Name: Fantasy Premier League 2019 -20
Creator: Kaggle
License: https://creativecommons.org/publicdomain/zero/1.0/

About this Dataset

Fantasy Premier League 2019 -20

Context

Hi,

This is my first year at Fantasy Premier League. I have created this database till the GW21 of EPL 2019-20. I was intrigued by the data intensive nature of this game and decided to jump into this.

Content

The database includes the player names, team names and all the FPL related stats for each player in every gameweek.

Acknowledgements

All the data has been extracted through the FPL API.
https://fantasy.premierleague.com/api/bootstrap-static/
https://fantasy.premierleague.com/api/element-summary/1/ --(1 is the player_id)
https://fantasy.premierleague.com/api/fixtures/

Inspiration

I want to predict the potential points of a player using this data. Using this I can calculate the opportunity cost I would be incurring if I select a player A over player B.
For instance,
This season, the top 6 GKs are seperated by just 5 points till GW21. So, is it worth spending the extra bucks on a different GK among this top 6?

Tables

Fixture Details

@kaggle.aj3907_fantasy_premier_league_2019_20.fixture_details

7.96 kB
380 rows
6 columns

CREATE TABLE fixture_details (
  "id" BIGINT,
  "event" BIGINT,
  "team_h" BIGINT,
  "team_a" BIGINT,
  "team_h_difficulty" BIGINT,
  "team_a_difficulty" BIGINT
);

Gw History

@kaggle.aj3907_fantasy_premier_league_2019_20.gw_history

222.63 kB
11,624 rows
29 columns

CREATE TABLE gw_history (
  "player_id" BIGINT,
  "element_type" BIGINT,
  "home_team" BIGINT,
  "opponent_team" BIGINT,
  "fdr_p" BIGINT,
  "fdr_o" BIGINT,
  "total_points" BIGINT,
  "was_home" BOOLEAN,
  "round" BIGINT,
  "minutes" BIGINT,
  "goals_scored" BIGINT,
  "assists" BIGINT,
  "clean_sheets" BIGINT,
  "goals_conceded" BIGINT,
  "own_goals" BIGINT,
  "penalties_saved" BIGINT,
  "penalties_missed" BIGINT,
  "yellow_cards" BIGINT,
  "red_cards" BIGINT,
  "saves" BIGINT,
  "bonus" BIGINT,
  "bps" BIGINT,
  "influence" DOUBLE,
  "creativity" DOUBLE,
  "threat" BIGINT,
  "ict_index" DOUBLE,
  "value" BIGINT,
  "transfers_balance" BIGINT,
  "selected" BIGINT
);

Player Details

@kaggle.aj3907_fantasy_premier_league_2019_20.player_details

111.95 kB
587 rows
53 columns

CREATE TABLE player_details (
  "id" BIGINT,
  "chance_of_playing_next_round" DOUBLE,
  "chance_of_playing_this_round" DOUBLE,
  "code" BIGINT,
  "cost_change_event" BIGINT,
  "cost_change_event_fall" BIGINT,
  "cost_change_start" BIGINT,
  "cost_change_start_fall" BIGINT,
  "dreamteam_count" BIGINT,
  "element_type" BIGINT,
  "ep_next" DOUBLE,
  "ep_this" DOUBLE,
  "event_points" BIGINT,
  "first_name" VARCHAR,
  "form" DOUBLE,
  "in_dreamteam" BOOLEAN,
  "news" VARCHAR,
  "news_added" VARCHAR,
  "now_cost" BIGINT,
  "photo" VARCHAR,
  "points_per_game" DOUBLE,
  "second_name" VARCHAR,
  "selected_by_percent" DOUBLE,
  "special" BOOLEAN,
  "squad_number" VARCHAR,
  "status" VARCHAR,
  "team" BIGINT,
  "team_code" BIGINT,
  "total_points" BIGINT,
  "transfers_in" BIGINT,
  "transfers_in_event" BIGINT,
  "transfers_out" BIGINT,
  "transfers_out_event" BIGINT,
  "value_form" DOUBLE,
  "value_season" DOUBLE,
  "web_name" VARCHAR,
  "minutes" BIGINT,
  "goals_scored" BIGINT,
  "assists" BIGINT,
  "clean_sheets" BIGINT,
  "goals_conceded" BIGINT,
  "own_goals" BIGINT,
  "penalties_saved" BIGINT,
  "penalties_missed" BIGINT,
  "yellow_cards" BIGINT,
  "red_cards" BIGINT,
  "saves" BIGINT,
  "bonus" BIGINT,
  "bps" BIGINT,
  "influence" DOUBLE,
  "creativity" DOUBLE,
  "threat" DOUBLE,
  "ict_index" DOUBLE
);

Team Details

@kaggle.aj3907_fantasy_premier_league_2019_20.team_details

8.84 kB
20 rows
10 columns

CREATE TABLE team_details (
  "id" BIGINT,
  "name" VARCHAR,
  "short_name" VARCHAR,
  "strength" BIGINT,
  "strength_overall_home" BIGINT,
  "strength_overall_away" BIGINT,
  "strength_attack_home" BIGINT,
  "strength_attack_away" BIGINT,
  "strength_defence_home" BIGINT,
  "strength_defence_away" BIGINT
);