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Case Study • Sport Technology • Artificial Intelligence

Building a Scalable AI Platform for Real-Time Sports Analytics

Turning a single smartphone into a real-time sports intelligence platform.

Computer Vision Multimodal AI Mobile Platforms Cloud Infrastructure
client: PocketBlue Jul 2026
Pocket_blue_cover
Single
Smartphone camera
for live game analysis
03
Integrated applications
working as one platform
Real-time
Player tracking &
game-state analysis
Distributed
Cloud & Edge, AI architecture
Overview
Pocket_blue_overview

Turning Live Games into Real-Time Insights

PocketBlue is a sports technology platform that uses AI and computer vision to analyze live baseball games from a single smartphone. By combining multiple machine learning models running directly on the device, the platform detects players, understands game context, tracks actions, and delivers real-time scoring and analytics without requiring specialized hardware or complex camera systems.

AccelOne partnered with PocketBlue to architect and build the platform's end-to-end AI ecosystem, developing edge AI capabilities, cloud infrastructure, and a suite of integrated applications for game capture, remote operations,
and fan engagement.

The result is a scalable, real-time sports analytics platform that transforms live video into actionable game intelligence, delivering accurate updates to fans while continuously improving through AI and user feedback.

The challenge

Real-Time Intelligence without Complex Infrastructure

PocketBlue set out to solve one of the hardest problems in sports technology:
understanding a live baseball game in real time, using nothing but video from a single smartphone.

The scope went well beyond player tracking. The platform needed to identify players, read game context, detect actions, infer outcomes, maintain scoring accuracy, and deliver live updates to fans. All of it had to run on a mobile device positioned behind home plate, with no heavy infrastructure to lean on. The intelligence had to live on the device itself.

That meant bringing together multiple specialized AI systems into a single platform capable of performing reliably, in real time, under real-world conditions.

The Challenge

The Hard Problems

01

Problem title

Problem description.

02

Problem title

Problem description.

03

Problem title

Problem description.

Our Solution

A connected AI Ecosystem

AccelOne architected and built a distributed AI ecosystem made up of three tightly integrated applications, each handling a distinct layer of the platform.

Recorder App

RESULT 01

The Recorder App captures live game video and runs multiple computer vision models directly on the device. It detects players, formations, movements, and gameplay signals in real time, then sends enriched game-state data to the cloud instead of raw video.

Capturing VideoCamera

Controller App

RESULT 02

The Controller App gives operators remote visibility and control over recorder devices deployed across different locations. It handles calibration, monitoring, scoring review, and exception management, and provides a human oversight layer for edge cases and model validation.

Remote Visibility RadioButton

Fan App

RESULT 03

The Fan App delivers live updates and analytics while allowing spectators to review events and flag potential scoring errors. Their feedback helps improve the platform’s accuracy with every game.

Live Analytics UsersThree
Highlights

Engineering Highlights

Real-Time AI on Mobile

The team trained and deployed multiple proprietary computer vision models that run simultaneously on a mobile device without compromising real-time performance. Together, they detect and identify players, estimate poses, analyze movement, classify formations, and recognize game states.

Multi-Model Game Analysis

Rather than relying on a single model, the system combines outputs from specialized models and heuristic engines to understand each play. The enriched data is then sent to the cloud, where multimodal AI analyzes visual, spatial, temporal, and contextual signals to determine the outcome.

Intelligence Built for Scale

The architecture distributes intelligence across edge devices, cloud infrastructure, and human validation workflows. This approach balances accuracy, speed, and scalability under real-world game conditions.
The Outcome

Results & Impact

Turning live video into accurate, actionable game intelligence.

 

01 Delivered

Delivered a complete multi-application sports intelligence platform

02 Realtime

Enabled real-time player detection, tracking, and game-state analysis from a single smartphone camera

03 Automated

Automated significant portions of scorekeeping and event recognition workflows

04 Cloud-1

Established a scalable cloud architecture supporting real-time AI inference

05 System

Created a feedback loop between AI systems, operators, and fans to improve accuracy over time

06 Build

Built a foundation for large-scale deployment across amateur and competitive sports environments

Output

Strategic Value

The platform was designed with longevity in mind. The combination of proprietary computer vision models, multimodal AI, distributed inference architecture, and a growing gameplay dataset represents a significant and compounding intellectual property asset.

Architecture is not limited to baseball. It can be extended to other sports and real-time event analysis applications, giving PocketBlue a strong foundation for expansion well beyond the original use case.

For AccelOne, this engagement reflects the kind of work we do best: bringing together edge computing, cloud AI, computer vision, mobile platforms, and human feedback into a system that works as one.
AccelOne has been transparent and collaborative. I see progress in real time, and the team is consistently open and responsive. It feels like a true partnership.

Mike C., Founder, PocketBlue

Impact

Business Impact

PocketBlue came in with a bold idea: take something that has always required trained human scorekeepers and turn it into an AI-driven platform. That shift has real
consequences for how leagues, teams, athletes, and fans engage in the game.

The operational benefits are real:

Lower overhead, more consistent scoring, and advanced analytics now accessible to leagues and audiences that previously had none.

For the Fans:

But the more interesting shift is what it does for fans. They get live game intelligence and become active participants in keeping the platform accurate.

Frequently asked questions

What is PocketBlue?
PockedBlue is an AI-powered sports analytics platform that uses a single smartphone camera to analyze baseball games in real time, automatically tracking players, recognizing game events, and generating live insights.
How does PocketBlue analyze a baseball game using only a smartphone?
The platform runs multiple computer vision models directly on the mobile device to detect players, track movements, identify formations, and understand game context. AI systems then combine this information to determine the outcome of each play.
What role did AccelOne play in the project?
AccelOne designed and developed the platform's AI architecture, computer vision systems, mobile applications, cloud infrastructure, and real-time analytics capabilities, creating a complete sports intelligence ecosystem.
What technologies power PocketBlue?
The platform combines computer vision, multimodal AI, edge computing, mobile development, cloud infrastructure, player tracking, and game-state analysis to deliver real-time sports intelligence.
How does PocketBlue improve scorekeeping and game analytics?
By automating player tracking, event recognition, and game-state analysis, PocketBlue reduces manual scorekeeping effort, improves consistency, and provides real-time insights for teams, leagues, and fans.
Can this technology be used beyond baseball?
Yes. The architecture was designed to be adaptable, allowing the underlying AI, computer vision, and real-time analytics capabilities to be extended to other sports and event-based applications.
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