F1 Live Timing Streamer

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An Apify Actor that connects to the official Formula 1 live timing SignalR feed and streams real-time telemetry, timing data, and derived analytics to Apify's Dataset.

Features

• Real-time SignalR connection to the official F1 timing endpoint with automatic reconnection and exponential backoff
• Three output formats: raw JSON, flat SQL-ready schemas, or ML-ready timeseries
• Derived micro-metrics computed on the fly: pit time breakdowns, tire degradation projections, sector deltas
• Webhook notifications for high-value events: pit entries, flag changes, lead changes, fastest laps, DRS changes
• Safety Car filtering for clean ML training data

Quick Start

Deploy to Apify

1. Push this repository to Apify using the Apify CLI:

   $apify push

2. Configure the input in the Apify Console (see Input Configuration)
3. Start the Actor during a live F1 session

Run Locally

pip install-r requirements.txt
exportAPIFY_TOKEN=your_token_here
python main.py

Input Configuration

Output Formats

Raw (default)

Parsed F1 events as JSON objects. Each object has a topic field indicating its type:

{
"topic":"TimingData",
"driver_id":"1",
"timestamp":"2025-03-16T14:32:01+00:00",
"position":1,
"last_lap_time":"1:31.456",
"sector_0_time":"28.123",
"sector_1_time":"34.567",
"sector_2_time":"28.766",
"gap_to_leader":null,
"in_pit":false,
"lap_number":42
}

Flat (SQL-ready)

Events decomposed into relational rows, each with a _table field for routing to SQL tables:

Tables: laps, sectors, positions, telemetry, track_events, weather, stints, metrics, pit_stops

{
"_table":"laps",
"timestamp":"2025-03-16T14:32:01+00:00",
"driver_id":"1",
"lap_number":42,
"lap_time_ms":91456.0,
"sector_1_ms":28123.0,
"sector_2_ms":34567.0,
"sector_3_ms":28766.0,
"compound":"MEDIUM",
"is_personal_best":false,
"is_overall_fastest":false,
"track_status":"GREEN"
}

Timeseries (ML-ready)

Normalized metric-per-row format optimized for machine learning pipelines:

{
"_table":"timeseries",
"timestamp":"2025-03-16T14:32:01+00:00",
"driver_id":"1",
"metric":"speed",
"value":312.5,
"lap_number":42,
"compound":"MEDIUM",
"track_status":"GREEN",
"is_valid_lap":true
}

Metrics emitted: speed, throttle, brake, gear, drs, sector_1_time, sector_2_time, sector_3_time, lap_time, gap_to_leader

Laps under Safety Car, VSC, or red flags are tagged is_valid_lap: false when exclude_sc_laps is enabled, so ML pipelines can filter anomalous data without manual scrubbing.

Derived Micro-Metrics

When enable_metrics is true, these are computed in real-time and pushed alongside primary data:

Pit Time Breakdown

{
"topic":"Metric",
"metric_type":"pit_time",
"driver_id":"1",
"lap_number":25,
"pit_total_ms":23456.0,
"pit_stationary_ms":2500.0,
"pit_transit_ms":20956.0
}

Tire Cliff Projection

Rolling 5-lap average of lap time degradation over the current stint (green-flag laps only):

{
"topic":"Metric",
"metric_type":"tire_cliff",
"driver_id":"1",
"lap_number":30,
"compound":"SOFT",
"stint_lap_count":12,
"tire_degradation_per_lap_ms":85.3,
"rolling_window":5
}

Sector Delta vs. Personal Best

{
"topic":"Metric",
"metric_type":"sector_delta",
"driver_id":"1",
"sector":2,
"lap_number":42,
"sector_time_ms":34567.0,
"personal_best_ms":34123.0,
"delta_ms":444.0
}

Webhooks

Configure webhook_url and optionally webhook_events to receive HTTP POST notifications for high-value race events.

Supported Event Types

Webhook Payload

{
"event_type":"lead_change",
"timestamp":"2025-03-16T14:45:00+00:00",
"data":{
"new_leader":"4",
"previous_leader":"1",
"lap_number":38
}
}

Webhook delivery is async with a 5-second timeout. Failed deliveries are logged but never block the main data stream.

Architecture

F1 SignalR Feed
 │
 ▼
┌─────────────┐
│ f1client │ SignalR negotiate → connect → subscribe → listen
│ │ Exponential backoff reconnection(max 10 retries)
└──────┬──────┘
 │ on_message callback
 ▼
┌──────────────┐
│ msg_parser │ Decompress .z topics, parse by type, safe field access
└──────┬───────┘
 │ typed event dicts
 ▼
┌──────────────┐ ┌─────────────┐
│ driver_state │────▶│ metrics │ Pit time, tire cliff, sector delta
└──────┬───────┘ └─────────────┘
 │
 ├──▶ schemas.py(flat mode) → SQL-ready rows
 ├──▶ timeseries.py(ts mode) → ML-ready rows
 └──▶ webhooks.py → HTTPPOST on events
 │
 ▼
 Apify Dataset

File Structure