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Expanding the research of "which countries have the most inflated elo"

Chess
Follow up of https://en.chessbase.com/post/which-countries-have-the-most-inflated-elo-chess-players (but I am not the original author!)

With the help of claude sonnet 4.5 (that shortens the prototyping and testing time by a lot) I collected and analyzed the data of the u18 WCh from 2015 to 2019 as follow up of this article https://en.chessbase.com/post/which-countries-have-the-most-inflated-elo-chess-players. Why pre covid? Because then one cannot argue about possible inflation/deflation due to covid measures (IMO the covid rating lag ended around 2023). Additionally I considered the u18 WCh from 2023 to 2025 and the Grand swisses (2019, 2023, 2025). More tournaments may be added later. Of course in the WCh u18 there are likely the best young players of each country, so underrated if anything, but still it gives an idea which countries gain rating as a whole and which don't.

Result directly from the output

================================================================================
EXTRACTION SUMMARY
================================================================================
Total players extracted: 4032
Federations represented: 117
Tournaments covered: 36

Overall statistics:
  Average rating change: +0.18
  Players with gains: 1834 (45.5%)
  Players with losses: 1998 (49.6%)

Top 20 federations by player count:
  IND: 543 players
  RUS: 228 players
  ESP: 207 players
  GER: 181 players
  FRA: 152 players
  None: 150 players
  USA: 149 players
  ISL: 128 players
  CHN: 98 players
  ENG: 95 players
  NOR: 86 players
  UKR: 80 players
  ARM: 75 players
  MEX: 70 players
  ISR: 67 players
  KAZ: 67 players
  ITA: 64 players
  POL: 62 players
  HUN: 62 players
  IRI: 58 players

 Analyzed 97 federations with 3+ players
 Analyzed 7 continents

==========================================================================================
RATING CHANGES BY CONTINENT
==========================================================================================
Rank   Continent          Players   Feds   Avg Change    Total        % Gainers
------------------------------------------------------------------------------------------
1      Oceania            34        2            +10.4        +354       58.8%
2      Africa             68        13            +5.3        +361       45.6%
3      North America      281       8             +1.8        +499       47.0%
4      Other              236       13            +1.1        +255       48.3%
5      Asia               1004      28            +0.9        +900       47.4%
6      Europe             2230      44            -0.7       -1506       44.4%
7      South America      179       10            -0.7        -129       39.7%

