Over the first half of the 20th century, the Soviet Union's GDP per capita rose from 1,218 U.S. dollars to 2,8334 U.S. dollars. There was a slight decrease between 1913 and 1929 due to the devastation caused by the First World War and Russian Revolution and the transition to a communist government and socialist economic structure. However, GDP per capita grew over the following three intervals, and the Soviet Union's relative isolation in the 1920s and 1930s meant that it was relatively untouched by the Great Depression in the 1930s. At the end of the recovery period after the Second World War, in 1950, GDP per capita had already exceeded pre-war levels by a significant margin, and the Soviet Union emerged as one of the two global superpowers, alongside the United States.

In the first half of the 20th century, the Soviet Union's GDP per capita rose from roughly one-third of Western Europe's GDP per capita in 1900 to one-half of its rate in 1950. Although it grew gradually between the given intervals, it did drop between 1913 and 1929 due to the devastation caused by the First World War and Russian Revolution. However, this year also marked the beginning of the Great Depression, which caused a significant economic downturn across Western Europe while being relatively unfelt in the Soviet Union.

In 1950, at the end of the recovery period that followed the Second World War, GDP per capita across the Eastern Bloc varied greatly by country. Czechoslovakia, the most industrialized country in the Bloc after East Germany, had a GDP per capita that was 69 percent of the rate across Western European** countries. In contrast, Romania's GDP per capita was less than a quarter of the Western European average in 1950. 1950-1989 Generally speaking, Eastern European economies grew faster and made gains on those of the west (not including Mediterranean region) in the 1950s and 1960s, however, a series of recessions and increasing debts meant that this gap widened in the 1970s and 1980s. By 1989, as communism in Europe came to an end, the difference between overall GDP per capita in the Eastern and Western Blocs returned to a similar rate as in 1950, although it varied by country. The Soviet Union, Czechoslovakia, and Poland, three of the larger economies of those given, had a lower share of western GDP per capita in 1989 than in 1950, while the smaller economies of the Balkans saw an increase. 1989-2000 Between 1989 and 2000, the European Union's GDP per capita grew faster than in the former Eastern Bloc countries. However, the end of communism did negatively impact EU economies in the early 1990s. Poland was the only Eastern Bloc country to make gains on the west in these years, although this was more to do with its poor economy in the 1980s. The former-Soviet states, in particular, saw GDP per capita drop below one-quarter of the European Union's rate over this decade, as post-Soviet economic recovery did not realistically begin until the late 1990s.

In each half-decade between the mid-1960s to the mid-1980s, there was a consistent decline in the growth rate of the Soviet Union's national income, industrial output, and agricultural production. In the early 1980s, national income and industrial output growth dropped below half of their respective rates in the late 1960s, while agricultural output fell to almost a quarter of its previous level.

In the build up to the Second World War, the United States was the major power with the highest gross domestic product (GDP) per capita in the world. In 1938, the United States also had the highest overall GDP in the world, and by a significant margin, however differences in GDP per person were much smaller. Switzerland In terms of countries that played a notable economic role in the war, the neutral country of Switzerland had the highest GDP per capita in the world. A large part of this was due to the strength of Switzerland's financial system. Most major currencies abandoned the gold standard early in the Great Depression, however the Swiss Franc remained tied to it until late 1936. This meant that it was the most stable, freely convertible currency available as the world recovered from the Depression, and other major powers of the time sold large amounts of gold to Swiss banks in order to trade internationally. Switzerland was eventually surrounded on all sides by Axis territories and lived under the constant threat of invasion in the war's early years, however Swiss strategic military planning and economic leverage made an invasion potentially more expensive than it was worth. Switzerland maintained its neutrality throughout the war, trading with both sides, although its financial involvement in the Holocaust remains a point of controversy. Why look at GDP per capita? While overall GDP is a stronger indicator of a state's ability to fund its war effort, GDP per capita is more useful in giving context to a country's economic power in relation to its size and providing an insight into living standards and wealth distribution across societies. For example, Germany and the USSR had fairly similar GDPs in 1938, whereas Germany's per capita GDP was more than double that of the Soviet Union. Germany was much more industrialized and technologically advanced than the USSR, and its citizens generally had a greater quality of life. However these factors did not guarantee victory - the fact that the Soviet Union could better withstand the war of attrition and call upon its larger population to replenish its forces greatly contributed to its eventual victory over Germany in 1945.

