Basketball players-to-coffee consumption rate (proxy)
Proxy index of basketball participation relative to coffee consumption, computed as basketball interest (0–100) divided by coffee consumption (kg/person/year).
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Key Insights
- •The index tends to spike where estimated coffee consumption is very low; these are mathematical artifacts rather than definitive evidence of very high basketball participation.
- •High-coffee Nordic/Western European countries can show lower ratios even if basketball interest is moderate, because the denominator is large.
- •To approximate actual ‘players’, replace Google Trends with registered-player counts from national federations (where available) and recompute the same ratio.
- •For stable mapping, apply denominator floors (e.g., coffee >= 0.5 kg/person/year) and cap/winsorize the top 1–2% of ratios.
Country Rankings
Top 10 Countries
Bottom 10 Countries
Data Analysis
Value Distribution
How countries are distributed across the value range
Regional Comparison
Average values by world region (Global avg: 59.0index (Trends points per kg/person/year))
About This Statistic
A direct global statistic for the “rate of basketball players to coffee consumption” is not published as a single official indicator. To make a map-ready, country-level dataset with broad coverage, this statistic uses a defensible proxy approach that combines (1) a country-level basketball participation/interest indicator and (2) coffee consumption per capita.
Because globally comparable counts of registered basketball players are not available in a single consistent database across most countries, this version uses Google Trends “Basketball” topic interest (0–100) as a scalable proxy for basketball activity/interest, and divides it by estimated coffee consumption per capita (kg/person/year). Higher values indicate higher basketball interest relative to coffee consumption.
Methodology
1) Coffee consumption: use per-capita coffee consumption (kg/person/year) from widely cited country tables (ideally aligned to an ICO reporting year; for prototyping, values are taken from the compiled ranking list). 2) Basketball proxy: use Google Trends topic interest for “Basketball” by country (0–100) for a recent year. 3) Compute the derived rate: Rate = (Basketball interest index) / (Coffee kg/person/year). Notes: This produces a dimension-like ratio (Trends points per kg/person/year). It is intended for comparative mapping, not as a literal count of players.
Full Data
| Rank ↑ | Country ↕ | Value ↕ |
|---|---|---|
| 1 | People's Republic of China | 900.0index (Trends points per kg/person/year) |
| 2 | Kenya | 800.0index (Trends points per kg/person/year) |
| 3 | Philippines | 100.0index (Trends points per kg/person/year) |
| 4 | Morocco | 90.0index (Trends points per kg/person/year) |
| 5 | India | 85.7index (Trends points per kg/person/year) |
| 6 | Türkiye | 80.0index (Trends points per kg/person/year) |
| 7 | Ukraine | 64.3index (Trends points per kg/person/year) |
| 8 | Indonesia | 54.5index (Trends points per kg/person/year) |
| 9 | South Africa | 54.5index (Trends points per kg/person/year) |
| 10 | Russian Federation | 47.1index (Trends points per kg/person/year) |
| 11 | Malaysia | 46.2index (Trends points per kg/person/year) |
| 12 | Thailand | 45.5index (Trends points per kg/person/year) |
| 13 | Saudi Arabia | 40.0index (Trends points per kg/person/year) |
| 14 | Vietnam | 40.0index (Trends points per kg/person/year) |
| 15 | Romania | 39.1index (Trends points per kg/person/year) |
| 16 | Mexico | 35.7index (Trends points per kg/person/year) |
| 17 | Venezuela | 35.3index (Trends points per kg/person/year) |
| 18 | Singapore | 30.0index (Trends points per kg/person/year) |
| 19 | Poland | 26.7index (Trends points per kg/person/year) |
| 20 | Lebanon | 23.3index (Trends points per kg/person/year) |
| 21 | United Arab Emirates | 22.9index (Trends points per kg/person/year) |
| 22 | Hungary | 22.2index (Trends points per kg/person/year) |
| 23 | South Korea | 18.5index (Trends points per kg/person/year) |
| 24 | United Kingdom | 17.9index (Trends points per kg/person/year) |
| 25 | New Zealand | 16.7index (Trends points per kg/person/year) |
| 26 | United States of America | 16.7index (Trends points per kg/person/year) |
| 27 | Portugal | 15.0index (Trends points per kg/person/year) |
| 28 | Slovenia | 14.0index (Trends points per kg/person/year) |
| 29 | Ireland | 13.3index (Trends points per kg/person/year) |
| 30 | Slovakia | 13.0index (Trends points per kg/person/year) |
| 31 | Ethiopia | 12.5index (Trends points per kg/person/year) |
| 32 | Czech Republic | 12.1index (Trends points per kg/person/year) |
| 33 | Colombia | 12.0index (Trends points per kg/person/year) |
| 34 | Japan | 11.8index (Trends points per kg/person/year) |
| 35 | Austria | 11.3index (Trends points per kg/person/year) |
| 36 | Spain | 11.1index (Trends points per kg/person/year) |
| 37 | Greece | 11.0index (Trends points per kg/person/year) |
| 38 | Israel | 11.0index (Trends points per kg/person/year) |
| 39 | Denmark | 10.3index (Trends points per kg/person/year) |
| 40 | Belgium | 10.3index (Trends points per kg/person/year) |
| 41 | Switzerland | 10.1index (Trends points per kg/person/year) |
| 42 | Sweden | 9.8index (Trends points per kg/person/year) |
| 43 | Brazil | 9.8index (Trends points per kg/person/year) |
| 44 | Italy | 9.6index (Trends points per kg/person/year) |
| 45 | Netherlands | 9.5index (Trends points per kg/person/year) |
| 46 | France | 9.3index (Trends points per kg/person/year) |
| 47 | Germany | 9.1index (Trends points per kg/person/year) |
| 48 | Canada | 8.1index (Trends points per kg/person/year) |
| 49 | Norway | 8.1index (Trends points per kg/person/year) |
| 50 | Australia | 5.0index (Trends points per kg/person/year) |
| 51 | Country 3920 | 0.0index (Trends points per kg/person/year) |
Topics
Data Source
This data comes from International Coffee Organization (ICO) + Google Trends (proxy) (2023).
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