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The POST /get_sportive_impact endpoint calculates the sportive (on-field) impact of a player joining a specific club. It uses SquadAssist’s model to simulate how the player would affect the club’s squad and expected league position over a 3-year period. The result is expressed both as a monetary value — the revenue contribution the improvement is expected to generate — and as concrete simulation metrics such as the change in expected finishing position and projected playing time. This is the on-field component of the full ROI analysis; no transfer fee or wage inputs are needed here because those belong to the cost side of the calculation.
The analysis always uses the 2025/2026 season as the starting point. Season is not a request parameter. The club_id must be a club in your coverage.

Request

The request body must be a JSON object.
string
required
The SquadAssist player ID. Use POST /query_player to look up the ID from a Transfermarkt or Wyscout ID.
string
required
The SquadAssist club ID for the club the player would be joining. The model compares the player against the club’s existing squad to determine playing time and positional improvement. Use POST /query_club to look up the ID.
string
An ISO 4217 currency code (e.g. EUR, GBP, USD). The on-field monetary value will be returned in this currency alongside the EUR value. Defaults to EUR.

Response

string
The ISO 4217 currency code for the on_field_value output. Matches your currency_code input, or EUR if none was provided.
integer
The estimated monetary value of the player’s on-field contribution to the club over the 3-year simulation, in the requested currency. This reflects the additional revenue the club is expected to generate from the improvement in league position.
integer
The estimated on-field value contribution, in EUR.
object
The league position simulation results showing how the player changes the club’s expected finishing position.
number
The projected fraction of available matches the player is expected to participate in at the signing club (0.0–0.9). For example, 0.72 means the player is expected to play in approximately 72% of matches.

Example

Errors