Adding removal of 6-mn lineups from showdown pull

database_queries.py

@@ -14,6 +14,26 @@ right_nfl_name_teams = ['Broncos', 'Commanders', 'Bengals', 'Cardinals', 'Rams',
 'Jets', 'Colts', 'Dolphins', 'Lions', 'Raiders', 'Falcons', 'Seahawks', 'Texans', 'Saints', 'Panthers', 'Giants', 'Browns', 'Titans', 'Eagles', 'Cowboys',
 'Chiefs', 'Chargers', 'Ravens', 'Bills', 'Vikings', 'Bears']
 
+
+def _showdown_seed_filter(salary_min, salary_max):
+    """Mongo filter for Showdown seed collections: salary band and at least one team."""
+    return {
+        'salary': {'$gte': salary_min, '$lte': salary_max},
+        'Team_count': {'$gte': 1},
+    }
+
+
+def _showdown_seed_filter_with_players(filter_query, salary_min, salary_max):
+    """AND player filter (e.g. {'$or': [...]}) with salary and Team_count constraints."""
+    return {
+        '$and': [
+            filter_query,
+            {'salary': {'$gte': salary_min, '$lte': salary_max}},
+            {'Team_count': {'$gte': 1}},
+        ],
+    }
+
+
 def init_nfl_baselines(type_var: str, site_var: str, slate_var: str):
 
     if slate_var == 'Main':
@@ -246,18 +266,18 @@ def init_DK_NFL_lineups(type_var, slate_var, prio_var, prio_mix, nfl_db_translat
                 # Combine all player conditions with $or
                 if query_conditions:
                     filter_query = {'$or': query_conditions}
-                    cursor1 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                    cursor2 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                    cursor1 = collection.find(_showdown_seed_filter_with_players(filter_query, salary_min, salary_max)).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                    cursor2 = collection.find(_showdown_seed_filter_with_players(filter_query, salary_min, salary_max)).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 else:
-                    cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                    cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                    cursor1 = collection.find(_showdown_seed_filter(salary_min, salary_max)).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                    cursor2 = collection.find(_showdown_seed_filter(salary_min, salary_max)).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
             else:
-                cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                cursor1 = collection.find(_showdown_seed_filter(salary_min, salary_max)).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                cursor2 = collection.find(_showdown_seed_filter(salary_min, salary_max)).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
         else:
-            cursor = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort(prio_var, -1).limit(lineup_num)
+            cursor = collection.find(_showdown_seed_filter(salary_min, salary_max)).sort(prio_var, -1).limit(lineup_num)
             raw_display = pd.DataFrame(list(cursor))
 
         raw_display = raw_display.drop_duplicates(subset=['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'])
@@ -416,18 +436,18 @@ def init_FD_NFL_lineups(type_var, slate_var, prio_var, prio_mix, nfl_db_translat
                 # Combine all player conditions with $or
                 if query_conditions:
                     filter_query = {'$or': query_conditions}
-                    cursor1 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                    cursor2 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                    cursor1 = collection.find(_showdown_seed_filter_with_players(filter_query, salary_min, salary_max)).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                    cursor2 = collection.find(_showdown_seed_filter_with_players(filter_query, salary_min, salary_max)).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 else:
-                    cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                    cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                    cursor1 = collection.find(_showdown_seed_filter(salary_min, salary_max)).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                    cursor2 = collection.find(_showdown_seed_filter(salary_min, salary_max)).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
             else:
-                cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                cursor1 = collection.find(_showdown_seed_filter(salary_min, salary_max)).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                cursor2 = collection.find(_showdown_seed_filter(salary_min, salary_max)).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
         else:
-            cursor = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort(prio_var, -1).limit(lineup_num)
+            cursor = collection.find(_showdown_seed_filter(salary_min, salary_max)).sort(prio_var, -1).limit(lineup_num)
             raw_display = pd.DataFrame(list(cursor))
 
