Game On: Unlocking NBA Insights with Tableau Sports Analytics

Introduction

You've always dreamed of being a pro player...

Growing up, basketball was more than just a game—it was a passion that fueled my competitive spirit. In between classes, my friends and I would sneak in a few shots, honing our skills and aiming for the stars. While my playing days may be behind me, my love for the sport remains as strong as ever, especially when it comes to the NBA. ??

As a kid, I found myself mesmerized by the athleticism and finesse of players like Kawhi Leonard, whose prowess on both ends of the court left an indelible impression on me. Now, armed with a passion for data analytics and a deep-seated love for basketball, I set out to explore the world of NBA sports analytics using Tableau.

Imagining myself as an NBA coach or analyst, I delve into the data to uncover the insights needed to create an all-star team tailored to my vision and strategy.

?? Key Takeaways

Through a series of visualizations crafted using Tableau, I aim to provide valuable insights into player performance, team dynamics, and positional trends within the NBA:

• Player Performance: A dynamic bubble plot offers a quick overview of each player's point, assist, and rebound statistics.
• Player Contribution: Stacked bar charts highlight the total points scored by each player, allowing for easy comparison and analysis.
• Assist Distribution: Treemaps visually represent assist distribution across different player positions, offering insights into playmaking abilities.
• 3-Point Efficiency: Heatmaps showcase the 3-point shooting percentage across various player positions and teams, revealing shooting trends and patterns.

?? Dataset

The dataset, sourced from Basketball Reference, provides comprehensive statistics from the 2022-2023 NBA season. While conducting this analysis, the 2024 season was still ongoing, prompting a focus on the most recent completed season.

Featuring player personal information, performance metrics, and team affiliations, the dataset offers a wealth of insights into player dynamics and team strategies.

However, it's important to note the presence of "TOT" entries, indicating combined statistics for players who were traded or transferred mid-season. Therefore, I filtered out the "TOT" rows for a more in-depth view on each player.

With this project, I aim to uncover hidden patterns and trends within NBA data, offering a deeper understanding of the game and its players through the lens of data analytics. So, join me as we unlock the secrets of NBA sports analytics and take our love for the game to new heights! ??????

Analysis

The analysis and charts below showcase the images from the stories I created from Tableau. Feel free to click this link to directly get to my Tableau analysis!

?? Player Performance

In the bubble chart above, each dot represents a player, with different colors indicating their respective positions.

Players positioned towards the upper right corner exhibit superior scoring and assisting statistics. Additionally, the size of each bubble corresponds to the player's rebounding prowess, with larger bubbles signifying more rebounds. To identify the top-performing players in the NBA, simply locate the bubbles in the upper-left corner with the largest sizes.

For example, to examine player statistics for the San Antonio Spurs, you can utilize the filter at the upper right of the chart and select "SAS." This action filters out players associated with the Spurs, allowing for a focused analysis of their performance.

From the chart, it's apparent that Tre Jones and Keldon Johnson stand out as star players for the Spurs. Tre Jones excels in assists, while Keldon Johnson boasts the highest total score during the season.

?? Player Contribution

While this horizontal stacked bar chart may appear complex due to its vibrant color palette ??, it actually provides a wealth of information.

Firstly, the total length of each bar represents the total points scored by each team throughout the season. A longer bar indicates that the team scored more points.

Secondly, each color section within the bar represents an individual player. By examining the chart, the leftmost player in each bar is likely the highest-scoring player on their respective team.

However, there's a caveat ??: players who were traded or transferred mid-season may have shorter individual sections within the bar. Yet, when combining the points they scored for each team, their bar may extend further than players on their right.

For instance, let's consider the San Antonio Spurs again. By clicking on their bar, the chart instantly filters out other teams, displaying only the players from the Spurs. Keldon Johnson emerges as the leftmost player, indicating that he scored the most points for his team during the season.

?? Assist Distribution

This treemap provides a detailed focus on players' assist numbers, with positions color-coded similarly to the bubble chart.

It's evident that passing guards dominate in assists compared to any other position, as they play a vital role in facilitating team play. Without their contributions, scoring points could prove challenging for players.

The treemap specifically includes players with at least 200 assists during the season. If players with fewer assists were included, the plot would resemble five large cephalopods, which would be quite intriguing to observe. ????

Interestingly, only one player from the San Antonio Spurs achieved the milestone of at least 200 assists. This observation leads to the assumption that either Spurs players distribute assists more evenly across the team or that the Spurs scored fewer points compared to other teams.

?? 3-Point Percentage by Each Position

Heatmaps offer a compelling visualization of continuous values, such as the 3-point scoring percentage showcased here. The clear palette applied to the chart ensures that darker squares correspond to higher shooting percentages for each position within a team.

However, it's essential to interpret this data cautiously. While the Centers at the Brooklyn Nets exhibit the highest percentage of scoring 3-pointers, this doesn't necessarily imply superior performance in this aspect compared to other positions.

This disparity could stem from only a few Center players attempting shots from the 3-point line and successfully converting them. However, drawing conclusions solely based on these few shots may be misleading, as it doesn't provide a comprehensive assessment of their shooting abilities.

Upon examining the Excel data extracted from Center players at the Brooklyn Nets, we find that they scored only 6 shots out of 13 attempts from beyond the arc. ??

Similarly, this situation applies to the Centers at the New York Knicks. Despite having the lowest 3-point shooting percentage, it doesn't imply inferior performance.

Conclusion

With the aid of Tableau visualizations, I've gained invaluable insights into player information and performance, akin to the perspective of a coach or analyst seeking ideal team compositions. ??

Commencing with the bubble chart, I delved into player statistics, obtaining a comprehensive overview of points scored, assists, and rebounds for each player. Through the team filter, individual player profiles emerged, forming discernible patterns indicative of skill levels across positions.

Subsequently, the rainbow palette stacked bar chart illuminated the point distribution within each team. Positioned at the leftmost end of each bar were players showcasing prowess in scoring, offering valuable insights into team dynamics and player contributions.

Transitioning to the treemap, I scrutinized assist distribution across five distinct spirals, each representing players with a minimum of 200 assists per position. Notably, Passing Guards emerged as pivotal playmakers, underscoring their significance in facilitating team offense.

Finally, exploration of 3-point shooting percentages by position revealed varying skill levels across teams, with darker palettes indicating superior long-range shooting capabilities. Interestingly, Centers typically exhibited lower proficiency in this aspect, challenging traditional perceptions.

In essence, these four charts provided foundational insights into player attributes essential for strategic decision-making. Armed with this knowledge, further analysis can be conducted to unearth deeper statistical nuances and optimize team compositions for success. ?

Call to Action

I trust you found the basketball analysis as enthralling as I did, tapping into the nostalgia of our childhood games on the court! It's been a rewarding journey to analyze something I'm truly passionate about. ??

Whether you have inquiries about the analysis or wish to explore further collaborations, don't hesitate to reach out via email at [email protected]!

For more captivating projects like this one, feel free to explore my portfolio website through this link!

Wishing you all an incredible day ahead! ??