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Visualizing Value

US Presswire

US Presswire


 
On Friday, Ming Wang wrote a post here at Hickory-High, sharing a new method for measuring the value of NBA contracts. Here’s the method and rationale in his own words:

A few weeks ago, Kevin Pelton of ESPN looked at the best contracts in the NBA by multiplying a player’s WARP (wins above replacement level) by the average amount that teams pay for each WARP. I’d like to approach this same problem from a different angle: namely, how much value are teams getting out of the salaries they pay their players? Instead of looking at WARP, I’ll focus on win shares, another metric of player value. While Pelton’s methodology assumes that the overall NBA salary market is priced correctly (therefore attaching a value to each WARP a team pays for), my method makes no assumptions about overall pricing accuracy and instead seeks to evaluate relative player salary and performance.

At a basic level, my goal is to quantitatively evaluate the best and worst contracts in the NBA. To do so, I construct a simple metric that I call the “value ratio.” This is defined as: (Player Salary/Median Salary)/(Player Win Share/Median Win Share). In effect, I am comparing the amount over (or under) which a player is being paid vs. the median NBA player with that player’s production over (or under) that of a median player. Comparing salaries and win shares with median values serves as a way of normalizing these metrics and making them more readily comparable to each other. A simple way to think about this metric is the following: if the ratio is less than 1, the player is undervalued; if the ratio is greater than one, the player is overvalued; if the ratio equals one, the player is properly valued. In short, the most valuable players will be those with the smallest value ratios.

Ming has plenty of interesting analysis of both the method and results in his original post, but I thought it might be interesting to extend the work a little. Ming was nice enough to share his entire dataset with me and I created a Tableau visualization to display the results. Each player is marked by their three-year Win Share average and the per-year average of the salary remaining on their contract. The darker and more red a player’s mark is, the more their contract overpays for their production. A lighter, yellow mark means they are more appropriately valued. The few hapless players providing negative production are marked in blue. Play around with the filters and let us know what you find!

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