New dataset: FIFA World Cup prize money — per team, per position, per edition

[amautadev]

Summary

Proposing (and have a working draft of) a World Cup prize-money dataset for the
football collection — a granular companion to the existing
football/worldcup dataset, which records
results but not the money behind them.

It answers: what did each team actually earn, and how has the prize pool grown — including the men's vs women's gap?

What's in it

Resource	Rows	Content
prize_by_team	128	What every team earned at the 2010/2014/2018/2022 men's World Cups, by finishing bracket (Winner → Group stage)
prize_schedule	36	FIFA's per-position payout schedule per edition 2010–2026, including the 48-team 2026 expansion (new Round-of-32 band)
prize_pool_by_edition	22	Total prize pool per edition — men's 1982–2026 and women's 1991–2027

All figures nominal USD millions.

Why it's interesting

• 2022: Argentina earned $42M for winning; every group-stage team got $9M.
• The gap: the 2022 men's pool ($440M) was 4× the 2023 women's pool ($110M). Women's prize money was $0 until 2007. FIFA has announced
  parity by 2027.
• Growth: the winner's cheque went $30M (2010) → $42M (2022) → $50M (2026).

Data quality / validation

Every figure is self-checking:

• Per-team payouts sum exactly to the published pool for all four editions ($348M / $358M / $400M / $440M).
• Each per-position schedule sums to its pool (e.g. 2026: 50+33+29+27 + 19×4 + 15×8 + 11×16 + 9×16 = $655M).
• Finishing brackets are derived from match data — top-4 from FIFA standings, deeper rounds from each team's furthest stage in the Fjelstul World Cup
  Database.

Sources & licence

• Payout schedules & pools: FIFA prize-money announcements (compiled via reporting / topendsports).
• Placements: Fjelstul World Cup Database.
• Figures are public facts; proposed licence PDDL, consistent with the other league datasets.

Status / discussion

Working draft is packaged (datapackage.json + CSVs + README + STORY.md + datahub views) and committed on a branch in datasets/football-datasets.

Open questions for discussion:

1. Keep men's + women's in one dataset, or split?
2. Worth adding per-team women's payouts (2015/2019/2023 mix team + individual-player components)?
3. Should pre-2014 men's total-contribution figures be excluded, or kept (flagged) for historical context?

Feedback welcome.