Indian Premier League - Brand value
Description
This Python script visualizes data related to the brand value of the Indian Premier League (IPL), its constituent teams, and a comparison with other major football leagues. It uses matplotlib for creating the plots and numpy for numerical operations. The script generates four key visualizations: IPL Brand Value Over Time: A line plot illustrating the growth of the IPL's brand value in billion dollars from 2009 to 2024. This chart shows the increasing financial strength and popularity of the league over the years. Top 10 IPL Team Brand Values (2024): A bar chart displaying the brand values (in million dollars) of the top 10 IPL teams in 2024. This visualization allows for a comparison of the brand strength of different teams within the league. The numerical value of each bar is displayed on top of the bar. Growth Rate of IPL Team Brand Values (2024): Another bar chart presenting the growth rates (in percentage) of the top 10 IPL teams' brand values in 2024. This plot highlights the teams with the highest growth potential and increasing popularity. The growth rate in percentage is displayed on top of the bar. Comparison of IPL and Football League Brand Values: A bar chart comparing the brand values (in billion dollars) of the IPL (top 5 teams), Bundesliga, and English Premier League (EPL). This visualization contextualizes the IPL's financial strength in comparison to other prominent sports leagues worldwide.
Files
Steps to reproduce
import matplotlib.pyplot as plt import numpy as np # Data for IPL Brand Value Over Time years = [2009, 2023, 2024] brand_values = [2, 10.7, 12] # In billion dollars # Data for Top 10 IPL Teams Brand Value 2024 teams = ["CSK", "MI", "RCB", "KKR", "SRH", "RR", "DC", "GT", "PBKS", "LSG"] brand_values_teams = [122, 119, 117, 109, 85, 81, 80, 69, 68, 60] # In million dollars growth_rates = [52, 36, 67, 38, 76, 30, 24, 5, 49, 29] # In percentage # Data for Football League Comparison leagues = ["Bundesliga", "EPL"] league_values = [2.9, 6.7] # In billion dollars # Plot IPL Brand Value Over Time plt.figure(figsize=(10, 5)) plt.plot(years, brand_values, marker='o', linestyle='-', color='b', label="IPL Brand Value") plt.xlabel("Year") plt.ylabel("Brand Value (Billion $)") plt.title("IPL Brand Value Over Time") plt.grid(True) plt.legend() plt.show() # Plot IPL Team Brand Values 2024 plt.figure(figsize=(12, 6)) bars = plt.bar(teams, brand_values_teams, color='orange') plt.xlabel("IPL Teams") plt.ylabel("Brand Value (Million $)") plt.title("Top 10 IPL Team Brand Values (2024)") # Adding values on top of bars for bar in bars: yval = bar.get_height() plt.text(bar.get_x() + bar.get_width()/2, yval, int(yval), ha='center', va='bottom') plt.show() # Plot Growth Rate of IPL Teams plt.figure(figsize=(12, 6)) bars = plt.bar(teams, growth_rates, color='green') plt.xlabel("IPL Teams") plt.ylabel("Growth Rate (%)") plt.title("Growth Rate of IPL Team Brand Values (2024)") # Adding values on top of bars for bar in bars: yval = bar.get_height() plt.text(bar.get_x() + bar.get_width()/2, yval, f"{int(yval)}%", ha='center', va='bottom') plt.show() # Compare IPL and Football League Brand Values plt.figure(figsize=(8, 5)) bars = plt.bar(["IPL (Top 5 Teams)", "Bundesliga", "EPL"], [0.551, 2.9, 6.7], color=['red', 'blue', 'purple']) plt.xlabel("Leagues") plt.ylabel("Brand Value (Billion $)") plt.title("Comparison of IPL and Football League Brand Values") # Adding values on top of bars for bar in bars: yval = bar.get_height() plt.text(bar.get_x() + bar.get_width()/2, yval, f"{yval}B", ha='center', va='bottom') plt.show()