Datasets:

Tomertg
/

stats

# NBA Evolution Analysis (1990–2024)
# Course: Introduction to Data Science
# Author: Tomer Golan
# Objective: Analyze how the NBA game evolved over time using EDA and visualization
url = "https://huggingface.co/datasets/Tomertg/stats/resolve/main/players_stats_by_season_full_details.csv"
df = pd.read_csv(url)

# Step 1: Import required libraries
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.ticker import MaxNLocator

# Configure visual style
sns.set(style="whitegrid", font_scale=1.1, palette="muted")

# Load dataset
df = pd.read_csv("players_stats_by_season_full_details.csv")

# Focus only on NBA data
df = df[df['League'] == 'NBA']

# Select relevant columns
cols = ['Season', 'PTS', 'AST', '3PM', 'height_cm']
df = df[cols]

# Preview
print("Shape:", df.shape)
df.head()

# Drop missing values and ensure numeric columns
df = df.dropna()
df['Season_Year'] = df['Season'].str.extract(r'(\d{4})').astype(int)

# Filter for reasonable timeline
df = df[df['Season_Year'].between(1990, 2024)]

# Convert numeric columns properly
for col in ['PTS', 'AST', '3PM', 'height_cm']:
    df[col] = pd.to_numeric(df[col], errors='coerce')

print("After cleaning:", df.shape)

# Summary statistics
df[['PTS', 'AST', '3PM', 'height_cm']].describe()

# Group by year and calculate averages
trend = df.groupby('Season_Year')[['PTS', 'AST', '3PM', 'height_cm']].mean().reset_index()

# Rename columns for clarity
trend.rename(columns={
    'PTS': 'Avg_Points',
    'AST': 'Avg_Assists',
    '3PM': 'Avg_3PM',
    'height_cm': 'Avg_Height'
}, inplace=True)

trend.head()

# Helper function for clean, clear barplots
def make_barplot(data, x, y, color, title, xlabel, ylabel):
    plt.figure(figsize=(9,6))
    ax = sns.barplot(x=x, y=y, data=data, color=color, edgecolor='black')
    ax.yaxis.set_major_locator(MaxNLocator(integer=True))
    plt.title(title, fontsize=15, weight='bold')
    plt.xlabel(xlabel)
    plt.ylabel(ylabel)
    plt.grid(True, linestyle='--', alpha=0.6)
    plt.tight_layout()
    plt.show()

# Average Points & Assists 

plt.figure(figsize=(10,6))

bar_data = trend.melt(id_vars='Season_Year', value_vars=['Avg_Points', 'Avg_Assists'],
                      var_name='Metric', value_name='Average')

ax = sns.barplot(x='Season_Year', y='Average', hue='Metric', data=bar_data,
                 palette=['orange', 'mediumseagreen'], edgecolor='black')

ax.yaxis.set_major_locator(MaxNLocator(integer=True))
plt.title("Average Points and Assists per Player (NBA 1990–2024)", fontsize=15, weight='bold')
plt.xlabel("Season Year")
plt.ylabel("Average per Player")
plt.xticks(rotation=45)
plt.legend(title="Metric", loc='upper left')
plt.grid(True, linestyle='--', alpha=0.6)
plt.tight_layout()
plt.show()


# The 3 Point Revolution 

plt.figure(figsize=(10,6))
ax = sns.barplot(x='Season_Year', y='Avg_3PM', data=trend, color='dodgerblue', edgecolor='black')
ax.yaxis.set_major_locator(MaxNLocator(integer=True))
plt.title("Average 3-Point Shots Made per Player (1990–2024)", fontsize=15, weight='bold')
plt.xlabel("Season Year")
plt.ylabel("Average 3PM per Player")
plt.xticks(rotation=45)
plt.grid(True, linestyle='--', alpha=0.6)
plt.tight_layout()
plt.show()

# Average Height Over Time
plt.figure(figsize=(10,6))
ax = sns.lineplot(x='Season_Year', y='Avg_Height', data=trend, color='purple', linewidth=2.5, marker='o')
ax.yaxis.set_major_locator(MaxNLocator(integer=True))
plt.title("Average Player Height (1990–2024)", fontsize=15, weight='bold')
plt.xlabel("Season Year")
plt.ylabel("Average Height (cm)")
plt.grid(True, linestyle='--', alpha=0.6)
plt.tight_layout()
plt.show()

