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Sports Tech • AI/ML • IoT

AI, Sensors, and Real-Time Coaching for Fast Bowlers

PaceLab is a UK-based sports tech startup focused on elite cricket fast bowlers. They needed to capture bowling motion live, score it with AI, flag fatigue risk, and give coaches instant guidance — all on the field.

IoT Sensors
AI Motion Analysis
Fatigue Risk Alerts
Coach Mobile App
See Measurable Impact ➜ Start a Conversation
Live Session Dashboard
Bowling Session
Release Speed
142 km/h
Arm Angle
32.4°
• Elbow stress trending high
• Fatigue risk: Elevated
• Technique drift vs baseline
COACH REVIEW
SAVE CLIP
Coach Console
Athlete
Bowler #27
Fatigue Risk
High
Speed Trend
12
Injury Warning
Watch Shoulder
Recommended Focus
  • Shorter run-up next 5 balls
  • Shoulder unload drill
  • Cooldown protocol after session
Technique Drift
Headless CMS
Injury Watch
Theecode Contribution
Real-time data ingestion from sensors, AI scoring, mobile UX for athlete & coach.
Challenge

Stream high-speed motion data, score it instantly, warn before injury

Fast bowling is violent on joints. PaceLab needed to capture release speed, arm angle, and workload in real time — then translate that into coaching cues and fatigue warnings on the spot, not in a lab the next day.

The data had to move from radar / IoT → cloud → ML → coach/athlete in seconds. And the UI still had to make sense to a 17-year-old bowler.
  • Real-time capture from Speed Radar / Pocket Radar and on-body sensors.
  • AI/ML to detect technique drift and joint stress load.
  • Fatigue + injury risk surfaced immediately to coach and athlete.
  • All of this delivered in a friendly mobile app, not a spreadsheet.

Before Theecode

• Manual coach notes
• Slow video review
• No objective fatigue warnings
• Hard to compare sessions

After Theecode

• Live IoT stream
• AI technique scoring
• Fatigue & injury risk alerts
• Session-to-session comparison
What We Delivered

From raw motion to “fix your arm slot now.”

IoT Sensor Integration
We wired radar devices and on-body sensors to capture delivery metrics like speed, release angle, and shoulder load — live.
Speed Radar
Release Angle
Stress Load
AI/ML Performance Insights
ML models translated biomechanics into coaching language: drifting arm slot, high elbow stress, elevated fatigue.
Technique Drift
Fatigue Risk
Injury Watch
Coach + Athlete Mobile App
Real-time dashboard for both: athlete sees progress, coach gets alerts and suggested corrections on the spot.
Flutter
Swift/Kotlin
Live Overlay
Secure Cloud & Data
High-speed ingest + secure storage. Metrics and video stay private, but stay available for comparison and rehab.
AWS Infra
Encrypted Data
Scalable Metrics Store
Platform Snapshot

How the PaceLab system actually runs

Ticketech platform architecture diagram: iOS POS, scanner / printer SDK, microservices (Identity, Payments, Analytics, Bulk Close, Revenue & Ticket Dashboard), and Unified Back Office.
Sensors and radar stream delivery data into the platform. Our ML layer scores stress, technique, and fatigue. Coaches and athletes instantly see “what happened and what to fix.”
  • Live IoT intake → cloud ingestion → ML scoring pipeline.
  • Risk flags (“high elbow load”, “fatigue spike”) surfaced in real time.
  • Coach notes & corrective drills attached to the exact delivery, not generic feedback.
Screens & Flows (Illustrative)

Capture. Score. Coach. Prevent injury.

Theecode delivered the on-field workflow: collect sensor data, auto-analyze mechanics, show the risk, and tell the bowler exactly what to adjust. No lab. No laptop. Just phone + tablet.
Live Throw Capture
Speed, release angle, and arm path are recorded per ball. This becomes the athlete’s “motion fingerprint.”
Ticketech iOS POS session mock: open ticket, tip, close, print receipt
Instant AI Feedback
The ML engine highlights stress load and technique drift, and flags fatigue risk before it becomes injury.
Unified back office dashboard mock: revenue book, per-location revenue, tickets and touchpoints
Coach + Athlete Review
Coach and player look at the same dashboard. Suggested drills are attached to that exact delivery.
Fraud & Bulk Close dashboard mock: risk alerts, stale overrides, bulk close status
Impact

Faster improvements, fewer injuries, higher trust

PaceLab can now prove performance gains with hard data — and intervene before a bowler breaks down. This is not “coach’s opinion”; it’s evidence.
+25%
Speed & accuracy gains across pilots
-40%
Reduction in fatigue-related injury incidents
80%+
Coach / athlete adoption in first 3 months
Real-time
Feedback arrives during the session, not days later
Tech Stack & Approach

Wearable data + AI + usable coaching UI

This is what next-gen sports coaching looks like: live telemetry, automated analysis, and actionable guidance where it matters — on the turf.
  • Flutter + Native Swift/Kotlin mobile experience
  • Python / Scikit-learn style ML models for motion & stress load
  • AWS data pipeline for ingestion, scoring, and retention
  • Secure athlete data storage, including video + telemetry
  • Coach dashboard with fatigue / risk alerts
Sample Ops Snapshot
illustrative only
Sessions Logged
1,500+
pilot rollout
Fatigue Alerts
3 / session
actionable flags
Speed Gain
+3 km/h
avg improvement
Adoption
>80%
regular use
  • IoT Capture
  • AI Feedback
  • Coach Console

Build your analytics platform with Theecode

Start with a Dev+AI pod. Go live in days, not months.

  • IoT ingestion patterns
  • AI features & role‑based KPIs
  • Flutter apps with offline sync
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