Setup

import math
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator as MNL
import pandas as pd
from random import choice as RC
import seaborn as sns

#Create 36 Lineups, defined by placement of sluggers.
#C - 7 contact hitters.
#S - 2 single slugger.
def createLineups2() :
    
    lineups = []
    
    for s1 in range(8):
        for s2 in range(0, 8-s1) :
            lineup = ["C"]*s1 + ["S"] + ["C"]*s2 + ["S"] + ["C"]*(7-s1-s2)
            lineups.append(lineup)
    
    return(lineups)
   




#Base outcome from hitter.
def calcBase(hitter):

    #Pure Contact hitter gets one base one third of time.
    if hitter == "C" :
        p = RC([0,0,1])
    #Slugger hits a homerun one tenth of time.
    else :
        p = RC([0,0,0,0,0,0,0,0,0,3])
        
    return(p)





#Calculates score of one inning for a lineup.
def calcHomeRuns2(lineup):
    
    #Initialize.
    inning = 1
    score = 0

    while inning < 10 :
        
        #Initialize.
        outs = 0
        base1 = 0
        base2 = 0
        base3 = 0

    
        #Play ball!
        while outs < 3 :
            
            #Current hitter.
            hitter = lineup[0]
            #Update lineup. Places current hitter at end.
            lineup = lineup[1:9] + lineup[0:1]
        
            hit = calcBase(hitter)
        
            #No hit = struck out.
            if hit == 0:
                outs += 1
        
            #Pure Contact.
            elif hit == 1:
                score += base3
                base3 = base2
                base2 = base1
                base1 = 1
        
            #Slugger
            else:
                score += base3 + base2 + base1 + 1
                base1 = 0
                base2 = 0
                base3 = 0
                
        inning += 1
    
    return(score)
    





#Calculate average number of runs achieved by a lineup.
def scoreMonteCarlo(lineup, iterations):
    scores = []
    
    for i in range(iterations):
        scores.append(calcHomeRuns2(lineup))
    
    return(sum(scores) / iterations)

lineups = createLineups2()
lineups

[['S', 'S', 'C', 'C', 'C', 'C', 'C', 'C', 'C'],
 ['S', 'C', 'S', 'C', 'C', 'C', 'C', 'C', 'C'],
 ['S', 'C', 'C', 'S', 'C', 'C', 'C', 'C', 'C'],
 ['S', 'C', 'C', 'C', 'S', 'C', 'C', 'C', 'C'],
 ['S', 'C', 'C', 'C', 'C', 'S', 'C', 'C', 'C'],
 ['S', 'C', 'C', 'C', 'C', 'C', 'S', 'C', 'C'],
 ['S', 'C', 'C', 'C', 'C', 'C', 'C', 'S', 'C'],
 ['S', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'S'],
 ['C', 'S', 'S', 'C', 'C', 'C', 'C', 'C', 'C'],
 ['C', 'S', 'C', 'S', 'C', 'C', 'C', 'C', 'C'],
 ['C', 'S', 'C', 'C', 'S', 'C', 'C', 'C', 'C'],
 ['C', 'S', 'C', 'C', 'C', 'S', 'C', 'C', 'C'],
 ['C', 'S', 'C', 'C', 'C', 'C', 'S', 'C', 'C'],
 ['C', 'S', 'C', 'C', 'C', 'C', 'C', 'S', 'C'],
 ['C', 'S', 'C', 'C', 'C', 'C', 'C', 'C', 'S'],
 ['C', 'C', 'S', 'S', 'C', 'C', 'C', 'C', 'C'],
 ['C', 'C', 'S', 'C', 'S', 'C', 'C', 'C', 'C'],
 ['C', 'C', 'S', 'C', 'C', 'S', 'C', 'C', 'C'],
 ['C', 'C', 'S', 'C', 'C', 'C', 'S', 'C', 'C'],
 ['C', 'C', 'S', 'C', 'C', 'C', 'C', 'S', 'C'],
 ['C', 'C', 'S', 'C', 'C', 'C', 'C', 'C', 'S'],
 ['C', 'C', 'C', 'S', 'S', 'C', 'C', 'C', 'C'],
 ['C', 'C', 'C', 'S', 'C', 'S', 'C', 'C', 'C'],
 ['C', 'C', 'C', 'S', 'C', 'C', 'S', 'C', 'C'],
 ['C', 'C', 'C', 'S', 'C', 'C', 'C', 'S', 'C'],
 ['C', 'C', 'C', 'S', 'C', 'C', 'C', 'C', 'S'],
 ['C', 'C', 'C', 'C', 'S', 'S', 'C', 'C', 'C'],
 ['C', 'C', 'C', 'C', 'S', 'C', 'S', 'C', 'C'],
 ['C', 'C', 'C', 'C', 'S', 'C', 'C', 'S', 'C'],
 ['C', 'C', 'C', 'C', 'S', 'C', 'C', 'C', 'S'],
 ['C', 'C', 'C', 'C', 'C', 'S', 'S', 'C', 'C'],
 ['C', 'C', 'C', 'C', 'C', 'S', 'C', 'S', 'C'],
 ['C', 'C', 'C', 'C', 'C', 'S', 'C', 'C', 'S'],
 ['C', 'C', 'C', 'C', 'C', 'C', 'S', 'S', 'C'],
 ['C', 'C', 'C', 'C', 'C', 'C', 'S', 'C', 'S'],
 ['C', 'C', 'C', 'C', 'C', 'C', 'C', 'S', 'S']]

#Create DataFrame.
data = pd.DataFrame(columns = ['S1',
                               'S2',
                               'HR'])
for l in lineups :
    temp = []
    for i in range(9):
        if l[i] == 'S':
            temp.append(i+1)
            
    x = scoreMonteCarlo(l, 10000000)
    data = data.append({'S1' : temp[0],
                        'S2' : temp[1],
                        'HR' : x},
                       ignore_index = True)
    
data['HR2'] = (data['HR'] - min(data['HR'])) / (max(data['HR']) - min(data['HR']))
data

	S1	S2	HR	HR2
0	1.0	2.0	1.936012	0.450360
1	1.0	3.0	1.958608	0.651445
2	1.0	4.0	1.942155	0.505034
3	1.0	5.0	1.940540	0.490654
4	1.0	6.0	1.922464	0.329806
5	1.0	7.0	1.915298	0.266031
6	1.0	8.0	1.916399	0.275833
7	1.0	9.0	1.897650	0.108984
8	2.0	3.0	1.986645	0.900942
9	2.0	4.0	1.974857	0.796042
10	2.0	5.0	1.942430	0.507477
11	2.0	6.0	1.946846	0.546779
12	2.0	7.0	1.960492	0.668206
13	2.0	8.0	1.938087	0.468833
14	2.0	9.0	1.922038	0.326012
15	3.0	4.0	1.997777	1.000000
16	3.0	5.0	1.968829	0.742397
17	3.0	6.0	1.948324	0.559922
18	3.0	7.0	1.984759	0.884155
19	3.0	8.0	1.980183	0.843434
20	3.0	9.0	1.941643	0.500473
21	4.0	5.0	1.966499	0.721662
22	4.0	6.0	1.948560	0.562026
23	4.0	7.0	1.961313	0.675517
24	4.0	8.0	1.979668	0.838851
25	4.0	9.0	1.966783	0.724192
26	5.0	6.0	1.920294	0.310489
27	5.0	7.0	1.933488	0.427905
28	5.0	8.0	1.929339	0.390979
29	5.0	9.0	1.938087	0.468827
30	6.0	7.0	1.902867	0.155414
31	6.0	8.0	1.901741	0.145387
32	6.0	9.0	1.885403	0.000000
33	7.0	8.0	1.925010	0.352460
34	7.0	9.0	1.912497	0.241103
35	8.0	9.0	1.917738	0.287746

sns.set()

fig = plt.figure(figsize = (15, 15))
ax = fig.add_subplot()

cmap = plt.cm.get_cmap('viridis_r')

plt.scatter(data["S1"],
            data["S2"],
            c = data["HR2"],
            cmap = cmap,
            s = 2000,
            alpha = 0.5 )

for i in range(36) :
    plt.text(data["S1"][i],
             data["S2"][i],
             round(data["HR"][i], 4),
             ha = 'center',
             va = 'center',
             size = 16)

#Title setup.
ax.set_title('Average Number of Home Runs After 9 Innings', fontsize = 24)

#X-axis setup.
ax.set_xlabel('Slugger 1 Position', fontsize = 22)
ax.xaxis.set_major_locator(MNL(integer = True))

#Y-axis setup.
ax.set_ylabel('Slugger 2 Position', fontsize = 22)
ax.yaxis.set_major_locator(MNL(integer = True))

ax.tick_params(axis = 'both', which = 'major', labelsize = 18)

fig.savefig("2023.08.18 Extra Credit.png");

Rohan Lewis

2023.08.21