Add showdown slate handling and dynamic database integration

- Introduced new showdown selections for DraftKings and FanDuel.
- Implemented dynamic database collection retrieval for showdown seed frames.
- Updated functions to utilize translation dictionaries for slate names.
- Enhanced data loading and state management for showdown contest simulations.

src/sim_func_hold/showdown_functions.py

@@ -5,14 +5,9 @@ from pymongo import MongoClient
 from database import db
 
 @st.cache_data(ttl = 599)
-def init_DK_SD_seed_frames(slate, split):
-    if slate == 'Showdown #1':
-        collection = db["DK_NFL_SD_seed_frame"] 
-    elif slate == 'Showdown #2':
-        collection = db["DK_NFL_Secondary_SD_seed_frame"] 
-    elif slate == 'Showdown #3':
-        collection = db["DK_NFL_Auxiliary_SD_seed_frame"] 
-
+def init_DK_SD_seed_frames(slate, split, translation_dict):
+    # Now dynamic
+    collection = db[translation_dict[slate]]
     cursor = collection.find().limit(split)
 
     raw_display = pd.DataFrame(list(cursor))
@@ -22,14 +17,9 @@ def init_DK_SD_seed_frames(slate, split):
     return DK_seed
 
 @st.cache_data(ttl = 599)
-def init_FD_SD_seed_frames(slate, split):
-    if slate == 'Showdown #1':
-        collection = db[f"FD_NFL_SD_seed_frame"] 
-    elif slate == 'Showdown #2':
-        collection = db[f"FD_NFL_Secondary_SD_seed_frame"] 
-    elif slate == 'Showdown #3':
-        collection = db[f"FD_NFL_Auxiliary_SD_seed_frame"] 
-
+def init_FD_SD_seed_frames(slate, split, translation_dict):
+    # Now dynamic
+    collection = db[translation_dict[slate]]
     cursor = collection.find().limit(split)
 
     raw_display = pd.DataFrame(list(cursor))
@@ -44,7 +34,6 @@ def init_SD_baselines(slate_var):
     cursor = collection.find()
 
     raw_display = pd.DataFrame(list(cursor))
-    raw_display = raw_display[raw_display['slate'] == slate_var]
     raw_display = raw_display[raw_display['version'] == 'overall']
     raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%',
                                 'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']]
@@ -63,7 +52,6 @@ def init_SD_baselines(slate_var):
     cursor = collection.find()
 
     raw_display = pd.DataFrame(list(cursor))
-    raw_display = raw_display[raw_display['slate'] == slate_var]
     raw_display = raw_display[raw_display['version'] == 'overall']
     raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%',
                                 'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']]

src/streamlit_app.py

@@ -20,6 +20,27 @@ fd_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'sal
 dk_sd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
 fd_sd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
 