In the decade that followed the dissolution of the Soviet Union and the collapse of communist systems in Eastern Europe, economic conditions across the region generally got worse before they improved. GDP per capita had been declining throughout most of the 1980s but fell dramatically as communism ended. In Central and Eastern Europe, economic recovery began in the early 1990s, whereas this process did not start until 1996 in the former-Soviet states. As a result, GDP per capita in Central and Eastern Europe had almost returned to its 1989 level within a decade, whereas GDP per capita in the former-Soviet states had dropped by 45 percent between 1989 and 1998. This transitionary period in the continent's east did have a knock-on effect on the continent's West. However, growth did continue. Additionally, GDP per capita was 2.2 times larger in the West than in the Soviet Union in 1989, but by 1998 it was 4.6 times larger.

Regression results for Ukraine, Belarus, and Russia using Model 1.

The period between 1950 and 1973, known as the "Golden Age of capitalism" in the west, was the most prosperous period in Europe's modern history. The economic boom in the post-war period saw GDP grow by an average of almost four percent in Western and Eastern Europe, and almost five percent in the south. Although the west was the most technologically advanced of the three, this period did see a significant amount of catching up in the other two regions, whose rapid industrialization and urbanization changed the lives of its citizens forever. Recession hits the west The recession of 1973-1975 brought this economic and industrial growth to an end, however, as conflict in the Middle East saw oil prices skyrocket. Virtually all of Western Europe's industrial powers went into recession, and this had a detrimental knock-on effect in Poland and Romania due to their indebtedness to the west. While the recession ended in most countries by 1976, factors such as unemployment, inflation, and industrial output often remained high until the 1980s. The 1980s and 1990s also saw the rapid economic growth of countries such as Ireland and Finland. However, growth was much slower in these decades for most western economies than it had been in the 1950s and1960s. Collapse of communism The 1970s marked the beginning of the economic decline in Eastern Europe, as the command economies of the East Bloc could not maintain pace with the capitalist west and failed to adapt to the challenges that emerged in this period. Communism in Eastern Europe eventually ended around the early 1990s, and the largest power, the Soviet Union, was dissolved. This resulted in severe economic hardships in the former communist states, and recovery in the former-Soviet states did not begin until the late 1990s. The effects of communism's collapse in Europe was so severe that GDP in the east actually fell by an average of 0.9 percent per year between 1973 and 1998

Regression results for Ukraine using Ministry of Health and HFA data.

Regression using Model 2: Significance of the inverse of GDP as a covariate.

Analysis of ‘Winter Olympics Prediction - Fantasy Draft Picks’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/ericsbrown/winter-olympics-prediction-fantasy-draft-picks on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Olympic Draft Predictive Model

Our family runs an Olympic Draft - similar to fantasy football or baseball - for each Olympic cycle. The purpose of this case study is to identify trends in medal count / point value to create a predictive analysis of which teams should be selected in which order.

There are a few assumptions that will impact the final analysis: Point Value - Each medal is worth the following: Gold - 6 points Silver - 4 points Bronze - 3 points For analysis reviewing the last 10 Olympic cycles. Winter Olympics only.

All GDP numbers are in USD

My initial hypothesis is that larger GDP per capita and size of contingency are correlated with better points values for the Olympic draft.

