def calculate_epv_added( event_id, events, tracking_home, tracking_away, GK_numbers, EPV, params):
""" calculate_epv_added
Calculates the expected possession value added by a pass
Parameters
-----------
event_id: Index (not row) of the pass event to calculate EPV-added score
events: Dataframe containing the event data
tracking_home: tracking DataFrame for the Home team
tracking_away: tracking DataFrame for the Away team
GK_numbers: tuple containing the player id of the goalkeepers for the (home team, away team)
EPV: tuple Expected Possession value grid (loaded using load_EPV_grid() )
params: Dictionary of pitch control model parameters (default model parameters can be generated using default_model_params() )
Returrns
-----------
EEPV_added: Expected EPV value-added of pass defined by event_id
EPV_difference: The raw change in EPV (ignoring pitch control) between end and start points of pass
"""
# pull out pass details from the event data
pass_start_pos = np.array([events.loc[event_id]['Start X'],events.loc[event_id]['Start Y']])
pass_target_pos = np.array([events.loc[event_id]['End X'],events.loc[event_id]['End Y']])
pass_frame = events.loc[event_id]['Start Frame']
pass_team = events.loc[event_id].Team
# direction of play for atacking team (so we know whether to flip the EPV grid)
home_attack_direction = mio.find_playing_direction(tracking_home,'Home')
if pass_team=='Home':
attack_direction = home_attack_direction
attacking_players = mpc.initialise_players(tracking_home.loc[pass_frame],'Home',params,GK_numbers[0])
defending_players = mpc.initialise_players(tracking_away.loc[pass_frame],'Away',params,GK_numbers[1])
elif pass_team=='Away':
attack_direction = home_attack_direction*-1
defending_players = mpc.initialise_players(tracking_home.loc[pass_frame],'Home',params,GK_numbers[0])
attacking_players = mpc.initialise_players(tracking_away.loc[pass_frame],'Away',params,GK_numbers[1])
# flag any players that are offside
attacking_players = mpc.check_offsides( attacking_players, defending_players, pass_start_pos, GK_numbers)
# pitch control grid at pass start location
Patt_start,_ = mpc.calculate_pitch_control_at_target(pass_start_pos, attacking_players, defending_players, pass_start_pos, params)
# pitch control grid at pass end location
Patt_target,_ = mpc.calculate_pitch_control_at_target(pass_target_pos, attacking_players, defending_players, pass_start_pos, params)
# EPV at start location
EPV_start = get_EPV_at_location(pass_start_pos, EPV, attack_direction=attack_direction)
# EPV at end location
EPV_target = get_EPV_at_location(pass_target_pos,EPV,attack_direction=attack_direction)
# 'Expected' EPV at target and start location
EEPV_target = Patt_target*EPV_target
EEPV_start = Patt_start*EPV_start
# difference is the (expected) EPV added
EEPV_added = EEPV_target - EEPV_start
# Also calculate the straight up change in EPV
EPV_difference = EPV_target - EPV_start
return EEPV_added, EPV_difference
def find_max_value_added_target(event_id, events, tracking_home, tracking_away,
GK_numbers, EPV, params):
""" find_max_value_added_target
Finds the *maximum* expected possession value that could have been achieved for a pass (defined by the event_id) by searching the entire field for the best target.
