Rank and Draft Smarter: Value Model for GameDay Squad and AFL Fantasy - 2026

Every season in GameDay Squad, AFL Fantasy and Supercoach the same question comes up:

“How do I actually compare players across positions, card rarities, age profiles and salary cap?”

Raw averages don’t cut it.Price tags lie.And scarcity is real — whether the platform likes it or not.

So we built a model.

This model incorporates no opinions on how a player will do, what will happen if they have moved clubs or any other intangible not reflected in the numbers. So there are players like flanders that are low down this list that what the opinion of them this, nic martin is high despite his ACL. This is purely numbers as a jumping off point.

This is the BOBs GDS Card Value Model — a framework designed to rank every relevant player in the pool while accounting for performance, age, scarcity, salary cap efficiency, and card rarity. It’s not about picking names. It’s about understanding value.

Below is how it works, step by step.

Rank and Draft Smarter: Value Model for GameDay Squad and AFL Fantasy - 2026

Start With the Only Thing That Matters: Scoring

At its core, Fantasy is a scoring game. Everything else is a constraint layered on top.

For every player in the model, we start with three seasons of fantasy scoring:

• 2023 average

• 2024 average

• 2025 average

If a player missed a season (injury, debut year, role change), we don’t punish or inflate them artificially. Instead:

👉 Any missing season is filled using that player’s own multi-year average

Why?

• It avoids small-sample traps

• It stops injury seasons nuking value

• It keeps rookies and mid-career movers usable in the model

Once this is done, every player has a clean 3-year scoring profile.

Weighted Averages: Recent Form > Old History

Not all seasons are created equal.

We weight scoring to reflect how fantasy actually works — recency matters:

• 2025 = 65%

• 2024 = 25%

• 2023 = 10%

This creates a Weighted Average that:

• Rewards players who are currently elite

• Keeps proven stars relevant

• Softens the impact of one-off career years

This is the backbone of the model — everything else builds off this number.

Age Adjustment: Development, Prime, Decline

Next comes age — and this is where most fantasy models get lazy.

We don’t.

The Age Curve

• 21 and under → no decay (growth baked in elsewhere)

• 22–30 → 2% decline per year (compounded) this accounts for playable years for cards and in draft keeper models

• 31+ → 4% decline per year (compounded)

Why compounded?Because decline isn’t linear. Once it starts, it accelerates.

This produces an Age Multiplier, applied directly to the Weighted Average:

Age-Adjusted Score = Weighted Average × Age Multiplier

This means:

• Young stars aren’t penalised

• Prime-age guns are rewarded

• Veterans need elite scoring to hold value

No vibes. No narratives. Just math.

Scarcity: Why Position Actually Matters

Here’s the uncomfortable truth:

A 95-average ruck is more valuable than a 95-average mid.

Why?Because you can’t replace them.

Scarcity in this model is position-based, not vibes-based.

How Scarcity Is Defined

• Top 15 players per position (DEF / MID / FWD)

• Top 4 players for RUCKS (because that’s the reality of the pool)

Scarcity is assessed within each position, not across the entire competition.

This produces a Scarcity Multiplier that:

• Rewards players who separate from the pack

• Identifies positional cliffs

• Stops midfield depth from drowning out elite defenders and rucks

When applied, we get:

Scarcity-Adjusted Value = Age-Adjusted Score × Scarcity Multiplier

This is the number the model ultimately ranks.

5️⃣ Final Rankings: One Board, All Players

Once scarcity is applied, every player is ranked together.

That’s the key point.

Not “best mid”.Not “best ruck”.Not “best forward”.

Just:

Who provides the most value in a constrained GDS environment?

The result is a single, unified ranking that:

• Exposes the top cards

• Highlights under-owned value due to opinion

• Shows where positional drop-offs actually occur

Rank and Draft Smarter: Value Model for GameDay Squad and AFL Fantasy - 2026. This is the list you draft from: Top 100

Full list is downloadable here: https://www.blokesontheball.com/post/the-full-rank-and-draft-smarter-value-model-for-gameday-squad-and-afl-fantasy-2026

