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Developing ML Models for Football Prediction

Football Game Outcome Prediction Analysis

Introduction​

Welcome to the Football Game Outcome Prediction Analysis Confluence page! This project aims to predict the outcomes of football games, specifically focusing on the Premier League games that occurred during the 2022-23 season. The predictive models are built using a dataset publicly available on Football-Data.co.uk.

Overview​

Objective​

The primary objective of this project is to develop machine learning models capable of predicting the results of Premier League football matches. By leveraging historical data from the 2022-23 season, we seek to uncover patterns and factors that contribute to the success or failure of teams in a given match.

Dataset Details​

Data Source​

The dataset utilized for this analysis is sourced from Football-Data.co.uk. This platform provides comprehensive football statistics, including match outcomes, team performance metrics, and other relevant information. The dataset specifically focuses on Premier League matches during the 2022-23 season, ensuring relevance and timeliness.

Data Format and Source​

The dataset is provided in CSV format, making it compatible with standard spreadsheet applications. It is sourced from Football-Data.co.uk. Note that some abbreviations, particularly those related to odds from specific bookmakers, may refer to data collected in earlier seasons.

Key Fields in the Dataset​

  • Div: League Division

  • Date: Match Date (dd/mm/yy)

  • Time: Time of match kick-off

  • HomeTeam: Home Team

  • AwayTeam: Away Team

  • FTHG and HG: Full Time Home Team Goals

  • FTAG and AG: Full Time Away Team Goals

  • FTR and Res: Full Time Result (H=Home Win, D=Draw, A=Away Win)

  • HTHG: Half Time Home Team Goals

  • HTAG: Half Time Away Team Goals

  • HTR: Half Time Result (H=Home Win, D=Draw, A=Away Win)

Match Statistics​

Where available, the dataset includes match statistics such as:

  • Attendance: Crowd Attendance

  • Referee: Match Referee

  • HS and AS: Home and Away Team Shots

  • HST and AST: Home and Away Team Shots on Target

  • HC and AC: Home and Away Team Corners

  • HF and AF: Home and Away Team Fouls Committed

  • HFKC and AFKC: Home and Away Team Free Kicks Conceded

  • HO and AO: Home and Away Team Offsides

  • HY and AY: Home and Away Team Yellow Cards

  • HR and AR: Home and Away Team Red Cards

  • HBP and ABP: Home and Away Team Bookings Points

Betting Odds​

The dataset includes betting odds from various bookmakers, with key abbreviations such as:

  • B365H, B365D, B365A: Bet365 home, draw, and away win odds

  • BSH, BSD, BSA: Blue Square home, draw, and away win odds

  • BWH, BWD, BWA: Bet&Win home, draw, and away win odds

Asian Handicap Betting Odds​

For Asian handicap betting, key fields include:

  • BbAH, BbAHh: Number of BetBrain bookmakers used and size of handicap (home team)

  • BbMxAHH, BbAvAHH: Betbrain maximum and average Asian handicap home team odds

  • BbMxAHA, BbAvAHA: Betbrain maximum and average Asian handicap away team odds

Closing Odds​

Closing odds, denoted with a "C" character, represent the last odds before the match starts.

Methodology​

Feature Selection​

The analysis involves utilizing key features from the dataset, including team performance metrics, match details, game context, and betting odds.The models are trained and evaluated using the aforementioned dataset. Various features from the dataset, such as team statistics, match location, and historical performance, are considered in the modeling process.

Data Preprocessing​

Before training machine learning models, the dataset undergoes preprocessing, including handling missing data, encoding categorical variables, and scaling numerical features.

Scope​

This project serves as a demonstration of applying machine learning techniques to football analytics. The findings can offer insights into the factors influencing match outcomes.

E0.csv