If you can’t shape it upfront (when scraping), here is one way of some post-processing :

from itertools import chain, batched # >=3.12

df = (
    pd.DataFrame(batched(chain.from_iterable(table_data), 8))
    # @ouroboros1's variant
    # pd.DataFrame(np.array(table_data).reshape(-1, int(20*8/20)))
    .pipe(
        lambda raw: pd.concat(
            [
                raw.iloc[:, :2],                           # club's pos
                raw[2].str.split(expand=True).astype(int), # game details
                raw.iloc[:, 3:].agg(" ".join, axis=1),     # form
            ],
            axis=1,
        )
    )
    .set_axis(
        [
            "Position", "Club", "Played",
            "Won", "Draw", "Lost", "GF", "GA", "GD",
            "Points", "Form",
        ],
        axis=1,
    )
)

NB: You may want to add an identifier (here: 1, 2 and 3) to distinguish ranking times.

Output :

   Position             Club  Played  Won  Draw  Lost  GF  GA  GD  Points       Form
0         1          Arsenal      33   23     5     5  77  26  51      74  D W W L W
1         2        Liverpool      33   22     8     3  75  32  43      74  W W D L W
2         3  Manchester City      32   22     7     3  76  32  44      73  D D W W W
3         4      Aston Villa      34   20     6     8  71  50  21      66  W L D W W
..      ...              ...     ...  ...   ...   ...  ..  ..  ..     ...        ...
56       17     Leeds United      38    9    11    18  42  79 -37      38  L L L D W
57       18          Burnley      38    7    14    17  34  53 -19      35  W L L D L
58       19          Watford      38    6     5    27  34  77 -43      23  L L D L L
59       20     Norwich City      38    5     7    26  23  84 -61      22  L L L D L

[60 rows x 11 columns]

You need to fix your input first. Notice that the stats from Played all the way to points are all in the same string. This isn’t the prettiest code but it gets the job done

import pandas as pd


def fix_season(list_output):
    stats_counter = 0
    current_team = []
    fixed_output = []
    for idx in range(len(list_output)):
        element = list_output[idx]
        # fix formatting on stats
        if stats_counter==2:
            current_team.extend(element.split(" "))
            stats_counter += 1
        # Appends team to the final season table
        elif stats_counter == 8:
            fixed_output.append(current_team)
            stats_counter = 1
            current_team = [element]
        # Add Stat to current team
        else:
            current_team.append(element)
            stats_counter += 1
    return fixed_output

season1 = fix_season(output[0])

result = pd.DataFrame(season1)
print(result)

Frankly, I think you should change code which you used for scraping and format it during scraping.
Maybe you should use more complex rules to scrape table by table and row by row instead of scraping all at once.

Now you have two different problems.

• many rows in one row
• values as single string in single row
  and you should resolve every problem separatelly using different code. You can’t do this in one command.

First doesn’t need pandas.DataFrame but you can split every row to many rows using for-loop with slice row[i:i+8]

data  = [[ ... your data ... ]]

new_data = []

for row in data:
    for i in range(0, len(row), 8):
        part = row[i:i+8]
        new_data.apped(part)
        print(part)

Result:

