NYC Data Science Academy| Blog
Bootcamps
Lifetime Job Support Available Financing Available
Bootcamps
Data Science with Machine Learning Flagship ๐Ÿ† Data Analytics Bootcamp Artificial Intelligence Bootcamp New Release ๐ŸŽ‰
Free Lesson
Intro to Data Science New Release ๐ŸŽ‰
Find Inspiration
Find Alumni with Similar Background
Job Outlook
Occupational Outlook Graduate Outcomes Must See ๐Ÿ”ฅ
Alumni
Success Stories Testimonials Alumni Directory Alumni Exclusive Study Program
Courses
View Bundled Courses
Financing Available
Bootcamp Prep Popular ๐Ÿ”ฅ Data Science Mastery Data Science Launchpad with Python View AI Courses Generative AI for Everyone New ๐ŸŽ‰ Generative AI for Finance New ๐ŸŽ‰ Generative AI for Marketing New ๐ŸŽ‰
Bundle Up
Learn More and Save More
Combination of data science courses.
View Data Science Courses
Beginner
Introductory Python
Intermediate
Data Science Python: Data Analysis and Visualization Popular ๐Ÿ”ฅ Data Science R: Data Analysis and Visualization
Advanced
Data Science Python: Machine Learning Popular ๐Ÿ”ฅ Data Science R: Machine Learning Designing and Implementing Production MLOps New ๐ŸŽ‰ Natural Language Processing for Production (NLP) New ๐ŸŽ‰
Find Inspiration
Get Course Recommendation Must Try ๐Ÿ’Ž An Ultimate Guide to Become a Data Scientist
For Companies
For Companies
Corporate Offerings Hiring Partners Candidate Portfolio Hire Our Graduates
Students Work
Students Work
All Posts Capstone Data Visualization Machine Learning Python Projects R Projects
Tutorials
About
About
About Us Accreditation Contact Us Join Us FAQ Webinars Subscription An Ultimate Guide to
Become a Data Scientist
    Login
NYC Data Science Acedemy
Bootcamps
Courses
Students Work
About
Bootcamps
Bootcamps
Data Science with Machine Learning Flagship
Data Analytics Bootcamp
Artificial Intelligence Bootcamp New Release ๐ŸŽ‰
Free Lessons
Intro to Data Science New Release ๐ŸŽ‰
Find Inspiration
Find Alumni with Similar Background
Job Outlook
Occupational Outlook
Graduate Outcomes Must See ๐Ÿ”ฅ
Alumni
Success Stories
Testimonials
Alumni Directory
Alumni Exclusive Study Program
Courses
Bundles
financing available
View All Bundles
Bootcamp Prep
Data Science Mastery
Data Science Launchpad with Python NEW!
View AI Courses
Generative AI for Everyone
Generative AI for Finance
Generative AI for Marketing
View Data Science Courses
View All Professional Development Courses
Beginner
Introductory Python
Intermediate
Python: Data Analysis and Visualization
R: Data Analysis and Visualization
Advanced
Python: Machine Learning
R: Machine Learning
Designing and Implementing Production MLOps
Natural Language Processing for Production (NLP)
For Companies
Corporate Offerings
Hiring Partners
Candidate Portfolio
Hire Our Graduates
Students Work
All Posts
Capstone
Data Visualization
Machine Learning
Python Projects
R Projects
About
Accreditation
About Us
Contact Us
Join Us
FAQ
Webinars
Subscription
An Ultimate Guide to Become a Data Scientist
Tutorials
Data Analytics
  • Learn Pandas
  • Learn NumPy
  • Learn SciPy
  • Learn Matplotlib
Machine Learning
  • Boosting
  • Random Forest
  • Linear Regression
  • Decision Tree
  • PCA
Interview by Companies
  • JPMC
  • Google
  • Facebook
Artificial Intelligence
  • Learn Generative AI
  • Learn ChatGPT-3.5
  • Learn ChatGPT-4
  • Learn Google Bard
Coding
  • Learn Python
  • Learn SQL
  • Learn MySQL
  • Learn NoSQL
  • Learn PySpark
  • Learn PyTorch
Interview Questions
  • Python Hard
  • R Easy
  • R Hard
  • SQL Easy
  • SQL Hard
  • Python Easy
Data Science Blog > Python > Soccer Team Popularity Data From Reddit

Soccer Team Popularity Data From Reddit

Sharan Naribole
Posted on Jan 6, 2017

Contributed by Sharan Naribole. He is currently in the NYC Data Science Academy Online Bootcamp program taking place between December 2016 to April 2017. This blog is based on his bootcamp project - Python Web-scraping and Data Analysis.