+showdown_selections = ['Showdown #1', 'Showdown #2', 'Showdown #3', 'Showdown #4', 'Showdown #5', 'Showdown #6', 'Showdown #7', 'Showdown #8', 'Showdown #9', 'Showdown #10', 'Showdown #11', 'Showdown #12', 'Showdown #13', 'Showdown #14', 'Showdown #15']
+dk_db_showdown_selections = ['DK_NFL_SD_seed_frame_Showdown #1', 'DK_NFL_SD_seed_frame_Showdown #2', 'DK_NFL_SD_seed_frame_Showdown #3', 'DK_NFL_SD_seed_frame_Showdown #4', 'DK_NFL_SD_seed_frame_Showdown #5', 'DK_NFL_SD_seed_frame_Showdown #6',
+'DK_NFL_SD_seed_frame_Showdown #7', 'DK_NFL_SD_seed_frame_Showdown #8', 'DK_NFL_SD_seed_frame_Showdown #9', 'DK_NFL_SD_seed_frame_Showdown #10', 'DK_NFL_SD_seed_frame_Showdown #11', 'DK_NFL_SD_seed_frame_Showdown #12', 'DK_NFL_SD_seed_frame_Showdown #13',
+'DK_NFL_SD_seed_frame_Showdown #14', 'DK_NFL_SD_seed_frame_Showdown #15']
+fd_db_showdown_selections = ['FD_NFL_SD_seed_frame_Showdown #1', 'FD_NFL_SD_seed_frame_Showdown #2', 'FD_NFL_SD_seed_frame_Showdown #3', 'FD_NFL_SD_seed_frame_Showdown #4', 'FD_NFL_SD_seed_frame_Showdown #5', 'FD_NFL_SD_seed_frame_Showdown #6',
+'FD_NFL_SD_seed_frame_Showdown #7', 'FD_NFL_SD_seed_frame_Showdown #8', 'FD_NFL_SD_seed_frame_Showdown #9', 'FD_NFL_SD_seed_frame_Showdown #10', 'FD_NFL_SD_seed_frame_Showdown #11', 'FD_NFL_SD_seed_frame_Showdown #12', 'FD_NFL_SD_seed_frame_Showdown #13',
+'FD_NFL_SD_seed_frame_Showdown #14', 'FD_NFL_SD_seed_frame_Showdown #15']
+
+dk_showdown_db_translation = dict(zip(showdown_selections, dk_db_showdown_selections))
+fd_showdown_db_translation = dict(zip(showdown_selections, fd_db_showdown_selections))
+
+# Probably should have done this in a dictionary to start with
+wrong_team_names = ['Denver Broncos', 'Washington Commanders', 'Cincinnati Bengals', 'Arizona Cardinals', 'Los Angeles Rams', 'Pittsburgh Steelers',
+'Jacksonville Jaguars', 'New England Patriots', 'Tampa Bay Buccaneers', 'San Francisco 49ers', 'Green Bay Packers', 'New York Jets',
+'Indianapolis Colts', 'Miami Dolphins', 'Detroit Lions', 'Las Vegas Raiders', 'Atlanta Falcons', 'Seattle Seahawks', 'Houston Texans',
+'New Orleans Saints', 'Carolina Panthers', 'New York Giants', 'Cleveland Browns', 'Tennessee Titans', 'Philadelphia Eagles', 'Dallas Cowboys',
+'Kansas City Chiefs', 'Los Angeles Chargers', 'Baltimore Ravens', 'Buffalo Bills', 'Minnesota Vikings', 'Chicago Bears']
+right_name_teams = ['Broncos', 'Commanders', 'Bengals', 'Cardinals', 'Rams', 'Steelers', 'Jaguars', 'Patriots', 'Buccaneers', '49ers', 'Packers', 
+'Jets', 'Colts', 'Dolphins', 'Lions', 'Raiders', 'Falcons', 'Seahawks', 'Texans', 'Saints', 'Panthers', 'Giants', 'Browns', 'Titans', 'Eagles', 'Cowboys',
+'Chiefs', 'Chargers', 'Ravens', 'Bills', 'Vikings', 'Bears']
+
 st.markdown("""
 <style>
     /* Tab styling */
@@ -57,16 +78,55 @@ st.markdown("""
 
 </style>""", unsafe_allow_html=True)
 
+@st.cache_resource(ttl=60)
+def define_dk_showdown_slates():
+    collection = db["DK_SD_NFL_ROO"]
+    cursor = collection.find()
+    raw_display = pd.DataFrame(list(cursor))
+    unique_slates = raw_display['slate'].unique()
+
+    slate_names = []
+
+    for slate in unique_slates:
+        slate_data = raw_display[raw_display['slate'] == slate]
+        slate_name = slate_data.iloc[0]['Team'] + ' vs. ' + slate_data.iloc[0]['Opp']
+        slate_names.append(slate_name)
+    
+    slate_name_lookup = dict(zip(slate_names, unique_slates))
+    return slate_names, slate_name_lookup
+
+@st.cache_resource(ttl=60)
+def define_fd_showdown_slates():
+    collection = db["FD_SD_NFL_ROO"]
+    cursor = collection.find()
+    raw_display = pd.DataFrame(list(cursor))
+    unique_slates = raw_display['slate'].unique()
+
+    slate_names = []
+
+    for slate in unique_slates:
+        slate_data = raw_display[raw_display['slate'] == slate]
+        slate_name = slate_data.iloc[0]['Team'] + ' vs. ' + slate_data.iloc[0]['Opp']
+        slate_names.append(slate_name)
+    
+    slate_name_lookup = dict(zip(slate_names, unique_slates))
+    return slate_names, slate_name_lookup
+
+slate_names_dk, slate_name_lookup_dk = define_dk_showdown_slates()
+slate_names_fd, slate_name_lookup_fd = define_fd_showdown_slates()
+
 if st.button("Load/Reset Data", key='reset2'):
     st.cache_data.clear()
     for key in st.session_state.keys():
         del st.session_state[key]
+    slate_names_dk, slate_name_lookup_dk = define_dk_showdown_slates()
+    slate_names_fd, slate_name_lookup_fd = define_fd_showdown_slates()
     DK_seed = init_DK_seed_frames('Main Slate', 10000)
-    DK_sd_seed = init_DK_SD_seed_frames('Main Slate', 10000)
+    DK_sd_seed = init_DK_SD_seed_frames("Showdown #1", 10000, dk_showdown_db_translation)
     FD_seed = init_FD_seed_frames('Main Slate', 10000)
-    FD_sd_seed = init_FD_SD_seed_frames('Main Slate', 10000)
+    FD_sd_seed = init_FD_SD_seed_frames("Showdown #1", 10000, fd_showdown_db_translation)
     dk_raw, fd_raw = init_baselines('Main Slate')
-    dk_sd_raw, fd_sd_raw = init_SD_baselines('Main Slate')
+    dk_sd_raw, fd_sd_raw = init_SD_baselines('Showdown #1')
     dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_ID))
     dk_sd_id_dict = dict(zip(dk_sd_raw.Player, dk_sd_raw.player_ID))
     fd_id_dict = dict(zip(fd_raw.Player, fd_raw.player_ID))
@@ -548,8 +608,8 @@ if selected_tab == "Regular Slate Contest Sims":
                 )
 
