01 // OVERVIEW

Wemby Climate

The Evolution of the NBA

Victor Wembanyama is a 7'4" alien who shoots step-back three-pointers and runs the fast break. Twenty years ago, a player like him wouldn't and *couldn't* exist.

Scroll down to explore the extreme evolutionary pressure that forced the NBA to adapt, and ultimately created the exact climate necessary for a player like Wemby to thrive especially with the NBA finals going on.

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02 // THE QUESTION

Much slower league

For decades, the NBA was a predictable, bruising battle of giants. Teams drafted massive, slow-moving centers, dumped the ball into the paint, AND methodically ground out the shot clock.

We wanted to measure exactly how this ecosystem was shattered. By tracking Pace, Field Goal Attempts, and Three-Point Volume over time, we can map the extinction of the traditional big man and the birth of a hyper-speed, perimeter-focused league.

The modern NBA looks completely different than it did a decade ago. We set out to answer a core question: How exactly have team playstyles and offensive strategies evolved in the past 10 years?

03 // THE DATASET

Where does the data come from?

Our visualization is powered by historical NBA team statistics spanning multiple seasons.

The dataset integrates per-game averages (such as FGA/G and 3PA/G) with overall team summaries (Wins) and geographical mapping data. By connecting team performance metrics with their physical locations, we aim to map out both the statistical and regional landscape of the league.

By analyzing specific metrics like Pace, Three-Point Attempts, and Field Goal Attempts, we can visualize the league's shift from a physical, paint-oriented game to a perimeter-focused, high-speed sport.

04 // THE TAKEAWAY

Quick Summary: A Faster, More Skilled League

The traditional, physical, paint-oriented era of basketball is officially dead.

The analytics revolution proved the supreme efficiency of the three-point shot, forcing every team in the league to play significantly faster, shoot from further away, and prioritize high-volume transition offense just to survive.

Pace

Game Speed

The estimated number of possessions a team has per 48 minutes. Higher numbers indicate a faster, transition-heavy style of play.

FGA / G

Shot Volume

Field Goal Attempts per Game. The total number of shots a team takes. This scales directly with how fast a team pushes the pace.

3PA / G

Perimeter Focus

Three-Point Attempts per Game. Indicates how heavily a team relies on spacing the floor and generating points from beyond the arc.

Wins

Overall Success

Regular season win totals. This helps us see if adopting a modern, fast-paced, 3-point heavy strategy actually translates to winning games.

05 // MACRO TRENDS

The Evolution of the League

Before we look at individual teams, let's observe how the entire NBA's shot profile has changed. Notice the dramatic convergence of 2-point and 3-point attempts, and how consistent free throws (fouls) are over time.

06 // THE "BUT"

"BUT" What Changed?

For years, basketball purists mocked the three-point shot.

Charles Barkley famously declared, "A jump-shooting team will never win a championship."
BUT, the analytics revolution revealed a mathematical truth that couldn't be ignored: 33% from deep equals 50% from mid-range.

In 2014-2015, the Golden State Warriors leaned into this math, broke the league, and won the title. Suddenly, shooting threes wasn't a gimmick—it was a matter of survival.

Notice the sharp spike in our macro trends chart right at this exact moment.

Three point shot
07 // GEOGRAPHY

Select Your Teams

Select the Golden State Warriors (the pioneers) and the Houston Rockets (the extremes). Watch how their 3PA lines detach from the rest of the league. Scroll down to see their historical trends.

Analyze Selected Teams ↓
08 // SPEED

The Pace of the Game

It was no longer enough to be big; you had to be fast. The game evolved into a track meet, punishing teams that couldn't keep up.

Pace measures the number of possessions a team has per 48 minutes. In traditional basketball, teams would methodically walk the ball up the court to set up a half-court play. Today, the NBA has evolved into a track meet.

Setting the Tempo

Look at the chart to your right. The dashed gray line represents the league average. Notice the steady, undeniable climb from 2014 to the modern era. Analytics revealed that transition points are highly efficient, pushing teams to play faster than ever before to exploit defensive mismatches before they can set up.

09 // OFFENSE

Field Goal Attempts (FGA)

With a faster pace comes more opportunities to score. Field Goal Attempts per game (FGA/G) measures sheer offensive volume—how many shots a team manages to put up before the buzzer sounds.

The Ripple Effect As the chart updates, you'll see a trendline that heavily mirrors Pace. More possessions equal more opportunities to score. Teams are shooting earlier in the shot clock, prioritizing volume over grinding out a full 24 seconds.
10 // STRATEGY

The 3-Point Surge (3PA)

For decades, the 3-pointer was treated as a desperation shot. Then, the analytics revolution revealed a simple mathematical reality: shooting 33% from the three-point range is equivalent to shooting 50% on mid-range jumpers.

Watch the line chart morph. Compared to Pace and FGA, the upward trajectory of Three-Point Attempts is staggering. The baseline for what is considered "normal" has effectively doubled in a decade.

The Strategic Shift

The traditional "paint-oriented" big man has been phased out. Now, every player on the court is expected to shoot from the perimeter, stretching the defense to its breaking point.

11 // Effectiveness

More Shots, But are they Efficient?

Although more teams shoot more and shoot faster, is it truly effective? We can use Effective Field Goal Percentage to bridge this gap. Effective Field Goal Percentage accounts for the fact that 3-point shots are worth more than 2-point shots, providing a more accurate measure of a team's shooting efficiency.

12 // SUCCESS

Translating to Wins

Metrics like Pace and 3PA define how the game is played, but Wins define why it is played. Look at your selected teams to see their specific peaks and valleys across seasons.

The Bottom Line While playing fast and shooting threes is the new mandatory baseline to compete in the NBA, execution and defensive rating still determine who actually hoists the trophy. Notice how erratic this chart looks compared to the smooth, league-wide trends above.

The Pace of the Game

12 // THE FULL TAKEAWAY

The Wemby Climate

The traditional era of basketball is officially dead, killed by the supreme efficiency of the three-point shot. Teams were forced to play significantly faster and shoot from further away. THEREFORE, this hyper-spaced, high-speed environment demanded a new type of player.

The Core Takeaway: The NBA's statistical revolution didn't just change team strategies; it completely redefined human athletic archetypes. Our visualization succeeds in proving this by layering macro league-wide convergence (the stacked bar) with micro team-level erraticism (the scrollytelling line chart). It shows that while every team was forced into this new climate, only those with the ultimate modern personnel—like Victor Wembanyama—will truly rule it.

Effectiveness of our Visualizations

Why our visualizations work: By tying macro-level league trends to micro-level geographic team data, this visualization effectively demonstrates the inescapability of the analytics revolution. THEREFORE, this hyper-spaced, high-speed environment demanded a new type of player.

The interactive scrollytelling isolates individual metrics (Pace, FGA, 3PA) so the user isn't overwhelmed, while the synchronized map proves that this wasn't just a regional trend—it was a league-wide climate shift. Seeing the Golden State Warriors' line spike on the graph right as the user reads about the 2014 tipping point connects the historical narrative directly to the mathematical reality.

13 // CHECKPOINT WRITEUP

Overview

Our team’s final project aims to create an interactive data visualization experience that helps users better understand patterns and insights within our chosen dataset through engaging and intuitive visual storytelling. We plan to use D3.js to build dynamic and interactive components that allow users to explore the data from multiple perspectives, following the course requirement of publishing an interactive web visualization.

Methodology & Progress

We made a line chart that looks at 4 different metrics (Pace, FGA/G, 3PA/G, and Wins) that you can filter between. We also created a temporary dropdown where you can select multiple teams to compare their stats. We also have an interactive map where you can select a team on the map that they are based in and it would display the selected statistic on the line plot. We will also look to add a reset button in the future and a zoom function since some teams on the map may overlap in terms of location.

Challenges

The challenging part of the project to design will probably be the website itself to make it as explorative and immersive as possible. We want the visualization to be visually appealing while also having it clearly convey our question and message. Additionally, making a functional map that represents each team will be a challenge since some teams relocate or get phased out of the league over time. This means that we have to ensure that not only are team roster changes updated, but we also have to make sure that changes in playstyle are also clearly reflected in our visualization.