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Sports Anomaly Detection - Technical Deep Dive
PptxGenJS Presentation
DATA 110 - UNC Chapel Hill
DATA 110 - UNC Chapel Hill
1
2026-03-18T20:56:28Z
2026-03-18T20:56:28Z
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Athlete Anomaly DetectionTechnical Deep DiveModels, Feature Engineering, and Performance EvaluationFor: Sports Science / Data Team | DATA 110 ProjectSchool of Data Science and Society • UNC Chapel HillPK
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Dataset OverviewFeature Set (8 Performance Metrics) Feature Unit Normal Range Training Load AU (0-100+) 30 – 95 Sprint Speed km/h 22 – 34 Resting Heart Rate bpm 45 – 72 Recovery Heart Rate bpm 88 – 130 Sleep Hours hours 6 – 9 Hydration Level % 48 – 62 Perceived Exertion RPE 1-10 3 – 8 Performance Score 0-100 60 – 90 Class Distribution n = 600 (20 athletes × 30 days) | Longitudinal panel data | 10% anomaly rate | 70/30 stratified splitPK
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Methodology: Three Approaches Random ForestSupervisedn_estimators=100max_depth=10class_weight='balanced'How it works:Ensemble of decision trees. Binary (Normal vs Anomaly) AND multi-class (specific type) models trained.+ Highest accuracy; identifies anomaly type− Requires labeled historical data Isolation ForestUnsupervisedn_estimators=100contamination=0.10How it works:Isolates anomalies via random partitioning. Shorter path length = more anomalous.+ No labels needed; fast− Can't classify anomaly type LOFUnsupervisedn_neighbors=20contamination=0.10How it works:Compares local density of each point to neighbors. Low relative density = outlier.+ Detects local context outliers− Slower on large data; parameter-sensitivePK
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Model Evaluation Metrics Detection by Anomaly Type RF IF LOF Overtraining 95% 80% 70% Injury Risk 90% 75% 65% Peak Perf. 85% 70% 60% RF excels at overtraining detection (highest feature separation). Peak performance is hardest to detect — positive outliers blend with normal high performers.Note: Multi-class RF also trained — can predict WHICH anomaly type, enabling specific coaching interventionsPK
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Feature Importance Analysis InterpretationTop 3 predictors account for 57% of model importance.Performance Score is the strongest signal — drops sharply in overtraining/injury, spikes in peak performance.Recovery HR and Sleep Hours are key wellness indicators — elevated recovery HR + poor sleep strongly predict overtraining.Resting HR ranks lowest individually but contributes to interaction effects with training load.PK
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