FIDE chess player ratings

TidyTuesday dataset of 2025-09-23

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import textwrap
from plotnine import *

fide_ratings_august = pd.read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-09-23/fide_ratings_august.csv')
fide_ratings_september = pd.read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-09-23/fide_ratings_september.csv')

fide_ratings_august

	id	name	fed	sex	title	wtitle	otitle	foa	rating	games	k	bday
0	53707043	A Darshil	IND	M	NaN	NaN	NaN	NaN	1412	4	40	2013
1	53200465	A F M Ehteshamul, Hoque (tuhin	BAN	M	NaN	NaN	NaN	NaN	1797	0	40	1977
2	5716365	A Hamid, Harman	MAS	M	NaN	NaN	NaN	NaN	1552	0	20	1970
3	53200553	A I Sabbir	BAN	M	NaN	NaN	NaN	NaN	1607	0	40	1995
4	5045886	A K, Kalshyan	IND	M	NaN	NaN	NaN	NaN	1747	0	20	1964
...	...	...	...	...	...	...	...	...	...	...	...	...
201010	4600410	Zysk, Robert	GER	M	IM	NaN	NaN	NaN	2373	0	10	1966
201011	1141589	Zysko, Jan	POL	M	NaN	NaN	NaN	NaN	2129	6	20	1990
201012	80411533	Zyskowski, Jean-Luc	FRA	M	NaN	NaN	NaN	NaN	1517	6	20	1972
201013	21836060	Zyto, Karol	POL	M	NaN	NaN	NaN	NaN	1748	0	20	2006
201014	55812104	Zyuryunina, Tatiana	RUS	F	NaN	NaN	NaN	NaN	1416	0	40	2013

201015 rows × 12 columns

fide_Aug_fed_titles = (
    fide_ratings_august[fide_ratings_august['title'].notna()]
    .groupby(['fed','sex'])
    .count()
    .loc[lambda x: x['id']>100]
    .sort_values(by='id', ascending=False)
    .reset_index()
)
fide_Aug_fed_titles

	fed	sex	id	name	title	wtitle	otitle	rating	games	k	bday
0	GER	M	902	902	902	0	29	902	902	902	902
1	ESP	M	646	646	646	0	45	646	646	646	646
2	FRA	M	404	404	404	0	8	404	404	404	404
3	RUS	M	375	375	375	0	3	375	375	375	375
4	SRB	M	360	360	360	0	24	360	360	360	360
5	IND	M	349	349	349	0	24	349	349	349	349
6	USA	M	344	344	344	0	14	344	344	344	344
7	HUN	M	338	338	338	0	29	338	338	338	338
8	POL	M	304	304	304	0	5	304	304	304	304
9	NED	M	289	289	289	0	6	289	289	289	289
10	CZE	M	282	282	282	0	12	282	282	282	282
11	CRO	M	196	196	196	0	17	196	196	196	196
12	AUT	M	186	186	186	0	6	186	186	186	186
13	ISR	M	175	175	175	0	8	175	175	175	175
14	ITA	M	175	175	175	0	7	175	175	175	175
15	UKR	M	169	169	169	0	3	169	169	169	169
16	TUR	M	159	159	159	0	5	159	159	159	159
17	SWE	M	155	155	155	0	7	155	155	155	155
18	ENG	M	152	152	152	0	6	152	152	152	152
19	ROU	M	150	150	150	0	8	150	150	150	150
20	NOR	M	133	133	133	0	5	133	133	133	133
21	ARG	M	133	133	133	0	10	133	133	133	133
22	AUS	M	123	123	123	0	7	123	123	123	123
23	IND	F	123	123	123	122	7	123	123	123	123
24	FID	M	122	122	122	0	6	122	122	122	122
25	GRE	M	115	115	115	0	9	115	115	115	115
26	DEN	M	115	115	115	0	3	115	115	115	115
27	COL	M	114	114	114	0	9	114	114	114	114
28	MEX	M	112	112	112	0	3	112	112	112	112
29	CUB	M	111	111	111	0	11	111	111	111	111
30	SUI	M	108	108	108	0	7	108	108	108	108
31	BRA	M	107	107	107	0	8	107	107	107	107
32	SVK	M	104	104	104	0	7	104	104	104	104
33	GER	F	103	103	103	102	6	103	103	103	103

