Initial commit and modernize

.streamlit/secrets.toml

@@ -0,0 +1 @@
+mongo_uri = "mongodb+srv://multichem:Xr1q5wZdXPbxdUmJ@testcluster.lgwtp5i.mongodb.net/?retryWrites=true&w=majority&appName=TestCluster"

Dockerfile

@@ -5,11 +5,23 @@ WORKDIR /app
 RUN apt-get update && apt-get install -y \
     build-essential \
     curl \
+    software-properties-common \
     git \
     && rm -rf /var/lib/apt/lists/*
 
 COPY requirements.txt ./
 COPY src/ ./src/
+COPY .streamlit/ ./.streamlit/
+
+
+ENV MONGO_URI="mongodb+srv://multichem:Xr1q5wZdXPbxdUmJ@testcluster.lgwtp5i.mongodb.net/?retryWrites=true&w=majority&appName=TestCluster"
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user\
+	PATH=/home/user/.local/bin:$PATH
+WORKDIR $HOME/app
+RUN pip install --no-cache-dir --upgrade pip
+COPY --chown=user . $HOME/app
 
 RUN pip3 install -r requirements.txt
 
@@ -17,4 +29,4 @@ EXPOSE 8501
 
 HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
 
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+ENTRYPOINT ["streamlit", "run", "src/application.py", "--server.port=8501", "--server.address=0.0.0.0"]

requirements.txt

@@ -1,3 +1,7 @@
-altair
-pandas
-streamlit
+streamlit
+matplotlib
+pymongo
+pulp
+docker
+plotly
+scipy

src/database.py

@@ -0,0 +1,14 @@
+import streamlit as st
+from pymongo import MongoClient
+
+@st.cache_resource
+def init_conn():
+
+        uri = st.secrets['mongo_uri']
+        client = MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=100000)
+        dfs_db = client["NCAAF_Database"]
+        props_db = client["Props_DB"]
+
+        return props_db, dfs_db
+    
+props_db, dfs_db = init_conn()

