Unveiling the NBA Through Analytics

Introduction

While new to American basketball and not necessarily a sports fan, I've been fascinated by the passion it ignites in viewers. This curiosity spurred me to delve deeper into the sport. To gain a better understanding and develop my research skills, I decided to explore the 2021-2022 NBA season, analyzing key performance metrics.? This project presented a unique challenge, allowing me to build upon basic knowledge and showcase my ability to learn and analyze data.

Data and Tools

The dataset, sourced from basketball-reference, is an Excel file containing information on 605 distinct NBA players across 30 teams. While the dataset includes 30 attributes, this analysis focuses on key metrics such as player names, position, team, 3-pointers made and attempted, total rebounds, assists, and total points scored.

The dataset contains 813 rows, indicating some redundancies. This is because players traded mid-season have an additional row with "TOL" stats for their new team. Similarly, some players may have played multiple positions for different teams, resulting in positions like "SG-PG." For clarity, this analysis will only consider players with a single primary position, filtering out those with dual positions.

Tableau Public was used to create data visualizations for this analysis.

Key Findings

Three-Point Distribution: The Miami Heat, Atlanta Hawks, and Los Angeles Clippers boasted the most potent 3-point shooting teams in the NBA. As expected, shooting guards dominated the league in 3-point scoring, reflecting their focus on perimeter play.

Player Performance by Position: A positive correlation existed between points and assists. Point guards unsurprisingly led the league in assists, with James Harden at the forefront. Notably, Trae Young excelled in both scoring and assisting, while some centers like Nikola Jokic, Joel Embiid, and Karl-Anthony Towns emerged as scoring and rebounding outliers.

Team Performance: The Minnesota Timberwolves topped the league in total points scored, while the Oklahoma City Thunder finished last. DeMar DeRozan of the Chicago Bulls led the league in individual scoring.

Assist Distribution by Position: Point guards, as the primary facilitators on offense, generated the most assists, followed by shooting guards. Centers, due to their focus on interior play, contributed the fewest assists.

Analysis

1. Three-Point Distribution

In basketball, scoring points is paramount. Field goals, either 2 points for shots inside the arc or 3 points for long-range shots beyond it, are the primary means of achieving this. Free throws, awarded for fouls while shooting, offer additional points. Teams strategically score these types of shots to outscore their opponents and win.

This analysis investigates the allocation of 3-point shooters among teams and positions using a heat map. Heat maps leverage color to visually reveal patterns, trends, and areas of importance, making complex information clear. The five player positions formed the map columns, and the 30 teams were the rows.

To obtain a more representative value for each team's 3-point scoring efficiency (as players have varying scoring ranges), I used an aggregate function that calculated the average 3-point score per 3-point attempt. Teams were then sorted in descending order by this value. The color range used three shades, with red indicating the lowest efficiency and dark blue representing the highest. This analysis revealed the Miami Heat, Atlanta Hawks, and Los Angeles Clippers as the top three teams with the most prolific 3-point shooting squads. As expected, shooting guards, known for their outside shooting skills, led the league in 3-point scoring.

2. Points, Assists, and Rebounds by Position

While points, assists, and rebounds are all crucial statistics, a well-rounded evaluation goes beyond just the numbers. High scorers are valuable, but their shooting efficiency also matters. Assists showcase a player's ability to create scoring opportunities for teammates, and ideally, a good scorer will also rack up assists. Rebounding helps secure extra possessions or prevent the other team from getting easy points.

To analyze individual player performance across these categories during the NBA season, I employed a bubble plot. This visualization revealed a positive correlation between points and assists. Notably, most of the green-colored point guard data points occupy the top half of the chart, indicating that point guards, as expected, generate the most assists for their teams. Interestingly, while centers tend to score more points than other positions, some standout players like Nikola Jokic, Joel Embiid, and Karl-Anthony Towns emerged as outliers, excelling in both scoring and rebounding. Trae Young exemplifies a player adept at both scoring (2,155 points) and assisting, despite not having a high number of rebounds.

3. Team Total Scores and Top Performers

?To provide a general picture of team performance, I used a stacked bar chart to demonstrate score rankings and individual player scores within each team. Color differentiation aided in distinguishing individual players on each team. The analysis revealed the Minnesota Timberwolves as the league leader in total points scored, while the Oklahoma City Thunder finished last. Karl-Anthony Towns was the top performer on the Timberwolves with 1,818 points. However, DeMar DeRozan of the Chicago Bulls led the entire league in individual scoring with 2,118 points.

4. Assist Distribution by Position

A high assist count signifies a team functioning well offensively. Players work together unselfishly, finding open teammates for high-percentage shots and exploiting weaknesses in the defense through their court vision and passing skills. This ball movement creates challenges for the opposing team, as they cannot solely focus on shutting down a single scorer. While scoring and defense are undeniably important, a team with a high assist count demonstrates the characteristics of an offensive juggernaut with a strong chance of winning.

To visualize the distribution of assists among positions, I utilized a treemap. Each position was represented by a different color, and each cell within the treemap represented a player with their corresponding number of assists. As expected, point guards led the league in assists, with James Harden at the top with an impressive 1,334 assists. Shooting guards followed closely behind, while centers generally contributed the fewest assists to their teams. This makes sense because centers typically focus on rebounding, rim protection, and scoring in the paint, leaving less opportunity to initiate plays or make long passes for assists. While some centers are gifted passers, their skillset usually prioritizes strength, size, and footwork for battling down low in the paint. In contrast, guards and forwards have the agility, ball-handling, and court vision to navigate defenses and find open teammates for scoring opportunities, leading to more assists.

Conclusion

This deep dive into the 2021-2022 NBA season provided me with valuable insights into the league's landscape. The importance of the 3-point shot and its impact on team success was undeniable. It was fascinating to see how teams with strong 3-point shooting squads dominated the league. The analysis also highlighted the diverse skill sets required for players to excel at their positions. Examining points, assists, and rebounds revealed a well-rounded picture of individual and team performance.

?This project served as a successful exploration of basketball, not only expanding my knowledge of the sport but also showcasing my ability to learn and analyze data.? I effectively utilized various data visualization techniques, which helped me present complex information in a clear and engaging way.

If you'd like to delve deeper into the data and visualizations, you can find my Tableau Story here.

?Thank you for your Interest!

I appreciate you taking the time to read this analysis. I'm always eager to learn and improve, so your feedback and contributions are highly valued. Feel free to connect with me on LinkedIn if you have any questions or insights to share.

As I'm currently exploring data analyst roles, I'd be grateful if you'd consider referring me to any suitable positions you may come across. Your support would be greatly appreciated!

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