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NBA Game Outcome Prediction

NBA

This project explores whether NBA game outcomes can be predicted using historical team statistics.

The project was developed as the final project for the Introduction to Pattern Recognition course during my Bachelor's degree in Computer Engineering at Erciyes University.

The goal was to apply machine learning algorithms to sports analytics data and evaluate how well game results can be predicted based on statistical metrics.

Project Overview

Using NBA match statistics from multiple seasons, the project attempts to predict game outcomes using a classification approach.

Each game is represented by a set of statistical features describing team performance during the match.

The model attempts to classify game results based on these features.

Dataset

The dataset contains NBA game statistics from two seasons:

  • 2016–2017
  • 2017–2018

Files included in the repository:

  • 2016-17_teamBoxScore.csv
  • 2017-18_teamBoxScore.csv

Dataset characteristics:

  • 4920 samples
  • 123 features per sample

The features include various team performance metrics recorded during NBA games.

Machine Learning Workflow

The following steps were applied in the project:

  1. Data loading and preprocessing
  2. Feature selection
  3. Dataset preparation
  4. Model training
  5. Cross-validation
  6. Model evaluation

The workflow is implemented in:

main.ipynb

Classification Algorithms

Several machine learning algorithms were evaluated:

  • Random Forest
  • Support Vector Classifier (SVC)
  • K-Nearest Neighbors
  • Decision Tree

These models were trained and compared based on their classification performance.

Performance Evaluation

The models were evaluated using the following metrics:

  • Sensitivity
  • Specificity
  • AUC (Area Under ROC Curve)

Metric Definitions

Sensitivity

Correct predictions for class 1 divided by total samples in class 1.

Specificity

Correct predictions for class 2 divided by total samples in class 2.

AUC

Area under the ROC curve.

Project Structure

IPR-Final-NBA
├── main.ipynb
├── 2016-17_teamBoxScore.csv
├── 2017-18_teamBoxScore.csv
├── IPR_final.pdf
├── Report[ENG].txt
├── Rapor[TR].txt
├── nbalogo.jpeg
└── README.md

Technologies Used

  • Python
  • Jupyter Notebook
  • Pandas
  • NumPy
  • Scikit-learn

Development Context

This project was developed as part of the Introduction to Pattern Recognition course.

The objective was to build a machine learning pipeline using real-world sports data and evaluate classification algorithms.

Author

Furkan Yilmaz

Developed during my Bachelor's degree in Computer Engineering at Erciyes University.

Currently pursuing an M.Sc. in Computer Science at HAW Kiel University of Applied Sciences (Germany).