Gracenote Confidential

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Projecting the Olympic Games: The Virtual Medal Table

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Prediction

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Projecting Olympic Medals

○ Economic Factors

○ Expertise

○ Results data

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Economic Factors

○ Needs to include previous Olympic performance

○ Static

○ No information on who wins medals

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Expertise

○ Available only just before the Olympic Games

○ Static

○ Potentially subject to recency bias

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Results

○ More information

○ Dynamic

○ Allocates medals

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Predicting Olympic medals using data

○ Relative Medal Table

○ Virtual Medal Table

○ Ranking methodology

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Virtual Medal Table 1.0

○ Developed in 2010

○ Published by media

○ Uses weighted results data

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Virtual Medal Table 1.0

○ All significant events

○ Four year time period

○ Result

○ Competition importance

○ Time

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Virtual Medal Table 1.0 – how did it perform?

○ 54% of medals correct

○ 22% of gold medals correct

○ 78% of medallists in top-8

○ 15 of top-20 NOCs within 4 medals

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Virtual Medal Table 1.0 – how did it perform?

○ 89% of all NOCs within 4 medals

○ Predicted ranking highly correlated

○ More accurate than econ models

○ Comparable with Sports Illustrated

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Virtual Medal Table improvements

○ Adding home advantage

○ Taking account of margins

○ Personal bests

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Virtual Medal Table 2016

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Virtual Medal Table 2016 - Insights

○ Best Games for the Netherlands & New Zealand

○ 25 medals for Korea for 8th successive Olympics

○ Asia to continue its rise

○ Four gold medals for Simone Biles

○ Most GBR medals in swimming since 1908

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Application

○ Media

○ Sports organisations

○ Anti-doping

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Room for further improvement

○ Generic model

○ Subjective weightings

○ Probabilities not possible

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What’s next?

○ Rio 2016

○ Ranking methodologies

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