fig,ax = plt.subplots(figsize=(6,8))
sns.scatterplot(data=fide_Aug_fed_titles, x='id', y='fed', hue='sex')
# add fed label to points
for i, row in fide_Aug_fed_titles.iterrows():
    plt.text(row['id']+10, row['fed'], row['fed'], fontsize=12, ha='left', va='center')
sns.despine()
plt.show()

fide_Aug_fed_titles['symbol'] = fide_Aug_fed_titles['sex'].apply(
    lambda s: '♔' if s == 'M' else '♕'
)
fide_Aug_fed_titles

	fed	sex	id	name	title	wtitle	otitle	rating	games	k	bday	symbol
0	GER	M	902	902	902	0	29	902	902	902	902	♔
1	ESP	M	646	646	646	0	45	646	646	646	646	♔
2	FRA	M	404	404	404	0	8	404	404	404	404	♔
3	RUS	M	375	375	375	0	3	375	375	375	375	♔
4	SRB	M	360	360	360	0	24	360	360	360	360	♔
5	IND	M	349	349	349	0	24	349	349	349	349	♔
6	USA	M	344	344	344	0	14	344	344	344	344	♔
7	HUN	M	338	338	338	0	29	338	338	338	338	♔
8	POL	M	304	304	304	0	5	304	304	304	304	♔
9	NED	M	289	289	289	0	6	289	289	289	289	♔
10	CZE	M	282	282	282	0	12	282	282	282	282	♔
11	CRO	M	196	196	196	0	17	196	196	196	196	♔
12	AUT	M	186	186	186	0	6	186	186	186	186	♔
13	ISR	M	175	175	175	0	8	175	175	175	175	♔
14	ITA	M	175	175	175	0	7	175	175	175	175	♔
15	UKR	M	169	169	169	0	3	169	169	169	169	♔
16	TUR	M	159	159	159	0	5	159	159	159	159	♔
17	SWE	M	155	155	155	0	7	155	155	155	155	♔
18	ENG	M	152	152	152	0	6	152	152	152	152	♔
19	ROU	M	150	150	150	0	8	150	150	150	150	♔
20	NOR	M	133	133	133	0	5	133	133	133	133	♔
21	ARG	M	133	133	133	0	10	133	133	133	133	♔
22	AUS	M	123	123	123	0	7	123	123	123	123	♔
23	IND	F	123	123	123	122	7	123	123	123	123	♕
24	FID	M	122	122	122	0	6	122	122	122	122	♔
25	GRE	M	115	115	115	0	9	115	115	115	115	♔
26	DEN	M	115	115	115	0	3	115	115	115	115	♔
27	COL	M	114	114	114	0	9	114	114	114	114	♔
28	MEX	M	112	112	112	0	3	112	112	112	112	♔
29	CUB	M	111	111	111	0	11	111	111	111	111	♔
30	SUI	M	108	108	108	0	7	108	108	108	108	♔
31	BRA	M	107	107	107	0	8	107	107	107	107	♔
32	SVK	M	104	104	104	0	7	104	104	104	104	♔
33	GER	F	103	103	103	102	6	103	103	103	103	♕

unique_fed_order = fide_Aug_fed_titles['fed'].drop_duplicates()[::-1].tolist()
fide_Aug_fed_titles['fed'] = pd.Categorical(
    fide_Aug_fed_titles['fed'],
    categories=unique_fed_order,
    ordered=True
)
fide_Aug_fed_titles['fed_label'] = fide_Aug_fed_titles.apply(
    lambda row: f"{row['fed']} ({row['id']})", axis=1
)
fed_list = ['IND', 'GER', 'ESP', 'FRA', 'CRO', 'SVK']
#label_df = fide_Aug_fed_titles[fide_Aug_fed_titles['fed'].isin(fed_list)].copy()
label_df = fide_Aug_fed_titles.copy()
label_df['label'] = label_df.apply(lambda row: f'({row.id})', axis=1)
wrapped_title = textwrap.fill("Number of male (♔) and female (♕) chess players with titles in August 2025. Countries with more than 100 titled players are shown.", \
                    width=35)

