sports video search6 min read

How sports agencies can automate highlight discovery and sponsor reporting with VideoVector

Why sports agencies struggle with video at scale

Sports agencies sit on a uniquely valuable media archive: full games, training sessions, interviews, sponsor activations, behind-the-scenes footage, and broadcast feeds. The operational problem is that the archive is "available" but not usable.

When the same athlete appears across hundreds of hours of content, finding the right clip becomes a manual scavenger hunt. That slows down:

Sponsorship deliverables (proof of placement, highlight reels, recap decks)
Athlete marketing packages (career highlights, role-specific examples, press moments)
Rights and licensing workflows (what can be shared, where, and with which restrictions)
Rapid-response storytelling (trade rumors, playoffs, injuries, record-breaking moments)

The core issue is not storage. It is the lack of time-coded, structured metadata that matches how agencies actually work.

What "searchable sports footage" actually requires

Most teams start with filenames, folder structures, transcripts, and a small set of manual tags. That is rarely enough for sports workflows, where the value lives in moments:

The exact 12 seconds where an athlete reacts to a call
A sponsor board visible during a celebration sequence
A sequence of defensive transitions for a scouting cut
A specific camera angle during a replay-worthy play

To make these moments retrievable, you need:

Time-based segments (so every result points to a precise timestamp)
A schema that reflects your operating language (athlete, play type, sponsor, crowd reaction, camera context)
Multimodal retrieval (search by meaning, not only by exact strings)

VideoVector is designed around these requirements: it converts raw media into structured, timestamped outputs that support search, filtering, SQL-style analysis, and downstream automation.

This tutorial shows how a sports agency can build a repeatable workflow that:

Imports full-event footage into an index
Extracts a strict JSON schema per segment
Supports moment search (text, multimodal, and filters)
Generates sponsor and highlight reporting via SQL

Along the way, you will use:

Sports video intelligence for highlights and live event workflows
Time-based video metadata for searchable media assets
Schema-aware video metadata extraction with JSON schema outputs
Multimodal media search and video vector retrieval

Step 1: Create an index strategy that matches agency workflows

A sports agency typically needs to search across multiple "cuts" of the same event:

Broadcast program feed
Clean feed
In-venue camera angles
Social clips
Training footage

A practical approach is to create indexes by:

Season + league (for cross-team searches)
Athlete (for fast packaging)
Client + campaign (for sponsor deliverables)

Once your index strategy is decided, import footage using the Python SDK.

Python example: create an import job (from the official guide)

from videovector import VideoVector

client = VideoVector(api_key="sk_live_...")

job = client.import_jobs.create(
    connector_id="conn_archive",
    index_id="idx_sports_2026",
    source_prefix="incoming/2026/season/",
    file_pattern="*.mp4",
    recursive=True,
)

print(job.id)

For more details on connector intake settings and recurring imports, see Create import jobs and connector intake settings.

Step 2: Define a sports moment schema (the "contract" your agency can trust)

Instead of relying on generic tags, define a schema that makes your downstream tasks trivial.

Here is an example segment-level schema pattern you can adapt:

{
  "type": "object",
  "properties": {
    "scene": {
      "type": "object",
      "properties": {
        "athletes": {"type": "array", "items": {"type": "string"}},
        "teams": {"type": "array", "items": {"type": "string"}},
        "sport": {"type": "string"},
        "play_type": {"type": "string"},
        "outcome": {"type": "string"},
        "camera_context": {"type": "string"},
        "crowd_reaction": {"type": "string"},
        "sponsor_signals": {
          "type": "array",
          "items": {
            "type": "object",
            "properties": {
              "brand": {"type": "string"},
              "signal_type": {"type": "string"},
              "evidence": {"type": "string"}
            },
            "required": ["brand", "signal_type"]
          }
        },
        "highlight_value": {"type": "integer", "minimum": 1, "maximum": 5},
        "notes": {"type": "string"}
      },
      "required": ["sport", "play_type", "highlight_value"]
    }
  },
  "required": ["scene"]
}

Important: treat this schema as a versioned contract. When your agency changes the schema, you can re-run extraction on selected footage and keep downstream dashboards consistent.

Step 3: Validate the schema before you lock it into production

Use the schema test endpoint to validate your JSON schema structure before saving a prompt:

curl -X POST /api/v2/prompts/test-schema \
  -H "X-API-Key: sk_live_..." \
  -H "Content-Type: application/json" \
  -d '{
    "json_schema": {"type": "object", "properties": {"headline": {"type": "string"}}},
    "sample_data": {"headline": "Example"}
  }'

Then create and manage prompts via the Prompts API for schema outputs.

Step 4: Execute the extraction run across your sports index

Once the prompt is created (for example, prompt_sports_scene_extract), execute it against the index.

