Usage Rate and First Shot Tendency: Why Stars Don’t Always Take the Opening Attempt
Available in USTable of Contents
- The Doncic problem that breaks the usage-rate shortcut
- Defining first-shot share – the metric you actually want
- Why some superstars rank low on first-shot share
- The screen and isolation script – how the first shot gets distributed
- How coaches script the opening play
- Applying first-shot share to betting edge
The Doncic problem that breaks the usage-rate shortcut
Luka Doncic is one of the highest-usage players in the NBA. By season-long usage rate he ranks in the top five most years he plays. And yet his first basket conversion rate is consistently middle-of-the-pack – sometimes outright unimpressive – because the Mavericks’ opening play is built around getting their secondary scorer the first look. By the time Doncic touches the ball, somebody else has already taken or missed the opening shot. The lesson is simple: usage rate as a season-long metric does not predict first basket frequency. First-shot share – measured specifically on the opening possession – is a different metric, and it is the one that pays.
This is the trap that catches most casual UK bettors. They look at a usage-rate leaderboard, find the obvious stars, and assume those names are the value side on first basket props. The math falls apart on contact with reality. The first basket market is governed by who takes the opening shot, not who takes the most shots over a full game, and the two metrics correlate weakly enough that one is essentially useless as a substitute for the other.
What I want to do here is define what first-shot share actually measures, explain why some superstars rank low on it despite their overall scoring volume, walk through the screen-and-isolation scripts that determine which player takes the first shot, examine how coaches script the opening play, and then translate the data into actionable betting edge. By the end you should be able to look at a usage-rate leaderboard and know which names to ignore.
Defining first-shot share – the metric you actually want
First-shot share is the percentage of a player’s team’s first shots that the player himself attempts. So if a player’s team takes 82 first shots across the season and the player attempts 20 of them, his first-shot share is 24.4%. This is operationally different from his game-long usage rate, which is the share of team possessions ending in his shot, free throw or turnover across the entire game.
The two numbers can diverge sharply. Victor Wembanyama showed a 20.7% first-basket rate across 58 starts, with the San Antonio Spurs winning the opening tip 77.0% of the time and Wembanyama taking 27.6% of his team’s first shots. His season-long usage rate was higher than 27.6% – meaning he took a smaller share of first shots than of total shots. Jalen Brunson posted a 21.2% first-basket rate across 80 starts and took 23.8% of his team’s opening shots. His season-long usage rate, again, was higher than 23.8%.
The pattern: high-usage players generally have first-shot shares that are slightly lower than their season usage rates, because the opening play is more often a designed set than a player-driven possession. The exceptions are stars whose teams genuinely script the first possession around them – Wembanyama close to a one-to-one match, Edwards the same, but Doncic a much wider gap.
The practical takeaway: when researching a first basket bet, you want first-shot share, not usage rate. The two metrics are related but not interchangeable, and the gap between them is where most pricing inefficiencies live.
Why some superstars rank low on first-shot share
Three patterns explain low first-shot share for high-usage stars. The first is the early-screen role – players whose primary opening-play function is to set a screen for a teammate, not to shoot. Tall forwards and scoring centres often play this role; they post high game-long usage because they receive secondary touches throughout the offence, but their opening-play role is screening rather than shooting.
The second is the off-ball decoy. Some elite scorers run hard off-ball cuts on the opening play to draw defensive attention away from the actual designed shooter. The decoy gets used on most opening possessions, never gets the ball, and his first-shot share is microscopic despite his season-long volume. This is rarer than the first pattern but it shows up on a handful of teams every year.
The third is the late-trigger isolation. Some teams script the opening play as an early ball-movement sequence with the star receiving the ball as a third or fourth touch – by which point the shot clock has dropped enough that the star is making a decision under pressure rather than running the play himself. These plays produce shots that come 12 to 18 seconds into the possession, which means the first shot of the game has already been taken by a teammate during the build-up.
