Aggregate Stats

Overview

The aggregateStats GraphQL query provides aggregated statistical insights from various events. Below, you'll find a field catalog detailing available fields, followed by query examples demonstrating their usage.

Field Catalog

Each field listed here can be used in Aggregate Stats queries.

Metadata Fields

Metadata fields are available for all events.

Field Name	Description	Usages
match.metadata[match_series_id]	ID of the MatchSeries	Term Filter
match.metadata[tournament_id]	ID of the Tournament	Term Filter
match.metadata[season_id]	ID of the Season	Term Filter
match.metadata[competition_id]	ID of the Competition	Term Filter
match.metadata[circuit_id]	ID of the Circuit	Term Filter
player.metadata[player_id]	ID of the Player	Term Filter, Term Bucket
team.metadata[team_id]	ID of the Team	Term Filter, Term Bucket
match.metadata[match_id]	ID of the Match	Term Filter

Common Fields

These fields are accessible across all events.

Field Name	Description	Usages
event.type	Type of event	Term Filter
event.timestamp	UTC timestamp of the event occurrence	Range Filter
game.map.name	In-game map name (e.g., "de_dust2")	Term Filter, Term Bucket
game.round.id	Unique round identifier	Term Bucket
game.round.occurrence	Event occurrence count within the round	Term Filter, Range Filter
game.round.result[WIN]	Count of rounds won	Count
game.round.result[LOSS]	Count of rounds lost	Count
team.role	Same as field "name" on GameRole	Term Filter, Term Bucket

Event-Specific Fields

These fields apply to specific event types.

Field Name	Description	Usages	Available Events
item.name	In-game item name (e.g., "m4a1")	Term Filter, Term Bucket	GameEventPlayerItemPurchase, GameEventPlayerItemThrow, GameEventPlayerAttack, GameEventPlayerKill
item.type	see GameItemType	Term Filter, Term Bucket	GameEventPlayerItemPurchase, GameEventPlayerItemThrow, GameEventPlayerAttack, GameEventPlayerKill
event.player_situation[CLUTCH]	1vsX clutch situations count	Count	GameEventPlayerSituation
event.player_situation.clutch.opponents_alive	Opponents alive in 1vsX clutch	Term Filter, Term Bucket	GameEventPlayerSituation
event.game_result	Relates to GameEventGameResult	Count	GameEventGameResult
event.game_result[WIN]	Total maps won	Count	GameEventGameResult
event.game_result[LOSS]	Total maps lost	Count	GameEventGameResult
event.game_score	Relates to GameEventGameScore	Count	GameEventGameScore
event.player_assist	Relates to GameEventPlayerAssist	Count	GameEventPlayerAssist
event.player_attack.classification[REGULAR].damage.health	Health damage dealt (excluding team damage)	Sum	GameEventPlayerAttack
event.player_death	Relates to GameEventPlayerDeath	Count	GameEventPlayerDeath
event.player_kill.classification	see GameKillClassification	Term Filter	GameEventPlayerKill
event.player_kill.classification[REGULAR]	Kills against opponents (excluding team kills)	Count	GameEventPlayerKill
event.player_purchase	Relates to GameEventPlayerItemPurchase	Count	GameEventPlayerItemPurchase

Query Examples

Clutch 1vsX Wins/Totals

{
    aggregateStats {
        data {
            players: termBucket(fields: ["player.metadata[player_id]", "event.player_situation.clutch.opponents_alive"]) {
                playerId: term(field: "player.metadata[player_id]")
                numOpponentsAlive: term(field: "event.player_situation.clutch.opponents_alive")
                wins: count(field: "game.round.result[WIN]")
                total: count
            }
        }
    }
}

Fetch data accuracy and timestamp of the latest update of the given request

{
    aggregateStats {
        meta {
            dataAccuracy
            lastUpdatedAt
        }
        data {
            # ...
        }
    }
}

Sort term bucket by highest map win percentage

{
    aggregateStats {
        data {
            players: termBucket(fields: ["player.metadata[player_id]"], sort: [{field: "_mapWinRate", order: ORDER_DESC }]) {
                playerId: term(field: "player.metadata[player_id]")
                mapTotal: count(field: "event.game_result")
                mapWins: count(field: "event.game_result[WIN]")
                mapWinRate: rate(fields: ["_mapWins", "_mapTotal"])
            }
        }
    }
}

First Kills/Deaths Round Win%

{
    aggregateStats {
        data {
            players: termBucket(fields: ["player.metadata[player_id]"]) {
                entry: filter(filters: [ { field: "game.round.occurrence", filter: { term: { values: ["1"] }} } ]) {
                    kills: filter(filters: [ { field: "event.player_kill.classification", filter: { term: { values: ["REGULAR"] }} } ]) {
                        roundTotal: count()
                        roundWins: count(field: "game.round.result[WIN]")
                    }
                    deaths: filter(filters: [ { field: "event.type", filter: { term: { values: ["PLAYER_DEATH"] }} } ]) {
                        roundTotal: count()
                        roundWins: count(field: "game.round.result[WIN]")
                    }
                }
            }
        }
    }
}

Average damage/kills per round

{
    aggregateStats {
        data {
            players: termBucket(fields: ["player.metadata[player_id]"]) {
                roundsTotal: count(field: "event.game_action[ROUND_ENDED]")
                damageTotal: sum(field: "event.player_attack.classification[REGULAR].damage.health")
                killsTotal: count(field: "event.player_kill.classification[REGULAR]")
                avgRoundDamage: rate(fields: ["_damageTotal", "_roundsTotal"])
                avgRoundKills: rate(fields: ["_killsTotal", "_roundsTotal"])
            }
        }
    }
}

Filter results by Player IDs

{
    aggregateStats(filters: [
      { term: { field: "player.metadata[player_id]", values: ["4357ee21-8021-415f-b7c8-d75123de6647", "4357ee21-8021-415f-b7c8-d75124e6657"] }}
    ]) {
        # ...
    }  
}

Filter results by Team IDs

{
    aggregateStats(filters: [
      { term: { field: "team.metadata[team_id]", values: ["2c3fda30-e913-4938-89de-5e28c2ff26c2", "b55a8d10-223b-11ee-be56-0242ac120002"] }}
    ]) {
        # ...
    }  
}

Filter results by time period

{
    aggregateStats(filters: [
      { range: { field: "event.timestamp", gte: "2022-01-01T01:02:03.004Z", lte: "2022-02-02T01:02:03.004Z" }}
    ]) {
        # ...
    }  
}