Forecasting injuries in sport
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Transcript of Forecasting injuries in sport
E’ possibile prevedere gli infortuni dai
dati di allenamento?
24,360 Giorni di assenza
16.23% of season absence
188,058,072 €
Alto costo
“[…] any illness related to training load are commonly viewed as preventable”
Gabbett, 2016
In letteratura come interpretano il problema?Analisi monodimensionaliGabbett 2016 and Rogalski B et al. 2013
Il problema di predizione degli infortuni
lo interpretiamo comeclassification problem
FeaturesFeatures di allenamento (GPS)
• Total Distance • High Speed Running (>19.8 km/h)• Metabolic Distance (>20W/kg)
• High Metabolic Load Distance (>25.5 W/Kg) • High Metabolic Load Distance Per Minute • Explosive Distance (>25 W/kg <19.8 Km/h)
• Accelerations >2m/s2
• Accelerations >3m/s2 • Decelerations >2m/s2 • Decelerations >3m/s2
• Dynamic Stress Load (>2g) • Fatigue Index (Dynamic Stress Load/Speed Intensity)
Features dei calciatori• Age• Height• Weight• Role
• Previous injuries
• Injury
Numero di infortuni che un giocatore ha avuto fino a quel momento.
0 = no-infortunio1 = infortunato
FeaturesFeatures di allenamento (GPS)
• Total Distance • High Speed Running (>19.8 km/h)• Metabolic Distance (>20W/kg)
• High Metabolic Load Distance (>25.5 W/Kg) • High Metabolic Load Distance Per Minute • Explosive Distance (>25 W/kg <19.8 Km/h)
• Accelerations >2m/s2
• Accelerations >3m/s2 • Decelerations >2m/s2 • Decelerations >3m/s2
• Dynamic Stress Load (>2g) • Fatigue Index (Dynamic Stress Load/Speed Intensity)
Features dei calciatori• Age• Height• Weight• Role
• Previous injuries
• Injury
Numero di infortuni che un giocatore ha avuto fino a quel momento.
0 = no-infortunio1 = infortunato
Moving average
Best window: 6 sessions
Decision Tree
Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5 Scenario 6 Scenario 7
Previous injuries ≤ 1.36 ≤ 1.36 ≤ 1.08 ≥ 2.05 ≥ 1.08 ≤ 1.51 ≤ 0.40
Acceleration above 2 m·s-2 ≤ 53.96 ≤ 57.93 ≤ 49.20 ≤ 57.96 ≤ 78.42 ≤ 77.16 ---
HML per min --- ≥ 7.53 ≤ 6.80 --- --- --- ---
Acceleration above 3 m·s-2 --- --- --- ≥ 16.67 --- --- ---
Probability of injury 80% 50% 33% 33% 12% 9% 6%
Number of injury observed 3 1 1 2 1 2 11
Number of injury predicted 1 1 1 1 0 1 8
Injury scenarios
Potentially avoided injuries ≥ 43%
Take home message•Data mining è utile per la predizione degli infortuni• In questo dataset la variabile Previous injuries
influenza gli infortuni successivi
• In uno scenario teorico noi otteniamo F1-score = 0.78• In uno scenario reale noi otteniamo un massimo di
F1-score = 0.40
