2018-19 Premier League Season Analysis

Soccer Data

This dataset contains data of every game from the 2018-2019 season in the English Premier League.

Discover the possible outcomes of matches and how those outcomes change between half time and full time. Analyse the results to see what takeaways can be taken.

Data Dictionary

Column	Explanation
Div	Division the game was played in
Date	The date the game was played
HomeTeam	The home team
AwayTeam	The away team
FTHG	Full time home goals
FTAG	Full time away goals
FTR	Full time result
HTHG	Half time home goals
HTAG	Half time away goals
HTR	Half time result
Referee	The referee of the game
HS	Number of shots taken by home team
AS	Number of shots taken by away team
HST	Number of shots taken by home team on target
AST	Number of shots taken by away team on target
HF	Number of fouls made by home team
AF	Number of fouls made by away team
HC	Number of corners taken by home team
AC	Number of corners taken by away team
HY	Number of yellow cards received by home team
AY	Number of yellow cards received by away team
HR	Number of red cards received by home team
AR	Number of red cards received by away team

Part 1 - Exploratory Data Analysis

# Import the data and display it
import pandas as pd

match_data = pd.read_csv("soccer18-19.csv", parse_dates=['Date'])

display(match_data.head())

display(match_data.tail())

# Info display the object types to check they have been imported correctly
# Info also highlights any NULLs to check
match_data.info()

# Look for NULLs
print(match_data.isna().sum().sort_values())

# Using describe to look for any odd numeric data points.
match_data.describe()

# Look at the population of Home and Away team names to identify any typos in the names.
Home_Teams = match_data.groupby('HomeTeam').size()
Away_Teams = match_data.groupby('AwayTeam').size()

All_Teams = pd.concat([Home_Teams, Away_Teams], axis=1)
All_Teams.columns = ['HomeMatchCount', 'AwayMatchCount']

print(All_Teams)

# Given Team names are repeated it would be sensible to convert it to categorical. This will help increase the speed of the data and reduce the storage
match_data['HomeTeam'] = match_data['HomeTeam'].astype('category')
match_data['AwayTeam'] = match_data['AwayTeam'].astype('category')

# Look again at the population of Home and Away team names to identify any typos in the names.
Home_TeamsCategorical = match_data.groupby('HomeTeam').size()
Away_TeamsCategorical = match_data.groupby('AwayTeam').size()

All_TeamsCategorical = pd.concat([Home_TeamsCategorical, Away_TeamsCategorical], axis=1)
All_TeamsCategorical.columns = ['HomeMatchCount', 'AwayMatchCount']

print(All_Teams)

Raw data review

The data is loaded correctly now the date column is parsed correctly.

On review of the info display, the data is fully populated with no NULL data points that need to be dropped.
On review of the describe display, the data would seem to be populated sensibly, with no obvious outliers or incorrect data, like negative goals etc.
We have revieed the Teams used in the home and away, to check for typos. There are none. This has been converted to categorical data to improve performance

It would be safe to move on to transform the data for analytics.

Part 2 - Analysing Match Results

Using the half time and full time results of matches

# Find the number of matches played in the season
MatchCount = len(match_data)
print(MatchCount)

# Show the different results and how many games for each at half time 
HalfTimeResults = match_data.groupby('HTR').size().sort_values(ascending=False)
FullTimeResults = match_data.groupby('FTR').size().sort_values(ascending=False)
                                                               
Results_df = pd.concat([HalfTimeResults, FullTimeResults], axis=1)
Results_df.columns = ['HTR', 'FTR']
display(Results_df)