Analyzing the movement of soccer players from one association to another using Excel. Dataset utilized from 365DataScience platform, aiming to unravel patterns, trends, and noteworthy occurrences in player movements.

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.

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 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

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.

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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

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!