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https://github.com/ysayaovong/nba-game-performance-analytics

Analyzing NBA team performance based on 2022-23 season data.
https://github.com/ysayaovong/nba-game-performance-analytics

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Analyzing NBA team performance based on 2022-23 season data.

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README 

        
# NBA Game Performance Analytics



![NBA Analysis Banner](images/top_10_nba_teams_chart.png)



## Overview



Welcome to my **NBA Game Performance Analytics** project! This repository showcases my journey in sports data analytics by analyzing team performance during the 2022-2023 NBA season. By leveraging Python, APIs, and data visualization, I derived meaningful insights into team dynamics and game performance.



## Objectives



- Extract real-time game data from the **NBA API**.

- Process and clean the data to ensure accuracy and consistency.

- Analyze team performance metrics, with a focus on average points scored per game.

- Visualize findings to provide actionable insights into NBA team performance.



## Key Accomplishments



1. **Data Extraction**:

   - Utilized the NBA API to collect detailed game data from the 2022-2023 season.

   - Ensured real-time relevance and data accuracy for analysis.

   - ![Data Retrieval Process](images/data_retrieval.png)



2. **Data Cleaning & Transformation**:

   - Handled missing and inconsistent values.

   - Transformed raw data into structured, actionable datasets.

   - ![Filtered Teams](images/filtered_teams.png)



3. **Performance Analysis**:

   - Ranked teams based on average points scored per game.

   - Identified the top 10 teams with standout offensive performances.



4. **Visualization**:

   - Designed clear, visually appealing graphs to represent team performance metrics.

   - Focused on making insights accessible to both technical and non-technical audiences.



## Skills Demonstrated



- **Programming**: Python

- **Data Analysis Libraries**: Pandas, NumPy

- **Visualization Tools**: Matplotlib, Seaborn

- **APIs**: NBA API

- **Data Wrangling**: Cleaning, Transforming, and Structuring Data



## Example Visualizations



### Team Rankings by Average Points Per Game



#### Chart Representation

![Top 10 NBA Teams Chart](images/top_10_nba_teams_chart.png)



#### Visual Representation

![Top 10 NBA Teams](images/top_10_nba_teams.png)



## Results and Insights



- The top-performing teams in the 2022-2023 NBA season were identified based on average points per game.

- Offensive strategies and game dynamics were highlighted through data-driven insights.



## Why Sports Data Analysis?



As a passionate follower of sports, I am motivated to contribute my skills in data engineering and analysis to the field of sports analytics. This project is a stepping stone towards a career where I can merge my technical expertise with my love for sports to create impactful insights that drive decisions.



My ultimate goal is to work as a **Sports Data Analyst**, contributing to the evolving landscape of sports by combining data-driven insights with strategic decision-making.



## Next Steps



- Expanding this project to include advanced metrics such as player efficiency ratings (PER) and defensive stats.

- Incorporating machine learning techniques for predictive analysis (e.g., game outcomes, player performance).

- Collaborating on open-source sports data projects to further enhance my portfolio.



## Connect With Me



- **LinkedIn**: [ysayaovong](https://linkedin.com/in/ysayaovong)

- **GitHub**: [YSayaovong](https://github.com/YSayaovong)

- **Email**: [[email protected]](mailto:[email protected])



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Feel free to explore this repository and provide any feedback or suggestions. I'm excited to grow and learn as I continue my journey into sports data analytics!