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MLB_Poisson_Check
File size: 5,883 Bytes 
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import streamlit as st
st.set_page_config(layout="wide")
import pandas as pd
import gspread
import pymongo
import time
import numpy as np
from scipy.stats import poisson

@st.cache_resource
def init_conn():
        scope = ['https://spreadsheets.google.com/feeds', 'https://www.googleapis.com/auth/drive']

        credentials = {
          "type": "service_account",
          "project_id": "model-sheets-connect",
          "private_key_id": "0e0bc2fdef04e771172fe5807392b9d6639d945e",
          "private_key": "-----BEGIN PRIVATE KEY-----\nMIIEvgIBADANBgkqhkiG9w0BAQEFAASCBKgwggSkAgEAAoIBAQDiu1v/e6KBKOcK\ncx0KQ23nZK3ZVvADYy8u/RUn/EDI82QKxTd/DizRLIV81JiNQxDJXSzgkbwKYEDm\n48E8zGvupU8+Nk76xNPakrQKy2Y8+VJlq5psBtGchJTuUSHcXU5Mg2JhQsB376PJ\nsCw552K6Pw8fpeMDJDZuxpKSkaJR6k9G5Dhf5q8HDXnC5Rh/PRFuKJ2GGRpX7n+2\nhT/sCax0J8jfdTy/MDGiDfJqfQrOPrMKELtsGHR9Iv6F4vKiDqXpKfqH+02E9ptz\nBk+MNcbZ3m90M8ShfRu28ebebsASfarNMzc3dk7tb3utHOGXKCf4tF8yYKo7x8BZ\noO9X4gSfAgMBAAECggEAU8ByyMpSKlTCF32TJhXnVJi/kS+IhC/Qn5JUDMuk4LXr\naAEWsWO6kV/ZRVXArjmuSzuUVrXumISapM9Ps5Ytbl95CJmGDiLDwRL815nvv6k3\nUyAS8EGKjz74RpoIoH6E7EWCAzxlnUgTn+5oP9Flije97epYk3H+e2f1f5e1Nn1d\nYNe8U+1HqJgILcxA1TAUsARBfoD7+K3z/8DVPHI8IpzAh6kTHqhqC23Rram4XoQ6\nzj/ZdVBjvnKuazETfsD+Vl3jGLQA8cKQVV70xdz3xwLcNeHsbPbpGBpZUoF73c65\nkAXOrjYl0JD5yAk+hmYhXr6H9c6z5AieuZGDrhmlFQKBgQDzV6LRXmjn4854DP/J\nI82oX2GcI4eioDZPRukhiQLzYerMQBmyqZIRC+/LTCAhYQSjNgMa+ZKyvLqv48M0\n/x398op/+n3xTs+8L49SPI48/iV+mnH7k0WI/ycd4OOKh8rrmhl/0EWb9iitwJYe\nMjTV/QxNEpPBEXfR1/mvrN/lVQKBgQDuhomOxUhWVRVH6x03slmyRBn0Oiw4MW+r\nrt1hlNgtVmTc5Mu+4G0USMZwYuOB7F8xG4Foc7rIlwS7Ic83jMJxemtqAelwOLdV\nXRLrLWJfX8+O1z/UE15l2q3SUEnQ4esPHbQnZowHLm0mdL14qSVMl1mu1XfsoZ3z\nJZTQb48CIwKBgEWbzQRtKD8lKDupJEYqSrseRbK/ax43DDITS77/DWwHl33D3FYC\nMblUm8ygwxQpR4VUfwDpYXBlklWcJovzamXpSnsfcYVkkQH47NuOXPXPkXQsw+w+\nDYcJzeu7F/vZqk9I7oBkWHUrrik9zPNoUzrfPvSRGtkAoTDSwibhoc5dAoGBAMHE\nK0T/ANeZQLNuzQps6S7G4eqjwz5W8qeeYxsdZkvWThOgDd/ewt3ijMnJm5X05hOn\ni4XF1euTuvUl7wbqYx76Wv3/1ZojiNNgy7ie4rYlyB/6vlBS97F4ZxJdxMlabbCW\n6b3EMWa4EVVXKoA1sCY7IVDE+yoQ1JYsZmq45YzPAoGBANWWHuVueFGZRDZlkNlK\nh5OmySmA0NdNug3G1upaTthyaTZ+CxGliwBqMHAwpkIRPwxUJpUwBTSEGztGTAxs\nWsUOVWlD2/1JaKSmHE8JbNg6sxLilcG6WEDzxjC5dLL1OrGOXj9WhC9KX3sq6qb6\nF/j9eUXfXjAlb042MphoF3ZC\n-----END PRIVATE KEY-----\n",
          "client_email": "[email protected]",
          "client_id": "100369174533302798535",
          "auth_uri": "https://accounts.google.com/o/oauth2/auth",
          "token_uri": "https://oauth2.googleapis.com/token",
          "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
          "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40model-sheets-connect.iam.gserviceaccount.com"
        }
     
