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Analyzed over 650 matches from Europe’s top 5 football leagues to asnwer the research question: "Why do teams with highest xG still end up not winning the league?".

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Group 01 - Football Data

  • Football Data: Expected Goals and Other Metrics

Milestones

Details for Milestone are available on Canvas (left sidebar, Course Project) or here.

Describe your topic/interest in about 150-200 words

We wish to use these metrics to analyse and predict outcomes of football games. Each team has its own playstyle which can be narrowed down to either offensive or defensive. We want to understand which playstyle is more effective in each of the top 5 european leagues. Additionally, upsets are another part of football. Using this comprehensive dataset we also wish to find out what factors may cause these upsets. Hence, we have two main research questions: What type of playstyle (offensive/defensive) leads to most success in each league (Top 5 European Leagues)? Why do teams with the highest xG(expected goals) still end up not winning the league and which factors cause the upset? This data can be displayed in the form of a dashboard because it involves variables and metrics that have numerical values. These values are best represented in the form of graphs and charts.

Describe your dataset in about 150-200 words

The nail-biting ending of the Euro Final reminded us why football is known as the beautiful game. We both are football fanatics and we chose a football dataset to help us understand the game better. We found the dataset on Kaggle and the data is owned by understat.com. The dataset contains statistical summary data of each season from 2014 to 2019 for 5 UEFA Leagues: La Liga, EPL, BundesLiga, Serie A, Ligue 1. The dataset includes standard parameters such as team standings, amount of matches played, wins, draws and other additional metrics such as xG(expected goals) , xG_diff(difference between actual goals and expected goals), ppda_coef(passes allowed per defensive action), oppda_coef(passes allowed per defensive action in the opposition half). According to understat these metrics were collected by forming neural network prediction algorithms with large dataset.

Team Members

  • Lakshay Karnwal: I am a 3rd year Computer Science student who loves playing the guitar.
  • Chinmay Jain: I am a 2nd year Management student who's never seen campus and supports FC Barcelona.

References

https://www.kaggle.com/slehkyi/extended-football-stats-for-european-leagues-xg

https://understat.com/

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Analyzed over 650 matches from Europe’s top 5 football leagues to asnwer the research question: "Why do teams with highest xG still end up not winning the league?".

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