Delete app.py

"""
This Program is intended to provide a glimpse into daily NBA player projections from the app Prize-Picks and conduct a
statistical analysis into each line to identify potential value spots and different angles and trends from which to
justify the selection.

Author: Khizr Ali Khizr89@gmail.com

Created: March 6th, 2022
"""
# Imports
import math # Using floor method to get odds for above line
import sys # used to exit the program
import time # Using sleep() between thread requests
from datetime import date, datetime

import pandas as pd # Using DataFrames to store and manipulate data
import requests # Using GET to load data from some API
import unidecode # Decode the player names to remove accents
from bs4 import BeautifulSoup # Needed in get_player_position_list()
from scipy.stats import poisson # using poisson odds as one metric
from nba_api.stats.static import players # a list of players
from nba_api.stats.endpoints import playergamelogs, leaguegamelog # methods from nba-api to get data
from selenium import webdriver # Needed in get_dvp_ranking()
from selenium.webdriver.support.ui import Select # Needed in get_dvp_ranking()
from selenium.webdriver.common.by import By # Needed in get_dvp_ranking()
from selenium.webdriver.chrome.options import Options # Needed in get_dvp_ranking()
from selenium.webdriver.chrome.service import Service # Needed in get_dvp_ranking()
from webdriver_manager.chrome import ChromeDriverManager # Needed in get_dvp_ranking()


# Modules
# Get the Prizepicks Projections (Starting Data)
# Method is GOOD
def get_prizepicks_projections():
""" Returns a DataFrame of the PrizePicks Projections

Parameters:
-----------

Returns
---------
df: <pandas.DataFrame>
A DataFrame of the player's projection data
"""
# URL of the Prize Picks Projections page
url = 'https://partner-api.prizepicks.com/projections?single_stat=True&league_id=7&per_page=1000'
resp = requests.get(url).json()
if len(resp['data']) != 0:
# Normalizes the JSON File into a Data Frame
data = pd.json_normalize(resp['data'], max_level=3)
included = pd.json_normalize(resp['included'], max_level=3)
inc_cop = included[included['type'] == 'new_player'].copy().dropna(axis=1)

# Joins on the 'id' to add the player name to the projections
data = pd.merge(data, inc_cop,
how='left',
left_on=['relationships.new_player.data.id', 'relationships.new_player.data.type'],
right_on=['id', 'type'],
suffixes=('', '_new_player'))

# Return the data with necessary columns
data = data.rename(
columns={'attributes.name': 'name', 'attributes.line_score': 'line_score',
'attributes.stat_type': 'stat_type', 'attributes.updated_at': 'updated_at',
'attributes.description': 'opponent', 'attributes.start_time': 'start_time',
'attributes.is_promo': 'is_promo', 'attributes.position': 'position', 'attributes.team': 'team',
'attributes.team_name': 'team_name', 'attributes.market': 'market'})
return data[['id', 'name', 'line_score', 'stat_type', 'updated_at',
'opponent', 'start_time', 'is_promo', 'position',
'team', 'team_name', 'market']]
else:
print('There Are Currently no NBA Lines Available.')
sys.exit()


# Get the DVP Rankings (Starting Data)
# Method is GOOD
def get_dvp_rankings():
""" Returns a pandas.DataFrame of the NBA DVP Rankings from the last 30 days

Parameters:
-----------

Returns
---------
dvp_list: <pandas.DataFrame>
A DataFrame of each team dvp and its position
"""
chrome_options = Options()
chrome_options.add_argument("--headless")
s = Service(ChromeDriverManager().install())
driver = webdriver.Chrome(service=s, options=chrome_options)
driver.get('https://hashtagbasketball.com/nba-defense-vs-position')
select = Select(driver.find_element(By.NAME, 'ctl00$ContentPlaceHolder1$DDDURATION'))
select.select_by_value("30")
time.sleep(.500)
table = driver.find_element(By.ID, 'ContentPlaceHolder1_GridView1').get_attribute('outerHTML')
dvp_table = pd.read_html(table)[0]
dvp_table = dvp_table.rename(
columns={'Sort: Team': 'Team', 'Sort: Position': 'Position', 'Sort: PTS': 'PTS', 'Sort: FG%': 'FG%',
'Sort: FT%': 'FT%', 'Sort: 3PM': '3PM', 'Sort: REB': 'REB', 'Sort: AST': 'AST', 'Sort: STL': 'STL',
'Sort: BLK': 'BLK', 'Sort: TO': 'TO'})

dvp_table['Team'] = dvp_table['Team'].str[:3]
dvp_table['PTS'] = dvp_table['PTS'].str[:4]
dvp_table['FG%'] = dvp_table['FG%'].str[:4]
dvp_table['FT%'] = dvp_table['FT%'].str[:4]
dvp_table['3PM'] = dvp_table['3PM'].str[:3]
dvp_table['REB'] = dvp_table['REB'].str[:-3]
dvp_table['AST'] = dvp_table['AST'].str[:-3]
dvp_table['STL'] = dvp_table['STL'].str[:3]
dvp_table['BLK'] = dvp_table['BLK'].str[:3]
dvp_table['TO'] = dvp_table['TO'].str[:3]

dvp_table['PTS'] = pd.to_numeric(dvp_table['PTS'])
dvp_table['FG%'] = pd.to_numeric(dvp_table['FG%'])
dvp_table['FT%'] = pd.to_numeric(dvp_table['FT%'])
dvp_table['3PM'] = pd.to_numeric(dvp_table['3PM'])
dvp_table['REB'] = pd.to_numeric(dvp_table['REB'])
dvp_table['AST'] = pd.to_numeric(dvp_table['AST'])
dvp_table['STL'] = pd.to_numeric(dvp_table['STL'])
dvp_table['BLK'] = pd.to_numeric(dvp_table['BLK'])
dvp_table['TO'] = pd.to_numeric(dvp_table['TO'])

driver.close()
return dvp_table


# Get a list of players and their position (Starting Data)
# Take a look at .replace() method issuing a warning
def get_player_position_list():
""" Returns a pandas.DataFrame of the NBA DVP Rankings from the last 30 days

Parameters:
-----------

Returns
---------
player_info_list: <list>
A list of players information including their position
"""
url = 'https://www.basketball-reference.com/leagues/NBA_2022_per_game.html'
r = requests.get(url)
r_html = r.text
soup = BeautifulSoup(r_html, 'html.parser')

table = soup.find_all(class_="full_table")

""" Extracting List of column names"""
head = soup.find(class_="thead")
column_names_raw = [head.text for _ in head][0]
column_names_polished = column_names_raw.replace("\n", ",").split(",")[2:-1]

"""Extracting full list of player_data"""
players_list = []

for i in range(len(table)):

player_ = []

for td in table[i].find_all("td"):
player_.append(unidecode.unidecode(td.text))

players_list.append(player_)

player_info_list = pd.DataFrame(players_list, columns=column_names_polished).set_index("Player")
# cleaning the player's name from occasional special characters
# player_info_list.index = player_info_list.index.str.encode('utf-8')
player_info_list.index = player_info_list.index.str.replace('*', '', regex=True)
return player_info_list


# Get a log of the last 10 games of all players listed in projections (Starting Data) (PrizePicks Projections Required)
# Look into changing the data to be streamlined and per player instead of all together
def get_game_logs(list_of_player_names):
""" Returns Two Lists. 1. List of Player Names 2. list of pandas.DataFrame containing logs of the last 10 games

Parameters:
-----------
list_of_projections: <pandas.DataFrame>
A DataFrame containing the Prize Picks Projections

Returns
---------
player_names: <List>
A List of the player's names
game_logs: <List>
A List of type pandas.DataFrame where each index contains the logs of the last 10 games played
"""
player_names = []
no_logs = []
game_logs = []
print('GETTING GAME LOGS')
for name in list_of_player_names:
if name not in player_names:
player = players.find_players_by_full_name(name)[0]
if player['is_active']:
player_id = str(player['id'])
player_logs = playergamelogs.PlayerGameLogs(date_from_nullable='10/31/2021',
player_id_nullable=player_id,
season_nullable='2021-22',
last_n_games_nullable=10).get_data_frames()[0]
player_names.append(name)
player_logs['ID'] = player_id
game_logs.append(player_logs)
time.sleep(.700)
print(name, ' added to logs')
else:
no_logs.append(name)
game_logs = pd.concat(game_logs, axis=0)
print('FINISHED ALL ELIGIBLE GAME LOGS', no_logs)
return game_logs, no_logs


def get_player_position(position_list, name):
""" Returns Two Lists. 1. List of Player Names 2. list of pandas.DataFrame containing logs of the last 10 games

