NRL | Round 25

alphr.com.au

MEL
Storm
VS
CBY
Bulldogs
AAMI PARK, MELBOURNE • FRIDAY 22 AUG, 8:00 PM

Win Probability

AI Game Review

Our model correctly predicted Storm to win at 63% probability. The margin model was sharp, predicting Storm by 7.1 vs the actual margin of 6 points. Total score prediction of 38 was close to the actual 34, within 4 points. A clean sweep, all 3 model picks hit for this match.

Model vs actual outcomes • Post-match analysis

🏁

AI Referee Insights

Adam Gee officiated this match (295 career games). The combined score of 34 points was 9 points below Adam Gee's career average of 43. Storm's victory aligns with Adam Gee's historical trend, Storm have a 68% win rate under this referee. Storm's home victory fits Adam Gee's profile, home teams win 57% of the time under this referee. Adam Gee averaged 13.7 penalties per game heading in, a whistle-heavy referee profile. 69% of his career sin bins go against away teams, a statistically significant away-team bias.

Based on referee career statistics • Post-match analysis

Momentum Replay
Beta
80', Storm firmly in control (99%)
STO20
99%80'1%
14BUL
HT100%50%0%0'20'40'60'80'
Bulldogs momentumMomentum -0Storm momentum →
Next Try (within 10 min)
AI Model
85% none
STO 8%No try 85%BUL 7%
Biggest Swings

AI Win Probability

63%StormFavourite

Storm

63%

Bulldogs

37%

AI Match Overview

Storm hold the advantage at 63% win probability, though Bulldogs are far from out of this at 37%. The model sees Storm ahead on 7 of 7 key factors including ELO Difference, Forward Pack and Backline Quality. Storm carry a 184-point ELO rating advantage (1731 vs 1547). Recent form favours Storm with 4 wins from their last 5 compared to 3 for Bulldogs. The margin model predicts Storm by 7.1 points with a combined total of 38.

Generated from model features • Pre-kick-off analysis

Edge Analysis

1 ACTIVE EDGE

Each market is predicted by an independent model, H2H, margin, and totals may occasionally disagree.

H2H Recommendation

Storm to Win @1.40

Winner ✓

Edge

-5.2%

Line / Spread

Storm -7.5 @1.91

Winner ✓

Edge

-5.2%

Total Points

Under 40.5 @1.91

Winner ✓

Edge

+5.4%

Form & History

TeamLast 5Avg Pts
Storm
W
W
W
W
L
22.0
Bulldogs
W
W
W
L
L
24.0

Avg Conceded

15.6

Storm

19.2

Bulldogs

Avg Margin

6.4

Storm

4.8

Bulldogs

Run Metres

1706

Storm

1546

Bulldogs

Line Breaks

3.4

Storm

5.8

Bulldogs

Referee Indicator

Favours Storm

Adam Gee

295 career games · since 2013

AI Analysis

Win rate when Adam Gee refs each team (vs any opponent)

Storm
30W – 14L
68%
Bulldogs
14W – 14L
50%

When Adam Gee officiates, Storm have won 30 of 44 games (68%), significantly stronger than Bulldogs's 14 from 28 (50%). Home teams win 57% of his matches (vs ~52% league avg).

Avg Total

42.8 pts

Home Win %

57%

Home Bias

Leans home

Penalty & Discipline

Pen / Game

13.7

Sin Bins / Gm

0.24

SB Away %

69%

Avg Penalties Per Game

vs Home Teams6.3
vs Away Teams7.4

Penalty Advantage Under This Ref

Positive = opponent penalised more than your team

Storm
+0.0
Bulldogs
+1.7

Adam Gee averages 13.7 penalties per game, above the league norm. Expect frequent stoppages. Penalises away teams more, 6.3 against home vs 7.4 against away. Bulldogs get a +1.7 penalty advantage under Adam Gee vs Storm's +0.0. 69% of his 36 career sin bins go to away teams.

H2H History (Last 5)Storm lead 4-1
Sep 2025MEL 26 - 18 CBY
Apr 2024MEL 16 - 14 CBY
Mar 2023MEL 12 - 26 CBY
Mar 2022MEL 44 - 0 CBY
Apr 2021MEL 52 - 18 CBY
Prediction BreakdownPure Alpha Model

📊Team ELO Ratings

MEL
1731Overall1547
CBY
ELO difference: +184 in favour of Storm

🏈Positional Matchups

Player ELO aggregated by position group, higher = stronger unit

1117Forwards988
Best: 1358MEL +128Best: 1196
1138Backs941
Best: 1205MEL +197Best: 1050
1218Halves1083
Best: 1218MEL +135Best: 1083
1222Hooker1004
MEL +218

📈Recent Form (Last 5)

MEL
Stat
CBY
4.0
Wins (Last 5)
3.0
22.0pts
Avg Score
24.0pts
15.6pts
Avg Conceded
19.2pts
6.4pts
Avg Margin
4.8pts
1705.8m
Run Metres
1545.8m
3.4
Line Breaks
5.8
362.0
Tackles
338.8
10.2
Errors
9.8

🔑Key Prediction Factors

What the model weighted most in this prediction

1
ELO Difference14.0%
Storm
2
Forward Pack12.0%
Storm
3
Backline Quality10.0%
Storm
4
Halves Control9.0%
Storm
5
Recent Win Rate9.0%
Storm
6
Referee Tendency7.0%
Storm
7
Venue Advantage7.0%
Storm

Model Confidence

63%

Storm predicted to win by 7 points

Predicted total: 38 · Line: +7.1

3/3 match predictions correct
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