saturn·

← all readings

olympics olympic medals data

source /home/coolhand/html/datavis/data_trove/data/cultural/olympics/olympic_medals_data.json 1,433 rows 8 columns profiled 2026-05-01 raw JSON static .html .ipynb Report Notebook

Reading

dataset summary · high confidence anthropic:claude-opus-4-7

This dataset contains 1,433 rows of Olympic medal counts by country and year, spanning 1896 to 2024 across 165 countries. Medal columns (gold, silver, bronze, total) are heavily right-skewed with high kurtosis and many outliers — a small number of dominant nations pull the means well above the medians (e.g. total has a median of 5 but a max of 234). Zero-rates are notable too: 33.9% of rows have zero gold medals and 25.3% zero silver, reflecting how often countries leave a Games empty-handed in a category. Country participation is fairly even at the top, with France and Great Britain tied as most-frequent entries (30 appearances each). Start by examining the shape of `total` and `gold` distributions and the `year` coverage to understand era effects.

citing: row_count · column_count · columns.total.stats · columns.gold.stats · columns.silver.stats · columns.bronze.stats · columns.country.top_values · columns.year.stats

total · Highly skewed total medal counts — most rows are small while a few mega-hauls reach 234.
Show data table
bincount
1 – 7.297907
7.297 – 13.59176
13.59 – 19.8999
19.89 – 26.1977
26.19 – 32.4942
32.49 – 38.7828
38.78 – 45.0820
45.08 – 51.3811
51.38 – 57.687
57.68 – 63.977
63.97 – 70.2712
70.27 – 76.575
76.57 – 82.861
82.86 – 89.163
89.16 – 95.4611
95.46 – 101.88
101.8 – 108.15
108.1 – 114.45
114.4 – 120.60
120.6 – 126.94
126.9 – 133.21
133.2 – 139.50
139.5 – 145.80
145.8 – 152.11
152.1 – 158.40
158.4 – 164.70
164.7 – 1710
171 – 177.31
177.3 – 183.60
183.6 – 189.90
189.9 – 196.21
196.2 – 202.50
202.5 – 208.80
208.8 – 215.10
215.1 – 221.40
221.4 – 227.70
227.7 – 2341
gold · Gold medals are zero for ~34% of rows; check the long right tail of dominant performances.
Show data table
bincount
0 – 2.243948
2.243 – 4.486175
4.486 – 6.7376
6.73 – 8.97361
8.973 – 11.2252
11.22 – 13.4625
13.46 – 15.711
15.7 – 17.9513
17.95 – 20.1912
20.19 – 22.433
22.43 – 24.684
24.68 – 26.923
26.92 – 29.166
29.16 – 31.412
31.41 – 33.655
33.65 – 35.892
35.89 – 38.1411
38.14 – 40.386
40.38 – 42.621
42.62 – 44.863
44.86 – 47.115
47.11 – 49.353
49.35 – 51.591
51.59 – 53.840
53.84 – 56.082
56.08 – 58.320
58.32 – 60.570
60.57 – 62.810
62.81 – 65.050
65.05 – 67.30
67.3 – 69.540
69.54 – 71.780
71.78 – 74.030
74.03 – 76.270
76.27 – 78.511
78.51 – 80.761
80.76 – 831
year · Coverage spans 1896–2024 with a left skew — more recent Games contribute more rows.
Show data table
bincount
1896 – 189910
1899 – 190318
1903 – 190612
1906 – 191019
1910 – 191319
1913 – 19170
1917 – 192022
1920 – 19240
1924 – 192727
1927 – 193133
1931 – 193428
1934 – 193832
1938 – 19410
1941 – 19440
1944 – 19480
1948 – 195138
1951 – 195543
1955 – 195838
1958 – 196244
1962 – 196541
1965 – 196944
1969 – 197248
1972 – 19760
1976 – 197941
1979 – 198236
1982 – 198647
1986 – 198952
1989 – 199364
1993 – 199679
1996 – 20000
2000 – 200380
2003 – 200774
2007 – 201087
2010 – 201486
2014 – 201786
2017 – 202193
2021 – 202492
country · Top countries by appearances are tightly clustered, with France and Great Britain leading at 30 each.
Show data table
valuecountshare
FRA302.1%
GBR302.1%
USA292.0%
DEN292.0%
SUI292.0%
HUN282.0%
AUS282.0%
BEL282.0%
ITA282.0%
SWE282.0%
AUT271.9%
NED271.9%
CAN271.9%
NOR261.8%
FIN261.8%
JPN231.6%
NZL231.6%
POL231.6%
MEX221.5%
GRE211.5%
rank_total · Ranks distribute fairly evenly from 1 to 93 — useful context for interpreting medal totals.
Show data table
bincount
1 – 3.48690
3.486 – 5.97360
5.973 – 8.45990
8.459 – 10.9560
10.95 – 13.4386
13.43 – 15.9256
15.92 – 18.4184
18.41 – 20.8952
20.89 – 23.3874
23.38 – 25.8648
25.86 – 28.3571
28.35 – 30.8444
30.84 – 33.3265
33.32 – 35.8140
35.81 – 38.358
38.3 – 40.7834
40.78 – 43.2747
43.27 – 45.7626
45.76 – 48.2435
48.24 – 50.7320
50.73 – 53.2229
53.22 – 55.718
55.7 – 58.1927
58.19 – 60.6818
60.68 – 63.1627
63.16 – 65.6517
65.65 – 68.1424
68.14 – 70.6216
70.62 – 73.1124
73.11 – 75.5915
75.59 – 78.0821
78.08 – 80.5713
80.57 – 83.0515
83.05 – 85.5410
85.54 – 88.0310
88.03 – 90.514
90.51 – 935

