Spaces:
Running
Running
James McCool
commited on
Commit
·
53152e8
1
Parent(s):
9143c92
Removed custom ROO, adjusted some object names, added a TLL to data
Browse files
app.py
CHANGED
@@ -35,7 +35,7 @@ gspreadcon = init_conn()
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dk_player_url = 'https://docs.google.com/spreadsheets/d/1Yq0vGriWK-bS79e-bD6_u9pqrYE6Yrlbb_wEkmH-ot0/edit#gid=172632260'
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@st.cache_data
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def load_overall_stats():
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sh = gspreadcon.open_by_url(dk_player_url)
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worksheet = sh.worksheet('DK_Build_Up')
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@@ -83,8 +83,7 @@ def load_overall_stats():
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raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
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roo_raw = raw_display.sort_values(by='Median', ascending=False)
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-
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timestamp = worksheet.acell('A1').value
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return dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, timestamp
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@@ -94,25 +93,9 @@ def convert_df_to_csv(df):
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dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, timestamp = load_overall_stats()
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t_stamp = f"Last Update: " + str(timestamp) + f" CST"
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tab1, tab2, tab3 = st.tabs(['
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with tab1:
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st.info("The Projections file can have any columns in any order, but must contain columns explicitly named: 'Player', 'Salary', 'Position', 'Team', 'Opp', 'Minutes', 'Median', 'Own'.")
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col1, col2 = st.columns([1, 5])
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with col1:
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proj_file = st.file_uploader("Upload Projections File", key = 'proj_uploader')
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if proj_file is not None:
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try:
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proj_dataframe = pd.read_csv(proj_file)
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except:
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proj_dataframe = pd.read_excel(proj_file)
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with col2:
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if proj_file is not None:
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st.dataframe(proj_dataframe.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
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with tab2:
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col1, col2 = st.columns([1, 9])
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@@ -126,15 +109,15 @@ with tab2:
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del st.session_state[key]
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site_var2 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var2')
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if site_var2 == 'Draftkings':
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site_baselines = roo_raw[roo_raw['
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elif site_var2 == 'Fanduel':
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site_baselines = roo_raw[roo_raw['
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slate_split = st.radio("Are you viewing the main slate or the secondary slate?", ('Main Slate', 'Secondary'), key='slate_split')
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if slate_split == 'Main Slate':
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raw_baselines = site_baselines[site_baselines['
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elif slate_split == 'Secondary':
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raw_baselines = site_baselines[site_baselines['
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split_var2 = st.radio("Are you running the full slate or
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if split_var2 == 'Specific Games':
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team_var2 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var2')
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elif split_var2 == 'Full Slate Run':
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@@ -155,7 +138,7 @@ with tab2:
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st.session_state.display_proj = st.session_state.display_proj
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elif pos_var2 != 'All':
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st.session_state.display_proj = st.session_state.display_proj[st.session_state.display_proj['Position'].str.contains(pos_var2)]
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st.dataframe(st.session_state.display_proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
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with display_dl_container_1:
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display_dl_container = st.empty()
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@@ -167,147 +150,18 @@ with tab2:
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mime='text/csv',
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)
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with
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col1, col2 = st.columns([1,
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with col1:
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st.
