Who's Most Likely To - Party

Bachelorette Games for parties

Only for iPhone

Free · In‑App Purchases

iPhone

Most Likely To is a party game for adults, bachelorettes, teens, college students and everyone who wants to have a bit of fun. It is a crazy party game for frat-parties, house-parties, hen parties and all social gatherings. How to play Most Likely To: 1. Select a category from the six categories available. 2. Read the card out loud 3. Everyone points a finger at the person they think the statement suits best The game contains six different modes: 1. Starting Out 2. Casual 3. Party 4. Funny 5. Romantic 6. Dirty Choose the mode that best suits your gathering. If you like the game, share and rate us.

  • 2.6
    out of 5
    5 Ratings

Bug Fixes and Feature Enhancements!

The developer, Akash Ram, indicated that the app’s privacy practices may include handling of data as described below. For more information, see the developer’s privacy policy .

  • Data Used to Track You

    The following data may be used to track you across apps and websites owned by other companies:

    • Identifiers
  • Data Not Linked to You

    The following data may be collected but it is not linked to your identity:

    • Identifiers
    • Usage Data
    • Diagnostics

Privacy practices may vary, for example, based on the features you use or your age. Learn More

The developer has not yet indicated which accessibility features this app supports. Learn More

  • Seller
    • Akash Ram
  • Size
    • 71.4 MB
  • Category
    • Word
  • Compatibility
    Requires iOS 9.0 or later.
    • iPhone
      Requires iOS 9.0 or later.
    • iPod touch
      Requires iOS 9.0 or later.
    • Apple Vision
      Requires visionOS 1.0 or later.
  • Languages
    • English
  • Age Rating
    13+
    • 13+
    • Infrequent
      Alcohol, Tobacco, Drug Use or References
      Sexual Content or Nudity
  • In-App Purchases
    Yes
    • Dirty $2.99
    • Funny $1.99
    • Buy All $4.99
    • Romance $1.99
    • Party $1.99
    • Casual $0.99
  • Copyright
    • © 2022 Akash Ram