European Soccer Database Supplementary

Context

This dataset was built as a supplementary to "[European Soccer Database][1]". It includes data dictionary, extraction of detailed match information previously contains in XML columns.

Content

• PositionReference.csv: A reference of position x, y and map them to
  actual position in a play court.
• DataDictionary.xlsx: Data dictionary for all XML columns in "Match"
  data table.
• card_detail.csv: Detailed XML information extracted form "card"
  column in "Match" data table.
• corner_detail.csv: Detailed XML information extracted form "corner"
  column in "Match" data table.
• cross_detail.csv: Detailed XML information extracted form "cross"
  column in "Match" data table.
• foulcommit_detail.csv: Detailed XML information extracted form
  "foulcommit" column in "Match" data table.
• goal_detail.csv: Detailed XML information extracted form "goal"
  column in "Match" data table.
• possession_detail.csv: Detailed XML information extracted form
  "possession" column in "Match" data table.
• shotoff_detail.csv: Detailed XML information extracted form
  "shotoffl" column in "Match" data table.
• shoton_detail.csv: Detailed XML information extracted form
  "shoton" column in "Match" data table.

Acknowledgements

Original data comes from [European Soccer Database][1] by Hugo Mathien. I personally thank him for all his efforts.

Inspiration

Since this is a open dataset with no specific goals / objectives, I would like to explore the following aspects by data analytics / data mining:

1. Team statistics
   Including overall team ranking, team points, winning possibility, team lineup, etc. Mostly descriptive analysis.
2. Team Transferring
   Track and study team players transferring in the market. Study team's strength and weakness, construct models to suggest best fit players to the team.
3. Player Statistics
   Summarize player's performance (goal, assist, cross, corner, pass, block, etc). Identify key factors of players by position. Based on these factors, evaluate player's characteristics.
4. Player Evolution
   Construct model to predict player's rating of future.
5. New Player's Template
   Identify template and model player for young players cater to their positions and characteristics.
6. Market Value Prediction
   Predict player's market value based on player's capacity and performance.
7. The Winning Eleven
   Given a season / league / other criteria, propose the best 11 players as a team based on their capacity and performance.