# Chicago Bears v Pittsburgh Steelers — NFL Week 12 2025 AI Prediction by Alphr

## Full Time: Chicago Bears 31 – 28 Pittsburgh Steelers

_Chicago Bears rally to bear down on Pittsburgh Steelers._

Half time: 17 – 21 · Alphr AI pre-game win probability: Chicago Bears 72% / Pittsburgh Steelers 28% · Soldier Field · Mon, 24 Nov, 05:00 am AEDT

**Touchdowns** — Chicago Bears: DJ Moore (7' Tipped ⚡), Colston Loveland (26'), DJ Moore (37' Tipped ⚡), Kyle Monangai (46' Tipped ⚡) · Pittsburgh Steelers: DK Metcalf (15' Tipped ⚡), Nick Herbig (16'), Jaylen Warren (29' Tipped ⚡), Pat Freiermuth (54')

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

| Team | Pre-game AI win probability | Result |
|---|---|---|
| Chicago Bears | 72% | Won |
| Pittsburgh Steelers | 28% | Lost |

Model call: **Correct** · Margin: 7.0 predicted → 3 actual · Markets hit: 2/3

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

### Full-Time Report

**Chicago Bears vs Pittsburgh Steelers Prediction and Tips - NFL Week 12, 2025**

_By Ben Dunlop · Published: 24 Nov 2025, 05:00 am_

Chicago Bears 31–28 Pittsburgh Steelers in NFL Week 12 at Soldier Field. Here's how the result stacked up against the model's call, with the win-probability story and where the value landed.

Down 14-21 at the break, Chicago Bears needed something to change and it did, fast. DJ Moore's second touchdown with 8:33 left in the third quarter flipped it to 23-21, the Bears added another to close the quarter at 24-21, then Kyle Monangai's score at the start of the fourth stretched it to 30-21. Sixteen unanswered points across that stretch, and suddenly a game Pittsburgh Steelers had led for most of the first half belonged to Chicago.

Pittsburgh Steelers didn't fold. Pat Freiermuth's touchdown with 6:32 left pulled them back to 31-27, and the final margin closed to 31-28. One score, right to the siren. It just wasn't quite enough.

Look at the yardage sheet and you'd think Pittsburgh Steelers should have won it. They outgained Chicago Bears 357 to 338 and doubled them up on the ground, 186 rushing yards to 99. But yards without efficiency are just yards. Chicago Bears' offensive EPA sat at 0.8 against Pittsburgh's 0.1, and that gap is where the game actually lived. Two takeaways apiece meant turnovers were a wash, so it came down to who did more with what they had.

Through the air, no contest. Caleb Williams went 19-of-35 for 239 yards and three touchdowns, while Mason Rudolph was tidier on completions at 24-of-31 for 171 but threw for one score and one interception. Three touchdown throws against a pick is the kind of margin that wins shootouts, and this was one.

DJ Moore was the difference-maker on the receiving end, five catches for 64 yards and both his touchdowns landing in the moments Chicago Bears needed points most. Colston Loveland and Kyle Monangai chipped in the other two majors

**Scoring by Quarter**

| Team | Q1 | Q2 | Q3 | Q4 | T |
|---|---|---|---|---|---|
| Chicago Bears | 7 | 10 | 7 | 7 | 31 |
| Pittsburgh Steelers | 7 | 14 | 0 | 7 | 28 |

**Key Match Stats**

| Chicago Bears | Stat | Pittsburgh Steelers |
|---|---|---|
| 338 | Total Yards | **357** |
| **239** | Passing Yards | 171 |
| 99 | Rushing Yards | **186** |
| **29** | First Downs | 28 |
| 2 | Turnovers | 2 |
| **2** | Sacks Made | 1 |
| **0.8** | Offensive EPA | 0.1 |

**Model Report Card**

Our model correctly predicted Chicago Bears to win at 72% probability. The margin model was sharp, predicting Chicago Bears by 7.0 vs the actual margin of 3 points. The model went 2/3 on this match. The over 46.5 total call was correct.

