From Python to Excel – Building an excel worksheet

First of all, the code that you are using is not actually wholly correct. E.g.:

analyze(46275)
(('Grind', '10 pts', 'roublard, ecaflip'), 
('SOLARY', '50 pts', 'enutrof, eniripsa, steamer, eliotrope'), 'A10')

Notice that the first player only has two image titles, and the second one has four. This is incorrect, and happens because your code assumes that img tags with the class ending in "dead" belong to the first player, and the ones that have a class named "class" belong to the second. This happens to be true for your first match (i.e. https://ktarena.com/fr/207-dofus-world-cup/match/46270), but very often this is not true at all. E.g. if I compare my result below with the same method applied to your analyze function, I end up with mismatches is 118 rows out of 248.

Here’s a suggested rewrite:

import requests
from bs4 import BeautifulSoup
import pandas as pd

def analyze_new(i):
    # You don't need `/1` at the end of the url
    url = f"https://ktarena.com/fr/207-dofus-world-cup/match/{i}"
    page = requests.get(url)
    soup = BeautifulSoup(page.content, "html.parser")
    
    arena = soup.find('span',class_='name').get_text()
    
    # find all teams, and look for info inside each team
    teams = soup.findAll('div',class_='team')
    my_teams = [tuple()]*2
    for idx, team in enumerate(teams):
        my_teams[idx] = my_teams[idx] + 
            (team.select(".name a")[0].get_text(),)
        my_teams[idx] = my_teams[idx] + 
            (soup.select(".result .points")[idx].get_text(),)
        my_teams[idx] = my_teams[idx] + 
            (', '.join([img['title'] for img in team.findAll('img')[1:]]),)

    # notice, we need `return` instead of `print` to use the data     
    return *my_teams,arena

print(analyze_new(46275))
(('Grind', '10 pts', 'roublard, ecaflip, enutrof'), 
('SOLARY', '50 pts', 'eniripsa, steamer, eliotrope'), 'A10')

Before writing this data to excel, I would create a pd.DataFrame, which can then be exported very easily:

# capture info per player in a single row
rows = []

for i in range(46270, 46394):
    one, two, arena = analyze_new(i)
    # adding `i` to rows, as "Match" seems like a useful `column` to have!
    # but if not, you can delete `i` here below (N.B. do NOT delete the COMMA!)
    # and cut 'Match' twice below
    rows.append(one+(arena,i))
    rows.append(two+(arena,i))

cols = ['Team','Points', 'Images', 'Arena','Match']

# create df
df = pd.DataFrame(data=rows,columns=cols)

# split up the images strings in `df.Images` and make new columns for them
# finally, drop the `df.Images` column itself
df = pd.concat([df,
                df.Images.str.split(',',expand=True)
                    .rename(columns={i:f'Image Title {i+1}' 
                                     for i in range(3)})], axis=1)
    .drop('Images', axis=1)

# Strip " pts" from the strings in `df.Points` and convert the type to an `int`
df['Points'] = df.Points.str.replace(' pts','').astype(int)

# Re-order the columns
df = df.loc[:, ['Match', 'Arena','Team', 'Image Title 1', 'Image Title 2', 
                'Image Title 3', 'Points']]

print(df.head())

   Match Arena         Team Image Title 1 Image Title 2 Image Title 3  Points
0  46270   A10      Thunder      roublard    huppermage       ecaflip       0
1  46270   A10       Tweaps       steamer          feca      sacrieur      60
2  46271   A10   Shadow Zoo          feca      osamodas       ouginak       0
3  46271   A10  UndisClosed      eniripsa          sram       pandawa      60
4  46272   A10   Laugh Tale      osamodas       ecaflip           iop       0

# Finally, write the `df` to an Excel file
df.to_excel('fname.xlsx')

Result:

If you dislike the default styles added to the header row and index column, you can write it away like so:

df.T.reset_index().T.to_excel('test.xlsx', index=False, header=False)

Result:

Incidentally, I assume you have a particular reason for wanting the function to return the relevant data as *my_teams,arena. If not, it would be better to let the function itself do most of the heavy lifting. E.g. we could write something like this, and return a df directly.

def analyze_dict(i):
    url = f"https://ktarena.com/fr/207-dofus-world-cup/match/{i}"
    page = requests.get(url)
    soup = BeautifulSoup(page.content, "html.parser")
    
    d = {'Match': [i]*2,
               'Arena': [soup.find('span',class_='name').get_text()]*2,
               'Team': [],
               'Image Title 1': [],
               'Image Title 2': [],
               'Image Title 3': [],
               'Points': [],
               }
    
    teams = soup.findAll('div',class_='team')
    for idx, team in enumerate(teams):
        d['Team'].append(team.select(".name a")[0].get_text())
        d['Points'].append(int(soup.select(".result .points")[idx].get_text().split(' ')[0]))
        for img_idx, img in enumerate(team.findAll('img')[1:]):
            d[f'Image Title {img_idx+1}'].append(img['title'])
        
    return pd.DataFrame(d)

print(analyze_dict(46275))

   Match Arena    Team Image Title 1 Image Title 2 Image Title 3  Points
0  46275   A10   Grind      roublard       ecaflip       enutrof      10
1  46275   A10  SOLARY      eniripsa       steamer     eliotrope      50

Now, we only need to do the following outside the function:

dfs = []

for i in range(46270, 46394):
    dfs.append(analyze_dict(i))

df = pd.concat(dfs, axis=0, ignore_index=True)

print(df.head())


   Match Arena         Team Image Title 1 Image Title 2 Image Title 3  Points
0  46270   A10      Thunder      roublard    huppermage       ecaflip       0
1  46270   A10       Tweaps       steamer          feca      sacrieur      60
2  46271   A10   Shadow Zoo          feca      osamodas       ouginak       0
3  46271   A10  UndisClosed      eniripsa          sram       pandawa      60
4  46272   A10   Laugh Tale      osamodas       ecaflip           iop       0