Spaces:
Sleeping
Sleeping
James McCool
commited on
Commit
·
8e462c9
1
Parent(s):
aef223f
Enhance exposure_spread function to include sport-specific position eligibility checks, ensuring accurate player replacements based on their eligible positions across various sports.
Browse files- app.py +1 -1
- global_func/exposure_spread.py +187 -3
app.py
CHANGED
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@@ -1156,7 +1156,7 @@ with tab2:
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submitted = st.form_submit_button("Submit")
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if submitted:
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st.session_state['settings_base'] = False
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-
parsed_frame = exposure_spread(st.session_state['working_frame'], exposure_player, exposure_target, exposure_stack_bool, st.session_state['projections_df'])
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st.session_state['working_frame'] = parsed_frame.reset_index(drop=True)
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st.session_state['export_merge'] = st.session_state['working_frame'].copy()
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with st.container():
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submitted = st.form_submit_button("Submit")
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if submitted:
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st.session_state['settings_base'] = False
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+
parsed_frame = exposure_spread(st.session_state['working_frame'], exposure_player, exposure_target, exposure_stack_bool, st.session_state['projections_df'], sport_var)
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st.session_state['working_frame'] = parsed_frame.reset_index(drop=True)
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st.session_state['export_merge'] = st.session_state['working_frame'].copy()
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with st.container():
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global_func/exposure_spread.py
CHANGED
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@@ -8,7 +8,186 @@ import math
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#### Find the player and the amount of rows that contain them and then find an exposure rate which is the percentage of total rows
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#### Use the exposure target argument and try to replace the player from as many rows as necessary to be at or just under the target
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-
def
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# Find comparable players in the projections
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comparable_players = projections_df[projections_df['player_names'] == exposure_player]
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@@ -70,8 +249,13 @@ def exposure_spread(working_frame, exposure_player, exposure_target, exposure_st
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row_data = working_frame.iloc[row]
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for col in working_frame.columns:
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if row_data[col] == exposure_player:
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-
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-
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change_counter += 1
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return working_frame
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#### Find the player and the amount of rows that contain them and then find an exposure rate which is the percentage of total rows
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#### Use the exposure target argument and try to replace the player from as many rows as necessary to be at or just under the target
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+
def check_nba_position_eligibility(column_name, player_positions):
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"""
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Check if a player is eligible for a specific NBA column position.
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Args:
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column_name (str): The column name (PG, PG1, PG2, SG, SG1, SG2, etc.)
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player_positions (list): List of positions the player is eligible for
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Returns:
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bool: True if player is eligible for the column
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"""
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if any(pos in column_name for pos in ['PG', 'SG', 'SF', 'PF', 'C']):
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# Extract the base position from the column name
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base_position = next(pos for pos in ['PG', 'SG', 'SF', 'PF', 'C'] if pos in column_name)
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return base_position in player_positions
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elif 'G' in column_name:
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return any(pos in ['PG', 'SG'] for pos in player_positions)
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elif 'F' in column_name:
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return any(pos in ['SF', 'PF'] for pos in player_positions)
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elif 'UTIL' in column_name:
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return True # UTIL can be any position
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return False
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def check_mlb_position_eligibility(column_name, player_positions):
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"""
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Check if a player is eligible for a specific MLB column position.
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Args:
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column_name (str): The column name (P, SP, RP, C, 1B, 2B, 3B, SS, OF)
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player_positions (list): List of positions the player is eligible for
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Returns:
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bool: True if player is eligible for the column
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"""
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if any(pos in column_name for pos in ['P', 'SP', 'RP']):
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return any(pos in ['P', 'SP', 'RP'] for pos in player_positions)
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elif any(pos in column_name for pos in ['C', '1B', '2B', '3B', 'SS', 'OF']):
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return any(pos in ['C', '1B', '2B', '3B', 'SS', 'OF'] for pos in player_positions)
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return False
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def check_nfl_position_eligibility(column_name, player_positions):
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"""
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Check if a player is eligible for a specific NFL column position.
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Args:
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column_name (str): The column name (QB, RB, WR, TE, FLEX, DST)
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player_positions (list): List of positions the player is eligible for
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Returns:
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bool: True if player is eligible for the column
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"""
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if any(pos in column_name for pos in ['QB', 'RB', 'WR', 'TE', 'DST']):
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return any(pos in ['QB', 'RB', 'WR', 'TE', 'DST'] for pos in player_positions)
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elif 'FLEX' in column_name:
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return any(pos in ['RB', 'WR', 'TE'] for pos in player_positions)
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elif 'UTIL' in column_name:
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return any(pos in ['RB', 'WR', 'TE'] for pos in player_positions)
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return False
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def check_golf_position_eligibility(column_name, player_positions):
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"""
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Check if a player is eligible for a specific Golf column position.
