# Chicago Bears v New York Giants — NFL Week 10 2025 AI Prediction by Alphr

## Full Time: Chicago Bears 24 – 20 New York Giants

_Chicago Bears hit back to maul New York Giants._

Half time: 7 – 10 · Alphr AI pre-game win probability: Chicago Bears 78% / New York Giants 22% · Soldier Field · Mon, 10 Nov, 05:00 am AEDT

**Touchdowns** — Chicago Bears: Kyle Monangai (10'), Rome Odunze (57' Tipped ⚡), Caleb Williams (59') · New York Giants: Jaxson Dart (17' Tipped ⚡), Jaxson Dart (33' Tipped ⚡)

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

| Team | Pre-game AI win probability | Result |
|---|---|---|
| Chicago Bears | 78% | Won |
| New York Giants | 22% | Lost |

Model call: **Correct** · Margin: 3.4 predicted → 4 actual · Markets hit: 2/3

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

### Full-Time Report

**Chicago Bears vs New York Giants Prediction and Tips - NFL Week 10, 2025**

_By Ben Dunlop · Published: 10 Nov 2025, 05:00 am_

Chicago Bears 24–20 New York Giants in NFL Week 10 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.

Alphr's model had Chicago Bears by 3.4 and a combined total of 42. The final read: Bears by 4, total of 44. Close enough to call it a good day at the office, but the path there was messier than the numbers suggest.

Chicago Bears won 24-20 at Soldier Field, and for most of the night it looked like the model had the wrong side winning. New York Giants led at half-time, 10-7, after Jaxson Dart's first touchdown flipped an early 7-0 Bears lead. Dart struck again early in the third to push the Giants out to 17-7, and by the end of that quarter New York led 17-10. Nobody watching at that point was thinking about a 78% pre-match win probability for Chicago.

Then the fourth quarter happened. Rome Odunze got the Bears within one score at 16-20 with 3:59 left, and Caleb Williams finished the job at 23-20 with 1:54 to go. Two scores, ninety seconds apart in game time, and the Bears had turned a seven-point deficit into a lead they didn't give back.

The stat sheet tells a strange story for a team that won by four. New York Giants outgained Chicago Bears 456 yards to 391 and matched them on first downs, 30 apiece. Jaxson Dart threw for 242 yards, more than Caleb Williams's 220, and Darius Slayton was the game's leading receiver with 89 yards on four catches. By the usual measures, Chicago Bears lost this game.

They didn't, because of two numbers: turnovers and sacks. New York Giants coughed up one turnover to Chicago Bears's zero, and the Bears' pass rush got home four times while the Giants' didn't register a single sack. That's the kind of ledger that doesn't show up in yards but shows up on the scoreboard, and it's exactly why the Offensive EPA split, 0.3 for New York Giants against minus-0.4 for Chicago, still ended with the Bears celebrating.

The positional read pre-match flagged a huge backfield gap, 86 to 47 in Chicago's favour, and it showed. Bears ran for 171 yards, 38% of their offensive output, led by D'Andre Swift's 80 yards on 13 carries. Kyle Monangai opened the scoring on the ground in the first quarter, and that early rushing identity is what kept Chicago in touching distance while Dart and the Giants passing game had its say.

New York Giants came in as the form underdog, one win from their last five against Chicago's four, and an average margin of minus-4.8 compared to the Bears' plus-1. For three quarters they played well above that billing, matching Chicago's first downs and out-throwing Williams through the air. But the turnover and the missing pass rush pressure cost them exactly the margin the model predicted before kickoff.

Alphr's model got the winner right at 78%, the margin right to within 0.6 of a point, and the total within 2. The stat sheet said New York Giants should have won. The turnover column and the fourth quarter said otherwise.

**Scoring by Quarter**

| Team | Q1 | Q2 | Q3 | Q4 | T |
|---|---|---|---|---|---|
| Chicago Bears | 7 | 0 | 3 | 14 | 24 |
| New York Giants | 0 | 10 | 7 | 3 | 20 |

**Key Match Stats**

| Chicago Bears | Stat | New York Giants |
|---|---|---|
| 391 | Total Yards | **456** |
| 220 | Passing Yards | **287** |
| **171** | Rushing Yards | 169 |
| 30 | First Downs | 30 |
| **0** | Turnovers | 1 |
| **4** | Sacks Made | 0 |
| -0.4 | Offensive EPA | **0.3** |

**Model Report Card**

Our model correctly predicted Chicago Bears to win at 78% probability. The margin model was sharp, predicting Chicago Bears by 3.4 vs the actual margin of 4 points. Total score prediction of 42 was close to the actual 44, within 2 points. The model went 2/3 on this match. The under 45.5 total call was correct.