Schema

8 columns
Alerts
year numeric 0.0% 30
country categorical 0.0% 159
country_name categorical 0.0% 165
gold numeric 0.0% 52
high_skew outliers
silver numeric 0.0% 45
high_skew outliers
bronze numeric 0.0% 44
high_skew outliers
total numeric 0.0% 97
high_skew outliers
rank_total numeric 0.0% 93

year

numeric timestamp
Four-digit calendar years spanning 1896 to 2024 with 30 distinct values across 1,433 rows and no nulls. The distribution is left-skewed (skew -0.76) toward recent decades, with a median of 1992 and IQR from 1960 to 2008, suggesting coverage is sparser in the early 20th century. No outliers were flagged. Treatment: Treat as a temporal feature; bucket by decade or use directly without scaling. high · anthropic:claude-opus-4-7
n
1,433
nulls
0 (0.0%)
unique
30
min
1,896
max
2,024
mean
1982
median
1,992
std
33.95
q1
1,960
q3
2,008
iqr
48
skew
-0.7568
kurtosis
-0.408
n_outliers
0
outlier_rate
0
zero_rate
0

country

categorical feature
Three-letter country codes (e.g., FRA, GBR, USA, DEN, SUI) covering 159 distinct nations across 1433 rows with no nulls. The distribution is remarkably flat — the top value FRA accounts for only 2.1% of rows and entropy ratio is 0.92, so no country dominates. Top counts cluster tightly between 28 and 30, suggesting a near-uniform sampling design rather than organic population weights. Treatment: Group rare codes or target-encode before modelling; one-hot would create 159 sparse columns. high · anthropic:claude-opus-4-7
n
1,433
nulls
0 (0.0%)
unique
159
top_value
FRA
top_rate
0.02094
cardinality
159
entropy
6.695
entropy_ratio
0.9155

country_name

categorical feature
Categorical country labels with 165 distinct values across 1433 rows and no nulls. Distribution is remarkably flat — the top value 'France' covers only 2.09% of rows, and the top ten countries each appear 28–30 times, giving an entropy ratio of 0.91 (near-uniform). This looks like a panel where each country contributes a similar number of observations rather than a skewed real-world sample. Treatment: Use as a grouping key or target-encode/one-hot for modelling; consider mapping to ISO codes for joins. high · anthropic:claude-opus-4-7
n
1,433
nulls
0 (0.0%)
unique
165
top_value
France
top_rate
0.02094
cardinality
165
entropy
6.715
entropy_ratio
0.9116