**Referee Watch**

John Hussey officiated this match (181 career games). The combined score of 59 points was 13 points above John Hussey's career average of 46. Chicago Bears bucked the trend, Pittsburgh Steelers historically win 54% of games under John Hussey, but couldn't convert that edge today. Chicago Bears's home victory fits John Hussey's profile, home teams win 66% of the time under this referee.

_Official NFL box score · Generated by Alphr's model_

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

- **Head to Head:** Chicago Bears to win @ $1.65 (edge +11.7%) — model probability 72.5% — result: WON
- **Line / Spread:** Chicago Bears -3 @ $1.91 (edge +0.0%) — result: PUSH
- **Total (Over/Under):** Over 46.5 @ $1.91 (edge +0.0%) — result: WON
- Predicted margin: Chicago Bears by 7 · Predicted total: 50

### Match Preview (pre-match read)

The model is firm on Chicago Bears here: 72% to see off Pittsburgh Steelers. The ratings say otherwise: Pittsburgh Steelers sit 42 ELO points clear, 1529 to 1487, so the model is backing Chicago Bears against the ratings gap. Form leans Chicago Bears' way: 4 from their last 5, against 2 for Pittsburgh Steelers.

The factors split evenly, which is why the margin stays tight. The margin model has Chicago Bears by 7.0, with the two sides combining for about 50. Chicago Bears to win, and win clearly. The model isn't hedging.

Chicago Bears have won 4 of the last 6 between these two, and Chicago Bears took the most recent meeting 31-28 in 2025 Week 12.

Chicago Bears went 11-6 in 2025 so far, 6-2 at home, averaging 25.9 points for and 24.4 against.

Pittsburgh Steelers went 10-7 in 2025 so far, 4-4 away, averaging 23.4 points for and 22.8 against.

Caleb Williams starts under centre for Chicago Bears, Aaron Rodgers for Pittsburgh Steelers, with Ben Johnson and Mike Tomlin calling the shots.

The market has Chicago Bears favourites, Chicago Bears $1.65 and Pittsburgh Steelers $2.30, with the spread at Chicago Bears -3 and the total at 46.5.

**By the numbers**

|  |  |
|---|---|
| H2H (last 6) | Chicago Bears 4 · Pittsburgh Steelers 2 |
| Last meeting | Chicago Bears 31-28 (2025) |
| Chicago Bears 2025 so far | 11-6 · 6-2 home |
| Chicago Bears scoring | 25.9 for · 24.4 against |
| Pittsburgh Steelers 2025 so far | 10-7 · 4-4 away |
| Pittsburgh Steelers scoring | 23.4 for · 22.8 against |
| Quarterbacks | Caleb Williams · Aaron Rodgers |
| Head coaches | Ben Johnson · Mike Tomlin |
| Market | Chicago Bears $1.65 · Pittsburgh Steelers $2.30 |
| Spread | Chicago Bears -3 · total 46.5 |
| Referee | John Hussey |

_Pre-match read · Alphr model_

### Referee Indicator — Favours Pittsburgh Steelers

**John Hussey** — 181 career games since 2015.

| Team | Record under John Hussey | Win rate |
|---|---|---|
| Chicago Bears | 3W–7L | 30% |
| Pittsburgh Steelers | 7W–6L | 54% |

Avg total: 45.8 pts · Home win %: 66% · Home bias: Leans home

When John Hussey officiates, Pittsburgh Steelers have won 7 of 13 games (54%), significantly stronger than Chicago Bears's 3 from 10 (30%). Home teams win 66% of their matches (vs ~55% league avg).

### Recent Form (Last 5)

| Chicago Bears | Stat | Pittsburgh Steelers |
|---|---|---|
| 4.0 | Wins (Last 5) | 2.0 |
| 26.4pts | Avg Score | 25.4pts |
| 24.6pts | Avg Conceded | 25.0pts |
| 1.8pts | Avg Margin | 0.4pts |
| 7.0d | Rest Days | 7.0d |

### Form & History

_Last 5 games, oldest → newest._

| Team | Last 5 | Avg Pts |
|---|---|---|
| Chicago Bears | W7 W · W8 L · W9 W · W10 W · W11 W | 26.4 |
| Pittsburgh Steelers | W7 L · W8 L · W9 W · W10 L · W11 W | 25.4 |

