Write a program to run simulations of the FIFA World Cup.

```
$ python tournament.py 2018m.csv
Belgium: 20.9% chance of winning
Brazil: 20.3% chance of winning
Portugal: 14.5% chance of winning
Spain: 13.6% chance of winning
Switzerland: 10.5% chance of winning
Argentina: 6.5% chance of winning
England: 3.7% chance of winning
France: 3.3% chance of winning
Denmark: 2.2% chance of winning
Croatia: 2.0% chance of winning
Colombia: 1.8% chance of winning
Sweden: 0.5% chance of winning
Uruguay: 0.1% chance of winning
Mexico: 0.1% chance of winning
```

## Background

In soccer’s World Cup, the knockout round consists of 16 teams. In each round, each team plays another team and the losing teams are eliminated. When only two teams remain, the winner of the final match is the champion.

In soccer, teams are given FIFA Ratings, which are numerical values representing each team’s relative skill level. Higher FIFA ratings indicate better previous game results, and given two teams’ FIFA ratings, it’s possible to estimate the probability that either team wins a game based on their current ratings. The FIFA Ratings from just before the two previous World Cups are available as the May 2018 Men’s FIFA Ratings and March 2019 Women’s FIFA Ratings.

Using this information, we can simulate the entire tournament by repeatedly simulating rounds until we’re left with just one team. And if we want to estimate how likely it is that any given team wins the tournament, we might simulate the tournament many times (e.g. 1000 simulations) and count how many times each team wins a simulated tournament.

Your task in this lab is to do just that using Python!

## Getting Started

- Log into ide.cs50.io using your GitHub account.
- In your terminal window, run
`wget https://cdn.cs50.net/2020/fall/labs/6/lab6.zip`

to download a Zip file of the lab distribution code. - In your terminal window, run
`unzip lab6.zip`

to unzip (i.e., decompress) that Zip file. - In your terminal window, run
`cd lab6`

to change directories into your`lab6`

directory.

## Understanding

Start by taking a look at the `2018m.csv`

file. This file contains the 16 teams in the knockout round of the 2018 Men’s World Cup and the ratings for each team. Notice that the CSV file has two columns, one called `team`

(representing the team’s country name) and one called `rating`

(representing the team’s rating).

The order in which the teams are listed determines which teams will play each other in each round (in the first round, for example, Uruguay will play Portugal and France will play Argentina; in the next round, the winner of the Uruguay-Portugal match will play the winner of the France-Argentina match). So be sure not to edit the order in which teams appear in this file!

Ultimately, in Python, we can represent each team as a dictionary that contains two values: the team name and the rating. Uruguay, for example, we would want to represent in Python as `{"team": "Uruguay", "rating": 976}`

.

Next, take a look at `2019w.csv`

, which contains data formatted the same way for the 2019 Women’s World Cup.

Now, open `tournament.py`

and see that we’ve already written some code for you. The variable `N`

at the top represents how many World Cup simulations to run: in this case, 1000.

The `simulate_game`

function accepts two teams as inputs (recall that each team is a dictionary containing the team name and the team’s rating), and simulates a game between them. If the first team wins, the function returns `True`

; otherwise, the function returns `False`

.

The `simulate_round`

function accepts a list of teams (in a variable called `teams`

) as input, and simulates games between each pair of teams. The function then returns a list of all of the teams that won the round.

In the `main`

function, notice that we first ensure that `len(sys.argv)`

(the number of command-line arguments) is 2. We’ll use command-line arguments to tell Python which team CSV file to use to run the tournament simulation. We’ve then defined a list called `teams`

(which will eventually be a list of teams) and a dictionary called `counts`

(which will associate team names with the number of times that team won a simulated tournament). Right now they’re both empty, so populating them is left up to you!

Finally, at the end of `main`

, we sort the teams in descending order of how many times they won simulations (according to `counts`

) and print the estimated probability that each team wins the World Cup.

Populating `teams`

and `counts`

and writing the `simulate_tournament`

function are left up to you!

## Implementation Details

Complete the implementation of `tournament.py`

, such that it simulates a number of tournaments and outputs each team’s probability of winning.

First, in `main`

, read the team data from the CSV file into your program’s memory, and add each team to the list `teams`

.

