Football Analytics
Bubble
Professional
Football Analytics is a global community of professionals and enthusiasts who use data science to analyze and innovate in association f...Show more
Football Analytics
Bubble
Professional
Football Analytics is a global community of professionals and enthusiasts who use data science to analyze and innovate in association football (soccer), from player recruitment to tactical analysis.
Statistics
Estimated Global Reach
75K
Popularity
Low
Regional Hotspot
Worldwide
General Q&A
Football analytics combines *football knowledge* with *data science* to reveal insights about player and team performances, aiming to inform decisions on strategy, recruitment, and tactics.
Community Q&A
Football analytics combines *football knowledge* with *data science* to reveal insights about player and team performances, aiming to inform decisions on strategy, recruitment, and tactics.
Most connect through *social media threads* (e.g., #FootballAnalytics), *blogs*, and events like the *OptaPro Forum*, where research and tools are openly shared.
Community Q&A
Summary
Key Findings
Metric Evangelism
Opinion ShiftsMembers fiercely defend and promote core metrics like xG and PPDA, viewing skepticism from traditionalists as a challenge to overcome rather than dismissal.
Data Storytelling
Communication PatternsInsiders use custom visualizations and jargon as key tools to translate complex data into compelling football narratives, signaling expertise and shaping debates.
Collaborative Rivalry
Community DynamicsThe community thrives on a culture of open yet critical sharing, where publishing methods are debated openly to sharpen models and challenge prevailing analytics.
Tech Identity
Identity MarkersProficiency in tools like Python, R, and open datasets acts as a strong insider marker, separating casual fans from elite analysts driving industry innovation.
Sub Groups
Professional Analysts
Employed by clubs, agencies, or analytics firms, focusing on advanced modeling, recruitment, and tactical analysis.
Academic Researchers
University-based groups and students conducting research and publishing on football analytics topics.
Enthusiast Data Scientists
Independent or hobbyist analysts sharing open-source projects, models, and match breakdowns.
Coaching & Scouting Staff
Practitioners integrating analytics into coaching, scouting, and player development.
Online Content Creators
YouTubers, bloggers, and social media personalities producing educational and analytical football content.
Statistics and Demographics
Platform Distribution
1 / 4
Reddit
20%Reddit hosts active football analytics subreddits where professionals and enthusiasts share data-driven insights, discuss methodologies, and analyze matches.
Twitter/X
18%Twitter/X is a major hub for real-time football analytics discussion, sharing of visualizations, and connecting analysts, clubs, and fans globally.
Conferences & Trade Shows
15%Industry conferences (e.g., OptaPro Forum, MIT Sloan Sports Analytics Conference) are key offline venues for networking, presenting research, and professional development in football analytics.
Professional Settingsoffline
Gender & Age Distribution
Ideological & Social Divides
Community Development
Discover Similar Bubbles
Insider Knowledge
Terminology
Greeting Salutations
Example Conversation
Insider
What's the xG?
Outsider
Huh? What's that mean?
Insider
It's short for 'expected goals,' a way we estimate scoring chances to understand how well a team creates opportunities.
Outsider
Oh, so you mean like how good their chances really are?
Insider
Exactly! It's a quick way to start conversations about match quality.
Cultural Context
This asymmetric greeting uses a central metric in football analytics, signaling insider knowledge and opening debates over team performance.
Inside Jokes
"That was a negative xG moment."
A humorous way analysts describe a very low-probability chance which ended in a goal, poking fun at how statistics can't predict everything perfectly.
"Packing more players than a Tetris game."
A lighthearted exaggeration about a team's ability to pass through opponent lines, comparing the tactical situation to the famous puzzle game's complexity.
Facts & Sayings
„xG“
Expected Goals, a statistical measure of the quality of scoring chances and the likelihood of those chances resulting in goals.
„PPDA“
Passes Per Defensive Action, a metric that quantifies how aggressively a team presses by counting the number of passes allowed per defensive action.
„Packing“
The act of bypassing opponent players with passes, 'packing' refers to the number of opponents bypassed by a single pass or run, indicating passing effectiveness.
„Data-driven scouting“
Refers to scouting players or tactics primarily based on quantitative metrics and analytics rather than subjective observation.
Unwritten Rules
Always cite data sources and methodologies transparently.
Credibility depends on openness about how metrics are calculated and the origins of datasets, fostering trust and reproducibility.
Respect traditional football knowledge while challenging it.
Balance is crucial; pure data rejection frustrates insiders, but arrogant dismissal of experience alienates traditionalists.
Use jargon carefully when communicating with non-analysts.
