def show_example():
batter = sing.get_players(name="Aaron Judge")[0]
pitching_team = "Rays"
candidate_pitchers = sing.get_players(team_name=pitching_team,
position=["P"],
active=True,
on_40=True)
atmosph = Atmosphere(sing.get_venues(stadium_name="Yankee Stadium")[0],
temperature=70,
home_team=sing.get_teams(name="Yankees")[0])
state = State(inning=9,
top=False,
bat_score=3,
fld_score=3,
on_1b=True,
on_2b=True,
on_3b=True,
outs=2)
matchups = [
Matchup(batter=batter, pitcher=p, atmosphere=atmosph, state=state)
for p in candidate_pitchers
]
results = pd.DataFrame(sing.get_pa_sim(matchups))
print(f'\nBest pitcher to face Aaron Judge with the game on the line')
pprint(results.sort_values(by=["obp_exp"]))
def show_example():
batter_list = [
'Mookie Betts', 'Cody Bellinger', 'Corey Seager', 'Manny Machado'
]
pitcher_list = ['Chris Paddack', 'Clayton Kershaw']
candidate_batters = [
sing.get_players(name=player_name)[0] for player_name in batter_list
]
candidate_pitchers = [
sing.get_players(name=player_name)[0] for player_name in pitcher_list
]
venue = sing.get_venues(stadium_name="Dodger Stadium")[0]
game_state = State(on_1b=False,
on_2b=True,
on_3b=False,
inning=4,
outs=0,
top=True,
bat_score=3,
fld_score=5,
pitch_number=50)
atmosph = Atmosphere(venue,
temperature=70,
home_team=sing.get_teams(name="Dodgers")[0])
matchups = ([
Matchup(batter=m, pitcher=p, state=game_state, atmosphere=atmosph)
for m in candidate_batters for p in candidate_pitchers
])
results = pd.DataFrame(sing.get_pa_sim(matchups))
print(f'\nResults')
print(results.sort_values(by=["woba_exp"], ascending=False))
def get_predictions(args):
batter_list = [
sing.get_players(name=b.strip())[0] for b in args.batters.split(',')
]
pitcher_list = [
sing.get_players(name=p.strip())[0] for p in args.pitchers.split(',')
]
state = State(inning=args.inning,
on_1b=args.on1b,
on_2b=args.on2b,
on_3b=args.on3b,
outs=args.outs,
top=not (args.bottom),
bat_score=args.batscore,
fld_score=args.fieldscore,
pitch_number=args.pitchnumber)
venue = sing.get_venues(stadium_name=args.venue)[0]
atmosphere = Atmosphere(venue=venue,
home_team=sing.get_teams(name=args.hometeam)[0],
temperature=args.temperature)
matchups = [
Matchup(batter=m,
pitcher=p,
atmosphere=atmosphere,
state=state,
date=args.date) for m in batter_list for p in pitcher_list
]
results = pd.DataFrame(
sing.get_pa_sim(matchups,
return_features=args.showinputs,
model_name=args.predictiontype))
return results
def show_example():
batter_list = ['Mookie Betts']
pitcher_list = ['Chris Paddack']
candidate_batters = [
sing.get_players(name=player_name)[0] for player_name in batter_list
]
candidate_pitchers = [
sing.get_players(name=player_name)[0] for player_name in pitcher_list
]
venue = sing.get_venues(stadium_name="Dodger Stadium")[0]
atmosph = Atmosphere(venue,
temperature=70,
home_team=sing.get_teams(name="Dodgers")[0])
state = State(on_1b=False,
on_2b=True,
on_3b=False,
pitch_number=79,
inning=4,
top=False,
bat_score=3,
fld_score=5)
matchups = ([
Matchup(batter=m, pitcher=p, atmosphere=atmosph, state=state)
for m in candidate_batters for p in candidate_pitchers
])
results = pd.DataFrame(sing.get_pa_sim(matchups, return_features=True))
print(f'\nResults')
print(results.sort_values(by=["woba_exp"], ascending=False))
def show_example():
candidate_batters = (sing.get_players(position=["1B", "DH"],
bat_side=["R"],
age_min=32,
active=True))
candidate_pitchers = [
sing.get_players(name=p)[0]
for p in ['J.A. Happ', 'James Paxton', 'Jordan Montgomery']
]
venue = sing.get_venues(stadium_name="Yankee Stadium")[0]
atmosph = Atmosphere(venue,
temperature=70,
home_team=sing.get_teams(name="Yankee")[0])
matchups = ([
Matchup(batter=m, pitcher=p, atmosphere=atmosph, state=State())
for m in candidate_batters for p in candidate_pitchers
])
results = pd.DataFrame(sing.get_pa_sim(matchups))
print(results.sort_values(by=["woba_exp"], ascending=False))
def find_best_reliever(pitchers, sims):
print(f'\nFinding the best closer by simulating {sims} innings')
home_lineup_positions = [
LineupPos(player=sing.get_players(name='Austin Hays')[0],
position='CF'),
LineupPos(player=sing.get_players(name='Anthony Santander')[0],
position='RF'),
LineupPos(player=sing.get_players(name='Jose Iglesias')[0],
position='SS'),
LineupPos(player=sing.get_players(name='Rio Ruiz')[0], position='3B'),
LineupPos(player=sing.get_players(name='Hanser Alberto')[0],
position='2B'),
