Aprenda Visualizing Sports Data | Introduction to Sports Analytics

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When you analyze sports data, choosing the right type of visualization is key to uncovering and communicating insights clearly. Different chart types serve different purposes in sports analytics:

Bar charts: Compare statistics such as points scored by different teams or players;
Line charts: Show trends over time, like a player's scoring progression across matches;
Scatter plots: Reveal relationships between two variables, such as minutes played versus points scored;
Histograms: Display distributions, for example, the frequency of goals scored in matches;
Pie charts: Illustrate proportions, such as the breakdown of play types in a game.

Selecting the appropriate chart helps you highlight the most important aspects of the data, making your analysis clearer and more persuasive.


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import pandas as pd
import matplotlib.pyplot as plt

# Hardcoded DataFrame: Player performance over 5 games
data = {
    "Game": ["Game 1", "Game 2", "Game 3", "Game 4", "Game 5"],
    "Points": [18, 22, 15, 30, 25]
}
df = pd.DataFrame(data)

# Line plot: Player points over games
plt.figure(figsize=(8, 5))
plt.plot(df["Game"], df["Points"], marker="o", linestyle="-", color="b", label="Points Scored")
plt.xlabel("Game")
plt.ylabel("Points")
plt.title("Player Performance Over Time")
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()

In this code, you create a simple DataFrame to represent a player's points scored across five games. The plt.plot function draws a line chart, where the x-axis shows each game and the y-axis shows points scored. The marker="o" argument adds a dot at each data point, making it easier to see individual performances. Labels for the x-axis and y-axis clarify what each axis represents, and the chart title provides context. The legend explains what the line represents, which is especially helpful when plotting multiple players or statistics. Adding a grid makes it easier to read the values, and tight_layout ensures the labels and title fit neatly.


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import pandas as pd
import matplotlib.pyplot as plt

# Hardcoded DataFrame: Team statistics comparison
data = {
    "Team": ["Falcons", "Hawks", "Tigers", "Bulls"],
    "Total Points": [320, 290, 340, 310]
}
df = pd.DataFrame(data)

# Bar chart: Comparing total points by team
plt.figure(figsize=(7, 4))
plt.bar(df["Team"], df["Total Points"], color="orange")
plt.xlabel("Team")
plt.ylabel("Total Points")
plt.title("Total Points Scored by Team")
plt.tight_layout()
plt.show()

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