Draft evaluation is hard precisely because it asks us to project college performance onto a pro game that looks different in almost every way. The HoopsWill Metric doesn't try to predict a player's NBA career. It tries to answer a narrower, more answerable question: given what this player did in college, from a raw analytical perspective how productive and impactful were they relative to their opportunity?
The framework is built around what I believe drives winning in the NBA — and more specifically, what produces 16-game players. That's the standard that matters: not who fills a box score in November, but who a coaching staff trusts when the margin is thin and the opposition has had weeks to prepare. Playoff basketball strips out the noise. It rewards skill, efficiency, and defensive impact in ways the regular season often masks. The metric is constructed with that context in mind.
What I wanted were stats that say something durable — that reflect how a player actually plays the game, not just what role their coach gave them. The six inputs below aren't just analytically meaningful. They're sticky. Players who score efficiently in college tend to score efficiently in the NBA. Players who take steals and blocks in college tend to do so in the pros. Players who create for others without turning it over tend to keep doing that at the next level. These aren't traits you coach in — they're expressions of how a player sees and plays the game.
The six inputs
True Shooting %
TS%
Scoring efficiency is the most portable skill in basketball. A player who scores efficiently in college — accounting for twos, threes, and free throws in a single number — is demonstrating shot quality and shot selection simultaneously. TS% cuts through volume to ask: when you shoot, how well do you do it? Elite scorers at the college level tend to remain elite scorers. That's not a coincidence — it reflects real skill in shot creation, touch, and decision-making that travels.
Points + Assists per Usage
P+A(2)/USG · pace-adjusted
This is the metric's most unusual input and probably its most important. It measures how much a player produces — points and assist-value, scaled for pace — relative to the possessions they consume. A player who scores 20 points but needs 25 possessions is less useful than one who scores 18 on 14. This stat rewards efficiency of impact rather than raw output, which is exactly what you want when evaluating players across wildly different roles and systems. It captures the dimension of basketball that matters most to winning teams: what did you produce for every opportunity you were given?
Steals + Blocks
Stl+Bl · pace-adjusted
Defensive impact is notoriously hard to quantify at the college level — but steals and blocks represent the two most direct ways a defense can alter possessions, and they are among the stickiest stats in basketball. A steal doesn't just stop a possession — it creates one. It eliminates the opponent's shot while generating a transition opportunity on the other end. A block reduces the opponent's shots on goal, and the best shot-blockers don't just alter attempts — they do so in ways that keep the ball in bounds and trigger quick-strike scoring on the other end. Combined and pace-adjusted, Stl+Bl identifies players who actively take possessions rather than simply contest them. That skill translates because it reflects athleticism, anticipation, and positioning — traits that don't diminish when the competition improves.
Assist-to-Turnover Ratio
A/TO
Ball security and decision-making. A high A/TO ratio tells you the player creates for others without giving the ball away — a signal of processing speed, court vision, and composure under pressure. It's one of the few stats that translates cleanly from college to the pros because it reflects how a player thinks, not just what they can physically do. Teams that protect the ball in the playoffs win. This is the stat that tracks the players who understand that.
Free Throw Rate
FTAr
The ability to draw fouls — to get to the line relative to shot attempts — reflects aggression, athleticism, and the physical assertiveness that creates pressure on defenses. It also captures something repeatable: some players just get calls, and that's a skill. In playoff basketball specifically, getting to the free throw line is one of the most reliable paths to efficient scoring when half-court offense is harder to come by.
Three-Point Attempt Rate
3PAr
Modern NBA offenses are built around three-point spacing, and 3PAr signals more than shooting volume — it signals a player's inherent value-add to an offense as a floor spacer. Research has shown that players with high three-point attempt rates force defenses to push out further on perimeter coverage, which generates driving lanes, post opportunities, and open cuts for teammates even when the shot doesn't go in. A player willing to operate from range at volume makes everyone around them more dangerous. That underlying value — spacing-driven offense creation — is something this stat quantifies beyond raw shooting efficiency. TS% handles whether the shots go in; 3PAr handles whether the threat is real.
The scaling factors
The six stats above are the core. But raw stats from an 18-year-old freshman carry different weight than the same numbers from a 22-year-old senior. The metric applies three scaling adjustments:
Age
Younger players who post strong numbers get a premium — and not just because the projection is longer. More importantly, it signals untapped upside. A freshman dominating from day one suggests a development arc that has barely begun. Older breakout ages, by contrast, can reflect a player whose performance was driven partly by competing against younger opponents — production that may not translate as cleanly when the age gap disappears.
Size
This is a recent addition to the model and one I expect to keep refining. The six core inputs are primarily skill-based — as they should be — but length and strength are always at a premium in the NBA, especially when playoff physicality demands it. A 6'8" guard running elite assist and steal numbers is rare in a way that matters. Size also affects how skills translate: a center with a high A/TO may be doing something more exceptional than a guard with the same number. I believe size deserves additional weighting in future iterations.
Position
A modest positional weighting reflects the reality that guards and forwards tend to produce higher raw metric inputs than traditional bigs — partly by design of the college game, partly by how possessions are structured. The adjustment is intentionally light. The goal isn't to correct for position so much as to ensure positional context is acknowledged.
Historical benchmarks
The metric is best read comparatively. Scores above 140 are genuinely rare — historically that tier has included some of the most impactful prospects in recent draft history, and the chart below shows how those scores align with where they were actually taken.
Lottery pick (1–14)
Late 1st round
2nd round
This is a living model
Basketball changes. The three-point revolution reshaped what efficiency looks like. Positional distinctions have blurred. Metrics that made sense five years ago may need revisiting — and as the game evolves, so should the model that tries to measure it.
The HoopsWill Metric is designed to be refined. The inputs may shift. The scaling weights may get adjusted. New variables may earn their way in. The size factor, for one, is already being reconsidered for more weight in future iterations. If a better way to measure something presents itself, the model should reflect that. The goal isn't to lock in a formula — it's to get closer to the truth about what makes a prospect.
