The stretch-five archetype expanded significantly over the past few seasons as front-court players ventured beyond the arc to capture greater offensive value. While this project skips the quantification of that value, the text alludes to it throughout. Instead, this analysis focuses entirely on mechanics, play-type distribution, tendencies, and much more surrounded each center’s perimeter attempts. The study combines manual film tracking with custom-built data metrics to paint a comprehensive picture of modern big-man shooting.
Why?
The decision to manually track perimeter attempts originated immediately following Karl-Anthony Towns’ arrival in New York. Consolidating his shot data had me interested for more and dive into how his perimeter shooting contrasted with other versatile bigs. Unpacking his differences in play-type usage and evaluating shot selection became the primary catalysts for this study.
Towns and other players aside, this project sharpened my basketball analytics and video tracking skills. It forced me to return to data visualization via R, the construction of a comprehensive data spreadsheet, and the formulation of film-derived metrics.
Baseline
I tracked 25 stretch fives from the previous season, establishing a baseline threshold of 200-plus three-point attempts and 400 plus minutes at the center position. Only Joel Embiid, Jock Landale, and Kristaps Porziņģis received exemptions from this criteria due to a combination of historical production and injury-shortened seasons. The entire dataset originated from direct video tracking, no system or outside assistance, other than looking up three-point attempts and manually logging the data through Court Sketch. I used thirteen columns to log data in a detailed and organized fashion, as shown in the Google Sheet. The following report analyzes these core categories, demonstrating their connections and key takeaways. Certain tracking columns will carry heavier weight and command more analytical focus than others.
Catch-and-shoot threes dominate the stretch big landscape, making this kind of analysis on a single player group indirectly “manageable.” Tracking shooting guard threes, for instance, would introduce far greater variation across every variable, likely making groupings unattainable. The tighter study of centers makes the groupings you’ll see far more defined. That said, this reflects my own definition of the stretch big archetype, others may attempt different methods.
Findings Part I: Play-Types
Play-types form the backbone of this analysis. The play-type category breaks down into eleven sub-groups: Spacing, Roll-man, Trailing, Relocation, ISO (Isolation), Pin-down, Screen Pop, P&R (Pick and Roll) Handler, Flare, Miscellaneous, and Offensive Rebound. Rather than covering every one, I’ll focus on the most impactful and common play-types throughout.
Roll-man
The roll-man play-type has potential to elevate an offense to new heights, a center who can shoot above the break(ATB) or well beyond the arc forces defenses into difficult schematic decisions. As we see with Towns, the gravitational effect extends far beyond himself. Creating difficult coverage decisions, stress in rotations, unappealing cross-matches, and opening up the rest of the floor. The mere threat can make it the most impactful play-type among stretch fives.
Joel Embiid, Nikola Jokić, Brook Lopez, and Nikola Vučević all rank among the higher-usage bigs in roll-man frequency. These centers often draw the opposing center as their matchup: teams rarely defend them with power forwards, switchable wings, or nimble defenders in ball-screen situations, fearing the post-up or a mauling on the offensive glass. Players like Naz Reid, Bobby Portis, and Evan Mobley either split time at the four or draw cross-matches from wings more frequently, limiting their roll-man volume. Towns and Wembanyama, both full-time centers this season, carry that same caveat in certain situations.
Teams have worked to leverage the play-type through play-calling, placing their big in positions to maximize the pick-and-pop action. The Bulls, Knicks, Clippers, Thunder, and Nuggets stood out as the best at creating space for their roll-man through various plays. One of the simplest, spread pick-and-roll, clears the break of offensive players, putting the opposing five (x5) into coverage with no immediate rotation towards the popping big.
Vučević sets the ball-screen to pop right while Coby White drives left, leaving only one help defender on the weak-side rather than two.
My favorite way to manufacture space above the break, starts with cutting. It’s more deceptive than any set play and works within both freelance and conceptual offenses. Anyone on the floor can execute a cut, instantly making themselves a rim threat in the process, benefitting perimeter players who don’t shoot it well from deep.
Concept: If pick-and-roll or handoff takes place one pass away → cut from wing (45 cut)
Peyton Watson executes a 45 cut to eliminate a potential rotation out to Jokić, who draws the center into coverage through a freelanced dribble handoff with Jamal Murray.
