# HBO Greatest Fighter of All Time Tournament Simulation¶

## Reverse ELO, Re-seeding, & Re-running¶

post @ endlesspint.com

In [1]:
import pandas as pd
import numpy as np
import os

import matplotlib.pyplot as plt
%matplotlib inline

In [2]:
match_ups_rev_chron = pd.read_excel("hbo_greatest_fighter.xlsx", sheet_name="tourn_matchups")

Out[2]:
Poll A Favorite Poll B Opponent
0 0.76 Ali 0.24 Leonard
1 0.55 Leonard 0.45 Mayweather
2 0.64 Ali 0.36 Tyson
3 0.81 Ali 0.19 Jones
4 0.69 Leonard 0.31 Hagler
In [3]:
fighter_elo = {fighter: '' for fighter in match_ups_rev_chron.Opponent.tolist()}
fighter_elo['Ali'] = 2600


## reverse ELO¶

In [4]:
# https://www.reddit.com/r/chess/comments/2y6ezm/how_to_guide_converting_elo_differences_to/

def rev_elo(a_elo, a_prob):
b_elo = a_elo + (np.log10((1.-a_prob)/a_prob) * 400)
return b_elo

# Ali v Leonard
rev_elo(fighter_elo['Ali'], 0.76)

Out[4]:
2399.759059772326

## win prob¶

In [5]:
def prob_elo(a_elo, b_elo):
a_prob = 1./(1. + 10**((b_elo - a_elo)/400.))
return a_prob

# Ali v Leonard
prob_elo(fighter_elo['Ali'], rev_elo(fighter_elo['Ali'], 0.76))

Out[5]:
0.7599999999999998

## reverse elo pandas¶

In [6]:
def rev_elo_pd(row):
a_elo = fighter_elo[row['Favorite']]
#     print (a_elo)
a_prob = row['Poll A']
#     print (a_prob)
b_elo = a_elo + (np.log10((1.-a_prob)/a_prob) * 400)
fighter_elo[row['Opponent']] = b_elo
return b_elo

match_ups_rev_chron.apply(rev_elo_pd, axis=1)

Out[6]:
0     2399.759060
1     2364.898989
2     2500.049011
3     2348.107433
4     2260.764101
5     2264.948000
6     2458.154871
7     2380.131231
8     2018.062056
9     2272.443459
10    2269.885892
11    2032.759267
12    2129.820527
13    2034.784882
14    2402.083389
15    2236.801071
16    1950.405096
17    2272.443459
18    2384.823003
19    2138.202277
20    2253.814463
21    2064.707060
22    2294.085084
23    2373.181593
24    1997.190851
25    1760.942019
26    1954.537726
27    1822.854111
28    2122.870889
29    1999.924811
30    2211.234887
dtype: float64
In [7]:
len(fighter_elo), fighter_elo

Out[7]:
(32,
{'Ali': 2600,
'Arguello': 2018.062055996596,
'Barrera': 1999.924811287957,
'Chavez': 2402.08338923934,
'Chocolatito': 1822.8541109415842,
'Cotto': 2236.801071405325,
'De La Hoya': 2138.202277167716,
'Duran': 2458.1548705053547,
'Foreman': 2264.948000198126,
'Frazier': 2269.885892090751,
'GGG': 2253.814462583364,
'Hagler': 2260.764101011133,
'Hearns': 2373.181592824921,
'Holmes': 2034.7848815755171,
'Holyfield': 2272.443459330131,
'Hopkins': 2129.8205272566006,
'Jones': 2348.107432829672,
'Leonard': 2399.759059772326,
'Lewis': 2380.13123125269,
'Lomachenko': 2272.443459330131,
'Marquez': 2211.2348873514748,
'Mayweather': 2364.898989484766,
'Morales': 2294.085084469735,
'Mosley': 1950.4050963564546,
'Pacquiao': 2384.8230033135146,
'Pryor': 1954.537725864619,
'Sanchez': 1997.1908513305673,
'Spinks': 1760.9420189489997,
'Tyson': 2500.04901071336,
'Ward': 2122.8708888288315,
'Whitaker': 2032.7592666035398})

## elo win prob matrix/grid¶

In [8]:
grid_rev_elo = pd.read_excel("hbo_greatest_fighter.xlsx", sheet_name="grid_RevELO")

grid_rev_elo = grid_rev_elo[grid_rev_elo.Region.notnull()]
grid_rev_elo = grid_rev_elo.reset_index(drop=True)
print(grid_rev_elo.shape)
print(grid_rev_elo.dtypes)