### H2H History (Last 5) — Chicago Bears lead 4-1

| Season | Round | Home | Score | Away |
|---|---|---|---|---|
| 2025 | W12 | Chicago Bears | 31 – 28 | Pittsburgh Steelers |
| 2021 | W9 | Pittsburgh Steelers | 29 – 27 | Chicago Bears |
| 2017 | W3 | Chicago Bears | 23 – 17 | Pittsburgh Steelers |
| 2013 | W3 | Pittsburgh Steelers | 23 – 40 | Chicago Bears |
| 2009 | W2 | Chicago Bears | 17 – 14 | Pittsburgh Steelers |

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

_Last 10 games, career record vs the opponent and at Soldier Field. ⚡ = 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._

**Chicago Bears** (team sheet)

| # | Player | Pos | 2025 | Last 10 | vs opp | Venue | TD % | Alphr play | This game |
|---|---|---|---|---|---|---|---|---|---|
| – | Rome Odunze | WR | 6 in 10g | 6/10g (1·2·1·1·0·0·0·0·1·0) | 0/0g | 3/12g | 32% | ⚡ Anytime TD $2.80 AI 30% ✗ No TD | — |
| – | D'Andre Swift | RB | 5 in 9g | 6/10g (1·0·1·0·1·1·1·1·0·0) | 0/1g | 5/14g | 44% | ⚡ Anytime TD $2.25 AI 41% ✗ No TD; 🎯 First TD $7.25 AI 10% ✗ Not first | — |
| – | Caleb Williams | QB | 4 in 10g | 4/10g (1·0·0·0·1·0·0·1·1·0) | 0/0g | 2/12g | 22% | — | — |
| – | Kyle Monangai | RB | 3 in 10g | 3/10g (0·0·0·0·0·1·0·0·1·1) | 0/0g | 2/4g | 30% | ⚡ Anytime TD $3.15 AI 32% ✓ Scored; 🎯 First TD $13.00 AI 7% ✗ Not first | 🏉 46' |
| – | Colston Loveland | TE | 2 in 9g | 2/9g (0·0·0·0·0·0·2·0·0) | 0/0g | 0/4g | 11% | — | 🏉 26' |
| – | DJ Moore | WR | 2 in 10g | 2/10g (0·0·1·0·0·0·0·1·0·0) | 1/2g | 9/20g | 26% | ⚡ Anytime TD $3.50 AI 24% ✓ Scored | 🏉 7' 37' |
| – | Cole Kmet | TE | 1 in 9g | 1/10g (0·0·0·1·0·0·0·0·0·0) | 0/1g | 10/44g | 18% | — | — |
| – | Luther Burden III | WR | 1 in 9g | 1/9g (0·0·1·0·0·0·0·0·0) | 0/0g | 1/4g | 11% | — | — |
| – | Olamide Zaccheaus | WR | 1 in 9g | 1/10g (0·0·0·0·0·0·0·0·1·0) | 0/2g | 0/4g | 12% | — | — |
| – | Devin Duvernay | WR | 0 in 10g | 0/10g (0·0·0·0·0·0·0·0·0·0) | 0/6g | 0/5g | 0% | — | — |
| – | Durham Smythe | TE | 0 in 2g | 0/10g (0·0·0·0·0·0·0·0·0·0) | 0/0g | 0/1g | 4% | — | — |
| – | Nikola Kalinic | TE | no games | 0/1g (0) | 0/0g | 0/0g | 0% | — | — |

