Multichem commited on
Commit
b482280
·
1 Parent(s): b8708f4

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +38 -50
app.py CHANGED
@@ -42,10 +42,12 @@ wr_te_util = {'Routes%': '{:.2%}','Targets%': '{:.2%}', 'Air Yards%': '{:.2%}',
42
 
43
  wr_matchups_form = {'Opp Man%': '{:.2%}','Opp Zone%': '{:.2%}'}
44
 
 
 
45
  all_dk_player_projections = 'https://docs.google.com/spreadsheets/d/1I_1Ve3F4tftgfLQQoRKOJ351XfEG48s36OxXUKxmgS8/edit#gid=179416653'
46
 
47
- @st.cache_resource(ttl = 300)
48
- def rb_util_weekly():
49
  sh = gc.open_by_url(all_dk_player_projections)
50
  worksheet = sh.worksheet('RB_Util')
51
  raw_display = pd.DataFrame(worksheet.get_all_records())
@@ -54,12 +56,8 @@ def rb_util_weekly():
54
  'tprr', 'player_SDD_snaps_per', 'inside_five_rush_per', 'player_LDD_snaps_per', 'two_min_per', 'exPPR', 'ppr_fantasy', 'UR_Rank']]
55
  raw_display = raw_display.set_axis(['Player', 'Position', 'Week', 'Team-Season', 'Player Snaps%', 'Rush Att%', 'Routes%', 'Targets%',
56
  'TPRR', 'SDD Snaps%', 'i5 Rush%', 'LDD Snaps%', '2-min%', 'Expected PPR', 'PPR', 'Utilization Rank'], axis='columns')
57
- raw_display = raw_display.sort_values(by='Utilization Rank', ascending=True)
58
- return raw_display
59
-
60
- @st.cache_resource(ttl = 300)
61
- def wr_te_util_weekly():
62
- sh = gc.open_by_url(all_dk_player_projections)
63
  worksheet = sh.worksheet('WR_TE_Util')
64
  raw_display = pd.DataFrame(worksheet.get_all_records())
65
  raw_display = raw_display.replace('', np.nan)
@@ -67,12 +65,8 @@ def wr_te_util_weekly():
67
  'ayprr', 'endzone_targets_per', 'third_fourth_per', 'third_fourth_target_per', 'play_action_targets_per', 'exPPR', 'ppr_fantasy', 'UR_Rank']]
68
  raw_display = raw_display.set_axis(['Player', 'Position', 'Week', 'Team-Season', 'Routes%', 'Targets%', 'TPRR' , 'ADOT', 'Air Yards%',
69
  'AYPRR', 'Endzone Targets%', 'Third/Fourth%', 'Third/Fourth Targets%', 'Play Action Targets%', 'Expected PPR', 'PPR', 'Utilization Rank'], axis='columns')
70
- raw_display = raw_display.sort_values(by='Utilization Rank', ascending=True)
71
- return raw_display
72
-
73
- @st.cache_resource(ttl = 300)
74
- def rb_util_season():
75
- sh = gc.open_by_url(all_dk_player_projections)
76
  worksheet = sh.worksheet('RB_Util_Season')
77
  raw_display = pd.DataFrame(worksheet.get_all_records())
78
  raw_display = raw_display.replace('', np.nan)
@@ -80,12 +74,8 @@ def rb_util_season():
80
  'tprr', 'player_SDD_snaps_per', 'inside_five_rush_per', 'player_LDD_snaps_per', 'two_min_per', 'exPPR', 'ppr_fantasy', 'UR_Rank']]
81
  raw_display = raw_display.set_axis(['Player', 'Position', 'Team-Season', 'Player Snaps%', 'Rush Att%', 'Routes%', 'Targets%',
82
  'TPRR', 'SDD Snaps%', 'i5 Rush%', 'LDD Snaps%', '2-min%', 'Expected PPR', 'PPR', 'Utilization Rank'], axis='columns')
83
- raw_display = raw_display.sort_values(by='Utilization Rank', ascending=True)
84
- return raw_display
85
-
86
- @st.cache_resource(ttl = 300)
87
- def wr_te_util_season():
88
- sh = gc.open_by_url(all_dk_player_projections)
89
  worksheet = sh.worksheet('WR_TE_Util_Season')
90
  raw_display = pd.DataFrame(worksheet.get_all_records())
91
  raw_display = raw_display.replace('', np.nan)
@@ -93,42 +83,35 @@ def wr_te_util_season():
93
  'ayprr', 'endzone_targets_per', 'third_fourth_per', 'third_fourth_target_per', 'play_action_targets_per', 'exPPR', 'ppr_fantasy', 'UR_Rank']]
94
  raw_display = raw_display.set_axis(['Player', 'Position', 'Team-Season', 'Routes%', 'Targets%', 'TPRR' , 'ADOT', 'Air Yards%',
95
  'AYPRR', 'Endzone Targets%', 'Third/Fourth%', 'Third/Fourth Targets%', 'Play Action Targets%', 'Expected PPR', 'PPR', 'Utilization Rank'], axis='columns')
96
- raw_display = raw_display.sort_values(by='Utilization Rank', ascending=True)
97
- return raw_display
98
-
99
- @st.cache_resource(ttl = 300)
100
- def coverage_matchups():
101
- sh = gc.open_by_url(all_dk_player_projections)
102
  worksheet = sh.worksheet('Defensive Matchups')
103
  raw_display = pd.DataFrame(worksheet.get_all_records())
104
  raw_display = raw_display.replace('', np.nan)
105
  raw_display = raw_display.dropna(subset='Weighted Targets')
106
  raw_display = raw_display[raw_display['Weighted Targets'] != '#DIV/0!']
107
  raw_display = raw_display[raw_display['Weighted Targets'] != '#N/A']
108
- raw_display = raw_display.sort_values(by='Weighted Targets', ascending=False)
109
 
