Dataset: European Soccer Database Supplementary

About this Dataset

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:

Team statistics
Including overall team ranking, team points, winning possibility, team lineup, etc. Mostly descriptive analysis.
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.
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.
Player Evolution
Construct model to predict player's rating of future.
New Player's Template
Identify template and model player for young players cater to their positions and characteristics.
Market Value Prediction
Predict player's market value based on player's capacity and performance.
The Winning Eleven
Given a season / league / other criteria, propose the best 11 players as a team based on their capacity and performance.

Tables

Card Detail

@kaggle.jiezi2004_soccer.card_detail

957.28 KB
62114 rows
22 columns


CREATE TABLE card_detail (
  "card" VARCHAR,
  "card_type" VARCHAR,
  "comment" VARCHAR,
  "del" DOUBLE,
  "elapsed" BIGINT,
  "elapsed_plus" DOUBLE,
  "event_incident_typefk" BIGINT,
  "goal_type" VARCHAR,
  "id" BIGINT,
  "match_id" BIGINT,
  "n" BIGINT,
  "player1" VARCHAR,
  "pos_x" VARCHAR,
  "pos_y" VARCHAR,
  "rcards" VARCHAR,
  "sortorder" BIGINT,
  "stats" VARCHAR,
  "subtype" VARCHAR,
  "team" DOUBLE,
  "type" VARCHAR,
  "value" VARCHAR,
  "ycards" VARCHAR
);

Corner Detail

@kaggle.jiezi2004_soccer.corner_detail

1.14 MB
87839 rows
20 columns


CREATE TABLE corner_detail (
  "coordinates" VARCHAR,
  "corner" VARCHAR,
  "corners" VARCHAR,
  "del" DOUBLE,
  "elapsed" BIGINT,
  "elapsed_plus" DOUBLE,
  "event_incident_typefk" BIGINT,
  "id" BIGINT,
  "match_id" BIGINT,
  "n" BIGINT,
  "player1" DOUBLE,
  "pos_x" DOUBLE,
  "pos_y" DOUBLE,
  "sortorder" BIGINT,
  "spectators" DOUBLE,
  "stats" VARCHAR,
  "subtype" VARCHAR,
  "team" DOUBLE,
  "type" VARCHAR,
  "value" VARCHAR
);

Cross Detail

@kaggle.jiezi2004_soccer.cross_detail

3.23 MB
284775 rows
23 columns


CREATE TABLE cross_detail (
  "coordinates" VARCHAR,
  "corners" VARCHAR,
  "cross" VARCHAR,
  "crosses" VARCHAR,
  "del" DOUBLE,
  "elapsed" BIGINT,
  "elapsed_plus" DOUBLE,
  "event_incident_typefk" BIGINT,
  "goal_type" VARCHAR,
  "id" BIGINT,
  "match_id" BIGINT,
  "n" BIGINT,
  "player1" DOUBLE,
  "pos_x" DOUBLE,
  "pos_y" DOUBLE,
  "sortorder" BIGINT,
  "spectators" DOUBLE,
  "stats" VARCHAR,
  "subtype" VARCHAR,
  "team" DOUBLE,
  "throwin" VARCHAR,
  "type" VARCHAR,
  "value" VARCHAR
);

Foulcommit Detail

@kaggle.jiezi2004_soccer.foulcommit_detail

3.07 MB
219234 rows
23 columns


CREATE TABLE foulcommit_detail (
  "card_type" VARCHAR,
  "coordinates" VARCHAR,
  "del" DOUBLE,
  "elapsed" BIGINT,
  "elapsed_plus" DOUBLE,
  "event_incident_typefk" BIGINT,
  "foulcommit" VARCHAR,
  "foulscommitted" VARCHAR,
  "id" BIGINT,
  "injury_time" DOUBLE,
  "match_id" BIGINT,
  "n" BIGINT,
  "player1" VARCHAR,
  "player2" DOUBLE,
  "pos_x" DOUBLE,
  "pos_y" DOUBLE,
  "sortorder" BIGINT,
  "stats" VARCHAR,
  "subtype" VARCHAR,
  "team" DOUBLE,
  "type" VARCHAR,
  "value" VARCHAR,
  "venue" VARCHAR
);

