Datasets:

AFOS-Analytics1
/

germany-2025-electoral-divergence

Every file and column, with type, unit and provenance. Party-level: polls measure party vote share; the market prices the probability of winning the most seats. Values are never imputed or smoothed — missing values are left blank.

`polls/germany-polls.csv`

Party vote-share polling, long format — one row per party × poll.

Column	Type	Notes
`poll_date`	date	Fieldwork-end date (`YYYY-MM-DD`).
`fieldwork`	string	Fieldwork window as published.
`pollster`	string	Polling firm (Forsa, INSA, Infratest dimap, …).
`sample`	integer	Sample size; blank when not declared (thousands separators normalized).
`party`	string	Party (canonical): CDU/CSU, SPD, AfD, Grüne, Die Linke, FDP, BSW, Freie Wähler.
`party_full`	string	Full party name.
`percent`	number	Party vote-share intention, %.

`polls/germany-polls.json`

Full structured polls (pollster, fieldwork, sample, per-party results). Deduplicated by (pollster, date), keeping the most complete row.

`data/germany-market-odds-timeseries.csv`

Polymarket "wins the most seats" probability per party, daily, from the "Germany Parliamentary Election Winner" market.

Column	Type	Notes
`date`	date	`YYYY-MM-DD`.
`party`	string	Canonical party name.
`polymarket_pct`	number	Implied probability of winning the most seats, %.
`volume_usd`	number	Cumulative traded volume for that party's market, USD.

`data/germany-divergence-timeseries.csv`

Market × poll divergence per party — each poll's party vote share joined to that party's market odds on the poll's date (nearest on or before).

Column	Type	Notes
`poll_date`	date	Poll date.
`pollster`	string	Polling firm.
`party`	string	Canonical party name.
`poll_pct`	number	Party vote share, %.
`polymarket_pct`	number	Market "wins most seats" probability on `polymarket_date`, %.
`polymarket_date`	date	Market date used (nearest on-or-before `poll_date`).
`divergence_pp`	number	`polymarket_pct − poll_pct`, percentage points.

Interpretation caveat: a poll reports vote share; the market prices probability of winning the most seats. The two are different quantities — divergence_pp is the spread AFOS tracks, not an error metric. A party (e.g. AfD) can hold a large vote share while its probability of winning the plurality is near zero; the gap is the signal.

`data/germany-poly-raw.json`

Raw Polymarket payload (event + per-party price histories), via a US-resolving function, kept for provenance.

Sources: Wikipedia — Opinion polling for the 2025 German federal election · Polymarket "Germany Parliamentary Election Winner". Maintained by AFOS Analytics. CC BY 4.0.

`data/germany-structural-context.csv`

Structural country context from the World Bank, complementary to the divergence data: it frames the country, it does not predict the electoral outcome. Long/tidy format, one row per indicator, latest available year per indicator.

Column	Type	Notes
`category`	string	`governance`, `economy`, or `education`.
`indicator`	string	Machine code (e.g. `political_stability`, `gdp_usd`, `expected_years_schooling`).
`label`	string	Human-readable indicator name (English).
`value`	number	Governance on a 0–100 scale; economy in US$ / %; education in % of GDP or years.
`unit`	string	`index_0_100`, `USD`, `percent`, or `years`.
`year`	integer	Reference year of the value (latest available).
`source`	string	World Bank WGI (governance, via Data360) or WDI (economy & education).
`iso3`	string	ISO 3166 alpha-3 country code.

Both sources are open-licensed (CC BY 4.0) and keyless. Governance = Worldwide Governance Indicators; economy & education = World Development Indicators.