Pre-Computed Injury Impact Predictions

The following describes how to retrieve pre-computed injury impact predictions through the Spors Data APIs.

Using the REST API to query for Injury Effect Simulations

"Related Sports Data APIS"

Method	Description	Availability
POST	/sports-data/{sport}/v2/injury_impacts	NFL, NBA, NHL

The injury impact api allows you to retrieve current Player Effect results that were generated using the Player Effect API. These are generated for each injured players on a roster and include the Player Effect results for if the player is in and out of the game. The results are provided as a quick way to retrieve the Player Effect results for comparison of an individual player's impact.

Use the event.uuid query parameter to filter to a specific event.

For example, here are the results for an NFL event:

GET https://api.fairplaybettech.com/american-football/v2/injury_impacts?event.uuid=d2656fe4-1c17-4aec-a0b2-a753122de63d

note

This result is very large so the example is not displayed here.

The result will contain a data array, each entity with an attribute of playerEffectIn and playerEffectOut. These properties contain the list of relevant player props for the players playing in the game.

• The playerEffectIn attribute contains prediction as if the target player was Playing in the game.
• The playerEffectOut attribute contains prediction as if the target player was NOT Playing in the game.

You can then use the two result sets to compare how a player's predicted performance is expected to change when the target player is in ou out.

For example:

{
  "links": {
    "self": "\/american-football\/v2\/injury_impacts?event.uuid=d2656fe4-1c17-4aec-a0b2-a753122de63d"
  },
  "meta": {
    "totalItems": 13,
    "itemsPerPage": 30,
    "currentPage": 1
  },
  "data": [
    {
      "id": "\/american-football\/v2\/injury_impacts\/c0b38013-8452-4bb3-b49c-ab4cc1ad8560",
      "type": "InjuryImpact",
      "attributes": {
        "playerEffectIn": {
          "c0b454d6-1640-4f8e-9360-61deb40ca6aa": {
            "team_id": "c023d5ce-83ec-4263-8662-1f411058d8de",
            "player_id": "c0b454d6-1640-4f8e-9360-61deb40ca6aa",
            "position": "QB",
            "stats": {
              "passing_touchdowns": 1.9,
              "passing_longest": 49.7,
              "passing_yards": 330.7,
              "passing_completions": 26.8,
              "passing_attempts": 40,
              "passing_cmp_pct": 0.7,
              "passing_sacks": 3.7,
              "passing_sack_yards": 24.6,
              "passing_interceptions": 0.4
            },
            "player_name": "Russell Wilson",
            "team_name": "Denver Broncos",
            "uniform": "3",
            "player_image": "https:\/\/avatar.api.quarter4.io\/american-football\/avatar\/c023d5ce-83ec-4263-8662-1f411058d8de\/256\/uniform\/3.png"
          },
---cut---
        "playerEffectOut": {
          "c0b454d6-1640-4f8e-9360-61deb40ca6aa": {
            "team_id": "c023d5ce-83ec-4263-8662-1f411058d8de",
            "player_id": "c0b454d6-1640-4f8e-9360-61deb40ca6aa",
            "position": "QB",
            "stats": {
              "passing_touchdowns": 1.9,
              "passing_longest": 50.2,
              "passing_yards": 332.1,
              "passing_completions": 27,
              "passing_attempts": 40.3,
              "passing_cmp_pct": 0.7,
              "passing_sacks": 3.7,
              "passing_sack_yards": 24.8,
              "passing_interceptions": 0.4
            },
            "player_name": "Russell Wilson",
            "team_name": "Denver Broncos",
            "uniform": "3",
            "player_image": "https:\/\/avatar.api.quarter4.io\/american-football\/avatar\/c023d5ce-83ec-4263-8662-1f411058d8de\/256\/uniform\/3.png"
          },
---cut---

warning

The Injury Impact API results only deals with a single player at a time and does not include pre-computed results for multiple players. For example it does not include results for if player A & B are out vs if player A & B are in. You can use the Player Effect API to generate more advanced results.

Here, you can see that when the target player is out, player c0b454d6-1640-4f8e-9360-61deb40ca6aa's predicted passing yards increase from 330.7 to 332.1.