# Pittsburgh Steelers v Seattle Seahawks — NFL Week 2 2025 AI Prediction by Alphr

## Full Time: Pittsburgh Steelers 17 – 31 Seattle Seahawks

_Seattle Seahawks surge back to soar past Pittsburgh Steelers._

Half time: 14 – 7 · Alphr AI pre-game win probability: Pittsburgh Steelers 73% / Seattle Seahawks 27% · Acrisure Stadium · Mon, 15 Sept, 03:00 am AEST

**Touchdowns** — Pittsburgh Steelers: DK Metcalf (29' Tipped ⚡) · Seattle Seahawks: Tory Horton (5'), AJ Barner (39'), George Holani (48'), Walker (57')

### Match Winner — AI Prediction Market (resolved)

| Team | Pre-game AI win probability | Result |
|---|---|---|
| Pittsburgh Steelers | 73% | Lost |
| Seattle Seahawks | 27% | Won |

Model call: **Missed** · Margin: 1.2 predicted → 14 actual · Markets hit: 2/3

_AI win probability priced pre-game · Markets hit = core betting markets._

### Full-Time Report

**Pittsburgh Steelers vs Seattle Seahawks Prediction and Tips - NFL Week 2, 2025**

_By Ben Dunlop · Published: 15 Sept 2025, 03:00 am_

Pittsburgh Steelers 17–31 Seattle Seahawks in NFL Week 2 at Acrisure Stadium. Here's how the result stacked up against the model's call, with the win-probability story and where the value landed.

Pittsburgh Steelers led 14-7 at the break and had every reason to feel comfortable. Seattle Seahawks had other ideas, and by the final whistle it was 31-17, a 14-point swing that turned a half-time lead into a fairly routine loss.

The turn started with AJ Barner's touchdown, cutting Seattle's deficit to one at 14-13, and by the end of the third quarter the game was locked at 14-14. From there it wasn't close. George Holani's score with 12:46 left in the fourth put Seattle up 23-14, and Kenneth Walker III added another with 3:47 remaining to make it 30-17 before the Steelers even had time to respond. Pittsburgh's only touchdown of the night, DK Metcalf's, had come right on the stroke of half-time. After that, nothing.

The stat sheet backs up exactly what the eye saw. Seattle outgained Pittsburgh 412 yards to 287, ran for 117 against 72, and piled up 34 first downs to the Steelers' 18. Offensive EPA tells the same story in blunter terms: Seattle at plus 4.8, Pittsburgh at minus 11.9. That's not a close game statistically, whatever the scoreboard suggested at the break.

Through the air, Sam Darnold had the better of it, 22 from 33 for 295 yards and two touchdowns against Aaron Rodgers' 18 from 33 for 203 and one. Both quarterbacks threw two interceptions, so the turnover column read even at two apiece, but Seattle made theirs count for less damage. Walker was the best player on the field by the numbers, 105 yards on just 13 carries plus a touchdown, and Jaxon Smith-Njigba backed him up with eight catches for 103 yards. Pittsburgh had no answer for either.

That's the strange part, given the pre-match read had Pittsburgh's quarterback spot rated 57 to Seattle's 34, and the receiving corps edge at 60 to 50. Those numbers didn't translate. Seattle's edges in pass rush, 59 to 51, and secondary, 56 to 55, were modest on paper but showed up exactly where it mattered, three sacks to two and a defence that limited Pittsburgh to 18 first downs.

Form lines pointed to this being tighter than it turned out. Seattle came in having won two of their last five, Pittsburgh just one, and Pittsburgh had actually been outscoring their opponents by less of a margin on average, minus 10 to Seattle's minus 3.2. Add in Alphr's pre-match model, which had Pittsburgh favoured at 73 percent and expected to win by 1.2 points on a combined total of 45. Seattle didn't just cover that gap, they turned it into a 14-point win, though the total of 48 landed within three of the model's number. Wins and misses, all of it.

Pittsburgh will look at the first half and wonder where it went. Seattle will look at the third and fourth quarters and know exactly how they won it.

**Scoring by Quarter**

| Team | Q1 | Q2 | Q3 | Q4 | T |
|---|---|---|---|---|---|
| Pittsburgh Steelers | 6 | 8 | 0 | 3 | 17 |
| Seattle Seahawks | 7 | 0 | 7 | 17 | 31 |

**Key Match Stats**

| Pittsburgh Steelers | Stat | Seattle Seahawks |
|---|---|---|
| 287 | Total Yards | **412** |
| 215 | Passing Yards | **295** |
| 72 | Rushing Yards | **117** |
| 18 | First Downs | **34** |
| 2 | Turnovers | 2 |
| 2 | Sacks Made | **3** |
| -11.9 | Offensive EPA | **4.8** |

**Model Report Card**

Seattle Seahawks defied the model's 73% prediction for Pittsburgh Steelers, a notable upset. The margin model missed here, predicting 1.2 but the actual margin was 14 points. Total score prediction of 45 was close to the actual 48, within 3 points. The model went 2/3 on this match. The over 40.5 total call was correct.