Reddit Introduction and Project Goal

I am a big fan of soccer supporting the English club Manchester United F.C. for the last 15 years. Most of my soccer news updates and discussions occur on the website Reddit. Reddit, claiming to be "the front page of the internet", is a social news aggregation, web content rating, and discussion website, as described by Wikipedia. Different topics of interest are organized within their groups called subreddits. For example, the popular subreddits include technology, world news, pics, players like Cristiano Ronaldo and etc.

Reddit includes a subreddit for soccer content entries/r/soccer, comprising of an enormous community of over 500,000 subscribers. Hundreds of submissions (posts) are voted based on their content and discussed on a daily basis. The common submissions include live goals, news articles and pre-, live and post-match analysis.

Many  time /r/soccer is where I vent my frustration whenever Manchester United suffers defeat!  The anonymity of Reddit provides a powerful tool for users to express their opinions about the clubs they support and also the clubs they don't like. This unique feature provides a mechanism to prevent the building of echo chambers in the discussions. The figure below illustrates a snapshot of the front page of /r/soccer.

rsoccer

   Fig 1. The front page of soccer subreddit.

/r/soccer also provides each subscriber the feature to select a "flair" which is the team crest (team logo) of the club/country the user supports. A miniature team logo is displayed beside the username in their posts and comments. This gives other readers context and insight into the user's thinking and adds another interesting dimension to the conversations that take place. Although one can observe the flairs for each users, Reddit does not provide the flair distribution across the whole of /r/soccer.

In this project, my objective is to scrape and analyze the flair distribution across the top posts in /r/soccer and this distribution's relationship with comments activity, submission score and submission type. 

flair

Fig 2. User flairs displayed beside their username in the comments.

Data Collection

For each submission, my goal is to collect the following features:

  1. Submission Title: to decipher the type of submission for e.g., goal video, news article, live match discussion etc.
  2. Submission Score (~= Upvotes - Downvotes): indicates the quality of the submission
  3. Number of comments: indicates the user activity on the submission
  4. Per-user flair map: dictionary of unique username and user flair  in the top 500 comments. This will be utilized for the flair distribution analysis.

Currently, for web-scraping, there are a wide variety of Python packages available for webscraping including BeautifulSoup, Selenium and Scrapy etc. I used Scrapy because it provides a simple and structured framework to design a Spider for crawling multiple levels of pages on a website. For my project, there are two levels of crawling. The top level is where the different submission titles are listed along with their scores (Fig. 1) and the second level is the comments for each submission (Fig 2.). Later, I will describe how we can design the multi-level crawl.

The two main files in a Scrapy project directory are the items.py file and the spider crawl file. In items.py, we can define containers for storing the scraped information.

Next step is to define how our spider should crawl. I define a FlairSpider class provided with the starting url and crawling motion. To get sufficient data for the analysis, I collect the above features for each of top 1000 posts in the period Nov 12- Dec 12 2016.

For this purpose, I provide the start_url to extract the top posts in the past month. Reddit provides the top 1000 posts in the past month in a descending order of 25 posts per page. Additionally, I provide parameters for the comments constraint and upper limit on the comments extracted per submission. I placed a constraint that a submission should at least contain 100 comments so that I have a big enough sample size for flair distribution conditional on the submission. Over 500 out of the 1000 submissions met this constraint.

Using the start_url, the spider begins the crawl. The parse() function is the default method called to handle the response downloaded for each of links in the start_urls. First, I extract the links pointing to comments page for each submission. As illustrated in Fig. 3, I utilize the SelectorGadget tool to provide me the CSS selector only for the comments and ignore other CSS selectors.

comments_css

Fig 3. SelectorGadget in action.

I filter the comment links based on the total number of comments as explained previously. If the number of comments of a submission exceeds the lower limit, the crawler stores the total number of comments in a new FlairsItem container. As we also want to extract user-flair map for each submission, a new request parse_submission() is made per submission with our partially filled FlairsItem container stored in the metadata. For this new request, I provide the url to the comments page of the submission sorted by the top 500 comments. These are typically the top comments any reader views on scrolling the comments page.

In the above request, I again utilize SelectorGadget to find out the CSS selectors for the submission title, score and username-flair mapping. I store the dictionary of unique users and their corresponding flairs as I am interesting in analyzing the flair diversity in the top comments per submission. Scrapy provides a command line argument to store the data in csv/json format for further processing.