(32, 36)
Region          object
SEED           float64
Fighter         object
ELO            float64
Ali            float64
Cotto          float64
Hearns         float64
Lewis          float64
Jones          float64
De La Hoya     float64
Whitaker       float64
Chocolatito    float64
Duran          float64
Morales        float64
Chavez         float64
Marquez        float64
Frazier        float64
Pryor          float64
Pacquiao       float64
Tyson          float64
Mayweather     float64
Lomachenko     float64
Holyfield      float64
Spinks         float64
Holmes         float64
Barrera        float64
Foreman        float64
Leonard        float64
Mosley         float64
Arguello       float64
Sanchez        float64
Hopkins        float64
Ward           float64
Hagler         float64
GGG            float64
dtype: object

Out[8]:
Region SEED Fighter ELO Ali Cotto Hearns Lewis Jones De La Hoya ... Foreman Trinidad Leonard Mosley Arguello Sanchez Hopkins Ward Hagler GGG
0 LAMPLEY 1.0 Ali 2600.000000 NaN 0.890000 0.786788 0.780000 0.810000 0.934522 ... 0.873107 0.956119 0.760000 0.976783 0.966102 0.969823 0.937413 0.939720 0.875752 0.880039
1 LAMPLEY 8.0 Cotto 2236.801071 0.110000 NaN 0.313228 0.304688 0.345081 0.638205 ... 0.459582 0.729217 0.281292 0.838706 0.778882 0.798880 0.649269 0.658324 0.465569 0.475535
2 LAMPLEY 4.0 Hearns 2373.181593 0.213212 0.686772 NaN 0.490000 0.536022 0.794562 ... 0.650910 0.855168 0.461826 0.919361 0.885364 0.897005 0.802326 0.808595 0.656363 0.665329
3 LAMPLEY 5.0 Lewis 2380.131231 0.220000 0.695312 0.510000 NaN 0.545956 0.801016 ... 0.659945 0.860053 0.471783 0.922278 0.889362 0.900642 0.808595 0.814710 0.665329 0.674177
4 LAMPLEY 3.0 Jones 2348.107433 0.190000 0.654919 0.463978 0.454044 NaN 0.770000 ... 0.617442 0.836359 0.426210 0.907992 0.869880 0.882885 0.778433 0.785256 0.623114 0.632462

5 rows × 36 columns

In [9]:
fighters = grid_rev_elo[['Region', 'SEED', 'Fighter', 'ELO']]

Out[9]:
Region SEED Fighter ELO
0 LAMPLEY 1.0 Ali 2600.000000
1 LAMPLEY 8.0 Cotto 2236.801071
2 LAMPLEY 4.0 Hearns 2373.181593
3 LAMPLEY 5.0 Lewis 2380.131231
4 LAMPLEY 3.0 Jones 2348.107433
In [10]:
fighter_win_prob = np.matrix(grid_rev_elo.iloc[:,4:])

# Ali v Leonard
i, j = fighters[fighters.Fighter=="Ali"].index[0], fighters[fighters.Fighter=="Leonard"].index[0]
fighter_win_prob[i, j]

Out[10]:
0.7599999999999998
In [11]:
np.random.seed(8)

fighter_cnt = int(fighters.shape[0])
tourn_rds = int(np.log2(fighter_cnt))

wins_tally = np.zeros(fighter_cnt * tourn_rds).reshape(fighter_cnt, tourn_rds)

for _ in range(10000):

fighter_no = range(fighters.shape[0])
round_ = 0

while len(fighter_no) >= 2:
# https://stackoverflow.com/questions/5389507/iterating-over-every-two-elements-in-a-list
for i,j in zip(fighter_no[0::2], fighter_no[1::2]):

toss_up = np.random.uniform()
#             print(rd_1_fighters[i], fighter_win_prob[i, j], rd_1_fighters[j], toss_up)

if fighter_win_prob[i, j] > toss_up:
wins_tally[i, round_] += 1

else:
wins_tally[j, round_] += 1

round_ += 1

print(wins_tally[:5])

[[8916. 6967. 5949. 4279. 3492.]
[1084.  343.  148.   35.   20.]
[4947. 1354.  856.  377.  203.]
[5053. 1336.  859.  384.  234.]
[7679. 6755. 1921.  753.  385.]]


## hbo_10k_sim¶

In [12]:
df_wins_tally = pd.DataFrame(wins_tally)
df_wins_tally.columns = ['1R', 'S16', 'QF', 'SF', 'F']
hbo_10k_sim = pd.concat([fighters, df_wins_tally], axis=1)
# hbo_10k_sim.to_excel("hbo_10k_sim.xlsx")

hbo_10k_sim.sort_values(by=['F'], ascending=False)

Out[12]:
Region SEED Fighter ELO 1R S16 QF SF F
0 LAMPLEY 1.0 Ali 2600.000000 8916.0 6967.0 5949.0 4279.0 3492.0
24 LEDERMAN 1.0 Leonard 2399.759060 9315.0 8437.0 6189.0 4080.0 1510.0
15 MERCHANT 7.0 Tyson 2500.049011 6570.0 5315.0 3322.0 1650.0 1189.0
8 MERCHANT 1.0 Duran 2458.154871 7221.0 4535.0 2536.0 1118.0 746.0
16 KELLERMAN 1.0 Mayweather 2364.898989 6302.0 4101.0 2964.0 1642.0 550.0
10 MERCHANT 4.0 Chavez 2402.083389 7534.0 3718.0 1788.0 655.0 414.0
4 LAMPLEY 3.0 Jones 2348.107433 7679.0 6755.0 1921.0 753.0 385.0
18 KELLERMAN 4.0 Holyfield 2272.443459 9514.0 3971.0 2534.0 1024.0 236.0
3 LAMPLEY 5.0 Lewis 2380.131231 5053.0 1336.0 859.0 384.0 234.0
14 MERCHANT 2.0 Pacquiao 2384.823003 3430.0 2367.0 1107.0 395.0 228.0
22 KELLERMAN 2.0 Foreman 2264.948000 7620.0 6070.0 2629.0 1091.0 220.0
2 LAMPLEY 4.0 Hearns 2373.181593 4947.0 1354.0 856.0 377.0 203.0
30 LEDERMAN 2.0 Hagler 2260.764101 5131.0 3555.0 1385.0 626.0 143.0
31 LEDERMAN 7.0 GGG 2253.814463 4869.0 3305.0 1273.0 601.0 136.0
17 KELLERMAN 8.0 Lomachenko 2272.443459 3698.0 1913.0 1174.0 486.0 112.0
12 MERCHANT 3.0 Frazier 2269.885892 8649.0 2249.0 695.0 196.0 74.0
9 MERCHANT 8.0 Morales 2294.085084 2779.0 1114.0 375.0 98.0 53.0
1 LAMPLEY 8.0 Cotto 2236.801071 1084.0 343.0 148.0 35.0 20.0
28 LEDERMAN 3.0 Hopkins 2129.820527 5139.0 1623.0 428.0 134.0 14.0
29 LEDERMAN 6.0 Ward 2122.870889 4861.0 1517.0 383.0 108.0 12.0
11 MERCHANT 5.0 Marquez 2211.234887 2466.0 633.0 173.0 28.0 9.0
23 KELLERMAN 7.0 Trinidad 2064.707060 2380.0 1364.0 273.0 58.0 4.0
20 KELLERMAN 3.0 Holmes 2034.784882 5500.0 1516.0 263.0 58.0 4.0
21 KELLERMAN 6.0 Barrera 1999.924811 4500.0 1050.0 162.0 26.0 4.0
5 LAMPLEY 6.0 De La Hoya 2138.202277 2321.0 1629.0 175.0 23.0 3.0
26 LEDERMAN 4.0 Arguello 2018.062056 5271.0 704.0 170.0 37.0 2.0
27 LEDERMAN 5.0 Sanchez 1997.190851 4729.0 583.0 133.0 22.0 2.0
6 LAMPLEY 2.0 Whitaker 2032.759267 7639.0 1467.0 91.0 9.0 1.0
25 LEDERMAN 8.0 Mosley 1950.405096 685.0 276.0 39.0 7.0 0.0
19 KELLERMAN 5.0 Spinks 1760.942019 486.0 15.0 1.0 0.0 0.0
7 LAMPLEY 7.0 Chocolatito 1822.854111 2361.0 149.0 1.0 0.0 0.0
13 MERCHANT 6.0 Pryor 1954.537726 1351.0 69.0 4.0 0.0 0.0

# reseed, rebracket & rerun¶

## reseed¶

In [13]:
fighters_reseed = fighters.sort_values(by='ELO', ascending=False)
fighters_reseed['RESEED'] = np.arange(1, fighters.shape[0] + 1)


Out[13]:
Region SEED Fighter ELO RESEED
0 LAMPLEY 1.0 Ali 2600.000000 1
15 MERCHANT 7.0 Tyson 2500.049011 2
8 MERCHANT 1.0 Duran 2458.154871 3
10 MERCHANT 4.0 Chavez 2402.083389 4
24 LEDERMAN 1.0 Leonard 2399.759060 5

## rebracket¶

In [14]:
def tourn_rebracket(no_seeds):

new_seeds = np.arange(1, no_seeds+1)

temp_bracket = new_seeds.copy()

folds = np.log2(len(new_seeds))

while folds > 0:
if len(temp_bracket.shape) == 1:
split_point = int(len(temp_bracket)/2)

top_half = temp_bracket[:split_point]
bot_half = np.flip(temp_bracket[split_point:], axis=0)

temp_bracket = np.vstack((top_half, bot_half))

else:
split_point = int(temp_bracket.shape[1]/2)

top_half = temp_bracket[:,:split_point]
bot_half = np.flip(np.flip(temp_bracket[:,split_point:], 0), 1)

temp_bracket = np.vstack((top_half, bot_half))

folds -= 1

return list(temp_bracket[:,0])

tourn_rebracket(16)

Out[14]:
[1, 16, 9, 8, 5, 12, 13, 4, 3, 14, 11, 6, 7, 10, 15, 2]

## rerun I¶

waaaay too slow, took to referencing pandas index below

these results can at least be used as a sanity check

In [15]:
# def tourn_sim(df, elo_col, seed_col, sims=10000, elo_update=False):

#     fighter_cnt = int(df.shape[0])
#     tourn_rds = int(np.log2(fighter_cnt))

# #     tourn_bracket =

#     wins_tally = np.zeros(fighter_cnt * tourn_rds).reshape(fighter_cnt, tourn_rds)

#     for _ in range(sims):

#         fighter_seeds = tourn_rebracket(fighter_cnt)
#         round_ = 0

#         while len(fighter_seeds) >= 2:

#             # https://stackoverflow.com/questions/5389507/iterating-over-every-two-elements-in-a-list
#             for i,j in zip(fighter_seeds[0::2], fighter_seeds[1::2]):

#                 fighter_a_elo = df[df[seed_col]==i][elo_col].values[0]
#                 fighter_b_elo = df[df[seed_col]==j][elo_col].values[0]

#                 fighter_a_idx = df[df[seed_col]==i].index[0]
#                 fighter_b_idx = df[df[seed_col]==j].index[0]

#                 toss_up = np.random.uniform()

#                 if prob_elo(fighter_a_elo, fighter_b_elo) > toss_up:
#                     wins_tally[fighter_a_idx, round_] += 1

#                 else:
#                     wins_tally[fighter_b_idx, round_] += 1

#             round_ += 1

#     return wins_tally

# np.random.seed(8)
# reseed_rerun = tourn_sim(fighters_reseed, "ELO", "RESEED", sims=10000)
# reseed_rerun[:5]

In [16]:
import time


## rerun slow¶

In [17]:
# same as above, commented out function, renamed

def tourn_sim_slow(df, elo_col, seed_col, sims=10000, elo_update=False):

fighter_cnt = int(df.shape[0])
tourn_rds = int(np.log2(fighter_cnt))

wins_tally = np.zeros(fighter_cnt * tourn_rds).reshape(fighter_cnt, tourn_rds)

for _ in range(sims):

fighter_seeds = tourn_rebracket(fighter_cnt)
round_ = 0

while len(fighter_seeds) >= 2:

# https://stackoverflow.com/questions/5389507/iterating-over-every-two-elements-in-a-list
for i,j in zip(fighter_seeds[0::2], fighter_seeds[1::2]):

fighter_a_elo = df[df[seed_col]==i][elo_col].values[0]
fighter_b_elo = df[df[seed_col]==j][elo_col].values[0]

fighter_a_idx = df[df[seed_col]==i].index[0]
fighter_b_idx = df[df[seed_col]==j].index[0]

toss_up = np.random.uniform()

if prob_elo(fighter_a_elo, fighter_b_elo) > toss_up:
wins_tally[fighter_a_idx, round_] += 1

else:
wins_tally[fighter_b_idx, round_] += 1

round_ += 1

return wins_tally

start = time.time()

np.random.seed(8)
reseed_rerun = tourn_sim_slow(fighters_reseed, "ELO", "RESEED", sims=200)

done = time.time()
elapsed = done - start
print(elapsed)

reseed_rerun[:5]

13.220763921737671

Out[17]:
array([[198., 174., 142., 118.,  84.],
[102.,  26.,  11.,   1.,   0.],
[175.,  96.,  23.,  11.,   1.],
[186., 110.,  35.,  19.,   8.],
[166.,  80.,  28.,  11.,   5.]])

## rerun, less slow¶

In [18]:
def tourn_sim(df, elo_col, seed_col, sims=10000, elo_update=False):

fighter_cnt = int(df.shape[0])
tourn_rds = int(np.log2(fighter_cnt))

wins_tally = np.zeros(fighter_cnt * tourn_rds).reshape(fighter_cnt, tourn_rds)

fighter_seeds = tourn_rebracket(fighter_cnt)
fighter_seeds_idx = []

for i,j in zip(fighter_seeds[0::2], fighter_seeds[1::2]):

fighter_seeds_idx.append(df[df[seed_col]==i].index[0])
fighter_seeds_idx.append(df[df[seed_col]==j].index[0])

#     print(fighter_seeds_idx)

for _ in range(sims):

fighter_seeds_idx_run = fighter_seeds_idx
round_ = 0

while len(fighter_seeds_idx_run) >= 2:

# https://stackoverflow.com/questions/5389507/iterating-over-every-two-elements-in-a-list
for i,j in zip(fighter_seeds_idx_run[0::2], fighter_seeds_idx_run[1::2]):

fighter_a_elo = df.loc[i][elo_col]
fighter_b_elo = df.loc[j][elo_col]

fighter_a_idx = i
fighter_b_idx = j

toss_up = np.random.uniform()

if prob_elo(fighter_a_elo, fighter_b_elo) > toss_up:
wins_tally[fighter_a_idx, round_] += 1

else:
wins_tally[fighter_b_idx, round_] += 1

round_ += 1

return wins_tally

start = time.time()

np.random.seed(8)
reseed_rerun = tourn_sim(fighters_reseed, "ELO", "RESEED", sims=200)

done = time.time()
elapsed = done - start
print(elapsed)

reseed_rerun[:5]

2.2258408069610596

Out[18]:
array([[198., 174., 142., 118.,  84.],
[102.,  26.,  11.,   1.,   0.],
[175.,  96.,  23.,  11.,   1.],
[186., 110.,  35.,  19.,   8.],
[166.,  80.,  28.,  11.,   5.]])
In [19]:
np.random.seed(8)
reseed_rerun = tourn_sim(fighters_reseed, "ELO", "RESEED", sims=10000)

reseed_rerun[:5]

Out[19]:
array([[9912., 8668., 6898., 5424., 3903.],
[4598.,  889.,  309.,   93.,   19.],
[8798., 4881., 1324.,  665.,  278.],
[8914., 5280., 2172., 1047.,  383.],
[8355., 4215., 1565.,  702.,  220.]])
In [20]:
df_reseed_rerun = pd.DataFrame(reseed_rerun)
df_reseed_rerun.columns = ['1R', 'S16', 'QF', 'SF', 'F']
hbo_10k_sim2 = pd.concat([fighters_reseed, df_reseed_rerun], axis=1)
# hbo_10k_sim2.to_excel("hbo_10k_sim2.xlsx")

hbo_10k_sim2.sort_values(by=['F', 'SF', 'QF'], ascending=False)

Out[20]:
Region SEED Fighter ELO RESEED 1R S16 QF SF F
0 LAMPLEY 1.0 Ali 2600.000000 1 9912.0 8668.0 6898.0 5424.0 3903.0
15 MERCHANT 7.0 Tyson 2500.049011 2 9804.0 7985.0 5472.0 3564.0 1793.0
8 MERCHANT 1.0 Duran 2458.154871 3 9471.0 7323.0 4823.0 2536.0 1152.0
24 LEDERMAN 1.0 Leonard 2399.759060 5 9126.0 6561.0 3788.0 1288.0 593.0
10 MERCHANT 4.0 Chavez 2402.083389 4 9292.0 6660.0 3819.0 1305.0 592.0
14 MERCHANT 2.0 Pacquiao 2384.823003 6 9065.0 6087.0 2884.0 1257.0 446.0
3 LAMPLEY 5.0 Lewis 2380.131231 7 8914.0 5280.0 2172.0 1047.0 383.0
2 LAMPLEY 4.0 Hearns 2373.181593 8 8798.0 4881.0 1324.0 665.0 278.0
16 KELLERMAN 1.0 Mayweather 2364.898989 9 8681.0 4677.0 1240.0 653.0 263.0
4 LAMPLEY 3.0 Jones 2348.107433 10 8355.0 4215.0 1565.0 702.0 220.0
9 MERCHANT 8.0 Morales 2294.085084 11 7304.0 3135.0 1092.0 354.0 90.0
17 KELLERMAN 8.0 Lomachenko 2272.443459 12 6941.0 2587.0 1044.0 202.0 59.0
18 KELLERMAN 4.0 Holyfield 2272.443459 13 6876.0 2500.0 992.0 239.0 54.0
12 MERCHANT 3.0 Frazier 2269.885892 14 5823.0 1642.0 710.0 200.0 40.0
22 KELLERMAN 2.0 Foreman 2264.948000 15 5402.0 1112.0 447.0 148.0 34.0
30 LEDERMAN 2.0 Hagler 2260.764101 16 5053.0 683.0 265.0 100.0 31.0
31 LEDERMAN 7.0 GGG 2253.814463 17 4947.0 646.0 249.0 80.0 29.0
1 LAMPLEY 8.0 Cotto 2236.801071 18 4598.0 889.0 309.0 93.0 19.0
11 MERCHANT 5.0 Marquez 2211.234887 19 4177.0 957.0 342.0 79.0 10.0
29 LEDERMAN 6.0 Ward 2122.870889 22 2696.0 585.0 119.0 16.0 5.0
28 LEDERMAN 3.0 Hopkins 2129.820527 21 3059.0 667.0 161.0 15.0 3.0
5 LAMPLEY 6.0 De La Hoya 2138.202277 20 3124.0 717.0 163.0 21.0 2.0
23 KELLERMAN 7.0 Trinidad 2064.707060 23 1645.0 319.0 25.0 2.0 1.0
6 LAMPLEY 2.0 Whitaker 2032.759267 25 1202.0 235.0 15.0 4.0 0.0
27 LEDERMAN 5.0 Sanchez 1997.190851 28 874.0 185.0 23.0 2.0 0.0
21 KELLERMAN 6.0 Barrera 1999.924811 27 935.0 193.0 20.0 1.0 0.0
13 MERCHANT 6.0 Pryor 1954.537726 29 708.0 123.0 10.0 1.0 0.0
26 LEDERMAN 4.0 Arguello 2018.062056 26 1086.0 186.0 10.0 1.0 0.0
20 KELLERMAN 3.0 Holmes 2034.784882 24 1319.0 207.0 9.0 1.0 0.0
25 LEDERMAN 8.0 Mosley 1950.405096 30 529.0 78.0 10.0 0.0 0.0
7 LAMPLEY 7.0 Chocolatito 1822.854111 31 196.0 14.0 0.0 0.0 0.0
19 KELLERMAN 5.0 Spinks 1760.942019 32 88.0 3.0 0.0 0.0 0.0

## reseed & rebracket (like above, but referencing index), rerun (n times), rerate <--repeat¶

In [21]:
def reseed_idx(elo_array):
return np.flip(np.argsort(elo_array),0)

reseed_idx(np.array(fighters.ELO))

Out[21]:
array([ 0, 15,  8, 10, 24, 14,  3,  2, 16,  4,  9, 18, 17, 12, 22, 30, 31,
1, 11,  5, 28, 29, 23, 20,  6, 26, 21, 27, 13, 25,  7, 19],
dtype=int64)
In [22]:
def tourn_rebracket_idx(seed_list):

temp_bracket = np.array(seed_list)

folds = np.log2(len(seed_list))

while folds > 0:
if len(temp_bracket.shape) == 1:
split_point = int(len(temp_bracket)/2)

top_half = temp_bracket[:split_point]
bot_half = np.flip(temp_bracket[split_point:], axis=0)

temp_bracket = np.vstack((top_half, bot_half))

else:
split_point = int(temp_bracket.shape[1]/2)

top_half = temp_bracket[:,:split_point]
bot_half = np.flip(np.flip(temp_bracket[:,split_point:], 0), 1)

temp_bracket = np.vstack((top_half, bot_half))

folds -= 1

return list(temp_bracket[:,0])

print(tourn_rebracket_idx(range(1,17)))
print(tourn_rebracket_idx(fighters.ELO.sort_values(ascending=False).index))

[1, 16, 9, 8, 5, 12, 13, 4, 3, 14, 11, 6, 7, 10, 15, 2]
[0, 19, 31, 30, 16, 20, 6, 2, 24, 27, 28, 18, 17, 5, 13, 10, 8, 25, 11, 12, 9, 29, 21, 14, 3, 26, 23, 4, 22, 1, 7, 15]