Player	Age	Position	2023	2024	2025	Average	Weighted_Average	Age Decay	Age Adjusted	Pos Baseline	Scarcity X	Scarcity_Adjusted	Rank
Harry Sheezel	21	FWD	97.3	112.1	109.2	106.2	108.7	1	108.7	77.13333333	1.409	153.2	1
Nasiah Wanganeen-Milera	22	DEF	91.5	97.7	110.8	100	105.6	0.98	103.5	81.62666667	1.268	131.2	2
Nick Daicos	22	MID	108.4	104.7	106.9	106.7	106.5	0.98	104.4	94.08666667	1.11	115.9	3
Nic Martin	24	FWD	85.2	107.2	97.4	96.6	98.6	0.941	92.8	77.13333333	1.203	111.6	4
Connor Rozee	25	DEF	103.5	95.7	104.1	101.1	101.9	0.922	94	81.62666667	1.152	108.3	5
Errol Gulden	23	MID	112.3	106.8	102.3	107.1	104.4	0.96	100.2	94.08666667	1.065	106.8	6
Tim English	28	RUC	118.7	103.9	110.7	111.1	109.8	0.868	95.3	91.3	1.044	99.5	7
Bailey Smith	25	MID	83.3	99.15	115	99.2	107.9	0.922	99.5	94.08666667	1	99.5	8
Tristan Xerri	26	RUC	66	114.5	105	95.2	103.5	0.904	93.6	91.3	1.025	95.8	9
Noah Anderson	24	MID	100.5	104	99.3	101.3	100.6	0.941	94.7	94.08666667	1.007	95.4	10
Jordan Dawson	28	MID	113.4	105.3	108.9	109.2	108.5	0.868	94.2	94.08666667	1	94.1	11
Max Holmes	23	MID	75.9	94.3	102.7	91	97.9	0.96	94	94.08666667	1	94	12
Will Ashcroft	21	MID	82.9	86.6	98.4	89.3	93.9	1	93.9	94.08666667	1	93.9	13
Tom Green	24	MID	108.7	99.8	97.5	102	99.2	0.941	93.3	94.08666667	1	93.3	14
Andrew Brayshaw	26	MID	110.3	104.6	101.6	105.5	103.2	0.904	93.3	94.08666667	1	93.3	15
Josh Dunkley	28	MID	102.7	105.6	108.4	105.6	107.1	0.868	93	94.08666667	1	93	16
Matt Rowell	24	MID	92	93.7	101.5	95.7	98.6	0.941	92.8	94.08666667	1	92.8	17
Caleb Serong	24	MID	108.1	104	94.3	102.1	98.1	0.941	92.3	94.08666667	1	92.3	18
Zak Butters	25	MID	100	100.5	99.9	100.1	100.1	0.922	92.3	94.08666667	1	92.3	19
Lachie Ash	24	DEF	84.8	75.8	99.6	86.7	92.2	0.941	86.8	81.62666667	1.063	92.2	20
Marcus Bontempelli	30	MID	117	105.9	111.3	111.4	110.5	0.834	92.2	94.08666667	1	92.2	21
Rowan Marshall	30	RUC	116.8	117.1	105.8	113.2	109.7	0.834	91.5	91.3	1.002	91.7	22
Jye Caldwell	25	MID	75.1	94.5	103.6	91.1	98.5	0.922	90.8	94.08666667	1	90.8	23
Finn Callaghan	22	MID	73	76.3	100.6	83.3	91.8	0.98	90	94.08666667	1	89.9	24
Sam Walsh	25	MID	97.9	105.3	93.1	98.8	96.6	0.922	89.1	94.08666667	1	89.1	25
Hugh McCluggage	27	MID	88.8	98.2	101.6	96.2	99.5	0.886	88.2	94.08666667	1	88.1	26
Zach Merrett	30	MID	112.9	108.7	102.8	108.1	105.3	0.834	87.8	94.08666667	1	87.8	27
Lachie Whitfield	31	DEF	95.8	109.6	105.9	103.8	105.8	0.8	84.6	81.62666667	1.036	87.7	28
Jack Sinclair	30	DEF	102.1	101.9	100.7	101.6	101.1	0.834	84.3	81.62666667	1.033	87.2	29
Archie Roberts	20	DEF	85.45	85	85.9	85.4	85.6	1	85.6	81.62666667	1	85.6	30
Tim Taranto	27	MID	112.4	95.5	93.9	100.6	96.2	0.886	85.2	94.08666667	1	85.2	31
Gryan Miers	26	FWD	75.5	87	92.8	85.1	89.6	0.904	81	77.13333333	1.05	85	32
Luke Jackson	24	RUC	84.7	78.7	95.3	86.2	90.1	0.941	84.8	91.3	1	84.8	33
Josh Daicos	27	DEF	90	94.8	94	92.9	93.8	0.886	83.1	81.62666667	1.018	84.6	34
Chad Warner	24	MID	92.5	93	88.1	91.2	89.8	0.941	84.5	94.08666667	1	84.5	35
Jordan Clark	25	DEF	76.5	96.9	89.4	87.6	90	0.922	83	81.62666667	1.017	84.4	36
Nic Newman	30	DEF	97.9	102.1	100	100	100.3	0.834	83.7	81.62666667	1	83.7	37
Ed Richards	26	MID	79.8	84.9	96.7	87.1	92.1	0.904	83.3	94.08666667	1	83.2	38
Isaac Heeney	29	MID	79.4	104.5	96.9	93.6	97.1	0.851	82.6	94.08666667	1	82.6	39
Jai Newcombe	24	MID	93.3	88.3	86.5	89.4	87.6	0.941	82.4	94.08666667	1	82.5	40
Jack Steele	30	MID	98.6	106.6	95.9	100.4	98.8	0.834	82.4	94.08666667	1	82.4	41
Luke Davies-Uniacke	26	MID	97.4	95.9	87.7	93.7	90.7	0.904	82	94.08666667	1	82	42
Darcy Cameron	30	RUC	82.5	96.2	101.2	93.3	98.1	0.834	81.8	91.3	1	81.8	43
Brodie Grundy	31	RUC	75	96	107	92.7	101	0.8	80.8	91.3	1	80.8	44
Adam Cerra	26	MID	94.9	72.5	95.1	87.5	89.4	0.904	80.8	94.08666667	1	80.8	45
Jason Horne-Francis	22	MID	67.8	87.6	82.2	79.2	82.1	0.98	80.5	94.08666667	1	80.5	46
Colby McKercher	20	DEF	80.4	82.3	78.5	80.4	79.6	1	79.6	81.62666667	1	79.6	47
Will Day	24	MID	95.3	74.1	86.8	85.4	84.5	0.941	79.5	94.08666667	1	79.5	48
Lloyd Meek	27	RUC	59.4	90.6	93.8	81.3	89.6	0.886	79.4	91.3	1	79.4	49
Christian Petracca	29	FWD	104.7	90	90.4	95	91.7	0.851	78	77.13333333	1.011	79	50
Matt Roberts	22	DEF	78.8	74.9	82.7	78.8	80.4	0.98	78.8	81.62666667	1	78.8	51
Tom Powell	23	MID	57.4	78.7	86.9	74.3	81.9	0.96	78.6	94.08666667	1	78.6	52
Sam Darcy	22	FWD	78.8	75.4	82.2	78.8	80.2	0.98	78.6	77.13333333	1	78.6	53
Touk Miller	29	MID	96.2	93.1	91.5	93.6	92.4	0.851	78.6	94.08666667	1	78.6	54
Max Gawn	34	RUC	93.5	111.8	113.3	106.2	110.9	0.708	78.5	91.3	1	78.5	55
Izak Rankine	25	FWD	74.2	79.8	87.2	80.4	84.1	0.922	77.5	77.13333333	1.005	77.9	56
Bailey Dale	29	DEF	85.3	91.5	92	89.6	91.2	0.851	77.6	81.62666667	1	77.6	57
Clayton Oliver	28	MID	115.1	77.7	89.7	94.2	89.2	0.868	77.4	94.08666667	1	77.5	58
George Hewett	30	MID	73.8	84.2	98.9	85.6	92.7	0.834	77.3	94.08666667	1	77.3	59
Jake Soligo	22	MID	67.6	76.9	81.4	75.3	78.9	0.98	77.3	94.08666667	1	77.3	60
Darcy Parish	28	MID	106.9	91.6	85.3	94.6	89	0.868	77.3	94.08666667	1	77.3	61
Tom McCarthy	25	DEF	83.5	83.5	83.5	83.5	83.5	0.922	77	81.62666667	1	77	62
Dylan Moore	26	FWD	88	91.6	82	87.2	85	0.904	76.8	77.13333333	1	76.8	63
Trent Rivers	24	DEF	74.7	82	82.4	79.7	81.5	0.941	76.7	81.62666667	1	76.7	64
Matthew Kennedy	28	MID	70	74.8	96.5	80.4	88.4	0.868	76.7	94.08666667	1	76.7	65
Patrick Cripps	30	MID	87.5	99.6	88.8	92	91.4	0.834	76.2	94.08666667	1	76.2	66
Nick Blakey	25	DEF	71.6	80.2	85	78.9	82.5	0.922	76.1	81.62666667	1	76	67
Toby Nankervis	31	RUC	102.3	100.6	91.1	98	94.6	0.8	75.7	91.3	1	75.7	68