Parameters:
-----------
list_of_projections: <pandas.DataFrame>
A DataFrame containing the Prize Picks Projections

Returns
---------
player_names: <List>
A List of the player's names
game_logs: <List>
A List of type pandas.DataFrame where each index contains the logs of the last 10 games played
"""
if name == 'Robert Williams III':
name = 'Robert Williams'

return position_list[position_list.index == name]['Pos'][0]


# Get the Poisson Odds of the player going OVER their projected total given previous 10 performances
# Method is GOOD
def get_poisson_odds(game_log, prop_type, line_score):
""" Returns a Value of the PrizePicks Projection Poisson odds to go over the suggested line_score

Parameters:
-----------
game_log: <pandas.DataFrame>
A DataFrame containing the players l10 Game Logs
prop_type: <String>
A string containing the type of prop it is eg. Points, Rebounds, Assists, etc.
line_score: <float>
A float valu

app.py

@@ -1,565 +0,0 @@
-import pandas
-import csv
-import os
-
-def prizepicks_6_legs_flex(wager, odds):
-    #calculate EV
-    value_win_6 = wager * 24
-    odds_win_6 = 1
-    for i in range(len(odds)):
-        odds_win_6 *= odds[i]
-    value_win_6 *= odds_win_6
-    
-    value_win_5 = wager
-    odds_win_5 = 0
-    for i in range(len(odds)):
-        temp = 1 - odds[i]
-        for n in range(len(odds)):
-            if n == i:
-                continue
-            temp *= odds[n]
-        odds_win_5 += temp
-    value_win_5 *= odds_win_5
-    
-    value_win_4 = 0.6 * wager * -1
-    odds_win_4 = 0
-    for i in range(len(odds)):
-        temp_1 = 1 - odds[i]
-        for n in range(len(odds)):
-            if n <= i:
-                continue
-            temp_2 = (1 - odds[n]) * temp_1
-            for j in range(len(odds)):
-                if j == i or j == n:
-                    continue
-                temp_2 *= odds[j]
-            odds_win_4 += temp_2
-    value_win_4 *= odds_win_4
-    
-    value_win_3 = wager * -1
-    odds_win_3 = 0
-    for i in range(len(odds)):
-        temp_1 = 1 - odds[i]
-        for n in range(len(odds)):
-            if n <= i:
-                continue
-            temp_2 = (1 - odds[n]) * temp_1
-            for j in range(len(odds)):
-                if j <= n:
-                    continue
-                temp_3 = (1 - odds[j]) * temp_2
-                for k in range(len(odds)):
-                    if k == i or k == n or k == j:
-                        continue
-                    temp_3 *= odds[k]
-                odds_win_3 += temp_3
-    value_win_3 *= odds_win_3
-    
-    odds_inverse = []
-    for i in range(len(odds)):
-        odds_inverse.append(1 - odds[i])
-    
-    value_win_2 = wager * -1
-    odds_win_2 = 0
-    for i in range(len(odds_inverse)):
-        temp_1 = 1 - odds_inverse[i]
-        for n in range(len(odds_inverse)):
-            if n <= i:
-                continue
-            temp_2 = (1 - odds_inverse[n]) * temp_1
-            for j in range(len(odds_inverse)):
-                if j == i or j == n:
-                    continue
-                temp_2 *= odds_inverse[j]
-            odds_win_2 += temp_2
-    value_win_2 *= odds_win_2
-    
-    value_win_1 = wager * -1
-    odds_win_1 = 0
-    for i in range(len(odds_inverse)):
-        temp = 1 - odds_inverse[i]
