A little Rugby with Data Science Studio!

IntroductionWhy? Because of the analysis of the last Rugby World Cup by this guy: http://andrewyuan.github.io/EDAV-project.htmlOutline: •Getting Data•Exploring the Data•Building the preliminary model•Discussing limits and possible improvements of the model

Collecting Data•Web scraping in python (beautifulSoup + urllib2)

•Thank you to rugbydata.com : http://www.rugbydata.com/italy/romania/gamesplayed/

•Easy to parse!

Team Dataset

Games Dataset

Exploration some features with Graph… and getting counter intuitive results!

Average number of points per gameJapan, Argentina and Namibia have the most points per game, while the 6 nations teams are the lowest…

Graph of games played

South Africa and Japan have never played each other!

Predicting the outcome of a game

•Outcome of a game : 0 if team 1 loses. 1 if team1 wins.

•Choosing the features: -Historic of the games (weighed or not) -Historic of points (weighed or not) -Historic of confrontations 1v1 (weighed or not) -Home game or not -Series of wins

•Particular precautions : No features like number of games played.

Choice of algorithm: Random Forest

Feature importance

accuracy : 0.7

ROC Graph

Results (good and not so good)

Assessing your predictions: - common good sense - Bookmakers

Comparison with four games played so far:France vs Italie : 0.881 (bookmaker : 0.909)England vs Fidji : 0.880 (bookmaker : 0.933)New-Zeeland vs Argentina : 0.943 (bookmaker : 0.980)

South Africa vs Japon : 0.496 (bookmaker : 0.964)

Limits and possible improvements• Predictions aren’t very good when there are very few

direct games between the two teams (Namibia and Japan for example)

• Adding the global rankings• No possible simulation on the long term• Doesn’t take into account bonus/malus

• Adding new features (teams in common…)• Taking into account the players that compose the team:

Thank you

And “ALLEZ LES BLEUS”