         raw_display = raw_display.drop_duplicates(subset=['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'])
@@ -669,18 +689,18 @@ def init_DK_NBA_lineups(type_var, slate_var, prio_var, prio_mix, nba_db_translat
                 # Combine all player conditions with $or
                 if query_conditions:
                     filter_query = {'$or': query_conditions}
-                    cursor1 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                    cursor2 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                    cursor1 = collection.find(_showdown_seed_filter_with_players(filter_query, salary_min, salary_max)).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                    cursor2 = collection.find(_showdown_seed_filter_with_players(filter_query, salary_min, salary_max)).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 else:
-                    cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                    cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                    cursor1 = collection.find(_showdown_seed_filter(salary_min, salary_max)).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                    cursor2 = collection.find(_showdown_seed_filter(salary_min, salary_max)).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
             else:
-                cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                cursor1 = collection.find(_showdown_seed_filter(salary_min, salary_max)).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                cursor2 = collection.find(_showdown_seed_filter(salary_min, salary_max)).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
         else:
-            cursor = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort(prio_var, -1).limit(lineup_num)
+            cursor = collection.find(_showdown_seed_filter(salary_min, salary_max)).sort(prio_var, -1).limit(lineup_num)
             raw_display = pd.DataFrame(list(cursor))
 
         raw_display = raw_display.drop_duplicates(subset=['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'])
@@ -834,18 +854,18 @@ def init_FD_NBA_lineups(type_var, slate_var, prio_var, prio_mix, nba_db_translat
                 # Combine all player conditions with $or
                 if query_conditions:
                     filter_query = {'$or': query_conditions}
-                    cursor1 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                    cursor2 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                    cursor1 = collection.find(_showdown_seed_filter_with_players(filter_query, salary_min, salary_max)).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                    cursor2 = collection.find(_showdown_seed_filter_with_players(filter_query, salary_min, salary_max)).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 else:
-                    cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                    cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                    cursor1 = collection.find(_showdown_seed_filter(salary_min, salary_max)).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                    cursor2 = collection.find(_showdown_seed_filter(salary_min, salary_max)).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
             else:
-                cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                cursor1 = collection.find(_showdown_seed_filter(salary_min, salary_max)).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                cursor2 = collection.find(_showdown_seed_filter(salary_min, salary_max)).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
         else:
-            cursor = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort(prio_var, -1).limit(lineup_num)
+            cursor = collection.find(_showdown_seed_filter(salary_min, salary_max)).sort(prio_var, -1).limit(lineup_num)
             raw_display = pd.DataFrame(list(cursor))
 
         raw_display = raw_display.drop_duplicates(subset=['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'])
@@ -1065,18 +1085,18 @@ def init_DK_NHL_lineups(type_var, slate_var, prio_var, prio_mix, nhl_db_translat
                 # Combine all player conditions with $or
                 if query_conditions:
                     filter_query = {'$or': query_conditions}
-                    cursor1 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                    cursor2 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                    cursor1 = collection.find(_showdown_seed_filter_with_players(filter_query, salary_min, salary_max)).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                    cursor2 = collection.find(_showdown_seed_filter_with_players(filter_query, salary_min, salary_max)).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 else:
-                    cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                    cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                    cursor1 = collection.find(_showdown_seed_filter(salary_min, salary_max)).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                    cursor2 = collection.find(_showdown_seed_filter(salary_min, salary_max)).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
             else:
-                cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                cursor1 = collection.find(_showdown_seed_filter(salary_min, salary_max)).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                cursor2 = collection.find(_showdown_seed_filter(salary_min, salary_max)).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
         else:
-            cursor = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort(prio_var, -1).limit(lineup_num)
+            cursor = collection.find(_showdown_seed_filter(salary_min, salary_max)).sort(prio_var, -1).limit(lineup_num)
             raw_display = pd.DataFrame(list(cursor))
 
