Our Data Scientist Maxime presented the results of his predictive model for the games of the 2015 Rugby World Cup. You can also read his blogpost here : http://www.dataiku.com/blog/2015/09/18/Sports_analytics_World_Cup.html

1. A little Rugby with Data
Science Studio!

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

3. Collecting Data
•Web scraping in python (beautifulSoup + urllib2)
•Thank you to rugbydata.com :
http://www.rugbydata.com/italy/romania/gamesplayed/
•Easy to parse!

4. Team Dataset

5. Games Dataset

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

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

8. Graph of games played
South Africa and Japan have never played each other!

9. 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

10. Feature importance
accuracy : 0.7

11. ROC Graph

12. 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)

13. 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:

14. Thank
you
And “ALLEZ LES BLEUS”