# Cincinnati Bengals v Chicago Bears — NFL Week 9 2025 AI Prediction by Alphr

## Full Time: Cincinnati Bengals 42 – 47 Chicago Bears

_Chicago Bears fight back to maul Cincinnati Bengals._

Half time: 20 – 17 · Alphr AI pre-game win probability: Cincinnati Bengals 34% / Chicago Bears 66% · Paycor Stadium · Mon, 03 Nov, 05:00 am AEDT

**Touchdowns** — Cincinnati Bengals: Carl Jones (1'), Tee Higgins (29' Tipped ⚡), Tee Higgins (40' Tipped ⚡), Noah Fant (59' Tipped ⚡), Andrei Iosivas (60') · Chicago Bears: Caleb Williams (7'), Olamide Zaccheaus (15'), Colston Loveland (37'), Brittain Brown (43'), DJ Moore (55' Tipped ⚡), Colston Loveland (60')

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

| Team | Pre-game AI win probability | Result |
|---|---|---|
| Chicago Bears | 66% | Won |
| Cincinnati Bengals | 34% | Lost |

Model call: **Correct** · Margin: 4.9 predicted → 5 actual · Markets hit: 1/3

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

### Full-Time Report

**Cincinnati Bengals vs Chicago Bears Prediction and Tips - NFL Week 9, 2025**

_By Ben Dunlop · Published: 3 Nov 2025, 05:00 am_

Cincinnati Bengals 42–47 Chicago Bears in NFL Week 9 at Paycor Stadium. Here's how the result stacked up against the model's call, with the win-probability story and where the value landed.

Chicago Bears beat Cincinnati Bengals 47-42 at Paycor Stadium. Five points in it at the death, but stack the stat sheet next to that scoreline and the gap looks a lot wider than a converted field goal.

Start with the ledger: Chicago outgained Cincinnati 585 yards to 516, and did it with a totally different toolkit. The Bears ran for 283 yards, roughly half their total output, while Cincinnati managed just 46 on the ground all night. Kyle Monangai alone put up 176 yards on 26 carries, more than the Bengals' entire rushing attack combined. When one side can win with its legs and the other can't get out of the passing lane, that's a structural mismatch, not a coin flip.

The turnover column tells the rest of it. Cincinnati coughed it up three times, Chicago none. Joe Flacco threw for 470 yards and 4 touchdowns on 31-of-47 passing, which sounds like a monster night, and it was, but he also handed over two interceptions the Bears didn't have to work for. Caleb Williams was tidier, 20-of-34 for 3 touchdowns, and a clean sheet on giveaways is worth more than any single highlight throw.

The scoring pattern backs up the idea this was tight on the board and lopsided underneath it. Charlie Jones opened the game for Cincinnati, Williams answered for Chicago, and the lead changed hands four times across the four quarters, last flipping with 25 seconds left when Colston Loveland scored his second. Loveland's double, plus touchdowns from Brittain Brown, Williams himself, DJ Moore and Zaccheaus, gave Chicago six different names on the board. Cincinnati's response leaned heavily on Tee Higgins, who caught both his touchdowns among 7 grabs for 121 yards, with Andrei Iosivas, Noah Fant and Jones rounding out the rest.

Efficiency numbers explain why Chicago could absorb a shootout and still come out ahead. The Bears ran up an offensive EPA of 34.9 to Cincinnati's 15, nearly twenty points clear on a per-snap basis, and got home on the pass rush three times to two. Cincinnati actually won first downs 47 to 45 and out-threw Chicago in yardage, 470 to 302, but volume through the air without the ground game or the turnover margin to back it up just meant more opportunities to give the ball back.

Form lines suggest this shouldn't have surprised anyone too much. Chicago arrived 4-1 across their last five with an average margin of plus 3.4, Cincinnati 1-4 at minus 9.2. The positional sheet had the Bengals' receiving corps and backfield rated higher, and Higgins and company delivered on that, but Chicago's edges at quarterback, pass rush and special teams were the ones that showed up when it mattered.

Alphr's model had Chicago at 66% and picked the margin almost to the point, forecasting Bears by 4.9 against an actual 5. The total was the one that got away, projected at 50, delivered at 89. Nobody was pricing in a track meet like that, but the winner and the margin both landed about where the numbers said they would.