As stated earlier, volume shooting and skill in this play-type reveal far more than surface-level three-point data. The ability to shoot ATB and demand gravity factors in just as heavily. Without a credible three-point threat, defenses key in on the handler rather than respect the popping big. Donovan Clingan illustrated that the best, while he produces at a solid rate as a pop big in both frequency and efficiency, his overall shooting talent doesn’t generate the kind of gravity Portland can leverage, in the way Jokić or Towns do for their respective offenses. For Clingan and others, we’ll see that it’s a reoccurring theme throughout many play-types despite substantial three-point volume.
Spacing
Many high-volume roll-man shooters don’t rank as high-volume spacers, and that makes plenty of sense. The skilled gravitational giants like Jokić, Embiid, and Towns see their defenders stick close off the ball, or they’re simply defended by the opposing center, so they’re more inclined to go toward the action rather than the corners. By extension, these bigs tend to position themselves ATB, or near the top of the key. As ultra-talented bigs who offer far more than just three-point shooting, seeing them space off-ball less comes as no surprise.
Jaylin Williams, Portis, and Reid lead the other side of this spectrum as elite spacers. As stated previously, these centers tend to share the floor with other bigs more often, while lacking in other dynamic areas of their game; subjecting them to heavier corner spacing volume, off-ball play and lower activity.
Advantage creation drives much of their spacing volume. All three played alongside elite creators, Williams with Shai Gilgeous-Alexander, Portis with Giannis Antetokounmpo, and Reid with Anthony Edwards and Julius Randle. Interestingly, Randle’s creation generated more shot attempts for Reid than Edwards did, 22% of Reid’s attempts came via Randle compared to just 12% from Edwards. I attribute that gap to Randle’s passing skill and paint gravity. Edwards operates more from the perimeter and, despite drawing double teams frequently, doesn’t consistently deliver the type of passes Randle does to register potential assists, or gets hockey assists.
Advantage creation alone doesn’t explain all of these looks. Spacers have the opportunity to exploit roll gravity and benefit from help defender manipulation. Portis capitalizes on the Bucks running double drag against high coverage, with Myles Turner setting the final screen and looking to roll into space.
As Turner rolls toward the basket, Jalen Duren executes his primary help assignment with Tobias Harris hedging. The play works exactly as designed, punishing high coverage and the single weak-side tagger to create an open three.
Spacing gravity travels as far as the shooter’s actual talent. Bam Adebayo fits my definition of another low-gravity shooter that teams gave open corner threes to, choosing instead to protect against the drive or the roll. This can be of significant concern heading into next season alongside Giannis. I have many questions about his ability to convert at the volume he’ll likely see, and the impact to Miami’s half-court offense if he’s forced to attempt large volumes. We’ll get into specifics later in the article.
A defensive tactic appearing frequently throughout this exercise is the peel switch: the center helps on a strong-side action while the point-of-attack defender peels off and switches onto the corner shooter. Teams deploy this far more often against lower-gravity stretch bigs in an effort to prioritize paint defense over the big’s perimeter threat.
Wendell Carter Jr., as a lower gravity shooter and corner spacer sees this tactic time and time again. Sandro Mamukelashvili helps off to smother a strong-side cut, prompting Immanuel Quickley to switch or “peel” onto Carter Jr. and contest the shot. Protect the rim with the center first and give up the three with the guard contesting.
Relocation
Unlike spacing, relocation frequency and efficiency spread widely across the twenty-five tracked players, exposing the volatility of this play-type. A relocation three-pointer, by my tracking definition, requires a player to travel to a separate section of the court. Examples include a wing-to-corner drift, a dunker-to-corner lift, or going from paint-to-break all within a half-court setting.
Dunker-to-corner leak outs represent a high-volume relocation sequence. Virtually every tracked center utilized this movement path, exploiting the dunker spot as the traditional space before clearing out to the perimeter. This sudden outward movement catches defenders off-guard or in help situations, creating shooting windows and separation.
Brook Lopez breaks for the right corner after the Lakers flood or overload Kawhi Leonard’s isolation. With all offensive players clearing out to give Leonard room to work, the Lakers bring help, Lopez reacts immediately finding a soft spot in the manufactured zone.