(
    ggplot(fide_Aug_fed_titles, aes(x='id', y='fed')) +
#    geom_point(size=2) +
    geom_text(aes(label='symbol'), size=15, va='center', color='#FFFFFF', show_legend=False) + 
    geom_text(aes(label='fed', color='sex'), nudge_x=20, size=10, ha='left', va='center',\
     show_legend=False, family='monospace') +
    geom_text(data=label_df, mapping=aes(label='label', color='sex'), nudge_x=65, \
    size=9, va='center', ha='left', family='monospace', show_legend=False) +
    annotate('text', x=325, y=10, label=wrapped_title, color='#EEEEEE', \
    size=14, ha='left', va='center') +
    theme_minimal() +
    scale_color_manual(values={'M':'skyblue', 'F':'pink'}) +
#    labs(x='Number of players with titles') +
    xlim(0, 985) +
    theme(figure_size=(6, 6),
        axis_title_y=element_blank(),
        axis_text_y=element_blank(),
        axis_ticks_major_y=element_blank(),
        axis_ticks_minor_y=element_blank(),
        panel_grid_major_y=element_blank(),
        axis_title_x=element_blank(),
        axis_text_x=element_blank(),
        axis_ticks_major_x=element_blank(),
        axis_ticks_minor_x=element_blank(),
        panel_grid_major_x=element_blank(),
        panel_grid_minor_x=element_blank(),
        plot_background=element_rect(fill='#2E2E2E'),
        panel_background=element_rect(fill='#2E2E2E')
    )
)#.save('chess_ratings.png', width=6, height=6, dpi=300)

df_sept_mod = (
    fide_ratings_september[(fide_ratings_september['rating']>2000) & 
    (fide_ratings_september['games']>0)]
    .groupby("fed")
    .obj
#    .loc[lambda x: x['id']>100]
#    .loc[lambda x: x['fed']=="IND"]
#    .sort_values(by='id', ascending=False)
    # change bday to datetime
    .assign(bday=lambda x: pd.to_datetime(x['bday'], format='%Y'))
    .assign(age=lambda x: 2025 - x['bday'].dt.year)
)

df_sept_mod

	id	name	fed	sex	title	wtitle	otitle	foa	rating	games	k	bday	age
57	33362041	Aadik Theophane Lenin	IND	M	NaN	NaN	NaN	AIM	2108	9	40	2012-01-01	13
77	25678191	Aaditya Dhingra	IND	M	IM	NaN	NaN	NaN	2391	17	10	2006-01-01	19
127	25644394	Aakash G	IND	M	FM	NaN	NaN	NaN	2349	17	20	2010-01-01	15
131	25033220	Aakash Sharadchandra, Dalvi	IND	M	IM	NaN	NaN	NaN	2420	18	10	2002-01-01	23
137	510726	Aalto, Patrik	FIN	M	FM	NaN	NaN	NaN	2296	9	20	2004-01-01	21
...	...	...	...	...	...	...	...	...	...	...	...	...	...
203110	393770	Zvolensky, David	CZE	M	NaN	NaN	NaN	NaN	2157	18	20	2002-01-01	23
203124	325511	Zwardon, Vojtech	CZE	M	IM	NaN	NaN	NaN	2431	10	10	1990-01-01	35
203127	1332252	Zweifel, Richard	SUI	M	CM	NaN	NaN	NaN	2196	1	20	1973-01-01	52
203145	1036432	Zwirs, Nico	NED	M	IM	NaN	NaN	NaN	2432	9	10	1994-01-01	31
203163	4200292	Zygouris, Hristos	GRE	M	NaN	NaN	NaN	NaN	2194	9	20	1974-01-01	51

7581 rows × 13 columns

sns.histplot(data=df_sept_mod, x='age', bins=20, hue='sex')

	fed	sex	id	name	title	wtitle	otitle	rating	games	k	bday
0	GER	M	902	902	902	0	29	902	902	902	902
1	ESP	M	646	646	646	0	45	646	646	646	646
2	FRA	M	404	404	404	0	8	404	404	404	404
3	RUS	M	375	375	375	0	3	375	375	375	375
4	SRB	M	360	360	360	0	24	360	360	360	360
5	IND	M	349	349	349	0	24	349	349	349	349
6	USA	M	344	344	344	0	14	344	344	344	344
7	HUN	M	338	338	338	0	29	338	338	338	338
8	POL	M	304	304	304	0	5	304	304	304	304
9	NED	M	289	289	289	0	6	289	289	289	289
10	CZE	M	282	282	282	0	12	282	282	282	282
11	CRO	M	196	196	196	0	17	196	196	196	196
12	AUT	M	186	186	186	0	6	186	186	186	186
13	ISR	M	175	175	175	0	8	175	175	175	175
14	ITA	M	175	175	175	0	7	175	175	175	175
15	UKR	M	169	169	169	0	3	169	169	169	169
16	TUR	M	159	159	159	0	5	159	159	159	159
17	SWE	M	155	155	155	0	7	155	155	155	155
18	ENG	M	152	152	152	0	6	152	152	152	152
19	ROU	M	150	150	150	0	8	150	150	150	150
20	NOR	M	133	133	133	0	5	133	133	133	133
21	ARG	M	133	133	133	0	10	133	133	133	133
22	AUS	M	123	123	123	0	7	123	123	123	123
23	IND	F	123	123	123	122	7	123	123	123	123
24	FID	M	122	122	122	0	6	122	122	122	122
25	GRE	M	115	115	115	0	9	115	115	115	115
26	DEN	M	115	115	115	0	3	115	115	115	115
27	COL	M	114	114	114	0	9	114	114	114	114
28	MEX	M	112	112	112	0	3	112	112	112	112
29	CUB	M	111	111	111	0	11	111	111	111	111
30	SUI	M	108	108	108	0	7	108	108	108	108
31	BRA	M	107	107	107	0	8	107	107	107	107
32	SVK	M	104	104	104	0	7	104	104	104	104
33	GER	F	103	103	103	102	6	103	103	103	103