**Scoring by Quarter**

| Team | Q1 | Q2 | Q3 | Q4 | T |
|---|---|---|---|---|---|
| Cincinnati Bengals | 10 | 10 | 7 | 15 | 42 |
| Chicago Bears | 7 | 10 | 14 | 16 | 47 |

**Key Match Stats**

| Cincinnati Bengals | Stat | Chicago Bears |
|---|---|---|
| 516 | Total Yards | **585** |
| **470** | Passing Yards | 302 |
| 46 | Rushing Yards | **283** |
| **47** | First Downs | 45 |
| 3 | Turnovers | **0** |
| 2 | Sacks Made | **3** |
| 15 | Offensive EPA | **34.9** |

**Model Report Card**

Our model correctly predicted Chicago Bears to win at 66% probability. The margin model was sharp, predicting Chicago Bears by 4.9 vs the actual margin of 5 points. The game's 89 points came in 39 points higher than the predicted 50. The model went 1/3 on this match.

**Referee Watch**

Clete Blakeman officiated this match (258 career games). The combined score of 89 points was 43 points above Clete Blakeman's career average of 46. Chicago Bears bucked the trend, Cincinnati Bengals historically win 50% of games under Clete Blakeman, but couldn't convert that edge today.

_Official NFL box score · Generated by Alphr's model_

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

- **Head to Head:** Chicago Bears to win @ $1.62 (edge +4.0%) — model probability 65.8% — result: WON
- **Line / Spread:** Cincinnati Bengals +3 @ $1.91 (edge +0.0%) — result: LOST
- **Total (Over/Under):** Under 51.5 @ $1.91 (edge +0.0%) — result: LOST
- Predicted margin: Chicago Bears by 5 · Predicted total: 50

### Match Preview (pre-match read)

The model is firm on Chicago Bears here: 66% to see off Cincinnati Bengals. The ratings say otherwise: Cincinnati Bengals sit 26 ELO points clear, 1469 to 1443, so the model is backing Chicago Bears against the ratings gap. Form leans Chicago Bears' way: 4 from their last 5, against 1 for Cincinnati Bengals.

The factors split evenly, which is why the margin stays tight. The margin model has Chicago Bears by 4.9, with the two sides combining for about 50. A big number on the win and a tighter one on the scoreboard. Back Chicago Bears to win, not to win big.

Chicago Bears have won 5 of the last 7 between these two, and Chicago Bears took the most recent meeting 47-42 in 2025 Week 9.

Cincinnati Bengals went 6-11 in 2025 so far, 3-6 at home, averaging 24.4 points for and 28.9 against.

Chicago Bears went 11-6 in 2025 so far, 5-4 away, averaging 25.9 points for and 24.4 against.

Joe Flacco starts under centre for Cincinnati Bengals, Caleb Williams for Chicago Bears, with Zac Taylor and Ben Johnson calling the shots.

The market has Chicago Bears favourites, Cincinnati Bengals $2.36 and Chicago Bears $1.62, with the spread at Chicago Bears -3 and the total at 51.5.

**By the numbers**

|  |  |
|---|---|
| H2H (last 7) | Cincinnati Bengals 2 · Chicago Bears 5 |
| Last meeting | Chicago Bears 47-42 (2025) |
| Cincinnati Bengals 2025 so far | 6-11 · 3-6 home |
| Cincinnati Bengals scoring | 24.4 for · 28.9 against |
| Chicago Bears 2025 so far | 11-6 · 5-4 away |
| Chicago Bears scoring | 25.9 for · 24.4 against |
| Quarterbacks | Joe Flacco · Caleb Williams |
| Head coaches | Zac Taylor · Ben Johnson |
| Market | Cincinnati Bengals $2.36 · Chicago Bears $1.62 |
| Spread | Chicago Bears -3 · total 51.5 |
| Referee | Clete Blakeman |

_Pre-match read · Alphr model_

### Referee Indicator — Favours Cincinnati Bengals

**Clete Blakeman** — 258 career games since 2010.

| Team | Record under Clete Blakeman | Win rate |
|---|---|---|
| Cincinnati Bengals | 8W–8L | 50% |
| Chicago Bears | 5W–7L | 42% |

Avg total: 46.2 pts · Home win %: 55% · Home bias: Leans home

Cincinnati Bengals hold a 8-point edge: 8W–8L (50%) vs Chicago Bears's 5W–7L (42%).