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slate_var1 = st.radio("Which data are you loading?", ('Paydirt', 'User'), key='slate_var1')
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site_var1 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var1')
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slate_split2 = st.radio("Are you viewing the main slate or the secondary slate?", ('Main Slate', 'Secondary'), key='slate_split2')
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if site_var1 == 'Draftkings':
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if slate_var1 == 'User':
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raw_baselines = proj_dataframe
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elif slate_var1 != 'User':
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if slate_split2 == 'Main Slate':
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raw_baselines = dk_raw
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elif slate_split2 == 'Secondary':
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raw_baselines = dk_raw_sec
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elif site_var1 == 'Fanduel':
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if slate_var1 == 'User':
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raw_baselines = proj_dataframe
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elif slate_var1 != 'User':
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if slate_split2 == 'Main Slate':
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raw_baselines = fd_raw
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elif slate_split2 == 'Secondary':
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raw_baselines = fd_raw_sec
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split_var1 = st.radio("Are you running the full slate or crtain games?", ('Full Slate Run', 'Specific Games'), key='split_var1')
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if split_var1 == 'Specific Games':
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team_var1 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var1')
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elif split_var1 == 'Full Slate Run':
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team_var1 = raw_baselines.Team.values.tolist()
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pos_var1 = st.selectbox('View specific position?', options = ['All', 'PG', 'SG', 'SF', 'PF', 'C'])
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with col2:
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hold_container = st.empty()
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if st.button('Create Range of Outcomes for Slate'):
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with hold_container:
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working_roo = raw_baselines
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working_roo = working_roo[working_roo['Team'].isin(team_var1)]
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own_dict = dict(zip(working_roo.Player, working_roo.Own))
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min_dict = dict(zip(working_roo.Player, working_roo.Minutes))
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team_dict = dict(zip(working_roo.Player, working_roo.Team))
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total_sims = 1000
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flex_file = working_roo[['Player', 'Position', 'Salary', 'Median', 'Minutes']]
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flex_file.rename(columns={"Agg": "Median"}, inplace = True)
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flex_file['Floor'] = (flex_file['Median'] * .25) + (flex_file['Minutes'] * .25)
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flex_file['Ceiling'] = flex_file['Median'] + 10 + (flex_file['Minutes'] * .25)
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flex_file['STD'] = (flex_file['Median']/4)
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flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']]
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hold_file = flex_file
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overall_file = flex_file
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salary_file = flex_file
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overall_players = overall_file[['Player']]
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for x in range(0,total_sims):
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salary_file[x] = salary_file['Salary']
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salary_file=salary_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
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salary_file.astype('int').dtypes
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salary_file = salary_file.div(1000)
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for x in range(0,total_sims):
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overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])
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overall_file=overall_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
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overall_file.astype('int').dtypes
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players_only = hold_file[['Player']]
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raw_lineups_file = players_only
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for x in range(0,total_sims):
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maps_dict = {'proj_map':dict(zip(hold_file.Player,hold_file[x]))}
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raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])])
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players_only[x] = raw_lineups_file[x].rank(ascending=False)
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players_only=players_only.drop(['Player'], axis=1)
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players_only.astype('int').dtypes
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salary_2x_check = (overall_file - (salary_file*4))
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salary_3x_check = (overall_file - (salary_file*5))
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salary_4x_check = (overall_file - (salary_file*6))
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gpp_check = (overall_file - ((salary_file*5)+10))
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players_only['Average_Rank'] = players_only.mean(axis=1)
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players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims
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players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims
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players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims
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players_only['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims)
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players_only['3x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims)
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players_only['4x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims)
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players_only['5x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)
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players_only['GPP%'] = salary_4x_check[gpp_check >= 1].count(axis=1)/float(total_sims)
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players_only['Player'] = hold_file[['Player']]
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final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '3x%', '4x%', '5x%', 'GPP%']]
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final_Proj = pd.merge(hold_file, final_outcomes, on="Player")
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final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '3x%', '4x%', '5x%', 'GPP%']]