The implication: do not assume the star is the first basket value side just because his name is on the marquee. Watch tape of three or four opening possessions and see whether he is actually taking the first shot. If the answer is no, his first-shot share is low and the implied first basket price should reflect that.
The screen and isolation script – how the first shot gets distributed
The most common opening-play script in the modern NBA is the high pick-and-roll. The point guard brings the ball up, a centre or forward sets a high screen near the top of the arc, and the ball-handler either shoots from the screen, drives the lane, or kicks to a shooter spotting up in the corner. In this script, the ball-handler is the most likely first-shot taker – but only if the defence allows the pull-up.
The next most common script is the off-ball curl. A star wing comes off a staggered double-screen on the weak side, catches the ball in motion, and rises into a jumper or attacks off the catch. In this script, the wing is the first-shot taker by design, and his first-shot share is high regardless of usage rate. Murray’s role on the Nuggets is essentially this script – and it is why his first-shot share is materially higher than you would expect from a guard with his nominal usage profile.
The third common script is the post-up isolation. A centre or forward establishes deep position on the block while the wings spread the floor, the entry pass goes inside, and the post player either scores, kicks out to a shooter, or draws a foul. Post-up isolation produces a first shot from the post player roughly 60% of the time when the defence does not double, and a kick-out from the post player to a perimeter shooter the rest of the time.
How coaches script the opening play
Coaching scripts for the opening play are remarkably consistent across multiple games once a coach settles on a particular set. Some coaches reuse the same first-possession play for weeks at a time, only switching when a defensive adjustment forces a change. Others vary the opening play more systematically, but even the more variable coaches have a small playbook of three or four sets that rotate predictably.
The coaching script matters because it tells you who the first-shot taker is going to be on a given night with much higher confidence than a season-long average. If a coach has been opening with the same play for the last six games, the seventh game’s opening play is overwhelmingly likely to be the same set – and the first-shot taker will be the same player who has taken it in the last six.
Anthony Edwards, Jamal Murray and Neemias Queta tied the NBA lead with 15 first baskets each in the 2025-26 regular season precisely because their coaches script them into the opening play with high consistency. Not because they have the highest usage rates in the league. Edwards’ Wolves run essentially the same high pick-and-roll for him on the opening possession; Murray’s Nuggets run the same staggered screen; Queta’s Celtics run the same high pick-and-roll with him as the roller.
Applying first-shot share to betting edge
The actionable methodology is straightforward. Identify the players whose first-shot share is materially higher than their season-long usage rate would suggest – those are the names UK books underprice on first basket markets. Identify the high-usage stars whose first-shot share is lower than their usage rate – those are the names books overprice because of name recognition.
The pricing inefficiency is largest on the second category. UK operators systematically price stars short on first basket because the trading desk leans on usage rate as the primary input. When the star’s first-shot share is materially below his usage rate, the operator is pricing higher implied probability than the actual fair rate – which means the bet is a negative-expected-value proposition no matter how attractive the headline odds look.
The pricing edge on the first category – high first-shot share, lower name recognition – is more subtle but more consistent. Players like Queta, where the season-long usage rate is moderate but the first-shot share is high, tend to be underpriced on first basket markets because the operator’s model treats them as moderate-usage names rather than as scripted first-shot takers. Tracking the coaching opening-play scripts is the practical way to find these names; without that input, you are betting on usage rate proxies that miss the actual mechanism driving first basket conversion.
Why is usage rate not the same as first-shot share?
Usage rate measures share of team possessions across an entire game. First-shot share measures share of opening shots specifically. The two metrics correlate weakly because opening plays are scripted differently from the rest of the game – coaches design the first possession around a specific shot for a specific player, regardless of who has the higher overall usage.
How do I find a player’s first-shot share?
Track the player who attempts the first shot for each of his team’s games over a meaningful sample of starts. Divide his count of first shots by the count of his team’s first shots in the sample. The result is the first-shot share. Public play-by-play data on basketball-reference.com and NBA.com supports this calculation.
Published by the nba First Basket Bets team.