LineupPos(player=sing.get_players(name='Renato Nunez')[0],
position='1B'),
LineupPos(player=sing.get_players(name='Dwight Smith')[0],
position='DH'),
LineupPos(player=sing.get_players(name='Pedro Severino')[0],
position='C'),
LineupPos(player=sing.get_players(name='DJ Stewart')[0],
position='CF'),
LineupPos(player=sing.get_players(name='Tommy Milone')[0],
position='P'),
]
visit_lineup_positions = [
LineupPos(player=sing.get_players(name='Yandy Diaz')[0],
position='3B'),
LineupPos(player=sing.get_players(name='Jose Martinez')[0],
position='1B'),
LineupPos(player=sing.get_players(name='Yoshi Tsutsugo')[0],
position='DH'),
LineupPos(player=sing.get_players(name='Hunter Renfroe')[0],
position='RF'),
LineupPos(player=sing.get_players(name='Manuel Margot')[0],
position='LF'),
LineupPos(player=sing.get_players(name='Mike Brosseau')[0],
position='3B'),
LineupPos(player=sing.get_players(name='Willy Adames')[0],
position='SS'),
LineupPos(player=sing.get_players(name='Kevin Kiermaier')[0],
position='CF'),
LineupPos(player=sing.get_players(name='Mike Zunino')[0],
position='C'),
LineupPos(player=sing.get_players(name='Tyler Glasnow')[0],
position='P'),
]
B = pd.Index([
f'{batter.player.full_name} ({batter.player.bat_side})'
for batter in home_lineup_positions
],
name="rows")[0:9]
P = pd.Index([
f'{pitcher.full_name} ({pitcher.pitch_hand})'
for pitcher in test_pitcher_list
],
name="columns")
home_lineup = Lineup(lineup=home_lineup_positions)
df = pd.DataFrame('', index=B, columns=P)
location = sing.get_venues(stadium_name='Oriole Park')[0]
home_team = sing.get_teams(name="Orioles")[0]
#assume, visit team has one run lead in the bottom of the 10th inning. Based on 2020 rules, home team starts with a runner on 2B and no one out
bat_score_start = 2
fld_score_start = 3
for bat_lineup_start in range(
1, 10
): #loop through batters. Skip last batter (pitcher) because of DH
st = State(inning=10,
top=False,
on_1b=False,
on_2b=True,
on_3b=False,
outs=0,
bat_score=bat_score_start,
fld_score=fld_score_start,
bat_lineup_order=bat_lineup_start)
pitcher_count = 0
for pitcher in pitchers: #loop through pitchers
pitcher_count = pitcher_count + 1 #track which pitcher we are using
visit_lineup_positions[9] = LineupPos(
player=sing.get_players(id=pitcher.mlb_id)[0], position='P')
visit_lineup = Lineup(lineup=visit_lineup_positions)
game = Game(visit_lineup=visit_lineup,
home_lineup=home_lineup,
atmosphere=Atmosphere(venue=location,
home_team=home_team))
#run the simulation. it will return an array of GameSimResults
game_sim_results = sing.get_game_sim(
{
'game': game,
'start_state': st
}, num_sims=sims)
saves = len(
[1 for r in game_sim_results if (r.away_score > r.home_score)])
losses = len(
[1 for r in game_sim_results if (r.away_score < r.home_score)])
ties = len([
1 for r in game_sim_results if (r.away_score == r.home_score)
])
print(
'Pitcher: {:<20s} Lineup Start: {} Save Percentage: {:.1f}% Loss Percentage: {:.1f}% Tie Percentage: {:.1f}%'
.format(pitcher.full_name, bat_lineup_start,
100 * saves / sims, 100 * losses / sims,
100 * ties / sims))
df.loc[B[bat_lineup_start - 1]][P[pitcher_count - 1]] = round(
saves / sims, 3)
for col in df.columns:
df[col] = df[col].astype(float)
return df
##########################################
# Simple batter vs. pitcher plate appearance prediction using defaults
##########################################
from common import sing
from pprint import pprint
from singlearity import State, Player, Team, Venue, Atmosphere, Matchup, ApiException
from singlearity.rest import ApiException
import pandas as pd
batter_list = ['Mookie Betts', 'Cody Bellinger', 'Gavin Lux']
pitcher_list = ['Chris Paddack']
candidate_batters = [sing.get_players(name = player_name)[0] for player_name in batter_list]
candidate_pitchers = [sing.get_players(name = player_name)[0] for player_name in pitcher_list]
venue = sing.get_venues(stadium_name = "Dodger Stadium")[0]
atmosph = Atmosphere(venue, temperature = 70, home_team = sing.get_teams(name = "Dodgers")[0])
matchups = ([Matchup(batter = m, pitcher = p,
atmosphere = atmosph, state=State()) for m in candidate_batters for p in candidate_pitchers])
results = pd.DataFrame(sing.get_pa_sim(matchups))
#just get some interesting stats. comment below line out to see all predictions
results = results[['batter_name', 'pitcher_name', 'hr_exp', 'so_exp', 'ba_exp', 'ops_exp', 'woba_exp']]
print(f'Results:\n{results}')