['1', 'Arsenal', '33 23 5 5 77 26 51 74', 'D', 'W', 'W', 'L', 'W']
['2', 'Liverpool', '33 22 8 3 75 32 43 74', 'W', 'W', 'D', 'L', 'W']
['3', 'Manchester City', '32 22 7 3 76 32 44 73', 'D', 'D', 'W', 'W', 'W']
['4', 'Aston Villa', '34 20 6 8 71 50 21 66', 'W', 'L', 'D', 'W', 'W']
['5', 'Tottenham Hotspur', '32 18 6 8 65 49 16 60', 'L', 'W', 'D', 'W', 'L']
['6', 'Newcastle United', '32 15 5 12 69 52 17 50', 'L', 'W', 'D', 'W', 'W']
['7', 'Manchester United', '32 15 5 12 47 48 -1 50', 'W', 'D', 'L', 'D', 'D']
['8', 'West Ham United', '34 13 9 12 54 63 -9 48', 'L', 'D', 'W', 'L', 'L']
['9', 'Chelsea', '31 13 8 10 61 52 9 47', 'W', 'D', 'W', 'D', 'W']
['10', 'Brighton And Hove Albion', '32 11 11 10 52 50 2 44', 'W', 'L', 'D', 'L', 'D']
['11', 'Wolverhampton Wanderers', '33 12 7 14 46 53 -7 43', 'L', 'D', 'L', 'D', 'L']
['12', 'Fulham', '34 12 6 16 50 54 -4 42', 'D', 'L', 'L', 'W', 'L']
['13', 'Bournemouth', '33 11 9 13 48 60 -12 42', 'W', 'W', 'L', 'D', 'L']
['14', 'Crystal Palace', '33 9 9 15 42 56 -14 36', 'D', 'L', 'L', 'W', 'W']
['15', 'Brentford', '34 9 8 17 52 59 -7 35', 'D', 'D', 'D', 'W', 'W']
['16', 'Everton', '33 10 8 15 34 48 -14 30', 'L', 'D', 'W', 'L', 'W']
['17', 'Nottingham Forest', '34 7 9 18 42 60 -18 26', 'D', 'W', 'L', 'D', 'L']
['18', 'Luton Town', '34 6 7 21 47 75 -28 25', 'L', 'L', 'W', 'L', 'L']
['19', 'Burnley', '34 5 8 21 37 69 -32 23', 'D', 'D', 'L', 'D', 'W']
['20', 'Sheffield United', '33 3 7 23 31 88 -57 16', 'D', 'L', 'D', 'L', 'L']
['1', 'Manchester City', '38 28 5 5 94 33 61 89', 'W', 'W', 'W', 'D', 'L']
['2', 'Arsenal', '38 26 6 6 88 43 45 84', 'W', 'W', 'L', 'L', 'W']
['3', 'Manchester United', '38 23 6 9 58 43 15 75', 'L', 'W', 'W', 'W', 'W']
['4', 'Newcastle United', '38 19 14 5 68 33 35 71', 'L', 'D', 'W', 'D', 'D']
['5', 'Liverpool', '38 19 10 9 75 47 28 67', 'W', 'W', 'W', 'D', 'D']
['6', 'Brighton And Hove Albion', '38 18 8 12 72 53 19 62', 'W', 'L', 'W', 'D', 'L']
['7', 'Aston Villa', '38 18 7 13 51 46 5 61', 'L', 'L', 'W', 'D', 'W']
['8', 'Tottenham Hotspur', '38 18 6 14 70 63 7 60', 'L', 'W', 'L', 'L', 'W']
['9', 'Brentford', '38 15 14 9 58 46 12 59', 'W', 'L', 'W', 'W', 'W']
['10', 'Fulham', '38 15 7 16 55 53 2 52', 'L', 'W', 'W', 'D', 'L']
