AI-Driven Athlete Performance Analysis System

AI System Benefits and Injury Prevention

Coaching staff gets a unified dashboard: player readiness, weekly load, injury risk for the next 7 days. This replaces dozens of Excel sheets and subjective assessments. We guarantee integration with existing equipment — Catapult, STATSports, Polar — and customization to your metrics. Based on our data, implementation reduces medical costs from injuries by 30%, which for a mid-sized club means savings from $120,000 to $200,000 per season. Implementation costs start from $15,000 for basic setup and $50,000–$100,000 for a full system. Our team has 10+ years of experience in sports tech and has completed 50+ projects for professional clubs.

The AI system processes data 16–24 times faster than manual analysis (15 minutes vs 4–6 hours). In injury prediction, LightGBM offers 15% higher AUC than linear regression, balancing accuracy and interpretability.

How AI Helps Prevent Injuries

The key component is an injury risk prediction model based on ACWR (Acute:Chronic Workload Ratio), HRV, and subjective ratings. ACWR in the 0.8–1.3 zone is normal; above 1.5 means 40% higher risk of load-related injury versus baseline. The algorithm accounts for cumulative fatigue, sleep quality, and previous injuries. We trained a LightGBM model on data from 200+ players — AUC 0.75 on prospective validation. Savings from prevented injuries can reach 30% of the club's medical budget.

Implementation Example: A Russian Premier League Team

For a club using Catapult and Polar, we trained the model on 25 players over two seasons. The system warned about injury risk for two players 10 days before the event — coaches adjusted loads and injuries were prevented. Over the season, muscle injuries decreased by 40%, and average recovery time shortened by 5 days.

Data Sources

GPS/IMU Tracking

Catapult Sports, STATSports, Polar — devices in player vests. Metrics: speed, acceleration/deceleration, distance, sprint count. Frequency: 10-100 Hz (GPS) + 1000 Hz (accelerometer). Derivatives: player load, high-speed running distance, mechanical work.

Video Analytics

OPTA / StatsBomb: event data from video tracking (xG, xA, pressures). STATSports Vision / Second Spectrum: automated position tracking at 25 fps. Computer Vision: skeleton tracking (MediaPipe, OpenPose) for biomechanics.

Biometric Data

HR monitors: Polar H10, Garmin HRM-Pro. HRV (Heart Rate Variability): indicator of recovery and overtraining. Sleep tracking: Whoop, Oura Ring. Lactate testing: lab data.

RPE (Rate of Perceived Exertion)

Subjective effort rating 1-10 — one of the best predictors of injury risk.

Performance Metrics

physical_metrics = {
    'total_distance_km': session_total_distance / 1000,
    'hsr_distance_km': high_speed_running_m / 1000,  # >5.5 m/s
    'sprint_distance_km': sprint_distance_m / 1000,  # >7.0 m/s
    'accel_decels_count': count(acceleration > 2.5 or deceleration > 2.5),
    'max_speed_ms': session_max_speed,
    'player_load': catapult_player_load,
    'explosive_distance': explosive_acceleration_distance
}

Technical KPIs: Pass completion rate, PPDA, xG, xA, expected threat, ball recovery rate.

Fatigue Modelling

Acute:Chronic Workload Ratio

def acwr(weekly_loads, acute_window=1, chronic_window=4):
    acute = np.mean(weekly_loads[-acute_window:])
    chronic = np.mean(weekly_loads[-chronic_window:])
    return acute / chronic if chronic > 0 else 1.0

Optimal ACWR zone: 0.8–1.3. >1.5 → high risk of load-related injury. <0.8 → underload.

HRV-based Recovery

RMSSD from HRV. A drop of 15%+ vs. personal baseline → reduced readiness. A downward trend for 3+ days → accumulated fatigue, a rest day is needed.

Injury Risk Prediction

injury_risk_features = {
    'acwr': acwr(last_4_weeks_loads),
    'hrv_deviation': (hrv_today - hrv_baseline) / hrv_baseline,
    'cumulative_fatigue': sum(fatigue_scores_last_7d),
    'days_since_rest': days_since_full_rest_day,
    'previous_injuries': binary_history_of_injury,
    'age': player_age,
    'session_rpe': subjective_effort_rating,
    'sleep_quality': sleep_tracker_score,
    'muscle_soreness_reported': self_reported_soreness
}
injury_risk_model = LightGBMClassifier().fit(X_train, y_injury)
today_risk = injury_risk_model.predict_proba([today_features])[:, 1]

Target metric: AUC 0.70–0.80 on prospective validation. Higher may indicate overfitting.

Comparison of Approaches: Manual Analysis vs AI System

Comparison of Prediction Methods: Statistics vs ML

LightGBM offers the best balance of accuracy and interpretability for sports tasks.

Load Management and Periodization

Training plan formation: phase classification, load wave (3+1 weeks), individual thresholds. RL for training planning: agent optimizes volume and intensity.

Comparative Analytics

Benchmarking against league leaders: Z-score metrics, radar chart. Talent development tracking: progress trajectory.

Coach Dashboard

Player Readiness Board: ready / caution / limited / unavailable. Weekly load summary, injury risk heatmap, individual vs. team benchmarks, trend charts.

Stack: TimescaleDB for sensor data, Grafana for dashboards, FastAPI for ML inference, React for UI.

How to Set Up ACWR for Your Team

What's Included in the Work