FIDE chess player ratings

TidyTuesday dataset of September 23, 2025

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