Portis and Jalen Smith stand out as clear elite performers in this play-type. Both have small-ball center stature and leverage their extra mobility to shoot on the move while relocating across the floor converting from multiple spots. Their versatility across different movement patterns and court locations makes them uniquely dangerous in this category.
Off-screens
Off-screens take into account pin-downs, flares, and screen pop actions, any play utilizing an off-screen. Some actions are niche and specific to certain players and teams, while others appeared consistently across the full sample. Much like relocation, this ranks as a movement-heavy play-type.
Read more about my favorite general actions for every stretch five in the study, including off-screen play:
Review of Play-types and Categorization
Each stretch big offers a unique profile: Nikola Jokić’s play-type diet looks nothing like Myles Turner’s, and Jock Landale’s output bears little resemblance to Bam Adebayo’s. Grouping them by shot tendencies, efficiency, and versatility can build tiers of play-styles across the board.
Venn diagrams don’t typically find their way into my work, but here the visual intersection of each player’s preferred and most efficient play-types makes a compelling case for making one. I included three categories: spacers, screening-action, and self-creators/movers.
Spacers — High-volume spacing bigs with little movement or involvement in action. Lack of efficiency in other play-types also pushes them into this category.
Screening-Action — High-volume bigs who leverage screens the most, whether through roll-man, pin-down, screen pops, and flares.
Self-Creators/Movers — Encompasses isolation and pick-and-roll handler threes, along with movement play-types like trailing and relocating. No sole movers or self-creators appear here, as bigs tend to perform better and have higher volumes in play-types that don’t demand heavy movement or ball-handling. Including it, however, allows me to identify hints within certain players.
A glimpse at my thought process in designing and placing each shooter into their respective category:
Left Intersection (Spacers + Self-creators/Movers) — Both Kel’el Ware and Jalen Smith carry play-type diets with over 12% relocation and trailing attempts each, the only two in the entire 25-player sample hitting those marks. With middling to high spacing volume and efficiency, both land in this category. Mamukelashvili skews heavily toward spacing, nearly earning a spot in the pure spacer category, but shoots a strong 43% in both trailing and relocation while taking an above-average amount of relocation threes. Okungwu ranks 4th in the sample as a trailer at 21% frequency, and while he doesn’t convert trailing threes well at 30%, he’s attempted only 35 roll-man threes, making this more of a volume case. Reid leans more toward self-creation than movement, a heavy spacer who dabbles in pick-and-roll handling chances (29 total) and isolations (17). More roll-man volume could have easily pushed him into middle-man territory.
Findings Part II: Movement
Analyzing and logging a player's shooting mechanics demands a level of intense focus, since information flashes by in such a small window of time. I did my best to break down and categorize six distinct types of lower-half body movements: step-ins, side steps, retreats, step-backs, jab steps, and standing or minimal movement.
Contrast of Step-ins and Standing Threes
I found that step-in threes are the most prevalent mechanism for generating lower-half load and creating power underneath the shot. But step-ins eat up time, and time can make or break a shot attempt with closeout defenders nearby. This creates an interesting trade-off, the extra power and load come at the cost of a later release. Contrast that with standing or minimal-movement threes, and it leads me to wonder if shooting without much load opens up more opportunities to actually get a shot off.
Joel Embiid is perhaps the greatest example of a step-in three merchant. I'd consider him a very heavy-load guy who takes his time getting into his shot, leaning on a ton of lower-body engagement and base establishment paired with a finesse release. He actively steps into shots, starting from a set distance to build his step-in load. No other stretch five in the study does anything remotely similar.
While there’s no strong correlation between three-point rate and step-in frequency, I want to display how much of an outlier Embiid is within the group, given the methodical nature in how he shoots three-pointers relative to everyone else. I’d love to see a case study trace his shot evolution and pinpoint where the slow-motion load originates. Does it have anything to do with his injury history? Or has he simply done it for so long that it's become a comfort thing? It’s one of the most intriguing finds I’ve noticed working on the project.
