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baseball_simulation.py
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import numpy as np
import pandas as pd
from enum import Enum
from copy import deepcopy
MAX_OUTS = 3
MAX_RUNS_PER_INNING = 5
MAX_INNINGS = 4
class Player:
p1b = 1.0
p2b = 1.0
p3b = 0.0
phr = 0.0
pso = 0.0 #struck out
pbo = 0.0 #hit but thrown out at first (useful for advancing a player)
def __init__(self, hit_probs):
self.p1b, self.p2b, self.p3b, self.phr, self.pso, self.pbo = hit_probs
if ((self.p1b + self.p2b + self.p3b + self.phr + self.pso + self.pbo) != 1.0):
print("Error with setting player")
print(hit_probs)
print((self.p1b + self.p2b + self.p3b + self.phr + self.pso + self.pbo))
self.hit_probs = [self.p1b, self.p2b, self.p3b, self.phr, self.pso, self.pbo]
def get_hit(self):
return np.random.choice(['p1b','p2b','p3b','phr','pso','pbo'], 1,
p = self.hit_probs)
class BaseBallAction(Enum):
Single = 1
Double = 2
Triple = 3
HomeRun = 4
StrikeOut = 5
ThrownOut = 6
class BaseballState():
def __init__(self, inning, runs, outs, base3, base2, base1, inning_start_runs = 0.0, likelihood = 1.0):
self.inning = inning
self.runs = runs
self.inning_start_runs = inning_start_runs
self.outs = outs
self.base3 = base3
self.base2 = base2
self.base1 = base1
self.likelihood = likelihood
def __repr__(self):
return "Inning: {}, Runs: {}, Inning Start Runs: {}, \n Outs: {}, First: {}, Second: {}, Third: {}, \n Likelihood: {}".format(self.inning, self.runs, self.inning_start_runs, self.outs, self.base1, self.base2, self.base3, self.likelihood)
def reset_bases(self):
self.base1 = 0
self.base2 = 0
self.base3 = 0
def increment_inning(self):
self.inning = self.inning + 1
self.reset_bases()
self.outs = 0
self.inning_start_runs = self.runs
def advance_bases(self, count, new_runner):
while(count != 0):
if (self.base3):
self.runs = self.runs + 1
self.base3 = self.base2
self.base2 = self.base1
self.base1 = new_runner
#max runs advance inning
if ((self.runs - self.inning_start_runs)%MAX_RUNS_PER_INNING == 0):
self.increment_inning()
break
else:
self.base3 = self.base2
self.base2 = self.base1
self.base1 = new_runner
new_runner = 0 #only new runner first time through
count = count - 1
def next_state(self,action, action_probability):
new_state = deepcopy(self)
new_state.likelihood = new_state.likelihood*action_probability
if (action == BaseBallAction.StrikeOut) or (action == BaseBallAction.ThrownOut):
new_state.outs = new_state.outs + 1
if (new_state.outs == MAX_OUTS):
#max outs advance inning
new_state.increment_inning()
elif (action == BaseBallAction.ThrownOut): #don't always advance base but will here...also usually throw to first
new_state.advance_bases(count = 1, new_runner = 0)
elif (action == BaseBallAction.Single):
new_state.advance_bases(count = 1, new_runner = 1)
elif (action == BaseBallAction.Double):
new_state.advance_bases(count = 2, new_runner = 1)
elif (action == BaseBallAction.Triple):
new_state.advance_bases(count = 3, new_runner = 1)
elif (action == BaseBallAction.HomeRun):
new_state.advance_bases(count = 4, new_runner = 1)
return new_state
