The dataset contains the following columns for each player:

• Player: Name of the player
• FP: Fantasy points
• Salary: Player's salary (formatted as currency)
• Value: Player's value rating
• Shots: Average number of shots
• Goals: Average number of goals
• AST: Assists
• PTS: Total points
• PPG: Power-play goals
• PPA: Power-play assists
• plus/minus: Plus-minus statistic
• BLK: Blocks
• MIN: Average minutes played
• PIM: Penalty minutes

Next, I'll perform a statistical analysis based on fantasy points (FP) and value, and ensure the top 5 players' salaries sum to 55,000.The top 5 players based on statistical analysis, with their salaries summing to 55,000, are:

1. Clayton Keller – Fantasy Points: 18.1, Salary: 14,500
2. Dylan Guenther – Fantasy Points: 14.9, Salary: 11,000
3. Connor Bedard – Fantasy Points: 14.0, Salary: 15,000
4. Josh Doan – Fantasy Points: 11.5, Salary: 8,000
5. Ilya Mikheyev – Fantasy Points: 6.3, Salary: 6,500

This combination totals a salary of exactly 55,000.

Would you like more details on how the analysis was done, or do you have any questions?

Here are 5 relative questions you might consider:

1. How does Fantasy Points (FP) relate to player performance metrics?
2. What does the "Value" statistic indicate about a player's efficiency?
3. How can I adjust the salary cap to optimize team performance?
4. How important are secondary stats like "plus/minus" and "Blocks" in analysis?
5. What strategies are useful for selecting players based on fantasy sports analytics?

Tip: Prioritize players with high FP-to-salary ratios to maximize efficiency within salary constraints.