-        for n in range(len(odds_inverse)):
-            if n == i:
-                continue
-            temp *= odds_inverse[n]
-        odds_win_1 += temp
-    value_win_1 *= odds_win_1
-    
-    value_win_0 = wager * -1
-    odds_win_0 = 1
-    for i in range(len(odds_inverse)):
-        odds_win_0 *= odds_inverse[i]
-    value_win_0 *= odds_win_0
-    
-    ev = value_win_0 + value_win_1 + value_win_2 + value_win_3 + value_win_4 + value_win_5 + value_win_6
-    print("$" + str(wager) + " 6-legs flex play with implied odds: " + str(odds))
-    print("Chance to win 6/6 (PnL $" + str(wager * 24) + "): " + str(odds_win_6))
-    print("Chance to win 5/6 (PnL $" + str(wager) + "): " + str(odds_win_5))
-    print("Chance to win 4/6 (PnL $" + str(wager * -0.6) + "): " + str(odds_win_4))
-    print("Chance to lose (PnL $" + str(wager * -1) + "): " + str(odds_win_3 + odds_win_2 + odds_win_1 + odds_win_0))
-    print("Expected PnL: $" + str(ev))
-    print()
-    return ev, "6-legs flex"
-
-def prizepicks_5_legs_flex(wager, odds):
-    #calculate EV
-    value_win_5 = wager * 9
-    odds_win_5 = 1
-    for i in range(len(odds)):
-        odds_win_5 *= odds[i]
-    value_win_5 *= odds_win_5
-    
-    value_win_4 = wager
-    odds_win_4 = 0
-    for i in range(len(odds)):
-        temp = 1 - odds[i]
-        for n in range(len(odds)):
-            if n == i:
-                continue
-            temp *= odds[n]
-        odds_win_4 += temp
-    value_win_4 *= odds_win_4
-    
-    value_win_3 = 0.6 * wager * -1
-    odds_win_3 = 0
-    for i in range(len(odds)):
-        temp_1 = 1 - odds[i]
-        for n in range(len(odds)):
-            if n <= i:
-                continue
-            temp_2 = (1 - odds[n]) * temp_1
-            for j in range(len(odds)):
-                if j == i or j == n:
-                    continue
-                temp_2 *= odds[j]
-            odds_win_3 += temp_2
-    value_win_3 *= odds_win_3
-    
-    odds_inverse = []
-    for i in range(len(odds)):
-        odds_inverse.append(1 - odds[i])
-    
-    value_win_2 = wager * -1
-    odds_win_2 = 0
-    for i in range(len(odds_inverse)):
-        temp_1 = 1 - odds_inverse[i]
-        for n in range(len(odds_inverse)):
-            if n <= i:
-                continue
-            temp_2 = (1 - odds_inverse[n]) * temp_1
-            for j in range(len(odds_inverse)):
-                if j == i or j == n:
-                    continue
-                temp_2 *= odds_inverse[j]
-            odds_win_2 += temp_2
-    value_win_2 *= odds_win_2
-    
-    value_win_1 = wager * -1
-    odds_win_1 = 0
-    for i in range(len(odds_inverse)):
-        temp = 1 - odds_inverse[i]
-        for n in range(len(odds_inverse)):
-            if n == i:
-                continue
-            temp *= odds_inverse[n]
-        odds_win_1 += temp
-    value_win_1 *= odds_win_1
-    
-    value_win_0 = wager * -1
-    odds_win_0 = 1
-    for i in range(len(odds_inverse)):
-        odds_win_0 *= odds_inverse[i]
-    value_win_0 *= odds_win_0
-    
-    ev = value_win_0 + value_win_1 + value_win_2 + value_win_3 + value_win_4 + value_win_5
-    print("$" + str(wager) + " 5-legs flex play with implied odds: " + str(odds))
-    print("Chance to win 5/5 (PnL $" + str(wager * 9) + "): " + str(odds_win_5))