	fed	sex	id	name	title	wtitle	otitle	rating	games	k	bday	symbol
0	GER	M	902	902	902	0	29	902	902	902	902	♔
1	ESP	M	646	646	646	0	45	646	646	646	646	♔
2	FRA	M	404	404	404	0	8	404	404	404	404	♔
3	RUS	M	375	375	375	0	3	375	375	375	375	♔
4	SRB	M	360	360	360	0	24	360	360	360	360	♔
5	IND	M	349	349	349	0	24	349	349	349	349	♔
6	USA	M	344	344	344	0	14	344	344	344	344	♔
7	HUN	M	338	338	338	0	29	338	338	338	338	♔
8	POL	M	304	304	304	0	5	304	304	304	304	♔
9	NED	M	289	289	289	0	6	289	289	289	289	♔
10	CZE	M	282	282	282	0	12	282	282	282	282	♔
11	CRO	M	196	196	196	0	17	196	196	196	196	♔
12	AUT	M	186	186	186	0	6	186	186	186	186	♔
13	ISR	M	175	175	175	0	8	175	175	175	175	♔
14	ITA	M	175	175	175	0	7	175	175	175	175	♔
15	UKR	M	169	169	169	0	3	169	169	169	169	♔
16	TUR	M	159	159	159	0	5	159	159	159	159	♔
17	SWE	M	155	155	155	0	7	155	155	155	155	♔
18	ENG	M	152	152	152	0	6	152	152	152	152	♔
19	ROU	M	150	150	150	0	8	150	150	150	150	♔
20	NOR	M	133	133	133	0	5	133	133	133	133	♔
21	ARG	M	133	133	133	0	10	133	133	133	133	♔
22	AUS	M	123	123	123	0	7	123	123	123	123	♔
23	IND	F	123	123	123	122	7	123	123	123	123	♕
24	FID	M	122	122	122	0	6	122	122	122	122	♔
25	GRE	M	115	115	115	0	9	115	115	115	115	♔
26	DEN	M	115	115	115	0	3	115	115	115	115	♔
27	COL	M	114	114	114	0	9	114	114	114	114	♔
28	MEX	M	112	112	112	0	3	112	112	112	112	♔
29	CUB	M	111	111	111	0	11	111	111	111	111	♔
30	SUI	M	108	108	108	0	7	108	108	108	108	♔
31	BRA	M	107	107	107	0	8	107	107	107	107	♔
32	SVK	M	104	104	104	0	7	104	104	104	104	♔
33	GER	F	103	103	103	102	6	103	103	103	103	♕

