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The condition strategy endpoint answers a single seller question: “I have a copy of this game — should I list it loose, CIB, or new, and why?” It is the composite counterpart to GET /api/v1/demand-intent/games/{id}/conditions: that endpoint reports per-condition demand, while this one recommends — blending where watch demand concentrates with each condition’s relative price into a 0100 strategy_score, then naming a single recommended condition. It requires the intelligence.read scope and a valid Authorization: Bearer <token> header.
This page covers the per-game condition recommendation. To rank or score whole titles by liquidity — or to score a lot/inventory in bulk — see Intelligence: Games.

How the recommendation is built

For each of the three sellable conditions — loose (cartridge/disc only), CIB (complete in box), and new (sealed) — the endpoint combines two normalised signals:
  • Demand concentration — how much of the game’s watch demand is attached to that condition, drawn from saved notification alerts where a buyer selected the condition they want.
  • Relative value — that condition’s catalog price, normalised against the highest of the title’s three condition prices.
These are blended into a strategy_score, and the highest-scoring condition becomes the recommended pick.

demand_share

Each condition’s share of the game’s watch demand, expressed between 0.0 and 1.0. A high demand_share on cib means buyers who alert on this title overwhelmingly want the complete-in-box version.

value_index

Each condition’s catalog price normalised against the highest of the title’s three condition prices (0.01.0). The priciest condition scores 1.0; the others are scored relative to it. This is the value component that the strategy_score weighs against demand.

strategy_score

A 0100 blend of demand concentration and relative value, normalised across the game’s three conditions:
The weights (w_demand and w_value) are set in the API’s serving configuration. The condition with the highest strategy_score is the one returned in recommended.

premiums

Price step-ups between conditions, expressed as ratios. complete_over_loose of 1.6786 means CIB sells for 168% of the loose price. Each ratio is null when its base price is 0.

GET /api/v1/intelligence//conditions

Returns the full condition breakdown for one game, scored and ranked, with a single recommended condition to list in.

Path parameters

integer
required
The numeric ID of the game. You can resolve IDs from the Games catalog endpoints.

Query parameters

string
default:"7d"
The time window to aggregate over. One of day, 7d, or 30d.
string
Override the target window-end date. Defaults to the latest computed date. Format: YYYY-MM-DD.

Response

object
object
Game identity fields.
integer
Game ID.
string
Display name.
string
URL slug.
integer
Console ID.
string
Console display name.
object
Current market prices from the catalog.
string
Loose price (decimal string).
string
Complete-in-box price (decimal string).
string
Sealed/new price (decimal string).
object
Price step-ups between conditions, expressed as ratios. Each is null when its base price is 0.
float
How much more CIB sells for than loose (e.g. 1.6786 = 168% of the loose price).
float
How much more sealed/new sells for than CIB.
array
One row per sellable condition — loose, cib, and new — sorted by strategy_score descending.
string
The condition: loose, cib, or new.
string
The catalog price for this condition (decimal string).
integer
Active watchlist alerts for this game in this condition.
float
This condition’s share of the game’s watch demand (0.01.0).
float
This condition’s price normalised against the highest of the three condition prices (0.01.0); the priciest condition is 1.0.
integer
A 0100 blend of demand concentration and relative value, normalised across the game’s three conditions.
The single suggested condition to list in.
string
The highest-scoring condition (ties break to the higher price).
string
A short rationale reconciling the pick against the pure-demand and pure-value winners — e.g. "where buyer demand concentrates", "best price with viable demand", "highest demand and best price", or "best demand/value balance".
object
string
The resolved period.
string
The window-end date the data was computed for (YYYY-MM-DD).
Example response:
In the example above, CIB wins despite “new” carrying the highest price: 55% of buyer demand for this title concentrates on the complete-in-box version, and only 3 alerts target a sealed copy. The reason"where buyer demand concentrates" — reconciles the recommendation against the pure-value winner (new).
Returns 404 not_found only when the id is not a real, enabled game.
This endpoint recommends a condition. To see the raw per-condition demand breakdown across all four conditions (including any) without the scoring layer, use GET /api/v1/demand-intent/games/{game_id}/conditions.