The Contract Value Calculator is one of the most revealing tools I have built, not because it reinvents basketball analytics, but because it reframes the way we ask the most important question in roster construction. In today’s NBA, evaluating players purely on performance is incomplete. Production exists inside a financial system shaped by max contracts, rookie scales, and cap percentage allocation. Teams are not simply judged on talent anymore. They are judged on how efficiently that talent fits under the cap.
That shift in thinking is exactly why I created this model. I wanted a way to translate raw box score production and salary into one clear, interpretable number. Not a ranking system. Not a legacy debate metric. A value per dollar calculator that shows whether a franchise is actually gaining something from a contract or quietly losing money.
At its core, the philosophy is grounded in a simple economic truth. Contract value equals production divided by cost. In a capped league, surplus value is the currency that builds contenders. Championship teams rarely run on fair contracts alone. They rely on underpaid stars, elite rookie deals, and mid tier signings outperforming their cap hits. The calculator measures three things simultaneously. How much production a player generates. How expensive that production is. And whether the team is extracting surplus or absorbing inefficiency.
To quantify production in a way that remains accessible but meaningful, I built the Production Index around five core box score metrics. Points per game, rebounds per game, assists per game, steals per game, and blocks per game. These are universal stats. Easy to track. Easy to contextualize. But when weighted correctly, they still paint a strong picture of impact.
The formula works as follows.
Production Score equals points multiplied by 1.0, rebounds by 0.7, assists by 0.9, steals by 1.2, and blocks by 1.2.
Each weight exists for a reason. Scoring carries the base value because offensive creation still drives primary impact. Rebounding matters for possession control but lacks creation leverage, so it scales lower. Assists approach scoring weight because they generate team offense, not just individual output. Steals and blocks carry the highest multipliers because defensive stocks directly end or prevent possessions, making them disproportionately impactful relative to raw volume.
This structure prevents one dimensional scorers from inflating unrealistically while rewarding true two way production.
When you apply the formula to a superstar like Luka Dončić, the scale becomes clear. With roughly 33 points, 8 rebounds, 9 assists, and solid defensive stocks, his Production Index lands around the high 40s. That places him firmly in MVP territory. For context, role players usually sit between 20 and 30, All Stars in the mid 30s to mid 40s, and MVP candidates from the mid 40s upward.
Production alone, however, is only half the equation. Cost has to be standardized to make comparisons readable. Dividing production directly by raw salary creates messy decimals that obscure interpretation. So I converted salary into cost units.
The scaling mechanism is simple. Salary divided by two million. Every two million dollars equals one cost unit.
This keeps the economics proportional while making outputs clean. A four million dollar player equals two cost units. Twenty million equals ten. Fifty million equals twenty five. It preserves financial reality without cluttering the math.
Once production and cost units are established, the contract efficiency equation activates. Production Score divided by Cost Units. To compress outputs onto a clean ten point scale, a league wide multiplier of 3.5 is applied. The final score is capped at ten so rookie scale contracts do not break the model.
The final formula reads as follows.
Contract Value out of ten equals Production Score divided by salary divided by two million, multiplied by 3.5, with a hard cap at ten.
When you run real examples, the philosophy comes alive.
Luka Dončić produces near fifty on the index but earns roughly forty six million. His efficiency lands around the mid sevens. That reflects elite production paired with elite cost. Strong value, but priced near market reality.
Shai Gilgeous Alexander lands in a similar range. Massive impact, but a near max salary compresses surplus.
Then you reach rookie scale players. Someone producing in the high twenties while earning four million generates enormous surplus. Their raw efficiency often exceeds the scale and gets capped at ten. That is intentional. It reflects the economic advantage of rookie contracts in a capped system.
Understanding the score tiers is critical for interpretation.
- Ten represents elite surplus contracts. Rookie stars and breakout bargains.
- Eight to nine signals underpaid All Stars or early extensions that beat market inflation.
- Six and a half to eight represents fair value max players.
- Five to six and a half indicates neutral contracts.
- Three to five highlights overpays or declining production.
- Below three reflects negative contracts that often require draft compensation to move.
One of the most important design choices I made was ensuring superstars rarely hit ten. That is not a flaw. It is economic reality. The max salary system artificially suppresses surplus at the top of the market. Even MVP level players consume a quarter to a third of the cap. Their production is unmatched, but their efficiency relative to cost is naturally compressed.
This dynamic explains modern championship construction.
- Draft hits create surplus.
