Task: NL2SQL (source dataset: Spider)

Natural language to SQL, also known as NL2SQL, is the problem of generating a SQL query that can be executed on a input table, for a given natural language question.

You will be given an input table called "table" in the markdown format, as well as a question. Your task is to generate an SQL query that can be executed on the input table to answer the question.

Please ensure that the generated SQL only return relevant columns being specifically asked in the question (e.g., in SQL please don't return more than one columns in SELECT if only one column is needed to answer the given question, and similarly please refrain from using SELECT * unless the entire row is needed to answer the question).
 
When generating SQL code, please use the SQLite syntax to ensure that your SQL is executable on SQLite3. Use only columns that are present in the table, always use quotes around your column and table names (some column names have punctuation or special characters), and refer to the columns using the column-names exactly as they appear in the table, do not add or remove punctuation (underscore, slash, etc.)

No explanation, return the code only with the following markdown codeblock format : ```sql<SQL CODE>```. For example, ```sql
SELECT * FROM "table"
``` is a valid format.

Database:
soccer_3

Tables:

Table: club
|   Club_ID | Name             | Manager        | Captain        | Manufacturer   | Sponsor                                      |
|----------:|:-----------------|:---------------|:---------------|:---------------|:---------------------------------------------|
|         1 | Arsenal          | Arsène Wenger  | Cesc Fàbregas  | Nike           | Fly Emirates                                 |
|         2 | Aston Villa      | Martin O'Neill | Martin Laursen | Nike           | Acorns                                       |
|         3 | Blackburn Rovers | Sam Allardyce  | Ryan Nelsen    | Umbro          | Crown Paints                                 |
|         4 | Bolton Wanderers | Gary Megson    | Kevin Davies   | Reebok         | Reebok                                       |
|         5 | Chelsea          | Guus Hiddink   | John Terry     | adidas         | Samsung                                      |
|         6 | Everton          | David Moyes    | Phil Neville   | Umbro          | Chang                                        |
|         7 | Fulham           | Roy Hodgson    | Danny Murphy   | Nike           | LG                                           |
|         8 | Hull City        | Phil Brown     | Ian Ashbee     | Umbro          | Karoo (H) / Kingston Communications (A, 3rd) |
|         9 | Liverpool        | Rafael Benítez | Steven Gerrard | adidas         | Carlsberg                                    |


Table: player
|   Player_ID | Name               | Country       |    Earnings |   Events_number |   Wins_count |   Club_ID |
|------------:|:-------------------|:--------------|------------:|----------------:|-------------:|----------:|
|           1 | Nick Price         | Zimbabwe      | 1.47856e+06 |              18 |            4 |         1 |
|           2 | Paul Azinger       | United States | 1.45846e+06 |              24 |            3 |         3 |
|           3 | Greg Norman        | Australia     | 1.35965e+06 |              15 |            2 |         5 |
|           4 | Jim Gallagher, Jr. | United States | 1.07887e+06 |              27 |            2 |         6 |
|           5 | David Frost        | South Africa  | 1.03072e+06 |              22 |            2 |         7 |


Question: What are the names of clubs, ordered descending by the average earnings of players within each?

db_id: soccer_3
tables: name: club
data: club.csv
name: player
data: player.csv
label: SELECT T1.Name FROM club AS T1 JOIN player AS T2 ON T1.Club_ID  =  T2.Club_ID GROUP BY T1.Club_ID ORDER BY avg(T2.Earnings) DESC
sqlite_path: $MMTU_HOME/data_sqlite/spider/spider_data/test_database/soccer_3/soccer_3.sqlite