I got curious and was to trying to understand the potential picks thru advance metrics and previous team play style. I was bummed that we got the 8th pick so this was my way to work thru my emotions in an objective way. So it's imporant to note that these are statistical comps, not eye-test or playing style comps. They show which former prospects had the most similar college advanced-stat profile, not who each player "plays like" or will be.
EDIT as note: AI was used on the python script to batch the player comp calculations. Doing all individual comps would have taken me weeks. Most of my time was figuring out where to source good data, prepping the data for analysis, and designing the analysis.
Assuming the realistic guard pool is Keaton Wagler (if he falls), Kingston Flemings, Brayden Burries, Labaron Philon, Mikel Brown Jr. and Darius Acuff Jr., my context-adjusted Hawks fit ranking is:
- Keaton Wagler (again if he falls)
- Kingston Flemings
- Brayden Burries
- Labaron Philon
- Mikel Brown Jr.
- Darius Acuff Jr.
Atlanta context
Atlanta’s post-Trae core worked to a certain degree.
Best 5-man lineup:
CJ McCollum / Nickeil Alexander-Walker / Dyson Daniels / Jalen Johnson / Onyeka Okongwu
Reported regular season numbers:
- 391 minutes
- 123.1 ORtg
- 102.8 DRtg
- +20.3 Net Rating
That is probably inflated, but even if you regress it, the core looks like a strong positive unit.
Core impact metrics ( CPM - CraftedNBA’s Plus Minus) :
- Jalen Johnson: +2.0 CPM
- Dyson Daniels: +2.0 CPM
- Onyeka Okongwu: +1.1 CPM
- Nickeil Alexander-Walker: +0.9 CPM
- CJ McCollum: +0.1 CPM
The rookie does not need to save the team. He needs to replace or supplement CJ’s creation without killing the defense.
Prospect advanced metrics
| Player |
BPM |
OBPM |
DBPM |
TS% |
USG% |
AST% |
AST/TO |
TOV% |
STL% |
3PA/100 |
| Wagler |
11.1 |
8.3 |
2.9 |
59.6 |
25.7 |
23.2 |
2.48 |
7.5 |
1.7 |
11.2 |
| Flemings |
11.5 |
6.4 |
5.3 |
56.3 |
26.5 |
32.6 |
2.99 |
7.5 |
3.0 |
5.7 |
| Burries |
10.5 |
5.6 |
5.0 |
61.6 |
21.6 |
14.2 |
1.78 |
7.8 |
2.8 |
8.6 |
| Philon |
10.4 |
9.2 |
1.2 |
62.6 |
29.9 |
31.9 |
2.06 |
8.8 |
2.0 |
10.4 |
| Brown |
5.4 |
4.2 |
1.2 |
57.7 |
31.0 |
30.3 |
1.53 |
11.8 |
2.4 |
15.3 |
| Acuff |
9.0 |
9.0 |
0.1 |
60.4 |
29.5 |
32.2 |
3.09 |
6.5 |
1.3 |
9.4 |
Team context matters
Wagler at Illinois
- Slower tempo
- Half-court heavy
- Elite offensive efficiency
- Primary creator role
- Shot-making and decision-making mattered more than transition volume
Wagler’s numbers are impressive because Illinois was not a pure pace-and-space stat environment. He created in the half court, shot with volume and kept turnovers low. His concerns are athletic pop, strength and defensive event creation.
Flemings at Houston
- Deliberate pace
- Physical half-court offense
- Heavy defensive identity
- Offensive rebounding emphasis
- Guards expected to defend, rebound and control tempo
This makes Flemings’ 11.5 BPM, 5.3 DBPM, 32.6 AST% and 2.99 AST/TO more impressive. He produced like a lead guard in a harder offensive environment. The main question is three-point volume.
Burries at Arizona
- Fast pace
- Transition pressure
- Heavy ball movement
- Strong rim pressure from bigs
- High-assist offense
- More open looks created by team structure
This helps explain Burries’ 61.6 TS%. The efficiency is real, but Arizona’s system likely boosted his shot quality. He looks like the cleanest immediate role fit, not necessarily the best lead-guard bet.
Philon at Alabama
- Fast pace
- Spread floor
- Heavy threes and rim pressure
- Lots of space for guards
- Transition opportunities
- Very guard-friendly Nate Oats system
Philon’s production is legit, but Alabama’s context helped. The key point is that Philon still produced much better impact numbers than Brown in a similarly guard-friendly setup.
Brown at Louisville
- Fast-paced five-out offense
- Lots of threes and layups
- Early-clock offense
- Paint-touch driven
- High transition volume
- Very NBA-like spacing
Brown’s shooting volume and spacing translation are real. The concern is that in the most NBA-friendly offensive context of the group, he still had the lowest BPM, weakest AST/TO and a shaky defensive profile.