==========================================================================================
ALL FEDERATIONS RANKED BY AVERAGE RATING CHANGE (per player)
==========================================================================================
Rank   Fed    Players   Avg Change    Total        % Gainers   Avg Rating
------------------------------------------------------------------------------------------
1      NZL    5               +38.0        +190       80.0%        1790
2      NAM    3               +37.7        +113      100.0%        1324
3      LAT    13              +37.0        +481       69.2%        2239
4      LTU    10              +28.8        +288       40.0%        2324
5      ALB    6               +28.5        +171       66.7%        2118
6      BIH    9               +21.1        +190       66.7%        2310
7      FIN    10              +20.9        +209       70.0%        1983
8      POR    8               +16.0        +128       50.0%        2148
9      PAR    8               +14.1        +113       75.0%        2431
10     LBN    3               +13.7         +41       66.7%        1882
11     TPE    22              +13.0        +285       59.1%        1675
12     WLS    7               +12.9         +90       57.1%        2045
13     RSA    25              +12.8        +319       52.0%        2031
14     EST    14              +12.1        +169       50.0%        2352
15     MNE    18              +11.5        +207       50.0%        1896
16     MEX    70              +10.0        +700       52.9%        1601
17     KOS    5                +9.2         +46       40.0%        1971
18     KGZ    8                +7.5         +60       62.5%        1752
19     BRA    29               +7.3        +213       51.7%        2048
20     BEL    34               +6.5        +222       50.0%        2228
21     MDA    11               +6.2         +68       45.5%        2382
22     VIE    12               +6.0         +72       83.3%        2446
23     FAI    5                +5.8         +29       60.0%        2120
24     AUS    29               +5.7        +164       55.2%        2238
25     ALG    3                +5.3         +16       66.7%        2078
26     BAN    4                +4.5         +18       25.0%        2116
27     IRL    20               +3.8         +75       50.0%        2118
28     EGY    15               +3.7         +55       33.3%        2439
29     SWE    57               +3.6        +207       45.6%        2351
30     COL    24               +3.3         +79       45.8%        2269
31     ISL    128              +3.1        +398       46.9%        2145
32     UZB    56               +3.0        +170       51.8%        2397
33     MAS    13               +3.0         +39       53.8%        2113
34     IND    543              +3.0       +1620       49.9%        2353
35     SVK    19               +2.9         +55       52.6%        2344
36     GRE    36               +2.8        +100       50.0%        2310
37     FID    48               +2.8        +132       52.1%        2450
38     CAN    47               +2.7        +125       51.1%        2132
39     SCO    9                +2.6         +23       66.7%        2193
40     NOR    86               +2.4        +206       51.2%        2295
41     CRO    18               +2.3         +42       50.0%        2508
42     SGP    22               +2.1         +47       36.4%        2327
43     CHI    20               +2.0         +41       30.0%        2315
44     CUB    10               +2.0         +20       40.0%        2407
45     IOM    3                +1.7          +5       33.3%        2210
46     INA    5                +1.6          +8       40.0%        2405
47     SLO    28               +1.2         +35       57.1%        2375
48     ARG    51               +1.2         +63       45.1%        2357
49     DEN    35               +1.2         +41       45.7%        2228
50     KEN    5                +1.0          +5       40.0%        1981
51     UAE    13               +0.1          +1       46.2%        2191
52     SRI    13               +0.1          +1       61.5%        1940
53     ENG    95               -0.0          -3       46.3%        2330
54     None   150              -0.0          -5       46.7%        2613
55     RUS    228              -0.3         -59       43.0%        2543
56     HUN    62               -0.3         -17       43.5%        2453
57     PHI    6                -0.8          -5       66.7%        2374
58     MGL    27               -1.1         -29       40.7%        2352
59     USA    149              -1.6        -241       44.3%        2409
60     CHN    98               -1.7        -165       39.8%        2457
61     POL    62               -1.7        -108       45.2%        2456
62     ITA    64               -1.8        -115       40.6%        2315
63     MKD    7                -2.0         -14       57.1%        2183
64     FRA    152              -2.1        -315       43.4%        2418
65     BLR    11               -2.2         -24       54.5%        2594
66     SRB    33               -2.3         -77       30.3%        2411
67     UKR    80               -2.4        -189       43.8%        2497
68     ESP    207              -2.6        -528       42.0%        2347
69     TUR    35               -2.8         -98       42.9%        2432
70     MAR    4                -3.0         -12       50.0%        2134
71     GEO    50               -3.1        -153       42.0%        2335
72     ARM    75               -3.1        -234       42.7%        2525
73     GER    181              -3.3        -590       45.9%        2369
74     NED    50               -3.4        -171       46.0%        2419
75     SUI    25               -3.8         -94       36.0%        2271
76     IRI    58               -3.9        -224       37.9%        2504
77     ISR    67               -4.8        -322       32.8%        2446
78     CZE    31               -4.9        -151       38.7%        2412
79     VEN    11               -4.9         -54       18.2%        2436
80     TJK    5                -6.0         -30       40.0%        2283
81     AZE    39               -6.7        -261       46.2%        2519
82     KAZ    67               -6.9        -461       37.3%        2295
83     ROU    45               -8.4        -379       40.0%        2415
84     HKG    4               -11.8         -47       50.0%        1962
85     AUT    35              -12.1        -423       28.6%        2369
86     PER    23              -12.6        -290       30.4%        2284
87     BUL    29              -12.8        -371       37.9%        2429
88     MRI    3               -13.3         -40       33.3%         552
89     NGR    3               -14.7         -44        0.0%        2242
90     CYP    5               -15.8         -79       40.0%        1781
91     KUW    3               -16.3         -49       33.3%        1830
92     LUX    4               -17.5         -70       50.0%        1930
93     JPN    4               -20.0         -80       25.0%        1703
94     NEP    4               -27.0        -108       50.0%        1870
95     TKM    7               -27.1        -190       28.6%        2079
96     URU    8               -27.6        -221        0.0%        1407
97     ECU    4               -32.2        -129        0.0%        2070

that is, this add support to my hypothesis that states more or less the following:

FIDE statistics from 2023-2025 indicate that India has between 32,000 and 75,000 active rated players. A massive proportion of these are Under-18 (U18) players. The Elo system has a known lag in tracking rapid improvement. A junior player might improve their skill by 200 Elo points in six months through intensive training. However, if they play only one rated tournament in that period, or—crucially—if they play primarily against other underrated juniors, their rating will not reflect this 200-point gain.

In the Indian ecosystem, underrated juniors largely compete against one another. Consider a tournament in Chennai where Player A (rated 1400, strength 1700) plays Player B (rated 1400, strength 1700). The game ends in a draw. The ratings remain 1400. Both players are essentially "smurfing"—carrying a rating far below their true strength. The "National" rating often diverges from the FIDE rating, creating a "heavy" pool where 1400 Elo requires 1700-level skill to maintain.

[...]
Recent analysis of global rating reliability has quantified this disparity. Research indicates that players from South Asia (dominated by India) are, on average, 150 to 250 Elo points underrated relative to a global skill baseline. Conversely, players from older, established European federations like Denmark are often overrated by ~162 points. (I wanted to double check those claims)

Europe (and to a lesser extent, the USA) serves as the "reserve currency" of the FIDE rating system. It has:

  1. High Tournament Density: The vast majority of FIDE-rated Open tournaments occur in Europe.
  2. Amateur Density: A large population of adult hobbyists who maintain ratings established decades ago.
  3. Inflationary Tendency: Older players tend to lose strength faster than their rating decays (due to low K-factors and floors), effectively "storing" points that should have been lost.

When an underrated Indian junior travels to play in the Grand Swiss, Cappelle-la-Grande, or the Gibraltar Masters, they engage in what is essentially rating arbitrage.
The Transaction:

  • The Matchup: An Indian Junior (Rated 2100, Strength 2350) vs. A German Amateur (Rated 2300, Strength 2250).
  • The Prediction: The Elo formula predicts the German (2300) will score 76% against the 2100 opponent.
  • The Reality: The Indian Junior is stronger. The match ends in a Draw or a Win for the Indian.
  • The Consequences:
    • For the German: He loses significantly more points than he would losing to a peer. A draw might cost him rating points (since he was expected to win). A loss is catastrophic (-15 to -20 points depending on K-factor).
    • For the Indian: He gains massive points.
    • For the System: The German player is now rated 2280. He goes back to his local club and plays a peer rated 2280. He is still 2250 strength (one bad game didn't change his skill). He beats his peer. The peer loses points. The deflation spreads.

The points gained by the Indian junior are eventually taken back to India. Once there, they are likely lost to another underrated Indian junior. The points essentially vanish into the "black hole" of the deflationary Asian pool, never returning to the European ecosystem.

The mechanism driving the Asian Deflation (Geographic Isolation) is mathematically identical to the mechanism driving Rapid/Blitz Deflation (Format Isolation/Inactivity).

  1. Isolation: Just as Chinese players don't play enough international games to normalize their ratings, top Grandmasters often don't play enough FIDE-rated Blitz tournaments to normalize their speed ratings. They play online (Speed Chess Championship, Titled Tuesday) which does not affect FIDE ratings.
  2. Inertia: The K-factor (rating volatility) drops to 10 for elites. To gain 100 points, a player must massively outperform expectations over a long period. If they play only one Blitz tournament a year, their rating remains "frozen" in the past.
  3. The "Heavy" Asset: This results in a player having a rating (e.g., 2600) that is far below their current skill level (e.g., 2800).

python code here .