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Table 2 Training load features in the highlighted papers

From: Machine Learning for Understanding and Predicting Injuries in Football

External load

[33]

[35]

[36, 37] *

[39]

[41]

[42]

[43]

[44]

Exposure

      

X

 

Jumps

 

X

    

X

 

Distance

X

X

   

X

 

X

Accelerations and decelerations

X

X

   

X

 

X

DSL (Total weighted impacts above 2 g)

X

       

Duration

 

X

   

X

  

Player load

 

X

   

X

 

X

Speed and velocity

 

X

   

X

 

X

Meterage per minute

 

X

      

Total efforts

 

X

      

High inertial movement analysis

 

X

      

Average metabolic power

       

X

Dynamic stress load

       

X

Impacts

       

X

Energy expenditure

       

X

Step Balance

       

X

Dribbling

   

X

    

Sprint

   

X

    

Jumping, moving and balancing

   

X

    

Internal loadphysical data

Body mass index

X

 

X

X

X

X

X

 

Fat percentage

   

X

  

X

 

Step yo-yo test

   

X

  

X

 

Heart rate

 

X

      

Ratings of perceived exertion (RPE)

     

X

  

Internal loadpsychological data

Sleep quality

  

X

  

X

  

Physical exhaustion

  

X

     

Reduced sense of exhaustion

  

X

     

Sport devaluation

  

X

     

Fatigue, shape, pain, pleasure, worry, satisfaction

     

X

  

Personal information

Height and weight

  

X

X

X

X

X

 

Age

X

 

X

X

X

X

X

X

Role of the player (Position)/field position

X

 

X

  

X

X

X

Previous injury

X

 

X

  

X

X

 

Minutes played in previous games

X

       

Number of games played

        

before each training session

X

       

Dominant leg

X

 

X

     

Current level of play

  

X

     

Injury details

       

X

Season stage

       

X

Activity

       

X

Phase of play

       

X

Footwear

       

X

Surface condition

       

X

Sitting height, curl-ups, leg length

   

X

    

75% Hop, SLCMJ, SLHD, Y-balance, TJ Knee

    

X

   

ACWR and MSWR of training loads

X

       

Neuromuscular training loads

   

X

X

   

Total training load features

55

65

151, 229

29

20

27

18

18

  1. Neuromuscular training loads is an over-arching “feature” which includes multiple variables not explicitly mentioned here
  2. *These two papers included 151 and 229 training load variables, under eight over-arching topics (with the most important ones noted here)