Context

This dataset contains 2021-2022 football player stats per 90 minutes. Only players of Premier League, Ligue 1, Bundesliga, Serie A and La Liga are listed.

• 2022-2023 Football Player Stats
• 2021-2022 Football Team Stats
• 2022-2023 Football Team Stats

Content

+2500 rows and 143 columns. Columns' description are listed below.

• Rk : Rank
• Player : Player's name
• Nation : Player's nation
• Pos : Position
• Squad : Squad’s name
• Comp : League that squat occupies
• Age : Player's age
• Born : Year of birth
• MP : Matches played
• Starts : Matches started
• Min : Minutes played
• 90s : Minutes played divided by 90
• Goals : Goals scored or allowed
• Shots : Shots total (Does not include penalty kicks)
• SoT : Shots on target (Does not include penalty kicks)
• SoT% : Shots on target percentage (Does not include penalty kicks)
• G/Sh : Goals per shot
• G/SoT : Goals per shot on target (Does not include penalty kicks)
• ShoDist : Average distance, in yards, from goal of all shots taken (Does not include penalty kicks)
• ShoFK : Shots from free kicks
• ShoPK : Penalty kicks made
• PKatt : Penalty kicks attempted
• PasTotCmp : Passes completed
• PasTotAtt : Passes attempted
• PasTotCmp% : Pass completion percentage
• PasTotDist : Total distance, in yards, that completed passes have traveled in any direction
• PasTotPrgDist : Total distance, in yards, that completed passes have traveled towards the opponent's goal
• PasShoCmp : Passes completed (Passes between 5 and 15 yards)
• PasShoAtt : Passes attempted (Passes between 5 and 15 yards)
• PasShoCmp% : Pass completion percentage (Passes between 5 and 15 yards)
• PasMedCmp : Passes completed (Passes between 15 and 30 yards)
• PasMedAtt : Passes attempted (Passes between 15 and 30 yards)
• PasMedCmp% : Pass completion percentage (Passes between 15 and 30 yards)
• PasLonCmp : Passes completed (Passes longer than 30 yards)
• PasLonAtt : Passes attempted (Passes longer than 30 yards)
• PasLonCmp% : Pass completion percentage (Passes longer than 30 yards)
• Assists : Assists
• PasAss : Passes that directly lead to a shot (assisted shots)
• Pas3rd : Completed passes that enter the 1/3 of the pitch closest to the goal
• PPA : Completed passes into the 18-yard box
• CrsPA : Completed crosses into the 18-yard box
• PasProg : Completed passes that move the ball towards the opponent's goal at least 10 yards from its furthest point in the last six passes, or any completed pass into the penalty area
• PasAtt : Passes attempted
• PasLive : Live-ball passes
• PasDead : Dead-ball passes
• PasFK : Passes attempted from free kicks
• TB : Completed pass sent between back defenders into open space
• PasPress : Passes made while under pressure from opponent
• Sw : Passes that travel more than 40 yards of the width of the pitch
• PasCrs : Crosses
• CK : Corner kicks
• CkIn : Inswinging corner kicks
• CkOut : Outswinging corner kicks
• CkStr : Straight corner kicks
• PasGround : Ground passes
• PasLow : Passes that leave the ground, but stay below shoulder-level
• PasHigh : Passes that are above shoulder-level at the peak height
• PaswLeft : Passes attempted using left foot
• PaswRight : Passes attempted using right foot
• PaswHead : Passes attempted using head
• TI : Throw-Ins taken
• PaswOther : Passes attempted using body parts other than the player's head or feet
• PasCmp : Passes completed
• PasOff : Offsides
• PasOut : Out of bounds
• PasInt : Intercepted
• PasBlocks : Blocked by the opponent who was standing it the path
• SCA : Shot-creating actions
• ScaPassLive : Completed live-ball passes that lead to a shot attempt
• ScaPassDead : Completed dead-ball passes that lead to a shot attempt
• ScaDrib : Successful dribbles that lead to a shot attempt
• ScaSh : Shots that lead to another shot attempt
• ScaFld : Fouls drawn that lead to a shot attempt
• ScaDef : Defensive actions that lead to a shot attempt
• GCA : Goal-creating actions
• GcaPassLive : Completed live-ball passes that lead to a goal
• GcaPassDead : Completed dead-ball passes that lead to a goal
• GcaDrib : Successful dribbles that lead to a goal
• GcaSh : Shots that lead to another goal-scoring shot
• GcaFld : Fouls drawn that lead to a goal
• GcaDef : Defensive actions that lead to a goal
• Tkl : Number of players tackled
• TklWon : Tackles in which the tackler's team won possession of the ball
• TklDef3rd : Tackles in defensive 1/3
• TklMid3rd : Tackles in middle 1/3
• TklAtt3rd : Tackles in attacking 1/3
• TklDri : Number of dribblers tackled
• TklDriAtt : Number of times dribbled past plus number of tackles
• TklDri% : Percentage of dribblers tackled
• TklDriPast : Number of t...

Premier League 2023/24: Match, Player, and Team Performance Insights

Dataset Description

This dataset offers an in-depth analysis of the 2023/24 Premier League season, capturing comprehensive data on team and player performances across all matchdays. With over 50 individual CSV files, this collection includes stats on passing accuracy, goal-scoring, defensive actions, possession metrics, and player ratings. Whether you're looking to analyze top scorers, assess team strengths, or delve into individual player contributions, this dataset provides a rich foundation for football analytics enthusiasts and professionals alike.

In addition to the core dataset, we have now added more files related to the league table, expanding the dataset with essential information on match outcomes, league standings, and advanced metrics.

Contents

The dataset contains the following types of data:

• Team Performance Metrics: Information on accurate passes, crosses, goals conceded, interceptions, and other team stats.
• Player Performance Metrics: Individual stats including expected goals (xG), assists, clearances, fouls committed, and tackles won.
• Match-Specific Insights: Detailed metrics on goals scored, scoring attempts, possession percentages, and cards issued per match.
• Match Details (New): Information about rounds, match IDs, teams, scores, and match statuses.
• League Tables (New):
  • Overall standings including matches played, wins, draws, losses, goals scored, goal differences, and points.
  • Separate breakdowns for home and away performances.
  • Advanced metrics including expected goals (xG), expected goals conceded, and expected points.

The file details provide an overview of each dataset, including a brief description of the data structure and potential uses for analysis. This helps users quickly navigate and understand the data available for analysis.

This dataset is ideal for statistical analysis, data visualization, and machine learning applications to uncover patterns in football performance.

Suggested Analysis

This dataset opens up multiple avenues for data analysis and visualization. Here are some ideas:

1. Team Performance Analysis

• Analyze team performance trends, such as comparing passing accuracy, possession, and expected goals (xG) across teams.
• Visualize which teams generate the most scoring opportunities and miss the most big chances.
• Identify the strongest and weakest defenses based on goals conceded, clean sheets, and clearances.

2. Player Performance Analysis

• Identify top-performing players by goals scored, assists, expected goals, and expected assists.
• Explore defensive contributions by analyzing tackles won, interceptions, and clearances per player.
• Assess attacking efficiency by comparing total attempts vs. on-target attempts for each player.

3. Goalkeeping and Defensive Analysis

• Compare goalkeepers on metrics like saves made, goals conceded, and clean sheets to highlight the top performers of the season.
• Evaluate defensive strength by analyzing interception rates and clearances by both teams and players.

4. League Table Insights (New)

• Analyze overall league standings to determine team performance trends.
• Explore home and away performance and identify strengths and weaknesses in different scenarios.
• Utilize advanced metrics to evaluate under- and overperforming teams.

5. Advanced Metrics Exploration

• Examine possession-based metrics, such as possession percentage and possessions won in the attacking third, to identify possession-dominant teams.
• Use expected goals and expected assists data to build profiles highlighting efficient playmaking and finishing among players and teams.

This dataset is a valuable resource for football enthusiasts, data scientists, and analysts interested in uncovering patterns, building predictive models, or generating insights into the Premier League 2023/24 season.

License and Disclaimer

License

This dataset is shared for non-commercial, educational, and personal analysis purposes only. It is not intended for redistribution, commercial use, or integration into other public datasets.

Disclaimer

This dataset was sourced from FotMob, a proprietary provider of football statistics. All rights to the original data belong to FotMob. The dataset is a restructured collection of publicly available data and does not claim ownership over FotMob's data. Users should reference FotMob as the original source when using this dataset for research or analysis.

Terms of Use

By using this dataset, you agree to the following: - Non-commercial Use: This dataset is only for educational, analytical, and personal use. It may not be used for commercial purposes or integrated into other public datasets. - **Proper Attri...

This dataset contains data for last 10 seasons of Italian Serie A including current season. The data is updated on weekly basis via Travis-CI. The dataset is sourced from http://www.football-data.co.u...

Royal Excel Mouscron undefined Get comprehensive football data including match results, statistics, team information, player details, league standings, and real-time updates from professional football leagues worldwide.

This is the Excel file for the PhD study of Jack Brimmell entitled: A longitudinal examination of executive function, visual attention, and soccer penalty performance.

Yearwise .csv's with [Date,HomeTeam,AwayTeam,FTR] as columns from 1993 to 2024

although the all columns can be downloaded by removing the argument usecols=['Date','HomeTeam','AwayTeam','FTR']) from the code in extraction.ipynb

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F17313823%2Fc364dd6637e754a9705980049af1fc50%2FBundesliga.png?generation=1709996972725452&alt=media" alt="">

Data Files: Germany Last updated: 03/03/24

the below dataset is extracted from the football-data.co.uk by me

Registering with any of the advertised bookmakers on Football-Data will help keep access to the historical results & betting odds data files FREE.

Below you will find download links to all available CSV data files to use for quantitative testing of betting systems in spreadsheet applications like Excel. League tables, head2head statistics and information on goalscrores, first scorers and top scorers can now be accessed through the Livescore service. Latest betting odds are available through the Odds Comparison.

You are free experiment with the data yourselves, but if you are looking for a bespoke Excel application that has been desinged specifically to work with Football-Data's files, visit BetGPS for an exceptional data analysis workbook. Like all of Football-Data's files, it free to download. Notes.txt (text file key to the data files and data source acknowledgements)

Contact Football-Data.co.uk if you believe there are any errors in the data files.

Notes for Football Data

All data is in csv format, ready for use within standard spreadsheet applications. Please note that some abbreviations are no longer in use (in particular odds from specific bookmakers no longer used) and refer to data collected in earlier seasons. For a current list of what bookmakers are included in the dataset please visit http://www.football-data.co.uk/matches.php

Key to results data:

Div = League Division Date = Match Date (dd/mm/yy) Time = Time of match kick off HomeTeam = Home Team AwayTeam = Away Team FTHG and HG = Full Time Home Team Goals FTAG and AG = Full Time Away Team Goals FTR and Res = Full Time Result (H=Home Win, D=Draw, A=Away Win) HTHG = Half Time Home Team Goals HTAG = Half Time Away Team Goals HTR = Half Time Result (H=Home Win, D=Draw, A=Away Win)

Match Statistics (where available) Attendance = Crowd Attendance Referee = Match Referee HS = Home Team Shots AS = Away Team Shots HST = Home Team Shots on Target AST = Away Team Shots on Target HHW = Home Team Hit Woodwork AHW = Away Team Hit Woodwork HC = Home Team Corners AC = Away Team Corners HF = Home Team Fouls Committed AF = Away Team Fouls Committed HFKC = Home Team Free Kicks Conceded AFKC = Away Team Free Kicks Conceded HO = Home Team Offsides AO = Away Team Offsides HY = Home Team Yellow Cards AY = Away Team Yellow Cards HR = Home Team Red Cards AR = Away Team Red Cards HBP = Home Team Bookings Points (10 = yellow, 25 = red) ABP = Away Team Bookings Points (10 = yellow, 25 = red)

Note that Free Kicks Conceeded includes fouls, offsides and any other offense commmitted and will always be equal to or higher than the number of fouls. Fouls make up the vast majority of Free Kicks Conceded. Free Kicks Conceded are shown when specific data on Fouls are not available (France 2nd, Belgium 1st and Greece 1st divisions).

Note also that English and Scottish yellow cards do not include the initial yellow card when a second is shown to a player converting it into a red, but this is included as a yellow (plus red) for European games.

Key to 1X2 (match) betting odds data:

B365H = Bet365 home win odds B365D = Bet365 draw odds B365A = Bet365 away win odds BSH = Blue Square home win odds BSD = Blue Square draw odds BSA = Blue Square away win odds BWH = Bet&Win home win odds BWD = Bet&Win draw odds BWA = Bet&Win away win odds GBH = Gamebookers home win odds GBD = Gamebookers draw odds GBA = Gamebookers away win odds IWH = Interwetten home win odds IWD = Interwetten draw odds IWA = Interwetten away win odds LBH = Ladbrokes home win odds LBD = Ladbrokes draw odds LBA = Ladbrokes away win odds PSH and PH = Pinnacle home win odds PSD and PD = Pinnacle draw odds PSA and PA = Pinnacle away win odds SOH = Sporting Odds home win odds SOD = Sporting Odds draw odds SOA = Sporting Odds away win odds SBH = Sportingbet home win odds SBD = Sportingbet draw odds SBA = Sportingbet away win odds SJH = Stan James home win odds SJD = Stan James draw odds SJA = Stan James away win odds SYH = Stanleybet home win odds SYD = St...

