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ARXIV:2604.18210 · SPORTS AI · SUBMITTED 21 APR · 04:14 UTC · FRESHNESS STALE
ARXIV:2604.18210SPORTS AISUBMITTED 21 APR · 04:14 UTCFRESHNESS STALESheng Xu · Guiliang Liu · Tarak Kharrat · Yudong Luo · Mohamed Aloulou · Javier López Peña · +9 at arXiv
Generative AI for creating adaptable and scalable football tactics based on game context and expert input.
Opportunity summary
Pain Generative AI for creating adaptable and scalable football tactics based on game context and expert input.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
Generative AI for creating adaptable and scalable football tactics based on game context and expert input. While recent advancements in spatio-temporal data and deep learning have enabled predictive analyses like trajectory forecasting, the development…
Success in association football relies on both individual skill and coordinated tactics. While recent advancements in spatio-temporal data and deep learning have enabled predictive analyses like trajectory forecasting, the development of tactical design remains…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Bridging this gap is essential, as prediction reveals what is likely to occur, whereas tactic generation determines what should occur to achieve strategic objectives.…
Sports AI moved forward this cycle; last verified April 2026. Public score 8.0/10. Production flags indicate code availability.
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Generative AI for creating adaptable and scalable football tactics based on game context and expert input.
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Paper Pack
10.48550/arXiv.2604.18210Generative AI for creating adaptable and scalable football tactics based on game context and expert input.
Abstract
Success in association football relies on both individual skill and coordinated tactics. While recent advancements in spatio-temporal data and deep learning have enabled predictive analyses like trajectory forecasting, the development of tactical design remains limited. Bridging this gap is essential, as prediction reveals what is likely to occur, whereas tactic generation determines what should occur to achieve strategic objectives. In this work, we present TacticGen, a generative model for adaptable and scalable tactic generation. TacticGen formulates tactics as sequences of multi-agent movements and interactions conditioned on the game context. It employs a multi-agent diffusion transformer with agent-wise self-attention and context-aware cross-attention to capture cooperative and competitive dynamics among players and the ball. Trained with over 3.3 million events and 100 million tracking frames from top-tier leagues, TacticGen achieves state-of-the-art precision in predicting player trajectories. Building on it, TacticGen enables adaptable tactic generation tailored to diverse inference-time objectives through classifier guidance mechanism, specified via rules, natural language, or neural models. Its modeling performance is also inherently scalable. A case study with football experts confirms that TacticGen generates realistic, strategically valuable tactics, demonstrating its practical utility for tactical planning in professional football. The project page is available at: https://shengxu.net/TacticGen/.
Source availability
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Proof status
unverified0 refs; 3 sources; 50% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
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Dimensions overall score 8.0
PROBLEM
Generative AI for creating adaptable and scalable football tactics based on game context and expert input. While recent advancements in spatio-temporal data and deep learning have enabled predictive analyses like trajectory forecasting, the development of tactical design remains...
METHOD
Success in association football relies on both individual skill and coordinated tactics. While recent advancements in spatio-temporal data and deep learning have enabled predictive analyses like trajectory forecasting, the development of tactical design remains limited.
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Bridging this gap is essential, as prediction reveals what is likely to occur, whereas tactic generation determines what should occur to achieve strategic objectives. Code availability is flagged in the p...
WHY NOW
Sports AI moved forward this cycle; last verified April 2026. Public score 8.0/10. Production flags indicate code availability.
{"file name": "input.pdf", "number of pages": 23, "author": "Sheng Xu; Guiliang Liu; Tarak Kharrat; Yudong Luo; Mohamed Aloulou; Javier L\u00f3pez Pe\u00f1a; Konstantin Sofeikov; Adam Reid; Paul Roberts; Steven Spencer
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verified
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Generative AI for creating adaptable and scalable football tactics based on game context and expert input.
Segment
Sports AI
Adoption evidence
No public code link in the paper record yet
Commercial read
8.0/10 public viability
Direct
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CITED BY
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2/3 checks · 67%
Build Passport
Build passport pending - Proof Lab budget No verified cost estimate / $7.00 cap
status
missing
reason
passport_row_missing
proof status
unverified
cost/budget
No verified cost estimate
confidence low
next verification path
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Source missing: Build Passport payload.
Experiment plan missing until prototype path is available.
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Validation checklist missing until required assets, cost, and regulatory flags are verified.
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Evidence coverage
OpportunityKernel evidence_receipt
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stale
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Build readiness
BuildPassport EvidenceState
passport absent
stale
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Artifact maturity
GitHub and Hugging Face maturity payloads
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stale
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Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
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Gaps
Next test
Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
Current read
Buyer urgency is not verified from source.
Evidence
0 references, 3 sources, 50% evidence coverage.
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Buyer clarity
missing
Current read
No budget owner is verified for this paper.
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Gaps
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Map target operator, economic buyer, and procurement trigger.
Defensibility
missing
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Defensibility signals are missing.
Evidence
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Gaps
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Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
Gaps
Next test
Write integration checklist from prototype path and target workflow.
Capital intensity
missing
Current read
No observed cost estimate is verified.
Evidence
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Regulatory load
missing
Current read
No regulatory classification is attached.
Evidence
Build Passport ledger does not include regulatory flags.
Gaps
Next test
Classify regulatory flags before commercialization planning.
No named scientific founder assigned.
Paper authors are not treated as operators without consent.
People
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Gaps
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Prototype owner missing.
Build Passport does not name an implementer.
People
No named person assigned.
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Operator workflow not sourced.
No buyer or workflow interview attached.
People
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Regulatory need unclassified.
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Gaps
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ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
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FORESIGHT
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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