Player performance prediction is a crucial aspect of basketball analytics. By leveraging machine learning, we can analyze historical player stats and predict their future performance, helping teams make data-driven decisions.
Dataset and Features
We'll use NBA player statistics from public sources such as Kaggle or the NBA API. The dataset contains key features like:
- Points per game (PPG)
- Assists per game (APG)
- Rebounds per game (RPG)
- Shooting percentages (FG%, 3P%)
- Turnovers per game (TOV)
- Defensive stats (blocks, steals)
Building the Prediction Model
We'll use Python along with pandas, scikit-learn, and Matplotlib to preprocess the data and build a machine learning model.
1. Load and Preprocess the Data
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
# Load dataset
data = pd.read_csv("nba_player_stats.csv")
# Select relevant features
features = ["PPG", "APG", "RPG", "FG%", "3P%", "TOV", "STL", "BLK"]
X = data[features]
y = data["Next_Season_PPG"]
# Split into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)2. Train the Machine Learning Model
from sklearn.ensemble import RandomForestRegressor
# Scale the data
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
# Train model
model = RandomForestRegressor(n_estimators=100, random_state=42)
model.fit(X_train_scaled, y_train)3. Make Predictions and Evaluate
from sklearn.metrics import mean_absolute_error
predictions = model.predict(X_test_scaled)
mae = mean_absolute_error(y_test, predictions)
print(f"Mean Absolute Error: {mae}")Conclusion
This machine learning model helps predict player performance, aiding in team decisions and fantasy basketball strategies. By integrating more advanced models, we can refine predictions and provide deeper insights into player development.