Crea tu primer informe con datos de fútbol en Power BI - TALLER PRÁCTICO📊⚽

Introduction to the Workshop

Welcome and Overview

• The workshop begins with a warm welcome from Dani and Luis, noting an impressive attendance of over 600 participants.

• They mention that this workshop has been conducted before but includes a significant new component focused on Python.

• Participants are encouraged to share their locations, highlighting the international reach of the event.

Goals of the Workshop

• The primary aim is to teach attendees how to work with data in football, emphasizing practical applications.

• Dani and Luis inquire about participants' familiarity with their platform, "Objetivo Analista," to tailor content accordingly.

Content Structure of the Workshop

Today's Focus: Power BI

• The first day will cover Power BI for data visualization, while the second day will focus on automating data retrieval using Python.

• They emphasize that these tools are essential for various roles in football, including coaches and analysts.

Importance of Data Tools

• Attendees will learn why Power BI and Python are valuable tools in sports analytics.

• Real-world examples from both professional and amateur football will be shared to inspire participants.

Practical Application and Future Opportunities

Hands-On Learning

• The goal is for participants to leave with actionable resources for creating their first reports using Power BI.

• At the end of the two-day workshop, they will introduce a new program called "Datos 360" aimed at furthering professional development.

Transitioning into Professional Programs

• This upcoming program responds to demand from students seeking deeper specialization in data analysis within football contexts.

Technical Issues During Live Session

Platform Challenges

• There were technical difficulties regarding video orientation; attendees were advised to switch to YouTube for better viewing experience.

• Links were provided in chat for those experiencing issues with vertical video display.

Importance of Data Analysis in Football

Relevance Across Roles

• Dani emphasizes that understanding data is crucial regardless of one's role within football organizations.

Future Training Opportunities

• After this introductory workshop series, they plan to offer more comprehensive training programs focusing on advanced skills in Power BI and Python.

Presenters' Background

Introducing Dani Pérez

• Dani shares his background as an analyst who has worked internationally, including notable experiences at Real Madrid and Nike Larnaca.

Achievements

• He highlights his success stories working with various teams and mentions having helped over 3,000 students through "Objetivo Analista."

Introducing Luis Mosquera

• Luis discusses his journey starting as a self-taught analyst leading up to collaborations with FIFA and participation in major tournaments like World Cups.

Project Development

• He explains how he developed a database project that opened doors for him professionally within sports analytics.

Why Work With Data?

Necessity of Data Skills

• Both presenters stress that proficiency in data analysis is no longer optional but essential for anyone looking to succeed in modern football environments.

Introduction to Data in Professional Football

Importance of Data Analysis

• The Brazilian national team utilizes data for player analysis, showcasing the growing trend in professional football.

• Villarreal employs data analytics before matches, indicating that this practice is now standard in modern football.

Embracing Data Analytics

• Acknowledgment of two approaches: rejecting data or embracing it to enhance skills and knowledge.

• The workshop aims to provide professional training in 360-degree data analysis, with examples from clubs like Real Madrid and Atlético Madrid.

Why Power BI and Python?

Demand for Skills

• Common inquiries about the necessity of Power BI and Python within the data ecosystem are addressed.

• Job postings from Athletic Club de Bilbao and the Portuguese Federation highlight a demand for professionals skilled in Power BI and Python.

Market Relevance

• The belief that mastering these tools is essential due to market demands for such expertise is emphasized.

Transitioning from Excel to Power BI

Limitations of Excel

• Excel is deemed insufficient for professional visualizations; it should be used primarily as a record-keeping tool rather than for reporting.

Challenges with Data Management

• Many professionals struggle with managing disparate datasets across various platforms, complicating their ability to analyze information effectively.

Automation and Simplification

Reducing Manual Work

• Manual data entry consumes valuable time; automation can streamline report preparation significantly.

Overcoming Programming Fears

• Participants often fear programming; however, they will learn how to use Power BI without coding while also being introduced to simple Python applications.

Visualizing Data Effectively

Comparison Between Tools

• A clear distinction between using Excel for record keeping versus utilizing Power BI for effective visualizations is made.

