1. Presentation du sujet et contexte de l’etude.
2. Demarche utilisee. 2)1) 2)2)
3. Limite des modeles et du pouvoir prédictif.
4. Difficultes.
5. Ce qui pourrait être améliorer à l’avenir.

• Lorsque nous parlons de premier League, nous faisons référence au championnat national de Football Anglais

Démarches utilisées

Notre démarche est consitutée de deux étapes :

• Scrapper différentes informations basiques les points, victoires, les défaites à travers le temps.
• Scrapper des informations en lien avec le championat actuel : Le budget
• Tenter d’apliquer des modèles statistiques et faire de la statistiques descriptive.

## Warning: package 'reticulate' was built under R version 4.1.2

## Warning in system2(command = python, args = shQuote(script), stdout =
## TRUE, : running command ''/Users/polo11/Documents/GitHub/Premierleague/
## Automatisation.ipynb' '/Library/Frameworks/R.framework/Versions/4.1/Resources/
## library/reticulate/config/config.py' 2>/dev/null' had status 126

## Error in python_config_impl(python): Error 126 occurred running /Users/polo11/Documents/GitHub/Premierleague/Automatisation.ipynb:

#Example of URL that could be pasted.
urlpage_4 = 'https://www.skysports.com/premier-league-table/2023'

# The objective of the function get_page is to extract and return HTML elements corresponding to a
# specified tag and a specific class from a given web page.

def get_page(urlpage_4,element,html_class):
    req_5 = urllib3.PoolManager()
    res_5 = req_5.request('GET', urlpage_4)
    row_html_5 = BeautifulSoup(res_5.data, 'html.parser')
    PL19 = row_html_5.find_all(element , 
    class_= html_class)
    return(PL19)

PL19 = str(get_page(urlpage_4, 'tr', 'row-body'))

list_team_20 = re.findall('<span class="team-name">(.*?)</span>', str(PL19))

def extract_team_20_stats(PL19, team):
    team= team.title()
    teams = re.findall('<span class="team-name">(.*?)</span>', 
    str(PL19))
    position= (list_team_20.index(team)+1)
    start = PL19.find(team)
    end = PL19.index("</tr>", start)
    team_data_20 = PL19[start:end]
    match_played= 38
    data = [int(s) for s in re.findall(r'<td.*?>(\d+)</td>', team_data_20)]
    points= data[0]
    wins= data [1]
    drawns= data [2]
    loses =data [3]
    goals_for = data [4]
    goals_against = data [5]
    team_stats20 = {'match_played': match_played,
    'position': position,'points': points,
                    'wins': wins,'loses': loses ,
                    'drawns':  drawns,'goals_for': goals_for,
        'goals_against':goals_against
    }
    return team_stats20

team_stats_20 = {}

for team in list_team_20:
    team_stats = extract_team_20_stats(PL19, team)
    team_stats_df = pd.DataFrame(team_stats, index=[0])
    team_stats_df['team'] = team
    team_stats_df['year'] = 2020
    team_stats_20[team] = team_stats_df

## Error in team_stats_20.replace({: could not find function "team_stats_20.replace"