- Early extensions lock in below market value.
- Mid tier bargains round out depth.
- Max veterans alone rarely produce enough surplus to build full contenders.
- From a usability standpoint, the model has several strengths that make it practical.
- It uses simple box stats and salary inputs.
- The math is transparent and reproducible.
- It scales across the entire league instantly.
- It reflects real front office cap logic.
And it remains accessible to fans without requiring advanced metrics literacy.
There are, however, clear limitations that must be acknowledged. Box score production cannot fully capture off ball gravity, defensive scheme versatility, playoff scalability, or on off impact. Advanced efficiency metrics would refine precision. This tool should be viewed as a baseline contract efficiency lens, not a definitive valuation system.
In practice, I use it to evaluate trade targets, identify surplus contracts, compare team cap sheets, and visualize extension windows. It transforms subjective contract debates into quantifiable discussions.
Building this calculator changed the way I look at roster construction. Talent still matters most, but contract context now sits beside it. The gap between contenders and mediocre teams is often not who has the best players, but who has the best value tied to those players.
That is the competitive edge this tool is designed to expose.
This was inspired by @StatDefender on X, give him a follow!
The Top 25 Players in the NBA (By PPG) and their CVS (Contract Value Score):
| Player | PPG | RPG | APG | SPG | BPG | GP | Age | Salary | Production | CVS |
|---|---|---|---|---|---|---|---|---|---|---|
| Luka Dončić | 33.4 | 7.9 | 8.7 | 1.5 | 0.5 | 41 | 26 | 45,999,660 | 49.16 | 7.480924859 |
| Shai Gilgeous-Alexander | 31.8 | 4.4 | 6.4 | 1.3 | 0.8 | 49 | 27 | 38,333,050 | 43.16 | 7.881449559 |
| Anthony Edwards | 29.7 | 5.2 | 3.7 | 1.3 | 0.8 | 41 | 24 | 45,550,512 | 39.19 | 6.022544818 |
| Jaylen Brown | 29.5 | 7.0 | 4.8 | 1.0 | 0.4 | 46 | 29 | 53,142,264 | 40.40 | 5.321564772 |
| Nikola Jokić | 29.1 | 12.1 | 10.5 | 1.4 | 0.7 | 35 | 30 | 55,224,526 | 49.54 | 6.279456342 |
| Tyrese Maxey | 28.9 | 4.2 | 6.8 | 2.0 | 0.8 | 48 | 25 | 37,958,760 | 41.32 | 7.619848488 |
| Donovan Mitchell | 28.8 | 4.6 | 5.8 | 1.5 | 0.2 | 47 | 29 | 46,394,100 | 39.28 | 5.926615669 |
| Giannis Antetokounmpo | 28.0 | 10.0 | 5.6 | 0.9 | 0.7 | 30 | 31 | 54,126,450 | 41.96 | 5.426552083 |
| Kawhi Leonard | 27.6 | 6.1 | 3.6 | 2.1 | 0.6 | 36 | 34 | 50,000,000 | 38.35 | 5.369 |
| Lauri Markkanen | 27.4 | 7.0 | 2.2 | 1.0 | 0.5 | 37 | 28 | 46,394,100 | 36.08 | 5.443795655 |
| Stephen Curry | 27.2 | 3.5 | 4.8 | 1.1 | 0.4 | 39 | 37 | 59,606,817 | 35.77 | 4.200694025 |
| Jalen Brunson | 27.1 | 3.2 | 6.0 | 0.7 | 0.1 | 45 | 29 | 34,944,001 | 35.70 | 7.151442103 |
| Kevin Durant | 26.2 | 5.4 | 4.6 | 0.7 | 0.9 | 45 | 37 | 54,708,609 | 36.04 | 4.611340054 |
| Joel Embiid | 26.1 | 7.4 | 3.8 | 0.7 | 1.1 | 29 | 31 | 55,224,526 | 36.86 | 4.67219945 |
| Austin Reaves | 26.1 | 5.2 | 6.0 | 1.0 | 0.2 | 24 | 27 | 13,937,574 | 36.58 | 10 |
| Jalen Johnson | 23.2 | 10.5 | 8.0 | 1.3 | 0.5 | 47 | 24 | 30,000,000 | 39.91 | 9.312333333 |
Welcome to the Contract Value Calculator
A tool designed to instantly measure how much on-court production a player delivers relative to their salary. Simply input the player’s basic stats and annual salary, and the model will generate a clear 0–10 contract value score.