Compare Embiid’s shot to Jaylin Williams’, a lightning quick load time, far more reliance on his arms than his lower half, with no-dip threes sprinkled in that showcase his lack of need for an Embiid-style load. His quick trigger compensates for a less creative off-ball game, allowing Williams to capitalize playing alongside a gravity heavy superstar like SGA.
Mapping stationary frequency relative to ATB three-point attempts indicates that movement varies by location. We find that all stretch bigs, to an extent, load up more often ATB than when they space in the corner. Portis, Landale, and Carter Jr. specifically exhibit higher activity metrics ATB, where they look to establish a pre-catch load for upward lift. Jokić, Towns, Huff, and Wembanyama show a more uniform distribution of low movement across both locations, avoiding multiple movements and shoot from a more upright position consistently. It’s another reason why Williams’ pairing with SGA proves very positive, since he’s able to space from anywhere without much of a load up.
Trailing play types generate the highest frequency of step-in threes. These deep, trailing attempts demand a greater physical load-up to create the necessary lift.
Towns ranks as the highest-volume trailer among stretch bigs in the NBA, attempting 96 threes in this play-type. Okongwu is the closest with 81, followed by Vučević at 62 and Kel'el Ware at 38. Tracking and logging Towns across a two-season sample reveals a surprisingly niche play-type, highlighting an attribute I should not take for granted. This establishes Towns as a premium gravitational center. For more, read this year’s data analysis on Towns that isolates his trailing volume and discusses his mechanical breakdown behind his poor 30% conversion rate on trailing threes this season.
Side step threes
Similar to other movement profiles, these attempts occur almost strictly off the catch. Side-step variations create separation, while producing an immediate shooting load within confined spaces like the corners.
As a high-volume relocation big, Myles Turner gets a bulk of his perimeter volume via side-step (24%). His lateral displacement serves as an alternative load, driving energy into his lower-leg lift as he relocates from the right corner up to the wing. Al Horford, Jaylin Williams, Kel’el Ware and Sandro Mamukelashvili excel at creating separation in a similar fashion, though they utilize different avenues to achieve this effect other than relocation.
Within the spacing play-type, Al Horford and others use side-steps to create passing windows for the ball-handler, optimizing their floor space prior to their catch-and-shoot attempt by either moving up the line or deeper into the baseline corner. Subtly side-stepping to the corner can expand the closeout distance between the closeout/off-ball defender, making their recovery and contest a bit tougher. Side-step movement can serve as a differentiator, separating elite spacers from the average ones.
Wendell Carter Jr. provides an excellent example of a primary spacer who barley attempts side-step threes, exposing a limitation in his shooting skill. In comparison, Nikola Jokić, who operates almost entirely outside the spacer archetype, allows him to eschew these extra movements due to his vastly different offensive role and play-type usage.
Kel’el Ware epitomizes a spacing marvel by combining high-volume side-step shooting with lethal above-the-break execution. He buries 41% of these attempts and leads the field in side step ATB volume, displaying ability to craft his own passing windows while finding the shooting slot to shoot comfortably from deep range. His consistency mirrors perimeter guards or wings, and highlights versatility that exceeds his spacing peers.
Review of Movement and Categorization
Tracking the diverse lower-body mechanics of these players was a highly rewarding exercise. This niche-specific area of basketball can entirely evade evaluation, and escape even the most advanced eye. Isolating these movements required a repetitive review of individual shot attempts to be as precise as possible. Just like distinct play-types, no two front-court shooters share identical habits.
For me, this data raises a compelling developmental question: do these movements stem from pure instinct, or do they reflect intentional practice? Unlike guard or wing shooters, stretch fives can have highly volatile shot trajectories, with some shooters already equipped with a defined stroke while others remain in the early stages of development.
Self-creation and isolation play-types remain statistically negligible across stretch fives, limiting individual tracking data for isolation movements, like step-backs, jab steps, and step-in pull-ups. The dataset lacks the volume to classify any single player exclusively within a self-created pull-up category. In total, this big-man sample registered 413 pull-up three-point attempts, with Victor Wembanyama, Nikola Jokić, and Naz Reid owning 49% of that collective volume.
Minimal Movement - Players with stationary profiles who shoot with little lower-body loading mechanics. This metric incorporates ATB frequencies to account for the increased physical demands of deep-range attempts.
