Match-State Fatigue Analysis Copilot

Detects match states automatically and links athlete fatigue metrics to the relevant video and performance analysis workflows for sports analysis staff.

The Problem

“Match-State Fatigue Analysis Copilot for Automated Sports Video and Athlete Metric Linking”

Organizations face these key challenges:

1

Analysts spend excessive time manually identifying and tagging match states

2

Video, event, and athlete fatigue data live in disconnected systems

3

Manual timestamp alignment between footage and sensor data is error-prone

4

Definitions of match states vary across analysts and coaching staff

Impact When Solved

Reduce manual match-state tagging time by 50-90% depending on data availabilityAccelerate post-match analysis package creation from hours to minutesImprove consistency of state labeling across analysts and matchesLink fatigue spikes to tactical contexts automatically for better coaching decisions

The Shift

Before AI~85% Manual

Human Does

  • •Review match video and manually tag transitions, set pieces, pressing phases, possession sequences, and stoppages
  • •Export and align video timestamps with event logs, GPS, heart-rate, IMU, and performance data across separate tools
  • •Interpret fatigue spikes in context and assemble clips, notes, and post-match analysis packages for coaches and sports scientists
  • •Reconcile inconsistent match-state definitions and retag unclear phases across analysts and staff

Automation

    With AI~75% Automated

    Human Does

    • •Approve or correct AI-detected match states and resolve ambiguous phases of play
    • •Set match-state definitions, fatigue thresholds, and review priorities for coaches and sports science needs
    • •Investigate flagged fatigue-context exceptions and decide whether intervention or deeper analysis is needed

    AI Handles

    • •Detect match states automatically from event, tracking, and video signals and align them to synchronized timelines
    • •Link athlete fatigue metrics to each match-state window and surface notable spikes, repeated patterns, and risk conditions
    • •Generate clip queues, searchable summaries, exports, and post-match review packages for analyst workflows
    • •Monitor live or post-match streams, prioritize review items, and trigger alerts for high-load tactical contexts requiring attention

    Operating Intelligence

    How it works

    AI surfaces what is hidden in the data.

    Humans do the substantive investigation.

    Closed cases sharpen future detection.

    Confidence89%
    ArchetypeDetect & Investigate
    Shape6-step funnel
    Human gates1
    Autonomy
    67%AI controls 4 of 6 steps

    Who is in control at each step

    Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.

    Loop shapefunnel

    Step 1

    Scan

    Step 2

    Detect

    Step 3

    Assemble Evidence

    Step 4

    Investigate

    Step 5

    Act

    Step 6

    Feedback

    AI lead

    Autonomous execution

    1AI
    2AI
    3AI
    5AI
    gate

    Human lead

    Approval, override, feedback

    4Human
    6↺ Loop
    AI-led step
    Human-controlled step
    Feedback loop
    TL;DR

    AI scans and assembles evidence autonomously. Humans do the substantive investigation. Closed cases improve future scanning.

    The Loop

    6 steps

    1 operating angles mapped

    Operational Depth

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