-    print("Chance to win 4/5 (PnL $" + str(wager) + "): " + str(odds_win_4))
-    print("Chance to win 3/5 (PnL $" + str(wager * -0.6) + "): " + str(odds_win_3))
-    print("Chance to lose (PnL $" + str(wager * -1) + "): " + str(odds_win_2 + odds_win_1 + odds_win_0))
-    print("Expected PnL: $" + str(ev))
-    print()
-    return ev, "5-legs flex"
-
-def prizepicks_4_legs_flex(wager, odds):
-    #calculate EV
-    value_win_4 = wager * 4
-    odds_win_4 = 1
-    for i in range(len(odds)):
-        odds_win_4 *= odds[i]
-    value_win_4 *= odds_win_4
-    
-    value_win_3 = wager * 0.5
-    odds_win_3 = 0
-    for i in range(len(odds)):
-        temp = 1 - odds[i]
-        for n in range(len(odds)):
-            if n == i:
-                continue
-            temp *= odds[n]
-        odds_win_3 += temp
-    value_win_3 *= odds_win_3
-    
-    value_win_2 = wager * -1
-    odds_win_2 = 0
-    for i in range(len(odds)):
-        temp_1 = 1 - odds[i]
-        for n in range(len(odds)):
-            if n <= i:
-                continue
-            temp_2 = (1 - odds[n]) * temp_1
-            for j in range(len(odds)):
-                if j == i or j == n:
-                    continue
-                temp_2 *= odds[j]
-            odds_win_2 += temp_2
-    value_win_2 *= odds_win_2
-    
-    odds_inverse = []
-    for i in range(len(odds)):
-        odds_inverse.append(1 - odds[i])
-    
-    value_win_1 = wager * -1
-    odds_win_1 = 0
-    for i in range(len(odds_inverse)):
-        temp = 1 - odds_inverse[i]
-        for n in range(len(odds_inverse)):
-            if n == i:
-                continue
-            temp *= odds_inverse[n]
-        odds_win_1 += temp
-    value_win_1 *= odds_win_1
-    
-    value_win_0 = wager * -1
-    odds_win_0 = 1
-    for i in range(len(odds_inverse)):
-        odds_win_0 *= odds_inverse[i]
-    value_win_0 *= odds_win_0
-    
-    ev = value_win_0 + value_win_1 + value_win_2 + value_win_3 + value_win_4
-    print("$" + str(wager) + " 4-legs flex play with implied odds: " + str(odds))
-    print("Chance to win 4/4 (PnL $" + str(wager * 4) + "): " + str(odds_win_4))
-    print("Chance to win 3/4 (PnL $" + str(wager * 0.5) + "): " + str(odds_win_3))
-    print("Chance to lose (PnL $" + str(wager * -1) + "): " + str(odds_win_2 + odds_win_1 + odds_win_0))
-    print("Expected PnL: $" + str(ev))
-    print()
-    return ev, "4-legs flex"
-
-def prizepicks_4_legs_power(wager, odds):
-    #calculate EV
-    value_win_4 = wager * 9
-    odds_win_4 = 1
-    for i in range(len(odds)):
-        odds_win_4 *= odds[i]
-    value_win_4 *= odds_win_4
-    
-    value_win_3 = wager * -1
-    odds_win_3 = 0
-    for i in range(len(odds)):
-        temp = 1 - odds[i]
-        for n in range(len(odds)):
-            if n == i:
-                continue
-            temp *= odds[n]
-        odds_win_3 += temp
-    value_win_3 *= odds_win_3
-    
-    value_win_2 = wager * -1
-    odds_win_2 = 0
-    for i in range(len(odds)):
-        temp_1 = 1 - odds[i]
-        for n in range(len(odds)):
-            if n <= i:
-                continue
-            temp_2 = (1 - odds[n]) * temp_1
-            for j in range(len(odds)):
-                if j == i or j == n:
-                    continue
-                temp_2 *= odds[j]
