Abstract

The Data

Our journey begins with the English Premier League results for the 2023 season, available in a CSV format at Football-Data.co.uk. This dataset is rich with match details including half-time results (HTR) and full-time results (FTR).

Getting Started with Python

To dig into this data, you'll need Python installed on your machine along with some essential libraries: Pandas for data manipulation and Matplotlib or Seaborn for visualization.

First, we import the necessary modules and load the data:

import pandas as pd

# Load the dataset
url = "https://www.football-data.co.uk/mmz4281/2223/E0.csv"
df = pd.read_csv(url)

# Selecting relevant columns for our analysis
columns_of_interest = ['Date', 'HomeTeam', 'AwayTeam', 'FTHG', 'FTAG', 'FTR', 'HTHG', 'HTAG', 'HTR']
df = df[columns_of_interest]

Exploring the Data

Let's first look at the overall distribution of full-time results:

# Full-Time Results Distribution
ftr_count = df['FTR'].value_counts(normalize=True) * 100
print(ftr_count)

This gives us a percentage breakdown of matches won by the home team (H), the away team (A), or ending in a draw (D).

Full time results:
H    0.484211
A    0.286842
D    0.228947

Half time results:
D    0.384211
H    0.363158
A    0.252632
Name: count, dtype: float64

The Confusion Matrix

To understand the relationship between half-time and full-time results, we create a confusion matrix:

# Confusion Matrix
confusion_matrix = pd.crosstab(df['HTR'], df['FTR'], rownames=['Half-Time'], colnames=['Full-Time'])
print(confusion_matrix)

This matrix shows us how many games had a certain result at half-time and how they ended at full-time.

Confusion matrix, half-time to full-time
Full-Time   A   D    H
Half-Time             
A          62  22   12
D          39  51   56
H           8  14  116

Insights

From the confusion matrix, several insights emerge:

1. Home Advantage: Home teams that lead at half-time tend to win the game. In our data set, they kept their lead at full-time in a significant majority of matches.

2. The Second-Half Swing: Matches drawn at half-time have an almost equal chance of ending as a win for either team or a draw. This tells us the second half is pivotal in such scenarios.

3. Away Resilience: Away teams leading at half-time often manage to carry their lead to the end of the match, highlighting their resilience.

Visualizing the Data

To better visualize this data, you could use Matplotlib or Seaborn to create a heatmap:

import seaborn as sns
import matplotlib.pyplot as plt

# Heatmap of the confusion matrix
sns.heatmap(confusion_matrix, annot=True, fmt="d", cmap="YlGnBu")
plt.show()

Conclusion

In this blog post, we've taken a dive into the Premier League's halftime and full-time results using Python. The home advantage is evident, especially when the home team starts the second half in the lead. For the neutral fan, a draw at half-time promises an exciting second half with all results still on the table.

The analysis conducted here is just the tip of the iceberg. With more detailed data, we could explore the impact of specific players, managers, or even weather conditions. Python provides a powerful toolkit for such analysis, making it accessible not only for data scientists but also for soccer enthusiasts looking to quantify the drama of the game.