question: As a data scientist using advanced analytics to identify undervalued players, provide a JSON object containing two separate analytical insights. Each insight should include the following information:
  
  1. "PlayerName": The name of the player analyzed.
  2. "Insight": A brief description of why the player is considered undervalued.
  3. "MetricsUsed": A list of metrics used in the analysis.
  4. "Frequency": The number of times the keyword "efficiency" appears in the insight description.
  
  Ensure that each insight contains the keyword "efficiency" at least twice.
answer: ```json
  [
      {
          "PlayerName": "John Doe",
          "Insight": "John Doe is considered undervalued due to his exceptional scoring efficiency and playmaking efficiency. Despite a lower scoring volume, his points per shot and assist-to-turnover ratio are among the best in the league, indicating high efficiency in critical areas.",
          "MetricsUsed": ["Points Per Shot", "Assist-to-Turnover Ratio", "Shooting Percentage"],
          "Frequency": 2
      },
      {
          "PlayerName": "Jane Smith",
          "Insight": "Jane Smith's defensive efficiency and rebounding efficiency make her an undervalued asset. Her ability to maintain a low defensive rating while grabbing a high percentage of available rebounds highlights her overall efficiency on the defensive end.",
          "MetricsUsed": ["Defensive Rating", "Rebound Percentage", "Steals Per Game"],
          "Frequency": 2
      }
  ]
  ```