Building an MTGA Cube Draft Assistant

July 5, 2026

I've been building a draft assistant for Magic: The Gathering Arena Cube. The goal is simple: given the current pack, my previous picks, the cards I've seen, and the cube list, recommend a pick.

This post summarizes the current state of the project: the models, the offline results, the first Monte Carlo tree search experiments, and one real-pack example where several model variants make noticeably different choices.

The setup

A draft pick is represented with only information available to the drafter:

the current pack
my drafted pool
cards I saw but did not pick
pack number and pick number
the known cube list

The base model is a contextual pick policy trained from public 17lands Powered Cube draft logs. Each card is represented by combining a card-specific learned vector with features extracted from Scryfall metadata and rules text: mana value, color and color-identity bits, card-type bits, rarity, power/toughness fields, keyword indicators, and oracle-text features.

In model terms, the representation is roughly:

card_repr = learned_card_embedding + alpha * MLP(scryfall_features)

where alpha starts high and is annealed to a nonzero floor. This lets common cube cards specialize through their learned embeddings while still giving reasonable representations to changed or unseen cube cards from their rules text and metadata. The policy scores every card in the current pack and is trained to imitate human picks.

There are several model variants in the post, and the names mean different things:

Human imitation / human continued is the pick policy trained only to match logged 17lands human picks. This is the most human-like baseline.
Deck-quality model is a separate value model trained from 17lands game results. It takes a completed 40-card deck plus sideboard and metadata, then predicts game win probability. I use it to ask: "if this draft pool became this deck, how good would the deck be?"
Value reranking means taking the human pick policy's score for each card in the pack, then adding a small bonus from the deck-quality model after hypothetically adding that card to the pool.
Card-bonus model is a simpler outcome model trained from game results. Instead of evaluating full deck structure, it learns a per-card win-rate bonus: cards that often appear in winning maindecks get positive bonuses, cards that underperform get lower bonuses. This is useful, but less contextual than the deck-quality model.
Safe game-data is a DPO fine-tune that nudges the pick policy toward card-bonus-preferred picks while staying close to the human-imitation model.
Aggressive game-data is a stronger DPO fine-tune that optimizes the card-bonus preferences harder. It improves game-data preference accuracy, but loses a lot of human-pick agreement and sometimes makes weird-looking picks.
MCTS value-conservative is not a separately trained model. It is inference-time search using the value-conservative policy as a prior and the deck-quality/value model to score simulated draft continuations.

Model and training details

The pick model is a cube-context contextual preference ranker. For a cube with C cards, every training example has a C-dimensional current-pack mask, pool count vector, seen-but-unpicked count vector, and cube mask. The model first computes card_repr for every card in the cube. It then uses separate DeepSets encoders for three unordered sets:

pool_repr = DeepSet(cards in my pool)
seen_repr = DeepSet(cards I saw but passed)
cube_repr = DeepSet(cards in the cube list)

The draft context is the concatenation of those three set embeddings, an explicit WUBRG color-commitment vector from the pool, and learned embeddings for pack number and pick number. A small MLP maps that context to the same dimension as a card representation. Each card is scored by a dot product plus a card bias:

context = MLP(pool_repr, seen_repr, cube_repr, color_commit, pack_idx, pick_idx)
score(card | context) = dot(context, card_repr(card)) + bias(card)

The policy is trained over the in-pack support only. The main loss is a contextual preference / triplet ranking loss: the logged human pick should score at least a margin above every other card in the pack.

loss = mean_over_unpicked_cards max(0, margin - score(picked) + score(unpicked))

In the current implementation the representation dimension is 128, the hidden dimension is 256, and the default margin is 0.2. The Scryfall feature contribution alpha anneals from 1.0 to a floor of 0.3 over early training. I also use random cube-list masking during training so the model is robust to partial or changing Arena Cube lists.

The outcome model is separate. It takes a built deck and sideboard, encodes maindeck and sideboard cards with DeepSets over the same card representation, concatenates rank/event/color covariates, and predicts game win probability. I train this on 17lands game data, both per-game and aggregated by (draft_id, build_index). For deployment, a simple heuristic deckbuilder maps a draft pool to a 40-card deck before scoring.

The game-card bonus model is an even simpler outcome model:

logit(win_probability) = intercept + sum(card_in_maindeck * card_bonus) + covariates

I tested pair-interaction variants, but they overfit relative to the card-only model. The learned top bonuses were plausible Powered Cube cards: Ancestral Recall, Time Walk, Black Lotus, Sol Ring, Mana Crypt, and the Moxen.