Data Processing

The goal of data processing is to compute the following metrics for each submission:

  1. Flair diversity: the unique number of flairs
  2. Percentage share per flair: The percentage of comments belonging to a given flair. This metric is computed for every team both club and country flairs for e.g. England, Real Madrid, Brazil etc.
  3. Top Percentage share: The highest percentage value among all flairs
  4. Number of comments

The function compute_flair_stats() computes the percentage share per flair given the flair_map scraped for a submission using Scrapy. Using Python pandas library and above function, I fill two pandas dataframes, per_submission_metrics and per_flair_metrics that prepare the scraped data for analysis. Fig. 4 illustrates the structure of the two dataframes.

dataframes_processing

Fig 4. Illustration of the key DataFrame structures.

Data Analysis

Flair Diversity Correlation

A user can participate in a submission either by a) upvoting/downvoting the submission consequently increasing/decreasing the submission score and/or b) a more involved participation by commenting on the submission. My hypothesis is that an average user's participation is higher in posts related to their flair (the team they support) in comparison to a generic post.

scatter_submission_comments

Fig 5. Flair Diversity Correlation.

Fig 5. illustrates a scatter plot between the number of comments and flair diversity with the size of the bubble proportional to the submission score.

Expectedly, the flair diversity increases with the number of comments. Also, with increase in diversity, even for low number of comments, the submission scores are much higher. These might be posts for which the quality of the content is enough to generate the high scores and increased flair diversity for e.g. world-class goal by a popular player, live match discussion between top teams Real Madrid and Barcelona. To analyze this hypothesis, next, I dive deeper into the relationship between the different metrics and the type of submission.

Submission Type Analysis

I classify the submissions into three high-level categories:

  1. Goal video submissions: Goal videos are posted in near real-time in the formats gfycat, streamable etc. My hypothesis is that goal submissions are expected to have a statistically higher flair diversity as the goals are discussed for their quality and not just the teams involved in the goal.
  2. Match Thread submissions: These submissions are automated submissions generated one hour before, during and immediately after a live soccer match. My hypothesis is that these posts receive lower flair diversity as users supporting the two teams taking part in the match are expected to have higher share of comments including the top ones.  At the same time, because these threads are meant for discussion of various events during a match, they are expected to contain higher number of comments in comparison to goal submissions.
  3. Rest: All other types of submissions are grouped into this  category.

I achieve the detection of Match Thread and Goal video submissions by applying the word_locate() and check_goal() functions respectively.

Fig 6. illustrates the distributions of the metrics based on the submission type.

Typically, the match thread discussions are mostly active during a live match and in its immediate aftermath. This results in the highest number of comments for Match Threads. In contrast, the submission score continues to rise many hours after the Goal submission is posted as they are rated on quality. Hence, the submission score is highest for goal submissions. We can observe even for the Rest category, there are quite a few outliers having a high submission score. These are typically news articles/tweets etc. transcending the interest of a particular section of soccer fans and appealing to a larger audience.

                                                                                  
 boxplot_score_comparison

(a)  Submission Score

     boxplot_comments_comparison

(b) Number of Comments

 boxplot_diversity_comparison

(c) Flair Diversity

Fig 6. Submission type analysis.

 Interestingly, my hypothesis that flair diversity for Goal submissions would be highest turned out to be false. The Match Threads edge the other two categories. This is because we have particularly selected the top 1000 submissions for analysis and not a random sampling of 1000 submissions in the same duration. Because the Match Threads are among the top posts, these are submissions for matches watched by a wider audience in comparison to an average match. I expect this result to not to be true if we randomly selected the submissions.

Flair Activity

Last, I analyze the distributions of the percentage share of the different flairs across all the submissions. We observe that the leaderboard is dominated by the English Premier League clubs followed by the two Spanish giants, Real Madrid and Barcelona. Reddit being a predominantly English-speaking website and English Premier League being the most popular league in the world, this result is expected.

boxplot_clubs

Going Forward

There are several directions in which my framework can be expanded. Few of them include:

  1. Sentiment Analysis: Perform team-specific sentiment analysis of the posts over the period of a season ( year). An interesting case study would be Leicester City, a lower-ranked team that went on to win the English Premier League 2015-2016 season for the first time in their 132-year history.
  2. Topic Modeling: Apply topic modeling for deeper classification of the submissions

Conclusion

In this project, I have scraped data from Reddit's soccer subreddit to analyze the user flair distribution across submissions and its correlation with Submission Score and comments activity. My results show that the metrics vary significantly based on the type of submissions and validate the popularity of English Premier League clubs in the subreddit.

The code for the project can be found here.

