Korean pop culture, in numbers

Faker and Peanut get a year of readers in one month. Son Heung-min never does

Reads of an esports player and reads of a footballer do not arrive the same way. One arrives in a single month.

If a year's reads were spread evenly, the busiest month would hold 8.3%. All 11 esports players here peak in November 2025, and their busiest month holds between 30.1% and 58.4%.

Two sports, two shapes

SportPeopleMedian busiest-month shareRangeCommonest peakSharing it
Football926.5%10.7–60.3%June 20263 of 9
Esports1136.6%30.1–58.4%November 202511 of 11 — all of them

The range matters as much as the median. The least concentrated esports player still has 30.1% of a year in one month. The least concentrated footballer has 10.7%, which is close to what an even year would give.

Every person, and what else was on that month

NameSportBusiest monthIts shareReads in the yearWhat else was on that month
Hong Myung-boFootballJune 202660.3%16,6002026 FIFA World Cup
Kim Soo-hwanEsportsNovember 202558.4%43,1612025 League of Legends World Championship
Lee Min-hyungEsportsNovember 202553.2%52,7242025 League of Legends World Championship
Jung Ji-hunEsportsNovember 202548.2%46,7182025 League of Legends World Championship
Kim Sang-sikFootballJanuary 202646.3%81,597—
FakerEsportsNovember 202540.5%153,2412025 League of Legends World Championship
Ryu Min-seokEsportsNovember 202539.9%40,6652025 League of Legends World Championship
DoranEsportsNovember 202536.6%35,4082025 League of Legends World Championship
OnerEsportsNovember 202535.9%25,5942025 League of Legends World Championship
Choi Woo-jeEsportsNovember 202534.7%26,6492025 League of Legends World Championship
Park Jae-hyukEsportsNovember 202533%20,0002025 League of Legends World Championship
Park Do-hyeonEsportsNovember 202531.7%25,5542025 League of Legends World Championship
Bergson Gustavo Silveira da SilvaFootballMay 202631%5,587—
PeanutEsportsNovember 202530.1%57,2632025 League of Legends World Championship
Shin Tae-yongFootballOctober 202527.7%70,328—
Lee Kang-inFootballJune 202626.5%29,4332026 FIFA World Cup
Park Hang-seoFootballJanuary 202623.1%41,974—
Son Heung-minFootballAugust 202522.6%194,608—
Kim Min-jaeFootballJune 202616%22,0882026 FIFA World Cup
Park Ji-SungFootballAugust 202510.7%42,654—

Does the same month come back every year?

One year is one point. To see whether this is a property of the sport rather than of 2025, we ran the same measure back to 2021. A person is counted for a year only if their article was already being read before that year began, and only if it drew at least 300 reads that year.

YearEsports: sharing a peak monthFootball: sharing a peak month
20221 of 1 in November 2022 — all of them4 of 9 in November 2022
20232 of 2 in November 2023 — all of them4 of 9 in October 2023
20244 of 5 in November 20244 of 9 in February 2024
20259 of 9 in November 2025 — all of them3 of 9 in January 2025

The footballers reach agreement in 0 of the 4 years measured; their best is 4 of 9. The esports players reach it in 3 of 4.

An article created midway through a year shows all of its reads in the months after it existed, which our measure would read as a spike. Four people showed exactly 100% in December 2024 for that reason. A year is only counted for someone whose article was already being read before that year began.

The last column is a calendar, not a cause

We measured when reads arrived. The tournament dates come from the calendar, not from our data. The two are printed side by side because that is the honest shape of what we know: a month is crowded, and something happened that month. We did not measure that one caused the other.

How this was counted, and what it cannot say

A month with more reads is a month with more reads. This does not say what caused it. Where we name a cause we name the source for it separately.

Four Southeast Asian Wikipedias, four different athletes · Which game the readers are actually reading about · How we count

What we wrote from this data

How this was measured

We report the median rather than the mean. A handful of very large values would pull a mean away from where most of the sample actually sits, and in reads-per-title data those large values are the norm, not an error. Correlations are Pearson coefficients. Where hours are involved they are taken on the logarithm of hours, because viewing hours span several orders of magnitude and a handful of enormous titles would otherwise decide the coefficient on their own. A person's peak month is the month with their highest reads in the window, and the question is whether those peaks land together or scatter.

What this cannot tell you

A median tells you where the middle sits and nothing about the shape around it, so it should be read next to the range or the full distribution, not alone. A correlation is not a cause, and it only detects the straight-line part of a relationship — two things can move together tightly in a curve and still score near zero. Taking logs changes what is being correlated: it is the ranking-like structure of hours, not hours themselves. A coefficient from a chosen sample also inherits that choice; ours are titles that reached a chart, which is not a sample of titles. With eleven people a shared peak month can happen by chance, and this page cannot separate that from a shared cause. A month with more reads is a month with more reads — nothing here identifies why, and a single event that lifted an entire edition would look identical to eleven separate reasons.