#!/usr/bin/env python
# coding: utf-8

# <div align="right"><a href="http://norvig.com">Peter Norvig</a><br><a href="https://github.com/norvig/pytudes">pytudes</a><br>March 2019</div>
# 
# # Dice Baseball
# 
# The [538 Riddler for March 22, 2019](https://fivethirtyeight.com/features/can-you-turn-americas-pastime-into-a-game-of-yahtzee/) asks us to simulate baseball using probabilities from a 19th century dice game called *Our National Ball Game*:
# 
#     1,1: double         2,2: strike    3,3: out at 1st  4,4: fly out
#     1,2: single         2,3: strike    3,4: out at 1st  4,5: fly out
#     1,3: single         2,4: strike    3,5: out at 1st  4,6: fly out
#     1,4: single         2,5: strike    3,6: out at 1st  5,5: double play
#     1,5: base on error  2,6: foul out                   5,6: triple
#     1,6: base on balls                                  6,6: home run
# 
# 
# The rules left some things unspecified; the following are my current choices (in an early version I made different choices that resulted in slightly more runs):
# 
# * On a*&nbsp;b*-base hit, runners advance*&nbsp;b* bases, except that a runner on second scores on a 1-base hit.
# * On an "out at first", all runners advance one base.
# * A double play only applies if there is a runner on first; in that case other runners advance.
# * On a fly out, a runner on third scores; other runners do not advance.
# * On an error all runners advance one base. 
# * On a base on balls, only forced runners advance.
# 
# I also made some choices about the implementation:
# 
# - Exactly one outcome happens to each batter. We call that an *event*.
# - I'll represent events with the following one letter codes:
#   - `K`, `O`, `o`, `f`, `D`: strikeout, foul out, out at first, fly out, double play
#   - `1`, `2`, `3`, `4`: single, double, triple, home run
#   - `E`, `B`: error, base on balls
# - Note the "strike" dice roll is not an event; it is only part of an event. From the probability of a "strike" dice roll, I compute the probability of three strikes in a row, and call that a strikeout event. Sice there are 7 dice rolls giving "strike", the probability of a strike is 7/36, and the probability of a strikeout is (7/36)**3.
# - Note that a die roll such as `1,1` is a 1/36 event, whereas `1,2` is a 2/36 event, because it also represents (2, 1).
# - I'll keep track of runners with a list of occupied bases; `runners = [1, 2]` means runners on first and second.
# - A runner who advances to base 4 or higher has scored a run (unless there are already 3 outs).
# - The function `inning` simulates a half inning and returns the number of runs scored.
# - I want to be able to test `inning` by feeding it specific events, and I also want to generate random innings. So I'll make the interface be that I pass in an *iterable* of events. The function `event_stream` generates an endless stream of randomly sampled events.
# - Note that it is consider good Pythonic style to automatically convert Booleans to integers, so for a runner on second (`r = 2`) when the event is a single (`e = '1'`), the expression `r + int(e) + (r == 2)` evaluates to `2 + 1 + 1` or `4`, meaning the runner on second scores.
# - I'll play 1 million innings and store the resulting scores in `innings`.
# - To simulate a game I just sample 9 elements of `innings` and sum them.
# 
# # The Code

# In[1]:


get_ipython().run_line_magic('matplotlib', 'inline')
import matplotlib.pyplot as plt
import random


# In[2]:


def event_stream(events='2111111EEBBOOooooooofffffD334', strike=7/36):
    "An iterator of random events. Defaults from `Our National Ball Game`."
    while True:
        yield 'K' if (random.random() < strike ** 3) else random.choice(events)
        
def inning(events=event_stream(), verbose=False) -> int:
    "Simulate a half inning based on events, and return number of runs scored."
    outs = runs = 0 # Inning starts with no outs and no runs,
    runners = []    # ... and with nobody on base
    for e in events:
        if verbose: print(f'{outs} outs, {runs} runs, event: {e}, runners: {runners}')
        # What happens to the batter?
        if   e in 'KOofD':  outs += 1         # Batter is out
        elif e in '1234EB': runners.append(0) # Batter becomes a runner
        # What happens to the runners?
        if e == 'D' and 1 in runners: # double play: runner on 1st out, others advance
            outs += 1
            runners = [r + 1 for r in runners if r != 1]
        elif e in 'oE': # out at first or error: runners advance
            runners = [r + 1 for r in runners]
        elif e == 'f' and 3 in runners and outs < 3: # fly out: runner on 3rd scores
            runners.remove(3)
            runs += 1
        elif e in '1234': # single, double, triple, homer
            runners = [r + int(e) + (r == 2) for r in runners]
        elif e == 'B': # base on balls: forced runners advance 
            runners = [r + forced(runners, r) for r in runners]
        # See if inning is over, and if not, whether anyone scored
        if outs >= 3:
            return runs
        runs += sum(r >= 4 for r in runners)
        runners = [r for r in runners if r < 4]
        
def forced(runners, r) -> bool: return all(b in runners for b in range(r))


# # Testing
# 
# Let's peek at some random innings:

# In[3]:


inning(verbose=True)


# In[4]:


inning(verbose=True)


# And we can feed in any events we want to test the code:

# In[5]:


inning('2EBB1DB12f', verbose=True)


# That looks good.
# 
# # Simulating
# 
# Now, simulate a million innings, and then sample from them to simulate a million nine-inning games (for one team):

# In[6]:


N = 1000000
innings = [inning() for _ in range(N)]
games = [sum(random.sample(innings, 9)) for _ in range(N)]


# Let's see histograms:

# In[7]:


def hist(nums, title): 
    "Plot a histogram."
    plt.hist(nums, ec='black', bins=max(nums)-min(nums)+1, align='left')
    plt.title(f'{title} Mean: {sum(nums)/len(nums):.3f}, Min: {min(nums)}, Max: {max(nums)}')
    
hist(innings, 'Runs per inning:')


# In[8]:


hist(games, 'Runs per game:')