Practical Application Examples

• Real-life examples demonstrate what students have achieved using Power BI, including reports generated by alumni working at various levels of football.

Workshop Engagement

Participant Incentives

• Attendees receive a downloadable report created by Sergio Ramos as an example of what can be accomplished with Power BI.

Guest Speaker Announcement

• Sergio Ramos will join the workshop briefly to showcase his capabilities using the software.

Features of Power BI

Key Functionalities

• Luis outlines three main functions of Power BI: loading, modeling, and analyzing data efficiently.

User-Friendly Interface

Power BI caters to users at all experience levels, helping them overcome initial apprehensions about working with data.

Quick Results

The tool provides rapid results which encourage continued engagement with data analysis practices.

Automation Capabilities

• Combining Python with Power BI allows users to automate workflows effectively.

Centralized Data Management

Streamlined Processes

• Users can connect multiple tables into a unified model within Power BI, simplifying their workflow significantly.

Time Efficiency

• Once set up correctly, adding new data becomes quick and easy compared to initial setup times.

Industry Demand Insights

Professional Training Needs

• Crystal Palace has reached out seeking comprehensive training in Power BI tailored specifically towards their staff's needs.

Global Reach

• The program has attracted interest from various international teams looking for similar training opportunities.

Installation Guidance

Getting Started With Software

• Instructions on installing Power BI are provided through a YouTube video link shared during the session.

Encouragement To Engage Post-Class

• Participants are encouraged not only to install but also engage fully after class hours when they can focus on learning without distractions.

Introduction to Data Loading in Power BI

Overview of Tables

• The speaker introduces four tables: classification, teams, statistics, and matches, which are preloaded into an Excel file for demonstration.

• The process begins by selecting all four tables and loading them directly into Power BI instead of transforming data first.

• This marks the completion of the first phase: loading tables into the Power BI report.

Data Transformation Phase

• The speaker explains that data transformation includes tasks like removing unnecessary columns or creating new calculated columns.

• In Power BI, users can edit loaded tables through a dedicated transformation window to ensure data accuracy and integrity.

Ensuring Data Accuracy

Checking Data Types

• Users must verify that each column has the correct data type (e.g., text, numeric).

• Power BI automatically analyzes and displays the data types for each column upon inspection.

Modifying Columns

• If any column's data type is incorrect, it can be easily changed within Power BI by selecting the appropriate type from a dropdown menu.

Transforming Team Table

Correcting Column Headers

• The team table initially shows incorrect headers ("Column 1" and "Column 2") instead of expected names.

• To fix this issue, users can select "Use First Row as Headers," which updates the table correctly.

Automation of Steps

• Each transformation step is recorded in a list called "Applied Steps," allowing users to navigate back through their changes if needed.

Advantages of Using Power BI

Automation Benefits

• Once transformations are applied in Power BI, they become automated; future updates will not require repeating these steps.

Safety Features

• Changes made in Power BI do not affect the original Excel file, providing a safety net against accidental deletions or modifications.

Cleaning Other Tables

Statistics Table Review

• The statistics table is checked for header correctness and proper data types; no changes are necessary here.

Matches Table Adjustments

• In the matches table, some columns need separation (date and time), while others may be removed entirely based on analysis needs.

Finalizing Data Preparation

Removing Unnecessary Columns

• Users can select multiple columns using Ctrl key to remove those not needed for analysis without affecting original Excel content.

Dividing Date and Time Columns

• To separate date from time in one column, users choose "Transform" > "Split Column" by delimiter (space).

Applying Changes

Saving Cleaned Tables

• After cleaning up all tables, users click “Close & Apply” to save changes back into their final report setup.

Establishing Relationships Between Tables

Model View Introduction

• Users transition to model view where they can visualize relationships between different tables within their dataset.

Creating Relationships

• By dragging common fields (like team names), users establish connections between independent datasets ensuring coherent analysis across related information.

Utilizing Relationships for Analysis

Filtering Capabilities

• With established relationships among tables, filtering options allow dynamic interaction with visualizations based on selected criteria (e.g., specific teams).

Visualizing Data Insights

Creating Reports

• Users begin constructing reports using intuitive drag-and-drop features available in Power BI’s visualization panel.

