Simpler Version:

Problem:

You are a data analyst for a football team and have been tasked with analyzing the performance of the team’s players. Your goal is to identify the most effective players based on their statistical performance in the last season.

Data:

You are provided with a dataset containing the following statistics for each player:

- Goals scored: The number of goals scored by each player in the last season.
- Assists: The number of assists made by each player in the last season.

Task:

- Calculate the average number of goals scored by all players in the dataset.
- Identify the player(s) who scored more goals than the average.
- Calculate the average number of assists made by all players in the dataset.
- Identify the player(s) who made more assists than the average.

Same Version:

Problem:

You are a data analyst for a football team and have been tasked with analyzing the performance of the team’s players. Your goal is to identify the most effective players based on their statistical performance in the last season.

Data:

You are provided with a dataset containing the following statistics for each player:

- Goals scored: The number of goals scored by each player in the last season.
- Assists: The number of assists made by each player in the last season.
- Pass completion rate: The percentage of successful passes made by each player.
- Tackles won: The number of successful tackles made by each player.
- Shots on target: The number of shots on target made by each player.
- Minutes played: The total number of minutes played by each player in the last season.

Task:

- Calculate the average number of goals scored by all players in the dataset.
- Identify the player(s) who scored more goals than the average.
- Calculate the average number of assists made by all players in the dataset.
- Identify the player(s) who made more assists than the average.
- Calculate the average pass completion rate for all players in the dataset.
- Identify the player(s) who achieved a pass completion rate higher than the average.
- Calculate the average number of tackles won by all players in the dataset.
- Identify the player(s) who won more tackles than the average.
- Calculate the average number of shots on target made by all players in the dataset.
- Identify the player(s) who had more shots on target than the average.
- Calculate the average minutes played by all players in the dataset.
- Identify the player(s) who played more minutes than the average.

More Complex Version:

Problem:

You are a data analyst for a football team and have been tasked with analyzing the performance of the team’s players. Your goal is to identify the most effective players based on their statistical performance in the last season.

Data:

You are provided with a dataset containing the following statistics for each player:

- Goals scored: The number of goals scored by each player in the last season.
- Assists: The number of assists made by each player in the last season.
- Pass completion rate: The percentage of successful passes made by each player.
- Tackles won: The number of successful tackles made by each player.
- Shots on target: The number of shots on target made by each player.
- Minutes played: The total number of minutes played by each player in the last season.
- Yellow cards: The number of yellow cards received by each player.
- Red cards: The number of red cards received by each player.
- Distance covered: The total distance covered by each player in kilometers.

Task:

- Calculate the average number of goals scored by all players in the dataset.
- Identify the player(s) who scored more goals than the average.
- Calculate the average number of assists made by all players in the dataset.
- Identify the player(s) who made more assists than the average.
- Calculate the average pass completion rate for all players in the dataset.
- Identify the player(s) who achieved a pass completion rate higher than the average.
- Calculate the average number of tackles won by all players in the dataset.
- Identify the player(s) who won more tackles than the average.
- Calculate the average number of shots on target made by all players in the dataset.
- Identify the player(s) who had more shots on target than the average.
- Calculate the average minutes played by all players in the dataset.
- Identify the player(s) who played more minutes than the average.
- Calculate the average number of yellow cards received by all players in the dataset.
- Identify the player(s) who received fewer yellow cards than the average.
- Calculate the average number of red cards received by all players in the dataset.
- Identify the player(s) who received fewer red cards than the average.
- Calculate the average distance covered by all players in the dataset.
- Identify the player(s) who covered more distance than the average.

Extensions (optional):

- Create a composite score for each player by assigning weights to each statistic (e.g., goals scored = 4, assists = 3, pass completion rate = 2, tackles won = 2, shots on target = 1). Calculate the composite score for each player and identify the player(s) with the highest composite score.
- Analyze the correlation between goals scored and assists made. Determine if there is a strong relationship between these two statistics.
- Create a scatter plot to visualize the relationship between pass completion rate and shots on target. Analyze if there is any correlation between these two statistics.