Background

This notebook seeks to quantify the value of leaving a certain number of tiles in the bag during the pre-endgame based on a repository of games. We will then implement these values as a pre-endgame heuristic in the Macondo speedy player to improve simulation quality.

Initial questions:

What is the probability that you will go out first if you make a play leaving N tiles in the bag?
(slightly harder) What is the expected improvement in end-of-game spread after making a play that leaves N tiles in the bag?

Implementation details

We'll need to make several passes through the log file to obtain the following:

Final spread of each simulated game
Delta between pre-endgame and final spread

Assumptions

We're only analyzing complete games
The last two rows in the log file for a given game ID are also the last two turns of the game

Next steps

Standardize sign convention for spread.
Start figuring out how to calculate pre-endgame spread



In [1]:

    
import csv
import pandas as pd

log_folder = '../logs/'
log_file = log_folder + 'log_20200411_short.csv'



In [2]:

    
second_to_last_move_dict = {}
last_move_dict = {}
spread_dict = {}
p1_minus_p2_spread_dict = {}



In [3]:

    
n=10000000

with open(log_file,'r') as f:
    moveReader = csv.reader(f)
    next(moveReader)
    
    for i,row in enumerate(moveReader):
        if i<n:
            if row[1] in last_move_dict.keys():
                second_to_last_move_dict[row[1]] = last_move_dict[row[1]]
            last_move_dict[row[1]] = row
        
        if i==n:
            break



In [4]:

    
# whoever made the final move went out first
went_out_first_dict = {game_id:last_move_dict[game_id][0] for game_id in last_move_dict.keys()}

# good sanity check - player 1 should go out a bit more often
print('Analyzing {} games, player 1 went out first {}% of the time'.format(
    len(went_out_first_dict),
    100*pd.Series(went_out_first_dict).value_counts(normalize=True)[0]))









    



Analyzing 438887 games, player 1 went out first 50.09307634994885% of the time



In [5]:

    
for game_id in last_move_dict.keys():
    spread_dict[game_id] = int(last_move_dict[game_id][6])-int(second_to_last_move_dict[game_id][6])
    p1_minus_p2_spread_dict[game_id] = (int(last_move_dict[game_id][6])-int(second_to_last_move_dict[game_id][6]))*\
        -(int(last_move_dict[game_id][0])-int(second_to_last_move_dict[game_id][0]))



In [6]:

    
print('The person who went out first won by an average of {} points'.format(pd.Series(spread_dict).mean()))
print('Player 1 won by an average of {} points'.format(pd.Series(p1_minus_p2_spread_dict).mean()))









    



The person who went out first won by an average of 17.206246710428882 points
Player 1 won by an average of 13.565596611428454 points



In [ ]:



In [ ]: