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The Premier League

Analyzing Trends and Outcomes

@kaggle.thedevastator_uncovering_betting_patterns_in_the_premier_leagu

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About this Dataset

The Premier League

The Premier League

Analyzing Trends and Outcomes

By [source]

About this dataset

This dataset contains results and pre-match odds from matches of the English Premier League (EPL) from 2011 to 2021, providing a rich source of information to gain insights into betting patterns and match outcomes. The data has been collected from multiple sources, including popular betting sites such as Bet365, William Hill, Interwetten, Sportingbet and Ladbrokes. We investigate the EPL's recent history with columns such as home team shots on target, away team red cards or both teams hit woodwork among others. Moreover there is extensive in-depth analysis on bookmaker offering including home/away win/draw bets as well their respective over/under 2.5 goals offerings by Bet 365 or Betbrain for example. This dataset provides an interesting opportunity for those interested in football analytics and sports betting enthusiasts alike to explore how decisions around play make-or-break a game's result...and why not turn it into a profitable endeavour!

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How to use the dataset

This guide will provide a few tips for getting started with this insightful dataset. With these tips, you can use this dataset to help predict match outcomes and learn more about betting patterns in professional football.

  • Understand The Variables
    The first step is to familiarize yourself with all of the column titles in order to understand what each column tells us about each different match. The columns give us information such as date, attendance, referee name, number of shots by each team, results at half time and full time.

  • Investigation
    Once you are aware of which variables are available it’s important that you start your investigation properly by exploring the data through aggregations or visuals tools like graphs or matrices so that those relevant insights jump out at you quickly instead of taking too long looking for them in too many different directions. Through visualisations we can get an overview quickly instead spending time and efforts doing it manually by looking for correlations between variables as well as understanding missing values if any exist (e.g., does one team have a tendency get more cards than another?).

  • Evaluate Your Options
    It’s essential beginning any prediction process by talking over options – which data should be used? Which actions ought to be taken? After we have evaluated our options based on available data points it is possible then focus models only after we know they will likely produce meaningful predictions beforehand due diligence is always important when working with new datasets such predicting probabilities given certain combinations parameters particulary when profanity involves such large underlying variables within sports markets being able capture predictive accuracy levels using models which comparative enough make sense only then consider deploying any sort automation even test tretment conditions first through conduction experiments designs isolate feature engineering performance Thus before making final model selection/deployment would need confirm degree exposure pricing coverage seek adequate feedback select appropriate approaches either through algorithmic techniques traditional approaches combine both together where necessary most importantly its important have solid confidence strategy once start building model automate decisions based around that strategy

Research Ideas

  • Analyzing betting patterns and trends in the EPL. Analysing the pre-match odds from various betting sites can provide insights into how certain teams, players or coaches are favored to win certain matches. This can then be used to predict outcomes of future matches or plan strategies for squads.

  • Investigating the effects of different environmental factors on match results by analyzing attendance rates and referee profiles at different matches across seasons and divisions. By examining this data, correlations between attendance rates and match outcomes can be studied among other correlations, providing potential insights into player morale, enthusiasm and performance levels of each side in particular games along with referees that may influence results during a game.

  • Studying team success metrics over time by looking at post-match statistics such as shots on target (HST/AST), hit woodwork (HHW/AHW) corners (HC/AC) and offsides (HO/AO). These metrics illustrate how teams perform in a specific match compared to their opponents thus giving an indication of team objectives set before kick-off as well as which goals were achieved during play time allowing better judgement upon management decisions leading into each fixture up until its conclusion

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: 1995.csv

Column name Description
Div Division of the match. (String)
Date Date of the match. (Date)
HomeTeam Name of the home team. (String)
AwayTeam Name of the away team. (String)
FTHG Full time home goals. (Integer)
FTAG Full time away goals. (Integer)
FTR Full time result. (String)
HTHG Half time home goals. (Integer)
HTAG Half time away goals. (Integer)
HTR Half time result. (String)

File: 1996.csv

Column name Description
Div Division of the match. (String)
Date Date of the match. (Date)
HomeTeam Name of the home team. (String)
AwayTeam Name of the away team. (String)
FTHG Full time home goals. (Integer)
FTAG Full time away goals. (Integer)
FTR Full time result. (String)
HTHG Half time home goals. (Integer)
HTAG Half time away goals. (Integer)
HTR Half time result. (String)

File: 1997.csv

Column name Description
Div Division of the match. (String)
Date Date of the match. (Date)
HomeTeam Name of the home team. (String)
AwayTeam Name of the away team. (String)
FTHG Full time home goals. (Integer)
FTAG Full time away goals. (Integer)
FTR Full time result. (String)
HTHG Half time home goals. (Integer)
HTAG Half time away goals. (Integer)
HTR Half time result. (String)

File: 1998.csv

Column name Description
Div Division of the match. (String)
Date Date of the match. (Date)
HomeTeam Name of the home team. (String)
AwayTeam Name of the away team. (String)
FTHG Full time home goals. (Integer)
FTAG Full time away goals. (Integer)
FTR Full time result. (String)
HTHG Half time home goals. (Integer)
HTAG Half time away goals. (Integer)
HTR Half time result. (String)

File: 1999.csv

Column name Description
Div Division of the match. (String)
Date Date of the match. (Date)
HomeTeam Name of the home team. (String)
AwayTeam Name of the away team. (String)
FTHG Full time home goals. (Integer)
FTAG Full time away goals. (Integer)
FTR Full time result. (String)
HTHG Half time home goals. (Integer)
HTAG Half time away goals. (Integer)
HTR Half time result. (String)

File: 2000.csv

Column name Description
Div Division of the match. (String)
Date Date of the match. (Date)
HomeTeam Name of the home team. (String)
AwayTeam Name of the away team. (String)
FTHG Full time home goals. (Integer)
FTAG Full time away goals. (Integer)
FTR Full time result. (String)
HTHG Half time home goals. (Integer)
HTAG Half time away goals. (Integer)
HTR Half time result. (String)
Attendance Number of spectators at the match. (Integer)
Referee Name of the referee officiating the match. (String)
HS Home team shots. (Integer)
AS Away team shots. (Integer)
HST Home team shots on target. (Integer)
AST Away team shots on target. (Integer)
HHW Home team woodworks hit. (Integer)
AHW Away team woodworks hit. (Integer)
HC Home team corners. (Integer)
AC Away team corners. (Integer)
HF Home team fouls. (Integer)
AF Away team fouls. (Integer)
HO Home team offsides. (Integer)
AO Away team offsides. (Integer)
HY Home team yellow cards. (Integer)
AY Away team yellow cards. (Integer)
HR Home team red cards. (Integer)
AR Away team red cards. (Integer)
HBP Home team bookings points. (Integer)
ABP Away team bookings points. (Integer)
GBH Good Bookmakers Home Win Odds. (Float)
GBD Good Bookmakers Draw Odds. (Float)
GBA Good Bookmakers Away Win Odds. (Float)
IWH Interwetten Home Win Odds. (Float)
IWD Interwetten Draw Odds. (Float)
IWA Interwetten Away Win Odds. (Float)
LBH Ladbrokes Home Win Odds. (Float)
LBD Ladbrokes Draw Odds. (Float)
LBA Ladbrokes Away Win Odds. (Float)
SBH Sportingbet Home Win Odds. (Float)
SBD Sportingbet Draw Odds. (Float)
SBA Sportingbet Away Win Odds. (Float)
WHH William Hill Home Win Odds. (Float)
WHD William Hill Draw Odds. (Float)

File: 2001.csv

Column name Description
Div Division of the match. (String)
Date Date of the match. (Date)
HomeTeam Name of the home team. (String)
AwayTeam Name of the away team. (String)
FTHG Full time home goals. (Integer)
FTAG Full time away goals. (Integer)
FTR Full time result. (String)
HTHG Half time home goals. (Integer)
HTAG Half time away goals. (Integer)
HTR Half time result. (String)
Attendance Number of spectators at the match. (Integer)
Referee Name of the referee officiating the match. (String)
HS Home team shots. (Integer)
AS Away team shots. (Integer)
HST Home team shots on target. (Integer)
AST Away team shots on target. (Integer)
HHW Home team woodworks hit. (Integer)
AHW Away team woodworks hit. (Integer)
HC Home team corners. (Integer)
AC Away team corners. (Integer)
HF Home team fouls. (Integer)
AF Away team fouls. (Integer)
HO Home team offsides. (Integer)
AO Away team offsides. (Integer)
HY Home team yellow cards. (Integer)
AY Away team yellow cards. (Integer)
HR Home team red cards. (Integer)
AR Away team red cards. (Integer)
HBP Home team bookings points. (Integer)
ABP Away team bookings points. (Integer)
GBH Good Bookmakers Home Win Odds. (Float)
GBD Good Bookmakers Draw Odds. (Float)
GBA Good Bookmakers Away Win Odds. (Float)
IWH Interwetten Home Win Odds. (Float)
IWD Interwetten Draw Odds. (Float)
IWA Interwetten Away Win Odds. (Float)
LBH Ladbrokes Home Win Odds. (Float)
LBD Ladbrokes Draw Odds. (Float)
LBA Ladbrokes Away Win Odds. (Float)
SBH Sportingbet Home Win Odds. (Float)
SBD Sportingbet Draw Odds. (Float)
SBA Sportingbet Away Win Odds. (Float)
WHH William Hill Home Win Odds. (Float)
WHD William Hill Draw Odds. (Float)

File: 2002.csv

Column name Description
Div Division of the match. (String)
Date Date of the match. (Date)
HomeTeam Name of the home team. (String)
AwayTeam Name of the away team. (String)
FTHG Full time home goals. (Integer)
FTAG Full time away goals. (Integer)
FTR Full time result. (String)
HTHG Half time home goals. (Integer)
HTAG Half time away goals. (Integer)
HTR Half time result. (String)
Referee Name of the referee officiating the match. (String)
HS Home team shots. (Integer)
AS Away team shots. (Integer)
HST Home team shots on target. (Integer)
AST Away team shots on target. (Integer)
HF Home team fouls. (Integer)
AF Away team fouls. (Integer)
HC Home team corners. (Integer)
AC Away team corners. (Integer)
HY Home team yellow cards. (Integer)
AY Away team yellow cards. (Integer)
HR Home team red cards. (Integer)
AR Away team red cards. (Integer)
GBH Good Bookmakers Home Win Odds. (Float)
GBD Good Bookmakers Draw Odds. (Float)
GBA Good Bookmakers Away Win Odds. (Float)
IWH Interwetten Home Win Odds. (Float)
IWD Interwetten Draw Odds. (Float)
IWA Interwetten Away Win Odds. (Float)
LBH Ladbrokes Home Win Odds. (Float)
LBD Ladbrokes Draw Odds. (Float)
LBA Ladbrokes Away Win Odds. (Float)
SBH Sportingbet Home Win Odds. (Float)
SBD Sportingbet Draw Odds. (Float)
SBA Sportingbet Away Win Odds. (Float)
WHH William Hill Home Win Odds. (Float)
WHD William Hill Draw Odds. (Float)
B365H Bet365 Home Win Odds. (Float)
B365D Bet365 Draw Odds. (Float)
B365A Bet365 Away Win Odds. (Float)
SOH Sporting Odds Home Win Odds. (Float)
SOD Sporting Odds Draw Odds. (Float)
SOA Sporting Odds Away Win Odds. (Float)
WHA William Hill Asian Handicap Odds. (Float)
GB>2.5 Good Bookmakers Over 2.5 Goals Odds. (Float)
GB<2.5 Good Bookmakers Under 2.5 Goals Odds. (Float)
B365>2.5 Bet365 Over 2.5 Goals Odds. (Float)
B365<2.5 Bet365 Under 2.5 Goals Odds. (Float)

File: 2005.csv

Column name Description
Div Division of the match. (String)
Date Date of the match. (Date)
HomeTeam Name of the home team. (String)
AwayTeam Name of the away team. (String)
FTHG Full time home goals. (Integer)
FTAG Full time away goals. (Integer)
FTR Full time result. (String)
HTHG Half time home goals. (Integer)
HTAG Half time away goals. (Integer)
HTR Half time result. (String)
Referee Name of the referee officiating the match. (String)
HS Home team shots. (Integer)
AS Away team shots. (Integer)
HST Home team shots on target. (Integer)
AST Away team shots on target. (Integer)
HF Home team fouls. (Integer)
AF Away team fouls. (Integer)
HC Home team corners. (Integer)
AC Away team corners. (Integer)
HY Home team yellow cards. (Integer)
AY Away team yellow cards. (Integer)
HR Home team red cards. (Integer)
AR Away team red cards. (Integer)
B365H Bet365 Home Win Odds. (Float)
B365D Bet365 Draw Odds. (Float)
B365A Bet365 Away Win Odds. (Float)
GBH Good Bookmakers Home Win Odds. (Float)
GBD Good Bookmakers Draw Odds. (Float)
GBA Good Bookmakers Away Win Odds. (Float)
IWH Interwetten Home Win Odds. (Float)
IWD Interwetten Draw Odds. (Float)
IWA Interwetten Away Win Odds. (Float)
LBH Ladbrokes Home Win Odds. (Float)
LBD Ladbrokes Draw Odds. (Float)
LBA Ladbrokes Away Win Odds. (Float)
SBH Sportingbet Home Win Odds. (Float)
SBD Sportingbet Draw Odds. (Float)
SBA Sportingbet Away Win Odds. (Float)
WHH William Hill Home Win Odds. (Float)
WHD William Hill Draw Odds. (Float)
WHA William Hill Asian Handicap Odds. (Float)
BbAv<2.5 Average betting odds for a match with less than 2.5 goals. (Float)
BbAH Average betting odds for Asian Handicap. (Float)
BbAHh Average betting odds for Asian Handicap Home team. (Float)
BbMxAHH Maximum betting odds for Asian Handicap Home team. (Float)
BbAvAHH Average betting odds for Asian Handicap Home team. (Float)
BbMxAHA Maximum betting odds for Asian Handicap Away team. (Float)

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit .

Tables

N 1995

@kaggle.thedevastator_uncovering_betting_patterns_in_the_premier_leagu.n_1995
  • 14.87 KB
  • 380 rows
  • 21 columns
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CREATE TABLE n_1995 (
  "div" VARCHAR,
  "date" VARCHAR,
  "hometeam" VARCHAR,
  "awayteam" VARCHAR,
  "fthg" BIGINT,
  "ftag" BIGINT,
  "ftr" VARCHAR,
  "hthg" BIGINT,
  "htag" BIGINT,
  "htr" VARCHAR,
  "unnamed_10" VARCHAR,
  "unnamed_11" VARCHAR,
  "unnamed_12" VARCHAR,
  "unnamed_13" VARCHAR,
  "unnamed_14" VARCHAR,
  "unnamed_15" VARCHAR,
  "unnamed_16" VARCHAR,
  "unnamed_17" VARCHAR,
  "unnamed_18" VARCHAR,
  "unnamed_19" VARCHAR,
  "unnamed_20" VARCHAR
);

N 1996

@kaggle.thedevastator_uncovering_betting_patterns_in_the_premier_leagu.n_1996
  • 14.99 KB
  • 380 rows
  • 21 columns
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CREATE TABLE n_1996 (
  "div" VARCHAR,
  "date" VARCHAR,
  "hometeam" VARCHAR,
  "awayteam" VARCHAR,
  "fthg" BIGINT,
  "ftag" BIGINT,
  "ftr" VARCHAR,
  "hthg" BIGINT,
  "htag" BIGINT,
  "htr" VARCHAR,
  "unnamed_10" VARCHAR,
  "unnamed_11" VARCHAR,
  "unnamed_12" VARCHAR,
  "unnamed_13" VARCHAR,
  "unnamed_14" VARCHAR,
  "unnamed_15" VARCHAR,
  "unnamed_16" VARCHAR,
  "unnamed_17" VARCHAR,
  "unnamed_18" VARCHAR,
  "unnamed_19" VARCHAR,
  "unnamed_20" VARCHAR
);

N 1997

@kaggle.thedevastator_uncovering_betting_patterns_in_the_premier_leagu.n_1997
  • 14.95 KB
  • 380 rows
  • 21 columns
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CREATE TABLE n_1997 (
  "div" VARCHAR,
  "date" VARCHAR,
  "hometeam" VARCHAR,
  "awayteam" VARCHAR,
  "fthg" BIGINT,
  "ftag" BIGINT,
  "ftr" VARCHAR,
  "hthg" BIGINT,
  "htag" BIGINT,
  "htr" VARCHAR,
  "unnamed_10" VARCHAR,
  "unnamed_11" VARCHAR,
  "unnamed_12" VARCHAR,
  "unnamed_13" VARCHAR,
  "unnamed_14" VARCHAR,
  "unnamed_15" VARCHAR,
  "unnamed_16" VARCHAR,
  "unnamed_17" VARCHAR,
  "unnamed_18" VARCHAR,
  "unnamed_19" VARCHAR,
  "unnamed_20" VARCHAR
);

N 1998

@kaggle.thedevastator_uncovering_betting_patterns_in_the_premier_leagu.n_1998
  • 14.98 KB
  • 380 rows
  • 21 columns
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CREATE TABLE n_1998 (
  "div" VARCHAR,
  "date" VARCHAR,
  "hometeam" VARCHAR,
  "awayteam" VARCHAR,
  "fthg" BIGINT,
  "ftag" BIGINT,
  "ftr" VARCHAR,
  "hthg" BIGINT,
  "htag" BIGINT,
  "htr" VARCHAR,
  "unnamed_10" VARCHAR,
  "unnamed_11" VARCHAR,
  "unnamed_12" VARCHAR,
  "unnamed_13" VARCHAR,
  "unnamed_14" VARCHAR,
  "unnamed_15" VARCHAR,
  "unnamed_16" VARCHAR,
  "unnamed_17" VARCHAR,
  "unnamed_18" VARCHAR,
  "unnamed_19" VARCHAR,
  "unnamed_20" VARCHAR
);

N 1999

@kaggle.thedevastator_uncovering_betting_patterns_in_the_premier_leagu.n_1999
  • 15.45 KB
  • 380 rows
  • 22 columns
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CREATE TABLE n_1999 (
  "div" VARCHAR,
  "date" VARCHAR,
  "hometeam" VARCHAR,
  "awayteam" VARCHAR,
  "fthg" BIGINT,
  "ftag" BIGINT,
  "ftr" VARCHAR,
  "hthg" BIGINT,
  "htag" BIGINT,
  "htr" VARCHAR,
  "unnamed_10" VARCHAR,
  "unnamed_11" VARCHAR,
  "unnamed_12" VARCHAR,
  "unnamed_13" VARCHAR,
  "unnamed_14" VARCHAR,
  "unnamed_15" VARCHAR,
  "unnamed_16" VARCHAR,
  "unnamed_17" VARCHAR,
  "unnamed_18" VARCHAR,
  "unnamed_19" VARCHAR,
  "unnamed_20" VARCHAR,
  "unnamed_21" VARCHAR
);

N 2000

@kaggle.thedevastator_uncovering_betting_patterns_in_the_premier_leagu.n_2000
  • 44.18 KB
  • 380 rows
  • 45 columns
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CREATE TABLE n_2000 (
  "div" VARCHAR,
  "date" VARCHAR,
  "hometeam" VARCHAR,
  "awayteam" VARCHAR,
  "fthg" BIGINT,
  "ftag" BIGINT,
  "ftr" VARCHAR,
  "hthg" BIGINT,
  "htag" BIGINT,
  "htr" VARCHAR,
  "attendance" BIGINT,
  "referee" VARCHAR,
  "hs" BIGINT,
  "as" BIGINT,
  "hst" BIGINT,
  "ast" BIGINT,
  "hhw" BIGINT,
  "ahw" BIGINT,
  "hc" BIGINT,
  "ac" BIGINT,
  "hf" BIGINT,
  "af" BIGINT,
  "ho" BIGINT,
  "ao" BIGINT,
  "hy" BIGINT,
  "ay" BIGINT,
  "hr" BIGINT,
  "ar" BIGINT,
  "hbp" BIGINT,
  "abp" BIGINT,
  "gbh" DOUBLE,
  "gbd" DOUBLE,
  "gba" DOUBLE,
  "iwh" DOUBLE,
  "iwd" DOUBLE,
  "iwa" DOUBLE,
  "lbh" DOUBLE,
  "lbd" DOUBLE,
  "lba" DOUBLE,
  "sbh" DOUBLE,
  "sbd" DOUBLE,
  "sba" DOUBLE,
  "whh" DOUBLE,
  "whd" DOUBLE,
  "wha" DOUBLE
);

N 2001

@kaggle.thedevastator_uncovering_betting_patterns_in_the_premier_leagu.n_2001
  • 48.55 KB
  • 380 rows
  • 48 columns
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CREATE TABLE n_2001 (
  "div" VARCHAR,
  "date" VARCHAR,
  "hometeam" VARCHAR,
  "awayteam" VARCHAR,
  "fthg" BIGINT,
  "ftag" BIGINT,
  "ftr" VARCHAR,
  "hthg" BIGINT,
  "htag" BIGINT,
  "htr" VARCHAR,
  "attendance" DOUBLE,
  "referee" VARCHAR,
  "hs" BIGINT,
  "as" BIGINT,
  "hst" BIGINT,
  "ast" BIGINT,
  "hhw" BIGINT,
  "ahw" BIGINT,
  "hc" BIGINT,
  "ac" BIGINT,
  "hf" BIGINT,
  "af" BIGINT,
  "ho" BIGINT,
  "ao" BIGINT,
  "hy" BIGINT,
  "ay" BIGINT,
  "hr" BIGINT,
  "ar" BIGINT,
  "hbp" BIGINT,
  "abp" BIGINT,
  "gbh" DOUBLE,
  "gbd" DOUBLE,
  "gba" DOUBLE,
  "iwh" DOUBLE,
  "iwd" DOUBLE,
  "iwa" DOUBLE,
  "lbh" DOUBLE,
  "lbd" DOUBLE,
  "lba" DOUBLE,
  "sbh" DOUBLE,
  "sbd" DOUBLE,
  "sba" DOUBLE,
  "syh" DOUBLE,
  "syd" DOUBLE,
  "sya" DOUBLE,
  "whh" DOUBLE,
  "whd" DOUBLE,
  "wha" DOUBLE
);

N 2002

@kaggle.thedevastator_uncovering_betting_patterns_in_the_premier_leagu.n_2002
  • 51.05 KB
  • 380 rows
  • 53 columns
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CREATE TABLE n_2002 (
  "div" VARCHAR,
  "date" TIMESTAMP,
  "hometeam" VARCHAR,
  "awayteam" VARCHAR,
  "fthg" BIGINT,
  "ftag" BIGINT,
  "ftr" VARCHAR,
  "hthg" BIGINT,
  "htag" BIGINT,
  "htr" VARCHAR,
  "referee" VARCHAR,
  "hs" BIGINT,
  "as" BIGINT,
  "hst" BIGINT,
  "ast" BIGINT,
  "hf" BIGINT,
  "af" BIGINT,
  "hc" BIGINT,
  "ac" BIGINT,
  "hy" BIGINT,
  "ay" BIGINT,
  "hr" BIGINT,
  "ar" BIGINT,
  "b365h" DOUBLE,
  "b365d" DOUBLE,
  "b365a" DOUBLE,
  "gbh" DOUBLE,
  "gbd" DOUBLE,
  "gba" DOUBLE,
  "iwh" DOUBLE,
  "iwd" DOUBLE,
  "iwa" DOUBLE,
  "lbh" DOUBLE,
  "lbd" DOUBLE,
  "lba" DOUBLE,
  "soh" DOUBLE,
  "sod" DOUBLE,
  "soa" DOUBLE,
  "sbh" DOUBLE,
  "sbd" DOUBLE,
  "sba" DOUBLE,
  "whh" DOUBLE,
  "whd" DOUBLE,
  "wha" DOUBLE,
  "gb_2_5" DOUBLE,
  "gb_2_5_a81079" DOUBLE,
  "b365_2_5" DOUBLE,
  "b365_2_5_c5d2e2" DOUBLE,
  "unnamed_48" VARCHAR,
  "unnamed_49" VARCHAR,
  "unnamed_50" VARCHAR,
  "unnamed_51" VARCHAR,
  "unnamed_52" VARCHAR
);

N 2005

@kaggle.thedevastator_uncovering_betting_patterns_in_the_premier_leagu.n_2005
  • 74.64 KB
  • 380 rows
  • 68 columns
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CREATE TABLE n_2005 (
  "div" VARCHAR,
  "date" VARCHAR,
  "hometeam" VARCHAR,
  "awayteam" VARCHAR,
  "fthg" BIGINT,
  "ftag" BIGINT,
  "ftr" VARCHAR,
  "hthg" BIGINT,
  "htag" BIGINT,
  "htr" VARCHAR,
  "referee" VARCHAR,
  "hs" BIGINT,
  "as" BIGINT,
  "hst" BIGINT,
  "ast" BIGINT,
  "hf" BIGINT,
  "af" BIGINT,
  "hc" BIGINT,
  "ac" BIGINT,
  "hy" BIGINT,
  "ay" BIGINT,
  "hr" BIGINT,
  "ar" BIGINT,
  "b365h" DOUBLE,
  "b365d" DOUBLE,
  "b365a" DOUBLE,
  "bwh" DOUBLE,
  "bwd" DOUBLE,
  "bwa" DOUBLE,
  "gbh" DOUBLE,
  "gbd" DOUBLE,
  "gba" DOUBLE,
  "iwh" DOUBLE,
  "iwd" DOUBLE,
  "iwa" DOUBLE,
  "lbh" DOUBLE,
  "lbd" DOUBLE,
  "lba" DOUBLE,
  "sbh" DOUBLE,
  "sbd" DOUBLE,
  "sba" DOUBLE,
  "whh" DOUBLE,
  "whd" DOUBLE,
  "wha" DOUBLE,
  "sjh" DOUBLE,
  "sjd" DOUBLE,
  "sja" DOUBLE,
  "vch" DOUBLE,
  "vcd" DOUBLE,
  "vca" DOUBLE,
  "bb1x2" BIGINT,
  "bbmxh" DOUBLE,
  "bbavh" DOUBLE,
  "bbmxd" DOUBLE,
  "bbavd" DOUBLE,
  "bbmxa" DOUBLE,
  "bbava" DOUBLE,
  "bbou" BIGINT,
  "bbmx_2_5" DOUBLE,
  "bbav_2_5" DOUBLE,
  "bbmx_2_5_994fdb" DOUBLE,
  "bbav_2_5_b3ab09" DOUBLE,
  "bbah" BIGINT,
  "bbahh" DOUBLE,
  "bbmxahh" DOUBLE,
  "bbavahh" DOUBLE,
  "bbmxaha" DOUBLE,
  "bbavaha" DOUBLE
);

N 2006

@kaggle.thedevastator_uncovering_betting_patterns_in_the_premier_leagu.n_2006
  • 74.58 KB
  • 380 rows
  • 68 columns
Loading... 

CREATE TABLE n_2006 (
  "div" VARCHAR,
  "date" VARCHAR,
  "hometeam" VARCHAR,
  "awayteam" VARCHAR,
  "fthg" BIGINT,
  "ftag" BIGINT,
  "ftr" VARCHAR,
  "hthg" BIGINT,
  "htag" BIGINT,
  "htr" VARCHAR,
  "referee" VARCHAR,
  "hs" BIGINT,
  "as" BIGINT,
  "hst" BIGINT,
  "ast" BIGINT,
  "hf" BIGINT,
  "af" BIGINT,
  "hc" BIGINT,
  "ac" BIGINT,
  "hy" BIGINT,
  "ay" BIGINT,
  "hr" BIGINT,
  "ar" BIGINT,
  "b365h" DOUBLE,
  "b365d" DOUBLE,
  "b365a" DOUBLE,
  "bwh" DOUBLE,
  "bwd" DOUBLE,
  "bwa" DOUBLE,
  "gbh" DOUBLE,
  "gbd" DOUBLE,
  "gba" DOUBLE,
  "iwh" DOUBLE,
  "iwd" DOUBLE,
  "iwa" DOUBLE,
  "lbh" DOUBLE,
  "lbd" DOUBLE,
  "lba" DOUBLE,
  "sbh" DOUBLE,
  "sbd" DOUBLE,
  "sba" DOUBLE,
  "whh" DOUBLE,
  "whd" DOUBLE,
  "wha" DOUBLE,
  "sjh" DOUBLE,
  "sjd" DOUBLE,
  "sja" DOUBLE,
  "vch" DOUBLE,
  "vcd" DOUBLE,
  "vca" DOUBLE,
  "bb1x2" BIGINT,
  "bbmxh" DOUBLE,
  "bbavh" DOUBLE,
  "bbmxd" DOUBLE,
  "bbavd" DOUBLE,
  "bbmxa" DOUBLE,
  "bbava" DOUBLE,
  "bbou" BIGINT,
  "bbmx_2_5" DOUBLE,
  "bbav_2_5" DOUBLE,
  "bbmx_2_5_994fdb" DOUBLE,
  "bbav_2_5_b3ab09" DOUBLE,
  "bbah" BIGINT,
  "bbahh" DOUBLE,
  "bbmxahh" DOUBLE,
  "bbavahh" DOUBLE,
  "bbmxaha" DOUBLE,
  "bbavaha" DOUBLE
);

N 2007

@kaggle.thedevastator_uncovering_betting_patterns_in_the_premier_leagu.n_2007
  • 79.78 KB
  • 380 rows
  • 71 columns
Loading... 

CREATE TABLE n_2007 (
  "div" VARCHAR,
  "date" VARCHAR,
  "hometeam" VARCHAR,
  "awayteam" VARCHAR,
  "fthg" BIGINT,
  "ftag" BIGINT,
  "ftr" VARCHAR,
  "hthg" BIGINT,
  "htag" BIGINT,
  "htr" VARCHAR,
  "referee" VARCHAR,
  "hs" BIGINT,
  "as" BIGINT,
  "hst" BIGINT,
  "ast" BIGINT,
  "hf" BIGINT,
  "af" BIGINT,
  "hc" BIGINT,
  "ac" BIGINT,
  "hy" BIGINT,
  "ay" BIGINT,
  "hr" BIGINT,
  "ar" BIGINT,
  "b365h" DOUBLE,
  "b365d" DOUBLE,
  "b365a" DOUBLE,
  "bwh" DOUBLE,
  "bwd" DOUBLE,
  "bwa" DOUBLE,
  "gbh" DOUBLE,
  "gbd" DOUBLE,
  "gba" DOUBLE,
  "iwh" DOUBLE,
  "iwd" DOUBLE,
  "iwa" DOUBLE,
  "lbh" DOUBLE,
  "lbd" DOUBLE,
  "lba" DOUBLE,
  "sbh" DOUBLE,
  "sbd" DOUBLE,
  "sba" DOUBLE,
  "whh" DOUBLE,
  "whd" DOUBLE,
  "wha" DOUBLE,
  "sjh" DOUBLE,
  "sjd" DOUBLE,
  "sja" DOUBLE,
  "vch" DOUBLE,
  "vcd" DOUBLE,
  "vca" DOUBLE,
  "bsh" DOUBLE,
  "bsd" DOUBLE,
  "bsa" DOUBLE,
  "bb1x2" BIGINT,
  "bbmxh" DOUBLE,
  "bbavh" DOUBLE,
  "bbmxd" DOUBLE,
  "bbavd" DOUBLE,
  "bbmxa" DOUBLE,
  "bbava" DOUBLE,
  "bbou" BIGINT,
  "bbmx_2_5" DOUBLE,
  "bbav_2_5" DOUBLE,
  "bbmx_2_5_994fdb" DOUBLE,
  "bbav_2_5_b3ab09" DOUBLE,
  "bbah" BIGINT,
  "bbahh" DOUBLE,
  "bbmxahh" DOUBLE,
  "bbavahh" DOUBLE,
  "bbmxaha" DOUBLE,
  "bbavaha" DOUBLE
);

N 2008

@kaggle.thedevastator_uncovering_betting_patterns_in_the_premier_leagu.n_2008
  • 81.61 KB
  • 380 rows
  • 71 columns
Loading... 

CREATE TABLE n_2008 (
  "div" VARCHAR,
  "date" VARCHAR,
  "hometeam" VARCHAR,
  "awayteam" VARCHAR,
  "fthg" BIGINT,
  "ftag" BIGINT,
  "ftr" VARCHAR,
  "hthg" BIGINT,
  "htag" BIGINT,
  "htr" VARCHAR,
  "referee" VARCHAR,
  "hs" BIGINT,
  "as" BIGINT,
  "hst" BIGINT,
  "ast" BIGINT,
  "hf" BIGINT,
  "af" BIGINT,
  "hc" BIGINT,
  "ac" BIGINT,
  "hy" BIGINT,
  "ay" BIGINT,
  "hr" BIGINT,
  "ar" BIGINT,
  "b365h" DOUBLE,
  "b365d" DOUBLE,
  "b365a" DOUBLE,
  "bwh" DOUBLE,
  "bwd" DOUBLE,
  "bwa" DOUBLE,
  "gbh" DOUBLE,
  "gbd" DOUBLE,
  "gba" DOUBLE,
  "iwh" DOUBLE,
  "iwd" DOUBLE,
  "iwa" DOUBLE,
  "lbh" DOUBLE,
  "lbd" DOUBLE,
  "lba" DOUBLE,
  "sbh" DOUBLE,
  "sbd" DOUBLE,
  "sba" DOUBLE,
  "whh" DOUBLE,
  "whd" DOUBLE,
  "wha" DOUBLE,
  "sjh" DOUBLE,
  "sjd" DOUBLE,
  "sja" DOUBLE,
  "vch" DOUBLE,
  "vcd" DOUBLE,
  "vca" DOUBLE,
  "bsh" DOUBLE,
  "bsd" DOUBLE,
  "bsa" DOUBLE,
  "bb1x2" BIGINT,
  "bbmxh" DOUBLE,
  "bbavh" DOUBLE,
  "bbmxd" DOUBLE,
  "bbavd" DOUBLE,
  "bbmxa" DOUBLE,
  "bbava" DOUBLE,
  "bbou" BIGINT,
  "bbmx_2_5" DOUBLE,
  "bbav_2_5" DOUBLE,
  "bbmx_2_5_994fdb" DOUBLE,
  "bbav_2_5_b3ab09" DOUBLE,
  "bbah" BIGINT,
  "bbahh" DOUBLE,
  "bbmxahh" DOUBLE,
  "bbavahh" DOUBLE,
  "bbmxaha" DOUBLE,
  "bbavaha" DOUBLE
);

N 2009

@kaggle.thedevastator_uncovering_betting_patterns_in_the_premier_leagu.n_2009
  • 83.84 KB
  • 380 rows
  • 71 columns
Loading... 

CREATE TABLE n_2009 (
  "div" VARCHAR,
  "date" VARCHAR,
  "hometeam" VARCHAR,
  "awayteam" VARCHAR,
  "fthg" BIGINT,
  "ftag" BIGINT,
  "ftr" VARCHAR,
  "hthg" BIGINT,
  "htag" BIGINT,
  "htr" VARCHAR,
  "referee" VARCHAR,
  "hs" BIGINT,
  "as" BIGINT,
  "hst" BIGINT,
  "ast" BIGINT,
  "hf" BIGINT,
  "af" BIGINT,
  "hc" BIGINT,
  "ac" BIGINT,
  "hy" BIGINT,
  "ay" BIGINT,
  "hr" BIGINT,
  "ar" BIGINT,
  "b365h" DOUBLE,
  "b365d" DOUBLE,
  "b365a" DOUBLE,
  "bwh" DOUBLE,
  "bwd" DOUBLE,
  "bwa" DOUBLE,
  "gbh" DOUBLE,
  "gbd" DOUBLE,
  "gba" DOUBLE,
  "iwh" DOUBLE,
  "iwd" DOUBLE,
  "iwa" DOUBLE,
  "lbh" DOUBLE,
  "lbd" DOUBLE,
  "lba" DOUBLE,
  "sbh" DOUBLE,
  "sbd" DOUBLE,
  "sba" DOUBLE,
  "whh" DOUBLE,
  "whd" DOUBLE,
  "wha" DOUBLE,
  "sjh" DOUBLE,
  "sjd" DOUBLE,
  "sja" DOUBLE,
  "vch" DOUBLE,
  "vcd" DOUBLE,
  "vca" DOUBLE,
  "bsh" DOUBLE,
  "bsd" DOUBLE,
  "bsa" DOUBLE,
  "bb1x2" BIGINT,
  "bbmxh" DOUBLE,
  "bbavh" DOUBLE,
  "bbmxd" DOUBLE,
  "bbavd" DOUBLE,
  "bbmxa" DOUBLE,
  "bbava" DOUBLE,
  "bbou" BIGINT,
  "bbmx_2_5" DOUBLE,
  "bbav_2_5" DOUBLE,
  "bbmx_2_5_994fdb" DOUBLE,
  "bbav_2_5_b3ab09" DOUBLE,
  "bbah" BIGINT,
  "bbahh" DOUBLE,
  "bbmxahh" DOUBLE,
  "bbavahh" DOUBLE,
  "bbmxaha" DOUBLE,
  "bbavaha" DOUBLE
);

N 2010

@kaggle.thedevastator_uncovering_betting_patterns_in_the_premier_leagu.n_2010
  • 81.86 KB
  • 380 rows
  • 71 columns
Loading... 

CREATE TABLE n_2010 (
  "div" VARCHAR,
  "date" VARCHAR,
  "hometeam" VARCHAR,
  "awayteam" VARCHAR,
  "fthg" BIGINT,
  "ftag" BIGINT,
  "ftr" VARCHAR,
  "hthg" BIGINT,
  "htag" BIGINT,
  "htr" VARCHAR,
  "referee" VARCHAR,
  "hs" BIGINT,
  "as" BIGINT,
  "hst" BIGINT,
  "ast" BIGINT,
  "hf" BIGINT,
  "af" BIGINT,
  "hc" BIGINT,
  "ac" BIGINT,
  "hy" BIGINT,
  "ay" BIGINT,
  "hr" BIGINT,
  "ar" BIGINT,
  "b365h" DOUBLE,
  "b365d" DOUBLE,
  "b365a" DOUBLE,
  "bwh" DOUBLE,
  "bwd" DOUBLE,
  "bwa" DOUBLE,
  "gbh" DOUBLE,
  "gbd" DOUBLE,
  "gba" DOUBLE,
  "iwh" DOUBLE,
  "iwd" DOUBLE,
  "iwa" DOUBLE,
  "lbh" DOUBLE,
  "lbd" DOUBLE,
  "lba" DOUBLE,
  "sbh" DOUBLE,
  "sbd" DOUBLE,
  "sba" DOUBLE,
  "whh" DOUBLE,
  "whd" DOUBLE,
  "wha" DOUBLE,
  "sjh" DOUBLE,
  "sjd" DOUBLE,
  "sja" DOUBLE,
  "vch" DOUBLE,
  "vcd" DOUBLE,
  "vca" DOUBLE,
  "bsh" DOUBLE,
  "bsd" DOUBLE,
  "bsa" DOUBLE,
  "bb1x2" BIGINT,
  "bbmxh" DOUBLE,
  "bbavh" DOUBLE,
  "bbmxd" DOUBLE,
  "bbavd" DOUBLE,
  "bbmxa" DOUBLE,
  "bbava" DOUBLE,
  "bbou" BIGINT,
  "bbmx_2_5" DOUBLE,
  "bbav_2_5" DOUBLE,
  "bbmx_2_5_994fdb" DOUBLE,
  "bbav_2_5_b3ab09" DOUBLE,
  "bbah" BIGINT,
  "bbahh" DOUBLE,
  "bbmxahh" DOUBLE,
  "bbavahh" DOUBLE,
  "bbmxaha" DOUBLE,
  "bbavaha" DOUBLE
);

N 2011

@kaggle.thedevastator_uncovering_betting_patterns_in_the_premier_leagu.n_2011
  • 81.43 KB
  • 380 rows
  • 71 columns
Loading... 

CREATE TABLE n_2011 (
  "div" VARCHAR,
  "date" VARCHAR,
  "hometeam" VARCHAR,
  "awayteam" VARCHAR,
  "fthg" BIGINT,
  "ftag" BIGINT,
  "ftr" VARCHAR,
  "hthg" BIGINT,
  "htag" BIGINT,
  "htr" VARCHAR,
  "referee" VARCHAR,
  "hs" BIGINT,
  "as" BIGINT,
  "hst" BIGINT,
  "ast" BIGINT,
  "hf" BIGINT,
  "af" BIGINT,
  "hc" BIGINT,
  "ac" BIGINT,
  "hy" BIGINT,
  "ay" BIGINT,
  "hr" BIGINT,
  "ar" BIGINT,
  "b365h" DOUBLE,
  "b365d" DOUBLE,
  "b365a" DOUBLE,
  "bwh" DOUBLE,
  "bwd" DOUBLE,
  "bwa" DOUBLE,
  "gbh" DOUBLE,
  "gbd" DOUBLE,
  "gba" DOUBLE,
  "iwh" DOUBLE,
  "iwd" DOUBLE,
  "iwa" DOUBLE,
  "lbh" DOUBLE,
  "lbd" DOUBLE,
  "lba" DOUBLE,
  "sbh" DOUBLE,
  "sbd" DOUBLE,
  "sba" DOUBLE,
  "whh" DOUBLE,
  "whd" DOUBLE,
  "wha" DOUBLE,
  "sjh" DOUBLE,
  "sjd" DOUBLE,
  "sja" DOUBLE,
  "vch" DOUBLE,
  "vcd" DOUBLE,
  "vca" DOUBLE,
  "bsh" DOUBLE,
  "bsd" DOUBLE,
  "bsa" DOUBLE,
  "bb1x2" BIGINT,
  "bbmxh" DOUBLE,
  "bbavh" DOUBLE,
  "bbmxd" DOUBLE,
  "bbavd" DOUBLE,
  "bbmxa" DOUBLE,
  "bbava" DOUBLE,
  "bbou" BIGINT,
  "bbmx_2_5" DOUBLE,
  "bbav_2_5" DOUBLE,
  "bbmx_2_5_994fdb" DOUBLE,
  "bbav_2_5_b3ab09" DOUBLE,
  "bbah" BIGINT,
  "bbahh" DOUBLE,
  "bbmxahh" DOUBLE,
  "bbavahh" DOUBLE,
  "bbmxaha" DOUBLE,
  "bbavaha" DOUBLE
);

N 2012

@kaggle.thedevastator_uncovering_betting_patterns_in_the_premier_leagu.n_2012
  • 89.2 KB
  • 380 rows
  • 74 columns
Loading... 

CREATE TABLE n_2012 (
  "div" VARCHAR,
  "date" VARCHAR,
  "hometeam" VARCHAR,
  "awayteam" VARCHAR,
  "fthg" BIGINT,
  "ftag" BIGINT,
  "ftr" VARCHAR,
  "hthg" BIGINT,
  "htag" BIGINT,
  "htr" VARCHAR,
  "referee" VARCHAR,
  "hs" BIGINT,
  "as" BIGINT,
  "hst" BIGINT,
  "ast" BIGINT,
  "hf" BIGINT,
  "af" BIGINT,
  "hc" BIGINT,
  "ac" BIGINT,
  "hy" BIGINT,
  "ay" BIGINT,
  "hr" BIGINT,
  "ar" BIGINT,
  "b365h" DOUBLE,
  "b365d" DOUBLE,
  "b365a" DOUBLE,
  "bwh" DOUBLE,
  "bwd" DOUBLE,
  "bwa" DOUBLE,
  "gbh" DOUBLE,
  "gbd" DOUBLE,
  "gba" DOUBLE,
  "iwh" DOUBLE,
  "iwd" DOUBLE,
  "iwa" DOUBLE,
  "lbh" DOUBLE,
  "lbd" DOUBLE,
  "lba" DOUBLE,
  "psh" DOUBLE,
  "psd" DOUBLE,
  "psa" DOUBLE,
  "whh" DOUBLE,
  "whd" DOUBLE,
  "wha" DOUBLE,
  "sjh" DOUBLE,
  "sjd" DOUBLE,
  "sja" DOUBLE,
  "vch" DOUBLE,
  "vcd" DOUBLE,
  "vca" DOUBLE,
  "bsh" DOUBLE,
  "bsd" DOUBLE,
  "bsa" DOUBLE,
  "bb1x2" BIGINT,
  "bbmxh" DOUBLE,
  "bbavh" DOUBLE,
  "bbmxd" DOUBLE,
  "bbavd" DOUBLE,
  "bbmxa" DOUBLE,
  "bbava" DOUBLE,
  "bbou" BIGINT,
  "bbmx_2_5" DOUBLE,
  "bbav_2_5" DOUBLE,
  "bbmx_2_5_994fdb" DOUBLE,
  "bbav_2_5_b3ab09" DOUBLE,
  "bbah" BIGINT,
  "bbahh" DOUBLE,
  "bbmxahh" DOUBLE,
  "bbavahh" DOUBLE,
  "bbmxaha" DOUBLE,
  "bbavaha" DOUBLE,
  "psch" DOUBLE,
  "pscd" DOUBLE,
  "psca" DOUBLE
);

N 2013

@kaggle.thedevastator_uncovering_betting_patterns_in_the_premier_leagu.n_2013
  • 83.25 KB
  • 380 rows
  • 68 columns
Loading... 

CREATE TABLE n_2013 (
  "div" VARCHAR,
  "date" VARCHAR,
  "hometeam" VARCHAR,
  "awayteam" VARCHAR,
  "fthg" BIGINT,
  "ftag" BIGINT,
  "ftr" VARCHAR,
  "hthg" BIGINT,
  "htag" BIGINT,
  "htr" VARCHAR,
  "referee" VARCHAR,
  "hs" BIGINT,
  "as" BIGINT,
  "hst" BIGINT,
  "ast" BIGINT,
  "hf" BIGINT,
  "af" BIGINT,
  "hc" BIGINT,
  "ac" BIGINT,
  "hy" BIGINT,
  "ay" BIGINT,
  "hr" BIGINT,
  "ar" BIGINT,
  "b365h" DOUBLE,
  "b365d" DOUBLE,
  "b365a" DOUBLE,
  "bwh" DOUBLE,
  "bwd" DOUBLE,
  "bwa" DOUBLE,
  "iwh" DOUBLE,
  "iwd" DOUBLE,
  "iwa" DOUBLE,
  "lbh" DOUBLE,
  "lbd" DOUBLE,
  "lba" DOUBLE,
  "psh" DOUBLE,
  "psd" DOUBLE,
  "psa" DOUBLE,
  "whh" DOUBLE,
  "whd" DOUBLE,
  "wha" DOUBLE,
  "sjh" DOUBLE,
  "sjd" DOUBLE,
  "sja" DOUBLE,
  "vch" DOUBLE,
  "vcd" DOUBLE,
  "vca" DOUBLE,
  "bb1x2" BIGINT,
  "bbmxh" DOUBLE,
  "bbavh" DOUBLE,
  "bbmxd" DOUBLE,
  "bbavd" DOUBLE,
  "bbmxa" DOUBLE,
  "bbava" DOUBLE,
  "bbou" BIGINT,
  "bbmx_2_5" DOUBLE,
  "bbav_2_5" DOUBLE,
  "bbmx_2_5_994fdb" DOUBLE,
  "bbav_2_5_b3ab09" DOUBLE,
  "bbah" DOUBLE,
  "bbahh" DOUBLE,
  "bbmxahh" DOUBLE,
  "bbavahh" DOUBLE,
  "bbmxaha" DOUBLE,
  "bbavaha" DOUBLE,
  "psch" DOUBLE,
  "pscd" DOUBLE,
  "psca" DOUBLE
);

N 2014

@kaggle.thedevastator_uncovering_betting_patterns_in_the_premier_leagu.n_2014
  • 81.08 KB
  • 380 rows
  • 68 columns
Loading... 

CREATE TABLE n_2014 (
  "div" VARCHAR,
  "date" VARCHAR,
  "hometeam" VARCHAR,
  "awayteam" VARCHAR,
  "fthg" BIGINT,
  "ftag" BIGINT,
  "ftr" VARCHAR,
  "hthg" BIGINT,
  "htag" BIGINT,
  "htr" VARCHAR,
  "referee" VARCHAR,
  "hs" BIGINT,
  "as" BIGINT,
  "hst" BIGINT,
  "ast" BIGINT,
  "hf" BIGINT,
  "af" BIGINT,
  "hc" BIGINT,
  "ac" BIGINT,
  "hy" BIGINT,
  "ay" BIGINT,
  "hr" BIGINT,
  "ar" BIGINT,
  "b365h" DOUBLE,
  "b365d" DOUBLE,
  "b365a" DOUBLE,
  "bwh" DOUBLE,
  "bwd" DOUBLE,
  "bwa" DOUBLE,
  "iwh" DOUBLE,
  "iwd" DOUBLE,
  "iwa" DOUBLE,
  "lbh" DOUBLE,
  "lbd" DOUBLE,
  "lba" DOUBLE,
  "psh" DOUBLE,
  "psd" DOUBLE,
  "psa" DOUBLE,
  "whh" DOUBLE,
  "whd" DOUBLE,
  "wha" DOUBLE,
  "sjh" DOUBLE,
  "sjd" DOUBLE,
  "sja" DOUBLE,
  "vch" DOUBLE,
  "vcd" DOUBLE,
  "vca" DOUBLE,
  "bb1x2" BIGINT,
  "bbmxh" DOUBLE,
  "bbavh" DOUBLE,
  "bbmxd" DOUBLE,
  "bbavd" DOUBLE,
  "bbmxa" DOUBLE,
  "bbava" DOUBLE,
  "bbou" BIGINT,
  "bbmx_2_5" DOUBLE,
  "bbav_2_5" DOUBLE,
  "bbmx_2_5_994fdb" DOUBLE,
  "bbav_2_5_b3ab09" DOUBLE,
  "bbah" BIGINT,
  "bbahh" DOUBLE,
  "bbmxahh" DOUBLE,
  "bbavahh" DOUBLE,
  "bbmxaha" DOUBLE,
  "bbavaha" DOUBLE,
  "psch" DOUBLE,
  "pscd" DOUBLE,
  "psca" DOUBLE
);

N 2015

@kaggle.thedevastator_uncovering_betting_patterns_in_the_premier_leagu.n_2015
  • 78.4 KB
  • 380 rows
  • 65 columns
Loading... 

CREATE TABLE n_2015 (
  "div" VARCHAR,
  "date" TIMESTAMP,
  "hometeam" VARCHAR,
  "awayteam" VARCHAR,
  "fthg" BIGINT,
  "ftag" BIGINT,
  "ftr" VARCHAR,
  "hthg" BIGINT,
  "htag" BIGINT,
  "htr" VARCHAR,
  "referee" VARCHAR,
  "hs" BIGINT,
  "as" BIGINT,
  "hst" BIGINT,
  "ast" BIGINT,
  "hf" BIGINT,
  "af" BIGINT,
  "hc" BIGINT,
  "ac" BIGINT,
  "hy" BIGINT,
  "ay" BIGINT,
  "hr" BIGINT,
  "ar" BIGINT,
  "b365h" DOUBLE,
  "b365d" DOUBLE,
  "b365a" DOUBLE,
  "bwh" DOUBLE,
  "bwd" DOUBLE,
  "bwa" DOUBLE,
  "iwh" DOUBLE,
  "iwd" DOUBLE,
  "iwa" DOUBLE,
  "lbh" DOUBLE,
  "lbd" DOUBLE,
  "lba" DOUBLE,
  "psh" DOUBLE,
  "psd" DOUBLE,
  "psa" DOUBLE,
  "whh" DOUBLE,
  "whd" DOUBLE,
  "wha" DOUBLE,
  "vch" DOUBLE,
  "vcd" DOUBLE,
  "vca" DOUBLE,
  "bb1x2" BIGINT,
  "bbmxh" DOUBLE,
  "bbavh" DOUBLE,
  "bbmxd" DOUBLE,
  "bbavd" DOUBLE,
  "bbmxa" DOUBLE,
  "bbava" DOUBLE,
  "bbou" BIGINT,
  "bbmx_2_5" DOUBLE,
  "bbav_2_5" DOUBLE,
  "bbmx_2_5_994fdb" DOUBLE,
  "bbav_2_5_b3ab09" DOUBLE,
  "bbah" BIGINT,
  "bbahh" DOUBLE,
  "bbmxahh" DOUBLE,
  "bbavahh" DOUBLE,
  "bbmxaha" DOUBLE,
  "bbavaha" DOUBLE,
  "psch" DOUBLE,
  "pscd" DOUBLE,
  "psca" DOUBLE
);

N 2016

@kaggle.thedevastator_uncovering_betting_patterns_in_the_premier_leagu.n_2016
  • 81.11 KB
  • 380 rows
  • 65 columns
Loading... 

CREATE TABLE n_2016 (
  "div" VARCHAR,
  "date" VARCHAR,
  "hometeam" VARCHAR,
  "awayteam" VARCHAR,
  "fthg" BIGINT,
  "ftag" BIGINT,
  "ftr" VARCHAR,
  "hthg" BIGINT,
  "htag" BIGINT,
  "htr" VARCHAR,
  "referee" VARCHAR,
  "hs" BIGINT,
  "as" BIGINT,
  "hst" BIGINT,
  "ast" BIGINT,
  "hf" BIGINT,
  "af" BIGINT,
  "hc" BIGINT,
  "ac" BIGINT,
  "hy" BIGINT,
  "ay" BIGINT,
  "hr" BIGINT,
  "ar" BIGINT,
  "b365h" DOUBLE,
  "b365d" DOUBLE,
  "b365a" DOUBLE,
  "bwh" DOUBLE,
  "bwd" DOUBLE,
  "bwa" DOUBLE,
  "iwh" DOUBLE,
  "iwd" DOUBLE,
  "iwa" DOUBLE,
  "lbh" DOUBLE,
  "lbd" DOUBLE,
  "lba" DOUBLE,
  "psh" DOUBLE,
  "psd" DOUBLE,
  "psa" DOUBLE,
  "whh" DOUBLE,
  "whd" DOUBLE,
  "wha" DOUBLE,
  "vch" DOUBLE,
  "vcd" DOUBLE,
  "vca" DOUBLE,
  "bb1x2" BIGINT,
  "bbmxh" DOUBLE,
  "bbavh" DOUBLE,
  "bbmxd" DOUBLE,
  "bbavd" DOUBLE,
  "bbmxa" DOUBLE,
  "bbava" DOUBLE,
  "bbou" BIGINT,
  "bbmx_2_5" DOUBLE,
  "bbav_2_5" DOUBLE,
  "bbmx_2_5_994fdb" DOUBLE,
  "bbav_2_5_b3ab09" DOUBLE,
  "bbah" BIGINT,
  "bbahh" DOUBLE,
  "bbmxahh" DOUBLE,
  "bbavahh" DOUBLE,
  "bbmxaha" DOUBLE,
  "bbavaha" DOUBLE,
  "psch" DOUBLE,
  "pscd" DOUBLE,
  "psca" DOUBLE
);

N 2017

@kaggle.thedevastator_uncovering_betting_patterns_in_the_premier_leagu.n_2017
  • 81.37 KB
  • 380 rows
  • 65 columns
Loading... 

CREATE TABLE n_2017 (
  "div" VARCHAR,
  "date" TIMESTAMP,
  "hometeam" VARCHAR,
  "awayteam" VARCHAR,
  "fthg" BIGINT,
  "ftag" BIGINT,
  "ftr" VARCHAR,
  "hthg" BIGINT,
  "htag" BIGINT,
  "htr" VARCHAR,
  "referee" VARCHAR,
  "hs" BIGINT,
  "as" BIGINT,
  "hst" BIGINT,
  "ast" BIGINT,
  "hf" BIGINT,
  "af" BIGINT,
  "hc" BIGINT,
  "ac" BIGINT,
  "hy" BIGINT,
  "ay" BIGINT,
  "hr" BIGINT,
  "ar" BIGINT,
  "b365h" DOUBLE,
  "b365d" DOUBLE,
  "b365a" DOUBLE,
  "bwh" DOUBLE,
  "bwd" DOUBLE,
  "bwa" DOUBLE,
  "iwh" DOUBLE,
  "iwd" DOUBLE,
  "iwa" DOUBLE,
  "lbh" DOUBLE,
  "lbd" DOUBLE,
  "lba" DOUBLE,
  "psh" DOUBLE,
  "psd" DOUBLE,
  "psa" DOUBLE,
  "whh" DOUBLE,
  "whd" DOUBLE,
  "wha" DOUBLE,
  "vch" DOUBLE,
  "vcd" DOUBLE,
  "vca" DOUBLE,
  "bb1x2" BIGINT,
  "bbmxh" DOUBLE,
  "bbavh" DOUBLE,
  "bbmxd" DOUBLE,
  "bbavd" DOUBLE,
  "bbmxa" DOUBLE,
  "bbava" DOUBLE,
  "bbou" BIGINT,
  "bbmx_2_5" DOUBLE,
  "bbav_2_5" DOUBLE,
  "bbmx_2_5_994fdb" DOUBLE,
  "bbav_2_5_b3ab09" DOUBLE,
  "bbah" BIGINT,
  "bbahh" DOUBLE,
  "bbmxahh" DOUBLE,
  "bbavahh" DOUBLE,
  "bbmxaha" DOUBLE,
  "bbavaha" DOUBLE,
  "psch" DOUBLE,
  "pscd" DOUBLE,
  "psca" DOUBLE
);

N 2018

@kaggle.thedevastator_uncovering_betting_patterns_in_the_premier_leagu.n_2018
  • 79.28 KB
  • 380 rows
  • 62 columns
Loading... 

CREATE TABLE n_2018 (
  "div" VARCHAR,
  "date" TIMESTAMP,
  "hometeam" VARCHAR,
  "awayteam" VARCHAR,
  "fthg" BIGINT,
  "ftag" BIGINT,
  "ftr" VARCHAR,
  "hthg" BIGINT,
  "htag" BIGINT,
  "htr" VARCHAR,
  "referee" VARCHAR,
  "hs" BIGINT,
  "as" BIGINT,
  "hst" BIGINT,
  "ast" BIGINT,
  "hf" BIGINT,
  "af" BIGINT,
  "hc" BIGINT,
  "ac" BIGINT,
  "hy" BIGINT,
  "ay" BIGINT,
  "hr" BIGINT,
  "ar" BIGINT,
  "b365h" DOUBLE,
  "b365d" DOUBLE,
  "b365a" DOUBLE,
  "bwh" DOUBLE,
  "bwd" DOUBLE,
  "bwa" DOUBLE,
  "iwh" DOUBLE,
  "iwd" DOUBLE,
  "iwa" DOUBLE,
  "psh" DOUBLE,
  "psd" DOUBLE,
  "psa" DOUBLE,
  "whh" DOUBLE,
  "whd" DOUBLE,
  "wha" DOUBLE,
  "vch" DOUBLE,
  "vcd" DOUBLE,
  "vca" DOUBLE,
  "bb1x2" BIGINT,
  "bbmxh" DOUBLE,
  "bbavh" DOUBLE,
  "bbmxd" DOUBLE,
  "bbavd" DOUBLE,
  "bbmxa" DOUBLE,
  "bbava" DOUBLE,
  "bbou" BIGINT,
  "bbmx_2_5" DOUBLE,
  "bbav_2_5" DOUBLE,
  "bbmx_2_5_994fdb" DOUBLE,
  "bbav_2_5_b3ab09" DOUBLE,
  "bbah" BIGINT,
  "bbahh" DOUBLE,
  "bbmxahh" DOUBLE,
  "bbavahh" DOUBLE,
  "bbmxaha" DOUBLE,
  "bbavaha" DOUBLE,
  "psch" DOUBLE,
  "pscd" DOUBLE,
  "psca" DOUBLE
);

N 2019

@kaggle.thedevastator_uncovering_betting_patterns_in_the_premier_leagu.n_2019
  • 140.57 KB
  • 380 rows
  • 106 columns
Loading... 

CREATE TABLE n_2019 (
  "div" VARCHAR,
  "date" TIMESTAMP,
  "time" VARCHAR,
  "hometeam" VARCHAR,
  "awayteam" VARCHAR,
  "fthg" BIGINT,
  "ftag" BIGINT,
  "ftr" VARCHAR,
  "hthg" BIGINT,
  "htag" BIGINT,
  "htr" VARCHAR,
  "referee" VARCHAR,
  "hs" BIGINT,
  "as" BIGINT,
  "hst" BIGINT,
  "ast" BIGINT,
  "hf" BIGINT,
  "af" BIGINT,
  "hc" BIGINT,
  "ac" BIGINT,
  "hy" BIGINT,
  "ay" BIGINT,
  "hr" BIGINT,
  "ar" BIGINT,
  "b365h" DOUBLE,
  "b365d" DOUBLE,
  "b365a" DOUBLE,
  "bwh" DOUBLE,
  "bwd" DOUBLE,
  "bwa" DOUBLE,
  "iwh" DOUBLE,
  "iwd" DOUBLE,
  "iwa" DOUBLE,
  "psh" DOUBLE,
  "psd" DOUBLE,
  "psa" DOUBLE,
  "whh" DOUBLE,
  "whd" DOUBLE,
  "wha" DOUBLE,
  "vch" DOUBLE,
  "vcd" DOUBLE,
  "vca" DOUBLE,
  "maxh" DOUBLE,
  "maxd" DOUBLE,
  "maxa" DOUBLE,
  "avgh" DOUBLE,
  "avgd" DOUBLE,
  "avga" DOUBLE,
  "b365_2_5" DOUBLE,
  "b365_2_5_c5d2e2" DOUBLE,
  "p_2_5" DOUBLE,
  "p_2_5_63c972" DOUBLE,
  "max_2_5" DOUBLE,
  "max_2_5_e1a24b" DOUBLE,
  "avg_2_5" DOUBLE,
  "avg_2_5_cdbfa9" DOUBLE,
  "ahh" DOUBLE,
  "b365ahh" DOUBLE,
  "b365aha" DOUBLE,
  "pahh" DOUBLE,
  "paha" DOUBLE,
  "maxahh" DOUBLE,
  "maxaha" DOUBLE,
  "avgahh" DOUBLE,
  "avgaha" DOUBLE,
  "b365ch" DOUBLE,
  "b365cd" DOUBLE,
  "b365ca" DOUBLE,
  "bwch" DOUBLE,
  "bwcd" DOUBLE,
  "bwca" DOUBLE,
  "iwch" DOUBLE,
  "iwcd" DOUBLE,
  "iwca" DOUBLE,
  "psch" DOUBLE,
  "pscd" DOUBLE,
  "psca" DOUBLE,
  "whch" DOUBLE,
  "whcd" DOUBLE,
  "whca" DOUBLE,
  "vcch" DOUBLE,
  "vccd" DOUBLE,
  "vcca" DOUBLE,
  "maxch" DOUBLE,
  "maxcd" DOUBLE,
  "maxca" DOUBLE,
  "avgch" DOUBLE,
  "avgcd" DOUBLE,
  "avgca" DOUBLE,
  "b365c_2_5" DOUBLE,
  "b365c_2_5_b914ed" DOUBLE,
  "pc_2_5" DOUBLE,
  "pc_2_5_bf2f8a" DOUBLE,
  "maxc_2_5" DOUBLE,
  "maxc_2_5_9de744" DOUBLE,
  "avgc_2_5" DOUBLE,
  "avgc_2_5_63f719" DOUBLE,
  "ahch" DOUBLE,
  "b365cahh" DOUBLE,
  "b365caha" DOUBLE
);

N 2020

@kaggle.thedevastator_uncovering_betting_patterns_in_the_premier_leagu.n_2020
  • 138.99 KB
  • 380 rows
  • 106 columns
Loading... 