# Points vs Assists over time
plt.figure(figsize=(10,6))
scatter = sns.scatterplot(
    x='Avg_Assists',
    y='Avg_Points',
    data=trend,
    hue='Season_Year',
    palette='coolwarm',
    size='Avg_3PM',
    sizes=(50, 300),
    alpha=0.8,
    edgecolor='black'
)

sns.regplot(
    x='Avg_Assists',
    y='Avg_Points',
    data=trend,
    scatter=False,
    color='black',
    line_kws={'linestyle':'--', 'linewidth':2}
)

plt.title("Points vs Assists Over Time (Color = Year, Size = 3PM)", fontsize=15, weight='bold')
plt.xlabel("Average Assists per Player")
plt.ylabel("Average Points per Player")
plt.grid(True, linestyle='--', alpha=0.6)
plt.legend(title="Season Year", loc='upper left', bbox_to_anchor=(1.02, 1))
plt.tight_layout()
plt.show()


# Ratio of Points to Assists 
trend['PTS_AST_Ratio'] = trend['Avg_Points'] / trend['Avg_Assists']

plt.figure(figsize=(10,6))
ax = sns.lineplot(x='Season_Year', y='PTS_AST_Ratio', data=trend, color='crimson', linewidth=2.5, marker='o')
ax.yaxis.set_major_locator(MaxNLocator(integer=True))
plt.title("Ratio of Points to Assists Over Time (Team Play Indicator)", fontsize=15, weight='bold')
plt.xlabel("Season Year")
plt.ylabel("Points / Assists Ratio")
plt.grid(True, linestyle='--', alpha=0.6)
plt.tight_layout()
plt.show()

corr = trend[['Avg_Points', 'Avg_Assists', 'Avg_3PM', 'Avg_Height']].corr()

plt.figure(figsize=(6,4))
sns.heatmap(corr, annot=True, cmap='coolwarm', fmt=".2f")
plt.title("Correlation Between NBA Performance Metrics", fontsize=14, weight='bold')
plt.tight_layout()
plt.show()

# Summary of Findings
print("""
Summary of Findings (1990 - 2024):
1. Average points per player have risen slightly - the game became faster and more efficient.
2. Assists grew steadily - NBA teams now play with more collaboration and spacing.
3. The 3-point revolution exploded after 2010 - the single biggest transformation in modern basketball.
4. Average player height slightly decreased - speed and versatility are prioritized over size.
5. The ratio of points to assists dropped - showing that more scoring now comes from teamwork.
In short: The NBA has evolved from slow, isolation heavy basketball
into a fast, perimeter oriented, analytics-driven, and team first game.
""")

	# NBA Evolution Analysis (1990–2024)
	# Course: Introduction to Data Science
	# Author: Tomer Golan
	# Objective: Analyze how the NBA game evolved over time using EDA and visualization
	url = "https://huggingface.co/datasets/Tomertg/stats/resolve/main/players_stats_by_season_full_details.csv"
	df = pd.read_csv(url)

	# Step 1: Import required libraries
	import pandas as pd
	import matplotlib.pyplot as plt
	import seaborn as sns
	from matplotlib.ticker import MaxNLocator

	# Configure visual style
	sns.set(style="whitegrid", font_scale=1.1, palette="muted")

	# Load dataset
	df = pd.read_csv("players_stats_by_season_full_details.csv")

	# Focus only on NBA data
	df = df[df['League'] == 'NBA']

	# Select relevant columns
	cols = ['Season', 'PTS', 'AST', '3PM', 'height_cm']
	df = df[cols]

	# Preview
	print("Shape:", df.shape)
	df.head()

	# Drop missing values and ensure numeric columns
	df = df.dropna()
	df['Season_Year'] = df['Season'].str.extract(r'(\d{4})').astype(int)

	# Filter for reasonable timeline
	df = df[df['Season_Year'].between(1990, 2024)]

	# Convert numeric columns properly
	for col in ['PTS', 'AST', '3PM', 'height_cm']:
	df[col] = pd.to_numeric(df[col], errors='coerce')

	print("After cleaning:", df.shape)

	# Summary statistics
	df[['PTS', 'AST', '3PM', 'height_cm']].describe()

	# Group by year and calculate averages
	trend = df.groupby('Season_Year')[['PTS', 'AST', '3PM', 'height_cm']].mean().reset_index()

	# Rename columns for clarity
	trend.rename(columns={
	'PTS': 'Avg_Points',
	'AST': 'Avg_Assists',
	'3PM': 'Avg_3PM',
	'height_cm': 'Avg_Height'
	}, inplace=True)

	trend.head()

	# Helper function for clean, clear barplots
	def make_barplot(data, x, y, color, title, xlabel, ylabel):
	plt.figure(figsize=(9,6))
	ax = sns.barplot(x=x, y=y, data=data, color=color, edgecolor='black')
	ax.yaxis.set_major_locator(MaxNLocator(integer=True))
	plt.title(title, fontsize=15, weight='bold')
	plt.xlabel(xlabel)
	plt.ylabel(ylabel)
	plt.grid(True, linestyle='--', alpha=0.6)
	plt.tight_layout()
	plt.show()