110
- return raw_display
111
-
112
- @st.cache_resource(ttl = 300)
113
- def macro_pull():
114
- sh = gc.open_by_url(all_dk_player_projections)
115
  worksheet = sh.worksheet('FL_Macro')
116
  raw_display = pd.DataFrame(worksheet.get_all_records())
117
  raw_display = raw_display.replace('', np.nan)
118
  raw_display = raw_display.dropna(subset='team')
119
- raw_display = raw_display.sort_values(by='Team Total', ascending=False)
120
-
121
- return raw_display
 
 
 
 
 
 
122
 
123
  @st.cache_data
124
  def convert_df_to_csv(df):
125
  return df.to_csv().encode('utf-8')
126
- rb_search = rb_util_weekly()
127
- wr_search = wr_te_util_weekly()
128
- rb_season = rb_util_season()
129
- wr_season = wr_te_util_season()
130
- wr_matchups = coverage_matchups()
131
- macro_data = macro_pull()
132
  pos_list = ['RB', 'WR', 'TE']
133
 
134
  tab1, tab2 = st.tabs(["Season Long Research", "Slate Specific"])
@@ -138,11 +121,7 @@ with tab1:
138
  with col1:
139
  if st.button("Load/Reset Data", key='reset1'):
140
  st.cache_data.clear()
141
- rb_search = rb_util_season()
142
- wr_search = wr_te_util_season()
143
- rb_season = rb_util_season()
144
- wr_season = wr_te_util_season()
145
- macro_data = macro_pull()
146
  stat_type_var1 = st.radio("What table are you loading?", ('Macro Table', 'RB Usage (Weekly)', 'WR/TE Usage (Weekly)', 'RB Usage (Season)', 'WR/TE Usage (Season)'), key='stat_type_var1')
147
  split_var1 = st.radio("Are you running the the whole league or certain teams?", ('All Teams', 'Specific Teams'), key='split_var1')
148
  pos_split1 = st.radio("Are you viewing all positions or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split1')
@@ -198,14 +177,17 @@ with tab2:
198
  with col1:
199
  if st.button("Load/Reset Data", key='reset2'):
200
  st.cache_data.clear()
201
- wr_matchups = coverage_matchups()
202
- stat_type_var2 = st.radio("What table are you loading?", ('WR/TE Coverage Matchups', 'Nothing idk lol'))
203
  if stat_type_var2 == 'WR/TE Coverage Matchups':
204
  routes_var2 = st.slider("Is there a certain price range of routes you want to include?", 0, 50, (10, 50), key='sal_var2')
205
  split_var2 = st.radio("Are you running the the whole league or certain teams?", ('All Teams', 'Specific Teams'))
206
  pos_split2 = st.radio("Are you viewing all positions or specific positions?", ('All Positions', 'Specific Positions'))
207
  if pos_split2 == 'Specific Positions':
208
- pos_var2 = st.multiselect('What Positions would you like to view?', options = ['RB', 'WR', 'TE'])
 