CREATE TABLE n_2020 (
  "div" VARCHAR,
  "date" TIMESTAMP,
  "time" VARCHAR,
  "hometeam" VARCHAR,
  "awayteam" VARCHAR,
  "fthg" BIGINT,
  "ftag" BIGINT,
  "ftr" VARCHAR,
  "hthg" BIGINT,
  "htag" BIGINT,
  "htr" VARCHAR,
  "referee" VARCHAR,
  "hs" BIGINT,
  "as" BIGINT,
  "hst" BIGINT,
  "ast" BIGINT,
  "hf" BIGINT,
  "af" BIGINT,
  "hc" BIGINT,
  "ac" BIGINT,
  "hy" BIGINT,
  "ay" BIGINT,
  "hr" BIGINT,
  "ar" BIGINT,
  "b365h" DOUBLE,
  "b365d" DOUBLE,
  "b365a" DOUBLE,
  "bwh" DOUBLE,
  "bwd" DOUBLE,
  "bwa" DOUBLE,
  "iwh" DOUBLE,
  "iwd" DOUBLE,
  "iwa" DOUBLE,
  "psh" DOUBLE,
  "psd" DOUBLE,
  "psa" DOUBLE,
  "whh" DOUBLE,
  "whd" DOUBLE,
  "wha" DOUBLE,
  "vch" DOUBLE,
  "vcd" DOUBLE,
  "vca" DOUBLE,
  "maxh" DOUBLE,
  "maxd" DOUBLE,
  "maxa" DOUBLE,
  "avgh" DOUBLE,
  "avgd" DOUBLE,
  "avga" DOUBLE,
  "b365_2_5" DOUBLE,
  "b365_2_5_c5d2e2" DOUBLE,
  "p_2_5" DOUBLE,
  "p_2_5_63c972" DOUBLE,
  "max_2_5" DOUBLE,
  "max_2_5_e1a24b" DOUBLE,
  "avg_2_5" DOUBLE,
  "avg_2_5_cdbfa9" DOUBLE,
  "ahh" DOUBLE,
  "b365ahh" DOUBLE,
  "b365aha" DOUBLE,
  "pahh" DOUBLE,
  "paha" DOUBLE,
  "maxahh" DOUBLE,
  "maxaha" DOUBLE,
  "avgahh" DOUBLE,
  "avgaha" DOUBLE,
  "b365ch" DOUBLE,
  "b365cd" DOUBLE,
  "b365ca" DOUBLE,
  "bwch" DOUBLE,
  "bwcd" DOUBLE,
  "bwca" DOUBLE,
  "iwch" DOUBLE,
  "iwcd" DOUBLE,
  "iwca" DOUBLE,
  "psch" DOUBLE,
  "pscd" DOUBLE,
  "psca" DOUBLE,
  "whch" DOUBLE,
  "whcd" DOUBLE,
  "whca" DOUBLE,
  "vcch" DOUBLE,
  "vccd" DOUBLE,
  "vcca" DOUBLE,
  "maxch" DOUBLE,
  "maxcd" DOUBLE,
  "maxca" DOUBLE,
  "avgch" DOUBLE,
  "avgcd" DOUBLE,
  "avgca" DOUBLE,
  "b365c_2_5" DOUBLE,
  "b365c_2_5_b914ed" DOUBLE,
  "pc_2_5" DOUBLE,
  "pc_2_5_bf2f8a" DOUBLE,
  "maxc_2_5" DOUBLE,
  "maxc_2_5_9de744" DOUBLE,
  "avgc_2_5" DOUBLE,
  "avgc_2_5_63f719" DOUBLE,
  "ahch" DOUBLE,
  "b365cahh" DOUBLE,
  "b365caha" DOUBLE
);

N 2021

@kaggle.thedevastator_uncovering_betting_patterns_in_the_premier_leagu.n_2021
  • 139.97 KB
  • 380 rows
  • 106 columns
Loading... 

CREATE TABLE n_2021 (
  "div" VARCHAR,
  "date" TIMESTAMP,
  "time" VARCHAR,
  "hometeam" VARCHAR,
  "awayteam" VARCHAR,
  "fthg" BIGINT,
  "ftag" BIGINT,
  "ftr" VARCHAR,
  "hthg" BIGINT,
  "htag" BIGINT,
  "htr" VARCHAR,
  "referee" VARCHAR,
  "hs" BIGINT,
  "as" BIGINT,
  "hst" BIGINT,
  "ast" BIGINT,
  "hf" BIGINT,
  "af" BIGINT,
  "hc" BIGINT,
  "ac" BIGINT,
  "hy" BIGINT,
  "ay" BIGINT,
  "hr" BIGINT,
  "ar" BIGINT,
  "b365h" DOUBLE,
  "b365d" DOUBLE,
  "b365a" DOUBLE,
  "bwh" DOUBLE,
  "bwd" DOUBLE,
  "bwa" DOUBLE,
  "iwh" DOUBLE,
  "iwd" DOUBLE,
  "iwa" DOUBLE,
  "psh" DOUBLE,
  "psd" DOUBLE,
  "psa" DOUBLE,
  "whh" DOUBLE,
  "whd" DOUBLE,
  "wha" DOUBLE,
  "vch" DOUBLE,
  "vcd" DOUBLE,
  "vca" DOUBLE,
  "maxh" DOUBLE,
  "maxd" DOUBLE,
  "maxa" DOUBLE,
  "avgh" DOUBLE,
  "avgd" DOUBLE,
  "avga" DOUBLE,
  "b365_2_5" DOUBLE,
  "b365_2_5_c5d2e2" DOUBLE,
  "p_2_5" DOUBLE,
  "p_2_5_63c972" DOUBLE,
  "max_2_5" DOUBLE,
  "max_2_5_e1a24b" DOUBLE,
  "avg_2_5" DOUBLE,
  "avg_2_5_cdbfa9" DOUBLE,
  "ahh" DOUBLE,
  "b365ahh" DOUBLE,
  "b365aha" DOUBLE,
  "pahh" DOUBLE,
  "paha" DOUBLE,
  "maxahh" DOUBLE,
  "maxaha" DOUBLE,
  "avgahh" DOUBLE,
  "avgaha" DOUBLE,
  "b365ch" DOUBLE,
  "b365cd" DOUBLE,
  "b365ca" DOUBLE,
  "bwch" DOUBLE,
  "bwcd" DOUBLE,
  "bwca" DOUBLE,
  "iwch" DOUBLE,
  "iwcd" DOUBLE,
  "iwca" DOUBLE,
  "psch" DOUBLE,
  "pscd" DOUBLE,
  "psca" DOUBLE,
  "whch" DOUBLE,
  "whcd" DOUBLE,
  "whca" DOUBLE,
  "vcch" DOUBLE,
  "vccd" DOUBLE,
  "vcca" DOUBLE,
  "maxch" DOUBLE,
  "maxcd" DOUBLE,
  "maxca" DOUBLE,
  "avgch" DOUBLE,
  "avgcd" DOUBLE,
  "avgca" DOUBLE,
  "b365c_2_5" DOUBLE,
  "b365c_2_5_b914ed" DOUBLE,
  "pc_2_5" DOUBLE,
  "pc_2_5_bf2f8a" DOUBLE,
  "maxc_2_5" DOUBLE,
  "maxc_2_5_9de744" DOUBLE,
  "avgc_2_5" DOUBLE,
  "avgc_2_5_63f719" DOUBLE,
  "ahch" DOUBLE,
  "b365cahh" DOUBLE,
  "b365caha" DOUBLE
);

X Whole3

@kaggle.thedevastator_uncovering_betting_patterns_in_the_premier_leagu.x_whole3
  • 128.66 KB
  • 8750 rows
  • 21 columns
Loading... 

CREATE TABLE x_whole3 (
  "unnamed_0" BIGINT,
  "htgda" DOUBLE,
  "atgda" DOUBLE,
  "hm1_d" BIGINT,
  "hm1_l" BIGINT,
  "hm1_w" BIGINT,
  "am1_d" BIGINT,
  "am1_l" BIGINT,
  "am1_w" BIGINT,
  "hm2_d" BIGINT,
  "hm2_l" BIGINT,
  "hm2_w" BIGINT,
  "am2_d" BIGINT,
  "am2_l" BIGINT,
  "am2_w" BIGINT,
  "hm3_d" BIGINT,
  "hm3_l" BIGINT,
  "hm3_w" BIGINT,
  "am3_d" BIGINT,
  "am3_l" BIGINT,
  "am3_w" BIGINT
);

Y Whole3

@kaggle.thedevastator_uncovering_betting_patterns_in_the_premier_leagu.y_whole3
  • 52.36 KB
  • 8750 rows
  • 2 columns
Loading... 

CREATE TABLE y_whole3 (
  "unnamed_0" BIGINT,
  "ftr" VARCHAR
);

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