop_avg'] = players_only['Prop'].mean() / 100
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players_only['prop_threshold'] = .10
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+
players_only = players_only[players_only['Mean_Outcome'] > 0]
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players_only['Over_diff'] = players_only['Over%'] - players_only['Imp Over']
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players_only['Under_diff'] = players_only['Under%'] - players_only['Imp Under']
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players_only['Bet_check'] = np.where(players_only['Over_diff'] > players_only['Under_diff'], players_only['Over_diff'] , players_only['Under_diff'])
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prop_df_raw = prop_df_raw.rename(columns={"Full_name": "Player"})
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for books in book_selections:
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prop_df = prop_df_raw[prop_df_raw['book'] == books]
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if prop_type_var == "NBA_GAME_PLAYER_POINTS":
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prop_df = prop_df[prop_df['prop_type'] == 'NBA_GAME_PLAYER_POINTS']
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elif prop_type_var == "Points":
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prop_df = prop_df[prop_df['prop_type'] == 'Points']
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elif prop_type_var == "NBA_GAME_PLAYER_REBOUNDS":
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prop_df = prop_df[prop_df['prop_type'] == 'NBA_GAME_PLAYER_REBOUNDS']
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elif prop_type_var == "Rebounds":
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prop_df = prop_df[prop_df['prop_type'] == 'Rebounds']
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elif prop_type_var == "NBA_GAME_PLAYER_ASSISTS":
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prop_df = prop_df[prop_df['prop_type'] == 'NBA_GAME_PLAYER_ASSISTS']
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elif prop_type_var == "Assists":
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prop_df = prop_df[prop_df['prop_type'] == 'Assists']
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elif prop_type_var == "NBA_GAME_PLAYER_3_POINTERS_MADE":
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prop_df = prop_df[prop_df['prop_type'] == 'NBA_GAME_PLAYER_3_POINTERS_MADE']
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elif prop_type_var == "3-Pointers Made":
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prop_df = prop_df[prop_df['prop_type'] == '3-Pointers Made']
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elif prop_type_var == "NBA_GAME_PLAYER_POINTS_REBOUNDS_ASSISTS":
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prop_df = prop_df[prop_df['prop_type'] == 'NBA_GAME_PLAYER_POINTS_REBOUNDS_ASSISTS']
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elif prop_type_var == "Points + Assists + Rebounds":
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prop_df = prop_df[prop_df['prop_type'] == 'Points + Assists + Rebounds']
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elif prop_type_var == "NBA_GAME_PLAYER_POINTS_REBOUNDS":
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prop_df = prop_df[prop_df['prop_type'] == 'NBA_GAME_PLAYER_POINTS_REBOUNDS']
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elif prop_type_var == "Points + Rebounds":
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prop_df = prop_df[prop_df['prop_type'] == 'Points + Rebounds']
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elif prop_type_var == "NBA_GAME_PLAYER_POINTS_ASSISTS":
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prop_df = prop_df[prop_df['prop_type'] == 'NBA_GAME_PLAYER_POINTS_ASSISTS']
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elif prop_type_var == "Points + Assists":
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prop_df = prop_df[prop_df['prop_type'] == 'Points + Assists']
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elif prop_type_var == "NBA_GAME_PLAYER_REBOUNDS_ASSISTS":
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prop_df = prop_df[prop_df['prop_type'] == 'NBA_GAME_PLAYER_REBOUNDS_ASSISTS']
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elif prop_type_var == "Assists + Rebounds":
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prop_df = prop_df[prop_df['prop_type'] == 'Assists + Rebounds']
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prop_df = prop_df[['Player', 'book', 'over_prop', 'over_line', 'under_line', 'prop_type']]
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prop_df = prop_df.rename(columns={"over_prop": "Prop"})
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players_only['Prop'] = players_only['Player'].map(prop_dict)
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players_only['Prop_avg'] = players_only['Prop'].mean() / 100
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players_only['prop_threshold'] = .10
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+
players_only = players_only[players_only['Mean_Outcome'] > 0]
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players_only['Over_diff'] = players_only['Over%'] - players_only['Imp Over']
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players_only['Under_diff'] = players_only['Under%'] - players_only['Imp Under']
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players_only['Bet_check'] = np.where(players_only['Over_diff'] > players_only['Under_diff'], players_only['Over_diff'] , players_only['Under_diff'])
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