- The file to use will be provided as a command-line argument. You can access the name of the file, then, with
`sys.argv[1]`

. - Recall that you can open a file with
`open(filename)`

, where`filename`

is a variable storing the name of the file. - Once you have a file
`f`

, you can use`csv.DictReader(f)`

to give you a “reader”: an object in Python that you can loop over to read the file one row at a time, treating each row as a dictionary. - By default, all values read from the file will be strings. So be sure to first convert the team’s
`rating`

to an`int`

(you can use the`int`

function in Python to do this). - Ultimately, append each team’s dictionary to
`teams`

. The function call`teams.append(x)`

will append`x`

to the list`teams`

. - Recall that each team should be a dictionary with a
`team`

name and a`rating`

.

Next, implement the `simulate_tournament`

function. This function should accept as input a list of teams and should repeatedly simulate rounds until you’re left with one team. The function should the return the name of that team.

- You can call the
`simulate_round`

function, which simulates a single round, accepting a list of teams as input and returning a list of all of the winners. - Recall that if
`x`

is a list, you can use`len(x)`

to determine the length of the list. - You should not assume the number of teams in the tournament, but you may assume it will be a power of 2.

Finally, back in the `main`

function, run `N`

tournament simulations, and keep track of how many times each team wins in the `counts`

dictionary.

- For example, if Uruguay won 2 games and Portugal won 3 games, then your
`counts`

dictionary should be`{"Uruguay": 2, "Portugal": 3}`

. - You should use your
`simulate_tournament`

to simulate each tournament and determine the winner. - Recall that if
`counts`

is a dictionary, then syntax like`counts[team_name] = x`

will associate the key stored in`team_name`

with the value stored in`x`

. - You can use the
`in`

keyword in Python to check if a dictionary has a particular key already. For example,`if "Portugal" in counts:`

will check to see if`"Portugal"`

already has an existing value in the`counts`

dictionary.

## Walkthrough

### Hints

- When reading in the file, you may find this syntax helpful, with
`filename`

as the name of your file and`file`

as a variable.`with open(filename) as file: reader = csv.DictReader(file)`

- In Python, to append to the end of a list, use the
`.append()`

function.

### Testing

Your program should behave per the examples below. Since simulations have randomness within each, your output will likely not perfectly match the examples below.

```
$ python tournament.py 2018m.csv
Belgium: 20.9% chance of winning
Brazil: 20.3% chance of winning
Portugal: 14.5% chance of winning
Spain: 13.6% chance of winning
Switzerland: 10.5% chance of winning
Argentina: 6.5% chance of winning
England: 3.7% chance of winning
France: 3.3% chance of winning
Denmark: 2.2% chance of winning
Croatia: 2.0% chance of winning
Colombia: 1.8% chance of winning
Sweden: 0.5% chance of winning
Uruguay: 0.1% chance of winning
Mexico: 0.1% chance of winning
```

```
$ python tournament.py 2019w.csv
Germany: 17.1% chance of winning
United States: 14.8% chance of winning
England: 14.0% chance of winning
France: 9.2% chance of winning
Canada: 8.5% chance of winning
Japan: 7.1% chance of winning
Australia: 6.8% chance of winning
Netherlands: 5.4% chance of winning
Sweden: 3.9% chance of winning
Italy: 3.0% chance of winning
Norway: 2.9% chance of winning
Brazil: 2.9% chance of winning
Spain: 2.2% chance of winning
China PR: 2.1% chance of winning
Nigeria: 0.1% chance of winning
```

- You might be wondering what actually happened at the 2018 and 2019 World Cups! For Men’s, France won, defeating Croatia in the final. Belgium defeated England for the third place position. For Women’s, the United States won, defeating the Netherlands in the final. England defeated Sweden for the third place position.

## How to Test Your Code

Execute the below to evaluate the correctness of your code using `check50`

. But be sure to compile and test it yourself as well!

`check50 cs50/labs/2021/spring/worldcup`

Execute the below to evaluate the style of your code using `style50`

.

`style50 tournament.py`

## How to Submit

Execute the below, logging in with your GitHub username and password when prompted. For security, you’ll see asterisks (`*`

) instead of the actual characters in your password.

`submit50 cs50/labs/2021/x/worldcup`