Luke Ryan	29	DEF	99.4	102.1	82.3	94.6	89	0.851	75.7	81.62666667	1	75.7	69
Marcus Windhager	22	MID	62.4	70.4	82	71.6	77.1	0.98	75.6	94.08666667	1	75.6	70
Darcy Wilmot	22	DEF	61.7	73.5	80.6	71.9	76.9	0.98	75.4	81.62666667	1	75.4	71
Tom De Koning	26	RUC	66.1	86.2	84.7	79	83.2	0.904	75.2	91.3	1	75.2	72
Jake Bowey	23	DEF	62.7	62.5	86.4	70.5	78.1	0.96	75	81.62666667	1	74.9	73
Kysaiah Pickett	24	FWD	63.3	66	87.3	72.2	79.6	0.941	74.9	77.13333333	1	74.9	74
Sam Flanders	24	FWD	89.6	107.8	67.2	88.2	79.6	0.941	74.9	77.13333333	1	74.9	75
Reilly O'Brien	30	RUC	88.4	91	89.4	89.6	89.7	0.834	74.8	91.3	1	74.8	76
Jaspa Fletcher	21	DEF	52.4	60	83.7	65.4	74.6	1	74.6	81.62666667	1	74.6	77
Mason Redman	28	DEF	86	83.9	85.8	85.2	85.3	0.868	74	81.62666667	1	74.1	78
Rory Laird	32	DEF	109.2	99.3	93.2	100.6	96.3	0.768	74	81.62666667	1	74	79
James Rowbottom	25	MID	78	85.1	78.5	80.5	80.1	0.922	73.9	94.08666667	1	73.9	80
John Noble	28	DEF	81	74.4	89.9	81.8	85.1	0.868	73.9	81.62666667	1	73.9	81
Lachie Neale	32	MID	94.6	100.4	94.3	96.4	95.9	0.768	73.7	94.08666667	1	73.6	82
Jase Burgoyne	22	DEF	75.8	77.4	74.2	75.8	75.2	0.98	73.7	81.62666667	1	73.7	83
Christian Salem	30	DEF	76.6	80.3	93	83.3	88.2	0.834	73.6	81.62666667	1	73.5	84
Ewan Mackinlay	22	MID	75	75	75	75	75	0.98	73.5	94.08666667	1	73.5	85
Sam Durham	24	MID	63	83	78.4	74.8	78	0.941	73.4	94.08666667	1	73.4	86
Jarrod Berry	27	MID	72.3	85	83.7	80.3	82.9	0.886	73.4	94.08666667	1	73.4	87
Callum Mills	28	DEF	84.3	70.6	90	81.6	84.6	0.868	73.4	81.62666667	1	73.4	88
Riley Thilthorpe	23	FWD	52.9	68.3	83.2	68.1	76.4	0.96	73.3	77.13333333	1	73.4	89
Jayden Short	29	DEF	92.5	80.5	87	86.7	85.9	0.851	73.1	81.62666667	1	73.1	90
Shaun Mannagh	28	FWD	81.35	75.7	87	81.4	83.6	0.868	72.6	77.13333333	1	72.6	91
Karl Amon	30	DEF	86.8	86.5	87.4	86.9	87.1	0.834	72.6	81.62666667	1	72.7	92
Jacob Hopper	28	MID	80.8	82.5	84.3	82.5	83.5	0.868	72.5	94.08666667	1	72.5	93
Jordon Sweet	27	RUC	82.1	82.8	81.4	82.1	81.8	0.886	72.5	91.3	1	72.5	94
Zach Reid	23	DEF	75.4	75.4	75.4	75.4	75.4	0.96	72.4	81.62666667	1	72.4	95
Levi Ashcroft	19	MID	72.2	72.2	72.2	72.2	72.2	1	72.2	94.08666667	1	72.2	96
Xavier Duursma	25	MID	65.1	81.8	78.8	75.2	78.2	0.922	72.1	94.08666667	1	72.1	97
Oliver Dempsey	22	MID	73.25	72.4	74.1	73.2	73.6	0.98	72.1	94.08666667	1	72.1	98
Zac Bailey	26	FWD	71.1	69.5	85	75.2	79.7	0.904	72	77.13333333	1	72.1	99

The Big Takeaway

This model isn’t about predicting the future perfectly.

It’s about making better decisions under constraints:

• Salary cap

• Card supply

• Positional scarcity

• Age curves

• Draft formats

If two players score the same but one:

• costs less cap

• has a higher multiplier

• plays a scarcer position

…then one is clearly better value.

This model just makes that obvious.

And that’s the goal.