API example: execute a prompt run

curl -X POST /api/v2/prompt-runs/execute \
  -H "X-API-Key: sk_live_..." \
  -H "Content-Type: application/json" \
  -H "Idempotency-Key: run-sports-2026-06-23" \
  -d '{
    "prompt_id": "prompt_sports_scene_extract",
    "target": {
      "type": "index",
      "index_id": "idx_sports_2026"
    },
    "video_segmentation_type": "smart",
    "audio_segmentation_type": "content_aware",
    "processing_model": "gemini-2.5-flash"
  }'

When you need to poll and retrieve segment-level results, use the Prompt runs API.

Step 5: Retrieve results for a specific asset (for review tools and UI overlays)

A common agency requirement is a "review surface": a player card or campaign dashboard where users can jump to the exact timestamp.

API example: retrieve run results for one video

curl "/api/v2/prompt-runs/run_123/results?video_id=vid_456&limit=50" \
  -H "X-API-Key: sk_live_..."

This pattern lets you build:

Clip review queues
Athlete timelines
Sponsor evidence lists
Editing handoff packages

Step 6: Search for moments (text, multimodal, and strict filters)

Once the run outputs are available, you have multiple retrieval modes depending on the workflow.

Text search for "what happened"

Use text search when a producer or account manager starts with meaning:

"fast break leading to a dunk"
"coach argument with referee"
"sideline celebration after overtime win"

See Search and SQL search for the relevant endpoints.

Multimodal search when the reference is visual

If your agency has a reference image (a specific jersey, a sponsor board, a venue setup, a camera composition), multimodal search can combine a text intent and an image example.

curl -X POST /api/v2/indexes/idx_sports_2026/multimodal-search \
  -H "X-API-Key: sk_live_..." \
  -H "Content-Type: application/json" \
  -d '{
    "text_query": "celebration at the sideline with sponsor board visible",
    "image_data": "<base64-image>",
    "text_weight": 0.7,
    "image_weight": 0.3,
    "top_k": 20,
    "run_ids": ["run_123"]
  }'

Filter search is useful when you want deterministic constraints over extracted fields.

curl -X POST /api/v2/search/filter/idx_sports_2026 \
  -H "X-API-Key: sk_live_..." \
  -H "Content-Type: application/json" \
  -d '{
    "run_ids": ["run_123"],
    "conditions": [
      {
        "field": "scene.highlight_value",
        "operator": ">=",
        "value": 4,
        "type": "number"
      },
      {
        "field": "scene.sponsor_signals[].brand",
        "operator": "equals",
        "value": "Acme Sportswear",
        "type": "string"
      }
    ],
    "page_size": 50
  }'

When agencies move from "find a clip" to "prove exposure" and "summarize activity," they typically need analysis over many segments.

VideoVector supports analyst-style SQL over run-backed tables, plus an instruction-to-SQL generator.

API example: generate a draft SQL query from an instruction

curl -X POST /api/v2/search/sql/idx_sports_2026/generate \
  -H "X-API-Key: sk_live_..." \
  -H "Content-Type: application/json" \
  -d '{
    "instruction": "Show the top 50 segments with highlight_value >= 4 where sponsor_signals includes Acme Sportswear. Include timestamps and athlete names.",
    "run_ids": ["run_123"]
  }'

This pattern is a practical foundation for:

Sponsor recap tables
"Best moments" lists per athlete
Campaign reporting by event, venue, or broadcast feed

Where to take this next (agency-grade workflows)

Once the core pipeline works, most agencies expand in one of three directions:

Packaging automation: turn retrieved segments into clip queues, recap drafts, and short-form candidates using content repackaging and highlights.
RAG for internal research and rapid briefing: enable operators to ask multi-turn questions and get grounded, timestamped evidence using Video RAG and agentic MediaRAG workflows.
Retrieval substrate upgrades: strengthen semantic and similarity workflows using video-to-vector embeddings for multimodal media libraries.

Why this approach is aligned with current highlight automation research

Academic and industry research consistently shows that highlight automation is not only a "single model problem." It is a workflow problem: segmentation, evidence grounding, metadata structure, and retrieval quality all matter.

If you want deeper reading on the challenges of automated highlights and sports video understanding, these are useful starting points:

AI-Based Sports Highlight Generation for Social Media
SPNet: A deep network for broadcast sports video highlight generation
Fully Automatic Camera for Personalized Highlight Generation in Sporting Events

Getting started

If you want to prototype this workflow quickly, start with:

Access setup via Auth and API keys
Application integration via the Python SDK for VideoVector workflows

Then choose one high-value outcome (for example: sponsor recap reporting for a single athlete across one season), validate the schema, and scale from there.