'''
Runs a multi-hypothesis set of scenarios to determine likelihoods for each final state ate end of game
This looks like a tree once it is completed where the leaf nodes are the possible final states
'''
def run_player_scenario_mh(all_players, order):
players = [all_players[i] for i in order]
runs = [0.0 for i in range(0,MAX_INNINGS*MAX_RUNS_PER_INNING + 1)]
def run_next_iter(cur_state, player_idx, baseball_action, probability):
new_state = cur_state.next_state(baseball_action, probability)
if(new_state.inning > MAX_INNINGS):
runs[new_state.runs] = runs[new_state.runs] + new_state.likelihood
else:
run_next_player(new_state,(player_idx + 1)%len(players))
def run_next_player(cur_state, player_idx):
current_player = players[player_idx%len(players)]
if(current_player.p1b > 0.0):
run_next_iter(cur_state,player_idx,BaseBallAction.Single, current_player.p1b)
if(current_player.p2b > 0.0):
run_next_iter(cur_state,player_idx,BaseBallAction.Double, current_player.p2b)
if(current_player.p3b > 0.0):
run_next_iter(cur_state,player_idx,BaseBallAction.Triple, current_player.p3b)
if(current_player.phr > 0.0):
run_next_iter(cur_state,player_idx,BaseBallAction.HomeRun, current_player.phr)
if(current_player.pso > 0.0):
run_next_iter(cur_state,player_idx,BaseBallAction.StrikeOut, current_player.pso)
if(current_player.pbo > 0.0):
run_next_iter(cur_state,player_idx,BaseBallAction.ThrownOut, current_player.pbo)
start_state = BaseballState(1,0,0,0,0,0)
state_player_idx = 0
run_next_player(start_state, state_player_idx)
w_sum = 0
for idx, val in enumerate(runs):
w_sum = w_sum + val*idx
#return w_sum
return MAX_INNINGS*MAX_RUNS_PER_INNING - w_sum
'''
Runs a a monte-carlo using the players to figure out statistical return
'''
def run_player_scenario_mc_standalone(all_players, order):
players = [all_players[i] for i in order]
df = pd.DataFrame(data = [], columns = ['Inning', 'Runs', 'Outs', 'Inning Runs'])
OUTS = 3
MAX_RUNS_PER_INNING = 5
MAX_INNINGS = 4
MAX_MC_RUNS = 100
for sim_runs in range(0,MAX_MC_RUNS):
runs = 0
inning = 1
current_batter = 0
bases = [0,0,0] #b1, b2, b3
outs = 0
start_runs = 0
while inning <= MAX_INNINGS:
batting = True
result = players[current_batter % len(players)].get_hit()
current_batter = current_batter + 1
if (result == 'pbo') or (result == 'pso'):
outs = outs + 1
if (outs == 3):
#max outs advance inning
df.loc[sim_runs*MAX_INNINGS + inning] = (inning, runs, outs, runs - start_runs)
inning = inning + 1
bases = [0,0,0]
outs = 0
start_runs = runs
elif (result == 'pbo'):
if (bases[2]):
runs = runs + 1
bases[2] = bases[1]
bases[1] = bases[0]
bases[0] = 0
#max runs advance inning
if ((runs - start_runs)%MAX_RUNS_PER_INNING == 0):
df.loc[sim_runs*MAX_INNINGS + inning] = (inning, runs, outs, runs - start_runs)
inning = inning + 1
bases = [0,0,0]
outs = 0
start_runs = runs
else:
bases[2] = bases[1]
bases[1] = bases[0]
bases[0] = 0
elif (result == 'p1b'):
if (bases[2]):
runs = runs + 1
bases[2] = bases[1]
bases[1] = bases[0]
bases[0] = 1
#max runs advance inning
if ((runs - start_runs)%MAX_RUNS_PER_INNING == 0):
