SportsEdge – AI-Powered Performance Analysis
- TDCM sp. z o.o.
- Mar 20
- 4 min read
Updated: Jun 2
Client: Confidential Pro-Level Sports Team
Industry: Sports & Athletics
AI Solution: AthleteAI – Player Performance & Injury Risk Analytics
Team: 6 members (AI engineer, data scientist, sports scientist, frontend/backend devs, PM)
The Problem: Pushing Limits Comes at a Cost
In elite sports, fractions of a second matter—and every player’s condition can make or break a season.
Our client, a competitive professional sports team in Europe (whose name is under NDA), came to us with a mission: "We want to use AI to understand our players better—not just when they’re performing well, but when they’re approaching burnout or injury. Can you help us build something that gives us an edge?"
Despite investing in top-tier coaching, medical staff, and wearables, the team struggled with:
Recurring injuries to key players
Inconsistent performance under fatigue
Lack of visibility into hidden stressors (sleep, load, micro-movements)
Difficulty translating raw performance data into actionable insights
They didn’t just want dashboards—they wanted decisions.
The Goal: From Data to Advantage
The vision was clear: a system that could predict injury risks before they materialize, personalize training loads, and enhance game-day strategies based on data.
This required:
Analyzing real-time and historical data
Understanding player-specific baselines
Detecting subtle trends in movement or behavior
Presenting results in an intuitive way to coaches and medical staff
The Kickoff: Discovery Phase (Weeks 1–3)
Our TDCM.ai team kicked off with a 3-week immersion phase. We:
Attended training sessions and match days
Interviewed coaches, trainers, physiotherapists, and players
Audited their existing tech stack (including wearables, GPS trackers, and fitness platforms like Catapult and Polar)
Reviewed anonymized injury records and training logs from the last 3 seasons
We discovered that while the team had a lot of data, it was fragmented—wearables collected one kind of metric, coaching staff had another, and medical notes were siloed in yet another system.
The insight: they needed one intelligence layer to connect it all—and make sense of it, fast.
The Solution: AthleteAI (Weeks 4–18)
System Overview
AthleteAI was built to serve four core functions:
Injury Risk Prediction
Fatigue & Load Monitoring
Movement Pattern Analysis
AI-Driven Strategy Suggestions
Data Integration Layer
Our backend team developed secure connectors to pull in:
Biometric and movement data from GPS trackers
Session logs from training apps
Historical injury reports
In-game performance stats
This unified dataset was anonymized, cleaned, and normalized for AI processing.
Machine Learning Models
A Random Forest Classifier was trained to identify injury risk factors based on variables like sudden deceleration, overtraining patterns, previous injuries, sleep inconsistencies, and hydration status.
A Time-Series LSTM was used to model fatigue curves and predict drops in endurance or reactivity.
A Pose Estimation Module, powered by computer vision (using OpenPose + MediaPipe), helped detect subtle mechanical inefficiencies that coaches couldn’t always spot live.
Visual Dashboard & Alerts
Our frontend developers designed a sleek dashboard for the coaching and medical staff. Key features included:
Real-time risk scores per player
Training load heat maps
Recommendations (e.g., “Consider reducing sprint intensity for Player 7 tomorrow”)
Push alerts for high-risk conditions
Everything was color-coded and mobile-friendly—no need for staff to sift through spreadsheets post-practice.
The Team Behind the Tech
1 AI Engineer – designed the predictive injury models and oversaw ML logic
1 Data Scientist – worked on time-series fatigue modeling and anomaly detection
1 Sports Scientist – translated athletic benchmarks into AI features
2 Developers – built the data pipeline, integrations, and dashboard
1 Project Manager – coordinated between tech and the coaching staff
We collaborated weekly with the client’s lead physio and head coach, ensuring feedback loops were fast and field-driven.
Challenges We Faced
1. Dirty, Inconsistent Data
Players changed devices, skipped logs, or entered subjective wellness scores inconsistently. We had to build robust preprocessing scripts and augment the data with synthetic variations to get enough training volume.
2. Skepticism from Staff
Some coaching staff were initially wary of “robots replacing instinct.” We addressed this by involving them in training the models and demonstrating how AI confirmed or supported their gut feelings—rather than replacing them.
3. Dynamic Roster
Player turnover meant the models had to be adaptable to new baselines quickly. We implemented a rolling calibration system that re-learned player behavior within the first 10 days of joining.
Game Time: Deployment & Testing (Weeks 19–24)
AthleteAI was rolled out in the latter half of the competitive season. We piloted it across 18 players for 6 weeks, during both practices and matches.
The results were striking:
✅ Injury rates reduced by 32%Fewer soft-tissue injuries and muscle strains—especially in high-volume positions.
✅ Player endurance improved by 24%Personalized load balancing led to better performance deep into matches.
✅ 16% more winsThe team’s win ratio improved as more players stayed match-fit, and AI-recommended formations boosted efficiency under fatigue.
The coaching staff quickly became fans, citing new confidence in managing player availability and training intensity. The players, too, began asking to see their “AI scores” after sessions—creating a feedback culture around optimization, not punishment.
Client Testimonial
“TDCM.ai helped us connect dots we didn’t even know were related. AthleteAI is now as important to us as any assistant coach or physio.”— Anonymous Head Coach
Next Steps: Taking AthleteAI Further
Following the successful pilot, the client is now working with us to expand AthleteAI in several exciting ways:
Cross-team benchmarking – using anonymized data to compare across the league
Mental fatigue tracking – combining HRV, sleep, and mood logs for cognitive strain modeling
Youth Academy version – early intervention for injury prevention in younger players
We're also exploring integration with wearables that can detect hydration and glucose levels—further expanding what AthleteAI can predict.
Conclusion: AI Is the New Assistant Coach
AthleteAI wasn’t just a dashboard—it was a mindset shift. It showed what’s possible when deep domain expertise meets cutting-edge technology. By reducing injuries, boosting endurance, and helping win more games, our AI didn’t just crunch numbers. It made the team smarter, safer, and more strategic.
At TDCM.ai, we don’t believe in one-size-fits-all AI. We build tailored, intelligent systems that plug directly into real-world workflows—and AthleteAI is proof that the future of sports performance is data-driven.