	fed	sex	id	name	title	wtitle	otitle	rating	games	k	bday
0	GER	M	902	902	902	0	29	902	902	902	902
1	ESP	M	646	646	646	0	45	646	646	646	646
2	FRA	M	404	404	404	0	8	404	404	404	404
3	RUS	M	375	375	375	0	3	375	375	375	375
4	SRB	M	360	360	360	0	24	360	360	360	360
5	IND	M	349	349	349	0	24	349	349	349	349
6	USA	M	344	344	344	0	14	344	344	344	344
7	HUN	M	338	338	338	0	29	338	338	338	338
8	POL	M	304	304	304	0	5	304	304	304	304
9	NED	M	289	289	289	0	6	289	289	289	289
10	CZE	M	282	282	282	0	12	282	282	282	282
11	CRO	M	196	196	196	0	17	196	196	196	196
12	AUT	M	186	186	186	0	6	186	186	186	186
13	ISR	M	175	175	175	0	8	175	175	175	175
14	ITA	M	175	175	175	0	7	175	175	175	175
15	UKR	M	169	169	169	0	3	169	169	169	169
16	TUR	M	159	159	159	0	5	159	159	159	159
17	SWE	M	155	155	155	0	7	155	155	155	155
18	ENG	M	152	152	152	0	6	152	152	152	152
19	ROU	M	150	150	150	0	8	150	150	150	150
20	NOR	M	133	133	133	0	5	133	133	133	133
21	ARG	M	133	133	133	0	10	133	133	133	133
22	AUS	M	123	123	123	0	7	123	123	123	123
23	IND	F	123	123	123	122	7	123	123	123	123
24	FID	M	122	122	122	0	6	122	122	122	122
25	GRE	M	115	115	115	0	9	115	115	115	115
26	DEN	M	115	115	115	0	3	115	115	115	115
27	COL	M	114	114	114	0	9	114	114	114	114
28	MEX	M	112	112	112	0	3	112	112	112	112
29	CUB	M	111	111	111	0	11	111	111	111	111
30	SUI	M	108	108	108	0	7	108	108	108	108
31	BRA	M	107	107	107	0	8	107	107	107	107
32	SVK	M	104	104	104	0	7	104	104	104	104
33	GER	F	103	103	103	102	6	103	103	103	103

	fed	sex	id	name	title	wtitle	otitle	rating	games	k	bday	symbol
0	GER	M	902	902	902	0	29	902	902	902	902	♔
1	ESP	M	646	646	646	0	45	646	646	646	646	♔
2	FRA	M	404	404	404	0	8	404	404	404	404	♔
3	RUS	M	375	375	375	0	3	375	375	375	375	♔
4	SRB	M	360	360	360	0	24	360	360	360	360	♔
5	IND	M	349	349	349	0	24	349	349	349	349	♔
6	USA	M	344	344	344	0	14	344	344	344	344	♔
7	HUN	M	338	338	338	0	29	338	338	338	338	♔
8	POL	M	304	304	304	0	5	304	304	304	304	♔
9	NED	M	289	289	289	0	6	289	289	289	289	♔
10	CZE	M	282	282	282	0	12	282	282	282	282	♔
11	CRO	M	196	196	196	0	17	196	196	196	196	♔
12	AUT	M	186	186	186	0	6	186	186	186	186	♔
13	ISR	M	175	175	175	0	8	175	175	175	175	♔
14	ITA	M	175	175	175	0	7	175	175	175	175	♔
15	UKR	M	169	169	169	0	3	169	169	169	169	♔
16	TUR	M	159	159	159	0	5	159	159	159	159	♔
17	SWE	M	155	155	155	0	7	155	155	155	155	♔
18	ENG	M	152	152	152	0	6	152	152	152	152	♔
19	ROU	M	150	150	150	0	8	150	150	150	150	♔
20	NOR	M	133	133	133	0	5	133	133	133	133	♔
21	ARG	M	133	133	133	0	10	133	133	133	133	♔
22	AUS	M	123	123	123	0	7	123	123	123	123	♔
23	IND	F	123	123	123	122	7	123	123	123	123	♕
24	FID	M	122	122	122	0	6	122	122	122	122	♔
25	GRE	M	115	115	115	0	9	115	115	115	115	♔
26	DEN	M	115	115	115	0	3	115	115	115	115	♔
27	COL	M	114	114	114	0	9	114	114	114	114	♔
28	MEX	M	112	112	112	0	3	112	112	112	112	♔
29	CUB	M	111	111	111	0	11	111	111	111	111	♔
30	SUI	M	108	108	108	0	7	108	108	108	108	♔
31	BRA	M	107	107	107	0	8	107	107	107	107	♔
32	SVK	M	104	104	104	0	7	104	104	104	104	♔
33	GER	F	103	103	103	102	6	103	103	103	103	♕