Royal Excel Mouscron undefined Get comprehensive football data including match results, statistics, team information, player details, league standings, and real-time updates from professional football leagues worldwide.

This data set provides data related to measuring consumer behavior in the context of sports marketing among football fans in the Indonesia Premier League. The survey was conducted online using a Google form with a Likert scale. Questions in the questionnaire include marketing variables represented by brand commitment (12 questions), brand trust (4 questions), brand satisfaction (8 questions), brand loyalty (3 questions), and brand attachment (4 questions). The survey was conducted in June–September 2022. A total of 258 football fans across Indonesia were selected using non-probability sampling techniques. Survey data is analyzed using structural equation modeling (SEM) using Smart PLS software to identify estimates of primary construction relationships in the data. The data can help football club managers and business operators in the field of football sports map and plan marketing strategies for organizational development and gain valuable economic benefits. There are three attachments: 1. Analysis of Smart PLS data (this data shows the results of data analysis in the Smart-PLS output format that is exported to Microsoft Excel) 2. Questionnaire: "Sports Marketing in Indonesia: Football Fans" (This data contains the distribution of questionnaire questions to respondents in Microsoft Excel.) 3. Data in Brief: Sports Marketing in Indonesia Soccer Fans_revision This data contains the results of the questionnaire's completion by respondents. Authors replace province-based clusters to facilitate data encoding and reading and avoid multiple interpretations of domicile location in homepage data. The research data was collected using an online survey questionnaire, using a likerts scale of 1-5 accessible through https://forms.gle/Ask9YzAnhKx6yy9. WhatsApp was used to distribute questionnaires to respondents because it is the 3rd largest WhatsApp user in the world [2] with the largest number of football fans reaching 69% [1], as well as considering the effectiveness of research coverage where the Indonesian region consists of diversity. The questions in the questionnaire use Indonesian to facilitate the understanding of respondents in filling out the questionnaire. The English questionnaire is provided as an additional file. The total sample in the study amounted to 258 respondents from various club fans who had their membership status verified by the club's fan leader chairman. Researchers designed survey instruments using research designs based on previous research [1]. Part A of the survey asks about the sociodemographic profile of respondents, including name (optional), gender, occupation, and place of residence. Meanwhile, part B contains questions to measure consumer behavior variables namely commitment, trust, satisfaction, loyalty, and attachment in the context of sports marketing. as shown in Table 1.

This is an original document that utilizes the PMC model for analysis to evaluate football policy reforms. It mainly includes the original texts of ten representative policy documents, basic information on Excel data analysis, results of word frequency charts (showing the top 10 items), and a sample of the expert questionnaire (appendix). Given that the experts' scoring information is recorded in handwritten forms and to ensure the anonymity and privacy of the experts, the original expert scoring sheets will not be uploaded.

ABSTRACT Introduction Sports injuries in soccer are hardly avoided due to the characteristics of battles, such as intense conflict and high-level competitiveness related to soccer. Objective Investigate the most common sports injuries in professional soccer players. Methods A questionnaire survey was carried out with 365 valid returns, including 198 male and 177 female professional soccer players. Data were collected and distributed using Excel software. Results Among sports injuries in professional soccer athletes, minor injuries are more frequent, and the lower limbs are the most affected. The subjective cause of these injuries is mainly overwork. Among the objective causes, many injuries caused by the sports characteristics of soccer are inevitable, having a strong connection with the intrinsic factors of the sport. Treating injuries combines traditional Chinese medicine with the advantages of Western medicine. Conclusion It is recommended that athletes focus constantly on their injuries while playing the sport. Coaches should verify the safety of the athletes, taking precautions to reduce injuries as much as possible and improve the athlete’s competitive level, prolonging his professional activity. Level of evidence II; Therapeutic studies - investigation of treatment outcomes.

