Tracking 7 upcoming fixtures. Data updated every 6 hours from official league sources.

First Goal Time (AVG) is a very popular market for football betting, and this page aims to serve as a list of highly-qualified fixtures. It will show you upcoming fixtures (and the teams playing in those fixtures) ranked by First Goal Time (AVG) occurance in their current season.

As of November 1, 2020, the Italian soccer club AC Milan scored 14 goals in the Serie A season 2020/2021. The team scored most goals during the games' second half. In particular, it scored four goals between the minutes 45 and 60 and other four goals between the last fifteen minutes of a match.

At the latest World Cup in Russia in 2022, an average of 2.69 goals per game. The highest ever goals to game ratio was during the 1954 World Cup in Switzerland, where an average of 5.38 goals were scored in each of the 26 games. This number can be attributed to some unusually high-scoring games, which included Austria 7 Switzerland 5 and Hungary 9 South Korea 0. The final between West Germany and Hungary clocked in at a below-average 3-2.

World Cup goal-festsThe 171 goals scored during the 2014 World Cup in Brazil equals the record for the most goals in a single World Cup along with France 1998. The World Cup in France was also the first tournament which included 64 teams in the main event, a vast increase on the 18 teams that took part in the very first World Cup in Uruguay in 1930.

World Cup goal kingsGermany’s Miroslav Klose holds the record for the most goals ever scored at the World Cup. The striker scored his sixteenth and record-breaking goal in Germany's 7-1 semi-final demolition of Brazil during the 2014 World Cup. His first World Cup goals came 12 years earlier at the World Cup in South Korea and Japan in 2002. He scored a hat-trick during Germany’s 8-0 victory against Saudi Arabia in the group stage and went on to score two more goals during the tournament. The top goal scorer at the 2002 World Cup was Brazil’s Ronaldo, who comes in at second on the all-time leading scorers at the World Cup.

This dataset contains detailed information on all Premier League matches played between the 2021 and 2025 seasons. It includes match dates, times, venues, results, goals scored (gf), goals against (ga), expected goals (xg), possession percentages, attendance figures, team formations, referees, and other relevant statistics. This data can be used for analysis, modeling predictions, or exploring trends in Premier League football.

Columns:

First Half Goals: Stats, Bets & Odds is a very popular market for football betting, and this page aims to serve as a list of highly-qualified fixtures. It will show you upcoming fixtures (and the teams playing in those fixtures) ranked by First Half Goals: Stats, Bets & Odds occurance in their current season.

Scraped player stats per game from Understat from 2014/2015 to 2024/2025 (still in progress) seasons.

This contains more detailed information than the dataset from https://www.kaggle.com/datasets/codytipton/understat-data, which includes the individual player stats per game for the English Premier League, La Liga, Bundesliga, Serie A, Ligue 1, and the Russian Football Premier League. In particular, it contains each player's xG, xGBuildup, goals, and shots per game. Furthermore, it has the events for each shot in the events table, clubs and their stats per season in the clubs table, and each game with who lost, won, shots, possession, probabilities of who wins, ect..

This is for educational purposes in our data science bootcamp project.

lineup_stats

• match_id: the id for the match they played
• goals: number of goals for this match
• own_goals: number of own goals for this match
• shots: number of shots for this match
• xG: players xG for this match
• **time*: total amount of time this player played in this match
• player_id: player id
• team_id: id for the players team
• position: players position in this match (SUB means they were substituted in)
• player: player's name
• h_a: 'h' if they are in the home team and 'a' if they are in the away team
• yellow_card: number of yellow cards for this match
• red_card: number of red cards for this match
• **roster_in*: (there is roster information in another table that I did not get, will update later)
• roster_out: (same as roster_in)
• key_passes: number of key passes for this match
• assists: number of assists for this match
• xA: expected assists for this match
• xGChain: total xG for every possession the player is involved in this match
• xGBuildup: Total xG for every possession the player is involved in without key passes and shots in this match
• positionOrder: ordering in the lineup

general_game_stats

• id: this game id
• fid: not sure what this is
• h_id: home team id
• a_id: away team id
• date: date of this game
• league_id: id for the league
• season: which season which game was for
• h_goals: number of goals for the home team
• a_goals: number of goals for the away team
• team_h: home team name
• team_a: away team name
• h_xg: home xG
• a_xg: away xG
• h_w: home win probability
• h_d: home draw probability
• h_l: home loss probability
• league: league name
• h_shot: number of shots by the home team
• a_shot: number of shots by the away team
• h_shotOnTarget: number of shots on target by the home team
• a_shotOnTarget: number of shots on target by the away team
• h_deep:home team passes completed within an estimated 20 yards of goal (crosses excluded) -deap_allowed: opponent passes completed within an estimated 20 yards of goal (crosses excluded)
• a_deep: away team passes completed within an estimated 20 yards of goal (crosses excluded) -deap_allowed: opponent passes completed within an estimated 20 yards of goal (crosses excluded)
• h_ppda: home team passes allowed per defensive action in the opposition half.
• a_ppda:away team passes allowed per defensive action in the opposition half.