         raw_display = raw_display.drop_duplicates(subset=['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'])
@@ -1230,18 +1250,18 @@ def init_FD_NHL_lineups(type_var, slate_var, prio_var, prio_mix, nhl_db_translat
                 # Combine all player conditions with $or
                 if query_conditions:
                     filter_query = {'$or': query_conditions}
-                    cursor1 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                    cursor2 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                    cursor1 = collection.find(_showdown_seed_filter_with_players(filter_query, salary_min, salary_max)).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                    cursor2 = collection.find(_showdown_seed_filter_with_players(filter_query, salary_min, salary_max)).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 else:
-                    cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                    cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                    cursor1 = collection.find(_showdown_seed_filter(salary_min, salary_max)).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                    cursor2 = collection.find(_showdown_seed_filter(salary_min, salary_max)).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
             else:
-                cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                cursor1 = collection.find(_showdown_seed_filter(salary_min, salary_max)).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                cursor2 = collection.find(_showdown_seed_filter(salary_min, salary_max)).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
         else:
-            cursor = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort(prio_var, -1).limit(lineup_num)
+            cursor = collection.find(_showdown_seed_filter(salary_min, salary_max)).sort(prio_var, -1).limit(lineup_num)
             raw_display = pd.DataFrame(list(cursor))
 
         raw_display = raw_display.drop_duplicates(subset=['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'])
@@ -1461,18 +1481,18 @@ def init_DK_MLB_lineups(type_var, slate_var, prio_var, prio_mix, mlb_db_translat
                 # Combine all player conditions with $or
                 if query_conditions:
                     filter_query = {'$or': query_conditions}
-                    cursor1 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                    cursor2 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                    cursor1 = collection.find(_showdown_seed_filter_with_players(filter_query, salary_min, salary_max)).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                    cursor2 = collection.find(_showdown_seed_filter_with_players(filter_query, salary_min, salary_max)).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 else:
-                    cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                    cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                    cursor1 = collection.find(_showdown_seed_filter(salary_min, salary_max)).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                    cursor2 = collection.find(_showdown_seed_filter(salary_min, salary_max)).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
             else:
-                cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                cursor1 = collection.find(_showdown_seed_filter(salary_min, salary_max)).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                cursor2 = collection.find(_showdown_seed_filter(salary_min, salary_max)).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
         else:
-            cursor = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort(prio_var, -1).limit(lineup_num)
+            cursor = collection.find(_showdown_seed_filter(salary_min, salary_max)).sort(prio_var, -1).limit(lineup_num)
             raw_display = pd.DataFrame(list(cursor))
 
         raw_display = raw_display.drop_duplicates(subset=['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'])
@@ -1626,18 +1646,18 @@ def init_FD_MLB_lineups(type_var, slate_var, prio_var, prio_mix, mlb_db_translat
                 # Combine all player conditions with $or
                 if query_conditions:
                     filter_query = {'$or': query_conditions}
-                    cursor1 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                    cursor2 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                    cursor1 = collection.find(_showdown_seed_filter_with_players(filter_query, salary_min, salary_max)).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                    cursor2 = collection.find(_showdown_seed_filter_with_players(filter_query, salary_min, salary_max)).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 else:
-                    cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                    cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                    cursor1 = collection.find(_showdown_seed_filter(salary_min, salary_max)).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                    cursor2 = collection.find(_showdown_seed_filter(salary_min, salary_max)).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
             else:
-                cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                cursor1 = collection.find(_showdown_seed_filter(salary_min, salary_max)).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                cursor2 = collection.find(_showdown_seed_filter(salary_min, salary_max)).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
         else:
-            cursor = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort(prio_var, -1).limit(lineup_num)
+            cursor = collection.find(_showdown_seed_filter(salary_min, salary_max)).sort(prio_var, -1).limit(lineup_num)
             raw_display = pd.DataFrame(list(cursor))
 
