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Update app.py
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app.py
CHANGED
@@ -2,6 +2,7 @@ import streamlit as st
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import numpy as np
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import pandas as pd
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import gspread
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st.set_page_config(layout="wide")
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@st.cache_resource
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@@ -62,8 +63,7 @@ non_qb_stats = overall_stats.loc[overall_stats['Position'] != 'QB']
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team_dict = dict(zip(prop_frame['Player'], prop_frame['Team']))
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t_stamp = f"Last Update: " + str(timestamp) + f" CST"
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all_sim_vars = ['pass_yards', 'rush_yards', 'rec_yards', 'receptions', 'rush_attempts'
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'pass_attempts', 'pass_completions']
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sim_all_hold = pd.DataFrame(columns=['Player', 'Team', 'Prop type', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over%', 'Imp Under', 'Under%', 'Bet?', 'Edge'])
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tab1, tab2, tab3, tab4, tab5 = st.tabs(["Game Betting Model", "QB Projections", "RB/WR/TE Projections", "Player Prop Simulations", "Stat Specific Simulations"])
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@@ -316,8 +316,7 @@ with tab5:
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export_container = st.empty()
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with col1:
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prop_type_var = st.selectbox('Select prop category', options = ['All Props', 'pass_yards', 'rush_yards', 'rec_yards', 'receptions', 'rush_attempts'
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'pass_attempts', 'pass_completions'])
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if st.button('Simulate Prop Category'):
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with col2:
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@@ -351,7 +350,11 @@ with tab5:
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elif prop_type_var == "rec_yards":
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df['Median'] = df['rec_yards']
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elif prop_type_var == "receptions":
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df['Median'] = df['
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flex_file = df
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flex_file['Floor'] = flex_file['Median'] * .20
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@@ -488,7 +491,7 @@ with tab5:
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over_dict = dict(zip(df.Player, df.Over))
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under_dict = dict(zip(df.Player, df.Under))
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total_sims =
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df.replace("", 0, inplace=True)
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@@ -499,7 +502,11 @@ with tab5:
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elif prop_type_var == "rec_yards":
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df['Median'] = df['rec_yards']
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elif prop_type_var == "receptions":
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df['Median'] = df['
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flex_file = df
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flex_file['Floor'] = flex_file['Median'] * .20
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import numpy as np
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import pandas as pd
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import gspread
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import plotly_express as px
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st.set_page_config(layout="wide")
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@st.cache_resource
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team_dict = dict(zip(prop_frame['Player'], prop_frame['Team']))
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t_stamp = f"Last Update: " + str(timestamp) + f" CST"
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all_sim_vars = ['pass_yards', 'rush_yards', 'rec_yards', 'receptions', 'rush_attempts']
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sim_all_hold = pd.DataFrame(columns=['Player', 'Team', 'Prop type', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over%', 'Imp Under', 'Under%', 'Bet?', 'Edge'])
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tab1, tab2, tab3, tab4, tab5 = st.tabs(["Game Betting Model", "QB Projections", "RB/WR/TE Projections", "Player Prop Simulations", "Stat Specific Simulations"])
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export_container = st.empty()
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with col1:
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prop_type_var = st.selectbox('Select prop category', options = ['All Props', 'pass_yards', 'rush_yards', 'rec_yards', 'receptions', 'rush_attempts'])
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if st.button('Simulate Prop Category'):
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with col2:
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elif prop_type_var == "rec_yards":
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df['Median'] = df['rec_yards']
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elif prop_type_var == "receptions":
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df['Median'] = df['rec']
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elif prop_type_var == "receptions":
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df['Median'] = df['rec']
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elif prop_type_var == "rush_attempts":
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df['Median'] = df['rush_att']
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flex_file = df
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flex_file['Floor'] = flex_file['Median'] * .20
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over_dict = dict(zip(df.Player, df.Over))
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under_dict = dict(zip(df.Player, df.Under))
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total_sims = 5000
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df.replace("", 0, inplace=True)
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elif prop_type_var == "rec_yards":
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df['Median'] = df['rec_yards']
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elif prop_type_var == "receptions":
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df['Median'] = df['rec']
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elif prop_type_var == "receptions":
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df['Median'] = df['rec']
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elif prop_type_var == "rush_attempts":
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df['Median'] = df['rush_att']
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flex_file = df
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flex_file['Floor'] = flex_file['Median'] * .20
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