game_events

• id: id for event
• minute: minute the event happend
• result: result (blocked shot, saved shot, ect..)
• X: x-coordinate where the player took the shot
• Y: y-coordinate where the player took the shot
• xG: the xG for the shot
• player: player's name
• h_a: h for home team or a for away team
• player_id: player's id
• situation: situation where this shot happend (direct free kicks, set piece, open play, ect..)
• season: the match season
• shotType: what type of shot (left foot, right foot, head, ect..)
• ** match_id**: id for the match
• h_team: home team name
• ** a_team**: away team name
• ** h_goals**: number of home goals at this time
• ** a_goals**: number of away goals at this time
• date: date of the match
• ** player_assisted**: player who assisted
• lastAction: the last action before this shot

clubs

• club_id: id for the club
• ** club**: club name
• ** league_id** : league id
• ** league**: league name
• ** season**: which season these stats are from
• ** wins**: number of wins that season
• ** draws**: number of draws that season
• ** losses**: number of losses that season
• ** pts**: number of points for that season
• ** avg_xG**: average xG throughout the season
• ** total_goals**: total amount of goals for this season
• total_goals_cond: total amount of goals conceded this season

As of October 31, 2020, the Italian soccer club FC Internazionale Milano scored 15 goals in the Serie A season 2020/2021. The team scored most goals during the game's second half. In particular, it scored four goals between the last fifteen minutes of a match.

Liga Indo Football Season

Teams, Results, and Statistics

By Irnadia Fardila [source]

About this dataset

This dataset contains information on Liga Indo football matches. It includes such data as the teams playing, the date and time of the match, and the half-time and full-time scores

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset can be used to study the 2017 Liga Indo football season. It can be used to analyze team performance, results, and statistics

Research Ideas

• Sports betting
• Predicting the outcome of future matches
• Analyzing team and player performance over time

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: liga_indo_2017.csv | Column name | Description | |:--------------|:-------------------------------------------| | SEASON | The season of the match. (String) | | DATE_TIME | The date and time of the match. (String) | | TEAM_HOME | The home team. (String) | | TEAM_AWAY | The away team. (String) | | FTG_HOME | The home team's full time goals. (Integer) | | FTG_AWAY | The away team's full time goals. (Integer) | | HTG_HOME | The home team's half time goals. (Integer) | | HTG_AWAY | The away team's half time goals. (Integer) |

File: liga_indo_2019.csv | Column name | Description | |:--------------|:-------------------------------------------| | SEASON | The season of the match. (String) | | DATE_TIME | The date and time of the match. (String) | | TEAM_HOME | The home team. (String) | | TEAM_AWAY | The away team. (String) | | FTG_HOME | The home team's full time goals. (Integer) | | FTG_AWAY | The away team's full time goals. (Integer) | | HTG_HOME | The home team's half time goals. (Integer) | | HTG_AWAY | The away team's half time goals. (Integer) |

File: liga_indo_2018.csv | Column name | Description | |:--------------|:-------------------------------------------| | SEASON | The season of the match. (String) | | DATE_TIME | The date and time of the match. (String) | | TEAM_HOME | The home team. (String) | | TEAM_AWAY | The away team. (String) | | FTG_HOME | The home team's full time goals. (Integer) | | FTG_AWAY | The away team's full time goals. (Integer) | | HTG_HOME | The home team's half time goals. (Integer) | | HTG_AWAY | The away team's half time goals. (Integer) |

File: liga_indo_2021_2022.csv | Column name | Description | |:--------------|:-------------------------------------------| | SEASON | The season of the match. (String) | | DATE_TIME | The date and time of the match. (String) | | TEAM_HOME | The home team. (String) | | TEAM_AWAY | The away team. (String) | | FTG_HOME | The home team's full time goals. (Integer) | | FTG_AWAY | The away team's full time goals. (Integer) | | HTG_HOME | The home team's half time goals. (Integer) | | HTG_AWAY | The away team's half time goals. (Integer) |

Acknowledgements

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

Tracking 7 upcoming fixtures. Data updated every 6 hours from official league sources.