Using excessive technical language can isolate newcomers; effective communication bridges the gap between data experts and football fans.
Engage in open collaboration rather than proprietary gatekeeping.
Sharing code, datasets, and insights is a cultural expectation that accelerates collective progress in the field.
Fictional Portraits
1 / 3
Liam, 29
Data ScientistmaleLiam is a data scientist working in sports technology who has recently transitioned his passion for statistics into football analytics projects.
AccuracyInnovationCollaboration
Motivations
- To leverage data science skills to impact football decision-making
- To stay updated on best practices and innovations in football analytics
- To build credibility and network within the football analytics community
Challenges
- Keeping up with rapidly evolving data tools and methodologies
- Interpreting complex football tactics in quantitative terms
- Breaking into established networks dominated by traditional scouts and coaches
Platforms
TwitterLinkedIn groupsDiscord channels dedicated to football analytics
Expected Goals (xG)Packing ratePass maps
Insights & Background
Historical Timeline
Main Subjects
Concepts
1 / 3
1 / 3
Concepts
First Steps & Resources
Get-Started Steps
Time to basics: 2-3 weeks
1
Learn Football Analytics Basics
2-3 hoursBasic
Summary: Study core concepts: expected goals, passing networks, and key metrics in football analytics.
Details: Start by familiarizing yourself with the foundational concepts and terminology of football analytics. This includes understanding metrics like expected goals (xG), expected assists (xA), passing networks, possession value models, and player performance indicators. Read introductory articles, glossaries, and explainer videos to grasp how these metrics are calculated and applied. Beginners often struggle with jargon and statistical concepts, so take time to look up unfamiliar terms and revisit explanations as needed. Focus on understanding the 'why' behind each metric—how it informs tactical decisions or player evaluation. This step is crucial because it builds the vocabulary and conceptual framework you'll need to engage with the community and interpret analyses. To evaluate your progress, try explaining xG or another metric to someone else, or summarize a match report that uses analytics.
2
Explore Public Football Data
1-2 hoursBasic
Summary: Download open football datasets and browse them to understand their structure and contents.
Details: Accessing and exploring real football data is a key rite of passage in this bubble. Seek out open-source datasets—such as match event data, player stats, or tracking data—available from public repositories or community projects. Download a dataset and open it in spreadsheet software or a data visualization tool. Examine the columns, data types, and sample entries. Beginners often feel overwhelmed by the volume and complexity of data, so start small: focus on one match or player, and look for patterns or anomalies. Try to answer simple questions, like 'How many shots did a team take?' or 'Who completed the most passes?' This hands-on familiarity is essential for later analysis and helps demystify the data. Progress is measured by your comfort in navigating the dataset and extracting basic insights.
3
Join Analytics Community Discussions
2-3 hoursBasic
Summary: Participate in online forums or social media groups focused on football analytics discussions and sharing.
Details: Engaging with the football analytics community is vital for learning and growth. Join online forums, social media groups, or discussion threads where enthusiasts and professionals share analyses, debate findings, and post resources. Start by reading existing threads to observe the tone, common topics, and etiquette. When comfortable, introduce yourself and ask beginner questions or comment on analyses. Avoid spamming or asking for proprietary data; instead, show curiosity and willingness to learn. Many beginners hesitate to participate, fearing their questions are too basic—remember, most communities welcome newcomers and value thoughtful engagement. This step helps you stay updated on trends, find mentors, and get feedback on your ideas. Progress is evident when you can contribute meaningfully to discussions or receive responses to your questions.
1
Learn Football Analytics Basics
2-3 hoursBasic
Summary: Study core concepts: expected goals, passing networks, and key metrics in football analytics.
Details: Start by familiarizing yourself with the foundational concepts and terminology of football analytics. This includes understanding metrics like expected goals (xG), expected assists (xA), passing networks, possession value models, and player performance indicators. Read introductory articles, glossaries, and explainer videos to grasp how these metrics are calculated and applied. Beginners often struggle with jargon and statistical concepts, so take time to look up unfamiliar terms and revisit explanations as needed. Focus on understanding the 'why' behind each metric—how it informs tactical decisions or player evaluation. This step is crucial because it builds the vocabulary and conceptual framework you'll need to engage with the community and interpret analyses. To evaluate your progress, try explaining xG or another metric to someone else, or summarize a match report that uses analytics.
2
Explore Public Football Data
1-2 hoursBasic
Summary: Download open football datasets and browse them to understand their structure and contents.