Heavy Load - Step-in volume shooters who rely on forward (step-ins) or lateral momentum (side steps) to generate consistent upward lift. Once again, we’re factoring in ATB distribution, isolating the area where bigs most frequently use these loading mechanisms.
Findings Part III: Distance, and Teammate Analysis
Distance
I’ve never used a ridge chart before, so I’m excited to display and explain one. By logging individual shot distances we can effectively mapped out the shape of each player’s three-point attempts by distance. Sharper peaks define the heaviest concentration of their perimeter diet, whereas flat lines show little to no volume at certain depths. Focusing on the very beginning of the visual immediately isolates corner usage at 23 feet.
These trends match our earlier data on player movement and play types. The distance data provides more evidence of a gap between the more active, above-the-break shooters and stationary corner players.
It’s possible that Kristaps Porziņģis establishes himself as an outlier in this dataset had he stayed healthy. He paces the group with 15 deep three-point attempts from 30-plus feet, practically doubling Brook Lopez's second-place mark of eight, whereas 10 total players do not register a shot from this distance. As observed with Towns and Ware, perimeter distance heavily influences offensive gravity, allowing Porziņģis to bend defenses via elite deep-range skill.
Volume Teammate Combos
Teammate frequency percentage calculates from the remaining roster data. In simple terms, this metric isolates how much a specific player potentially assists a big man's three-point attempts relative to the rest of his team. I had two main takeaways: the data highlights the perimeter synergy between Shai Gilgeous-Alexander and Jaylin Williams, which we discussed at length, but even more pronounced when you compare to his other teammate, Chet Holmgren. I’m not suggesting that Chet and SGA necessarily lack synergy, more-so that it’s interesting seeing SGA assist a bench big much more from three than a starter logging almost 400-500 more minutes together.
The Jamal Murray–Nikola Jokić combination never fails. Their tracking connection across Jokić’s three-point attempts presents a small window into an elite, high-usage duo, showcasing strong interaction across a multitude of play-types. Both stars possess the versatility to operate off the ball, relocate, and quickly re-engage to make reads for each other. The Nuggets can place both players off-ball and run action through them. Murray and Jokić align in Horns, two in the slot. Murray touches the ball and initiates flex action for Jokić on the opposite slot, screening for a cutter while taking a simultaneous pin-down. This play works beautifully against a defense like Detroit, where the Nuggets use Murray as the hub with Ausar Thompson defending. The fact that Murray and Jokić can occupy either position in the set showcases their interchangeable roles.
Data Analysis: Crafted Metrics
Through film analysis and comprehensive data collection I was able to yield two proprietary metrics: shot quality and shooting talent. Shot quality quantifies a player's spatial freedom by converting the shot-openness tracking into a comparative value. Shooting talent introduces greater complexity than the typical three-point percentage or volume, it combines volume and efficiency across the many play-types, movements, locations, and self-created looks, variables that traditional three-point percentage does not capture.
Shot Quality
Having tracked the New York Knicks’ half-court shot quality for a full season, I like to think I’ve built a strong system for defining shot openness, something I initially considered a volatile exercise. Shot openness doesn’t track difficulty; it instead determines whether the shot was open or not. A player can pull up from 40 feet and the shot can register as open. Openness, like the other variables, feeds into a larger system that accounts for difficulty separately.
The basics of my system:
Priority One: Release point. I mainly track at the release, the basketball must sit on the end of the fingertips toward the end of the shot. I freeze frame the video at that moment to determine contest distance and other details (see priority two), in order to log the contest as open, light or heavy.
Priority Two: Defender’s hand location. It sounds straightforward, but closeout defenders can make no attempt or are late to contest. In these scenarios, it’s common to penalize the defense and log the shot as open, even with a defender in the general vicinity. For an active contest, the hand must be timed with the intention of bothering the shot at the release point.
Coach Mike Jagacki made a great video on this subject recently. I highly suggest watching it if you want a detailed breakdown of a process similar to how I built my shot quality metric and assigned these contest labels.