This dataset describes all the matches made available. Each match is a document consisting of the following fields:- competitionId: the identifier of the competition to which the match belongs to. It is a integer and refers to the field "wyId" of the competition document;- date and dateutc: the former specifies date and time when the match starts in explicit format (e.g., May 20, 2018 at 8:45:00 PM GMT+2), the latter contains the same information but in the compact format YYYY-MM-DD hh:mm:ss; - duration: the duration of the match. It can be "Regular" (matches of regular duration of 90 minutes + stoppage time), "ExtraTime" (matches with supplementary times, as it may happen for matches in continental or international competitions), or "Penalities" (matches which end at penalty kicks, as it may happen for continental or international competitions);- gameweek: the week of the league, starting from the beginning of the league;- label: contains the name of the two clubs and the result of the match (e.g., "Lazio - Internazionale, 2 - 3");- roundID: indicates the match-day of the competition to which the match belongs to. During a competition for soccer clubs, each of the participating clubs plays against each of the other clubs twice, once at home and once away. The matches are organized in match-days: all the matches in match-day i are played before the matches in match-day i + 1, even tough some matches can be anticipated or postponed to facilitate players and clubs participating in Continental or Intercontinental competitions. During a competition for national teams, the "roundID" indicates the stage of the competition (eliminatory round, round of 16, quarter finals, semifinals, final);- seasonId: indicates the season of the match;- status: it can be "Played" (the match has officially finished), "Cancelled" (the match has been canceled for some reason), "Postponed" (the match has been postponed and no new date and time is available yet) or "Suspended" (the match has been suspended and no new date and time is available yet);- venue: the stadium where the match was held (e.g., "Stadio Olimpico");- winner: the identifier of the team which won the game, or 0 if the match ended with a draw;- wyId: the identifier of the match, assigned by Wyscout;- teamsData: it contains several subfields describing information about each team that is playing that match: such as lineup, bench composition, list of substitutions, coach and scores: - hasFormation: it has value 0 if no formation (lineups and benches) is present, and 1 otherwise; - score: the number of goals scored by the team during the match (not counting penalties); - scoreET: the number of goals scored by the team during the match, including the extra time (not counting penalties); - scoreHT: the number of goals scored by the team during the first half of the match; - scoreP: the total number of goals scored by the team after the penalties; - side: the team side in the match (it can be "home" or "away"); - teamId: the identifier of the team; - coachId: the identifier of the team's coach; - bench: the list of the team's players that started the match in the bench and some basic statistics about their performance during the match (goals, own goals, cards); - lineup: the list of the team's players in the starting lineup and some basic statistics about their performance during the match (goals, own goals, cards); - substitutions: the list of team's substitutions during the match, describing the players involved and the minute of the substitution.

FIFA World Cup All-Time Team Standings Dataset Description This dataset contains detailed information on the performance of national football teams in the FIFA World Cup. The data is extracted from Wikipedia and covers various statistics such as matches played, wins, draws, losses, goals scored, goals conceded, goal difference, and total points.

Columns: Rank: The position of the team based on their overall performance in the FIFA World Cup. Team: The name of the national football team. Part: The number of times the team has participated in the FIFA World Cup. Pld: The number of matches played by the team. W: The number of matches won by the team. D: The number of matches drawn by the team. L: The number of matches lost by the team. GF: The number of goals scored by the team. GA: The number of goals conceded by the team. GD: The goal difference, calculated as goals scored minus goals conceded. Pts: The total points accumulated by the team. Data Source: The data was scraped from Wikipedia, ensuring accurate and up-to-date information on FIFA World Cup team standings.

Important Note: Please note that any occurrences of "−5" in the original data have been corrected to "-5" to accurately represent negative values. This correction has been made to ensure clarity and correctness in the dataset.

Usage: This dataset can be used for various analytical purposes, such as:

Analyzing the performance trends of different national teams over time. Predicting future performance based on historical data. Visualizing statistics to understand team strengths and weaknesses. Feel free to use this dataset for your analysis, and if you find it helpful, please consider upvoting or sharing your findings!

Half with Most Goals / Highest Scoring Half (2nd) is a very popular market for football betting, and this page aims to serve as a list of highly-qualified fixtures. It will show you upcoming fixtures (and the teams playing in those fixtures) ranked by Half with Most Goals / Highest Scoring Half (2nd) occurance in their current season.

A total of 172 goals were scored during the 2022 World Cup in Qatar, marking a new record for the tournament. This was three more goals than the previous tournament in 2018.

Goals galore The highest ever goals to game ratio was set during the 1954 World Cup in Switzerland, where an average of 5.38 goals were scored in each of the 26 games. Some of the highest scoring games during this tournament included Austria 7-5 Switzerland and Hungary 9-0 South Korea. The honor of the World Cup's all time top goal scorer belongs to Germany's Miroslav Klose. The iconic striker scored a total of 16 goals across four World Cups, with his 16th and record-breaking goal coming in Germany's semi-final demolition of Brazil in 2014.