**Pittsburgh Steelers** (team sheet)

| # | Player | Pos | 2025 | Last 10 | vs opp | Venue | TD % | Alphr play | This game |
|---|---|---|---|---|---|---|---|---|---|
| – | DK Metcalf | WR | 5 in 10g | 5/10g (0·1·1·1·1·0·1·0·0·0) | 1/2g | 0/1g | 40% | ⚡ Anytime TD $2.90 AI 33% ✓ Scored; 🎯 First TD $12.00 AI 8% ✗ Not first | 🏉 15' |
| – | Kenny Gainwell | RB | 5 in 10g | 5/10g (0·0·1·2·0·0·0·0·0·2) | 0/1g | 0/1g | 14% | — | — |
| – | Jaylen Warren | RB | 3 in 9g | 3/10g (0·1·0·0·0·0·0·2·0·0) | 0/0g | 0/0g | 18% | ⚡ Anytime TD $1.98 AI 39% ✓ Scored; 🎯 First TD $7.00 AI 10% ✗ Not first | 🏉 29' |
| – | Pat Freiermuth | TE | 3 in 9g | 3/10g (0·0·0·0·0·2·0·1·0·0) | 2/1g | 0/0g | 26% | — | 🏉 54' |
| – | Calvin Austin III | WR | 2 in 8g | 2/10g (0·0·1·0·1·0·0·0·0·0) | 0/0g | 0/0g | 20% | — | — |
| – | Jonnu Smith | TE | 2 in 10g | 2/10g (1·0·0·0·0·1·0·0·0·0) | 1/3g | 0/1g | 28% | — | — |
| – | Roman Wilson | WR | 2 in 7g | 2/7g (0·0·0·1·0·1·0) | 0/0g | 0/0g | 29% | — | — |
| – | Ben Skowronek | WR | 1 in 10g | 1/10g (1·0·0·0·0·0·0·0·0·0) | 0/0g | 0/0g | 6% | — | — |
| – | Connor Heyward | RB | 1 in 9g | 1/10g (0·0·0·0·1·0·0·0·0·0) | 0/0g | 0/0g | 6% | — | — |
| – | Darnell Washington | TE | 1 in 7g | 1/10g (0·0·0·0·0·0·1·0·0·0) | 0/0g | 0/0g | 8% | — | — |
| – | Ke'Shawn Williams | WR | 0 in 6g | 0/6g (0·0·0·0·0·0) | 0/0g | 0/0g | 0% | — | — |
| – | Mason Rudolph | QB | 0 in 3g | 1/10g (0·1·0·0·0·0·0·0·0·0) | 0/0g | 0/0g | 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._

**Pittsburgh Steelers** (2 picks)
- ⚡ Jaylen Warren 🎯 — 2 TDs in last 5 (2 rush · 0 pass) · model 39% · **scored Q2**
- ⚡ DK Metcalf 🎯 — 1 TD in last 5 (0 rush · 1 pass) · model 33% · **scored Q1**
- Rest of the Steelers: 11 TDs across the team's last 5
- Other scorers this game: N.Herbig, P.Freiermuth

**Chicago Bears** (4 picks)
- ⚡ D'Andre Swift 🎯 — 3 TDs in last 5 (2 rush · 1 pass) · model 41% · no TD
- ⚡ Kyle Monangai 🎯 — 3 TDs in last 5 (3 rush · 0 pass) · model 32% · **scored Q4**
- ⚡ Rome Odunze — 1 TD in last 5 (0 rush · 1 pass) · model 30% · no TD
- ⚡ DJ Moore — 1 TD in last 5 (1 rush · 0 pass) · model 24% · **scored Q1, Q3**
- Rest of the Bears: 6 TDs across the team's last 5
- Other scorers this game: C.Loveland

### Prediction Breakdown — Pure Alpha Model

**ELO–Market Disagreement:** Pittsburgh Steelers hold the ELO advantage (1529 vs 1487), but the market favours Chicago Bears (@1.65). The model sides with the market, other factors override the ELO gap.

**Team ELO Ratings** — Chicago Bears 1487 · Pittsburgh Steelers 1529 · ELO difference: 42 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 | Chicago Bears | Pittsburgh Steelers | Edge |
|---|---|---|---|
| Quarterback | 57 | 50 | Chicago Bears +7 |
| Receiving Corps | 56 | 54 | Even |
| Backfield | 82 | 57 | Chicago Bears +25 |
| Pass Rush | 47 | 53 | Pittsburgh Steelers +6 |
| Secondary | 57 | 60 | Even |
| Special Teams | 77 | 71 | Chicago Bears +6 |

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

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

**Model Confidence: 72%** — Chicago Bears predicted to win by 7 points · Predicted total: 50 pts

**Record:** 2/2 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: 2025-11-23 18:00 UTC (latest tip update)