Example
This sequence fails to meet the criteria for a contested shot. Although Kel’el Ware closes ground, frame-by-frame analysis at the point of release sees a poor and late contest: Ware’s contest arrives well after Towns begins his upward shooting motion. Towns fires the ball prior to Ware establishing defensive proximity with his hand, qualifying the attempt as an open look. I will only evaluate follow-through activity if a defender significantly impacts the shooter’s motion, which can potentially upgrade the contest.
I tagged every logged three-pointer as an Open, Light, or Heavy contest based on the release-point and hand-location rules. The formulas then execute the following calculations:
Raw Open% → Adjusted Open%: Since players have wildly different sample sizes, Raw Open% is shrunk toward the group average before it's “trusted” data:
Adjusted Open% = (Open attempts + k × League Open%) / (Total attempts + k)
The data model maintains a volume-weighted baseline average league open rate of 44.75% across all twenty-five players, alongside a sample size smoothing constraint (k = 50). k can be explained as a low-volume player's raw rate blended with 50 "fake" league-average attempts; a high-volume player’s would barely move. The model integrates Light and Heavy defensive contests by incorporating specific weighted values into the final calculation of the Openness Score:
Openness Score = Open% × 1.0 + Light% × 0.65 + Heavy% × (-0.2)
A lightly contested shot counts for 65% of a fully open one, treating “light” as closer to open than to heavy. Heavy contests subtract from the score (-0.2) rather than contributing zero, penalizing heavily-contested shots instead of just not rewarding them.
Openness Score → Z-Score: Standardizing the adjusted openness metrics against the collective mean and standard deviation of the twenty-five player sample transforms raw numbers into comparative statistics.
Takeaways
Evaluating Wendell Carter Jr. and Donovan Clingan reveals that a high Shot Quality score does not automatically equal positive offensive impact. Instead, this metric merely tracks openness, and can point to how much cushion an opponent willingly gives up. Both Carter Jr. and Clingan hover near the top of the leaderboard because they lack meaningful shooting gravity. Opposing defenses do not close out aggressively enough, rather they smother drivers and stay in pick-and-roll coverage. This registers in the data as “elite openness,” when the film reveals it as a lack of defensive respect or gravity. Contrast that with elite shooters like Jokić or Towns, who see much lower openness scores. High-level shooters face hard closeouts and tighter coverage through actions/play-types, because defenses cannot afford to give multiple clean looks across many possessions to them.
This introduces an analytical paradox: to a degree, Shot Quality measures the space a defense willingly gives up, granting low-gravity big men the highest openness metrics in the dataset. Even when facing accurate and legitimate shooters (Okungwu, Vučević, Landale etc.), defensive big men may instinctively or intentionally get into help positions to contest drive attempts or muck up action. The value of rim protection, rebounding and providing other sources of help may outweigh the risk of a close out three-pointer from a stretch five. Modern NBA offenses incorporate incredible amounts of talent leading to unprecedented levels of efficiency, so playing defense has never been more difficult on a night to night basis. These offenses force defenses to accept trade-offs, and the rise of the stretch five perfectly exemplifies how some teams may pursue that.
Remember dunker-to-corner relocation? As the driver penetrates the defense, the defensive big, Goga Bitadze, helps off Okongwu to execute a standard help side contest. Despite Okongwu’s proven perimeter range, Bitadze prioritizes rim protection abandoning his assignment to contest the drive. This choice highlights ingrained defensive decision making, as most centers protect the rim in the help position before accounting for the perimeter.
This possession begs the question: why don’t defenses simply help off non-shooters or assign their rim-protecting big man to non-shooters or below-average shooters? Well, coaching staffs do deploy this exact adjustment across the league. That explains why centers may guard players like Dyson Daniels, Josh Hart, or Alex Caruso, allowing the rim protector to park near the paint without worrying about a stretch five dragging him out. The cross-matching tactic, however, introduces an entirely new set of trade-offs, reinforcing that today’s defense demands constant adjustment. My substack features extensive content exploring this exact strategic approach, particularly regarding how the New York Knicks navigated these matchups with Towns and Hart.