['11', 'Crystal Palace', '38 11 12 15 40 49 -9 45', 'W', 'L', 'W', 'D', 'D']
['12', 'Chelsea', '38 11 11 16 38 47 -9 44', 'W', 'D', 'L', 'L', 'D']
['13', 'Wolverhampton Wanderers', '38 11 8 19 31 58 -27 41', 'L', 'W', 'L', 'D', 'L']
['14', 'West Ham United', '38 11 7 20 42 55 -13 40', 'L', 'W', 'L', 'W', 'L']
['15', 'Bournemouth', '38 11 6 21 37 71 -34 39', 'W', 'L', 'L', 'L', 'L']
['16', 'Nottingham Forest', '38 9 11 18 38 68 -30 38', 'L', 'W', 'D', 'W', 'D']
['17', 'Everton', '38 8 12 18 34 57 -23 36', 'D', 'W', 'L', 'D', 'W']
['18', 'Leicester City', '38 9 7 22 51 68 -17 34', 'D', 'L', 'L', 'D', 'W']
['19', 'Leeds United', '38 7 10 21 48 78 -30 31', 'L', 'L', 'D', 'L', 'L']
['20', 'Southampton', '38 6 7 25 36 73 -37 25', 'L', 'L', 'L', 'L', 'D']
['1', 'Manchester City', '38 29 6 3 99 26 73 93', 'W', 'W', 'W', 'D', 'W']
['2', 'Liverpool', '38 28 8 2 94 26 68 92', 'W', 'D', 'W', 'W', 'W']
['3', 'Chelsea', '38 21 11 6 76 33 43 74', 'L', 'D', 'W', 'D', 'W']
['4', 'Tottenham Hotspur', '38 22 5 11 69 40 29 71', 'W', 'D', 'W', 'W', 'W']
['5', 'Arsenal', '38 22 3 13 61 48 13 69', 'W', 'W', 'L', 'L', 'W']
['6', 'Manchester United', '38 16 10 12 57 57 0 58', 'L', 'D', 'W', 'L', 'L']
['7', 'West Ham United', '38 16 8 14 60 51 9 56', 'L', 'L', 'W', 'D', 'L']
['8', 'Leicester City', '38 14 10 14 62 59 3 52', 'L', 'W', 'W', 'D', 'W']
['9', 'Brighton And Hove Albion', '38 12 15 11 42 44 -2 51', 'D', 'W', 'W', 'D', 'W']
['10', 'Wolverhampton Wanderers', '38 15 6 17 38 43 -5 51', 'L', 'D', 'L', 'D', 'L']
['11', 'Newcastle United', '38 13 10 15 44 62 -18 49', 'W', 'L', 'L', 'W', 'W']
['12', 'Crystal Palace', '38 11 15 12 50 46 4 48', 'W', 'W', 'D', 'L', 'W']
['13', 'Brentford', '38 13 7 18 48 56 -8 46', 'D', 'L', 'W', 'W', 'L']
['14', 'Aston Villa', '38 13 6 19 52 54 -2 45', 'W', 'L', 'D', 'D', 'L']
['15', 'Southampton', '38 9 13 16 43 67 -24 40', 'D', 'L', 'L', 'L', 'L']
['16', 'Everton', '38 11 6 21 43 66 -23 39', 'W', 'D', 'L', 'W', 'L']
['17', 'Leeds United', '38 9 11 18 42 79 -37 38', 'L', 'L', 'L', 'D', 'W']
['18', 'Burnley', '38 7 14 17 34 53 -19 35', 'W', 'L', 'L', 'D', 'L']
['19', 'Watford', '38 6 5 27 34 77 -43 23', 'L', 'L', 'D', 'L', 'L']
['20', 'Norwich City', '38 5 7 26 23 84 -61 22', 'L', 'L', 'L', 'D', 'L']