ABSTRACT Objectives: To analyze the results of pre-participation tests applied to soccer players from a professional club, aiming to compare the cardiographic findings with the literature and encourage the development of new strategies for the prevention of sudden death. Methods: We used a sample group of 110 male soccer players. Stages of the study: 1) collection of data from the pre-participation tests (cardiac history, electrocardiogram, exercise test and echocardiogram) using a form covering three years (2015 to 2017); 2) tabulation of data using Word and Excel Office 2010 software; 3) comparison with the literature. Results: Of the athletes studied, 55.5% had sinus bradycardia and 14.5% had ventricular repolarization abnormalities, 33.3% showed evidence of minimal tricuspid regurgitation, and 45.7% had physiological pulmonary regurgitation. The echocardiogram presented some interesting data when compared to the adult non-athlete population. In the ergometric test, 53.6% of the athletes reached the maximum stage and 46.4% discontinued the test due to physical fatigue. Regarding arrhythmias, in 21.8% of the patients we observed rare isolated ventricular extrasystoles and in 8.2% rare isolated supraventricular extrasystoles. Conclusion: The findings corroborate data from the literature on exercise and sports cardiology, since they mainly represent physiological adaptations of the athlete's heart. The sports physician is responsible for monitoring athletes to prevent sudden death. Level of Evidence II; Retrospective study.

Data and do-file are provided to replicate the analysis about Johan Cruyff and his contribution to success and stadium attendance at Feyenoord. The paper is published as a Tinbergen Institute Discussion Paper and (in shorter version) in Applied Economics Letters. The excel-file provides a description of the data.DOI for Applied Economics Letters paper: https://doi.org/10.1080/13504851.2021.1967275

Data from: https://www.kaggle.com/kriegsmaschine/soccer-players-values-and-their-statistics Changes: Transformed from .csv to .xlsx Modified certain values: Values replaced in excel using search and replace function (transfermarkt database 1718 and 1819): é -- é ó -- ó á -- á ë -- ë ú -- ú ï -- ï ô -- ô ü -- ü Ö -- Ö Ã -- í í± -- ñ í¶ -- ö í² -- ò í‰ -- É í“ -- Ó È™ -- ș í® -- î ć -- ć í£ -- ã í‘ -- Ñ í§ -- ç Values replaced in excel using search and replace function (transfermarkt database 1920): Ăş -- ú Ă© -- é ĂŻ -- ï à -- í ÄŤ -- č í§ -- ç š -- š íˇ -- á í˛ -- ò í« -- ë í‰ -- É í¶ -- ö í– -- Ö í§ -- ç í± -- ñ íł -- ó í‘ -- Ñ í“ -- Ó í´ -- ô ć -- ć ü -- ü í“ -- Ó

Context

This dataset contains all the player stats of UEFA Champions League season 2021-22 .

Challenges

• Discover the weak points of any team.
• Suggest players need to be sold, based on performance analysis.
• Nominate Player of the season

About UEFA Champions League

The UEFA Champions League is an annual club football competition organised by the Union of European Football Associations and contested by top-division European clubs, deciding the competition winners through a round robin group stage to qualify for a double-legged knockout format, and a single leg final.

https://m.media-amazon.com/images/M/MV5BNTViYjI5M2MtNDYzZS00MDZkLTkzOWItMzkyM2FmMDhhZjc4XkEyXkFqcGdeQXVyNDg4MjkzNDk@._V1_.jpg" alt="UCL">

Content

• attacking.csv
• attempts.csv
• defending.csv
• disciplinary.csv
• distributon.csv
• goalkeeping.csv
• key_stats.csv

Related Datasets

• EPL ⚽ 2021-22 ⭐ English Premier League
• ⚽ Cristiano Ronaldo ⭐ All Club Goals 📈📊
• ⚽ Lionel Messi ⭐ All Club Goals 📈📊

Related Notebooks

• CR7 - Extensive EDA & Analytics-Cristiano Ronaldo
• "https://www.kaggle.com/code/azminetoushikwasi/lionel-messi-extended-eda-goals?scriptVersionId=97083243">▶️ Lionel Messi 📊 Extended EDA ⚽ Goals

Download

• kaggle API Command !kaggle datasets download -d azminetoushikwasi/ucl-202122-uefa-champions-league

About UEFA Champions League 2021-22

The 2022 UEFA Champions League Final was the final match of the 2021–22 UEFA Champions League, the 67th season of Europe's premier club football tournament organised by UEFA, and the 30th season since it was renamed from the European Champion Clubs' Cup to the UEFA Champions League. It was played at the Stade de France in Saint-Denis, France, on 28 May 2022, between English club Liverpool and Spanish club Real Madrid. It was the third time the two sides have met in the European Cup final, after 1981 and 2018, the third final held here, after the 2000 and 2006 finals, and the first time the same two teams have met in three finals.