All Data pulled from the following Datasets:

Winter Olympics Medal Count - https://www.kaggle.com/ramontanoeiro/winter-olympic-medals-1924-2018 Worldwide GDP History - https://data.worldbank.org/indicator/NY.GDP.MKTP.CD?end=2020&start=1984&view=chart

GDP data was a wide format when downloaded from the World Bank. Opened file in Excel, removed irrelevant years, and saved as .csv.

Process

In RStudio utilized the following code to convert wide data to long:

install.packages("tidyverse") library(tidyverse) library(tidyr)

Converting to long data from wide

long <- newgdpdata %>% gather(year, value, -c("Country Name","Country Code"))

Completed these same steps for GDP per capita.

Primary Key Creation

Differing types of data between these two databases and there is not a good primary key to utilize. Used CONCAT to create a new key column in both combining the year and country code to create a unique identifier that matches between the datasets.

SELECT *, CONCAT(year,country_code) AS "Primary" FROM medal_count

Saved as new table "medals_w_primary"

Utilized Excel to concatenate the primary key for GDP and GDP per capita utilizing:

=CONCAT()

Saved as new csv files.

Uploaded all to SSMS.

Contingent Size

Next need to add contingent size.

No existing database had this information. Pulled data from Wikipedia.

2018 - No problem, pulled existing table. 2014 - Table was not created. Pulled information into excel, needed to convert the country NAMES into the country CODES.

Created excel document with all ISO Country Codes. Items were broken down between both formats, either 2 or 3 letters. Example:

AF/AFG

Used =RIGHT(C1,3) to extract only the country codes.

For the country participants list in 2014, copied source data from Wikipedia and pasted as plain text (not HTML).

Items then showed as: Albania (2)

Broke cells using "(" as the delimiter to separate country names and numbers, then find and replace to remove all parenthesis from this data.

We were left with: Albania 2

Used VLOOKUP to create correct country code: =VLOOKUP(A1,'Country Codes'!A:D,4,FALSE)

This worked for almost all items with a few exceptions that didn't match. Based on nature and size of items, manually checked on which items were incorrect.

Chinese Taipei 3 #N/A Great Britain 56 #N/A Virgin Islands 1 #N/A

This was relatively easy to fix by adding corresponding line items to the Country Codes sheet to account for future variability in the country code names.

Copied over to main sheet.

Repeated this process for additional years.

Once complete created sheet with all 10 cycles of data. In total there are 731 items.

Data Cleaning

Filtered by Country Code since this was an issue early on.

Found a number of N/A Country Codes:

Serbia and Montenegro FR Yugoslavia FR Yugoslavia Czechoslovakia Unified Team Yugoslavia Czechoslovakia East Germany West Germany Soviet Union Yugoslavia Czechoslovakia East Germany West Germany Soviet Union Yugoslavia

Appears to be issues with older codes, Soviet Union block countries especially. Referred to historical data and filled in these country codes manually. Codes found on iso.org.

Filled all in, one issue that was more difficult is the Unified Team of 1992 and Soviet Union. For simplicity used code for Russia - GDP data does not recognize the Soviet Union, breaks the union down to constituent countries. Using Russia is a reasonable figure for approximations and analysis to attempt to find trends.

From here created a filter and scanned through the country names to ensure there were no obvious outliers. Found the following:

Olympic Athletes from Russia[b] -- This is a one-off due to the recent PED controversy for Russia. Amended the Country Code to RUS to more accurately reflect the trends.

Korea[a] and South Korea -- both were listed in 2018. This is due to the unified Korean team that competed. This is an outlier and does not warrant standing on its own as the 2022 Olympics will not have this team (as of this writing on 01/14/2022). Removed the COR country code item.

Confirmed Primary Key was created for all entries.

Ran minimum and maximum years, no unexpected values. Ran minimum and maximum Athlete numbers, no unexpected values. Confirmed length of columns for Country Code and Primary Key.

No NULL values in any columns. Ready to import to SSMS.

