Hugging Face's logo

Spaces:
Multichem
/
MLB_Poisson_Check
Sleeping

App Files Community
MLB_Poisson_Check
File size: 6,880 Bytes 
e6aed42
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5f38c80
e6aed42
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
404a89f
e6aed42
 
 
 
404a89f
 
e6aed42
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
71c1236
 
0197b24
5f38c80
 
0197b24
 
5f38c80
 
0197b24
 
404a89f
 
e6aed42
 
404a89f
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
 
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-----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\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():
    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, sp_count, bp_count):
    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 = sp_count
    bp_instances = bp_count
    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())
    working_pitcher = pitcher_rhh.copy()
    pitcher_check = working_pitcher[working_pitcher['Names'] == pitcher_var1]
    pitcher_hand = pitcher_check['Hand'].iloc[0]
    if pitcher_hand == 'RHP':
        hitter_var1 = st.selectbox("What hitter are you looking at?", options = hitter_rhp['Player'].unique())
        working_hitters = hitter_rhp.copy()
        hitter_check = working_hitters[working_hitters['Player'] == hitter_var1]
    else:
        hitter_var1 = st.selectbox("What hitter are you looking at?", options = hitter_lhp['Player'].unique())
        working_hitters = hitter_lhp.copy()
        hitter_check = working_hitters[working_hitters['Player'] == hitter_var1]
    sp_count = st.number_input("How many PA against the Pitcher?")
    bp_count = st.number_input("How many PA against the Bullpen?")

with col2:
    if st.button('calculate theoretical means'):
        hitter_val = hitter_check['BB%'].iloc[0]
        sp_val = pitcher_check['BB%'].iloc[0]
        value = calc_poisson(hitter_val, sp_val, sp_count, bp_count)
        st.write(f"Theoretical mean of the SP instances: {value[0]}")
        st.write(f"Theoretical mean of the BP instances: {value[1]}")
        st.write(f"Sample mean from generated data: {value[2]}")