Second problem may need to use pandas.DataFrame

It can use .str.split(' ') to convert string to list.
Later it can convert it to new DataFrame with values in separated columns and join it to original DataFrame

import pandas as pd

df1 = pd.DataFrame(new_data)
#print(df1)

# convert string to list
df1.iloc[:,2] = df1.iloc[:,2].str.split(' ')
#print(df1)

# create new dataframe with values in separated columns
df2 = pd.DataFrame(df1.iloc[:,2].to_list(), columns=['Played', 'Won', 'Drawn', 'Lost', 'GF', 'GA', 'GD', 'Points'])
#print(df2)

# add new columns to old dataframe
df = df1.join(df2)

# remove old column with list
df.drop(columns=[2], inplace=True)

print(df)

Result:

    0                         1  3  4  5  6  7 Played Won Drawn Lost  GF  GA   GD Points
0    1                   Arsenal  D  W  W  L  W     33  23     5    5  77  26   51     74
1    2                 Liverpool  W  W  D  L  W     33  22     8    3  75  32   43     74
2    3           Manchester City  D  D  W  W  W     32  22     7    3  76  32   44     73
3    4               Aston Villa  W  L  D  W  W     34  20     6    8  71  50   21     66
4    5         Tottenham Hotspur  L  W  D  W  L     32  18     6    8  65  49   16     60
5    6          Newcastle United  L  W  D  W  W     32  15     5   12  69  52   17     50
6    7         Manchester United  W  D  L  D  D     32  15     5   12  47  48   -1     50
7    8           West Ham United  L  D  W  L  L     34  13     9   12  54  63   -9     48
8    9                   Chelsea  W  D  W  D  W     31  13     8   10  61  52    9     47
9   10  Brighton And Hove Albion  W  L  D  L  D     32  11    11   10  52  50    2     44
10  11   Wolverhampton Wanderers  L  D  L  D  L     33  12     7   14  46  53   -7     43
11  12                    Fulham  D  L  L  W  L     34  12     6   16  50  54   -4     42
12  13               Bournemouth  W  W  L  D  L     33  11     9   13  48  60  -12     42
13  14            Crystal Palace  D  L  L  W  W     33   9     9   15  42  56  -14     36
14  15                 Brentford  D  D  D  W  W     34   9     8   17  52  59   -7     35
15  16                   Everton  L  D  W  L  W     33  10     8   15  34  48  -14     30
16  17         Nottingham Forest  D  W  L  D  L     34   7     9   18  42  60  -18     26
17  18                Luton Town  L  L  W  L  L     34   6     7   21  47  75  -28     25
18  19                   Burnley  D  D  L  D  W     34   5     8   21  37  69  -32     23
19  20          Sheffield United  D  L  D  L  L     33   3     7   23  31  88  -57     16
20   1           Manchester City  W  W  W  D  L     38  28     5    5  94  33   61     89
21   2                   Arsenal  W  W  L  L  W     38  26     6    6  88  43   45     84
22   3         Manchester United  L  W  W  W  W     38  23     6    9  58  43   15     75
23   4          Newcastle United  L  D  W  D  D     38  19    14    5  68  33   35     71
24   5                 Liverpool  W  W  W  D  D     38  19    10    9  75  47   28     67
25   6  Brighton And Hove Albion  W  L  W  D  L     38  18     8   12  72  53   19     62
26   7               Aston Villa  L  L  W  D  W     38  18     7   13  51  46    5     61
27   8         Tottenham Hotspur  L  W  L  L  W     38  18     6   14  70  63    7     60
28   9                 Brentford  W  L  W  W  W     38  15    14    9  58  46   12     59
29  10                    Fulham  L  W  W  D  L     38  15     7   16  55  53    2     52
30  11            Crystal Palace  W  L  W  D  D     38  11    12   15  40  49   -9     45
31  12                   Chelsea  W  D  L  L  D     38  11    11   16  38  47   -9     44
32  13   Wolverhampton Wanderers  L  W  L  D  L     38  11     8   19  31  58  -27     41
33  14           West Ham United  L  W  L  W  L     38  11     7   20  42  55  -13     40
34  15               Bournemouth  W  L  L  L  L     38  11     6   21  37  71  -34     39
35  16         Nottingham Forest  L  W  D  W  D     38   9    11   18  38  68  -30     38
36  17                   Everton  D  W  L  D  W     38   8    12   18  34  57  -23     36
37  18            Leicester City  D  L  L  D  W     38   9     7   22  51  68  -17     34
38  19              Leeds United  L  L  D  L  L     38   7    10   21  48  78  -30     31
39  20               Southampton  L  L  L  L  D     38   6     7   25  36  73  -37     25
40   1           Manchester City  W  W  W  D  W     38  29     6    3  99  26   73     93
41   2                 Liverpool  W  D  W  W  W     38  28     8    2  94  26   68     92
42   3                   Chelsea  L  D  W  D  W     38  21    11    6  76  33   43     74
43   4         Tottenham Hotspur  W  D  W  W  W     38  22     5   11  69  40   29     71
44   5                   Arsenal  W  W  L  L  W     38  22     3   13  61  48   13     69
45   6         Manchester United  L  D  W  L  L     38  16    10   12  57  57    0     58
46   7           West Ham United  L  L  W  D  L     38  16     8   14  60  51    9     56
47   8            Leicester City  L  W  W  D  W     38  14    10   14  62  59    3     52
48   9  Brighton And Hove Albion  D  W  W  D  W     38  12    15   11  42  44   -2     51
49  10   Wolverhampton Wanderers  L  D  L  D  L     38  15     6   17  38  43   -5     51
50  11          Newcastle United  W  L  L  W  W     38  13    10   15  44  62  -18     49
51  12            Crystal Palace  W  W  D  L  W     38  11    15   12  50  46    4     48
52  13                 Brentford  D  L  W  W  L     38  13     7   18  48  56   -8     46
53  14               Aston Villa  W  L  D  D  L     38  13     6   19  52  54   -2     45
54  15               Southampton  D  L  L  L  L     38   9    13   16  43  67  -24     40
55  16                   Everton  W  D  L  W  L     38  11     6   21  43  66  -23     39
56  17              Leeds United  L  L  L  D  W     38   9    11   18  42  79  -37     38
57  18                   Burnley  W  L  L  D  L     38   7    14   17  34  53  -19     35
58  19                   Watford  L  L  D  L  L     38   6     5   27  34  77  -43     23
59  20              Norwich City  L  L  L  D  L     38   5     7   26  23  84  -61     22

Now you can only rename columns and put them in different order.