SQL work

We now have 4 tables, joined together to create the master table:

SELECT [OlympicDraft].[dbo].[medals_w_primary].[year], host_country, host_city, [OlympicDraft].[dbo].[medals_w_primary].[country_name], [OlympicDraft].[dbo].[medals_w_primary].[country_code], Gold, Silver, Bronze, [OlympicDraft].[dbo].[gdp_w_primary].[value] AS GDP, [OlympicDraft].[dbo].[convertedgdpdatapercapita].[gdp_per_capita], Atheletes FROM medals_w_primary INNER JOIN gdp_w_primary ON [OlympicDraft].[dbo].[medals_w_primary].[primary] = [OlympicDraft].[dbo].[gdp_w_primary].[year_country] INNER JOIN contingency_cleaned ON [OlympicDraft].[dbo].[medals_w_primary].[primary] = [OlympicDraft].[dbo].[contingency_cleaned].[Year_Country] INNER JOIN convertedgdpdatapercapita ON [OlympicDraft].[dbo].[medals_w_primary].[primary] = [OlympicDraft].[dbo].[convertedgdpdatapercapita].[Year_Country] ORDER BY year DESC

This left us with the following table:

https://i.imgur.com/tpNhiNs.png" alt="Imgur">

Performed some basic cleaning tasks to ensure no outliers:

Checked GDP numbers: 1992 North Korea shows as null. Updated this row with information from countryeconomy.com - $12,458,000,000

Checked GDP per capita:

1992 North Korea again missing. Updated this to $595, utilized same source.

UPDATE [OlympicDraft].[dbo].[gdp_w_primary] SET [OlympicDraft].[dbo].[gdp_w_primary].[value] = 12458000000 WHERE [OlympicDraft].[dbo].[gdp_w_primary].[year_country] = '1992PRK'

UPDATE [OlympicDraft].[dbo].[convertedgdpdatapercapita] SET [OlympicDraft].[dbo].[convertedgdpdatapercapita].[gdp_per_capita] = 595 WHERE [OlympicDraft].[dbo].[convertedgdpdatapercapita].[year_country] = '1992PRK'

Liechtenstein showed as an outlier with GDP per capita at 180,366 in 2018. Confirmed this number is correct per the World Bank, appears Liechtenstein does not often have atheletes in the winter olympics. Performing a quick SQL search to verify this shows that they fielded 3 atheletes in 2018, with a Bronze medal being won. Initially this appears to be a good ratio for win/loss.

Finally, need to create a column that shows the total point value for each of these rows based on the above formula (6 points for Gold, 4 points for Silver, 3 points for Bronze).

Updated query as follows:

SELECT [OlympicDraft].[dbo].[medals_w_primary].[year], host_country, host_city, [OlympicDraft].[dbo].[medals_w_primary].[country_name], [OlympicDraft].[dbo].[medals_w_primary].[country_code], Gold, Silver, Bronze, [OlympicDraft].[dbo].[gdp_w_primary].[value] AS GDP, [OlympicDraft].[dbo].[convertedgdpdatapercapita].[gdp_per_capita], Atheletes, (Gold*6) + (Silver*4) + (Bronze*3) AS 'Total_Points' FROM [OlympicDraft].[dbo].[medals_w_primary] INNER JOIN gdp_w_primary ON [OlympicDraft].[dbo].[medals_w_primary].[primary] = [OlympicDraft].[dbo].[gdp_w_primary].[year_country] INNER JOIN contingency_cleaned ON [OlympicDraft].[dbo].[medals_w_primary].[primary] = [OlympicDraft].[dbo].[contingency_cleaned].[Year_Country] INNER JOIN convertedgdpdatapercapita ON [OlympicDraft].[dbo].[medals_w_primary].[primary] = [OlympicDraft].[dbo].[convertedgdpdatapercapita].[Year_Country] ORDER BY [OlympicDraft].[dbo].[convertedgdpdatapercapita].[year]

Spot checked, calculating correctly.