Finally, I generated static preference pairs for DPO. For a logged pick state, if one in-pack candidate has a sufficiently higher learned game-card bonus than another, that pair becomes (preferred, rejected). DPO fine-tunes the pick policy toward those game-data preferences while anchoring it to the human-pick reference policy with a KL penalty. This gives the safe/aggressive trade-off in the table below.

Current offline results

Model / setting	Human top-1	Human top-3	Game-data pref. acc.
Human continued, no value rerank	0.6240	0.9093	0.5494
Human continued + value rerank	0.6234	0.9077	0.6062
Safer game-data DPO + value rerank	0.6161	0.9048	0.6406
Aggressive game-data + value rerank	0.5092	0.8411	0.7262

The trade-off is clear: the safer game-data model keeps most of the human-pick accuracy while improving game-data preference agreement. The aggressive model optimizes game-data preferences much harder, but it no longer behaves like a human drafter and often makes suspicious-looking picks.

How much does previous-pick context matter?

A lot. I tested the human-continued model by removing or corrupting the previous-pick context on a 20k held-out sample:

Context	Top-1	Top-3	Same top pick as full context
full pool + seen	0.6168	0.9070	1.0000
no pool, keep seen	0.4268	0.7545	0.5116
keep pool, no seen	0.6160	0.9065	0.9409
no pool, no seen	0.4070	0.7384	0.4856
shuffled pool	0.3507	0.6533	0.4088

The pool matters enormously. The seen-but-unpicked channel matters much less in the current model.

Adding inference-time search

The next experiment was to add Monte Carlo tree search at inference time. This is not training. The model is already trained; MCTS is a search procedure that evaluates each legal pick by simulating possible continuations of the draft and scoring the resulting pools.

For each candidate pick, the search forces that pick, samples plausible future packs from the cube, completes the draft with the policy, builds/scores the final pool with the value model, and combines rollout value with the policy prior.

Because MTGA logs do not reveal hidden packs or opponent picks, this is an information-set approximation. It knows my pack and my pool, but future packs are sampled rather than perfectly reconstructed.

Simulations	p50	p95	Mean
10	418 ms	574 ms	378 ms
25	942 ms	1370 ms	850 ms
50	1825 ms	2697 ms	1642 ms

So 25 simulations is a reasonable default for live search-based reranking.

--simulations 25
--rank-by final
--root-policy-weight 0.15
--root-value-weight 1.0
--root-value-mode prob
--rollout-temperature 0.0
--cpuct 1.5

A real depleted-pack example

The following trace uses real packs from the held-out 17lands eval data. These are not independently sampled fake packs. They are the packs as seen by a real drafter, already depleted by previous picks. Pack sizes go from 14 down to 7.

Caveat: the later packs are real for the human's actual draft path. If a model makes a different earlier pick, the later packs are not a perfect counterfactual.

The real packs

Pick 1, pack size 14

Arena of Glory; Blood Crypt; Containment Priest; Endurance; Godless Shrine; Grief; Omnath, Locus of Creation; Ouroboroid; Skyclave Apparition; Snapcaster Mage; Sundering Titan; The Wandering Emperor; Wishclaw Talisman; Yavimaya, Cradle of Growth

Pick 2, pack size 13

Birds of Paradise; Chain Lightning; Cosmogrand Zenith; Gitaxian Probe; Hymn to Tourach; Life // Death; Multiversal Passage; Soul-Guide Lantern; Talisman of Indulgence; Talisman of Unity; Torsten, Founder of Benalia; Wasteland; Witch Enchanter // Witch-Blessed Meadow

Pick 3, pack size 12

Dark Confidant; Deathrite Shaman; Demonic Tutor; Expressive Iteration; Get Lost; Liliana of the Veil; Questing Druid // Seek the Beast; Razorverge Thicket; Sylvan Caryatid; Underground Mortuary; Virtue of Loyalty // Ardenvale Fealty; Windswept Heath

Pick 4, pack size 11

Copperline Gorge; Elvish Reclaimer; Flickerwisp; Mine Collapse; Savai Triome; Scrubland; Talisman of Progress; Trumpeting Carnosaur; Unexpectedly Absent; Valki, God of Lies // Tibalt, Cosmic Impostor; Zuran Orb

Pick 5, pack size 10

Archon of Cruelty; Bleachbone Verge; Mana Confluence; Questing Beast; Sacred Foundry; Blazing Firesinger // Seething Song; Stomping Ground; Taiga; Tersa Lightshatter; Titania, Protector of Argoth

Pick 6, pack size 9

Crucible of Worlds; Emperor of Bones; Grim Lavamancer; Jetmir's Garden; Restless Cottage; Restless Fortress; Rofellos, Llanowar Emissary; Sanguine Evangelist; Vampire Hexmage

Pick 7, pack size 8

Bone Shards; Collective Brutality; Exploration; Keen-Eyed Curator; Overgrown Tomb; Restless Vents; Utopia Sprawl; Winds of Abandon

Pick 8, pack size 7

Celestial Colonnade; Deep-Cavern Bat; Gloomlake Verge; Jadar, Ghoulcaller of Nephalia; Prismatic Ending; Sink into Stupor // Soporific Springs; Talisman of Conviction

Model choices

Pick	Human log	Human imitation	Safe game-data	Aggressive game-data	MCTS
1	Skyclave Apparition	Snapcaster Mage	Skyclave Apparition	Ouroboroid	Blood Crypt
2	Gitaxian Probe	Gitaxian Probe	Cosmogrand Zenith	Birds of Paradise	Hymn to Tourach
3	Windswept Heath	Demonic Tutor	Windswept Heath	Demonic Tutor	Demonic Tutor
4	Scrubland	Scrubland	Unexpectedly Absent	Zuran Orb	Valki, God of Lies // Tibalt, Cosmic Impostor
5	Sacred Foundry	Archon of Cruelty	Sacred Foundry	Titania, Protector of Argoth	Archon of Cruelty
6	Sanguine Evangelist	Emperor of Bones	Sanguine Evangelist	Emperor of Bones	Emperor of Bones
7	Winds of Abandon	Collective Brutality	Winds of Abandon	Keen-Eyed Curator	Bone Shards
8	Deep-Cavern Bat	Deep-Cavern Bat	Prismatic Ending	Deep-Cavern Bat	Deep-Cavern Bat

Final pools

Human log: Skyclave Apparition; Gitaxian Probe; Windswept Heath; Scrubland; Sacred Foundry; Sanguine Evangelist; Winds of Abandon; Deep-Cavern Bat

Human imitation: Snapcaster Mage; Gitaxian Probe; Demonic Tutor; Scrubland; Archon of Cruelty; Emperor of Bones; Collective Brutality; Deep-Cavern Bat

Safe game-data: Skyclave Apparition; Cosmogrand Zenith; Windswept Heath; Unexpectedly Absent; Sacred Foundry; Sanguine Evangelist; Winds of Abandon; Prismatic Ending

Aggressive game-data: Ouroboroid; Birds of Paradise; Demonic Tutor; Zuran Orb; Titania, Protector of Argoth; Emperor of Bones; Keen-Eyed Curator; Deep-Cavern Bat

MCTS value-conservative: Blood Crypt; Hymn to Tourach; Demonic Tutor; Valki, God of Lies // Tibalt, Cosmic Impostor; Archon of Cruelty; Emperor of Bones; Bone Shards; Deep-Cavern Bat

What happened?

The safe game-data model most closely follows the human's white/fixing lane. It takes Skyclave Apparition, Windswept Heath, Sacred Foundry, Sanguine Evangelist, and Winds of Abandon.

The human-imitation model actually chases more raw power here: Snapcaster Mage, Demonic Tutor, Archon of Cruelty. That is interesting because it is the model optimized for human agreement overall, but on this trace it is less committed to the logged human's fixing-heavy path.

The aggressive game-data model still looks unsafe. Picks like Ouroboroid, Zuran Orb, Titania, and Keen-Eyed Curator are plausible outputs of a model chasing static game-outcome bonuses, but they look contextually dubious.

MCTS is heavily path-dependent. After taking Blood Crypt, it moves into a black/red power lane: Hymn to Tourach, Demonic Tutor, Valki/Tibalt, Archon, Emperor of Bones, Bone Shards. This may be a coherent line, but it is also a reminder that search amplifies early assumptions. If the first pick is off, the whole searched line can drift.

Current takeaways

The base policy is already strong at human-pick imitation.
Prior picks matter a lot; removing the pool cuts top-1 accuracy by about 20 points.
Safe game-data tuning improves game-data preference agreement while mostly preserving human agreement.
Aggressive game-data tuning is useful for analysis, but not safe as a default recommendation policy.
MCTS is promising as an inference-time search/reranking method, especially at 25 simulations, but its disagreements need auditing.
Real depleted-pack traces are much more informative than random pack examples.

The next thing I want is a larger disagreement audit: sample many real draft prefixes, compare human / safe / MCTS / aggressive choices, and manually review the cases where MCTS changes the pick.