JSON Output Shape

gem.to_json() and gem.parse_to_json() serialize the same ParsedMatch object returned by gem.parse(). The JSON is useful when you want the full nested match structure for an API response, a saved artifact, or a downstream system that does not use pandas.

import gem

json_str = gem.parse_to_json("my_replay.dem", indent=2)

match = gem.parse("my_replay.dem")
json_str = gem.to_json(match, indent=2)
data = gem.to_dict(match)

From the CLI:

python -m gem my_replay.dem --format json > match.json

Top-level shape

The top-level object mirrors ParsedMatch:

{
  "match_id": 8461735141,
  "game_mode": 2,
  "leagueid": 18324,
  "radiant_win": true,
  "duration": 3264,
  "radiant_score": 31,
  "dire_score": 18,
  "players": [],
  "draft": [],
  "combat_log": [],
  "wards": [],
  "objectives": [],
  "teamfights": [],
  "opendota_teamfights": [],
  "smoke_events": [],
  "neutral_item_finds": [],
  "radiant_gold_adv": [],
  "radiant_xp_adv": []
}

The actual output includes more fields than this abbreviated example. For the generated model documentation, see the Models API Reference.

Players

players contains one object per player slot, ordered 0-4 for Radiant and 5-9 for Dire.

{
  "player_id": 0,
  "hero_name": "npc_dota_hero_sven",
  "player_name": "Ame",
  "team": 2,
  "kills": 11,
  "deaths": 0,
  "assists": 3,
  "net_worth": 37348,
  "last_hits": 605,
  "denies": 32,
  "lane_role": 1,
  "damage_by_type": {
    "physical": 8920,
    "magical": 12340,
    "pure": 450
  },
  "purchase_log": [],
  "position_log": []
}

Use gem.constants.hero_display() or gem.catalog.hero_display() to turn internal hero NPC names into display names.

Event arrays

Most match events are arrays of records:

Field	Contents
combat_log	Normalized damage, death, heal, item, ability, modifier, gold, XP, rune, and buyback events
draft	Picks and bans in replay order
wards	Observer and sentry placements with coordinates
objectives	OpenDota-shaped objective timeline
towers, barracks, roshans, tormentors, shrines	Typed Gem objective lists
teamfights	Gem teamfight windows with richer participant stats
opendota_teamfights	OpenDota-compatible teamfight windows
smoke_events	Smoke of Deceit activations and grouped heroes
neutral_item_finds	Neutral item find user messages
vision_modifiers	Reveal/vision modifier windows used by vision analysis

Combat log entries

Combat log records share one schema. log_type tells you which fields are meaningful for that row.

{
  "tick": 25969,
  "log_type": "DAMAGE",
  "attacker_name": "npc_dota_hero_sven",
  "target_name": "npc_dota_hero_pangolier",
  "inflictor_name": "sven_storm_bolt",
  "value": 58,
  "attacker_is_hero": true,
  "target_is_hero": true,
  "damage_type": "magical"
}

Common log_type values include DAMAGE, DEATH, HEAL, ITEM, PURCHASE, ABILITY, MODIFIER_ADD, MODIFIER_REMOVE, GOLD, XP, PICKUP_RUNE, and BUYBACK.

JSON vs DataFrames

Use JSON when you need the complete nested match object. Use gem.parse_to_dataframe() when you want analysis-ready tables with stable row shapes.

frames = gem.parse_to_dataframe("my_replay.dem")

players = frames["players"]
combat = frames["combat_log"]
teamfights = frames["opendota_teamfights"]

See Time-Series & DataFrames for the table list and export examples.