### Recent Form (Last 5)

| Cincinnati Bengals | Stat | Chicago Bears |
|---|---|---|
| 1.0 | Wins (Last 5) | 4.0 |
| 23.2pts | Avg Score | 24.6pts |
| 32.4pts | Avg Conceded | 21.2pts |
| -9.2pts | Avg Margin | 3.4pts |
| 7.0d | Rest Days | 7.0d |

### Form & History

_Last 5 games, oldest → newest._

| Team | Last 5 | Avg Pts |
|---|---|---|
| Cincinnati Bengals | W4 L · W5 L · W6 L · W7 W · W8 L | 23.2 |
| Chicago Bears | W3 W · W4 W · W6 W · W7 W · W8 L | 24.6 |

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

| Season | Round | Home | Score | Away |
|---|---|---|---|---|
| 2025 | W9 | Cincinnati Bengals | 42 – 47 | Chicago Bears |
| 2021 | W2 | Chicago Bears | 20 – 17 | Cincinnati Bengals |
| 2017 | W14 | Cincinnati Bengals | 7 – 33 | Chicago Bears |
| 2013 | W1 | Chicago Bears | 24 – 21 | Cincinnati Bengals |
| 2009 | W7 | Cincinnati Bengals | 45 – 10 | Chicago Bears |

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

_Last 10 games, career record vs the opponent and at Paycor 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._

**Cincinnati Bengals** (team sheet)

| # | Player | Pos | 2025 | Last 10 | vs opp | Venue | TD % | Alphr play | This game |
|---|---|---|---|---|---|---|---|---|---|
| – | Ja'Marr Chase | WR | 5 in 8g | 6/10g (0·1·0·1·0·0·2·1·1·0) | 1/1g | 18/28g | 50% | ⚡ Anytime TD $1.89 AI 41% ✗ No TD; 🎯 First TD $8.00 AI 10% ✗ Not first | — |
| – | Tee Higgins | WR | 4 in 8g | 7/10g (3·0·0·1·0·0·1·0·1·1) | 1/1g | 17/22g | 48% | ⚡ Anytime TD $2.25 AI 33% ✓ Scored | 🏉 29' 40' |
| – | Chase Brown | RB | 3 in 8g | 3/10g (0·0·1·0·0·0·0·0·0·2) | 0/0g | 7/19g | 30% | ⚡ Anytime TD $1.98 AI 39% ✗ No TD; 🎯 First TD $8.50 AI 10% ✗ Not first | — |
| – | Noah Fant | TE | 2 in 6g | 3/10g (0·0·0·1·1·0·0·0·1·0) | 0/2g | 1/4g | 16% | ⚡ Anytime TD $4.75 AI 24% ✓ Scored | 🏉 59' |
| – | Joe Flacco | QB | 1 in 7g | 1/10g (0·0·0·0·0·0·0·0·0·1) | 0/3g | 1/2g | 4% | — | — |
| – | Mitchell Tinsley | WR | 1 in 3g | 1/3g (1·0·0) | 0/0g | 1/1g | 33% | — | — |
| – | Samaje Perine | RB | 1 in 8g | 1/10g (0·0·0·0·0·0·0·0·0·1) | 0/2g | 2/12g | 10% | — | — |
| – | Tanner Hudson | TE | 1 in 2g | 2/10g (0·0·1·0·0·0·0·0·1·0) | 0/2g | 1/11g | 10% | — | — |
| – | Andrei Iosivas | WR | 0 in 6g | 1/10g (0·1·0·0·0·0·0·0·0·0) | 0/0g | 7/18g | 21% | — | 🏉 60' |
| – | Charlie Jones | WR | 0 in 8g | 1/10g (1·0·0·0·0·0·0·0·0·0) | 0/0g | 1/13g | 8% | — | 🏉 1' |
| – | Tahj Brooks | RB | 0 in 6g | 0/6g (0·0·0·0·0·0) | 0/0g | 0/3g | 0% | — | — |