	fed	sex	id	name	title	wtitle	otitle	rating	games	k	bday
0	GER	M	902	902	902	0	29	902	902	902	902
1	ESP	M	646	646	646	0	45	646	646	646	646
2	FRA	M	404	404	404	0	8	404	404	404	404
3	RUS	M	375	375	375	0	3	375	375	375	375
4	SRB	M	360	360	360	0	24	360	360	360	360
5	IND	M	349	349	349	0	24	349	349	349	349
6	USA	M	344	344	344	0	14	344	344	344	344
7	HUN	M	338	338	338	0	29	338	338	338	338
8	POL	M	304	304	304	0	5	304	304	304	304
9	NED	M	289	289	289	0	6	289	289	289	289
10	CZE	M	282	282	282	0	12	282	282	282	282
11	CRO	M	196	196	196	0	17	196	196	196	196
12	AUT	M	186	186	186	0	6	186	186	186	186
13	ISR	M	175	175	175	0	8	175	175	175	175
14	ITA	M	175	175	175	0	7	175	175	175	175
15	UKR	M	169	169	169	0	3	169	169	169	169
16	TUR	M	159	159	159	0	5	159	159	159	159
17	SWE	M	155	155	155	0	7	155	155	155	155
18	ENG	M	152	152	152	0	6	152	152	152	152
19	ROU	M	150	150	150	0	8	150	150	150	150
20	NOR	M	133	133	133	0	5	133	133	133	133
21	ARG	M	133	133	133	0	10	133	133	133	133
22	AUS	M	123	123	123	0	7	123	123	123	123
23	IND	F	123	123	123	122	7	123	123	123	123
24	FID	M	122	122	122	0	6	122	122	122	122
25	GRE	M	115	115	115	0	9	115	115	115	115
26	DEN	M	115	115	115	0	3	115	115	115	115
27	COL	M	114	114	114	0	9	114	114	114	114
28	MEX	M	112	112	112	0	3	112	112	112	112
29	CUB	M	111	111	111	0	11	111	111	111	111
30	SUI	M	108	108	108	0	7	108	108	108	108
31	BRA	M	107	107	107	0	8	107	107	107	107
32	SVK	M	104	104	104	0	7	104	104	104	104
33	GER	F	103	103	103	102	6	103	103	103	103

	fed	sex	id	name	title	wtitle	otitle	rating	games	k	bday	symbol
0	GER	M	902	902	902	0	29	902	902	902	902	♔
1	ESP	M	646	646	646	0	45	646	646	646	646	♔
2	FRA	M	404	404	404	0	8	404	404	404	404	♔
3	RUS	M	375	375	375	0	3	375	375	375	375	♔
4	SRB	M	360	360	360	0	24	360	360	360	360	♔
5	IND	M	349	349	349	0	24	349	349	349	349	♔
6	USA	M	344	344	344	0	14	344	344	344	344	♔
7	HUN	M	338	338	338	0	29	338	338	338	338	♔
8	POL	M	304	304	304	0	5	304	304	304	304	♔
9	NED	M	289	289	289	0	6	289	289	289	289	♔
10	CZE	M	282	282	282	0	12	282	282	282	282	♔
11	CRO	M	196	196	196	0	17	196	196	196	196	♔
12	AUT	M	186	186	186	0	6	186	186	186	186	♔
13	ISR	M	175	175	175	0	8	175	175	175	175	♔
14	ITA	M	175	175	175	0	7	175	175	175	175	♔
15	UKR	M	169	169	169	0	3	169	169	169	169	♔
16	TUR	M	159	159	159	0	5	159	159	159	159	♔
17	SWE	M	155	155	155	0	7	155	155	155	155	♔
18	ENG	M	152	152	152	0	6	152	152	152	152	♔
19	ROU	M	150	150	150	0	8	150	150	150	150	♔
20	NOR	M	133	133	133	0	5	133	133	133	133	♔
21	ARG	M	133	133	133	0	10	133	133	133	133	♔
22	AUS	M	123	123	123	0	7	123	123	123	123	♔
23	IND	F	123	123	123	122	7	123	123	123	123	♕
24	FID	M	122	122	122	0	6	122	122	122	122	♔
25	GRE	M	115	115	115	0	9	115	115	115	115	♔
26	DEN	M	115	115	115	0	3	115	115	115	115	♔
27	COL	M	114	114	114	0	9	114	114	114	114	♔
28	MEX	M	112	112	112	0	3	112	112	112	112	♔
29	CUB	M	111	111	111	0	11	111	111	111	111	♔
30	SUI	M	108	108	108	0	7	108	108	108	108	♔
31	BRA	M	107	107	107	0	8	107	107	107	107	♔
32	SVK	M	104	104	104	0	7	104	104	104	104	♔
33	GER	F	103	103	103	102	6	103	103	103	103	♕