Parameters
-----------
event_id: Index (not row) of the pass event to calculate EPV-added score
events: Dataframe containing the event data
tracking_home: tracking DataFrame for the Home team
tracking_away: tracking DataFrame for the Away team
GK_numbers: tuple containing the player id of the goalkeepers for the (home team, away team)
EPV: tuple Expected Possession value grid (loaded using load_EPV_grid() )
params: Dictionary of pitch control model parameters (default model parameters can be generated using default_model_params() )
Returrns
-----------
maxEPV_added: maximum EPV value-added that could be achieved at the current instant
max_target_location: (x,y) location of the position of the maxEPV_added
"""
# pull out pass details from the event data
pass_start_pos = np.array(
[events.loc[event_id]["Start X"], events.loc[event_id]["Start Y"]])
pass_frame = events.loc[event_id]["Start Frame"]
pass_team = events.loc[event_id].Team
# direction of play for atacking team (so we know whether to flip the EPV grid)
home_attack_direction = mio.find_playing_direction(tracking_home, "Home")
if pass_team == "Home":
attack_direction = home_attack_direction
attacking_players = mpc.initialise_players(
tracking_home.loc[pass_frame], "Home", params, GK_numbers[0])
defending_players = mpc.initialise_players(
tracking_away.loc[pass_frame], "Away", params, GK_numbers[1])
elif pass_team == "Away":
attack_direction = home_attack_direction * -1
defending_players = mpc.initialise_players(
tracking_home.loc[pass_frame], "Home", params, GK_numbers[0])
attacking_players = mpc.initialise_players(
tracking_away.loc[pass_frame], "Away", params, GK_numbers[1])
# flag any players that are offside
attacking_players = mpc.check_offsides(attacking_players,
defending_players, pass_start_pos,
GK_numbers)
# pitch control grid at pass start location
Patt_start, _ = mpc.calculate_pitch_control_at_target(
pass_start_pos, attacking_players, defending_players, pass_start_pos,
params)
# EPV at start location
EPV_start = get_EPV_at_location(pass_start_pos,
EPV,
attack_direction=attack_direction)
# calculate pitch control surface at moment of the pass
PPCF, xgrid, ygrid = mpc.generate_pitch_control_for_event(
event_id,
events,
tracking_home,
tracking_away,
params,
GK_numbers,
field_dimen=(
106.0,
68.0,
),
n_grid_cells_x=50,
offsides=True,
)
# EPV surface at instance of the pass
if attack_direction == -1:
EEPV = np.fliplr(EPV) * PPCF
else:
EEPV = EPV * PPCF
# find indices of the maxEPV
maxEPV_idx = np.unravel_index(EEPV.argmax(), EEPV.shape)
# Expected EPV at current ball position
EEPV_start = Patt_start * EPV_start
# maxEPV_added (difference between max location and current ball location)
maxEPV_added = EEPV.max() - EEPV_start
# location of maximum
max_target_location = (xgrid[maxEPV_idx[1]], ygrid[maxEPV_idx[0]])
return maxEPV_added, max_target_location
# direction of play for atacking team (so we know whether to flip the EPV grid)
home_attack_direction = mio.find_playing_direction(tracking_home, 'Home')
if pass_team == 'Home':
attack_direction = home_attack_direction
attacking_players = mpc.initialise_players(tracking_home.loc[pass_frame],
'Home', params, GK_numbers[0])
defending_players = mpc.initialise_players(tracking_away.loc[pass_frame],
'Away', params, GK_numbers[1])
elif pass_team == 'Away':
attack_direction = home_attack_direction * -1
defending_players = mpc.initialise_players(tracking_home.loc[pass_frame],
'Home', params, GK_numbers[0])
attacking_players = mpc.initialise_players(tracking_away.loc[pass_frame],
'Away', params, GK_numbers[1])
# flag any players that are offside
attacking_players = mpc.check_offsides(attacking_players, defending_players,
pass_start_pos, GK_numbers)
# pitch control grid at pass start location
# Patt_start,_ = mpc.calculate_pitch_control_at_target(pass_start_pos, attacking_players, defending_players, pass_start_pos, params)
#%%
ball_start_pos = pass_start_pos.copy()
target_position = pass_start_pos.copy()
if ball_start_pos is None or any(
np.isnan(ball_start_pos)): # assume that ball is already at location
ball_travel_time = 0.0
else:
# ball travel time is distance to target position from current ball position divided assumed average ball speed
ball_travel_time = np.linalg.norm(
target_position - ball_start_pos) / params['average_ball_speed']
#%%