Saved result as winter_olympics_study.csv.

We can now see that all relevant information is in this table:

https://i.imgur.com/ceZvqCA.png" alt="Imgur">

RStudio Work

To continue our analysis, opened this CSV in RStudio.

install.packages("tidyverse") library(tidyverse) library(ggplot2) install.packages("forecast") library(forecast) install.packages("GGally") library(GGally) install.packages("modelr") library(modelr)

View(winter_olympic_study)

Finding correlation between gdp_per_capita and Total_Points

ggplot(data = winter_olympic_study) + geom_point(aes(x=gdp_per_capita,y=Total_Points,color=country_name)) + facet_wrap(~country_name)

cor(winter_olympic_study$gdp_per_capita, winter_olympic_study$Total_Points, method = c("pearson"))

Result is .347, showing a moderate correlation between these two figures.

Looked next at GDP vs. Total_Points ggplot(data = winter_olympic_study) + geom_point(aes(x=GDP,y=Total_Points,color=country_name))+ facet_wrap(~country_name)

cor(winter_olympic_study$GDP, winter_olympic_study$Total_Points, method = c("pearson")) This resulted in 0.35, statistically insignificant difference between this and GDP Per Capita

Next looked at contingent size vs. total points ggplot(data = winter_olympic_study) + geom_point(aes(x=Atheletes,y=Total_Points,color=country_name)) +

Perinatal mortality data from the HFA database and the Ministry of Public Health.

Olympic Draft Predictive Model

Our family runs an Olympic Draft - similar to fantasy football or baseball - for each Olympic cycle. The purpose of this case study is to identify trends in medal count / point value to create a predictive analysis of which teams should be selected in which order.

There are a few assumptions that will impact the final analysis: Point Value - Each medal is worth the following: Gold - 6 points Silver - 4 points Bronze - 3 points For analysis reviewing the last 10 Olympic cycles. Winter Olympics only.

All GDP numbers are in USD

My initial hypothesis is that larger GDP per capita and size of contingency are correlated with better points values for the Olympic draft.

All Data pulled from the following Datasets:

Winter Olympics Medal Count - https://www.kaggle.com/ramontanoeiro/winter-olympic-medals-1924-2018 Worldwide GDP History - https://data.worldbank.org/indicator/NY.GDP.MKTP.CD?end=2020&start=1984&view=chart

GDP data was a wide format when downloaded from the World Bank. Opened file in Excel, removed irrelevant years, and saved as .csv.

Process

In RStudio utilized the following code to convert wide data to long:

install.packages("tidyverse") library(tidyverse) library(tidyr)

Converting to long data from wide

long <- newgdpdata %>% gather(year, value, -c("Country Name","Country Code"))

Completed these same steps for GDP per capita.

Primary Key Creation

Differing types of data between these two databases and there is not a good primary key to utilize. Used CONCAT to create a new key column in both combining the year and country code to create a unique identifier that matches between the datasets.

SELECT *, CONCAT(year,country_code) AS "Primary" FROM medal_count

Saved as new table "medals_w_primary"

Utilized Excel to concatenate the primary key for GDP and GDP per capita utilizing:

=CONCAT()

Saved as new csv files.

Uploaded all to SSMS.

Contingent Size

Next need to add contingent size.

No existing database had this information. Pulled data from Wikipedia.

2018 - No problem, pulled existing table. 2014 - Table was not created. Pulled information into excel, needed to convert the country NAMES into the country CODES.

Created excel document with all ISO Country Codes. Items were broken down between both formats, either 2 or 3 letters. Example:

AF/AFG

Used =RIGHT(C1,3) to extract only the country codes.

For the country participants list in 2014, copied source data from Wikipedia and pasted as plain text (not HTML).

Items then showed as: Albania (2)

Broke cells using "(" as the delimiter to separate country names and numbers, then find and replace to remove all parenthesis from this data.