src/streamlit_app.py

@@ -1,40 +1,123 @@
-import altair as alt
+import streamlit as st
 import numpy as np
 import pandas as pd
-import streamlit as st
+from database import props_db, dfs_db
+st.set_page_config(layout="wide")
+
+game_format = {'Win%': '{:.2%}', 'Vegas': '{:.2%}', 'Win% Diff': '{:.2%}'}
+american_format = {'First Inning Lead Percentage': '{:.2%}', 'Fifth Inning Lead Percentage': '{:.2%}'}
+
+@st.cache_resource(ttl=600)
+def init_baselines():
+    collection = dfs_db["NCAAF_GameModel"] 
+    cursor = collection.find()
+    raw_display = pd.DataFrame(list(cursor))
+    game_model = raw_display[['Team', 'Opp', 'Win%', 'Vegas', 'Win% Diff', 'Win Line', 'Vegas Line', 'Line Diff', 'PD Spread', 'Vegas Spread', 'Spread Diff', 'O/U']]
+    game_model = game_model.replace('', np.nan)
+    game_model = game_model.sort_values(by='O/U', ascending=False)
+    game_model.loc[:, ~game_model.columns.isin(['Team', 'Opp'])] = game_model.loc[:, ~game_model.columns.isin(['Team', 'Opp'])].apply(pd.to_numeric)
+
+    collection = props_db["NCAAF_Props"] 
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+    market_props = raw_display[['Name', 'Position', 'Projection', 'PropType', 'OddsType', 'over_pay', 'under_pay']]
+    market_props['over_prop'] = market_props['Projection']
+    market_props['over_line'] = market_props['over_pay'].apply(lambda x: (x - 1) * 100 if x >= 2.0 else -100 / (x - 1))
+    market_props['under_prop'] = market_props['Projection']
+    market_props['under_line'] = market_props['under_pay'].apply(lambda x: (x - 1) * 100 if x >= 2.0 else -100 / (x - 1))
+
+    return game_model, market_props
+
+def convert_df_to_csv(df):
+    return df.to_csv().encode('utf-8')
+
+def calculate_no_vig(row):
+    def implied_probability(american_odds):
+        if american_odds < 0:
+            return (-american_odds) / ((-american_odds) + 100)
+        else:
+            return 100 / (american_odds + 100)
+
+    over_line = row['over_line']
+    under_line = row['under_line']
+    over_prop = row['over_prop']
+    
+    over_prob = implied_probability(over_line)
+    under_prob = implied_probability(under_line)
+    
+    total_prob = over_prob + under_prob
+    no_vig_prob = (over_prob / total_prob + 0.5) * over_prop
+    
+    return no_vig_prob
+
+prop_table_options = ['NCAAF_GAME_PLAYER_PASSING_ATTEMPTS', 'NCAAF_GAME_PLAYER_PASSING_COMPLETIONS', 'NCAAF_GAME_PLAYER_PASSING_INTERCEPTIONS',
+                'NCAAF_GAME_PLAYER_PASSING_RUSHING_YARDS', 'NCAAF_GAME_PLAYER_PASSING_TOUCHDOWNS', 'NCAAF_GAME_PLAYER_PASSING_YARDS',
+                'NCAAF_GAME_PLAYER_RECEIVING_RECEPTIONS', 'NCAAF_GAME_PLAYER_RECEIVING_TOUCHDOWNS', 'NCAAF_GAME_PLAYER_RECEIVING_YARDS',
+                'NCAAF_GAME_PLAYER_RUSHING_ATTEMPTS', 'NCAAF_GAME_PLAYER_RUSHING_RECEIVING_YARDS', 'NCAAF_GAME_PLAYER_RUSHING_TOUCHDOWNS',
+                'NCAAF_GAME_PLAYER_RUSHING_YARDS', 'NCAAF_GAME_PLAYER_SCORE_TOUCHDOWN']
+prop_format = {'L3 Success': '{:.2%}', 'L6_Success': '{:.2%}', 'L10_success': '{:.2%}', 'Trending Over': '{:.2%}', 'Trending Under': '{:.2%}',
+               'Implied Over': '{:.2%}', 'Implied Under': '{:.2%}', 'Over Edge': '{:.2%}', 'Under Edge': '{:.2%}'}
+
+game_model, market_props = init_baselines()
+
+tab1, tab2 = st.tabs(["Game Model", "Prop Market"])
+
+with tab1:
+    if st.button("Reset Data", key='reset1'):
+              st.cache_data.clear()
+              game_model, market_props = init_baselines()
+    line_var1 = st.radio('How would you like to display odds?', options = ['Percentage', 'American'], key='line_var1')
+    team_frame = game_model
+    if line_var1 == 'Percentage':
+        team_frame = team_frame[['Team', 'Opp', 'Win%', 'Vegas', 'Win% Diff', 'PD Spread', 'Vegas Spread', 'Spread Diff']]
+        team_frame = team_frame.set_index('Team')
+        try:
+            st.dataframe(team_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['PD Spread', 'Vegas Spread', 'Spread Diff']).format(game_format, precision=2), use_container_width = True)
+        except:
+            st.dataframe(team_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['PD Spread', 'Vegas Spread']).format(precision=2), use_container_width = True)
+    if line_var1 == 'American':
+        team_frame = team_frame[['Team', 'Opp', 'Win Line', 'Vegas Line', 'Line Diff', 'PD Spread', 'Vegas Spread', 'Spread Diff']]
+        team_frame = team_frame.set_index('Team')
+        st.dataframe(team_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn_r').format(precision=2), height = 1000, use_container_width = True)
+    
+    st.download_button(
+        label="Export Team Model",
+        data=convert_df_to_csv(team_frame),
+        file_name='NCAAF_team_betting_export.csv',
+        mime='text/csv',
+        key='team_export',
+    )
+
+with tab2:
+    if st.button("Reset Data", key='reset4'):
+                st.cache_data.clear()
+                game_model, market_props = init_baselines()
+    market_type = st.selectbox('Select type of prop are you wanting to view', options = prop_table_options, key = 'market_type_key')
+    disp_market = market_props.copy()
+    disp_market = disp_market[disp_market['PropType'] == market_type]
+    disp_market['No_Vig_Prop'] = disp_market.apply(calculate_no_vig, axis=1)
+    fanduel_frame = disp_market[disp_market['OddsType'] == 'FANDUEL']
+    fanduel_dict = dict(zip(fanduel_frame['Name'], fanduel_frame['No_Vig_Prop']))
+    draftkings_frame = disp_market[disp_market['OddsType'] == 'DRAFTKINGS']
+    draftkings_dict = dict(zip(draftkings_frame['Name'], draftkings_frame['No_Vig_Prop']))
+    mgm_frame = disp_market[disp_market['OddsType'] == 'MGM']
+    mgm_dict = dict(zip(mgm_frame['Name'], mgm_frame['No_Vig_Prop']))
+    bet365_frame = disp_market[disp_market['OddsType'] == 'BET_365']
+    bet365_dict = dict(zip(bet365_frame['Name'], bet365_frame['No_Vig_Prop']))
+
+    disp_market['FANDUEL'] = disp_market['Name'].map(fanduel_dict)
+    disp_market['DRAFTKINGS'] = disp_market['Name'].map(draftkings_dict)
+    disp_market['MGM'] = disp_market['Name'].map(mgm_dict)
+    disp_market['BET365'] = disp_market['Name'].map(bet365_dict)
+
+    disp_market = disp_market[['Name', 'Position','FANDUEL', 'DRAFTKINGS', 'MGM', 'BET365']]
+    disp_market = disp_market.drop_duplicates(subset=['Name'], keep='first', ignore_index=True)
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+    st.dataframe(disp_market.style.background_gradient(axis=1, subset=['FANDUEL', 'DRAFTKINGS', 'MGM', 'BET365'], cmap='RdYlGn').format(prop_format, precision=2), height = 1000, use_container_width = True)
+    st.download_button(
+        label="Export Market Props",
+        data=convert_df_to_csv(disp_market),
+        file_name='NCAAF_market_props_export.csv',
+        mime='text/csv',
+    )