-            odds_win_2 += temp_2
-    value_win_2 *= odds_win_2
-    
-    odds_inverse = []
-    for i in range(len(odds)):
-        odds_inverse.append(1 - odds[i])
-    
-    value_win_1 = wager * -1
-    odds_win_1 = 0
-    for i in range(len(odds_inverse)):
-        temp = 1 - odds_inverse[i]
-        for n in range(len(odds_inverse)):
-            if n == i:
-                continue
-            temp *= odds_inverse[n]
-        odds_win_1 += temp
-    value_win_1 *= odds_win_1
-    
-    value_win_0 = wager * -1
-    odds_win_0 = 1
-    for i in range(len(odds_inverse)):
-        odds_win_0 *= odds_inverse[i]
-    value_win_0 *= odds_win_0
-    
-    ev = value_win_0 + value_win_1 + value_win_2 + value_win_3 + value_win_4
-    print("$" + str(wager) + " 4-legs power play with implied odds: " + str(odds))
-    print("Chance to win 4/4 (PnL $" + str(wager * 9) + "): " + str(odds_win_4))
-    print("Chance to lose (PnL $" + str(wager * -1) + "): " + str(odds_win_3 + odds_win_2 + odds_win_1 + odds_win_0))
-    print("Expected PnL: $" + str(ev))
-    print()
-    return ev, "4-legs power"
-
-def prizepicks_3_legs_flex(wager, odds):
-    #calculate EV
-    value_win_3 = wager * 1.25
-    odds_win_3 = 1
-    for i in range(len(odds)):
-        odds_win_3 *= odds[i]
-    value_win_3 *= odds_win_3
-    
-    value_win_2 = wager * 0.25
-    odds_win_2 = 0
-    for i in range(len(odds)):
-        temp = 1 - odds[i]
-        for n in range(len(odds)):
-            if n == i:
-                continue
-            temp *= odds[n]
-        odds_win_2 += temp
-    value_win_2 *= odds_win_2
-    
-    odds_inverse = []
-    for i in range(len(odds)):
-        odds_inverse.append(1 - odds[i])
-    
-    value_win_1 = wager * -1
-    odds_win_1 = 0
-    for i in range(len(odds_inverse)):
-        temp = 1 - odds_inverse[i]
-        for n in range(len(odds_inverse)):
-            if n == i:
-                continue
-            temp *= odds_inverse[n]
-        odds_win_1 += temp
-    value_win_1 *= odds_win_1
-    
-    value_win_0 = wager * -1
-    odds_win_0 = 1
-    for i in range(len(odds_inverse)):
-        odds_win_0 *= odds_inverse[i]
-    value_win_0 *= odds_win_0
-    
-    ev = value_win_0 + value_win_1 + value_win_2 + value_win_3
-    print("$" + str(wager) + " 3-legs flex play with implied odds: " + str(odds))
-    print("Chance to win 3/3 (PnL $" + str(wager * 1.25) + "): " + str(odds_win_3))
-    print("Chance to win 2/3 (PnL $" + str(wager * 0.25) + "): " + str(odds_win_2))
-    print("Chance to lose (PnL $" + str(wager * -1) + "): " + str(odds_win_1 + odds_win_0))
-    print("Expected PnL: $" + str(ev))
-    print()
-    return ev, "3-legs flex"
-
-def prizepicks_3_legs_power(wager, odds):
-    #calculate EV
-    value_win_3 = wager * 4
-    odds_win_3 = 1
-    for i in range(len(odds)):
-        odds_win_3 *= odds[i]
-    value_win_3 *= odds_win_3
-    
-    value_win_2 = wager * -1
-    odds_win_2 = 0
-    for i in range(len(odds)):
-        temp = 1 - odds[i]
-        for n in range(len(odds)):
-            if n == i:
-                continue
-            temp *= odds[n]
-        odds_win_2 += temp
-    value_win_2 *= odds_win_2
-    
-    odds_inverse = []
-    for i in range(len(odds)):
-        odds_inverse.append(1 - odds[i])
-    