 if selected_tab == "Showdown Contest Sims":
-    dk_raw, fd_raw = init_SD_baselines('Showdown #1')
-    raw_baselines = dk_raw
+    dk_sd_raw, fd_sd_raw = init_SD_baselines('Showdown #1')
+    raw_baselines = dk_sd_raw
     column_names = dk_sd_columns
     with st.expander("Info and Filters"):
         site_data_col, slate_data_col, contest_size_col, contest_sharpness_col = st.columns([1, 1, 1, 1])
@@ -557,7 +617,7 @@ if selected_tab == "Showdown Contest Sims":
             sim_site_var2 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='sim_site_var2')
 
         with slate_data_col:
-            sim_slate_var2 = st.radio("Which data are you loading?", ('Showdown #1', 'Showdown #2', 'Showdown #3'), key='sim_slate_var2')
+            sim_slate_var2 = st.radio("Which data are you loading?", slate_names_dk if sim_site_var2 == 'Draftkings' else slate_names_fd, key='sim_slate_var2')
             
         with contest_size_col:
             contest_var2 = st.selectbox("What contest size are you simulating?", ('Small', 'Medium', 'Large'), key='contest_var2')
@@ -586,14 +646,14 @@ if selected_tab == "Showdown Contest Sims":
             
         if 'sd_working_seed' not in st.session_state:
             if sim_site_var2 == 'Draftkings':
-                st.session_state.sd_working_seed = init_DK_SD_seed_frames(sim_slate_var2, sharp_split)
-                export_id_dict = dict(zip(dk_raw.Player, dk_raw.player_ID)) 
-                raw_baselines = dk_raw
+                st.session_state.sd_working_seed = init_DK_SD_seed_frames(slate_name_lookup_dk[sim_slate_var2], sharp_split, dk_showdown_db_translation)
+                export_id_dict = dict(zip(dk_sd_raw.Player, dk_sd_raw.player_ID)) 
+                raw_baselines = dk_sd_raw
                 column_names = dk_sd_columns
             elif sim_site_var2 == 'Fanduel':
-                st.session_state.sd_working_seed = init_FD_SD_seed_frames(sim_slate_var2, sharp_split)
-                export_id_dict = dict(zip(fd_raw.Player, fd_raw.player_ID))
-                raw_baselines = fd_raw
+                st.session_state.sd_working_seed = init_FD_SD_seed_frames(slate_name_lookup_fd[sim_slate_var2], sharp_split, fd_showdown_db_translation)
+                export_id_dict = dict(zip(fd_sd_raw.Player, fd_sd_raw.player_ID))
+                raw_baselines = fd_sd_raw
                 column_names = fd_sd_columns
             maps_dict = {
                     'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)),
@@ -646,7 +706,7 @@ if selected_tab == "Showdown Contest Sims":
                     'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev)),
                     'cpt_STDev_map':dict(zip(raw_baselines.Player,raw_baselines['CPT_STDev']))
                     }
-            Sim_Winners = sim_SD_contest(1000, st.session_state.working_seed, maps_dict, Contest_Size)
+            Sim_Winners = sim_SD_contest(1000, st.session_state.sd_working_seed, maps_dict, Contest_Size)
             Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners))
             
             #st.table(Sim_Winner_Frame)
@@ -677,7 +737,7 @@ if selected_tab == "Showdown Contest Sims":
             freq_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:6].values, return_counts=True)),
                                             columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
         elif sim_site_var2 == 'Fanduel':
-            freq_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:5].values, return_counts=True)),
+            freq_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:6].values, return_counts=True)),
                                             columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
         freq_working['Freq'] = freq_working['Freq'].astype(int)
         freq_working['Position'] = freq_working['Player'].map(maps_dict['Pos_map'])