Predicting and Comparing Medal Counts in the Summer

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Predicting and Comparing Medal Counts in the Summer and Winter Olympics Emmy Yarmy University

Predicting and Comparing Medal Counts in the Summer and Winter Olympics Emmy Yarmy University of Lynchburg Advisor: Dr. Bahaddine Taoufik

Objectives ● Predict the number of medals a country will win at the summer

Objectives ● Predict the number of medals a country will win at the summer olympics using GDP, political history, and latitude of the country’s capital as predictors. ● Predict the number of medals a country will win at the winter olympics using the same predictors. ● Evaluate the difference between the coefficients of these predictors in the summer versus winter olympics.

Data ● GDP- measured GDP per capita, in thousands of U. S. dollars ●

Data ● GDP- measured GDP per capita, in thousands of U. S. dollars ● Political history- dummy variable where 0 indicates no socialist history and 1 indicated socialist history ● Latitude- the latitude of the country’s capital

Summer Olympics Winter Olympics ● 42 Sports ● 205 Countries ● 11. 238 Athletes

Summer Olympics Winter Olympics ● 42 Sports ● 205 Countries ● 11. 238 Athletes ● 15 Sports ● 92 Countries ● 2, 952 Athletes

Summer Olympics

Summer Olympics

Descriptive Statistics Min: 0. 00 First Quartile: 0. 00 Median: 0. 00 Mean: 4.

Descriptive Statistics Min: 0. 00 First Quartile: 0. 00 Median: 0. 00 Mean: 4. 727 Third Quartile: 3. 00 Maximum: 121. 00 Variance: 171. 151 Summer Olympics

Zero-Inflated Count Model

Zero-Inflated Count Model

Interpretation of Count Model ● Of the countries with a propensity to win medals:

Interpretation of Count Model ● Of the countries with a propensity to win medals: ○ For every $1000 increase in GDP per capita, holding social history and latitude constant, expected number of medals will increase 3. 9%. ○ When a country has a socialist history, their expected number of medals rises 173% holding GDP constant. ● In this model, latitude is not significant ● We note that log(theta) is significant, indicating the use of the zero-inflated negative binomial model is correct.

Zero-Inflated Model

Zero-Inflated Model

Interpretation of Zero Model ● Of those who tend to win 0 medals, by

Interpretation of Zero Model ● Of those who tend to win 0 medals, by increasing the capital’s latitude by 1 degree while holding the GDP and social history constant, the country becomes 4. 3% less likely to be in the zero group. ● GDP per capita and social history cannot be deemed significant due to their high p values.

Winter Olympics

Winter Olympics

Descriptive Statistics Min: 0. 00 First Quartile: 0. 00 Median: 0. 00 Mean: 3.

Descriptive Statistics Min: 0. 00 First Quartile: 0. 00 Median: 0. 00 Mean: 3. 231 Third Quartile: 1. 50 Max: 39. 00 Variance: 56. 25 Winter Olympics

Zero-Inflated Count Model

Zero-Inflated Count Model

Interpretation of Count Model ● ● ● Of the countries with a propensity to

Interpretation of Count Model ● ● ● Of the countries with a propensity to win medals ○ When GDP per capita is increased by $1, 000, the expected number of medals will increase by a factor of about 8%, holding social history and latitude constant. ○ When the latitude of a country’s capital increases by 1 degree, the expected number of medals will increase by a factor of about 3% holding all else constant. ○ When a country has a socialist history, medal count is 3. 39 times higher on average than countries without a socialist history, holding GDP and latitude constant. Since log(theta) is significant, we are correct in using the zero inflated negative binomial model. Average GDP per capita of non-zero: $40, 253. 85

Zero-Inflated Model

Zero-Inflated Model

Interpretation of Zero Inflated Model ● There are no significant variables in this group.

Interpretation of Zero Inflated Model ● There are no significant variables in this group. ● This means that there is no variable we have discussed which would significantly impact the likelihood of a country winning 0 medals in the Winter Olympics.