**Referee Watch**

Scott Novak officiated this match (111 career games). The 48-point combined total was right in line with Scott Novak's career average of 45. Seattle Seahawks's victory aligns with Scott Novak's historical trend, Seattle Seahawks have a 100% win rate under this referee.

_Official NFL box score · Generated by Alphr's model_

### Edge Analysis — the model's pre-game markets

- **Head to Head:** Pittsburgh Steelers to win @ $1.52 (edge +7.3%) — model probability 73.0% — result: LOST
- **Line / Spread:** Seattle Seahawks +3.5 @ $1.91 (edge +0.0%) — result: WON
- **Total (Over/Under):** Over 40.5 @ $1.91 (edge +0.0%) — result: WON
- Predicted margin: Pittsburgh Steelers by 1 · Predicted total: 45

### Match Preview (pre-match read)

The model is firm on Pittsburgh Steelers here: 73% to see off Seattle Seahawks. The heaviest factor is the ratings gap: Pittsburgh Steelers sit 26 ELO points clear, 1532 to 1506. Form leans Seattle Seahawks' way: 2 from their last 5, against 1 for Pittsburgh Steelers.

The factors split evenly, which is why the margin stays tight. The margin model has Pittsburgh Steelers by 1.2, with the two sides combining for about 45. A big number on the win and a tighter one on the scoreboard. Back Pittsburgh Steelers to win, not to win big.

The recent history is even: 5 wins each from the last 10 meetings, Seattle Seahawks taking the most recent 31-17 in 2025 Week 2.

Pittsburgh Steelers went 10-7 in 2025 so far, 6-3 at home, averaging 23.4 points for and 22.8 against.

Seattle Seahawks went 14-3 in 2025 so far, 8-1 away, averaging 28.4 points for and 17.2 against.

Aaron Rodgers starts under centre for Pittsburgh Steelers, Sam Darnold for Seattle Seahawks, with Mike Tomlin and Mike Macdonald calling the shots.

The market has Pittsburgh Steelers favourites, Pittsburgh Steelers $1.52 and Seattle Seahawks $2.60, with the spread at Pittsburgh Steelers -3.5 and the total at 40.5.

**By the numbers**

|  |  |
|---|---|
| H2H (last 10) | Pittsburgh Steelers 5 · Seattle Seahawks 5 |
| Last meeting | Seattle Seahawks 31-17 (2025) |
| Pittsburgh Steelers 2025 so far | 10-7 · 6-3 home |
| Pittsburgh Steelers scoring | 23.4 for · 22.8 against |
| Seattle Seahawks 2025 so far | 14-3 · 8-1 away |
| Seattle Seahawks scoring | 28.4 for · 17.2 against |
| Quarterbacks | Aaron Rodgers · Sam Darnold |
| Head coaches | Mike Tomlin · Mike Macdonald |
| Market | Pittsburgh Steelers $1.52 · Seattle Seahawks $2.60 |
| Spread | Pittsburgh Steelers -3.5 · total 40.5 |
| Referee | Scott Novak |

_Pre-match read · Alphr model_

### Referee Indicator — Favours Seattle Seahawks

**Scott Novak** — 111 career games since 2019.

| Team | Record under Scott Novak | Win rate |
|---|---|---|
| Pittsburgh Steelers | 5W–3L | 63% |
| Seattle Seahawks | 6W–0L | 100% |

Avg total: 45.3 pts · Home win %: 47% · Home bias: Leans away

When Scott Novak officiates, Seattle Seahawks have won 6 of 6 games (100%), significantly stronger than Pittsburgh Steelers's 5 from 8 (63%). Small sample (6 games for Seattle Seahawks).

### Recent Form (Last 5)

| Pittsburgh Steelers | Stat | Seattle Seahawks |
|---|---|---|
| 1.0 | Wins (Last 5) | 2.0 |
| 18.4pts | Avg Score | 17.2pts |
| 28.4pts | Avg Conceded | 20.4pts |
| -10.0pts | Avg Margin | -3.2pts |
| 7.0d | Rest Days | 7.0d |

### Form & History

_Last 5 games, oldest → newest._

| Team | Last 5 | Avg Pts |
|---|---|---|
| Pittsburgh Steelers | W16 L · W17 L · W18 L · WC L · W1 W | 18.4 |
| Seattle Seahawks | W15 L · W16 L · W17 W · W18 W · W1 L | 17.2 |