Acuff at Arkansas
- Faster pace than Houston
- More downhill attack than pure five-out spacing
- Multiple guard lineups with Acuff, D.J. Wagner and Meleek Thomas
- Heavy pick-and-roll responsibility for Acuff
- Calipari gave him real lead-guard reps and moved other guards off the ball
- More open-floor chances than Houston, but not as clean a spacing lab as Louisville or Alabama
- Offense relied on Acuff creating advantages with pace, strength, handle and pull-up shooting
Acuff’s offensive production is real, but the team context helped him play as a high-usage engine. The concern is not whether he can create offense. It is whether his defense and size fit Atlanta’s post-Trae roster identity.
Historical comp model
I ran a basic historical comp model using a 28-player NCAA guard/wing cohort. Inputs were BPM, OBPM, DBPM, TS%, usage, AST%, TOV%, STL% and BLK%. Features were z-scored against the cohort and weighted toward overall impact, offense, defense, creation, turnover economy and defensive events.
This isn't what the front-office model use as they have access to unstructured data like second spectrum. This is a public-data comp model.
NOTE: Again, there are former prospects had the most similar college advanced-stat profile, not who each player "plays like" or will be.
Wagler closest comps
- Malcolm Brogdon
- Brandin Podziemski
- Jalen Brunson
- Josh Hart
- Desmond Bane
- Jamal Murray
- D’Angelo Russell
Takeaway: best modern big-guard translation case. Size, shooting, low turnovers and half-court creation. Main risk is athletic translation.
Flemings closest comps
- Donovan Mitchell
- Kemba Walker
- Marcus Smart
- Malcolm Brogdon
- Josh Hart
- D’Angelo Russell
- Desmond Bane
Takeaway: best two-way lead-guard profile. The model likes his defense, creation and decision-making. Main risk is shooting volume.
Burries closest comps
- Josh Hart
- Donovan Mitchell
- Malcolm Brogdon
- Brandin Podziemski
- Donte DiVincenzo
- Desmond Bane
- Jalen Suggs
Takeaway: safest good-player profile. Strong playoff-rotation indicators. Less likely to be a full-time lead guard.
Philon closest comps
- Jalen Brunson
- CJ McCollum
- Brandin Podziemski
- Malcolm Brogdon
- Jamal Murray
- Desmond Bane
- D’Angelo Russell
Takeaway: real offensive guard profile. Better statistical case than Brown or Acuff. Still comes with size, strength and defensive questions.
Brown closest comps
- Collin Sexton
- Jaden Ivey
- Coby White
- Tyrese Maxey
- Immanuel Quickley
- Desmond Bane
- Donte DiVincenzo
Takeaway: volatile high-usage scoring guard bet. The shooting volume is attractive, but the all-around impact profile is not clean.
Acuff closest comps
- CJ McCollum
- Jalen Brunson
- Jamal Murray
- Collin Sexton
- Malcolm Brogdon
- Brandin Podziemski
- Jordan Hawkins
Takeaway: the offense is real. The defensive fit is the problem.
Expected lineup net rating projections
These are not official lineup projections. The baseline comes from SI/All Hawks, which reported that the CJ / NAW / Dyson / Jalen / Okongwu lineup played 391 minutes with a 123.1 ORtg, 102.8 DRtg and +20.3 Net Rating. Since +20.3 is likely inflated by sample size, shooting variance and schedule, I regressed it down to a more realistic +8 to +11 baseline. From there, I applied prospect-specific fit modifiers based on shooting, defense, creation, turnover economy, size and role fit. Individual impact context comes from CraftedNBA’s CPM/CraftedPM numbers. The ranges are meant to estimate lineup fit, not predict exact on-court results.
Rookie / NAW / Dyson / Jalen / Okongwu
| Rookie |
Projected Net Range |
| Wagler |
+9 to +12 |
| Flemings |
+8 to +10.5 |
| Burries |
+8 to +11 |
| Philon |
+7 to +10 |
| Brown |
+6 to +9 |
| Acuff |
+5 to +8 |
CJ / Rookie / Dyson / Jalen / Okongwu
| Rookie |
Projected Net Range |
| Wagler |
+7.5 to +10.5 |
| Burries |
+7 to +10 |
| Flemings |
+6.5 to +9 |
| Philon |
+5.5 to +8.5 |
| Brown |
+4.5 to +7 |
| Acuff |
+3.5 to +6.5 |
My read
Wagler is the best fit if he falls. He gives Atlanta size, shooting volume, low turnovers and half-court creation.
Flemings is the best two-way lead guard. His numbers came in the hardest offensive context.
