[USER (data scientist)]: Thank you. Finally, I need to know how the overall competitiveness of the tournament changes based on the updated odds compared to the original odds. Please provide summary statistics and visualizations (Density plot for Distribution of Odds, Bar plot for Changes in Match Favorites, Line plot for Shifts in Tournament Dynamics).
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pickle
from decision_company import read_csv_file,

# please import the necessary private functions from decision_company first

atp_tennis = read_csv_file('atp_tennis.csv')

create_figure((12, 6))

# YOUR SOLUTION BEGIN:
<code1>
[COMPLETE YOUR CODE]
</code1>
# YOUR SOLUTION END

# save data
save_plot('./pred_result/updated_odds_change_for_player_1.png')
show_plots()

create_figure((12, 6))

# YOUR SOLUTION BEGIN:
<code2>
[COMPLETE YOUR CODE]
</code2>
# YOUR SOLUTION END

# save data
save_plot('./pred_result/updated_odds_change_for_player_2.png')
show_plots()

create_figure((12, 6))

# YOUR SOLUTION BEGIN:
<code3>
[COMPLETE YOUR CODE]
</code3>
# YOUR SOLUTION END

# save data
save_plot('./pred_result/changes_in_match_favorites.png')
show_plots()

create_figure((12, 6))

# YOUR SOLUTION BEGIN:
<code4>
[COMPLETE YOUR CODE]
</code4>
# YOUR SOLUTION END

# save data
save_plot('./pred_result/shifts_in_tournament_dynamics.png')
show_plots()

---END CODE TEMPLATE---

[YOU (AI assistant)]: To analyze the overall competitiveness of the tournament based on the updated odds compared to the original odds, and provide summary statistics and visualizations, you can use the following code:
'''
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pickle
from decision_company import read_csv_file,

# please import the necessary private functions from decision_company first

atp_tennis = read_csv_file('atp_tennis.csv')

create_figure((12, 6))

# YOUR SOLUTION BEGIN:
