As with the MLB visualization, there’s a couple caveats. The biggest one is that I’m using contract data from Basketball Reference that shows what each player is being paid each year. The sum of these salaries almost always exceeds the salary cap each year. That’s because the salary cap is actually a soft cap. There’s a lot of different situations where a team can sign a player that would put them over the cap. I am no expert on all the details and there are better resources to learn about what is allowed and what is not. This visualization is great at providing a general idea of what each team’s cap space is, but it won’t tell you its exact size. Also, option years of any kind (team or player) are included in each player’s contract and thus the team totals as well.
There are a couple cool story lines that you can explore using the visualization. The one that may stand out the most is the current leader in payroll: the Brooklyn Nets. Scrolling through the years makes it pretty easy to identify when this guy swooped in to buy the team. It’ll be fun to watch this season to see if all the money spent since then can buy a good playoff performance.
Scrolling forward just one year shows the incredible amount of cap space the Lakers will have to work with next summer. The details of the cap mean that it won’t be quite as much as the chart seems to imply, but they still have a lot of options when the time comes.
Scrolling back to the 2005-06 season shows just how much money the Knicks were paying at the time. The amount that the Knicks have had to pay in luxury tax is simply astounding. What may be even more astonishing is their lack of results to show for it.
It’s time to come clean a bit about these visualizations. I absolutely love working with data and presenting it in a way that is beautiful, easy to understand, and educational. I’ve also really wanted to get a better grasp of the markets in these different sports so this was a natural project for me to take on. But mostly, I want to get a job doing this type of thing with any kind of data. Whether it’s making predictions based on a trove of past behavior or visualizing the trends to make sense of it, I have quite the passion for working with data. I can think of one website that is currently looking for that type of thing. Contact me if you’d like to learn more.