## rerate¶

In [23]:
# http://www.eloratings.net/system.html &
# http://gobase.org/studying/articles/elo/

def ratings_new(f1, f2, f1_result = 1.0):

f2_result = 1.0 - f1_result

# expected score
f1_exp = prob_elo(f1, f2)
f2_exp = 1.0 - f1_exp

# constant
K = 30.0
f1_new, f2_new = round(f1 + K * (f1_result - f1_exp)), round(f2 + K * (f2_result - f2_exp))

return f1_new, f2_new

print (prob_elo(2100, 2100), ratings_new(2100, 2100))
print (prob_elo(2100, 2100), ratings_new(2100, 2100, 0))
print (prob_elo(2100, 2000), ratings_new(2100, 2000))
print (prob_elo(1900, 2100), ratings_new(1900, 2100))

0.5 (2115, 2085)
0.5 (2085, 2115)
0.6400649998028851 (2111, 1989)
0.2402530733520421 (1923, 2077)

In [24]:
x = 1767

def elo_floor_rating(x):
return np.floor((x - 200)/100) * 100

elo_floor_rating(x)

Out[24]:
1500.0
In [25]:
print(fighters_reseed.ELO.sort_index().tolist())

np.apply_along_axis(elo_floor_rating, 0, fighters_reseed.ELO)

[2600.0, 2236.801071405325, 2373.181592824921, 2380.13123125269, 2348.107432829672, 2138.202277167716, 2032.7592666035398, 1822.8541109415842, 2458.1548705053547, 2294.085084469735, 2402.08338923934, 2211.2348873514748, 2269.885892090751, 1954.537725864619, 2384.8230033135146, 2500.04901071336, 2364.898989484766, 2272.443459330131, 2272.443459330131, 1760.9420189489997, 2034.7848815755171, 1999.924811287957, 2264.948000198126, 2064.707059970452, 2399.759059772326, 1950.4050963564546, 2018.062055996596, 1997.1908513305673, 2129.8205272566006, 2122.8708888288315, 2260.764101011133, 2253.814462583364]

Out[25]:
array([2400., 2300., 2200., 2200., 2100., 2100., 2100., 2100., 2100.,
2100., 2000., 2000., 2000., 2000., 2000., 2000., 2000., 2000.,
2000., 1900., 1900., 1900., 1800., 1800., 1800., 1800., 1700.,
1700., 1700., 1700., 1600., 1500.])
In [26]:
def tourn_sim_expanded(df, elo_col, seed_col, sims=10000, elo_update=False, elo_floor=True, elo_ceiling=3000, reseed_every_n_sim=0):

fighter_cnt = int(df.shape[0])
tourn_rds = int(np.log2(fighter_cnt))

wins_tally = np.zeros(fighter_cnt * tourn_rds).reshape(fighter_cnt, tourn_rds)

elo_grid  = np.zeros(fighter_cnt * (sims+1)).reshape(fighter_cnt, (sims+1))
elo_grid[:,0] = df[elo_col].sort_index()

seed_grid = np.zeros(fighter_cnt * (sims+1)).reshape(fighter_cnt, (sims+1))
seed_grid[:,0] = df[seed_col].sort_index()

reseed_cnt = 0

if elo_floor==True:
elo_floor_array = np.apply_along_axis(elo_floor_rating, 0, elo_grid[:,0])

for _ in range(sims):

if elo_update==True:
elo_temp = elo_grid[:,_].copy()
else:
elo_temp = elo_grid[:,0]

if reseed_every_n_sim > 0:
fighter_seeds_idx_run = reseed_idx(elo_grid[:, _])
fighter_seeds_bracket = tourn_rebracket_idx(fighter_seeds_idx_run)

#             for a,b in zip(np.flip(np.argsort(elo_grid[:, _]), 0),range(1,fighter_cnt+1)):
#                 seed_grid[a][_+1] = b

else:
fighter_seeds_idx_run = reseed_idx(elo_grid[:,0])
fighter_seeds_bracket = tourn_rebracket_idx(fighter_seeds_idx_run)

seed_grid[:,_+1] = seed_grid[:,0]

round_ = 0

while len(fighter_seeds_bracket) >= 2:

# https://stackoverflow.com/questions/5389507/iterating-over-every-two-elements-in-a-list
for i,j in zip(fighter_seeds_bracket[0::2], fighter_seeds_bracket[1::2]):

fighter_a_elo = elo_temp[i]
fighter_b_elo = elo_temp[j]

fighter_a_idx = i
fighter_b_idx = j

toss_up = np.random.uniform()

if prob_elo(fighter_a_elo, fighter_b_elo) > toss_up:
wins_tally[fighter_a_idx, round_] += 1

if elo_update==True:
new_a_elo, new_b_elo = ratings_new(fighter_a_elo, fighter_b_elo, 1.0)
elo_temp[fighter_a_idx], elo_temp[fighter_b_idx] = np.min((new_a_elo, elo_ceiling)), \
np.max((new_b_elo, elo_floor_array[fighter_b_idx]))

else:
wins_tally[fighter_b_idx, round_] += 1

if elo_update==True:
new_a_elo, new_b_elo = ratings_new(fighter_a_elo, fighter_b_elo, 0.0)
elo_temp[fighter_a_idx], elo_temp[fighter_b_idx] = np.max((new_a_elo, elo_floor_array[fighter_a_idx])), \
np.min((new_b_elo, elo_ceiling))

round_ += 1

elo_grid[:, _+1] = elo_temp

if reseed_every_n_sim > 0 and (_+1)%reseed_every_n_sim==0:

for a,b in zip(np.flip(np.argsort(elo_grid[:, _+1]), 0),range(1,fighter_cnt+1)):
seed_grid[a][_+1] = b

reseed_cnt += 1

if reseed_every_n_sim > 0 and (_+1)%reseed_every_n_sim!=0:

seed_grid[:,_+1] = seed_grid[:,_]

print("reseed count:", reseed_cnt)

if elo_update==False:
#         print(elo_grid[:,0])
#         print(seed_grid[:,0])
return wins_tally, elo_grid, seed_grid

else:
#         print(elo_grid)
#         print(seed_grid)
return wins_tally, elo_grid, seed_grid

start = time.time()

np.random.seed(8)
wins_X, elo_X, seed_X = tourn_sim_expanded(fighters_reseed, "ELO", "RESEED", sims=6, elo_update=True, reseed_every_n_sim=1)

done = time.time()
elapsed = done - start
print(elapsed)

print(wins_X[:5])
print(elo_X[:5])
print(seed_X[:5])

reseed count: 6
0.0
[[6. 6. 4. 4. 3.]
[4. 0. 0. 0. 0.]
[6. 2. 0. 0. 0.]
[6. 5. 2. 1. 1.]
[6. 0. 0. 0. 0.]]
[[2600.         2629.         2623.         2602.         2634.
2656.         2634.        ]
[2236.80107141 2247.         2259.         2264.         2250.
2234.         2219.        ]
[2373.18159282 2385.         2391.         2376.         2363.
2352.         2342.        ]
[2380.13123125 2387.         2371.         2379.         2391.
2392.         2455.        ]
[2348.10743283 2339.         2331.         2324.         2318.
2312.         2308.        ]]
[[ 1.  1.  1.  1.  1.  1.  1.]
[18. 17. 14. 14. 15. 17. 17.]
[ 8.  7.  5.  8.  8.  9.  9.]
[ 7.  6.  7.  6.  6.  5.  3.]
[10. 10. 11. 11. 11. 12. 12.]]


## some doodles¶

In [27]:
for row in range(elo_X.shape[0]):
plt.plot(elo_X[row])

In [28]:
for row in range(seed_X.shape[0]):
plt.plot(seed_X[row])
plt.ylim(33,0)

In [29]:
# wins_X, elo_X, seed_X = tourn_sim_expanded(fighters_reseed, "ELO", "RESEED", sims=10000, elo_update=True, reseed_every_n_sim=1)


## plotting ELO on multiple reseed options¶

In [30]:
np.random.seed(8)

reseed_every = [1, 10, 100, 1000]

for i in range(len(reseed_every)):
wins_X, elo_X, seed_X = tourn_sim_expanded(fighters_reseed, "ELO", "RESEED",
sims=10000, elo_update=True,
reseed_every_n_sim=reseed_every[i])

plt.figure(figsize=(18,6))
print("Fighters re-seeded every %i tournament run(s)" % reseed_every[i])
for row in range(elo_X.shape[0]):
plt.plot(elo_X[row])

reseed count: 10000
Fighters re-seeded every 1 tournament run(s)
reseed count: 1000
Fighters re-seeded every 10 tournament run(s)
reseed count: 100
Fighters re-seeded every 100 tournament run(s)
reseed count: 10
Fighters re-seeded every 1000 tournament run(s)