We were left with: Albania 2

Used VLOOKUP to create correct country code: =VLOOKUP(A1,'Country Codes'!A:D,4,FALSE)

This worked for almost all items with a few exceptions that didn't match. Based on nature and size of items, manually checked on which items were incorrect.

Chinese Taipei 3 #N/A Great Britain 56 #N/A Virgin Islands 1 #N/A

This was relatively easy to fix by adding corresponding line items to the Country Codes sheet to account for future variability in the country code names.

Copied over to main sheet.

Repeated this process for additional years.

Once complete created sheet with all 10 cycles of data. In total there are 731 items.

Data Cleaning

Filtered by Country Code since this was an issue early on.

Found a number of N/A Country Codes:

Serbia and Montenegro FR Yugoslavia FR Yugoslavia Czechoslovakia Unified Team Yugoslavia Czechoslovakia East Germany West Germany Soviet Union Yugoslavia Czechoslovakia East Germany West Germany Soviet Union Yugoslavia

Appears to be issues with older codes, Soviet Union block countries especially. Referred to historical data and filled in these country codes manually. Codes found on iso.org.

Filled all in, one issue that was more difficult is the Unified Team of 1992 and Soviet Union. For simplicity used code for Russia - GDP data does not recognize the Soviet Union, breaks the union down to constituent countries. Using Russia is a reasonable figure for approximations and analysis to attempt to find trends.

From here created a filter and scanned through the country names to ensure there were no obvious outliers. Found the following:

Olympic Athletes from Russia[b] -- This is a one-off due to the recent PED controversy for Russia. Amended the Country Code to RUS to more accurately reflect the trends.

Korea[a] and South Korea -- both were listed in 2018. This is due to the unified Korean team that competed. This is an outlier and does not warrant standing on its own as the 2022 Olympics will not have this team (as of this writing on 01/14/2022). Removed the COR country code item.

Confirmed Primary Key was created for all entries.

Ran minimum and maximum years, no...

Improvement in fit from the strontium term when added to the GDP term.

In 1950, GDP per capita in Western Europe (29 countries) was just 48 percent of GDP per capita in the U.S. The post-war economic boom from 1950 to 1973 was the most prosperous period in Western Europe's history, and GDP per capita more than doubled in this period, reaching 69 percent of the U.S.' rate. Due to several economic crises in Europe in the following decades, growth rates in Western Europe remained relatively stable. Still, they did not reach the same heights as seen during the so-called Golden Age of Capitalism.

In contrast, the U.S. had been harder hit than Western Europe by the economic difficulties of the 1970s and 1980s, but the dissolution of the Soviet Union in 1991 coincided with one of the most successful decades in U.S. history, with the economy thriving in the 1990s. For Western Europe, the fall of communism had a knock-on effect that limited growth in the early 1990s, although GDP per capita compared to the U.S. was fairly similar to 1973's rate (albeit lower) at 66 percent.

Comparison of HFA data from Belarus with data from the Ministry of Health.

Improvement in model fit with each level of model refinement.

Throughout the Second World War, the United States consistently had the largest gross domestic product (GDP) in the world. Additionally, U.S. GDP grew significantly throughout the war, whereas the economies of Europe and Japan saw relatively little growth, and were often in decline. The impact of key events in the war is also reflected in the trends shown here - the economic declines of France and the Soviet Union coincide with the years of German invasion, while the economies of the three Axis countries experienced their largest declines in the final year of the war.

Significance of the bell-shaped excess terms from regressions with Model 1.

Compared to Western Europe over the late 20th century, the GDP per capita across some of Western Europe's peripheral countries (Greece, Ireland, Portugal, and Spain) grew from approximately half of the rest of Western Europe's rate in 1950, to three quarters in 1998. By comparison, GDP per capita in the Eastern Bloc fell in the same period, from 42 to 29 percent in Central and Eastern Europe, and from 57 to 21 percent in the Soviet Union and its successor states.