Enhancing Visual Appeal

• Adding images such as team logos requires setting image URLs correctly within dataset properties before displaying them visually on reports.

Creating a Visual Report for Osasuna's Performance

Data Organization and Visualization

• The discussion begins with organizing data in ascending order, specifically from 1 to 25, focusing on Osasuna's matches.

• Customization of visual elements is emphasized, including changing colors for team goals (green) and opponent goals (red), enhancing clarity in performance tracking.

• A classification table is introduced to provide context alongside match data, filtering specifically for Osasuna while allowing future adjustments for other teams.

Enhancing Table Functionality

• Instructions are provided on how to ensure all teams appear in the classification table by adjusting filter settings effectively.

• Copying formatting from one table to another is suggested as a time-saving technique to maintain consistency across visuals.

Finalizing the Report

• Additional columns such as games played and points are added to the classification table, aiming for a comprehensive overview without overcomplicating the report.

• The importance of ensuring filters affect only specific visuals is highlighted, allowing general views without restrictions from selected filters.

Automating Updates in Power BI Reports

Streamlining Data Management

• The session illustrates how quickly an Excel report can be transformed into a dynamic Power BI report that provides deeper insights beyond mere numbers.

• Automation capabilities are showcased by demonstrating how changing player statistics (e.g., Lucas Boyet’s goals updated from 7 to 10 after a hat trick) reflects instantly in Power BI upon refreshing data.

Benefits of Automation

• The ease of updating reports through simple actions like refreshing data saves significant time compared to manual updates traditionally required in Excel.

• Emphasis is placed on the potential time savings throughout a season due to automation features within Power BI, enabling more efficient analysis and reporting.

Practical Applications of Excel and Power BI Integration

Accessibility of Tools

• Participants are encouraged that they can generate similar reports using basic Excel files provided during the workshop, applicable across various team levels.

• Luis demonstrates how simple Excel sheets can be connected with Power BI for powerful visualizations that enhance professional presentations.

Real-world Scenarios

• Discussion includes examples where automated systems allow quick adaptations when new game data arises without needing extensive rework on reports.

Exploring Advanced Use Cases Beyond Basic Reporting

Diverse Applications Across Roles

• Different use cases are presented for analysts, scouts, and physical trainers showcasing how versatile Power BI can be across various roles within sports management.

Examples of Scouting Reports

• An example scouting report based on FIFA video game data illustrates initial screening processes that help narrow down thousands of players efficiently through filtering metrics like finishing skills or speed.

Leveraging Data Providers for Enhanced Analysis

Filtering Techniques

• By applying multiple filters such as market value or contract expiration dates, users can refine their search results significantly from large datasets down to manageable lists suitable for further evaluation.

Integrating Subjective Insights

• Combining objective statistical data with subjective observations allows scouts to create comprehensive profiles that inform recruitment decisions effectively.

Utilizing Professional Data Sources

Accessing Comprehensive Football Statistics

The conversation highlights utilizing professional databases like StatsBomb which offer extensive football statistics useful for tactical analysis and performance evaluations.

Importance of Data Generation Skills

• It’s stressed that while tools like Power BI facilitate visualization and analysis, acquiring raw data remains essential; users must either source it themselves or utilize available providers effectively.

Exploring Data Visualization in Sports

Importance of Data Analysis in Sports

• The discussion emphasizes the potential benefits of data analysis, particularly in physical performance metrics within sports.

• Miguel Ángel García, a student and sports journalist, showcases his work using Power BI to create visualizations that enhance understanding of game statistics.

Visualizing Game Statistics

• The presentation highlights how shot maps can be represented visually on the field or goal area, providing insights into player performance during penalties.

• Miguel Ángel's articles utilize these visualizations for deeper analysis published in major sports outlets like Marca.

Video Analysis Tools

• A workshop is introduced where participants can create their own video analysis platforms, allowing them to upload and annotate match footage.

• Users can tag specific actions from highlight videos, such as shots taken by players, enhancing their analytical capabilities.

Comprehensive Match Analysis

• The platform allows users to filter and analyze all shots made by a team throughout a season, facilitating detailed performance reviews.