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Args:
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column_name (str): The column name (G)
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player_positions (list): List of positions the player is eligible for
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Returns:
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bool: True if player is eligible for the column
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"""
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return True
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def check_tennis_position_eligibility(column_name, player_positions):
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"""
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Check if a player is eligible for a specific Tennis column position.
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Args:
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column_name (str): The column name (TEN)
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player_positions (list): List of positions the player is eligible for
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Returns:
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bool: True if player is eligible for the column
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"""
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return True
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def check_mma_position_eligibility(column_name, player_positions):
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"""
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Check if a player is eligible for a specific MMA column position.
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Args:
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column_name (str): The column name (MMA)
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player_positions (list): List of positions the player is eligible for
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Returns:
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bool: True if player is eligible for the column
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"""
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return True
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def check_nascar_position_eligibility(column_name, player_positions):
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"""
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Check if a player is eligible for a specific NASCAR column position.
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Args:
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column_name (str): The column name (NAS)
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player_positions (list): List of positions the player is eligible for
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Returns:
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bool: True if player is eligible for the column
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"""
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return True
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def check_cfb_position_eligibility(column_name, player_positions):
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"""
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Check if a player is eligible for a specific CFB column position.
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Args:
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column_name (str): The column name (QB, RB, WR, TE, FLEX, DST)
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player_positions (list): List of positions the player is eligible for
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Returns:
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bool: True if player is eligible for the column
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"""
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if any(pos in column_name for pos in ['QB', 'RB', 'WR']):
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return any(pos in ['QB', 'RB', 'WR'] for pos in player_positions)
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elif 'FLEX' in column_name:
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return any(pos in ['RB', 'WR'] for pos in player_positions)
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elif 'SUPERFLEX' in column_name:
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return any(pos in ['RB', 'WR', 'QB'] for pos in player_positions)
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return False
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def check_nhl_position_eligibility(column_name, player_positions):
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"""
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Check if a player is eligible for a specific NHL column position.
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Args:
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column_name (str): The column name (C, LW, RW, D, G, UTIL)
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player_positions (list): List of positions the player is eligible for
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Returns:
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bool: True if player is eligible for the column
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"""
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if any(pos in column_name for pos in ['C', 'W', 'D', 'G']):
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return any(pos in ['C', 'W', 'D', 'G'] for pos in player_positions)
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elif 'FLEX' in column_name:
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return True # UTIL can be any position
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elif 'UTIL' in column_name:
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return True # UTIL can be any position
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return False
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def check_position_eligibility(sport, column_name, player_positions):
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"""
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Main function to check position eligibility based on sport.
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Args:
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sport (str): The sport (NBA, MLB, NFL, NHL)
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column_name (str): The column name
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player_positions (list): List of positions the player is eligible for
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Returns:
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bool: True if player is eligible for the column
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"""
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if sport == 'NBA':
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return check_nba_position_eligibility(column_name, player_positions)
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elif sport == 'MLB':
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return check_mlb_position_eligibility(column_name, player_positions)
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elif sport == 'NFL':
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return check_nfl_position_eligibility(column_name, player_positions)
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elif sport == 'NHL':
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return check_nhl_position_eligibility(column_name, player_positions)
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elif sport == 'MMA':
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return check_cfb_position_eligibility(column_name, player_positions)
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elif sport == 'Golf':
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return check_golf_position_eligibility(column_name, player_positions)
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elif sport == 'Tennis':
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return check_tennis_position_eligibility(column_name, player_positions)
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else:
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# Default fallback - assume exact position match
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return column_name in player_positions
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+
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def exposure_spread(working_frame, exposure_player, exposure_target, exposure_stack_bool, projections_df, sport_var):
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# Find comparable players in the projections
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comparable_players = projections_df[projections_df['player_names'] == exposure_player]
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row_data = working_frame.iloc[row]
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for col in working_frame.columns:
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if row_data[col] == exposure_player:
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# Get the replacement player's positions
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replacement_player_positions = projections_df[projections_df['player_names'] == insert_player]['position'].iloc[0].split('/')
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# Check if the replacement player is eligible for this column
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if check_position_eligibility(sport_var, col, replacement_player_positions):
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working_frame.at[row, col] = insert_player
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break
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change_counter += 1
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return working_frame
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