         raw_display = raw_display.drop_duplicates(subset=['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'])
@@ -1859,18 +1879,18 @@ def init_DK_MMA_lineups(type_var, slate_var, prio_var, prio_mix, lineup_num, sal
                 # Combine all player conditions with $or
                 if query_conditions:
                     filter_query = {'$or': query_conditions}
-                    cursor1 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                    cursor2 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                    cursor1 = collection.find(_showdown_seed_filter_with_players(filter_query, salary_min, salary_max)).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                    cursor2 = collection.find(_showdown_seed_filter_with_players(filter_query, salary_min, salary_max)).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 else:
-                    cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                    cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                    cursor1 = collection.find(_showdown_seed_filter(salary_min, salary_max)).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                    cursor2 = collection.find(_showdown_seed_filter(salary_min, salary_max)).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
             else:
-                cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                cursor1 = collection.find(_showdown_seed_filter(salary_min, salary_max)).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                cursor2 = collection.find(_showdown_seed_filter(salary_min, salary_max)).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
         else:
-            cursor = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort(prio_var, -1).limit(lineup_num)
+            cursor = collection.find(_showdown_seed_filter(salary_min, salary_max)).sort(prio_var, -1).limit(lineup_num)
             raw_display = pd.DataFrame(list(cursor))
 
         raw_display = raw_display.drop_duplicates(subset=['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'])
@@ -2024,18 +2044,18 @@ def init_FD_MMA_lineups(type_var, slate_var, prio_var, prio_mix, lineup_num, sal
                 # Combine all player conditions with $or
                 if query_conditions:
                     filter_query = {'$or': query_conditions}
-                    cursor1 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                    cursor2 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                    cursor1 = collection.find(_showdown_seed_filter_with_players(filter_query, salary_min, salary_max)).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                    cursor2 = collection.find(_showdown_seed_filter_with_players(filter_query, salary_min, salary_max)).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 else:
-                    cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                    cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                    cursor1 = collection.find(_showdown_seed_filter(salary_min, salary_max)).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                    cursor2 = collection.find(_showdown_seed_filter(salary_min, salary_max)).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
             else:
-                cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                cursor1 = collection.find(_showdown_seed_filter(salary_min, salary_max)).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                cursor2 = collection.find(_showdown_seed_filter(salary_min, salary_max)).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
         else:
-            cursor = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort(prio_var, -1).limit(lineup_num)
+            cursor = collection.find(_showdown_seed_filter(salary_min, salary_max)).sort(prio_var, -1).limit(lineup_num)
             raw_display = pd.DataFrame(list(cursor))
 
         raw_display = raw_display.drop_duplicates(subset=['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'])
@@ -2260,18 +2280,18 @@ def init_DK_PGA_lineups(type_var, slate_var, prio_var, prio_mix, lineup_num, sal
                 # Combine all player conditions with $or
                 if query_conditions:
                     filter_query = {'$or': query_conditions}
-                    cursor1 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                    cursor2 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                    cursor1 = collection.find(_showdown_seed_filter_with_players(filter_query, salary_min, salary_max)).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                    cursor2 = collection.find(_showdown_seed_filter_with_players(filter_query, salary_min, salary_max)).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 else:
-                    cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                    cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                    cursor1 = collection.find(_showdown_seed_filter(salary_min, salary_max)).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                    cursor2 = collection.find(_showdown_seed_filter(salary_min, salary_max)).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
             else:
-                cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                cursor1 = collection.find(_showdown_seed_filter(salary_min, salary_max)).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                cursor2 = collection.find(_showdown_seed_filter(salary_min, salary_max)).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
         else:
-            cursor = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort(prio_var, -1).limit(lineup_num)
+            cursor = collection.find(_showdown_seed_filter(salary_min, salary_max)).sort(prio_var, -1).limit(lineup_num)
             raw_display = pd.DataFrame(list(cursor))
 
