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)) +

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