-    value_win_1 = wager * -1
-    odds_win_1 = 0
-    for i in range(len(odds_inverse)):
-        temp = 1 - odds_inverse[i]
-        for n in range(len(odds_inverse)):
-            if n == i:
-                continue
-            temp *= odds_inverse[n]
-        odds_win_1 += temp
-    value_win_1 *= odds_win_1
-    
-    value_win_0 = wager * -1
-    odds_win_0 = 1
-    for i in range(len(odds_inverse)):
-        odds_win_0 *= odds_inverse[i]
-    value_win_0 *= odds_win_0
-    
-    ev = value_win_0 + value_win_1 + value_win_2 + value_win_3
-    print("$" + str(wager) + " 3-legs power play with implied odds: " + str(odds))
-    print("Chance to win 3/3 (PnL $" + str(wager * 4) + "): " + str(odds_win_3))
-    print("Chance to lose (PnL $" + str(wager * -1) + "): " + str(odds_win_2 + odds_win_1 + odds_win_0))
-    print("Expected PnL: $" + str(ev))
-    print()
-    return ev, "3-legs power"
-
-def prizepicks_2_legs_power(wager, odds):
-    #calculate EV
-    value_win_2 = wager * 2
-    odds_win_2 = 1
-    for i in range(len(odds)):
-        odds_win_2 *= odds[i]
-    value_win_2 *= odds_win_2
-    
-    value_win_1 = wager * -1
-    odds_win_1 = 0
-    for i in range(len(odds)):
-        temp = 1 - odds[i]
-        for n in range(len(odds)):
-            if n == i:
-                continue
-            temp *= odds[n]
-        odds_win_1 += temp
-    value_win_1 *= odds_win_1
-    
-    odds_inverse = []
-    for i in range(len(odds)):
-        odds_inverse.append(1 - odds[i])
-    
-    value_win_0 = wager * -1
-    odds_win_0 = 1
-    for i in range(len(odds_inverse)):
-        odds_win_0 *= odds_inverse[i]
-    value_win_0 *= odds_win_0
-    
-    ev = value_win_0 + value_win_1 + value_win_2
-    print("$" + str(wager) + " 2-legs power play with implied odds: " + str(odds))
-    print("Chance to win 2/2 (PnL $" + str(wager * 2) + "): " + str(odds_win_2))
-    print("Chance to lose (PnL $" + str(wager * -1) + "): " + str(odds_win_1 + odds_win_0))
-    print("Expected PnL: $" + str(ev))
-    print()
-    return ev, "2-legs power"
-
-def sort_plays(plays):
-    plays.sort(key = lambda x: x[0], reverse=True)
-    return plays
-
-def main():
-    input_fname = "All-Sports_PrizePicks Optimizer.csv"
-    output_fname = "dgf.csv"
-
-    with open(input_fname, newline='') as inFile, open(output_fname, 'w', newline='') as outFile:
-        r = csv.reader(inFile)
-        w = csv.writer(outFile)
-
-        next(r, None)
-        w.writerow(['First Name', 'Last Name', 'Sport', 'Team', 'Over/Under', 'Prop', 'PrizePicks Line',
-        'Sportsbook Line', 'DGF', 'Pinnacle', 'Fanduel', 'DraftKings', 'Barstool', 'MGM', 'Caesars',
-        'BetOnline', 'Bovada', 'Bet365', 'FoxBet', 'Odds to hit'])
-
-        for row in r:
-            w.writerow(row)
-
-    data = pandas.read_csv('dgf.csv')
-    top_lines = data.iloc [[0,1,2,3,4,5]]
-    print(top_lines)
-    odds = top_lines['Odds to hit'].tolist()
-
-    plays_6 = top_lines.iloc [[0,1,2,3,4,5], [0,1,4,5,6]]
-    plays_5 = top_lines.iloc [[0,1,2,3,4], [0,1,4,5,6]]
-    plays_4 = top_lines.iloc [[0,1,2,3], [0,1,4,5,6]]
-    plays_3 = top_lines.iloc [[0,1,2], [0,1,4,5,6]]
-    plays_2 = top_lines.iloc [[0,1], [0,1,4,5,6]]
-
-    for i in range(len(odds)):
-        odds[i] = odds[i]/100
-
-    wager = 100
-    plays = []
