Table:
Club

Season

League

National Cup

League Cup

Other

Total

Division

Apps
Goals

Apps
Goals

Apps
Goals

Apps
Goals

Apps
Goals

Melbourne Heart

2011–12

A-League

25
1
0
0
0
0
0
0
25
1

Newcastle United

2012–13

Premier League

0
0
0
0
0
0
0
0
0
0

2013–14

0
0
0
0
1
0
0
0
1
0

2014–15

0
0
0
0
0
0
0
0
0
0

2015–16

0
0
0
0
0
0
0
0
0
0

2016–17

Championship

0

0

1

0

0

0

0

0

1

0

2017–18

Premier League

0
0
0
0
0
0
0
0
0
0

Total

0

0

1

0

1

0

0

0

2

0

Bradford City (loan)

2012–13

League Two

3
0
1
0
3
0
2
0
9
0

Dundee United (loan)

2013–14

Scottish Premiership

4
1
1
0
0
0
0
0
5
1

Melbourne City

2018–19

A-League

0
0
0
0
—
0
0
0
0

Career Total

32
2
3
0
4
0
2
0
41
2

Analyze the provided table and extract the contents of specified rows or columns, identified by row or column indices. Show the cells in each row or column in the JSON format {"row_id/column_id":"<row or column index>", "cell_list":"<a list of cells in this row/column>"}. For example, {"row_id":"3", "cell_list": ["A", "B", "C"]}.
Target rows or columns:
row 14,
column 5,
column 11,
row 9,
row 13,

The cell values of target rows or columns are as follows:
For row 14: {"row_id": "14", "cell_list": ['career total', '', '', '32', '2', '3', '0', '4', '0', '2', '0', '41', '2']}
For column 5: {"column_id": "5", "cell_list": ['', 'goals', '1', '0', '0', '0', '0', '0', '0', '0', '0', '1', '0', '2']}
For column 11: {"column_id": "11", "cell_list": ['', 'goals', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0']}
For row 9: {"row_id": "9", "cell_list": ['', '2017-18', 'premier league', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0']}
For row 13: {"row_id": "13", "cell_list": ['melbourne city', '2018-19', 'a-league', '0', '0', '0', '0', '-', '', '0', '0', '0', '0']}