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ARXIV:2603.25199 · SPORTS AI · SUBMITTED 31 MAR · 20:30 UTC · FRESHNESS STALE
ARXIV:2603.25199SPORTS AISUBMITTED 31 MAR · 20:30 UTCFRESHNESS STALEPeng Wen · Yuting Wang · Qiurui Wang · arXiv
A new dataset and benchmark for accurately replicating real-world football team tactical behaviors, moving beyond simple reward optimization.
Opportunity summary
Pain A new dataset and benchmark for accurately replicating real-world football team tactical behaviors, moving beyond simple reward optimization.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence unverified
A new dataset and benchmark for accurately replicating real-world football team tactical behaviors, moving beyond simple reward optimization. We introduce Tac SIm, a large-scale dataset and benchmark for Tactical Style Imitation in football.
Current football imitation research primarily aims to opti mize reward-based objectives, such as goals scored or win rate proxies, paying less attention to accurately replicat ing real-world team tactical behaviors. We introduce Tac SIm,…
ScienceToStartup currently rates this 5.0/10 on the public viability pass. By using unified data and metrics from broadcast to simulation, TacSIm estab lishes a rigorous benchmark for measuring and modeling style-aligned tactical imitation task…
Sports AI moved forward this cycle; last verified April 2026. Public score 5.0/10. Production flags indicate code availability.
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A new dataset and benchmark for accurately replicating real-world football team tactical behaviors, moving beyond simple reward optimization.
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Paper Pack
10.48550/arXiv.2603.25199A new dataset and benchmark for accurately replicating real-world football team tactical behaviors, moving beyond simple reward optimization.
Abstract
Current football imitation research primarily aims to opti mize reward-based objectives, such as goals scored or win rate proxies, paying less attention to accurately replicat ing real-world team tactical behaviors. We introduce Tac SIm, a large-scale dataset and benchmark for Tactical Style Imitation in football. TacSIm imitates the acitons of all 11 players in one team in the given broadcast footage of Pre mier League matches under a single broadcast view. Under a offensive or defensive broadcast footage, TacSIm projects the beginning positions and actions of all 22 players from both sides onto a standard pitch coordinate system. Tac SIm offers an explicit style imitation task and evaluation protocols. Tactics style imitation is measured by using spatial occupancy similarity and movement vector similarity in defined time, supporting the evaluation of spatial and tem poral similarities for one team. We run multiple baseline methods in a unified virtual environment to generate full team behaviors, enabling both quantitative and visual as sessment of tactical coordination. By using unified data and metrics from broadcast to simulation, TacSIm estab lishes a rigorous benchmark for measuring and modeling style-aligned tactical imitation task in football.
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Extraction status
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Proof status
unverified0 refs; 0 sources; 33% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
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Dimensions overall score 5.0
PROBLEM
A new dataset and benchmark for accurately replicating real-world football team tactical behaviors, moving beyond simple reward optimization. We introduce Tac SIm, a large-scale dataset and benchmark for Tactical Style Imitation in football.
METHOD
Current football imitation research primarily aims to opti mize reward-based objectives, such as goals scored or win rate proxies, paying less attention to accurately replicat ing real-world team tactical behaviors. We introduce Tac SIm, a large-scale dataset and benchmark for T...
RESULT
ScienceToStartup currently rates this 5.0/10 on the public viability pass. By using unified data and metrics from broadcast to simulation, TacSIm estab lishes a rigorous benchmark for measuring and modeling style-aligned tactical imitation task in football. Code availability is...
WHY NOW
Sports AI moved forward this cycle; last verified April 2026. Public score 5.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A new dataset and benchmark for accurately replicating real-world football team tactical behaviors, moving beyond simple reward optimization. We introduce Tac SIm, a large-scale dataset and benchmark for Tactical Style Imitation in football.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Current football imitation research primarily aims to opti mize reward-based objectives, such as goals scored or win rate proxies, paying less attention to accurately replicat ing real-world team tactical behaviors. We introduce Tac SIm, a large-scale dataset and benchmark for Tactical Style Imitation in football.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. By using unified data and metrics from broadcast to simulation, TacSIm estab lishes a rigorous benchmark for measuring and modeling style-aligned tactical imitation task in football. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Sports AI moved forward this cycle; last verified April 2026. Public score 5.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A new dataset and benchmark for accurately replicating real-world football team tactical behaviors, moving beyond simple reward optimization.
Segment
Sports AI
Adoption evidence
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Commercial read
5.0/10 public viability
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missing
reason
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proof status
unverified
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confidence low
next verification path
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Evidence coverage
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stale
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Build readiness
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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
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Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
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Evidence
0 references, 0 sources, 33% evidence coverage.
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Buyer clarity
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Integration burden
missing
Current read
No public implementation surface observed.
Evidence
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Write integration checklist from prototype path and target workflow.
Capital intensity
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Regulatory load
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Current read
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Evidence
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Classify regulatory flags before commercialization planning.
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Paper authors are not treated as operators without consent.
People
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People
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Regulatory need unclassified.
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ARTIFACTS
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DEFENSIBILITY
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WATCHTOWER
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FORESIGHT
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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TIMELINE
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