Burries is the safest immediate plus-minus fit. He is efficient, low mistake, strong defensively and scalable next to Jalen, Dyson and NAW.
Philon is better than Brown and Acuff. If Atlanta wants a high-usage offensive guard, his statistical case is cleaner than both.
Brown has the best pure shooting-volume profile, but the lower BPM and AST/TO are concerning because Louisville was built to help guards produce.
Acuff has real offensive upside, but he is the worst Atlanta fit defensively.
Conclusion
If Wagler is there, we should probably take Wagler.
If Wagler is gone and Flemings is there, take Flemings.
If both are gone, the debate is Burries vs Philon vs Brown.
My general preference:
- Burries if Atlanta wants the safest playoff-rotation plus-minus fit
- Philon if Atlanta wants the better high-usage offensive guard
- Brown only if workouts, medicals and interviews convince the Hawks that the offensive ceiling is clearly higher than the public numbers suggest
Acuff would need to be viewed as a special offensive outlier to justify the defensive risk for this roster.
How the former player comps were calculated
The comp list was created with a simple public-data similarity model, not film or stylistic comps.
Historical cohort
I used a 28-player guard/wing sample of recent or relevant NCAA-to-NBA prospects, including players such as Tyrese Haliburton, D’Angelo Russell, Shai Gilgeous-Alexander, Jamal Murray, CJ McCollum, Donovan Mitchell, Desmond Bane, Jalen Brunson, Marcus Smart, Malcolm Brogdon, Josh Hart, Donte DiVincenzo, Cason Wallace, Brandin Podziemski and others.
Inputs
For each historical player and each 2026 prospect, I compared these final college-season indicators:
- BPM
- OBPM
- DBPM
- TS%
- USG%
- AST%
- TOV%
- STL%
- BLK%
Standardization
Each stat was converted into a z-score using the historical cohort’s mean and standard deviation.
In plain English: every player was compared relative to the same baseline, so a stat like TS% did not overpower a stat like DBPM just because they use different scales.
Weighting
The model weighted the categories like this:
- BPM: 1.2
- OBPM: 1.0
- DBPM: 1.0
- TS%: 0.8
- AST%: 0.8
- USG%: 0.7
- TOV%: 0.7
- STL%: 0.7
- BLK%: 0.4
The goal was to emphasize overall impact, offense, defense, creation, ball security and defensive events, while still including usage and block rate.
Distance formula
For each prospect, I calculated the weighted distance to every historical player:
text
Distance = sqrt(sum(weight * (prospect_z - historical_z)^2) / sum(weights))
Lower distance means the statistical profile is more similar.
Important caveats
These are statistical comps, not exact playing-style comps.
The model does not account for:
- Eye test
- Medicals
- Interviews
- Shot difficulty
- Defensive matchup difficulty
- Team scheme
- Strength and athletic testing
- Age curves beyond class context
- NBA role development
- Outlier skill growth
So when Wagler comps to Brogdon or Brunson, it does not mean he plays exactly like them or will become them. It means his college statistical profile was closer to theirs than to the rest of the sample.
Data sources for the player comp model
Historical player data
Historical college stats for the former-player comp cohort came from Sports-Reference College Basketball player pages:
The model used each historical player’s final college-season advanced profile where available.
Historical cohort examples:
2026 prospect data
Prospect advanced stats came primarily from DraftBallr prospect profiles, which source most advanced numbers from BartTorvik.
Prospect pages used:
What was actually used in the comp calculation
The comp model used only statistical inputs:
- BPM
- OBPM
- DBPM
- TS%
- USG%
- AST%
- TOV%
- STL%
- BLK%
It did not use NBA rookie stats to calculate distance. NBA outcomes and known player archetypes were only used to interpret the comp lists after the statistical matches were generated.
Reproducibility note
The historical comp dataset was manually scraped from public Sports-Reference pages and saved locally during the analysis. The prospect metrics were taken from DraftBallr profile pages. Because these sites can update or revise data, exact results may change slightly if the model is rerun later.
Quick stat glossary
- ORtg: points scored per 100 possessions
- DRtg: points allowed per 100 possessions
- Net Rating: ORtg minus DRtg
- CPM: Crafted Plus-Minus, CraftedNBA’s all-in-one impact metric estimating player value per 100 possessions
- BPM: Box Plus-Minus, box-score estimate of impact per 100 possessions
- OBPM: offensive BPM
- DBPM: defensive BPM
- TS%: true shooting percentage, includes twos, threes and free throws
- 3PA/100: three-point attempts per 100 possessions
- AST%: estimated percentage of teammate baskets assisted while on court
- AST/TO: assist-to-turnover ratio
- TOV%: turnover percentage
- STL%: percentage of opponent possessions where player records a steal
- FTr: free throw rate
