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import streamlit as st |
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import pandas as pd |
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import numpy as np |
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from pymongo import MongoClient |
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from database import db |
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@st.cache_data(ttl = 600) |
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def init_DK_seed_frames(slate_var, sharp_split): |
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if slate_var == 'Main Slate': |
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collection = db['DK_NFL_name_map'] |
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cursor = collection.find() |
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raw_data = pd.DataFrame(list(cursor)) |
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names_dict = dict(zip(raw_data['key'], raw_data['value'])) |
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collection = db[f"DK_NFL_seed_frame_Main Slate"] |
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elif slate_var == 'Secondary Slate': |
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collection = db['DK_NFL_Secondary_name_map'] |
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cursor = collection.find() |
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raw_data = pd.DataFrame(list(cursor)) |
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names_dict = dict(zip(raw_data['key'], raw_data['value'])) |
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collection = db[f"DK_NFL_seed_frame_Secondary Slate"] |
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elif slate_var == 'Late Slate': |
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collection = db['DK_NFL_Late_name_map'] |
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cursor = collection.find() |
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raw_data = pd.DataFrame(list(cursor)) |
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names_dict = dict(zip(raw_data['key'], raw_data['value'])) |
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collection = db[f"DK_NFL_seed_frame_Late Slate"] |
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cursor = collection.find().limit(sharp_split) |
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raw_display = pd.DataFrame(list(cursor)) |
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raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']] |
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dict_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST'] |
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for col in dict_columns: |
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raw_display[col] = raw_display[col].map(names_dict) |
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DK_seed = raw_display.to_numpy() |
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return DK_seed |
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@st.cache_data(ttl = 599) |
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def init_FD_seed_frames(slate_var, sharp_split): |
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if slate_var == 'Main Slate': |
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collection = db['FD_NFL_name_map'] |
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cursor = collection.find() |
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raw_data = pd.DataFrame(list(cursor)) |
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names_dict = dict(zip(raw_data['key'], raw_data['value'])) |
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collection = db[f"FD_NFL_seed_frame_Main Slate"] |
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cursor = collection.find().limit(sharp_split) |
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elif slate_var == 'Secondary Slate': |
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collection = db['FD_NFL_Secondary_name_map'] |
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cursor = collection.find() |
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raw_data = pd.DataFrame(list(cursor)) |
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names_dict = dict(zip(raw_data['key'], raw_data['value'])) |
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collection = db[f"FD_NFL_seed_frame_Secondary Slate"] |
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cursor = collection.find().limit(sharp_split) |
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elif slate_var == 'Late Slate': |
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collection = db['FD_NFL_Late_name_map'] |
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cursor = collection.find() |
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raw_data = pd.DataFrame(list(cursor)) |
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names_dict = dict(zip(raw_data['key'], raw_data['value'])) |
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collection = db[f"FD_NFL_seed_frame_Late Slate"] |
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cursor = collection.find().limit(sharp_split) |
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raw_display = pd.DataFrame(list(cursor)) |
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raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']] |
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dict_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST'] |
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for col in dict_columns: |
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raw_display[col] = raw_display[col].map(names_dict) |
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FD_seed = raw_display.to_numpy() |
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return FD_seed |
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@st.cache_data(ttl = 599) |
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def init_baselines(slate_var): |
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collection = db["DK_NFL_ROO"] |
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cursor = collection.find() |
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raw_display = pd.DataFrame(list(cursor)) |
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raw_display = raw_display[raw_display['slate'] == slate_var] |
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raw_display = raw_display[raw_display['version'] == 'overall'] |
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dk_raw = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', |
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'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_ID', 'site']] |
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dk_raw['STDev'] = (dk_raw['Ceiling'] - dk_raw['Floor']) / 4 |
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collection = db["FD_NFL_ROO"] |
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cursor = collection.find() |
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raw_display = pd.DataFrame(list(cursor)) |
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raw_display = raw_display[raw_display['slate'] == slate_var] |
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raw_display = raw_display[raw_display['version'] == 'overall'] |
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fd_raw = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', |
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'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_ID', 'site']] |
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fd_raw['STDev'] = (fd_raw['Ceiling'] - fd_raw['Floor']) / 4 |
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return dk_raw, fd_raw |
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@st.cache_data |
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def convert_df(array): |
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array = pd.DataFrame(array, columns=column_names) |
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return array.to_csv().encode('utf-8') |
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@st.cache_data |
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def calculate_DK_value_frequencies(np_array): |
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unique, counts = np.unique(np_array[:, :9], return_counts=True) |
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frequencies = counts / len(np_array) |
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combined_array = np.column_stack((unique, frequencies)) |
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return combined_array |
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@st.cache_data |
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def calculate_FD_value_frequencies(np_array): |
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unique, counts = np.unique(np_array[:, :9], return_counts=True) |
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frequencies = counts / len(np_array) |
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combined_array = np.column_stack((unique, frequencies)) |
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return combined_array |
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@st.cache_data |
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def sim_contest(Sim_size, seed_frame, maps_dict, Contest_Size): |
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SimVar = 1 |
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Sim_Winners = [] |
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fp_array = seed_frame.copy() |
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vec_projection_map = np.vectorize(maps_dict['Projection_map'].__getitem__) |
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vec_stdev_map = np.vectorize(maps_dict['STDev_map'].__getitem__) |
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st.write('Simulating contest on frames') |
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while SimVar <= Sim_size: |
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fp_random = fp_array[np.random.choice(fp_array.shape[0], Contest_Size)] |
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sample_arrays1 = np.c_[ |
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fp_random, |
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np.sum(np.random.normal( |
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loc=vec_projection_map(fp_random[:, :-7]), |
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scale=vec_stdev_map(fp_random[:, :-7])), |
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axis=1) |
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] |
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sample_arrays = sample_arrays1 |
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final_array = sample_arrays[sample_arrays[:, 10].argsort()[::-1]] |
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best_lineup = final_array[final_array[:, -1].argsort(kind='stable')[::-1][:1]] |
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Sim_Winners.append(best_lineup) |
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SimVar += 1 |
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return Sim_Winners |