	# Average Points & Assists

	plt.figure(figsize=(10,6))

	bar_data = trend.melt(id_vars='Season_Year', value_vars=['Avg_Points', 'Avg_Assists'],
	var_name='Metric', value_name='Average')

	ax = sns.barplot(x='Season_Year', y='Average', hue='Metric', data=bar_data,
	palette=['orange', 'mediumseagreen'], edgecolor='black')

	ax.yaxis.set_major_locator(MaxNLocator(integer=True))
	plt.title("Average Points and Assists per Player (NBA 1990–2024)", fontsize=15, weight='bold')
	plt.xlabel("Season Year")
	plt.ylabel("Average per Player")
	plt.xticks(rotation=45)
	plt.legend(title="Metric", loc='upper left')
	plt.grid(True, linestyle='--', alpha=0.6)
	plt.tight_layout()
	plt.show()


	# The 3 Point Revolution

	plt.figure(figsize=(10,6))
	ax = sns.barplot(x='Season_Year', y='Avg_3PM', data=trend, color='dodgerblue', edgecolor='black')
	ax.yaxis.set_major_locator(MaxNLocator(integer=True))
	plt.title("Average 3-Point Shots Made per Player (1990–2024)", fontsize=15, weight='bold')
	plt.xlabel("Season Year")
	plt.ylabel("Average 3PM per Player")
	plt.xticks(rotation=45)
	plt.grid(True, linestyle='--', alpha=0.6)
	plt.tight_layout()
	plt.show()

	# Average Height Over Time
	plt.figure(figsize=(10,6))
	ax = sns.lineplot(x='Season_Year', y='Avg_Height', data=trend, color='purple', linewidth=2.5, marker='o')
	ax.yaxis.set_major_locator(MaxNLocator(integer=True))
	plt.title("Average Player Height (1990–2024)", fontsize=15, weight='bold')
	plt.xlabel("Season Year")
	plt.ylabel("Average Height (cm)")
	plt.grid(True, linestyle='--', alpha=0.6)
	plt.tight_layout()
	plt.show()

	# Points vs Assists over time
	plt.figure(figsize=(10,6))
	scatter = sns.scatterplot(
	x='Avg_Assists',
	y='Avg_Points',
	data=trend,
	hue='Season_Year',
	palette='coolwarm',
	size='Avg_3PM',
	sizes=(50, 300),
	alpha=0.8,
	edgecolor='black'
	)

	sns.regplot(
	x='Avg_Assists',
	y='Avg_Points',
	data=trend,
	scatter=False,
	color='black',
	line_kws={'linestyle':'--', 'linewidth':2}
	)

	plt.title("Points vs Assists Over Time (Color = Year, Size = 3PM)", fontsize=15, weight='bold')
	plt.xlabel("Average Assists per Player")
	plt.ylabel("Average Points per Player")
	plt.grid(True, linestyle='--', alpha=0.6)
	plt.legend(title="Season Year", loc='upper left', bbox_to_anchor=(1.02, 1))
	plt.tight_layout()
	plt.show()


	# Ratio of Points to Assists
	trend['PTS_AST_Ratio'] = trend['Avg_Points'] / trend['Avg_Assists']

	plt.figure(figsize=(10,6))
	ax = sns.lineplot(x='Season_Year', y='PTS_AST_Ratio', data=trend, color='crimson', linewidth=2.5, marker='o')
	ax.yaxis.set_major_locator(MaxNLocator(integer=True))
	plt.title("Ratio of Points to Assists Over Time (Team Play Indicator)", fontsize=15, weight='bold')
	plt.xlabel("Season Year")
	plt.ylabel("Points / Assists Ratio")
	plt.grid(True, linestyle='--', alpha=0.6)
	plt.tight_layout()
	plt.show()

	corr = trend[['Avg_Points', 'Avg_Assists', 'Avg_3PM', 'Avg_Height']].corr()

	plt.figure(figsize=(6,4))
	sns.heatmap(corr, annot=True, cmap='coolwarm', fmt=".2f")
	plt.title("Correlation Between NBA Performance Metrics", fontsize=14, weight='bold')
	plt.tight_layout()
	plt.show()

	# Summary of Findings
	print("""
	Summary of Findings (1990 - 2024):

	1. Average points per player have risen slightly - the game became faster and more efficient.
	2. Assists grew steadily - NBA teams now play with more collaboration and spacing.
	3. The 3-point revolution exploded after 2010 - the single biggest transformation in modern basketball.
	4. Average player height slightly decreased - speed and versatility are prioritized over size.
	5. The ratio of points to assists dropped - showing that more scoring now comes from teamwork.

	In short: The NBA has evolved from slow, isolation heavy basketball
	into a fast, perimeter oriented, analytics-driven, and team first game.
	""")