**Chicago Bears** (team sheet)

| # | Player | Pos | 2025 | Last 10 | vs opp | Venue | TD % | Alphr play | This game |
|---|---|---|---|---|---|---|---|---|---|
| – | Rome Odunze | WR | 5 in 7g | 5/10g (0·0·0·1·2·1·1·0·0·0) | 0/0g | 0/0g | 29% | ⚡ Anytime TD $2.30 AI 32% ✗ No TD; 🎯 First TD $10.00 AI 10% ✗ Not first | — |
| – | Caleb Williams | QB | 2 in 7g | 2/10g (0·0·0·1·0·0·0·1·0·0) | 0/0g | 0/0g | 12% | — | 🏉 7' |
| – | Cole Kmet | TE | 1 in 6g | 2/10g (0·1·0·0·0·0·1·0·0·0) | 0/1g | 0/0g | 24% | — | — |
| – | DJ Moore | WR | 1 in 7g | 2/10g (0·0·1·0·0·1·0·0·0·0) | 0/2g | 0/1g | 28% | ⚡ Anytime TD $2.95 AI 31% ✓ Scored; 🎯 First TD $12.50 AI 9% ✗ Not first | 🏉 55' |
| – | Kyle Monangai | RB | 1 in 7g | 1/7g (0·0·0·0·0·1·0) | 0/0g | 0/0g | 14% | — | — |
| – | Brittain Brown | RB | no games | 0/1g (0) | 0/0g | 0/0g | 0% | — | 🏉 43' |
| – | Colston Loveland | TE | 0 in 6g | 0/6g (0·0·0·0·0·0) | 0/0g | 0/0g | 0% | — | 🏉 37' 60' |
| – | Devin Duvernay | WR | 0 in 7g | 0/10g (0·0·0·0·0·0·0·0·0·0) | 0/6g | 0/1g | 0% | — | — |
| – | Durham Smythe | TE | no games | 0/10g (0·0·0·0·0·0·0·0·0·0) | 0/4g | 0/1g | 4% | — | — |
| – | Jahdae Walker | WR | 0 in 2g | 0/2g (0·0) | 0/0g | 0/0g | 0% | — | — |
| – | Olamide Zaccheaus | WR | 0 in 7g | 0/10g (0·0·0·0·0·0·0·0·0·0) | 0/1g | 0/1g | 10% | — | 🏉 15' |
| – | Travis Homer | RB | 0 in 2g | 0/10g (0·0·0·0·0·0·0·0·0·0) | 0/0g | 0/0g | 6% | — | — |
| – | Tyson Bagent | QB | 0 in 1g | 2/10g (1·0·1·0·0·0·0·0·0·0) | 0/0g | 0/0g | 20% | — | — |

### 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._

**Chicago Bears** (2 picks)
- ⚡ Rome Odunze 🎯 — 2 TDs in last 5 (0 rush · 2 pass) · model 32% · no TD
- ⚡ DJ Moore 🎯 — 1 TD in last 5 (0 rush · 1 pass) · model 31% · **scored Q4**
- Rest of the Bears: 8 TDs across the team's last 5
- Other scorers this game: C.Williams, O.Zaccheaus, C.Loveland, B.Brown

**Cincinnati Bengals** (4 picks)
- ⚡ Ja'Marr Chase 🎯 — 4 TDs in last 5 (0 rush · 4 pass) · model 41% · no TD
- ⚡ Chase Brown 🎯 — 2 TDs in last 5 (1 rush · 1 pass) · model 39% · no TD
- ⚡ Tee Higgins — 3 TDs in last 5 (0 rush · 3 pass) · model 33% · **scored Q2, Q3**
- ⚡ Noah Fant — 1 TD in last 5 (0 rush · 1 pass) · model 24% · **scored Q4**
- Rest of the Bengals: 3 TDs across the team's last 5
- Other scorers this game: C.Jones, A.Iosivas

### Prediction Breakdown — Pure Alpha Model

**ELO–Market Disagreement:** Cincinnati Bengals hold the ELO advantage (1469 vs 1443), but the market favours Chicago Bears (@1.62). The model sides with the market, other factors override the ELO gap.

**Team ELO Ratings** — Cincinnati Bengals 1469 · Chicago Bears 1443 · ELO difference: 26 in favour of Cincinnati Bengals

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

| Unit | Cincinnati Bengals | Chicago Bears | Edge |
|---|---|---|---|
| Quarterback | 49 | 51 | Even |
| Receiving Corps | 57 | 50 | Cincinnati Bengals +7 |
| Backfield | 56 | 48 | Cincinnati Bengals +8 |
| Pass Rush | 50 | 53 | Even |
| Secondary | 55 | 57 | Even |
| Special Teams | 61 | 64 | Even |

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

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

**Model Confidence: 66%** — Chicago Bears predicted to win by 5 points · Predicted total: 50 pts

**Record:** 1/3 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: 2025-11-02 18:00 UTC (latest tip update)