 
 
209
  elif pos_split2 == 'All Positions':
210
  pos_var2 = pos_list
211
  if split_var2 == 'Specific Teams':
@@ -218,13 +200,19 @@ with tab2:
218
  slate_table_instance = slate_table_instance[slate_table_instance['Position'].isin(pos_var2)]
219
  slate_table_instance = slate_table_instance[slate_table_instance['Avg Routes'] >= routes_var2[0]]
220
  slate_table_instance = slate_table_instance[slate_table_instance['Avg Routes'] <= routes_var2[1]]
221
- slate_table_instance = slate_table_instance.set_index('name')
 
 
 
 
222
  elif stat_type_var1 == 'Nothing idk lol':
223
  slate_table_instance = wr_matchups
224
 
225
  with col2:
226
  if stat_type_var2 == 'WR/TE Coverage Matchups':
227
  st.dataframe(slate_table_instance.style.background_gradient(axis=0).background_gradient(cmap = 'RdYlGn').format(wr_matchups_form, precision=2), use_container_width = True)
 
 
228
  elif stat_type_var2 == 'Nothing idk lol':
229
  st.write('lol same bro but yo the vibes immaculate')
230
  if stat_type_var2 == 'WR/TE Coverage Matchups':
 
42
 
43
  wr_matchups_form = {'Opp Man%': '{:.2%}','Opp Zone%': '{:.2%}'}
44
 
45
+ trending_form = {'Trend': '{:.2%}'}
46
+
47
  all_dk_player_projections = 'https://docs.google.com/spreadsheets/d/1I_1Ve3F4tftgfLQQoRKOJ351XfEG48s36OxXUKxmgS8/edit#gid=179416653'
48
 
49
+ @st.cache_resource(ttl = 600)
50
+ def pull_baselines():
51
  sh = gc.open_by_url(all_dk_player_projections)
52
  worksheet = sh.worksheet('RB_Util')
53
  raw_display = pd.DataFrame(worksheet.get_all_records())
 
56
  'tprr', 'player_SDD_snaps_per', 'inside_five_rush_per', 'player_LDD_snaps_per', 'two_min_per', 'exPPR', 'ppr_fantasy', 'UR_Rank']]
57
  raw_display = raw_display.set_axis(['Player', 'Position', 'Week', 'Team-Season', 'Player Snaps%', 'Rush Att%', 'Routes%', 'Targets%',
58
  'TPRR', 'SDD Snaps%', 'i5 Rush%', 'LDD Snaps%', '2-min%', 'Expected PPR', 'PPR', 'Utilization Rank'], axis='columns')
59
+ rb_search = raw_display.sort_values(by='Utilization Rank', ascending=True)
60
+
 
 
 
 
61
  worksheet = sh.worksheet('WR_TE_Util')
62
  raw_display = pd.DataFrame(worksheet.get_all_records())
63
  raw_display = raw_display.replace('', np.nan)
 
