Football analytics can be a fascinating and valuable field for understanding the game on a deeper level. For beginners, here are ten essential football analytics concepts to explore:
- Expected Goals (xG): xG measures the probability of a shot resulting in a goal based on various factors such as shot location, angle, and the type of shot. It helps assess the quality of scoring chances created and taken by teams or players.
- Expected Assists (xA): Similar to xG, xA quantifies the probability of an assist being made from a pass based on the pass’s characteristics, location, and other factors.
- Passing Metrics: Explore various passing statistics like pass completion rate, successful passes in the final third, key passes, and pass accuracy to understand a team’s ability to control the game and create opportunities.
- Possession and Passing Networks: Analyze possession percentages and passing networks to grasp how teams build up play, maintain possession, and create passing patterns.
- Pressing and Defensive Metrics: Look into pressing intensity, successful tackles, interceptions, and defensive duels to understand how teams apply defensive strategies.
- Heat Maps and Player Movement: Heat maps illustrate the areas of the field where players are most active, providing insights into player positioning and team tactics.
- Expected Points (xPTS): This metric estimates the number of points a team should have earned based on their performance in matches, which can help identify overperforming or underperforming teams.
- Player Comparison: Compare player statistics to evaluate their contributions and impact on the team’s performance.
- Set Piece Analysis: Examine the effectiveness of teams in set-piece situations, such as corners, free-kicks, and penalties.
- Goalkeeper Metrics: Delve into goalkeeper performance statistics like save percentage, clean sheets, and goals conceded to assess their contribution to the team.
To get started with football analytics, you can find data and resources from websites like Opta, StatsBomb, and Wyscout. Additionally, learning data analysis and visualization tools like Excel, Tableau, or Python can be immensely helpful in exploring and interpreting football data. As you progress, you can dive deeper into advanced analytics concepts and techniques.