Comparing Summer and Winter

Comparing Summer and Winter

Overall Comparisons Summer 205 Nations Winter 4. 73 Medals on Average $14, 957 Average

Overall Comparisons Summer 205 Nations Winter 4. 73 Medals on Average $14, 957 Average GDP 91 Nations 3. 23 Medals on Average $26, 620 Average GDP

Average GDP Per Capita by Group $40, 253 Winter Non-Zero Group $20, 243 Winter

Average GDP Per Capita by Group $40, 253 Winter Non-Zero Group $20, 243 Winter Zero Group $18, 829 Summer Non-Zero Group $12, 070 Summer Zero Group

Significant Variables in the Count Models Summer: ● GDP ● Social History Winter: ●

Significant Variables in the Count Models Summer: ● GDP ● Social History Winter: ● GDP ● Social History ● Latitude ● GDP is more significant and generates a larger coefficient in the Winter Games ● Social History is more significant in the Summer Games, but has a larger coefficient in the winter model

Results ● In order to even gain entry into the Winter Olympics, you have

Results ● In order to even gain entry into the Winter Olympics, you have to be a wealthier country. The zero group average GDP per capita of the Winter Olympics is almost $2000 higher than the non-zero group of the Summer Olympics. ● In the Summer Games, higher latitude countries are less likely to be in the zero group. ● The most likely country to win a medal would be a wealthy country with a high latitude and socialist history competing in the winter games.

Works Cited “ 2016 Summer Olympics. ” Wikipedia, Wikimedia Foundation, 23 Apr. 2018, en.

Works Cited “ 2016 Summer Olympics. ” Wikipedia, Wikimedia Foundation, 23 Apr. 2018, en. wikipedia. org/wiki/2016_Summer_Olympics. “ 2018 Peyong. Chang Olympics Medal Standings. ” NBC Olympics, www. nbcolympics. com/medals. “ 2018 Winter Olympics. ” Wikipedia, Wikimedia Foundation, 23 Apr. 2018, en. wikipedia. org/wiki/2018_Winter_Olympics. Bian, Xun. “Predicting Olympic Medal Counts: the Effects of Economic Development on Olympic Performance. ” The Park Place Economist, XIII, pp. 37– 44. , www. iwu. edu/economics/PPE 13/bian. pdf. “COUNTRY COMPARISON : : GDP - PER CAPITA (PPP). ” Central Intelligence Agency, www. cia. gov/library/publications/the-world-factbook/rankorder/2004 rank. html. Econometricsacademy. Count Data Models in R. Youtube, Youtube. com, 17 Feb. 2013, www. youtube. com/watch? v=y. LF 45 x 70 A 3 o. Graettinger, Dan, and TIm Graettinger. “Predicting the 2014 Winter Olympic Medal Count. ” Discovery Corps Inc. , 2014, www. discoverycorpsinc. com/winter-olympic-medal-predict_1/.

“List of National Capitals by Latitude. ” Wikipedia, Wikimedia Foundation, 23 Apr. 2018, en.

“List of National Capitals by Latitude. ” Wikipedia, Wikimedia Foundation, 23 Apr. 2018, en. wikipedia. org/wiki/List_of_national_capitals_by_latitude. “List of Socialist States. ” Wikipedia, Wikimedia Foundation, 23 Apr. 2018, en. wikipedia. org/wiki/List_of_socialist_states. Stromberg, Joseph. “Can a Statistical Model Accurately Predict Olympic Medal Counts? ” Smithsonian. com, Smithsonian Institution, 5 Feb. 2014, www. smithsonianmag. com/science-nature/can-statistical-model-accurately-predict-olympic-medalcounts-180949627/. “WWS 509. ” Princeton University, The Trustees of Princeton University, data. princeton. edu/wws 509/r/overdispersion. html. “Zero-Inflated Negative Binomial Regression | Stata Annotated Output. ” IDRE Stats, UCLA, stats. idre. ucla. edu/stata/output/zeroinflated-negative-binomial-regression/.