G: Yes, I’m familiar with Pokémon GO. It’s an augmented reality mobile game where players explore the real world to catch Pokémon, battle in gyms, complete raids, and participate in various events. Are you looking for tips, event updates, or something else?

G: You can accomplish this in a spreadsheet if the dataset is relatively small and manageable. A database is better if you expect the data to grow significantly or require complex querying.

Broad Strokes Breakdown:

Spreadsheet Approach:

1. Data Organization: Create columns for move names, stats (damage, energy cost, etc.), and combinations.
2. Formulas: Use functions like SUM, AVERAGE, IF, and VLOOKUP to compare and analyze stats.
3. Filters/Conditional Formatting: Highlight key combinations and make filtering easier.
4. Pivot Tables: Summarize and compare statistics dynamically.

Database Approach:

1. Set Up Database: Use SQLite or a similar lightweight option.
2. Define Tables: Create tables for moves, stats, and combinations with relationships.
3. Query with SQL: Write queries to compare stats (e.g., SELECT SUM(damage) FROM moves WHERE...).
4. Interface: Optionally, connect the database to a Python script or use a GUI tool like DB Browser.

A spreadsheet is sufficient for small-scale analysis, but a database is better for scalability and advanced querying.

G:

Spreadsheet

Pros:

• Intuitive and easy to set up for small datasets.
• Built-in tools like filters, formulas, and pivot tables for quick analysis.
• Minimal overhead; no need to learn or manage additional tools.
• Great for visualizing and tweaking data manually.

Cons:

• Becomes unwieldy for larger datasets or complex calculations.
• Limited scalability and querying capabilities.
• Higher risk of errors when manually updating formulas or data.

Database

Pros:

• Scalable and better suited for handling relational data and complex queries.
• Easy to automate repetitive or complex calculations with SQL.
• Better data integrity through structured relationships.
• Easier to integrate with external tools (e.g., Python for custom analysis or visualization).

Cons:

• Higher initial setup time and learning curve.
• Requires external tools or scripts for visualization if needed.
• Overkill for small, static datasets and basic operations.

For your scenario, spreadsheets are simpler and sufficient, but if you anticipate scaling up or require frequent, complex analysis, consider a database.

G: Here’s a clear, step-by-step process for achieving your goal in LibreOffice Calc:

1. Structure Your Data

• Sheet 1: "Fast" Table
  Columns: Name, Damage, Energy, etc.
• Sheet 2: "Charge" Table
  Columns: Name, Damage, Energy Cost, etc.

2. Create a Selection Area

• In a new sheet ("Selections"):

  • Create two sections:
    • Fast Moves: Use checkboxes or a dropdown list for selecting 3-6 entries.
    • Charge Moves: Same setup as above.

  Tip: Use Data → Validity for dropdowns or a manual list.

3. Extract Selected Moves

• Use FILTER or conditional logic (IF and INDEX):
  • Extract selected rows into a dynamic table.
  • Example: =IF(A1=1, Sheet1.A2, "") (if using checkboxes).
  • Alternatively, list selected names, then use VLOOKUP to pull corresponding data.

4. Generate Combinations

• Use a helper table to pair each Fast move with each Charge move:
  • First column: All Fast moves (repeated for each Charge).
  • Second column: Each Charge move paired with every Fast move.
  • Example: If you have 3 Fast and 4 Charge, you'll create 12 rows.

5. Perform Calculations

• Add calculation columns for each combination:
  • Example:
    • Total Damage = Fast.Damage + Charge.Damage
    • Efficiency = (Fast.Damage / Energy) + (Charge.Damage / Energy Cost)
  • Use VLOOKUP or INDEX/MATCH to reference the stats dynamically.

6. Display Results

• Organize results in a readable table:
  • Columns: Fast Move, Charge Move, Total Damage, Efficiency, etc.
  • Use conditional formatting to highlight top combinations.

7. Automate Updates

• Use Named Ranges for data (to simplify references).
• Apply filters or sorting to focus on specific results.

This approach leverages basic spreadsheet functions while keeping it maintainable. For a more dynamic experience, you could consider using macros, but they aren't strictly necessary for your stated goal.

G: To handle variable numbers of selected moves and automate the creation of the helper table in step 4, here’s an improved approach:

4. Generate Combinations Dynamically

Dynamic Fast and Charge Selection

1. Use named ranges or dynamic arrays:

   • Extract selected moves from the "Selections" sheet into dynamic lists using FILTER or conditional logic (e.g., only pull rows where the selection is valid).
   • Example: In "Selections" sheet, list selected Fast moves in column A and Charge moves in column B.

2. Dynamic Row Count:

   • Count the selected moves using COUNTA or similar.
   • Example: =COUNTA(A:A) for the count of Fast moves.

Helper Table with Dynamic Pairing

To automate combinations:

1. Create a helper table with two dynamic columns:
   • First column: Repeat each Fast move for the count of Charge moves.
   • Second column: Cycle through Charge moves repeatedly to align with the Fast repeats.

Formulas for Pairing

Assume:

• SelectedFast is in "Selections" A1:A (dynamic range).
• SelectedCharge is in "Selections" B1:B (dynamic range).
• Place the helper table on a new sheet.

1. First Column (Fast Moves):

   • Use:
     =INDEX(Selections.A$1:A$100, INT((ROW()-1)/COUNTA(Selections.B$1:B$100))+1)
   • This repeats each Fast move enough times to match all Charge moves.

2. Second Column (Charge Moves):

   • Use:
     =INDEX(Selections.B$1:B$100, MOD(ROW()-1, COUNTA(Selections.B$1:B$100))+1)
   • This cycles through Charge moves.

Key Benefits

• The helper table automatically adjusts when you select a different number of moves.
• No need to manually rebuild for each test.

Final Note

With this setup, the helper table grows and shrinks based on the number of selected moves, ensuring full automation of the pairing process. All downstream calculations and results will update dynamically.