Scraping Soccer Popularity on Reddit from Sharan Naribole

About Author

Sharan Naribole

Sharan Naribole is a PhD Candidate in Electrical & Computer Engineering department at Rice University supported by Texas Instruments Fellowship. Sharan's research focuses on next-generation Wireless Networks protocol design and experimentation. Sharan has undertaken data science internship at...
View all posts by Sharan Naribole >

Related Articles

Capstone
Catching Fraud in the Healthcare System
Capstone
The Convenience Factor: How Grocery Stores Impact Property Values
Capstone
Acquisition Due Dilligence Automation for Smaller Firms
Machine Learning
Pandemic Effects on the Ames Housing Market and Lifestyle
Machine Learning
The Ames Data Set: Sales Price Tackled With Diverse Models

Leave a Comment

Cancel reply

You must be logged in to post a comment.

reddit los angeles โ€“ Download Free Movies and TV February 6, 2017
[โ€ฆ] Soccer Team Popularity on Reddit โ€“ NYC Data Science [โ€ฆ]
Strip Club Barcelona February 5, 2017
Thanks for the marvelous posting! I genuinely enjoyed reading it, you are a great author.I will be sure to bookmark your blog and will coje back later in life. I want to encourage one to continue your great posts, have a nice weekend!

View Posts by Categories

All Posts 2399 posts
AI 7 posts
AI Agent 2 posts
AI-based hotel recommendation 1 posts
AIForGood 1 posts
Alumni 60 posts
Animated Maps 1 posts
APIs 41 posts
Artificial Intelligence 2 posts
Artificial Intelligence 2 posts
AWS 13 posts
Banking 1 posts
Big Data 50 posts
Branch Analysis 1 posts
Capstone 206 posts
Career Education 7 posts
CLIP 1 posts
Community 72 posts
Congestion Zone 1 posts
Content Recommendation 1 posts
Cosine SImilarity 1 posts
Data Analysis 5 posts
Data Engineering 1 posts
Data Engineering 3 posts
Data Science 7 posts
Data Science News and Sharing 73 posts
Data Visualization 324 posts
Events 5 posts
Featured 37 posts
Function calling 1 posts
FutureTech 1 posts
Generative AI 5 posts
Hadoop 13 posts
Image Classification 1 posts
Innovation 2 posts
Kmeans Cluster 1 posts
LLM 6 posts
Machine Learning 364 posts
Marketing 1 posts
Meetup 144 posts
MLOPs 1 posts
Model Deployment 1 posts
Nagamas69 1 posts
NLP 1 posts
OpenAI 5 posts
OpenNYC Data 1 posts
pySpark 1 posts
Python 16 posts
Python 458 posts
Python data analysis 4 posts
Python Shiny 2 posts
R 404 posts
R Data Analysis 1 posts
R Shiny 560 posts
R Visualization 445 posts
RAG 1 posts
RoBERTa 1 posts
semantic rearch 2 posts
Spark 17 posts
SQL 1 posts
Streamlit 2 posts
Student Works 1687 posts
Tableau 12 posts
TensorFlow 3 posts
Traffic 1 posts
User Preference Modeling 1 posts
Vector database 2 posts
Web Scraping 483 posts
wukong138 1 posts

Our Recent Popular Posts

AI 4 AI: ChatGPT Unifies My Blog Posts
by Vinod Chugani
Dec 18, 2022
Meet Your Machine Learning Mentors: Kyle Gallatin
by Vivian Zhang
Nov 4, 2020
NICU Admissions and CCHD: Predicting Based on Data Analysis
by Paul Lee, Aron Berke, Bee Kim, Bettina Meier and Ira Villar
Jan 7, 2020

View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day ChatGPT citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay football gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income industry Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI

NYC Data Science Academy

NYC Data Science Academy teaches data science, trains companies and their employees to better profit from data, excels at big data project consulting, and connects trained Data Scientists to our industry.

NYC Data Science Academy is licensed by New York State Education Department.

Get detailed curriculum information about our
amazing bootcamp!

Please enter a valid email address
Sign up completed. Thank you!

Offerings

  • HOME
  • DATA SCIENCE BOOTCAMP
  • ONLINE DATA SCIENCE BOOTCAMP
  • Professional Development Courses
  • CORPORATE OFFERINGS
  • HIRING PARTNERS
  • About

  • About Us
  • Alumni
  • Blog
  • FAQ
  • Contact Us
  • Refund Policy
  • Join Us
  • SOCIAL MEDIA

    ยฉ 2025 NYC Data Science Academy
    All rights reserved. | Site Map
    Privacy Policy | Terms of Service
    Bootcamp Application