In this project, I explored how to predict the probability of Premier League teams winning games in the 2024/25 season, using their 2023/24 results as a baseline. I used Python, the API-Football API, and some light statistics to model each team's win probability.
Let’s break it down 👇
📊 The Goal
Predict how many games each team is likely to win in the 2024/25 season using:
- 🧮 Bernoulli Distribution (win or no win)
- 🎲 Binomial Probability Model
- 📈 Visualizations with Seaborn & Matplotlib
⚙️ Tools & Libraries
import requests
import pandas as pd
from scipy.stats import binom
import matplotlib.pyplot as plt
import seaborn as sns
📦 Step 1: Pull 2023/24 Match Data from API-Football
I used the API-Football service to get Premier League match data for the 2023/24 season:
API_KEY = 'your_api_key'
BASE_URL = 'https://v3.football.api-sports.io'
HEADERS = {'x-apisports-key': API_KEY}
params = {'league': 39, 'season': 2023}
response = requests.get(f'{BASE_URL}/fixtures', headers=HEADERS, params=params)
fixtures = response.json()['response']
🔍 Step 2: Process the Results
Each match was inspected to determine the winning team, and I counted how many matches each team played and won.
data = []
for match in fixtures:
if match['fixture']['status']['short'] == 'FT':
home_team = match['teams']['home']['name']
away_team = match['teams']['away']['name']
home_goals = match['goals']['home']
away_goals = match['goals']['away']
if home_goals > away_goals:
winner = home_team
elif away_goals > home_goals:
winner = away_team
else:
winner = None # draw
data.append({'home': home_team, 'away': away_team, 'winner': winner})
df = pd.DataFrame(data)
📊 Step 3: Calculate Win Probabilities
I grouped matches by team, calculated win rates (wins / games), and used the Binomial PMF to estimate their chance of winning a given number of games in 38-match season.
teams = list(set(df['home']).union(set(df['away'])))
records = []
for team in teams:
played = df[(df['home'] == team) | (df['away'] == team)]
wins = (df['winner'] == team).sum()
win_rate = wins / played.shape[0]
records.append({'team': team, 'wins': wins, 'played': played.shape[0], 'win_rate': win_rate})
df_stats = pd.DataFrame(records)
📈 Step 4: Visualize the Prediction
I used Seaborn to create a line plot for each team showing the probability distribution of their possible wins in the next season (assuming 38 games).
season_games = 38
plot_data = []
for _, row in df_stats.iterrows():
team = row['team']
p = row['win_rate']
for x in range(0, season_games + 1):
prob = binom.pmf(x, n=season_games, p=p)
plot_data.append({'team': team, 'wins': x, 'probability': prob})
viz_df = pd.DataFrame(plot_data)
plt.figure(figsize=(14, 8))
sns.lineplot(data=viz_df, x='wins', y='probability', hue='team')
plt.title('Predicted Win Probability Distribution (2024/25 Season)')
plt.xlabel('Number of Wins')
plt.ylabel('Probability')
plt.tight_layout()
plt.show()
🧠 Why This Matters
- This approach doesn’t predict exact results, but gives a solid probability profile for each team.
- It’s helpful for analysts and fans to understand team performance trends.
- One can improve this model by adding player-level data, home/away effects, injuries, or transfer impact.
💭 Final Thoughts
This was a fun exploration that blended sports and data science. Using historical data with probability theory gives deeper insights than just "gut feeling."
📂 GitHub: [https://github.com/loryneJoy/Python-Assignments.git]
🐍 Tags: #football #python #data-science #premier-league
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