Getgud.io

Adjustable sensitivity settings

Automated responses to disruptive behavior

Dashboard access for querying player data, reports, and insights

Capability to set up webhooks and actions for specific player groups

Match data retention for review

Ability to replay entire game matches

Visual representation of player activities during matches

In-game observability on weapons, maps, characters, and effects

Functions entirely server-side

Improved speed and precision in pinpointing disruptive conduct

Potential for improved gameplay experiences

Contributes to building non-toxic gaming environments

Analyzes conduct to spot griefing

Detects cheat categories including aim bots, wall hacks, and speed hacks

Identifies and addresses boosting/smurfing activities

Enables management of player status, rank, and reputation

Automated execution of rules based on player groups

Promotes transparency and confidence within the community

Forever preservation of in-match data

Compatible with a wide array of game genres

Supports all platforms including web/mobile

Control over match analysis, streaming, and storage

Management of in-match reports

Future visibility of in-match weaponry and characters

Scheduling controls for data filters

Supports player reputation systems

Supports informed choices in game balance

Encourages player involvement

Enables segmentation through campaigns/UTM parameters

Alerts for notifications of toxic players

Enables improvements in gameplay quality

Helps identify trends in gameplay

Stream-based filtering of data

Supports the automated execution of actions

Enhances analytical capabilities of match data

Helpful for modifying team matchmaking

Customizable actions for toxic players (e.g., bans, warnings)

Initiate email campaigns via webhooks

Visualized environment for unstructured game data

Automated demotion of disruptive players

Insights on weapon/character popularity

Insights on in-game milestones

Does not degrade server performance

SDK documentation is available

Insights applicable to adjusting in-game loot drop locations

Insights on popular maps/modes

Insights on player retention and toxicity

Uncertain precision in toxicity detection

Possible false positives in detection

Dependent on server-side information

Match replays might experience lag

Challenges with unconventional game types

Limited data stream control

Possible system overload