Data Scraping World Cup Fencing

Posted on Mar 20, 2016
The skills the author demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.
Contributed by Thomoas Kolasa. He attended in the NYC Data Science Academy 12 week full time Data Science Bootcamp program taking place between January 11th to April 1st, 2016. This post is based on his third class project -  python web scraping(due on the 6th week of the program).

As one of the five original Olympic events, fencing has a storied history. While some fencers trace their maestros' lineage back generations, data shows the globalization of fencing creates new schools that make the sport evolve more than ever. The Fédération Internationale d'Escrime (FIE) governs the sport and runs the global ranking system.

Women and men compete in three weapons: épée, foil, and sabre. Fencers improve their rankings through placement at five World Cups, three Grand Prix, the World Championships, and the Olympic Games over the previous year. Each country can send up to 12 fencers to World Cups and Grand Prix and only up to four fencers to the World Championships or Olympics.

Fencing results are available at https://fie.org/results-statistic/result from about 2002 onwards, so I collected them using URL manipulation and Beautiful Soup 4. Each tournament had a name, place, country, date, and tournament type. I made sure to record the entire tournaments list and check if they spread over multiple pages. Since I also accessed the URLs of the specific tournaments, I was also able to gather the results for each tournament, including points earned.

Data Scraping World Cup Fencing

The results for each tournament included:

  • Finishing placement
  • FIE points earned
  • Name
  • Nationality
  • Birth date
  • Competition
  • City
  • Country
  • Tournament dates
  • Weapon (√©p√©e, foil, or sabre)
  • Male/female
  • Tournament type

Investigating the data, I calculated the age of each fencer on the day of each tournament. Looking at a histogram of all tournament winners, fencers seem to get gold medals in their mid to late twenties.

Data Scraping World Cup Fencing

Is there home piste advantage in world cup fencing?

I next investigated if there is a home-field advantage in fencing like in other sports. I considered a home tournament to be in a fencer's home country. This means that a "home" tournament would include an American from the west coast fencing in a world cup in New York or a Russian fencer from Siberia fencing in St. Petersburg. I also only looked at world cup and grand prix results between 2010 and 2015, the period when the FIE standardized the number of world cups. Even though Grand Prix award 50% more points, the same fencers show up in nearly all the world cups and grand prix. I therefore normalized the points awarded at these two events to be equal.

Senior Men's Sabre average points earned at home (blue) and away (green)
world cups and grand prix, 2010-2015:
Data Scraping World Cup Fencing

The average number of home points earned includes the sum of points earned by local fencers divided by the number of world cups in that country. The average away points are calculated analogously, but for all other tournaments. However, this means the graph shows the averages of five world cups in Italy and only one world cup in South Korea.

To account for this, I calculated the weighted average of home-field advantage. I looked at high performing countries that scored on average at least ten FIE points abroad so as to avoid the effects of more fencers from weaker countries participating in tournaments hosted at home. I next multiplied the home percent improvement of a country times the home tournament count and divided by the total number of tournaments hosted by high-performing countries. This yielded that men's sabre fencers earned 42.7% more FIE points at home than abroad.

Does this advantage exist in épée?

I next investigated men's épée world cup and grand prix results. In épée, the referee does not have to determine which fencer has the right-of-way and thus makes far fewer subjective calls. I similarly looked at FIE points earned by fencers in their own countries.

Senior men's épée average points earned at home (blue) and away (green)
world cups and grand prix, 2010-2015:

sme_avg_points

Looking at a similar calculation of home country advantage for countries that on average score at least ten FIE points abroad, I found that men's épée has a home-country advantage of 26.9%.

Since men's sabre fencers had a 42.7% home country advantage, does this mean that right-of-way calls in sabre unfairly go to local fencers? Possibly. But with such a small sample of data, many confounding factors can account for this discrepancy. Since sabre is more fast-paced than épée, the psychological and physical advantages of fencing close to home may have a different effect. It would also be worth comparing these results with the advantage in foil and in all three women's disciplines.

To more thoroughly look into the fairness of fencing, one has to look at bout-level data.

Bout Score Data

For the tournaments with bout data, I collected direct elimination bout data including the fencers, their countries, and the rounds when the bouts took place, and the scores. The inconsistencies in the FIE's site made it a time-consuming process, especially with regards to preliminary rounds.

Results for each bout included:

  • round (64, 32, etc.)
  • fencer data
    • name
    • country
    • victory/defeat
    • touches
    • final placement
    • birthday
    • height
    • weight
    • dominant hand
  • opponent data (same features)
  • tournament data

A sample bout tableau from the quarterfinals onwards:

screenshot_bouts

While I originally wanted to analyze bout data going back many years, the website no longer has bout data available before 2014. Consequently, there was not enough data to perform much viable machine learning analysis on the data.

A further issue with the available data is that very few tournaments release referee data. Even though refereeing has improved drastically with the advent of video replays, consistent referee data would allow for better reviews of performance and an evidence-based certainty of impartiality among officials.

About Author

Thomas Kolasa

After working in econometric consulting, Thomas began learning programming in order to pursue data science: the perfect combination of his interests in computer science, statistics, and business strategy. Thomas earned his B.A. in economics from Harvard University where...
View all posts by Thomas Kolasa >

Related Articles

Leave a Comment

sisi gao March 20, 2021
Hello there, I am a maths student doing an internal assessment, is it ok if I have this data spreadsheet for finishing my work please? My email address is [email protected] Thank you so much!!
Rural Fencing June 20, 2016
Wowww......What's A BLog.. Good One..

View Posts by Categories


Our Recent Popular Posts


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 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 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 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