**Referee Watch**

Adrian Hill officiated this match (112 career games). The 44-point combined total was right in line with Adrian Hill's career average of 45. Chicago Bears's victory aligns with Adrian Hill's historical trend, Chicago Bears have a 63% 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:** Chicago Bears to win @ $1.46 (edge +9.7%) — model probability 78.3% — result: WON
- **Line / Spread:** Chicago Bears -4.5 @ $1.91 (edge +0.0%) — result: LOST
- **Total (Over/Under):** Under 45.5 @ $1.91 (edge +0.0%) — result: WON
- Predicted margin: Chicago Bears by 3 · Predicted total: 42

### Match Preview (pre-match read)

The model is firm on Chicago Bears here: 78% to see off New York Giants. The heaviest factor is the ratings gap: Chicago Bears sit 96 ELO points clear, 1465 to 1369. Form leans Chicago Bears' way: 4 from their last 5, against 1 for New York Giants.

Chicago Bears own the matchup too, leading 3 of 6 factors including Team Rating (ELO), Secondary and Recent Form. The margin model has Chicago Bears by 3.4, with the two sides combining for about 42. 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 7 of the last 12 between these two, and Chicago Bears took the most recent meeting 24-20 in 2025 Week 10.

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

New York Giants went 4-13 in 2025 so far, 1-8 away, averaging 22.4 points for and 25.8 against.

Caleb Williams starts under centre for Chicago Bears, Jaxson Dart for New York Giants, with Ben Johnson and Brian Daboll calling the shots.

The market has Chicago Bears favourites, Chicago Bears $1.46 and New York Giants $2.80, with the spread at Chicago Bears -4.5 and the total at 45.5.

**By the numbers**

|  |  |
|---|---|
| H2H (last 12) | Chicago Bears 7 · New York Giants 5 |
| Last meeting | Chicago Bears 24-20 (2025) |
| Chicago Bears 2025 so far | 11-6 · 6-2 home |
| Chicago Bears scoring | 25.9 for · 24.4 against |
| New York Giants 2025 so far | 4-13 · 1-8 away |
| New York Giants scoring | 22.4 for · 25.8 against |
| Quarterbacks | Caleb Williams · Jaxson Dart |
| Head coaches | Ben Johnson · Brian Daboll |
| Market | Chicago Bears $1.46 · New York Giants $2.80 |
| Spread | Chicago Bears -4.5 · total 45.5 |
| Referee | Adrian Hill |

_Pre-match read · Alphr model_

### Referee Indicator — Favours Chicago Bears

**Adrian Hill** — 112 career games since 2019.

| Team | Record under Adrian Hill | Win rate |
|---|---|---|
| Chicago Bears | 5W–3L | 63% |
| New York Giants | 2W–7L | 22% |

Avg total: 44.5 pts · Home win %: 47% · Home bias: Neutral

When Adrian Hill officiates, Chicago Bears have won 5 of 8 games (63%), significantly stronger than New York Giants's 2 from 9 (22%).

### Recent Form (Last 5)

| Chicago Bears | Stat | New York Giants |
|---|---|---|
| 4.0 | Wins (Last 5) | 1.0 |
| 27.8pts | Avg Score | 24.8pts |
| 26.8pts | Avg Conceded | 29.6pts |
| 1.0pts | Avg Margin | -4.8pts |
| 7.0d | Rest Days | 7.0d |

### Form & History

_Last 5 games, oldest → newest._

| Team | Last 5 | Avg Pts |
|---|---|---|
| Chicago Bears | W4 W · W6 W · W7 W · W8 L · W9 W | 27.8 |
| New York Giants | W5 L · W6 W · W7 L · W8 L · W9 L | 24.8 |

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

| Season | Round | Home | Score | Away |
|---|---|---|---|---|
| 2025 | W10 | Chicago Bears | 24 – 20 | New York Giants |
| 2022 | W4 | New York Giants | 20 – 12 | Chicago Bears |
| 2021 | W17 | Chicago Bears | 29 – 3 | New York Giants |
| 2020 | W2 | Chicago Bears | 17 – 13 | New York Giants |
| 2019 | W12 | Chicago Bears | 19 – 14 | New York Giants |

### 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 |
|---|---|---|---|---|---|---|---|---|---|
| – | D'Andre Swift | RB | 5 in 7g | 6/10g (0·0·1·0·1·0·1·1·1·1) | 2/2g | 5/13g | 46% | ⚡ Anytime TD $2.30 AI 58% ✗ No TD; 🎯 First TD $7.75 AI 13% ✗ Not first | — |
| – | Rome Odunze | WR | 5 in 8g | 5/10g (0·0·1·2·1·1·0·0·0·0) | 0/0g | 2/11g | 29% | ⚡ Anytime TD $2.75 AI 36% ✓ Scored | 🏉 57' |
| – | Caleb Williams | QB | 3 in 8g | 3/10g (0·0·1·0·0·0·1·0·0·1) | 0/0g | 1/11g | 17% | — | 🏉 59' |
| – | Colston Loveland | TE | 2 in 7g | 2/7g (0·0·0·0·0·0·2) | 0/0g | 0/3g | 14% | — | — |
| – | DJ Moore | WR | 2 in 8g | 3/10g (0·1·0·0·1·0·0·0·0·1) | 1/3g | 9/19g | 32% | ⚡ Anytime TD $3.20 AI 35% ✗ No TD | — |
| – | Cole Kmet | TE | 1 in 7g | 2/10g (1·0·0·0·0·1·0·0·0·0) | 0/3g | 10/43g | 22% | — | — |
| – | Kyle Monangai | RB | 1 in 8g | 1/8g (0·0·0·0·0·1·0·0) | 0/0g | 1/3g | 13% | — | 🏉 10' |
| – | Luther Burden III | WR | 1 in 7g | 1/7g (0·0·1·0·0·0·0) | 0/0g | 1/3g | 14% | — | — |
| – | Olamide Zaccheaus | WR | 1 in 8g | 1/10g (0·0·0·0·0·0·0·0·0·1) | 1/5g | 0/3g | 14% | — | — |
| – | Devin Duvernay | WR | 0 in 8g | 0/10g (0·0·0·0·0·0·0·0·0·0) | 0/2g | 0/4g | 0% | — | — |