Details: Accessing and exploring real football data is a key rite of passage in this bubble. Seek out open-source datasets—such as match event data, player stats, or tracking data—available from public repositories or community projects. Download a dataset and open it in spreadsheet software or a data visualization tool. Examine the columns, data types, and sample entries. Beginners often feel overwhelmed by the volume and complexity of data, so start small: focus on one match or player, and look for patterns or anomalies. Try to answer simple questions, like 'How many shots did a team take?' or 'Who completed the most passes?' This hands-on familiarity is essential for later analysis and helps demystify the data. Progress is measured by your comfort in navigating the dataset and extracting basic insights.
3
Join Analytics Community Discussions
2-3 hoursBasic
Summary: Participate in online forums or social media groups focused on football analytics discussions and sharing.
Details: Engaging with the football analytics community is vital for learning and growth. Join online forums, social media groups, or discussion threads where enthusiasts and professionals share analyses, debate findings, and post resources. Start by reading existing threads to observe the tone, common topics, and etiquette. When comfortable, introduce yourself and ask beginner questions or comment on analyses. Avoid spamming or asking for proprietary data; instead, show curiosity and willingness to learn. Many beginners hesitate to participate, fearing their questions are too basic—remember, most communities welcome newcomers and value thoughtful engagement. This step helps you stay updated on trends, find mentors, and get feedback on your ideas. Progress is evident when you can contribute meaningfully to discussions or receive responses to your questions.
4
Recreate a Simple Analysis
2-4 hoursIntermediate
Summary: Use spreadsheet software to replicate a basic football analysis, like calculating xG for a match.
Details: Apply your knowledge by conducting a simple analysis using accessible tools like spreadsheets. For example, use shot location data to calculate expected goals (xG) for a single match. Follow a step-by-step guide or tutorial, inputting data and applying formulas as instructed. Beginners often struggle with data formatting or formula errors, so double-check your work and consult troubleshooting guides if needed. This hands-on practice solidifies your understanding of both the metric and the data manipulation process. It also introduces you to the workflow of football analytics: data cleaning, calculation, and interpretation. This step is important because it bridges theory and practice, giving you a tangible result to share or discuss. Evaluate your progress by comparing your results to published analyses or sharing your work for feedback.
5
Follow and Critique Match Analyses
2-3 hoursIntermediate
Summary: Read published match analyses using analytics, then write your own short critique or summary.
Details: Regularly reading and critically engaging with published football analytics match reports is a core community practice. Find recent analyses from reputable blogs, community posts, or open-access journals. Read with attention to how data is used to support tactical or player insights. Take notes on the metrics, visualizations, and arguments presented. Then, write a brief critique or summary—highlight what you learned, question any assumptions, or suggest alternative interpretations. Beginners may feel intimidated by the depth of some analyses; focus on understanding the main points and gradually build up to more complex reports. This step is crucial for developing analytical thinking and learning how to communicate insights effectively. Progress is measured by your ability to identify strengths and weaknesses in others' work and by the clarity of your own summaries.
Welcoming Practices
„Sharing beginner-friendly blogs and tutorials.“
Helps newcomers understand complex analytics concepts and gradually integrate into the community.
„Inviting new members to participate in open data projects.“
Facilitates hands-on experience and connects newcomers with experienced analysts, fostering inclusion and learning.
Beginner Mistakes
Over-relying on a single metric like xG without context.
Learn to interpret multiple metrics together and consider tactical background to avoid misleading conclusions.
Using overly complex jargon in communication.
Simplify explanations and tailor language to the audience to improve understanding and engagement.
Pathway to Credibility
Tap a pathway step to view details
Develop strong foundational knowledge of football tactics and statistics.
Understanding both aspects is essential to analyze data meaningfully and gain respect.
Contribute original analyses or models publicly.
Publishing work demonstrates expertise and fosters recognition from peers.
Engage actively in community discussions and collaborations.
Participation builds relationships, trust, and awareness within the bubble.
Facts
Regional Differences
Europe
European football analytics communities tend to be more integrated with professional clubs and leagues, often influencing matchday tactical decisions directly.
North America
North American football analytics communities often emphasize data visualization and storytelling approaches popularized by American sports analytics.
Misconceptions
Misconception #1
Football analytics removes the emotion and soul from the game.
Reality
While analytics is data-driven, insiders use it to uncover deeper tactical and performance insights that enhance understanding and appreciation, not replace passion.
Misconception #2
Analysts just look at numbers and ignore on-field realities.
Reality
Analysts combine quantitative data with qualitative knowledge of football to interpret data in the context of game situations and tactics.
Misconception #3
Advanced metrics like xG are a guaranteed predictor of future matches.
Reality
Metrics are probabilistic tools that guide understanding but never predict exact outcomes due to football's inherent unpredictability.