Among the bigs with lower shot quality are those that either play the four next to another center or deal with the cross-match on occasion. Defenses use these tactics specifically to neutralize the more dangerous perimeter shooters. Lower shot quality serves as a direct proxy for real defensive gravity (Jokić, Towns, Smith, Ware, Portis, and Wembanyama). That said, Evan Mobley occupies an interesting spot in this data since his low shot quality doesn’t actually reflect that kind of perimeter talent, for him it’s more about his positional role or individual shot selection.
Would you say this represents a “good shot” for Evan Mobley? Well, the film shows a cluster of shots like this one, leading to an overall poor shot quality:
A part of Mobley’s shooting trajectory stems from the management of his physical makeup. Possessing an elite length-profile within this 25-player list, his wingspan and hand size show on the film as a handsy, slow release shooter. His shooting profile can be traced back to his collegiate sample, where he logged just 40 three-point attempts. Although Mobley has scaled his perimeter volume over multiple NBA seasons, showing brief efficient variance across years three and four, his efficiency regressed below 30% in the previous season. This regression proves once more that perimeter production and developmental skill acquisition remain entirely non-linear, particularly for stretch bigs adapting to large perimeter volumes.
Shooting Talent
Perimeter shooting extends beyond raw three-point conversion rates. A single-season player taking 300 attempts at a 38% clip does not automatically indicate superior shooting talent relative to someone shooting 34%. Volume, shot-type, versatility, and range all dictate the parameters of my shooting talent metric. This index combines six variables, each isolating a separate area of shooting and proficiency, into a singular composite score.
Weighted Zone Efficiency
The metric integrates a player's ATB and corner three-point percentages via a non-uniform weighting system. ATB efficiency carries greater statistical weight within the formula due to increased variability and difficulty. ATB attempts usually blend an increased average depth, signify higher gravity, and lead to specialized play-types (roll-man, trailing).
Weighted Zone Efficiency = (Above Break 3P% × 0.70) + (Corner 3P% × 0.30)
Play-Type Versatility Score
The metric evaluates attempts and efficiency together, measuring how many of the 11 tracked play types a player is a legitimate threat from. Each category earns partial credit on a 0-to-1 scale based on how close a player’s volume and percentage land relative to the two benchmarks: 10 attempts and 37% shooting. A category with real volume and a strong conversion rate earns close to full credit, while one with just a handful of attempts earns almost nothing. Summing all 11 categories and dividing by 11 standardizes the final versatility score.
Movement Versatility Score
Using identical mathematical constraints from the play-type score, this metric tracks the physical execution across nine distinct movement profiles: Catch-and-Shoot, Pull-up, Shot-Fake, Face-up, Step-in, Retreating, Step-back, Side-step, and Jab Step. A player who can efficiently knock down shots off a variety of shot-types and loads score higher here than a one who relies on a single type.
Standing Bonus
A direct reward for high-volume, high-efficiency minimal movement catch-and-shoot threes, the “low-load” shot in a big man’s arsenal:
Standing Bonus = (Standing attempts ÷ Total attempts) × Standing 3P% × 0.5
This ensures a player whose game is built around quick, standing shooting doesn’t get penalized for lacking movement variety, as long as he’s knocking those shots down at a decent rate.
Distance Bonus
Makes from farther away get progressively more credit, from 23 feet (baseline weight) up to 31 feet (35% more credit), scaled by how much of a player’s own shot diet comes from each distance. A player stretching defenses out to 29-31 feet gets rewarded more than someone whose volume is concentrated at the shorter distances.
Overall 3P%
Used as a whole-sample efficiency number, included as a check against all the more granular components.
Combine it
All six pieces get combined into a single weighted composite score:
Shooting Talent = (0.45 × Weighted Zone Efficiency) + (0.10 × Play-Type Versatility)
+ (0.10 × Movement Versatility) + (0.10 × Standing Bonus)
+ (0.10 × Distance Bonus) + (0.15 × Overall 3P%)
+ (0.10 × Movement Versatility) + (0.10 × Standing Bonus)
+ (0.10 × Distance Bonus) + (0.15 × Overall 3P%)
Zone efficiency carries the most weight as accuracy still matters more than anything else, but the other five components exist specifically so a player isn’t purely rewarded for their shooting percentages. Versatility, range, and reliability off different play types and releases all factor in: two players can finish with the same three-point percentage and still have very different levels of shooting talent once these variables are accounted for.