• This method encourages analysts to focus on specific game moments or transitions that are crucial for tactical assessments.

Integration with Existing Platforms

• While many analysts use established platforms like Wyscout for video analysis, this approach enables customization tailored to individual needs and methodologies.

• Participants are encouraged to adapt existing tagging methods used in their clubs into this new platform for enhanced data collection.

Enhancing Scouting and Injury Monitoring

Scouting Efficiency through Visualization

• Roberto from Atlético de Madrid shares how visualizations have streamlined scouting processes by saving time and improving player profiling accuracy.

Goalkeeper Preparation Insights

• Various examples illustrate how goalkeeper training can benefit from aggregated data visualization regarding shot locations and outcomes.

Upcoming Workshops on Python Integration

Introduction to Python for Football Analytics

• Tomorrow’s workshop will cover automating football data retrieval using Python—a long-requested topic among participants.

Simplifying Programming Concepts

• The session aims to demystify programming for attendees by providing straightforward instructions on installing Python and writing basic code snippets.

Launching New Educational Programs

Overview of Data 360 Program

• A new program called "Data 360 Applied to Football" will be introduced, focusing on professional development through Power BI and Python training.

Real-world Applications

• Participants will learn practical applications of Python in football analytics without needing extensive technical backgrounds.

Building Professional Portfolios

Importance of Practical Experience

• Emphasis is placed on creating a portfolio through real projects which enhances employability within the football industry.

Community Engagement

• Attendees are encouraged to ask questions about tools like heat maps and other advanced visualizations available in professional training modules.

Technical Support for Software Installation

Installing Power BI on Mac Systems

• Instructions are provided for Mac users wishing to install Power BI via virtual machines or alternative methods while ensuring compatibility with Windows-based software.

Conclusion: Future Learning Opportunities

• Continuous learning is emphasized as technology evolves; participants are urged to stay updated with advancements in data collection techniques using AI-driven solutions.

Introduction to Data 360 Training

Overview of the Program

• The training program aims to provide a comprehensive introduction to programming with Python, enabling participants to start coding in under three minutes.

• The primary goal is to alleviate fears associated with programming and emphasize that everyone can learn, regardless of their starting point.

• Participants will have their first exposure to data analysis tools, simplifying complex concepts related to football analytics.

Learning Path and Experience

• Emphasis on the importance of foundational knowledge in football analytics for career advancement; experience builds upon this knowledge.

• Personal anecdotes highlight that many successful professionals started without connections in football, stressing that everyone begins somewhere.

Steps Towards Professional Development

Gaining Experience

• Progression through various levels of football (from amateur teams to professional leagues) is essential for gaining relevant experience.

• Once initial contacts are made within the industry, opportunities become more accessible; networking plays a crucial role.

Internship Opportunities

• The program offers potential internship placements with various clubs but does not guarantee them due to external factors.

• Collaboration agreements with clubs ensure students receive opportunities based on their training quality and performance.

Job Placement and Networking

Employment Prospects

• The school claims a strong position as a leading institution for Spanish-speaking individuals interested in data analysis within sports.

• While job placement cannot be guaranteed, numerous opportunities arise from collaborations with clubs and alumni networks.

Sharing Work Effectively

• Importance of sharing analytical work using tools like Power BI; multiple methods exist for presenting findings effectively.

Knowledge Requirements in Football Analytics

Conceptual vs. Technical Knowledge

• A discussion on whether conceptual or technical knowledge holds more weight when entering the football industry reveals differing perspectives.

• Conceptual knowledge is subjective and difficult to demonstrate compared to technical skills which can be showcased through portfolios.

Competitive Landscape

• Many individuals possess theoretical knowledge about football; however, fewer have practical technical skills, creating an opportunity for those who do.

Differentiating Factors in Career Advancement

Unique Skill Sets

• Professionals often gain entry into clubs not solely based on tactical understanding but rather unique skill sets that set them apart from others.

Real-world Examples

• Anecdotes illustrate how presenting data-driven projects can capture attention from club decision-makers more effectively than traditional tactics discussions.

Conclusion and Next Steps

Final Thoughts

• Encouragement for participants to attend upcoming workshops as they offer valuable insights into pursuing careers within football analytics.