The Absolute Value of Picks & the Sweet 7-10 Draft Pocket

For rebuilding teams like the Jazz, getting a potential future superstar like Victor Wembanyama is the goal. (via ESPN.com)

For an NBA front office evaluating draft picks, ultimately what you’re hoping to do is use your picks in a way that maximizes the pick’s estimated lifetime value to value paid for ratio. Average players end right around a 1:1 ratio. Eventual max-contract guys will offer a value ratio of 2:1 or even 3:1 during their rookie contract, then move back to 110% or 120% when their salary catches up with their production during an extension or second contract. Factor in average player retention past team control¹ and add the years’ values together and you’ve got a score for total lifetime value over contract scale.

That gives front offices an advantageous window for team-building around a young star, with more flexibility to add complementary pieces while the guy in question is still on a rookie deal. But that’s precisely the window the Jazz missed with their previous rebuild; they waited until each of Gordon Hayward, Donovan Mitchell and Rudy Gobert were no longer 3:1 value pieces before they made moves to upgrade the roster around them.

If they could’ve pushed themselves up to the luxury tax with players contributing as much as they were being paid while those young stars were still underpaid on rookie-scale contracts, they’d have hit championship contention level, but Jazz stuck right over the cap until Mike Conley’s extension, or really the Derrick Favors 2020 signing at the midlevel exception. Instead, they refused to commit until the season after Hayward left.

The 2016-17 season had a salary cap of $99M and a tax of $119M, and the Jazz were paying Gobert and Hayward just $18M combined. That was just 15% of the tax. On average, players at or adding up to 15% of the tax will produce 10 extra wins, while Gobert and Hayward contributed roughly 23. That was the perfect moment to load up around them while their cheap contracts still offered flexibility to a team wanting to go all-in. That they didn’t is easily the biggest misstep in the Jazz’ rebuild. Instead, they were dead last in spending that year, with 15% of the cap available at the end of the season, which means they had room to buy another nine or so wins at market value.They should’ve not only paid in UFA, but they also didn’t make any trades to move up in contract value. Had they pushed the tax (they were at just 71% of the tax), they could’ve propelled the team to championship levels.

The Jazz’s spending relative to the cap and tax over the past several seasons.

Anyway, lets jump into draft pick value²

Let’s get a max contract expected value from a simple linear regression for impact vs. % of cap from last season, and assume you play 34 MPG in 74 games. We can then convert to regular season wins above replacement player by adding 2.2³ and multiplying by both 2.7⁴, and the percentage of possible minutes played⁵.

Impact compared to % of cap.

Then use the 2022-23 max contract scale⁶/rookie scale⁷ for every player since 1980 and their value multiplied by their % of minutes played to estimate how many wins they added over the expected wins the contract scale at that age would have added. If the given player is below max contract value and outside of their rookie contract, we’ll assume on average they’re paid 1:1 value, otherwise, we’ll count how many wins they’re contributing over how many the contract scale is paying for. Now you’ve got a players’ wins over max contract by season. Here’s NBA legend Michael Jordan as an example:

MJ’s performance relative to the cap in his era.

Let’s now use a logarithmic expression for converting wins added to championship value added by season and add all seasons up for each player, that’s a player’s individual lifetime champ value added over their career. Let’s assume the average team with a max contract player has a roster around them that would win 36-56 games⁸ if they were pulled down to the average value of a max contract. We’ll throw in a random variable to get a number from 36-56 for the supporting team, and adding in the wins added over contract scale from before will give us a new # for expected wins for a given team, which we can convert to championship odds.

More wins = higher championship odds

Applying that math to Jordan’s career, you see that he generally contributed far more wins and championship hopes than the expected max player in his era — or in other words, that the max contract would have artificially limited his earning potential to well below his actual win-per-dollar value. It should be noted that Jordan was paid 123.2% of the salary cap in 1997-98, prior to the introduction of max contracts⁹ and the luxury cap¹⁰; but for all intents and purposes, we’re using the 2023 NBA salary rules to project out what would happen in the case of drafting a player like Jordan with today’s rules.

MJ features prominently in the GOAT debates for a reason.

