This project focuses on analyzing NFL tackling data to gain insights into player and team performances. The analysis includes exploratory data analysis (EDA), player and team-level analysis, tackling type and success factors, and the development of new metrics to evaluate tackling efficiency.

The analysis utilizes several datasets:

• tackles.csv
• games.csv
• plays.csv
• players.csv
• tracking_week_{i}.csv for weeks 1 to 9

Data is mounted from Google Drive and processed using Python libraries including Pandas, NumPy, Matplotlib, and Seaborn.

• Identification of unique games, plays, and players.
• Analysis of the distribution of tackles, assists, and missed tackles.
• Exploration of player heights, weights, and their correlation with tackling efficiency.

• Calculation of tackling efficiency and ranking of players based on various metrics.
• Investigation into how player attributes like height and weight impact tackling efficiency.

• Examination of tackling proficiency among different teams.
• Analysis of total tackles and missed tackles for each team.

• Study of different types of tackles (e.g., open field tackles, gang tackles).
• Classification of play types based on play descriptions.

• Creation of a class nflBigData for advanced data analysis.
• Implementation of methods to calculate downfield speed, downfield acceleration, distance to ball carrier, and yards forward.
• Development of a custom metric to evaluate the importance of tackles (yards saved).

• Numerous plots and visualizations to support the analysis, including histograms, scatter plots, and heatmaps.

• Comparison of traditional tackling efficiency with the new yards saved metric.
• Visualization of player performance metrics for better understanding.