### H2H History (Last 5) — Seattle Seahawks lead 3-2

| Season | Round | Home | Score | Away |
|---|---|---|---|---|
| 2025 | W2 | Pittsburgh Steelers | 17 – 31 | Seattle Seahawks |
| 2023 | W17 | Seattle Seahawks | 23 – 30 | Pittsburgh Steelers |
| 2021 | W6 | Pittsburgh Steelers | 23 – 20 | Seattle Seahawks |
| 2019 | W2 | Pittsburgh Steelers | 26 – 28 | Seattle Seahawks |
| 2015 | W12 | Seattle Seahawks | 39 – 30 | Pittsburgh Steelers |

### TD Scorer History — Who Finds The End Zone

_Last 10 games, career record vs the opponent and at Acrisure Stadium. ⚡ = our AI backs them to score; 🎯 = our AI's first-td pick. 🏉 = scored in this game. TD % = share of games with a td, last 10 weighted double vs the 30 prior — historical frequency, not a market price._

**Pittsburgh Steelers** (team sheet)

| # | Player | Pos | 2025 | Last 10 | vs opp | Venue | TD % | Alphr play | This game |
|---|---|---|---|---|---|---|---|---|---|
| – | Ben Skowronek | WR | 1 in 1g | 1/10g (0·0·0·0·0·0·0·0·0·1) | 0/5g | 0/3g | 8% | — | — |
| – | Calvin Austin III | WR | 1 in 1g | 3/10g (0·1·1·0·0·0·0·0·0·1) | 0/1g | 4/17g | 24% | — | — |
| – | Jaylen Warren | RB | 1 in 1g | 2/10g (0·1·0·0·0·0·0·0·0·1) | 1/1g | 1/24g | 18% | — | — |
| – | Jonnu Smith | TE | 1 in 1g | 8/10g (0·2·1·0·1·1·0·1·1·1) | 1/2g | 0/1g | 36% | ⚡ Anytime TD $4.40 AI 34% ✗ No TD; 🎯 First TD $16.00 AI 9% ✗ Not first | — |
| – | Aaron Rodgers | QB | 0 in 1g | 0/10g (0·0·0·0·0·0·0·0·0·0) | 0/6g | 0/1g | 2% | — | — |
| – | Connor Heyward | RB | 0 in 1g | 1/10g (1·0·0·0·0·0·0·0·0·0) | 0/1g | 1/21g | 6% | — | — |
| – | Darnell Washington | TE | no games | 0/10g (0·0·0·0·0·0·0·0·0·0) | 0/0g | 0/11g | 3% | — | — |
| – | DK Metcalf | WR | 0 in 1g | 3/10g (1·0·0·0·0·0·1·0·1·0) | 0/0g | 0/0g | 34% | ⚡ Anytime TD $2.55 AI 27% ✓ Scored | 🏉 29' |
| – | Kaleb Johnson | RB | 0 in 1g | 0/1g (0) | 0/0g | 0/0g | 0% | — | — |
| – | Kenny Gainwell | RB | 0 in 1g | 0/10g (0·0·0·0·0·0·0·0·0·0) | 0/1g | 0/0g | 6% | — | — |
| – | Mason Rudolph | QB | no games | 1/10g (0·0·0·0·1·0·0·0·0·0) | 0/2g | 0/1g | 5% | — | — |
| – | Pat Freiermuth | TE | 0 in 1g | 4/10g (0·0·1·1·1·0·0·1·0·0) | 0/2g | 6/19g | 28% | ⚡ Anytime TD $5.10 AI 28% ✗ No TD | — |
| – | Roman Wilson | WR | no games | 0/0g | 0/0g | 0/0g | — | — | — |

**Seattle Seahawks** (team sheet)

| # | Player | Pos | 2025 | Last 10 | vs opp | Venue | TD % | Alphr play | This game |
|---|---|---|---|---|---|---|---|---|---|
| – | Zach Charbonnet | RB | 1 in 1g | 5/10g (0·0·0·1·2·1·0·0·0·1) | 0/1g | 0/0g | 30% | ⚡ Anytime TD $3.45 AI 35% ✗ No TD; 🎯 First TD $12.00 AI 11% ✗ Not first | — |
| – | AJ Barner | TE | 0 in 1g | 3/10g (0·0·0·0·1·0·0·1·1·0) | 0/0g | 0/0g | 27% | — | 🏉 39' |
| – | Brady Russell | RB | 0 in 1g | 0/10g (0·0·0·0·0·0·0·0·0·0) | 0/0g | 0/0g | 0% | — | — |
| – | Cooper Kupp | WR | 0 in 1g | 4/10g (2·1·0·1·0·0·0·0·0·0) | 0/2g | 0/0g | 44% | — | — |
| – | Dareke Young | WR | no games | 0/10g (0·0·0·0·0·0·0·0·0·0) | 0/0g | 0/0g | 0% | — | — |
| – | Elijah Arroyo | TE | 0 in 1g | 0/1g (0) | 0/0g | 0/0g | 0% | — | — |
| – | Eric Saubert | TE | 0 in 1g | 1/10g (0·0·0·0·0·0·1·0·0·0) | 0/2g | 0/0g | 8% | — | — |
| – | George Holani | RB | 0 in 1g | 0/4g (0·0·0·0) | 0/0g | 0/0g | 0% | — | 🏉 48' |
| – | Jaxon Smith-Njigba | WR | 0 in 1g | 5/10g (2·0·1·0·1·0·1·0·0·0) | 1/1g | 0/0g | 29% | ⚡ Anytime TD $2.80 AI 30% ✗ No TD; 🎯 First TD $10.50 AI 9% ✗ Not first | — |
| – | Kenneth Walker III | RB | 0 in 1g | 4/10g (0·1·2·0·0·1·0·0·0·0) | 1/1g | 0/0g | 44% | ⚡ Anytime TD $3.00 AI 33% ✓ Scored; 🎯 First TD $10.50 AI 10% ✗ Not first | — |
| – | Robbie Ouzts | RB | no games | 0/0g | 0/0g | 0/0g | — | — | — |
| – | Sam Darnold | QB | 0 in 1g | 1/10g (1·0·0·0·0·0·0·0·0·0) | 0/3g | 0/1g | 10% | — | — |
| – | Tory Horton | WR | 0 in 1g | 0/1g (0) | 0/0g | 0/0g | 0% | — | 🏉 5' |

### End Zone Map — Where Our Picks Have Been Scoring

_Real touchdowns, not predictions: every score each of our picks made in his last five games. The percentage is our model's chance he scores in this game._

**Seattle Seahawks** (3 picks)
- ⚡ Zach Charbonnet 🎯 — 2 TDs in last 5 (2 rush · 0 pass) · model 35% · no TD
- ⚡ Kenneth Walker III 🎯 — 1 TD in last 5 (1 rush · 0 pass) · model 33% · **scored Q4**
- ⚡ Jaxon Smith-Njigba 🎯 — 1 TD in last 5 (0 rush · 1 pass) · model 30% · no TD
- Rest of the Seahawks: 6 TDs across the team's last 5
- Other scorers this game: T.Horton, A.Barner, G.Holani

**Pittsburgh Steelers** (3 picks)
- ⚡ Jonnu Smith 🎯 — 4 TDs in last 5 (0 rush · 4 pass) · model 34% · new club · no TD
- ⚡ Pat Freiermuth — 1 TD in last 5 (0 rush · 1 pass) · model 28% · no TD
- ⚡ DK Metcalf — 2 TDs in last 5 (0 rush · 2 pass) · model 27% · new club · **scored Q2**
- Rest of the Steelers: 9 TDs across the team's last 5

### Prediction Breakdown — Pure Alpha Model

**Team ELO Ratings** — Pittsburgh Steelers 1532 · Seattle Seahawks 1506 · ELO difference: 26 in favour of Pittsburgh Steelers

**Positional Matchups** — unit strength index from player ratings: 50 is league average, 75+ is elite, 25 and under is a weakness

| Unit | Pittsburgh Steelers | Seattle Seahawks | Edge |
|---|---|---|---|
| Quarterback | 57 | 34 | Pittsburgh Steelers +23 |
| Receiving Corps | 60 | 50 | Pittsburgh Steelers +10 |
| Backfield | 48 | 47 | Even |
| Pass Rush | 51 | 59 | Seattle Seahawks +8 |
| Secondary | 55 | 56 | Even |
| Special Teams | 57 | 56 | Even |

**Key Prediction Factors** — what the model weighted most

| # | Factor | Weight | Favours |
|---|---|---|---|
| 1 | Team Rating (ELO) | 30.0% | Pittsburgh Steelers |
| 2 | Quarterback | 18.0% | Pittsburgh Steelers |
| 3 | Pass Rush | 12.0% | Seattle Seahawks |
| 4 | Secondary | 10.0% | — |
| 5 | Recent Form | 10.0% | Seattle Seahawks |
| 6 | Rest Advantage | 8.0% | — |

**Model Confidence: 73%** — Pittsburgh Steelers predicted to win by 1 points · Predicted total: 45 pts

**Record:** 2/3 match predictions correct

**Prediction by Alphr** (https://alphr.com.au) — free AI predictions for every AFL, NRL & Super League match, published before kick-off and never edited. Verified track record: https://alphr.com.au/accuracy

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Last updated: 2025-09-14 17:00 UTC (latest tip update)