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final_Proj['Own'] = final_Proj['Player'].map(own_dict)
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final_Proj['Minutes Proj'] = final_Proj['Player'].map(min_dict)
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final_Proj['Team'] = final_Proj['Player'].map(team_dict)
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final_Proj['Own'] = final_Proj['Own'].astype('float')
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final_Proj['LevX'] = ((final_Proj[['Top_finish', '4x%', 'Top_5_finish']].mean(axis=1))*100) - final_Proj['Own']
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final_Proj['ValX'] = ((final_Proj[['4x%', '5x%']].mean(axis=1))*100) + final_Proj['LevX']
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final_Proj = final_Proj[['Player', 'Minutes Proj', 'Position', 'Team', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '3x%', '4x%', '5x%', 'GPP%', 'Own', 'LevX', 'ValX']]
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final_Proj = final_Proj.set_index('Player')
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final_Proj = final_Proj.sort_values(by='Median', ascending=False)
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st.session_state.final_Proj = final_Proj
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hold_container = st.empty()
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with display_container:
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display_container = st.empty()
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if 'final_Proj' in st.session_state:
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if pos_var1 == 'All':
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st.session_state.final_Proj = st.session_state.final_Proj
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elif pos_var1 != 'All':
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st.session_state.final_Proj = st.session_state.final_Proj[st.session_state.final_Proj['Position'].str.contains(pos_var1)]
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st.dataframe(st.session_state.final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
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with display_dl_container:
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display_dl_container = st.empty()
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if 'final_Proj' in st.session_state:
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st.download_button(
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label="Export Tables",
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data=convert_df_to_csv(st.session_state.final_Proj),
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file_name='Custom_NBA_export.csv',
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mime='text/csv',
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)
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dk_player_url = 'https://docs.google.com/spreadsheets/d/1Yq0vGriWK-bS79e-bD6_u9pqrYE6Yrlbb_wEkmH-ot0/edit#gid=172632260'
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@st.cache_data(ttl=300)
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def load_overall_stats():
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sh = gspreadcon.open_by_url(dk_player_url)
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worksheet = sh.worksheet('DK_Build_Up')
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raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
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roo_raw = raw_display.sort_values(by='Median', ascending=False)
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timestamp = raw_display['timestamp'].values[0]
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return dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, timestamp
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dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, timestamp = load_overall_stats()
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t_stamp = f"Last Update: " + str(timestamp) + f" CST"
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tab1, tab2, tab3 = st.tabs(['Range of Outcomes', 'Uploads and Info'])
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with tab1:
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col1, col2 = st.columns([1, 9])
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del st.session_state[key]
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site_var2 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var2')
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if site_var2 == 'Draftkings':
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site_baselines = roo_raw[roo_raw['site'] == 'Draftkings']
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elif site_var2 == 'Fanduel':
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site_baselines = roo_raw[roo_raw['site'] == 'Fanduel']
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slate_split = st.radio("Are you viewing the main slate or the secondary slate?", ('Main Slate', 'Secondary'), key='slate_split')
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if slate_split == 'Main Slate':
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raw_baselines = site_baselines[site_baselines['slate'] == 'Main Slate']
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elif slate_split == 'Secondary':
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raw_baselines = site_baselines[site_baselines['slate'] == 'Secondary']
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split_var2 = st.radio("Are you running the full slate or certain games?", ('Full Slate Run', 'Specific Games'), key='split_var2')
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if split_var2 == 'Specific Games':
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team_var2 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var2')
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elif split_var2 == 'Full Slate Run':
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st.session_state.display_proj = st.session_state.display_proj
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elif pos_var2 != 'All':
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st.session_state.display_proj = st.session_state.display_proj[st.session_state.display_proj['Position'].str.contains(pos_var2)]
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st.dataframe(st.session_state.display_proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height=1000, use_container_width = True)
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with display_dl_container_1:
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display_dl_container = st.empty()
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mime='text/csv',
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)
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with tab2:
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st.info("The Projections file can have any columns in any order, but must contain columns explicitly named: 'Player', 'Salary', 'Position', 'Team', 'Opp', 'Minutes', 'Median', 'Own'.")
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col1, col2 = st.columns([1, 5])
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with col1:
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proj_file = st.file_uploader("Upload Projections File", key = 'proj_uploader')
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if proj_file is not None:
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try:
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proj_dataframe = pd.read_csv(proj_file)
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except:
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proj_dataframe = pd.read_excel(proj_file)
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with col2:
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if proj_file is not None:
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st.dataframe(proj_dataframe.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
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