Proceedings of the Advances in Materials, Machinery, Electrical Engineering (AMMEE 2017)

Olympic Medals: Does The Past Predict The Future?

Authors
Yi Han
Corresponding Author
Yi Han
Available Online June 2017.
DOI
10.2991/ammee-17.2017.158How to use a DOI?
Keywords
Gray Prediction Model, Olympic medal, Historical Achievements.
Abstract

With the United States retain its position as the top medal-winning nation at last year's Olympic Games in Rio, the most greatly desirable event came to the end. Is the result the same as your expectation? Is it possible to predict how many medals each nation will win? And what is it about a nation that allows it to produce Olympic medal-winning athletes? Even though the performances of individual athletes can vary unpredictably, we reasoned, there might be an overall relationship between a country's fundamental characteristics (its size and amount of wealth, for instance) and the number of medals it would likely take home. First, considering the past Olympic success, we developed a factor model with its weight in achievement of Games calculated by Grey Prediction Model. Based on this model, we chose china for further analysis and found that there is a link between them but not obvious.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the Advances in Materials, Machinery, Electrical Engineering (AMMEE 2017)
Series
Advances in Engineering Research
Publication Date
June 2017
ISBN
978-94-6252-350-0
ISSN
2352-5401
DOI
10.2991/ammee-17.2017.158How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Yi Han
PY  - 2017/06
DA  - 2017/06
TI  - Olympic Medals: Does The Past Predict The Future?
BT  - Proceedings of the Advances in Materials, Machinery, Electrical Engineering (AMMEE 2017)
PB  - Atlantis Press
SP  - 824
EP  - 827
SN  - 2352-5401
UR  - https://doi.org/10.2991/ammee-17.2017.158
DO  - 10.2991/ammee-17.2017.158
ID  - Han2017/06
ER  -