**New York Giants** (team sheet)

| # | Player | Pos | 2025 | Last 10 | vs opp | Venue | TD % | Alphr play | This game |
|---|---|---|---|---|---|---|---|---|---|
| – | Jaxson Dart | QB | 5 in 8g | 5/8g (0·0·1·0·1·1·1·1) | 0/0g | 0/0g | 63% | ⚡ Anytime TD $3.20 AI 36% ✓ Scored; 🎯 First TD $13.00 AI 10% ✗ Not first | 🏉 17' 33' |
| – | Theo Johnson | TE | 5 in 9g | 5/10g (0·0·0·0·1·2·0·1·0·1) | 0/0g | 0/0g | 30% | ⚡ Anytime TD $3.60 AI 30% ✗ No TD; 🎯 First TD $16.00 AI 8% ✗ Not first | — |
| – | Wan'Dale Robinson | WR | 2 in 9g | 2/10g (0·0·1·0·0·0·1·0·0·0) | 0/0g | 0/0g | 18% | ⚡ Anytime TD $3.60 AI 33% ✗ No TD; 🎯 First TD $15.00 AI 9% ✗ Not first | — |
| – | Daniel Bellinger | TE | 1 in 7g | 1/10g (0·0·0·0·0·0·0·0·1·0) | 0/1g | 0/0g | 6% | — | — |
| – | Gunner Olszewski | WR | 1 in 9g | 1/10g (0·0·0·0·0·0·0·0·0·1) | 0/0g | 0/0g | 6% | — | — |
| – | Tyrone Tracy Jr. | RB | 1 in 7g | 2/10g (1·0·0·0·0·0·0·1·0·0) | 0/0g | 0/0g | 27% | — | — |
| – | Darius Slayton | WR | 0 in 7g | 1/10g (0·1·0·0·0·0·0·0·0·0) | 0/3g | 0/2g | 14% | — | — |
| – | Devin Singletary | RB | 0 in 9g | 0/10g (0·0·0·0·0·0·0·0·0·0) | 1/1g | 1/1g | 18% | — | — |
| – | Ray-Ray McCloud | WR | 0 in 5g | 0/10g (0·0·0·0·0·0·0·0·0·0) | 0/3g | 0/1g | 2% | — | — |
| – | Russell Wilson | QB | 0 in 5g | 1/10g (0·0·1·0·0·0·0·0·0·0) | 0/3g | 0/2g | 14% | — | — |

### 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._

**New York Giants** (3 picks)
- ⚡ Jaxson Dart 🎯 — 4 TDs in last 5 (4 rush · 0 pass) · model 36% · **scored Q2, Q3**
- ⚡ Wan'Dale Robinson 🎯 — 1 TD in last 5 (0 rush · 1 pass) · model 33% · no TD
- ⚡ Theo Johnson 🎯 — 4 TDs in last 5 (0 rush · 4 pass) · model 30% · no TD
- Rest of the Giants: 8 TDs across the team's last 5

**Chicago Bears** (3 picks)
- ⚡ D'Andre Swift 🎯 — 4 TDs in last 5 (3 rush · 1 pass) · model 58% · no TD
- ⚡ Rome Odunze — 1 TD in last 5 (0 rush · 1 pass) · model 36% · **scored Q4**
- ⚡ DJ Moore — 1 TD in last 5 (1 rush · 0 pass) · model 35% · no TD
- Rest of the Bears: 7 TDs across the team's last 5
- Other scorers this game: K.Monangai, C.Williams

### Prediction Breakdown — Pure Alpha Model

**Team ELO Ratings** — Chicago Bears 1465 · New York Giants 1369 · ELO difference: 96 in favour of Chicago Bears

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

| Unit | Chicago Bears | New York Giants | Edge |
|---|---|---|---|
| Quarterback | 56 | 55 | Even |
| Receiving Corps | 60 | 59 | Even |
| Backfield | 86 | 47 | Chicago Bears +39 |
| Pass Rush | 58 | 55 | Even |
| Secondary | 64 | 51 | Chicago Bears +13 |
| Special Teams | 70 | 55 | Chicago Bears +15 |

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

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

**Model Confidence: 78%** — Chicago Bears predicted to win by 3 points · Predicted total: 42 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: 2025-11-09 18:00 UTC (latest tip update)