You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 movie_platform
【Schema】
# Table: lists
[
(user_id:INTEGER, Examples: [88260493, 45204418, 48905025]),
(list_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(list_title:TEXT, Examples: [Films that made your kid sister cry]),
(list_movie_number:INTEGER, Examples: [5, 3, 7]),
(list_update_timestamp_utc:TEXT, Examples: [2019-01-24 19:16:18, 2018-12-03 15:12:20, 2019-05-30 03:00:07]),
(list_creation_timestamp_utc:TEXT, Examples: [2009-11-11 00:02:21, 2009-11-11 00:05:11, 2009-11-11 00:20:00]),
(list_followers:INTEGER, Examples: [5, 1, 6]),
(list_url:TEXT),
(list_comments:INTEGER, Examples: [3, 2, 5]),
(list_description:TEXT),
(list_cover_image_url:TEXT),
(list_first_image_url:TEXT),
(list_second_image_url:TEXT),
(list_third_image_url:TEXT)
]
# Table: movies
[
(movie_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(movie_title:TEXT, Examples: [La Antena, Elementary Particles, It's Winter]),
(movie_release_year:INTEGER, Examples: [2007, 2006, 2005]),
(movie_url:TEXT),
(movie_title_language:TEXT, Examples: [en]),
(movie_popularity:INTEGER, Examples: [105, 23, 21]),
(movie_image_url:TEXT),
(director_id:TEXT, Examples: [131, 73, 82]),
(director_name:TEXT, Examples: [Esteban Sapir, Oskar Roehler, Rafi Pitts]),
(director_url:TEXT)
]
# Table: lists_users
[
(user_id:INTEGER, Primary Key, Examples: [2385, 15264, 15344]),
(list_id:INTEGER, Primary Key, Examples: [192287, 192313, 192318]),
(list_update_date_utc:TEXT, Examples: [2019-11-26, 2020-05-01, 2020-04-12]),
(list_creation_date_utc:TEXT, Examples: [2009-12-18, 2010-01-30, 2010-03-31]),
(user_trialist:INTEGER, Examples: [1, 0]),
(user_subscriber:INTEGER, Examples: [1, 0]),
(user_avatar_image_url:TEXT),
(user_cover_image_url:TEXT),
(user_eligible_for_trial:TEXT, Examples: [0, 1]),
(user_has_payment_method:TEXT, Examples: [1, 0])
]
# Table: ratings_users
[
(user_id:INTEGER, Examples: [41579158, 68654088, 84114365]),
(rating_date_utc:TEXT, Examples: [2017-06-10, 2012-10-02, 2010-12-25]),
(user_trialist:INTEGER, Examples: [0, 1]),
(user_subscriber:INTEGER, Examples: [0, 1]),
(user_avatar_image_url:TEXT),
(user_cover_image_url:TEXT),
(user_eligible_for_trial:INTEGER, Examples: [1, 0]),
(user_has_payment_method:INTEGER, Examples: [0, 1])
]
# Table: ratings
[
(movie_id:INTEGER, Examples: [1066, 1067, 1068]),
(rating_id:INTEGER, Examples: [15610495, 10704606, 10177114]),
(rating_url:TEXT),
(rating_score:INTEGER, Examples: [3, 2, 4]),
(rating_timestamp_utc:TEXT, Examples: [2017-06-10 12:38:33, 2014-08-15 23:42:31, 2014-01-30 13:21:57]),
(critic:TEXT),
(critic_likes:INTEGER, Examples: [0, 1, 2]),
(critic_comments:INTEGER, Examples: [0, 2, 1]),
(user_id:INTEGER, Examples: [41579158, 85981819, 4208563]),
(user_trialist:INTEGER, Examples: [0, 1]),
(user_subscriber:INTEGER, Examples: [0, 1]),
(user_eligible_for_trial:INTEGER, Examples: [1, 0]),
(user_has_payment_method:INTEGER, Examples: [0, 1])
]
【Foreign keys】
lists.user_id=lists_users.user_id
lists_users.list_id=lists.list_id
lists_users.user_id=lists.user_id
ratings_users.user_id=lists_users.user_id
ratings.movie_id=movies.movie_id
ratings.user_id=lists_users.user_id
ratings.rating_id=ratings.rating_id
ratings.user_id=ratings_users.user_id

    【Question】
    Among all movies in the list, calculate the percentage of movies that were never been rated? percentage of movies that were never been rated refers to DIVIDE(COUNT(main_movies.movie_id ! = main_ratings.movie_id),COUNT(movie_id))

    【Evidence】
    percentage of movies that were never been rated refers to DIVIDE(COUNT(main_movies.movie_id ! = main_ratings.movie_id),COUNT(movie_id))

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT CAST(SUM(CASE WHEN T2.movie_id IS NULL THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(T2.movie_id) FROM ratings AS T1 INNER JOIN movies AS T2 ON T1.movie_id = T2.movie_id