GWTTKB Research · Player Study

Should you draft Pat Freiermuth in 2026?

He has beaten his expected points in 2 of 5 seasons. Whether that is a skill or a countdown decides what he is worth at 179.4 overall.

Pat Freiermuth scored 116.4 fantasy points in 2025. The volume he earned was worth 103.5. That gap of 12.9 points is the single most important thing to understand before drafting him at 179.4 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 15: targets went up from 3.2 to 4.8, a 49.3% change, at 3.19 standard deviations. The cause was structural — the offense surged. He also missed Weeks 4, 5, 13.

Year over year the engine classifies 2025 as a decline that came from the offense, not the player. 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 7.28 points per game on 57 targets — down 24.2% from his 2024 rate with PIT. 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 traveled, what was that usage worth? For 2025 the answer was 103.5 points — 6.5 per game. He scored 12.9 more than that, a 12.5% overshoot.

The expected line underneath the season was 41.1 catches for 427 yards and 3.3 touchdowns, and he cleared it.

The miss was receiving yards, and almost nothing else

Against the expected line, he finished 42 catches, 504 receiving yards, 4 touchdowns — against 41.1, 427, 3.3 expected.

60% of the entire gain came from receiving yards alone. He hit 118% of his expected receiving yards, against 102% of his expected catches and 121% of his expected touchdowns.

01020301NYJ2SEA3NE6CLE7CIN8GB9IND10LAC11CIN12CHI14BAL15MIA16DET17CLE18BAL
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 8 of 15 games.

The season was one game

1 of his 15 games produced 25 or more points: Week 7 against CIN (28.1). Strip it and the remaining 14 games average 6.1.

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. 12 games came in under 10 points, including Week 1 against NYJ (5.8) and Week 2 against SEA (6.1).

The three largest overshoots — Week 7 (+17.2), Week 12 (+5.5), Week 9 (+4.4) — are the same games. The spikes were not extra volume. They were finishing.

This has happened before

0510152021PIT · 84 tgt2022PIT · 98 tgt2023PIT · 55 tgt2024PIT · 82 tgt2025PIT · 57 tgt
Expected /gActual — ran hotActual — in line
5 seasons, actual against expected. 2 of the 5 came in meaningfully above what the volume justified.

2 of his 5 seasons ran hot: 2024 (+19.1%), 2025 (+12.5%). 1 came in below what the usage justified: 2022 (-13.6%). Across all 5 he has scored 23.2 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 170 tgt+0.2366.8 y/tCOVER 266 tgt+0.3298.32 y/tCOVER 354 tgt+0.2787.81 y/tCOVER 444 tgt+0.4198.16 y/t2 MAN26 tgt+0.0247.92 y/tCOVER 621 tgt-0.1686.29 y/t
What he does against each coverage, 293 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 293 targets and four seasons, he has been a fundamentally different receiver depending on what the defense played. Against man he has caught 67% for 7.08 yards per target and 0.191 expected points added per target. Against zone: 74.1%, 8.2 yards, 0.277 EPA.

That is a gap of -0.086 EPA per target on a sample of 100 man targets against 193 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 1, single-high man is the look he has faced most: 70 targets, 6.8 yards per target, 0.236 EPA. His best is Cover 4 quarters at 0.419 on 44 targets.

The weakness is specific. Against Cover 6 he has caught 61.9% for 6.29 yards a target and -0.168 EPA across 21 looks — a gap of 0.6 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 him fairly priced

Across 58 graded prop lines, he has gone over 55.2% of the time. On receiving yards specifically the number is 48.3% across 29 lines, missing the posted number by an average of 41.7%.

That is close enough to a coin flip that the market has him fairly priced on his primary market. There is no edge to claim in either direction.

Touchdowns are the control. His anytime-TD market implied a 22.8% rate and he converted at 34.5% — a gap of 11.6 points, which is worth noting.

So the risk is not efficiency. It is targets.

DK Metcalf21%Michael Pittman Jr.13.9%Germie Bernard13.6%Pat Freiermuth13.1%Jaylen Warren10.4%Rico Dowdle7.1%Darnell Washington6.5%
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.

Pittsburgh returns 4 of 6 continuity slots — qb1, wr1, rb1, te1 — with Aaron Rodgers at quarterback, Mike McCarthy as head coach and Brian Angelichio calling it. The offense projects to 32.96 pass attempts a game at medium confidence.

The pie is stable. His slice is the question. Our share model has him at 13.1% against the 13.7% he ran, with a range of 10.5% to 15.5%. The biggest competing claim on that pie is DK Metcalf at 21%, 7.9 points ahead of him.

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 Tyler Higbee 14.5% to 15.7%, David Njoku 15.2% to 21.6%, Cole Kmet 15.9% to 9.1%. Two held, one did not.

The numbers that carry, and the ones that do not

Across 4,488 player-seasons we swept every stat for whether it predicts NEXT year's points, and kept only the ones that replicated in all five. target share (r 0.247), WOPR (r 0.218), air-yards share (r 0.142) carry. receiving EPA per game (r -0.127) and yards per target (r -0.114) and week-to-week volatility (r -0.075) 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: an 11.2% target share, 13.2% of the air yards, a WOPR of 0.26, a 52.5% snap share. He averaged 2.04 yards of separation, with +0.77 YAC over expected.

The offense he plays in

Vegas sets an implied total for every team every week, and some offenses beat theirs systematically. Over 106 games, PIT has come in +0.19 points against its own implied total, clearing it in 49% of weeks.

That is close to neutral — the offense performs about where the market expects, so there is no environment bonus or penalty to apply to him either way.

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

4.8 floor 6.8 ppg 10.4 ceiling bust 29% boom 53%
The 2026 range, not the point estimate. 6.8 is the middle. The spread from 4.8 to 10.4 is the actual bet.

Our board has him at 102.7 points, 6.8 per game across 15.2 expected games — 41.1 catches, 454 yards, 2.7 touchdowns. That is TE5, 177 overall, and -52 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.

Where to draft him

The books do not agree on him. Pat Freiermuth is a board split: Yahoo takes him ~125, Sleeper lets him slide to ~244 (118.7-pick gap). That 118.7-pick gap between Yahoo and Sleeper is the practical thing to act on: in a Sleeper-priced room he is available later than his consensus number suggests.

He goes at 179.4 overall on consensus, and the books are far apart — 118.7 picks between cheapest and priciest, which is a real value window. He is latest on sleeper and earliest on yahoo: sleeper 244, espn 168.8, yahoo 125.3, underdog 194.

He goes at 179.4 and our board has him 177. Those agree, so there is no market disagreement to exploit here — the question is whether you want the TE5 profile, not whether it is mispriced.

The case for: a 53% boom rate.

The case against: 2 of 5 seasons flagged as running hot — not simply finishing above expectation, but far enough above it to classify, which is the pattern that regresses; 3 games missed in the anchor season.

The call: fair value — draft him because you want this tight end, not because you are getting a deal.

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

Expected points are computed from realized usage — targets, air yards and field position — against league-average conversion, not from projections. Prop records cover 58 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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