
There is the dataset: ATP_tennis containing the following columns: ['Tournament', 'Date', 'Series', 'Court', 'Surface', 'Round', 'Best of', 'Player_1', 'Player_2', 'Winner', 'Rank_1', 'Rank_2', 'Pts_1', 'Pts_2', 'Odd_1', 'Odd_2', 'score'].  
--- The description for each column this dataset is:
Tournament: Name of the tennis tournament (Brisbane International, Chennai Open, Qatar Exxon Mobil Open ...etc)
Date: Date the match was played (year-month-day)
Series: Category or level of the tennis tournament (ATP250, ATP500, Masters1000 and Grand Slams offer 250, 500, 1000, and 2000 ranking points to the winner seperately.)
Court: Place the match was held (Indoors or Outdoors)
Surface: Type of court surface (Hard, Grass and Clay)
Round: Stage of the tournament (1st Round, 2nd Round, Quarterfinals, Semifinal and The Final)
Best of: Tourament systems ("best of 3" or "best of 5")
Player_1: Name of the first competitor in each match
Player_2: Name of the second competitor in each match
Winner: Name of the Player_1 or Player_2 who won the match 
Rank_1: World rankings of the Player_1 at the time of the match
Rank_2: World rankings of the Player_2 at the time of the match
Pts_1: Ranking points held by Player_1 before the match (accumulated over the season and higher points typically indicate a more successful player)
Pts_2: Ranking points held by Player_2 before the match
Odd_1: Betting odds for Player_1 (reflecting their perceived chances of winning)
Odd_2: Betting odds for Player_2 
Score: Final score of the match ("score of Player_1" - "score of Player_2")
---

You are a data scientist with an impressive array of skills including data analysis, statistics, machine learning, and proficiency in Pandas. The plot mentioned in the question below has been transformed into a Filtered Dataframe, and your task is to answer the following question from User based on this Filtered Dataframe below.

--- Filtered Dataframe: ---
TITLE | Win/Loss Ratios by Surface Type and Year
Year | Surface | Hard | Clay | Grass
2012 | 1.47 | 1.47 | 1.06 | 1.00
2014 | 1.30 | 1.30 | 1.09 | 1.14
2016 | 1.27 | 1.26 | 1.16 | 1.17
2018 | 1.29 | 1.15 | 1.15 | 1.27
2020 | 1.29 | 1.29 | 1.19 | 1.23
2022 | 1.24 | 1.23 | 1.24 | 1.24

--- Filtered Dataframe ---

[USER (data scientist)]: The performance on these surfaces over time is which? 
A. divergent
B. uniform
C. random
D. convergent
E. None of above

[YOU (AI assistant)]:
