K.J. Simpson Leads DataBaller's Scoring-Jump Forecast Ahead of Next NBA Season

DataBaller's Scoring Shift model projects Simpson for a +2.38 PPG rise, with Peyton Watson and Dillon Brooks close behind — driven largely by momentum signals that outpaced each player's trailing averages.

Published: August 23, 2026 · Figures as of August 23, 2026

Asked: Who will take the biggest scoring jump this season in the NBA?

K.J. Simpson leads the forecast, with Peyton Watson and Dillon Brooks close behind

Scoring Shift v3 (DataBaller's forward scoring model, as of August 23) leans toward K.J. Simpson for the biggest jump — a predicted +2.38 points per game — with Peyton Watson (+2.14) and Dillon Brooks (+1.99) the next-closest. All three carry high confidence (0.9, 1.0, and 1.0 respectively, above the model's 0.8 recommended floor). Before anything else: these are leans from a model with a rank correlation of roughly 0.26 against realized forward scoring change in backtesting — it orders who rises and who falls usefully, but explains a minority of the variance. A trade, a coaching change, or an injury inverts the forecast the day it happens.

K.J. Simpson, Denver — +2.38 PPG (conf 0.9)

Simpson averaged 4.6 PPG in just 20 games at 12.9 minutes last season. His deep-band baseline — games from one to two years back — was substantially higher, and his minutes last season ran about 10.6 below that level. The model interprets that gap as suppressed deployment, not declining ability: the 182-day momentum input is the single biggest driver (+1.57 of the +2.38), meaning he was scoring well above his trailing half-year average when he was on the floor. The confidence note says his deep-band game count (36 games) is the limiting factor, which is why confidence lands at 0.9 rather than 1.0. A player putting up 4.6 PPG who the model says will jump by 2.4 is still a 7-point player in projection — but if his role expands meaningfully, the runway is larger than those raw numbers suggest.

Peyton Watson, Denver — +2.14 PPG (conf 1.0)

Watson scored 14.6 PPG last season in 54 games at 29.6 minutes. His momentum signal is the overwhelming driver: he was scoring 7.2 points per game above his trailing 182-day average, and that persistence-of-momentum term contributed +3.08 to the forecast. The model does partially offset it: his internal window trend was declining (minutes and scoring both fell in the second half of his last-20-game stretch), which dragged the total down by about 0.6. Still, the lean is meaningfully up. Worth noting that Watson's minutes in his window actually exceeded his deep-band baseline by 7.2 — so the model isn't just rediscovering a role expansion; it sees genuine above-baseline production on top of it.

Dillon Brooks, Phoenix — +1.99 PPG (conf 1.0)

Brooks already averaged 20.2 PPG in 56 games, making him the highest-volume scorer in this group. A predicted +1.99 would put him at roughly 22 PPG — a genuine volume-scorer upgrade. His forecast rests primarily on the same momentum signal (+2.04 contribution): his recent scoring outpaced his 182-day trailing average, and his efficiency was slightly above his deep-band level (+0.23 from that component). His window trend was declining in both minutes and points, which costs him about 0.44, keeping this just under the +2.00 line.

Miles McBride, New York — +1.71 PPG (conf 1.0)

McBride averaged 12.0 PPG in 41 games. His momentum reading is the largest raw input in this entire table (5.58 PPG above trailing average), contributing +2.39. His internal trend was sharply negative — scoring and minutes both fell hard in the back half of his window — and that drag (-0.84 combined) is the only reason this isn't even higher. The model is essentially saying: the momentum is real, but the late-window fade tempers it.

Nikola Vucevic, Orlando — +1.67 PPG (conf 1.0)

Vucevic at 15.1 PPG last season has the cleanest, most balanced signal in the list: momentum (+1.02), an efficiency reading below his deep-band level by 7.3 percentage points that argues for reversion (+0.37), and a minutes-deployment gap of -8.0 against his baseline adding another +0.41. No single overwhelming driver, just multiple inputs aligned in the same direction.

Notes on notable absences and caveats

Cam Thomas (Brooklyn, no team listed in current data) at +1.90 carries a confidence of 0.625 — his deep-band sample is only 25 games, below the model's ideal floor. The lean is still "up," but the width of uncertainty around it is wider than the others.

Trae Young (Washington, +1.74) has a window of only 15 games with confidence 0.75, also below the 0.8 threshold. His momentum was real but his sample limits the read.

The model says nothing about why a player's scoring might change — a new team, a new offensive scheme, or a change in role is outside what a box-score model can see, and those are usually the bigger forces. These forecasts are built from last season's trajectories, and the offseason will have already moved several of these players to new contexts the model hasn't processed.