As @DSM points out, you can do this more directly using the vectorised string methods:

df['Date'].str[-4:].astype(int)

Or using extract (assuming there is only one set of digits of length 4 somewhere in each string):

df['Date'].str.extract('(?P<year>\d{4})').astype(int)

An alternative slightly more flexible way, might be to use apply (or equivalently map) to do this:

df['Date'] = df['Date'].apply(lambda x: int(str(x)[-4:]))
             #  converts the last 4 characters of the string to an integer

The lambda function, is taking the input from the Date and converting it to a year.
You could (and perhaps should) write this more verbosely as:

def convert_to_year(date_in_some_format):
    date_as_string = str(date_in_some_format)  # cast to string
    year_as_string = date_in_some_format[-4:] # last four characters
    return int(year_as_string)

df['Date'] = df['Date'].apply(convert_to_year)

Perhaps 'Year' is a better name for this column...

First of all, if you need to sort over datetimes, I would suggest to either use the YYYYMMDD string representation of dates (e.g. 20191108 for the first record) or to use actual datetime data types. Using the American notation is confusing and not easy to sort on.

In any case, to solve your issue I would advise to use pandas pivot function first, followed by a fill NaN (i.e. fillna) function with a backfill (i.e. bfill) method.

EDIT: If you want to keep the Country column, it seems that using it as a multi-index with the Date column won't work with pivot. What you can do is to keep the original df and join it with the new one on the Date column.

import pandas as pd
import datetime as dt    

# Create DataFrame similar to example
df = pd.DataFrame(data={'Date': ['11/8/2019','2/20/2019','9/22/2017','6/28/2016','6/27/2016','6/24/2016','6/12/2015','6/13/2014'], 
                        'Team': ['Team A','Team B','Team A','Team B','Team C','Team A','Team C','Team C'], 
                        'Rating': [95,90,90,90,90,95,100,100]})


# Convert strings to datetimes
df['Date'] = df['Date'].map(lambda x: dt.datetime.strptime(x, '%m/%d/%Y'))
df['Country'] = 'United Kingdom'

# Pivot DataFrame
dfp = df.pivot(columns='Team', values='Rating')

# Join with Country from original df
dfp = df[['Date', 'Country']].join(dfp)

# sort descending on Date
dfp.sort_values(by='Date', ascending=False, inplace=True)

# dfp is:
# Date        Country         Team A  Team B  Team C
# 2019-11-08  United Kingdom  95.0     NaN     NaN
# 2019-02-20  United Kingdom   NaN    90.0     NaN
# 2017-09-22  United Kingdom  90.0     NaN     NaN
# ...

# Fill NaN values using the "next" row value
dfp.fillna(method='bfill', inplace=True)

# dfp is:
# Date        Country         Team A  Team B  Team C                              
# 2019-11-08  United Kingdom    95.0    90.0    90.0
# 2019-02-20  United Kingdom    90.0    90.0    90.0
# 2017-09-22  United Kingdom    90.0    90.0    90.0
# ...

Tried a few approaches, and I believe the best working one so far is below:

df['v4'] = df['v2'].apply(myFunction.classify)