gold

numeric feature high_skew outliers
Numeric count-style feature 'gold' ranging from 0 to 83 with median 1 and mean 4.06, so most rows sit near zero (zero_rate 0.339) while a long tail pulls the average up. Distribution is severely right-skewed (skew 4.26, kurtosis 23.14) with 134 outliers (9.35% of rows) above the q3 of 4. Only 52 unique values across 1433 rows suggests a discrete tally rather than a continuous measurement. Treatment: Apply a log1p transform before modelling to tame the heavy right tail. high · anthropic:claude-opus-4-7
n
1,433
nulls
0 (0.0%)
unique
52
min
0
max
83
mean
4.059
median
1
std
8.419
q1
0
q3
4
iqr
4
skew
4.259
kurtosis
23.14
n_outliers
134
outlier_rate
0.09351
zero_rate
0.3391

silver

numeric feature high_skew outliers
A non-negative integer-like count of silver medals or items, with 45 distinct values ranging 0 to 79 and a median of 2. The distribution is heavily right-skewed (skew 4.03, kurtosis 23.2) with 25.3% zeros and 9.8% flagged as outliers, so a small set of large counts dominates the mean (4.04) versus the median. Treatment: Apply a log1p transform before modelling to tame the skew and heavy tail. high · anthropic:claude-opus-4-7
n
1,433
nulls
0 (0.0%)
unique
45
min
0
max
79
mean
4.038
median
2
std
7.121
q1
0
q3
4
iqr
4
skew
4.026
kurtosis
23.21
n_outliers
140
outlier_rate
0.0977
zero_rate
0.2526

bronze

numeric feature high_skew outliers
This is a count of bronze medals (or similar bronze-tier tally) per record, with 1433 rows, 44 distinct integer values from 0 to 78, and no nulls. The distribution is heavily right-skewed (skew 3.37, kurtosis 16.94): the median is 2 and Q3 is 5, yet the max reaches 78, producing 150 outliers (10.5%). Roughly 19.8% of rows are zero, so a sizeable share of entities have never won bronze. Treatment: Apply a log1p transform before modelling to tame the heavy right tail. high · anthropic:claude-opus-4-7
n
1,433
nulls
0 (0.0%)
unique
44
min
0
max
78
mean
4.398
median
2
std
6.853
q1
1
q3
5
iqr
4
skew
3.37
kurtosis
16.94
n_outliers
150
outlier_rate
0.1047
zero_rate
0.1982

total

numeric feature high_skew outliers
This appears to be a count-style numeric feature (total), heavily right-skewed: the median is 5 while the mean is 12.5 and the max reaches 234. Skew of 3.92 and kurtosis of 20.8 confirm a long tail, with 151 values (10.5%) flagged as outliers. No nulls or zeros, and only 97 unique values across 1,433 rows, suggesting a discrete count with a small repeating vocabulary. Treatment: log1p-transform before modelling to tame the heavy right tail. high · anthropic:claude-opus-4-7
n
1,433
nulls
0 (0.0%)
unique
97
min
1
max
234
mean
12.5
median
5
std
21.66
q1
2
q3
13
iqr
11
skew
3.922
kurtosis
20.8
n_outliers
151
outlier_rate
0.1054
zero_rate
0

rank_total

numeric feature
Integer-valued ranking field spanning 1 to 93 with 93 unique values across 1433 rows, suggesting a complete rank table repeated many times (e.g., per period or per group). Distribution is right-skewed (skew 0.74) with median 26 below mean 31.06, so lower ranks dominate while a tail extends toward 93. No nulls, no zeros, and no outliers flagged given the bounded range. Treatment: Use as an ordinal feature; consider inverting (e.g., 94 - rank) so higher means better before modelling. high · anthropic:claude-opus-4-7
n
1,433
nulls
0 (0.0%)
unique
93
min
1
max
93
mean
31.06
median
26
std
22.7
q1
13
q3
45
iqr
32
skew
0.739
kurtosis
-0.3677
n_outliers
0
outlier_rate
0
zero_rate
0