df.loc[sim_runs*MAX_INNINGS + inning] = (inning, runs, outs, runs - start_runs)
inning = inning + 1
bases = [0,0,0]
outs = 0
start_runs = runs
else:
bases[2] = bases[1]
bases[1] = bases[0]
bases[0] = 1
elif (result == 'p2b'):
if (bases[2] or bases[1]):
runs = min(runs + bases[2] + bases[1],start_runs + MAX_RUNS_PER_INNING)
bases[2] = bases[0]
bases[1] = 1
bases[0] = 0
#max runs advance inning
if ((runs - start_runs)%MAX_RUNS_PER_INNING == 0):
df.loc[sim_runs*MAX_INNINGS + inning] = (inning, runs, outs, runs - start_runs)
inning = inning + 1
bases = [0,0,0]
outs = 0
start_runs = runs
else:
bases[2] = bases[0]
bases[1] = 1
bases[0] = 0
elif (result == 'p3b'):
if (bases[2] or bases[1] or bases[0]):
runs = min(runs + bases[2] + bases[1] + bases[0],start_runs + MAX_RUNS_PER_INNING)
bases[2] = 1
bases[1] = 0
bases[0] = 0
#max runs advance inning
if ((runs - start_runs)%MAX_RUNS_PER_INNING == 0):
df.loc[sim_runs*MAX_INNINGS + inning] = (inning, runs, outs, runs - start_runs)
inning = inning + 1
bases = [0,0,0]
outs = 0
start_runs = runs
else:
bases[2] = 1
bases[1] = 0
bases[0] = 0
elif (result == 'phr'):
runs = min(runs + bases[2] + bases[1] + bases[0] + 1,start_runs + MAX_RUNS_PER_INNING)
bases[2] = 0
bases[1] = 0
bases[0] = 0
#max runs advance inning
if ((runs - start_runs)%MAX_RUNS_PER_INNING == 0):
df.loc[sim_runs*MAX_INNINGS + inning] = (inning, runs, outs, runs - start_runs)
inning = inning + 1
bases = [0,0,0]
outs = 0
start_runs = runs
return df
#Standalone runner to get a min
def run_player_scenario_mc(all_players, order):
df = run_player_scenario_mc_standalone(all_players,order)
average_total_runs = df[df.Inning == MAX_INNINGS]['Runs'].sum()/df[df.Inning == MAX_INNINGS]['Runs'].count()
#return max runs - average_total since it is a cost not a score
return MAX_INNINGS*MAX_RUNS_PER_INNING - average_total_runs
def test():
players =[Player([1.0, 0.0, 0.0, 0.0, 0.0, 0.0]),
Player([1.0, 0.0, 0.0, 0.0, 0.0, 0.0]),
Player([1.0, 0.0, 0.0, 0.0, 0.0, 0.0]),
Player([1.0, 0.0, 0.0, 0.0, 0.0, 0.0]),
Player([1.0, 0.0, 0.0, 0.0, 0.0, 0.0]),
Player([1.0, 0.0, 0.0, 0.0, 0.0, 0.0]),
Player([1.0, 0.0, 0.0, 0.0, 0.0, 0.0]),
Player([1.0, 0.0, 0.0, 0.0, 0.0, 0.0]),
Player([1.0, 0.0, 0.0, 0.0, 0.0, 0.0]),
Player([1.0, 0.0, 0.0, 0.0, 0.0, 0.0])]
w_sum_mh = run_player_scenario_mh(players, [0,1,2,3,4,5,6,7,8,9])
w_sum_mc = run_player_scenario_mc(players, [0,1,2,3,4,5,6,7,8,9])
print(w_sum_mh,w_sum_mc)
players =[Player([0.5, 0.0, 0.0, 0.0, 0.0, 0.5]),
Player([1.0, 0.0, 0.0, 0.0, 0.0, 0.0]),
Player([1.0, 0.0, 0.0, 0.0, 0.0, 0.0]),
Player([0.5, 0.0, 0.0, 0.0, 0.0, 0.5]),
Player([1.0, 0.0, 0.0, 0.0, 0.0, 0.0]),
Player([1.0, 0.0, 0.0, 0.0, 0.0, 0.0]),
Player([1.0, 0.0, 0.0, 0.0, 0.0, 0.0]),
Player([0.5, 0.0, 0.0, 0.0, 0.0, 0.5]),
Player([1.0, 0.0, 0.0, 0.0, 0.0, 0.0]),
Player([1.0, 0.0, 0.0, 0.0, 0.0, 0.0])]
w_sum_mh = run_player_scenario_mh(players, [0,1,2,3,4,5,6,7,8,9])
w_sum_mc = run_player_scenario_mc(players, [0,1,2,3,4,5,6,7,8,9])
print(w_sum_mh,w_sum_mc)
w_sum_mh = run_player_scenario_mh(players, [0,1,2,3,4,5,6,7,8,9])
w_sum_mc = run_player_scenario_mc(players, [0,1,2,3,4,5,6,7,8,9])
print(w_sum_mh,w_sum_mc)