GDP based Medal Count Analysis in Summer Olympics Games for two Decades – An Exploratory Analysis
Sumathi VP1, Vanitha V2, Divyadarshini M3
1Sumathi VP, Department of Computer Science and Engineering, Kumaraguru College of Technology, Coimbatore (Tamil Nadu), India.
2Vanitha V, Department of Computer Science and Engineering, Kumaraguru College of Technology, Coimbatore (Tamil Nadu), India.
3Divyadarshini M, Department of Computer Science and Engineering, Kumaraguru College of Technology, Coimbatore (Tamil Nadu), India.
Manuscript received on 10 December 2018 | Revised Manuscript received on 29 December 2018 | Manuscript Published on 09 January 2019 | PP: 12-16 | Volume-7 Issue-4S November 2018 | Retrieval Number: E1977017519/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The Olympics games started way long back with many participants and winners from all over the world. The game involved in many disciplines and made a bigger impact on the participants and the audience as well. A big data boom is on the horizon, so it’s more important than ever to take control of this data. Instinctively this analysis recognise that to perform better than the competitors, this need accurate evidence and data to base the decisions on. The game had its debut in the year 1896 and the progress till now is recorded with the athlete’s respective years,disciplines,total medal counts. The goal of this thesis included improved understanding of the competing countries and to develop the players’ skills more efficiently for both the extremes (First 10 and Last 10 countries). The analysis is taken by the data of last 5 summer Olympics Games using statistical methods such as correlation factor. Performance analysis is based on the correlation factor with respect to country’s GDP (Gross Domestic Product), total medal counts and gold medal counts. This analysis results in an outcome for both extremes meant to amplify the information, which can make the users get higher knowledge about their competitors and country to proceed. There are attributes(year,GDP in million) taken from the dataset and derived attributes(country wise total medal count and country wise men and women athletes count and distinct medal counts for men and women) obtained and analysed to give the knowledge of both extreme countries’ (First 10 and Last 10 countries) performance in each year. Finally, the analysed data is plotted in graphs, which can help to find the successes as well asdisappointments.
Keywords: Exploratory Data Analysis; Olympics Analytics ; Performance; Medal Count Analysis; Gross Domestic Product (GDP); Analysis; Competitors Skillsets; Disciplines; Regular Expository; Statistical Methods; Sample Variance; Graphs.
Scope of the Article: Game Playing