Goal droughts At the other end of the scale, the 1990 World Cup had the fewest average goals per match, with the ball hitting the back of the net 115 times in 52 matches, or around 2.21 goals per game. The fewest goals scored in a single tournament stands at 70, which occurred at the first two World Cups in 1930 and 1934. This can be explained by the fact that only 13 and 16 teams respectively took part in the finals, and so fewer games were played overall.

As of October 31, 2020, the Italian soccer club FC Internazionale Milano conceded 10 goals in the Serie A season 2020/2021. The team conceded most goals during the game's second half. In particular, it conceded three goals between the minutes 45 and 60 of a match.

Data: Summary statistics.

Context

I wanted to create a unique dataset which had not been made before. I was thinking about it for many days as to which data I should work upon because anything I was thinking was already there so I came up with this FIFA VAR dataset. I just wanted to keep it one hundred. VAR arrived in the Premier League in the season 19-20 and caused much controversy, with a total of 109 goals or incidents directly affected by the video ref.

What will the VAR review? - Goal/no goal - Penalty/no penalty - Direct red card (not second yellow card/caution) - Mistaken identity (when the referee cautions or sends off the wrong player)

What will it not review? - Any yellow card (including second yellow card leading to red) - Any free kick offence outside the box (other than red card offence)

Content

There are in all 2 files: 1. VAR_Incidents_Stats: This file contains the information about the various incidents that led to the inclusion of VAR in a football match. It contains 7 columns namely: - Team- Name of the team for which VAR was applied - Opponent Team - Name of the Opponent team for which VAR was applied - Date- The date when the match between team and opponent team was played - Site- Match was played Home(H) or Away(A) with respect to the Team - Incident- Description of the incident that led to the usage of VAR facility - Time- Time during the match when VAR was used or the incident happened - VAR used - VAR was used FOR or AGAINST with respect to the Team

1. VAR_Team_Stats: This file contains the statistics as to how VAR was used for a Team. It contains 10 columns namely:
2. Team - Name of the team for which VAR was applied
3. Overturns- How many times was the on field referees decision was overturned by the VAR
4. Leading to goals for - VAR Decisions that Lead to goal for the team
5. Disallowed goals for - VAR Decisions that Disallowed goals for the team
6. Leading to goals against - VAR Decisions that Leading to goals against the team
7. Disallowed goals against - VAR Decisions that Disallowed goals against the team
8. Net goal score - Total FOR and AGAINST VAR Decisions that led to a Goal
9. Subjective decisions for - VAR Subjective decisions for the team
10. Subjective decisions against - VAR Subjective decisions against the team
11. Net subjective score - Total FOR and AGAINST VAR Subjective Decisions

Acknowledgements

Data has been scraped from https://www.espn.in/football/english-premier-league/story/3929823/how-var-decisions-have-affected-every-premier-league-club.

Inspiration

• Has VAR been a good edition to the Premier League or Football in General?
• Has VAR been biased to any specific team?
• Is VAR improving with its decisions overtime?
• The name of the player on whom VAR has been used the most(FOR or AGAINST)?
• VAR has been used the most for which specific reason(Read cards, penalties, etc)?
• VAR has been used more on defenders, forwards, goalkeepers..?

Here are a few use cases for this project:

1. Real-time match analysis: The "Football" model can be used to provide real-time insights and statistics about the ongoing match, such as ball possession percentages, player movements, goal attempts, successful corner kicks, and identification of goalkeepers making crucial saves.

2. Automated highlight generation: By identifying critical events like goals, corners, and exceptional goalkeeper saves, the model can automatically create highlight reels of important moments in a football match, saving content creators and broadcasters significant editing effort.

3. Performance analytics for teams and coaching staff: The model can be used to analyze and quantify individual player performance and team dynamics during a match, providing valuable insights for coaching staff to optimize strategies, identify strengths and weaknesses, and enhance team performance.

4. Enhanced fan engagement: With its ability to identify various elements of a football match, the model can be used to develop interactive applications and augmented reality solutions that engage fans and provide them with additional information, such as player statistics, goal breakdowns, or immersive replays of key events.

5. Referee decision support: The model can be integrated into a decision support system for referees, assisting with offside calls or other contentious decisions by providing accurate information about the positions of the ball, players, and goalkeepers during critical moments.

This is the raw data set used to examine how setting goals for specific behaviors moderates the association between health coaching and change in health behavior over a 12 week period. This data set includes all data, including missing and incomplete data, for baseline data time point and for three follow up timepoints.