This is my final project, including the presentation video for my audience who are NBA head coaches and general managers, who do not have strong analytical backgrounds on average, assessing which variables impact three-point shooting the most.

My final project assesses variables that impact three-point shooting, which is one of the most valuable skills in the NBA that is a strong indicator of offensive success.

In this data dive, I assessed how the NBA's three-point shot attempt rate has changed over time for five NBA seasons, not included pandemic affected ones.

In this data dive, I assess how impactful variables such as average shot distance, corner 3pt%, and volume of three-point attempts impacts three-point shooting using a linear model.

In this week's data dive, I created a regression model to assess what variables impact the log-odds of predicting a high-volume three point shooter, calculating CI's for one of the variables, and ran a Cook's D diagnostic check on my model.

I added more variables to my linear regression model I made in week 8 and conducted the five diagnostic plots on this new model.

In this data dive, I conducted an ANOVA test to compared FG% and position and a linear regression model to assess FG% and Average Shot Distance.

This is my week 7 data dive where I conduct hypothesis testing using Fisher Significance Framework (FG% and shot distance) and Neyman-Pearson Framework (comparing FG% and position)

This is my week 6 data dive where I created two calculated metrics: Distance-Adjusted Efficiency (DAE) and Shooting Efficiency (SE). I then compared DAE to Average Shot Distance and SE to FG% by using correlation matrices confidence intervals, and visual plots.

this is my resubmission of the week 4 data dive based on the feedback provided by my TA including hiding libraries using include = FALSE and elaborating how the analysis done will impact future analysis.

This week, I dove into the data documentation for the NBA shooting analytics dataset to have better clarity on the data being presented.

Analyzing three point attempt samples analyzing the significance of outliers.

This is my submission for the week3datadive where I analyze shooting efficiencies grouped by position, teams, and age. I also analyze team roster construction by analyzing the combinations of teams and positions to understand how teams are building their rosters to maximize offensive success.

The analysis done is comparing NBA shooting statistics and distance, specifically how the different types of shots attempted by players and team impacts efficiency and if there is a difference in efficiency by distance among positions.