        MLB_Data = 'https://docs.google.com/spreadsheets/d/1f42Ergav8K1VsOLOK9MUn7DM_MLMvv4GR2Fy7EfnZTc/edit#gid=340831852'

        gc_con = gspread.service_account_from_dict(credentials, scope)

        return gc_con, MLB_Data
    
gcservice_account, MLB_Data = init_conn()

@st.cache_data(ttl = 599)
def init_baselines(long_form, short_form):
    sh = gcservice_account.open_by_url(MLB_Data)
    
    worksheet = sh.worksheet('Hitter_Data (RHP)')
    load_display = pd.DataFrame(worksheet.get_all_records())
    load_display.replace('', np.nan, inplace=True)
    load_display = load_display.dropna(subset=['PA'])
    load_display = load_display.drop_duplicates(subset=['Player'], keep='first')
    
    hitter_rhp = load_display.copy()
    
    time.sleep(.5)
    
    worksheet = sh.worksheet('Hitter_Data (LHP)')
    load_display = pd.DataFrame(worksheet.get_all_records())
    load_display.replace('', np.nan, inplace=True)
    load_display = load_display.dropna(subset=['PA'])
    load_display = load_display.drop_duplicates(subset=['Player'], keep='first')
    
    hitter_lhp = load_display.copy()
    
    time.sleep(.5)
    
    worksheet = sh.worksheet('Pitcher_Data (RHH)')
    load_display = pd.DataFrame(worksheet.get_all_records())
    load_display.replace('', np.nan, inplace=True)
    load_display = load_display.dropna(subset=['True AVG'])
    
    pitcher_rhh = load_display.copy()
    
    time.sleep(.5)
    
    worksheet = sh.worksheet('Pitcher_Data (LHH)')
    load_display = pd.DataFrame(worksheet.get_all_records())
    load_display.replace('', np.nan, inplace=True)
    load_display = load_display.dropna(subset=['True AVG'])
    
    pitcher_lhh = load_display.copy()

    return hitter_rhp, hitter_lhp, pitcher_rhh, pitcher_lhh

@st.cache_resource
def calc_poisson(hitter_val, sp_val):
    base_val = hitter_val
    opp_val = sp_val
    sp_combo_val = sum([base_val, opp_val]) / 2
    bp_combo_val = sum([base_val, .085]) / 2
    sp_instances = 1
    bp_instances = 0
    sp_mean = sp_combo_val * sp_instances
    bp_mean = bp_combo_val * bp_instances
    
    # Generate a large number of samples from the Poisson distribution
    SP_run = poisson.rvs(sp_mean, size=10000)
    BP_run = poisson.rvs(bp_mean, size=10000)
    
    # Calculate the sample mean
    sp_outcome = np.mean(SP_run)
    bp_outcome = np.mean(BP_run)
    
    mean_outcome = sp_outcome + bp_outcome
    
    return sp_outcome, bp_outcome, mean_outcome

hitter_rhp, hitter_lhp, pitcher_rhh, pitcher_lhh = init_baselines()

col1, col2 = st.columns([1, 7])

with col1:
    if st.button("Load/Reset Data", key='reset1'):
          st.cache_data.clear()
          hitter_rhp, hitter_lhp, pitcher_rhh, pitcher_lhh = init_baselines()
          
    pitcher_var1 = st.selectbox("Which pitcher are you looking at?", options = pitcher_rhh['Names'].unique())
    pitcher_check = pitcher_rhh[pitcher_rhh['Names'] == pitcher_var1]
    pitcher_hand = pitcher_check['Hand'][0]
    hitter_var1 = st.selectbox("What hitter are you looking at?", options = hitter_rhp['Player'].unique())

with col2:
    st.write(pitcher_hand)