March Madness Insights

this project graph was create in Power Bi and R :

The Kaggle dataset originates from an advanced March Madness Data Analysis dashboard created with Domo, offering a comprehensive collection of NCAA Division 1 men's basketball data. It is meticulously organized to cater to enthusiasts, analysts, and researchers interested in exploring March Madness dynamics from various perspectives, including team and conference filters, with a particular focus on teams within the Power 6 Conferences (ACC, Big 10, Big 12, Big East, Pac-12, SEC).

The dataset's key features include:

1. Team and Conference Filters: Users can delve into specific teams and conferences, gaining a detailed view of the tournament landscape.

2. Power 6 Conference Insights: Emphasizing the ACC, Big 10, Big 12, Big East, Pac-12, and SEC conferences, facilitating targeted analysis of these influential groups.

3. Tournament Projections: Based on Joe Lunardi's Bracketology on ESPN, offering forward-looking insights on tournament participants until the final selections on Selection Sunday.

4. Championship DNA Analysis: Identifying teams with the highest potential to win the tournament, utilizing historical data and winning patterns.

5. Season Efficiency Overviews: Providing data on the season's efficiency for teams within the March Madness tournament, offering insights into their performance.

6. Detailed Performance Metrics: Breaking down crucial metrics historically influencing March Madness outcomes, including Offensive vs. Defensive Efficiency and various rate comparisons.

Additionally, the dataset allows users to export data for further analysis in platforms like Domo, Power BI, Tableau, Excel, and Google Sheets. It also includes general bracket-filling tips based on trends from past seasons and credits the sources of raw data and analytical inspiration.

This dataset is tailored for users seeking to conduct their analysis, develop predictive models, or gain deeper insights into March Madness tournaments. Whether for academic research, personal interest, or professional analysis, this dataset serves as a fundamental tool for exploring the diverse landscape of college basketball's most thrilling season.