def value_pool_hitting(league, player_pool, type):
from datetime import date
from general import utilities
from analysis import calculations
from analysis import player_pool_stats
assert type in ['B', 'P']
player_pool['type'] = type
# calc % of season left for determining PAs for sample threshold
f_date = date(2021, 4, 1)
l_date = date(2021, 9, 30)
season_length = l_date - f_date
pct_through = (l_date - date.today()).days / (l_date - f_date).days
#player_pool['sample'] = player_pool['pa'] > 500*pct_through
player_pool['sample'] = True
for run in range(1, 3):
player_pool = calculations.calc_z(df=player_pool,
ls=league,
type='hitting')
player_pool['sample'] = player_pool.apply(lambda row: row.zar > 0,
axis=1)
columns = [
'name', 'fg_id', 'type', 'elig', 'pa', league.hitting_counting_stats,
league.hitting_rate_stats, 'zar', 'value'
]
columns = utilities.flatten(columns)
player_pool = player_pool[columns]
return player_pool
def create_pitcher_valuations(league, stats):
from general import utilities
from analysis import calculations
from analysis import player_pool_stats
assert league.league_name in ['SoS', 'Legacy']
pitchers = stats
pitchers['type'] = 'P'
pitchers['sample'] = pitchers.apply(lambda row: not (row['era'] == float(
'inf') or row['whip'] == float('inf')),
axis=1)
for run in range(1, 3):
pitchers = calculations.calc_z(df=pitchers, ls=league, type='pitching')
pitchers['sample'] = pitchers.apply(lambda row: row.zar > 0, axis=1)
columns = [
'name', 'fg_id', 'team', 'type', 'elig', 'ip',
league.pitching_counting_stats, league.pitching_rate_stats, 'zar',
'value', 'zar_skills', 'rank_sp', 'rank_rp'
]
columns = utilities.flatten(columns)
pitchers = pitchers[columns]
return pitchers
def create_hitter_valuations(league, stats):
from general import utilities
from analysis import calculations
from analysis import player_pool_stats
assert league.league_name in ['SoS', 'Legacy']
hitters = stats
hitters['type'] = 'B'
pa_threshold = hitters['pa'].quantile(.9)
hitters['sample'] = hitters.apply(lambda row: row['pa'] > pa_threshold,
axis=1)
for run in range(1, 3):
hitters = calculations.calc_z(df=hitters, ls=league, type='batting')
hitters['sample'] = hitters.apply(lambda row: row.zar > 0, axis=1)
columns = [
'name', 'fg_id', 'team', 'type', 'elig', 'pa',
league.hitting_counting_stats, league.hitting_rate_stats, 'zar',
'value'
]
columns = utilities.flatten(columns)
hitters = hitters[columns]
hitters.reset_index(inplace=True)
return hitters
def create_combined_pitcher_valuations(league):
from general import utilities
from analysis import calculations
from analysis import player_pool_stats
assert league.league_name in ['SoS', 'Legacy']
combined_pitchers = player_pool_stats.create_combined_pitchers(league)
combined_pitchers['type'] = 'P'
combined_pitchers['sample'] = True
for run in range(1, 3):
combined_pitchers = calculations.calc_z(df=combined_pitchers,
ls=league,
type='pitching')
combined_pitchers['sample'] = combined_pitchers.apply(
lambda row: row.zar > 0, axis=1)
columns = [
'name', 'fg_id', 'team', 'type', 'elig', 'ip',
league.pitching_counting_stats, league.pitching_rate_stats, 'zar',
'value', 'zar_skills', 'rank_sp', 'rank_rp'
]
columns = utilities.flatten(columns)
combined_pitchers = combined_pitchers[columns]
return combined_pitchers
def create_combined_hitter_valuations(league):
from datetime import date
from general import utilities
from analysis import calculations
from analysis import player_pool_stats
assert league.league_name in ['SoS', 'Legacy']
combined_hitters = player_pool_stats.create_combined_hitters(league)
combined_hitters['type'] = 'B'
# calc % of season left for determining PAs for sample threshold
f_date = date(2021, 4, 1)
l_date = date(2021, 9, 30)
season_length = l_date - f_date
pct_through = (l_date - date.today()).days / (l_date - f_date).days
combined_hitters['sample'] = combined_hitters['pa'] > 500 * pct_through
for run in range(1, 3):
combined_hitters = calculations.calc_z(df=combined_hitters,
ls=league,
type='hitting')
combined_hitters['sample'] = combined_hitters.apply(
lambda row: row.zar > 0, axis=1)
combined_hitters_600 = player_pool_stats.create_combined_hitters(league,
pa=600)
combined_hitters_600['type'] = 'B'
combined_hitters_600 = combined_hitters_600.merge(
combined_hitters[['fg_id', 'sample']], how='left', on='fg_id')
combined_hitters_600 = calculations.calc_z(df=combined_hitters_600,
ls=league,
type='hitting')
combined_hitters = combined_hitters.merge(
combined_hitters_600[['fg_id',
'value']].rename(columns={'value': 'value_600'}),
how='left',
on='fg_id')
columns = [
'name', 'fg_id', 'type', 'elig', 'pa', league.hitting_counting_stats,
league.hitting_rate_stats, 'zar', 'value', 'value_600'
]
columns = utilities.flatten(columns)
combined_hitters = combined_hitters[columns]
return combined_hitters