-    pp_6_legs_flex = prizepicks_6_legs_flex(wager, odds)
-    pp_6_legs_flex_ev = pp_6_legs_flex[0]
-    plays.append(pp_6_legs_flex)
-
-    pp_5_legs_flex = prizepicks_5_legs_flex(wager, odds[:5])
-    pp_5_legs_flex_ev = pp_5_legs_flex[0]
-    plays.append(pp_5_legs_flex)
-
-    pp_4_legs_flex = prizepicks_4_legs_flex(wager, odds[:4])
-    pp_4_legs_flex_ev = pp_4_legs_flex[0]
-    plays.append(pp_4_legs_flex)
-
-    pp_4_legs_power = prizepicks_4_legs_power(wager, odds[:4])
-    pp_4_legs_power_ev = pp_4_legs_power[0]
-    plays.append(pp_4_legs_power)
-
-    pp_3_legs_flex = prizepicks_3_legs_flex(wager, odds[:3])
-    pp_3_legs_flex_ev = pp_3_legs_flex[0]
-    plays.append(pp_3_legs_flex)
-
-    pp_3_legs_power = prizepicks_3_legs_power(wager, odds[:3])
-    pp_3_legs_power_ev = pp_3_legs_power[0]
-    plays.append(pp_3_legs_power)
-
-    pp_2_legs_power = prizepicks_2_legs_power(wager, odds[:2])
-    pp_2_legs_power_ev = pp_2_legs_power[0]
-    plays.append(pp_2_legs_power)
-    
-    outputs = []
-
-    output_6_flex = ""
-    output_6_flex += "$" + str(wager) + " 6-legs flex play expected PnL: $" + str(float("{:.2f}".format(pp_6_legs_flex_ev))) + "\n"
-    output_6_flex += str(plays_6)
-    output_6_flex += "\n"
-    tup_6_flex = (pp_6_legs_flex_ev, output_6_flex)
-    outputs.append(tup_6_flex)
-
-    output_5_flex = ""
-    output_5_flex += "$" + str(wager) + " 5-legs flex play expected PnL: $" + str(float("{:.2f}".format(pp_5_legs_flex_ev))) + "\n"
-    output_5_flex += str(plays_5)
-    output_5_flex += "\n"
-    tup_5_flex = (pp_5_legs_flex_ev, output_5_flex)
-    outputs.append(tup_5_flex)
-
-    output_4_flex = ""
-    output_4_flex += "$" + str(wager) + " 4-legs flex play expected PnL: $" + str(float("{:.2f}".format(pp_4_legs_flex_ev))) + "\n"
-    output_4_flex += str(plays_4)
-    output_4_flex += "\n"
-    tup_4_flex = (pp_4_legs_flex_ev, output_4_flex)
-    outputs.append(tup_4_flex)
-
-    output_4_power = ""
-    output_4_power += "$" + str(wager) + " 4-legs power play expected PnL: $" + str(float("{:.2f}".format(pp_4_legs_power_ev))) + "\n"
-    output_4_power += str(plays_4)
-    output_4_power += "\n"
-    tup_4_power = (pp_4_legs_power_ev, output_4_power)
-    outputs.append(tup_4_power)
-
-    output_3_flex = ""
-    output_3_flex += "$" + str(wager) + " 3-legs flex play expected PnL: $" + str(float("{:.2f}".format(pp_3_legs_flex_ev))) + "\n"
-    output_3_flex += str(plays_3)
-    output_3_flex += "\n"
-    tup_3_flex = (pp_3_legs_flex_ev, output_3_flex)
-    outputs.append(tup_3_flex)
-
-    output_3_power = ""
-    output_3_power += "$" + str(wager) + " 3-legs power play expected PnL: $" + str(float("{:.2f}".format(pp_3_legs_power_ev))) + "\n"
-    output_3_power += str(plays_3)
-    output_3_power += "\n"
-    tup_3_power = (pp_3_legs_power_ev, output_3_power)
-    outputs.append(tup_3_power)
-
-    output_2_power = ""
-    output_2_power += "$" + str(wager) + " 2-legs power play expected PnL: $" + str(float("{:.2f}".format(pp_2_legs_power_ev))) + "\n"
-    output_2_power += str(plays_2)
-    output_2_power += "\n"
-    tup_2_power = (pp_2_legs_power_ev, output_2_power)
-    outputs.append(tup_2_power)
-
-    sort_plays(outputs)
-    for i in range(len(outputs)):
-        print(outputs[i][1])
-
-if __name__ == "__main__":
-    main()