Let’s scan the top 15 players in championship odds over contract scale to see if it passes the gut check.

All time greats.

A quirk in RAPTOR’s valuation is it inflates the value of high-assist guards, so it’s no surprise players like John Stockton, James Harden, and Chris Paul appear a bit higher than they should. The rest of the top 15 feels reasonably accurate for players since 1980, at least to a pick-level aggregate.

We can now average out championship odds over contract scale for each draft pick, and run a regression on that to smooth outliers. Let’s run it a few times on all players in our database, add up the total championship odds over max contract for each player’s lifetime, and aggregate by average championship odds added for each draft position:

The value of each draft pick using “championship odds over contract” logic

We get a really, really nice value of pick number when going down this path. There’s a distinct drop off after the first round¹¹ that isn’t taken into account with the line of best fit, but let’s not worry about that too much since we’re only really looking at the first round. A power series regression gives us a super solid 1:1 look at average pick value.

Digging further, we can ask some interesting questions: how many championships are you expected to draft if you hold the 14th spot prior to the draft lottery? Can we equate a spot finished to expected wins pre-lottery, then equate it back to a spot, post-lottery? If you finished 9th, and you’ve got a 20% chance of jumping up to pick in the top 4, is your unprotected pick worth the 8th pick in the draft? 7th? Let’s go down that route:

Combining lottery odds with expected pick value

By taking the weighted sum of lottery pick number odds¹² multiplied by pick number value¹³, then you’ll get a true pick value for each final standing, as compared to first overall, post-lottery. We’ll convert this back to pick number by taking the inverse of the pick number odds function¹⁴ to get an approximate post-lottery pick value. Essentially, this is saying “This pick unprotected is worth the 5th pick in the draft even though they finished with the 10th worst record”

Here’s the fun thing: an unprotected 14th pick pre-lottery is worth the same as the 13th pick post-lottery, roughly. But 8th overall? It has the same value as 5th, post-lottery. You’re giving your team excess value by pushing to be mediocre.

Finally, let’s compare pre-lottery value to post-lottery value, to see which teams benefit the most from the lottery system:

The current lottery odds deflate the value of the worst teams’ picks, inflate those above.

This is just another way to illustrate an inherent truth of the NBA draft lottery: The worst team in the league is hurt by the lottery system, because only 14% of the time do they actually maintain the first overall pick. Meanwhile, mid-lottery teams have a significant chance of jumping up in the lottery¹⁵, so they’re gaining value by finishing where they do.

This leads me to one more truth: there’s a kink in the rookie scale at the 9th pick that allows teams to average slightly more value than what they’re paying for. Again, the first few picks are most valuable (but never guaranteed), and then picks 4-6 are slightly less valuable than 7-10.

There’s a very distinct win-win-win scenario for finishing in that 7-10 pocket. You’ll get more climbing value than anywhere else through the lottery, you don’t have to flesh out your non-replacement level talent for bad contracts, and when your odds don’t follow through you’re gaining slightly more value than those picking in the 4-6 pocket.

Value added-to-salary ratio for each pick in the current lottery system.

This has me convinced that the ideal way to build a team from the ground up is to maintain talent that can win a significant amount of games, and then use your cap flexibility to propel up as soon as you’re sure you’ve got a player that will contribute a significant amount of wins above their contract scale. Put another way; retooling, not all-out rebuilding, provides the highest ceiling for an individual team facing a reset.

You get bonus points if you can stack more of these mid-lottery unprotected picks from other teams once you’ve got a team already set with a base of 50-55 wins, since their upside provides a pathway directly to a championship. Sound familiar? In 2017, the Boston Celtics advanced to the Eastern Conference Finals the night before their pick swap from a previous trade landed top overall pick. They were already a contender and added Jayson Tatum¹⁶, now an All-NBA talent.

For the present-day Jazz, the implications here are clear: heading into draft lottery night with the best odds at a Victor Wembanyama-level franchise changer would be nice — but being 7th through 10th pre-lottery provides a similar chance at adding championship-level value without necessitating a purge of talent & winning culture – a luxury many previous lottery winners may admit eventually nullified their lottery-night luck¹⁷.