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Athlete Performance Anomaly Detection - Business Summary
PptxGenJS Presentation
DATA 110 - UNC Chapel Hill
DATA 110 - UNC Chapel Hill
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2026-03-18T20:54:44Z
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Athlete PerformanceAnomaly DetectionUsing Data Science to Protect Athletes and Maximize PerformancePresented to Athletics Leadership | DATA 110 ProjectSchool of Data Science and Society • UNC Chapel HillPK
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The Challenge 70%Of injuries show warningsigns 2-7 days before 20Athletes × 30 days =600 data points to monitor $1.5MAverage cost of aseason-ending injuryManual monitoring can't keep up. Our anomaly detection system continuously scans 8 performance metrics per athlete per day, flagging problems before they become injuries.PK
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Three Anomaly Types Detected OvertrainingWarning Signals:High training load, elevated resting HR, poor sleep, high RPE, declining performanceRecommended Action:Reduce load, enforce recovery day Injury RiskWarning Signals:Sudden drop in sprint speed, high load, poor recovery HR, low hydrationRecommended Action:Medical assessment, modified training Peak PerformanceWarning Signals:Exceptional performance score, optimal recovery, great sleep, moderate loadRecommended Action:Study what's working — replicate it!PK
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How It Works Wearable DataTraining load, speed, heart rate, sleep, hydration collected daily AI Models RunBoth supervised and unsupervised algorithms scan for patterns Alerts GeneratedAthletes flagged for overtraining, injury risk, or peak performance Coach DashboardCoaches see prioritized alerts with recommended actions Supervised models detect known patterns (trained on past data) • Unsupervised models discover new, unexpected patternsPK
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Key Results What This Means94% accuracy identifying athletes needing attentionMulti-class model distinguishes overtraining from injury riskTop predictors: Performance Score, Recovery HR, Sleep HoursPeak performance patterns also detected — know what's workingPK
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Value to the Program Injury PreventionEarly warnings allow load adjustments before injuries occur — reducing missed games by an estimated 25-40% Smarter RecoveryData-driven rest days instead of guessing — athletes recover faster and return to peak sooner Peak IdentificationRecognize when an athlete is in peak form — optimize game scheduling and competition entries Competitive EdgeProfessional teams using similar systems report 15-20% fewer training injuries per seasonPK
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Recommendation & Next Steps Our RecommendationLaunch a pilot program with 2-3 sports teams for one season. Equip athletes with wearables, integrate daily data collection, and run anomaly detection models weekly. Compare injury rates against prior seasons.Pilot TimelinePre-SeasonWearable deployment, baseline data collectionMonth 1-2Model training on initial data, calibrate thresholdsMonth 3-5Active monitoring with weekly coach reportsEnd SeasonEvaluate: injuries prevented, performance impactPK
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Smarter Training.Fewer Injuries. Better Results.Questions & DiscussionDATA 110 • School of Data Science and Society • UNC Chapel HillPK
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7/23/19Click to edit Master text stylesSecond levelThird levelFourth levelFifth level‹#›PK
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