65
  'ayprr', 'endzone_targets_per', 'third_fourth_per', 'third_fourth_target_per', 'play_action_targets_per', 'exPPR', 'ppr_fantasy', 'UR_Rank']]
66
  raw_display = raw_display.set_axis(['Player', 'Position', 'Week', 'Team-Season', 'Routes%', 'Targets%', 'TPRR' , 'ADOT', 'Air Yards%',
67
  'AYPRR', 'Endzone Targets%', 'Third/Fourth%', 'Third/Fourth Targets%', 'Play Action Targets%', 'Expected PPR', 'PPR', 'Utilization Rank'], axis='columns')
68
+ wr_search = raw_display.sort_values(by='Utilization Rank', ascending=True)
69
+
 
 
 
 
70
  worksheet = sh.worksheet('RB_Util_Season')
71
  raw_display = pd.DataFrame(worksheet.get_all_records())
72
  raw_display = raw_display.replace('', np.nan)
 
74
  'tprr', 'player_SDD_snaps_per', 'inside_five_rush_per', 'player_LDD_snaps_per', 'two_min_per', 'exPPR', 'ppr_fantasy', 'UR_Rank']]
75
  raw_display = raw_display.set_axis(['Player', 'Position', 'Team-Season', 'Player Snaps%', 'Rush Att%', 'Routes%', 'Targets%',
76
  'TPRR', 'SDD Snaps%', 'i5 Rush%', 'LDD Snaps%', '2-min%', 'Expected PPR', 'PPR', 'Utilization Rank'], axis='columns')
77
+ rb_season = raw_display.sort_values(by='Utilization Rank', ascending=True)
78
+
 
 
 
 
79
  worksheet = sh.worksheet('WR_TE_Util_Season')
80
  raw_display = pd.DataFrame(worksheet.get_all_records())
81
  raw_display = raw_display.replace('', np.nan)
 
83
  'ayprr', 'endzone_targets_per', 'third_fourth_per', 'third_fourth_target_per', 'play_action_targets_per', 'exPPR', 'ppr_fantasy', 'UR_Rank']]
84
  raw_display = raw_display.set_axis(['Player', 'Position', 'Team-Season', 'Routes%', 'Targets%', 'TPRR' , 'ADOT', 'Air Yards%',
85
  'AYPRR', 'Endzone Targets%', 'Third/Fourth%', 'Third/Fourth Targets%', 'Play Action Targets%', 'Expected PPR', 'PPR', 'Utilization Rank'], axis='columns')
86
+ wr_season = raw_display.sort_values(by='Utilization Rank', ascending=True)
87
+
 
 
 
 
88
  worksheet = sh.worksheet('Defensive Matchups')
89
  raw_display = pd.DataFrame(worksheet.get_all_records())
90
  raw_display = raw_display.replace('', np.nan)
91
  raw_display = raw_display.dropna(subset='Weighted Targets')
92
  raw_display = raw_display[raw_display['Weighted Targets'] != '#DIV/0!']
93
  raw_display = raw_display[raw_display['Weighted Targets'] != '#N/A']
94
+ wr_matchups = raw_display.sort_values(by='Weighted Targets', ascending=False)
95
 
 
 
 
 
 
96
  worksheet = sh.worksheet('FL_Macro')
97
  raw_display = pd.DataFrame(worksheet.get_all_records())
98
  raw_display = raw_display.replace('', np.nan)
99
  raw_display = raw_display.dropna(subset='team')
100
+ macro_data = raw_display.sort_values(by='Team Total', ascending=False)
101
+
102
+ worksheet = sh.worksheet('Ownership Trend')
103
+ raw_display = pd.DataFrame(worksheet.get_all_records())
104
+ raw_display = raw_display.replace('', np.nan)
105
+ raw_display = raw_display.dropna(subset='Team')
106
+ trending_data = raw_display.sort_values(by='Trend', ascending=False)
107
+
108
+ return rb_search, wr_search, rb_season, wr_season, wr_matchups, macro_data, trending_data
109
 
110
  @st.cache_data
111
  def convert_df_to_csv(df):
112
  return df.to_csv().encode('utf-8')
113
+
114
+ rb_search, wr_search, rb_season, wr_season, wr_matchups, macro_data, trending_data = pull_baselines()
 
