GWTTKB Research · Player Study

Should you draft Bijan Robinson in 2026?

What the expected-points record, the coverage data and the market say he is worth at 2 overall.

Bijan Robinson scored 370.8 fantasy points in 2025. The volume he earned was worth 344.3. That gap of 26.5 points is the single most important thing to understand before drafting him at 2 overall.

The week it changed

Rather than reading a season average, it is worth finding the point the season actually turned. Our shift detector flags in-season changes large enough to clear a statistical threshold, then asks what moved around them.

The largest single move was Week 4: rushing TDs went up from 0 to 0.5, at 2.88 standard deviations. The cause was structural — the offence around him got sharply better. He also missed Week 5.

Year over year the engine classifies 2025 as a breakout that leaned on touchdown luck. That is the label to carry into the projection, because it separates a player who lost snaps from one who kept them and converted worse.

What actually happened in 2025

He finished 21.81 points per game on 103 touches — a 8.5% jump on his 2024 rate with ATL. Every part of that is real and none of it is in dispute.

What is worth separating is how much of it he earned. Expected points strip out the finishing and ask a narrower question: given the targets he drew, where they came from on the field, and how far downfield they travelled, what was that usage worth? For 2025 the answer was 344.3 points — 20.3 per game. He scored 26.5 more than that, a 7.7% overshoot.

The expected line underneath the season was 80.5 catches for 608 yards and 3.7 touchdowns, and he cleared it.

The miss was receiving yards, and almost nothing else

The expected line was 80.5 catches for 608 yards and 3.7 touchdowns. He finished with 79, 820 and 4.

87% of the entire gain came from receiving yards alone. He hit 98% of his expected catches and 108% of his expected touchdowns — and 135% of his expected receiving yards.

0102030401TB2MIN3CAR4WAS6BUF7SF8MIA9NE10IND11CAR12NO13NYJ14SEA15TB16ARI17LA18NO
ExpectedBeat itMissed it
Every week of 2025, what he was worth against what he scored. Expected points come from the volume he actually earned — targets, air yards, and where on the field they came. He cleared it in 9 of 17 games.

The season was seven games

7 of his 17 games produced 25 or more points: Week 4 against WAS (28.1), Week 6 against BUF (35.8), Week 11 against CAR (30.3), Week 13 against NYJ (30.3), Week 15 against TB (29.5), Week 16 against ARI (29.8), Week 17 against LA (39.9). Strip them and the remaining 10 games average 14.7.

Spike weeks win fantasy games and a player who produces them is more valuable than one who never does — he is a ceiling play with real weeks off. 3 games came in under 10 points, including Week 8 against MIA (5.8) and Week 14 against SEA (9.4).

The three largest overshoots — Week 17 (+13.3), Week 6 (+11.1), Week 4 (+9.5) — are the same games. The spikes were not extra volume. They were finishing.

This has happened before

05101520252023ATL · 86 tgt2024ATL · 72 tgt2025ATL · 103 tgt
Expected /gActual — ran hotActual — in line
Four seasons, actual against expected. Two of the four came in meaningfully above what the volume justified.

1 of his 3 seasons ran hot: 2024 (+11.1%). Across all 3 he has scored 50.4 points more than his usage was worth.

The reflexive read is regression. Finishing above expectation is unstable, and a player who does it repeatedly is usually one whose luck has not run out yet.

But an expected-points model has a specific blind spot, and it is worth naming: it converts usage at league-average rates. A target twenty yards downfield is worth what the average receiver does with a target twenty yards downfield. If a player is reliably better than average at winning a particular kind of target, he will beat the model every single year, and calling that luck is just mislabelling a skill.

So the question is not whether he beat the number. It is whether there is a mechanism.

What the coverage data says

COVER 374 tgt+0.1076.45 y/tCOVER 264 tgt+0.0715.47 y/tCOVER 155 tgt+0.388.8 y/tCOVER 430 tgt-0.0575.57 y/t
What he does against each coverage, 261 targets over four seasons. Cover 1 is single-high man — the look he sees most and punishes hardest. Cover 0 and Cover 6 are the two he cannot solve.

Across 261 targets and four seasons, he has been a fundamentally different receiver depending on what the defence played. Against man he has caught 76.8% for 8.49 yards per target and 0.132 expected points added per target. Against zone: 75.4%, 5.86 yards, 0.046 EPA.

That is a gap of 0.086 EPA per target on a sample of 69 man targets against 191 zone. The catch rate goes the other way — he catches more against zone — which is exactly what you would expect from a receiver whose value is in winning contested downfield throws rather than in finding soft spots underneath.

The shell detail sharpens it. Cover 3 is the look he has faced most: 74 targets, 6.45 yards per target, 0.107 EPA. His best is Cover 1, single-high man at 0.38 on 55 targets.

The weakness is specific. Against Cover 4 quarters he has caught 63.3% for 5.57 yards a target and -0.057 EPA across 30 looks — a gap of 0.4 expected points a target between his best coverage and his worst.

