Documents Home » Data Files » Structured Data » Labeled Data » Football Data: Expected Goals and Other Metrics

Samar Patil's Documents

  • More »
  •  
  •  

Football Data: Expected Goals and Other Metrics

December 30, 2021

Context

I am a huge football fan and football statistics fascinate me. This data helps you to understand who is really who in the world of football. After discovering the understat.com platform with extended analytics about every single game in top European leagues I started to understand football even more. So I decided to scrape available summary data to take a look at some numbers of all teams of all leagues that not many people go for.

Content

The dataset contains statistical summary data by the end of each season from 2014 for 6 UEFA Leagues:

  • La Liga

  • EPL

  • BundesLiga

  • Serie A

  • Ligue 1

  • RFPL

Standard parameters: position, team, amount of matches played, wins, draws, loses, goals scored, goals missed, points.

Additional metrics:

  • xG - expected goals metric, it is a statistical measure of the quality of chances created and conceded. More at understat.com

  • xG_diff - difference between actual goals scored and expected goals.

  • npxG - expected goals without penalties and own goals.

  • xGA - expected goals against.

  • xGA_diff - difference between actual goals missed and expected goals against.

  • npxGA - expected goals against without penalties and own goals.

  • npxGD - difference between "for" and "against" expected goals without penalties and own goals.

  • ppda_coef - passes allowed per defensive action in the opposition half (power of pressure)

  • oppda_coef - opponent passes allowed per defensive action in the opposition half (power of opponent's pressure)

  • deep - passes completed within an estimated 20 yards of goal (crosses excluded)

  • deep_allowed - opponent passes completed within an estimated 20 yards of goal (crosses excluded)

  • xpts - expected points

  • xpts_diff - difference between actual and expected points

Acknowledgements

Huge thanks for the team of understat.com for collecting this data and make it open to the world.

Inspiration

With this dataset we can have more arguments in describing every league and can find answers to questions like:

Which teams create more chances to score a goal?

Which teams use pressure a lot and what results does this give?

Which teams play more defensive/offensive football?

Which teams have luck on their side, which do not?

Is there any particular characteristic of each league?

With this high overview dataset we can play a lot and understand more European football.

  • License Type Open Data Commons
  • Data Original Source Attribution https://www.kaggle.com/slehkyi/extended-football-stats-for-european-leagues-xg