 
 
 
115
  pos_list = ['RB', 'WR', 'TE']
116
 
117
  tab1, tab2 = st.tabs(["Season Long Research", "Slate Specific"])
 
121
  with col1:
122
  if st.button("Load/Reset Data", key='reset1'):
123
  st.cache_data.clear()
124
+ rb_search, wr_search, rb_season, wr_season, wr_matchups, macro_data, trending_data = pull_baselines()
 
 
 
 
125
  stat_type_var1 = st.radio("What table are you loading?", ('Macro Table', 'RB Usage (Weekly)', 'WR/TE Usage (Weekly)', 'RB Usage (Season)', 'WR/TE Usage (Season)'), key='stat_type_var1')
126
  split_var1 = st.radio("Are you running the the whole league or certain teams?", ('All Teams', 'Specific Teams'), key='split_var1')
127
  pos_split1 = st.radio("Are you viewing all positions or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split1')
 
177
  with col1:
178
  if st.button("Load/Reset Data", key='reset2'):
179
  st.cache_data.clear()
180
+ rb_search, wr_search, rb_season, wr_season, wr_matchups, macro_data, trending_data = pull_baselines()
181
+ stat_type_var2 = st.radio("What table are you loading?", ('WR/TE Coverage Matchups', 'Ownership Trends', 'Nothing idk lol'))
182
  if stat_type_var2 == 'WR/TE Coverage Matchups':
183
  routes_var2 = st.slider("Is there a certain price range of routes you want to include?", 0, 50, (10, 50), key='sal_var2')
184
  split_var2 = st.radio("Are you running the the whole league or certain teams?", ('All Teams', 'Specific Teams'))
185
  pos_split2 = st.radio("Are you viewing all positions or specific positions?", ('All Positions', 'Specific Positions'))
186
  if pos_split2 == 'Specific Positions':
187
+ if stat_type_var2 == 'WR/TE Coverage Matchups':
188
+ pos_var2 = st.multiselect('What Positions would you like to view?', options = ['RB', 'WR', 'TE'])
189
+ elif stat_type_var2 == 'Ownership Trends':
190
+ pos_var2 = st.multiselect('What Positions would you like to view?', options = ['QB', 'RB', 'WR', 'TE', 'DST'])
191
  elif pos_split2 == 'All Positions':
192
  pos_var2 = pos_list
193
  if split_var2 == 'Specific Teams':
 
200
  slate_table_instance = slate_table_instance[slate_table_instance['Position'].isin(pos_var2)]
201
  slate_table_instance = slate_table_instance[slate_table_instance['Avg Routes'] >= routes_var2[0]]
202
  slate_table_instance = slate_table_instance[slate_table_instance['Avg Routes'] <= routes_var2[1]]
203
+ slate_table_instance = slate_table_instance.set_index('name')
204
+ elif stat_type_var2 == 'Ownership Trends':
205
+ slate_table_instance = trending_data
206
+ slate_table_instance = slate_table_instance[slate_table_instance['Team'].isin(team_var2)]
207
+ slate_table_instance = slate_table_instance[slate_table_instance['Position'].isin(pos_var2)]
208
  elif stat_type_var1 == 'Nothing idk lol':
209
  slate_table_instance = wr_matchups
210
 
211
  with col2:
212
  if stat_type_var2 == 'WR/TE Coverage Matchups':
213
  st.dataframe(slate_table_instance.style.background_gradient(axis=0).background_gradient(cmap = 'RdYlGn').format(wr_matchups_form, precision=2), use_container_width = True)
214
+ elif stat_type_var2 == 'Ownership Trends':
215
+ st.dataframe(slate_table_instance.style.background_gradient(axis=0).background_gradient(cmap = 'RdYlGn').format(trending_form, precision=2), use_container_width = True)
216
  elif stat_type_var2 == 'Nothing idk lol':
217
  st.write('lol same bro but yo the vibes immaculate')
218
  if stat_type_var2 == 'WR/TE Coverage Matchups':