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A little Rugby with Data Science Studio!
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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
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Collecting Data•Web scraping in python (beautifulSoup + urllib2)
•Thank you to rugbydata.com : http://www.rugbydata.com/italy/romania/gamesplayed/
•Easy to parse!
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Team Dataset
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Games Dataset
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Exploration some features with Graph… and getting counter intuitive results!
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Average number of points per gameJapan, Argentina and Namibia have the most points per game, while the 6 nations teams are the lowest…
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Graph of games played
South Africa and Japan have never played each other!
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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
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Feature importance
accuracy : 0.7
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ROC Graph
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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)
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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:
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Thank you
And “ALLEZ LES BLEUS”