         raw_display = raw_display.drop_duplicates(subset=['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6'])
@@ -2431,18 +2451,18 @@ def init_FD_PGA_lineups(type_var, slate_var, prio_var, prio_mix, lineup_num, sal
                 # Combine all player conditions with $or
                 if query_conditions:
                     filter_query = {'$or': query_conditions}
-                    cursor1 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                    cursor2 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                    cursor1 = collection.find(_showdown_seed_filter_with_players(filter_query, salary_min, salary_max)).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                    cursor2 = collection.find(_showdown_seed_filter_with_players(filter_query, salary_min, salary_max)).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 else:
-                    cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                    cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                    cursor1 = collection.find(_showdown_seed_filter(salary_min, salary_max)).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                    cursor2 = collection.find(_showdown_seed_filter(salary_min, salary_max)).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
             else:
-                cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                cursor1 = collection.find(_showdown_seed_filter(salary_min, salary_max)).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                cursor2 = collection.find(_showdown_seed_filter(salary_min, salary_max)).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
         else:
-            cursor = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort(prio_var, -1).limit(lineup_num)
+            cursor = collection.find(_showdown_seed_filter(salary_min, salary_max)).sort(prio_var, -1).limit(lineup_num)
             raw_display = pd.DataFrame(list(cursor))
 
         raw_display = raw_display.drop_duplicates(subset=['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6'])
@@ -2674,18 +2694,18 @@ def init_DK_NASCAR_lineups(type_var, slate_var, prio_var, prio_mix, lineup_num,
                 # Combine all player conditions with $or
                 if query_conditions:
                     filter_query = {'$or': query_conditions}
-                    cursor1 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                    cursor2 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                    cursor1 = collection.find(_showdown_seed_filter_with_players(filter_query, salary_min, salary_max)).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                    cursor2 = collection.find(_showdown_seed_filter_with_players(filter_query, salary_min, salary_max)).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 else:
-                    cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                    cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                    cursor1 = collection.find(_showdown_seed_filter(salary_min, salary_max)).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                    cursor2 = collection.find(_showdown_seed_filter(salary_min, salary_max)).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
             else:
-                cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                cursor1 = collection.find(_showdown_seed_filter(salary_min, salary_max)).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                cursor2 = collection.find(_showdown_seed_filter(salary_min, salary_max)).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
         else:
-            cursor = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort(prio_var, -1).limit(lineup_num)
+            cursor = collection.find(_showdown_seed_filter(salary_min, salary_max)).sort(prio_var, -1).limit(lineup_num)
             raw_display = pd.DataFrame(list(cursor))
 
         raw_display = raw_display.drop_duplicates(subset=['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6'])
@@ -2845,18 +2865,18 @@ def init_FD_NASCAR_lineups(type_var, slate_var, prio_var, prio_mix, lineup_num,
                 # Combine all player conditions with $or
                 if query_conditions:
                     filter_query = {'$or': query_conditions}
-                    cursor1 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                    cursor2 = collection.find(filter_query, {'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                    cursor1 = collection.find(_showdown_seed_filter_with_players(filter_query, salary_min, salary_max)).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                    cursor2 = collection.find(_showdown_seed_filter_with_players(filter_query, salary_min, salary_max)).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 else:
-                    cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                    cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                    cursor1 = collection.find(_showdown_seed_filter(salary_min, salary_max)).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                    cursor2 = collection.find(_showdown_seed_filter(salary_min, salary_max)).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
             else:
-                cursor1 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).limit(math.ceil(lineup_num * (prio_mix / 100)))
-                cursor2 = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
+                cursor1 = collection.find(_showdown_seed_filter(salary_min, salary_max)).limit(math.ceil(lineup_num * (prio_mix / 100)))
+                cursor2 = collection.find(_showdown_seed_filter(salary_min, salary_max)).sort('Own', -1).limit(math.ceil(lineup_num * ((100 - prio_mix) / 100)))
                 raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
         else:
-            cursor = collection.find({'salary': {'$gte': salary_min, '$lte': salary_max}}).sort(prio_var, -1).limit(lineup_num)
+            cursor = collection.find(_showdown_seed_filter(salary_min, salary_max)).sort(prio_var, -1).limit(lineup_num)
             raw_display = pd.DataFrame(list(cursor))
 
         raw_display = raw_display.drop_duplicates(subset=['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6'])