How to use data and trees to measure injury risk in sport Presented at second Data Beers Tuscany by Alessio Rossi

1. ROSSI ALESSIO
alessio.rossi@unimi.it
Phone +39 3472339050
PREVEDERE GLI INFORTUNI
SPORTIVI CON I DATI

2. E’ possibile prevedere
gli infortuni dai dati di
allenamento?

3. 24,360
Giorni di assenza
16.23%
of season absence
188,058,072 €
Alto costo

4. “[…] any illness related to training load are
commonly viewed as preventable”
Gabbett, 2016
In letteratura come interpretano il problema?
Analisi monodimensionali
Gabbett 2016 and Rogalski B et al. 2013

5. Il problema di
predizione degli infortuni
lo interpretiamo come
classification problem

6. Features
Features 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-infortunio
1 = infortunato

7. Features
Features 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-infortunio
1 = infortunato

8. Moving average

9. Best window:
6 sessions

10. Decision Tree

11. 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

12. Potentially avoided injuries ≥ 43%

13. 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

14. ROSSI ALESSIO
alessio.rossi@unimi.it
Phone +39 3472339050
GRAZIE DELL’ATTENZIONE