This does not prove the overshoot repeats. It does mean the pattern has a stated cause rather than being a coin that keeps landing heads.

The market has been wrong about him in one direction

Across 128 graded prop lines, he has gone over 55.5% of the time. On rushing yards specifically the number is 62.5% across 32 lines, beating the posted number by an average of 14.4%.

A projection model can be wrong about conversion. A book adjusting weekly, with money on the other side, being wrong 63% of the time on the same market is a harder thing to explain away.

Touchdowns are the control. His anytime-TD market implied a 61.2% rate and he converted at 56.3% — a gap of -5.0 points, which is worth noting.

So the risk is not efficiency. It is targets.

Drake London24.2%Kyle Pitts16.9%Bijan Robinson14.5%Austin Hooper10.3%Zachariah Branch8.4%Olamide Zaccheaus7.4%Jahan Dotson5.8%
Projected 2026 target share. Pale bar is the p10–p90 range. Built from comparable receivers at the same age, share and offensive continuity — not from a depth chart.

Atlanta returns 4 of 6 continuity slots — qb1, wr1, rb1, te1 — with Michael Penix Jr at quarterback, Kevin Stefanski as head coach and Tommy Rees calling it. The offence projects to 32.83 pass attempts a game at medium confidence.

The pie is stable. His slice is the question. Our share model has him at 14.5% against the 17.8% he ran, with a range of 13.5% to 16.3%.

That number is built from 25 comparable players — same age band, same prior share, same team, same coordinator — not from a depth chart. The closest three went Austin Ekeler 16.7% to 14.7%, Breece Hall 16.1% to 14.1%, Austin Ekeler 16.8% to 17.3%. Two held, one fell hard.

The numbers that carry, and the ones that do not

Across 2,022 player-seasons we swept every stat for whether it predicts NEXT year's points, and kept only the ones that replicated in all five. f_timeToLOS (r 0.128), f_snapTrend (r 0.103), f_fantasyForm (r 0.078) carry. week-to-week volatility (r -0.163) and receiving EPA per game (r -0.143) run negative — a player who was hyper-efficient on low volume tends to come back down, so a gaudy per-touch number is a warning rather than a selling point.

So the figures worth weighing for him: a 19.8% target share, 2.1% of the air yards, a WOPR of 0.31, on the field for 78.2% of snaps. He finished 2 at the position — RB1.

The offence he plays in

Vegas sets an implied total for every team every week, and some offences beat theirs systematically. Over 101 games, ATL has come in -1.07 points against its own implied total, clearing it in 49% of weeks.

That is a structural drag. The market keeps expecting more from this offence than it delivers, and every skill player in it inherits that.

One related finding worth carrying: across 1,280 cases we measured what happens after a team's implied total slides, and the correlation is -0.37 — negative, meaning the market over-corrects. A falling team total is more often a buying window than a warning.

What we project for 2026

16.8 floor 23.5 ppg 32.7 ceiling bust 29% boom 39%
The 2026 range, not the point estimate. 23.5 is the middle. The spread from 16.8 to 32.7 is the actual bet.

Our board has him at 375.5 points, 23.5 per game across 16 expected games — 73.7 catches, 649 yards, 4.6 touchdowns. That is RB1, 2 overall, and 220 points of value over a replacement starter.

It is a step down from 2025, and the reason is volume rather than efficiency. The projection assumes he keeps doing what he does on slightly fewer looks.

What we have been saying about him

never missed a game outside one meaningless snap; Allgeier's departure (he was out-touching Bijan inside the 10 last year) could hand Bijan the full goal-line role on top of already being featured everywhere else.

BUY · high conviction · 2026-08

Where to draft him

The books do not agree on him. Bijan Robinson is a board split: Sleeper takes him ~2, ESPN lets him slide to ~3 (0.8-pick gap). That 0.8-pick gap between Sleeper and ESPN is the practical thing to act on: in a ESPN-priced room he is available later than his consensus number suggests.

He goes at 2 overall on consensus, and the books are tight on him — 0.8 picks between cheapest and priciest. He is latest on espn and earliest on yahoo: sleeper 1.8, espn 2.6, yahoo 1.8, underdog 2, cbs 1.9.

At 2 you are paying a WR2 price for a player whose median outcome is a WR2 season with five weeks that win you the game outright and four that lose it. The bust rate of 29% is real and so is the 39% boom rate.

The call: fair value, not a discount. The reason to take him is the ceiling and the betting record. The reason to pass is that you are buying a profile that has beaten its expected output four years running and only needs to stop once.

Ask the Coach about Bijan Robinson Every number in this piece, plus your league's roster and scoring, in one answer.

Expected points are computed from realised usage — targets, air yards and field position — against league-average conversion, not from projections. Prop records cover 128 graded lines. Projections are anchored to consensus with our engine applied as a capped tilt; the raw engine figure is stated where it differs. Nothing here is betting advice.

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