This was the first final to be played in front of a full attendance since the 2019 final, as the previous two finals were affected by the COVID-19 pandemic.The final was originally scheduled to be played at the Allianz Arena in Munich, Germany. After the postponement and relocation of the 2020 final, the final hosts were shifted back a year, so the 2022 final was given to the Krestovsky Stadium in Saint Petersburg. Following the Russian invasion of Ukraine on 24 February, UEFA called an extraordinary meeting of the executive committee, where it was expected to officially pull the match out of Russia.[8][9] A day later, it announced the final would move to the Stade de France in Saint-Denis, located just north of Paris.

Real Madrid won the match 1–0 via a 59th-minute goal from Vinícius Júnior for a record-extending 14th title, and their 5th in nine years. As the winners of the 2021–22 UEFA Champions League, Real Madrid earned the right to play against the winners of the 2021–22 UEFA Europa League, Eintracht Frankfurt, in the 2022 UEFA Super Cup. Additionally, the winners typically qualify for the annual FIFA Club World Cup. However, the tournament's status remains uncertain, following FIFA's proposal for a format overhaul.

Disclaimer

• The data collected are all publicly available and it's intended for educational purposes only.

Acknowledgement

• Cover image taken from internet.

Appreciate, Support, Share

Base de donnée officielle des équipements sportifs en FranceData ES est la base de données des équipements sportifs et des lieux de pratiques du Ministère chargé des Sports. Elle est mise à jour quotidiennement. Cette donnée découle d'une obligation légale où "tout propriétaire d'un équipement sportif est tenu d'en faire la déclaration à l'administration en vue de l'établissement d'un recensement des équipements" (Code du Sport L312-2). Depuis 2005, ce dispositif contribue à documenter et éclairer le développement de la pratique sportive en France. Aujourd'hui, avec plus de 330 000 lieux de pratiques recensés en France métropolitaine et dans les territoires d'outre-mer, DATA ES constitue un référentiel exhaustif, mis à jour quotidiennement. Chaque équipement recensé est associé à un code national unique, garantissant une identification précise et homogène. Data ES - Complet Ce jeu de données constitue l’agrégation complète des équipements sportifs, de leurs installations associées, et des activités sportives pratiquées sur chaque site. Il s'agit du fichier principal pour les analyses territoriales ou cartographiques à l’échelle nationale.Méthodologie et définitions Informations clés

Fréquence de mise à jour : quotidienne Format : CSV, JSON, via API Licence : Licence Ouverte / Etalab 2.0 Source : Ministère des Sports Contact : contact-equipements[AT]sports.gouv.fr

This dataset provides detailed statistics for 380 matches from the 2005-2006 English Premier League season. It includes:

Team performance: Full-time/half-time goals, shots, fouls, corners, and cards. Match outcomes: Results (Home Win, Draw, Away Win) for both full-time and half-time. Referee data: Names of referees for each match.

Ideal for analyzing team strategies, referee influence, or building predictive models. Data is structured in an Excel file (football-raw-data.xlsx) with clear column headers for easy analysis.

Use cases: - Sports analytics - Performance trend visualization - Machine learning (e.g., match outcome prediction) - Historical football research

Hello! I'm a French engineering student and I'm very interested in data analysis. I'm also a huge fan of football, and I wanted to mix both by studying the penalties of some european football championships.

In this dataset, I included data from Premier League, Ligue 1, Bundesliga, Serie A (in two separate tables) and Champions League (until the row of 8). I collected the data thanks to the mobile app "Match en Direct", going match by match to see if there was any penalty taken or not, and adding the data in an Excel sheet.

My goal is to see if it is possible to make some links between the succes in a penalty and some factors such as the moment of the game, the score of the game when the penalty is taken, home/away team, the player's main position on the pitch, if the penalty taker is a sub or not...

I started by studying all this in my Excel sheet and found out some interesting facts, but I want to improve my analysis by doing it with a notebook here on Kaggle.

I am very new in this data analysis field, so if you have any suggestion, i will be happy to listen!