Takeaways
Let’s compare a couple players so we understand what went into crafting this metric. We’ll go through a couple exercises between two players to illustrate this.
Nikola Jokić (#1, +2.34) vs. Joel Embiid (#18, -0.44)
Two of the league’s most skilled big men, who fueled one of the greatest positional rivalries of this generation, face a shooting comparison from the latest season. Understand that I’m not saying Joel Embiid is a poor shooter in any sense; he attempts a high volume of above-the-break threes and possesses the ability for deep-range shots. Embiid's low rating within my shooting metric stems entirely from deficiencies in versatility and slow load(movement and play-type) compared to the field. Jokić's movement value (0.85) swamps Embiid’s (0.67), and when you add on Jokić’s standing bonus (0.18) it dwarfs Embiid’s number (0.01) as well. As we touched on before, Embiid essentially avoids standing threes and does not attempt other alternative movements (heavy step-ins) or play-types (heavy roll-man) at any meaningful volume, whereas Jokić does them constantly and efficiently.
While Embiid has fantastic gravity beyond the arc, he occupies a separate tier when evaluated as a shooter across every action and movement pattern. His sub-optimal 32% clip ATB also doesn’t help his case, further tanking his rating within the metric.
Bobby Portis (#2, +1.67) vs. Bam Adebayo (#19, -0.70)
We’re perfectly set up for a realistic team situation, given that both front-court players now play alongside Giannis Antetokounmpo in Miami, a role that relies heavily on spacing. Portis perfectly fits the shooter profile designed for the pairing, boasting a 45.5% overall clip and 44.9% zone efficiency displaying elite shooting touch across the entire perimeter. His synergistic fit alongside Giannis should come as no surprise, given Milwaukee’s previous long-term commitment to him along with a stellar +6.3 net rating next to the star power-forward. The duo had an elite 121 offensive rating when sharing the floor for over 3,800 minutes across the last five seasons.
Bam Adebayo represents more of a question mark the Heat must answer in his own offensive synergy with Giannis: his 31.7% mark and matching zone efficiency should draw some concerns. Though it’s worth noting that both Adebayo’s movement versatility (0.71) and play-type versatility (0.51) metrics are promising. Bam projects as an ultra confident shooter, not hesitant to shoot and appearing quite malleable in how he’s utilized as a shooter, that will be of high importance with Giannis. Whether his efficiency scales up while taking on heavier spacing responsibilities remains to be seen, but for right now the film and data don’t him see him matching Portis’ perimeter talent.
Shot Quality + Shooting Talent
This visualization brings everything together, Shooting Talent against Shot Quality. Placing both variables side by side reveals the most advanced data this project can offer, identifying the best shooters in the sample while accounting for the contest pressure they faced.
BBall Index derives both Shot Quality and Shooting Talent values using a different methodology. However, there’s notable overlapping trends in how we both positioned specific players by our metrics. On both graphs, Wendell Carter Jr. remains the player with the worst shooting talent of the group while attempting the best shot quality. Evan Mobley lands in the same range as a low shot quality player with low talent level. The cluster of Williams, Okongwu, Mamukelashvili, Landale and Vučević present as high shot quality shooters with middling shooting talent compared to the pack. These players face better shot quality, leading to the conclusion that defenses live with the tradeoff of leaving this group open in exchange for their bigs stopping other actions.
We can now apply groupings based on the new data. Combining these two metrics reveal a level of shooting gravity and talent together, allowing us draw takeaways about their overall shooting skill.
One consistent takeaway I had from the film,: every center is different. Perimeter-shooting does not make the NBA a “cookie-cutter” league, differentiation exists, and those differences fuel diverse offensive systems and multiple styles of play. For skeptics criticizing modern league spacing, and the rise of three-point shooting, I encourage you to dig deeper.
For visualization R-codes on this project check out my GitHub
Credits:
Court Sketch created by Gabriel Guzman
Top and Bottom 10 table designs inspired by David Lee
Cover art created by TipOff
Assisted in acquiring extra video, Ben Pfeifer
legendary
Amazing. I love the fact that I sometimes have to reread passages of your work to make sure I fully understand them.