Goal Detail

@kaggle.jiezi2004_soccer.goal_detail

738.38 KB
39980 rows
26 columns


CREATE TABLE goal_detail (
  "comment" VARCHAR,
  "coordinates" VARCHAR,
  "del" DOUBLE,
  "elapsed" BIGINT,
  "elapsed_plus" DOUBLE,
  "event_incident_typefk" BIGINT,
  "goal" VARCHAR,
  "goal_type" VARCHAR,
  "goals" VARCHAR,
  "id" BIGINT,
  "match_id" BIGINT,
  "n" BIGINT,
  "owngoals" VARCHAR,
  "penalties" VARCHAR,
  "player1" DOUBLE,
  "player2" DOUBLE,
  "pos_x" DOUBLE,
  "pos_y" DOUBLE,
  "shotoff" VARCHAR,
  "shoton" VARCHAR,
  "sortorder" BIGINT,
  "stats" VARCHAR,
  "subtype" VARCHAR,
  "team" DOUBLE,
  "type" VARCHAR,
  "value" VARCHAR
);

Positionreference

@kaggle.jiezi2004_soccer.positionreference

4.16 KB
99 rows
5 columns


CREATE TABLE positionreference (
  "player_pos_x" BIGINT,
  "player_pos_y" BIGINT,
  "role_x" VARCHAR,
  "role_y" VARCHAR,
  "role_xy" VARCHAR
);

Possession Detail

@kaggle.jiezi2004_soccer.possession_detail

510.92 KB
34815 rows
21 columns


CREATE TABLE possession_detail (
  "awaypos" DOUBLE,
  "card_type" VARCHAR,
  "comment" VARCHAR,
  "del" DOUBLE,
  "elapsed" BIGINT,
  "elapsed_plus" DOUBLE,
  "event_incident_typefk" BIGINT,
  "goal_type" VARCHAR,
  "homepos" DOUBLE,
  "id" BIGINT,
  "injury_time" DOUBLE,
  "match_id" BIGINT,
  "n" BIGINT,
  "pos_x" VARCHAR,
  "pos_y" VARCHAR,
  "possession" VARCHAR,
  "sortorder" BIGINT,
  "stats" VARCHAR,
  "subtype" VARCHAR,
  "type" VARCHAR,
  "value" VARCHAR
);

Shotoff Detail

@kaggle.jiezi2004_soccer.shotoff_detail

1.38 MB
95303 rows
19 columns


CREATE TABLE shotoff_detail (
  "card_type" VARCHAR,
  "coordinates" VARCHAR,
  "del" DOUBLE,
  "elapsed" BIGINT,
  "elapsed_plus" DOUBLE,
  "event_incident_typefk" BIGINT,
  "id" BIGINT,
  "match_id" BIGINT,
  "n" BIGINT,
  "player1" DOUBLE,
  "pos_x" DOUBLE,
  "pos_y" DOUBLE,
  "shotoff" VARCHAR,
  "sortorder" BIGINT,
  "stats" VARCHAR,
  "subtype" VARCHAR,
  "team" DOUBLE,
  "type" VARCHAR,
  "value" VARCHAR
);

Shoton Detail

@kaggle.jiezi2004_soccer.shoton_detail

1.37 MB
93755 rows
21 columns


CREATE TABLE shoton_detail (
  "blocked" VARCHAR,
  "card_type" VARCHAR,
  "coordinates" VARCHAR,
  "del" DOUBLE,
  "elapsed" BIGINT,
  "elapsed_plus" DOUBLE,
  "event_incident_typefk" BIGINT,
  "goal_type" VARCHAR,
  "id" BIGINT,
  "match_id" BIGINT,
  "n" BIGINT,
  "player1" DOUBLE,
  "pos_x" DOUBLE,
  "pos_y" DOUBLE,
  "shoton" VARCHAR,
  "sortorder" BIGINT,
  "stats" VARCHAR,
  "subtype" VARCHAR,
  "team" DOUBLE,
  "type" VARCHAR,
  "value" VARCHAR
);