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Table 1 Study characteristics

From: Machine learning methods in sport injury prediction and prevention: a systematic review

Authors, Year Outcome Variable Predictor Variables Participants (Age Mean ± sd) Period Study Design Unit of Observation Number of Observations Total Amount of Injuries / No. Of Injured Athletes (N =)a Number of Features
AYALA ET AL., 2019 [2] Occurrence of Hamstring strain injury Individual (sport-related background, demographic, previous hamstring strain injury), psychological and neuromuscular measurements 96 Male professional soccer players from 4 teams in 1st and 2nd league in Spain
6 players that did not complete the tests and 4 players that left their teams were removed
1 season (2013–2014) Prospective cohort Player 86 NR/18 229
CAREY ET AL., 2018 [11] Occurrence of non-contact injury, non-contact causing time loss injury and hamstring injury Training load variables (+ Exponential Weighted Moving Average features and Acute Chronic Workload Ratio features) 75 male professional players from 1 team in the Australian Football League in Australia 3 seasons (2014–2016) Prospective cohort Player matches and player training sessions 13,867 Non-contact: 388/NR
Non-contact causing time loss: 198/NR
Hamstring: 72/NR
58
LÓPEZ-VALENCIANO ET AL., 2018 [25] Occurrence of lower extremity muscle injury Individual (sport-related background, demographic, previous injury), psychological and neuromuscular measurements 132 Male professional players in handball (34) and soccer (98) in the first three National Leagues in Spain
6 players that did not complete the tests and 4 players that left their teams were removed
1 season (2013–2014) Prospective cohort Player 122 32/29 151
MCCULLAGH ET AL., 2013 [27] Occurrence of injury and injury type (contact or non-contact) Workloads, squeeze test data, soft tissue scores, stress level, mood, sleep score, ankle flexibility, fatigue and player perceived performance, years played, player durability, age 39 male professional players from the Australian Football League in Australia 1 season (2010) Prospective cohort Player weeks 1210 163/NR 30
OLIVER ET AL., 2020 [32] Occurrence of non-contact lower limb injury Personal data (age, Body Mass Index, etc.) and neuromuscular control tests data 355 Male youth soccer players (age 14.3 ± 2.1) from Premier League and Championship clubs in England 1 season (2014–2015) Prospective cohort Player 355 NR/99 20
RODAS ET AL., 2019 [34] Occurrence of Tendinopathy Genetic markers 363 Male (89%) and female (11%) professional soccer, futsal, basketball, handball and roller hockey players (age 25 ± 6) from FC Barcelona in Spain 10 years (2008–2018) Case–control Player 363 199/199 1 419 369
ROMMERS ET AL., 2020 [35] Occurrence of injury and type of injury (acute and overuse) Anthropometric measurements, motor coordination and physical fitness 734 Male U10 to U15 youth soccer players (age 11.7 ± 1.7) of 7 premier league clubs in Belgium 1 season (2017–2018) Prospective cohort Player 734 NR /368 29
ROSSI ET AL., 2018 [36] Occurrence of injury Personal, Workload features from GPS Tracking data, previous injury 26 Male professional soccer players (age 26 ± 4) in Italy 1 season (2013–2014) Prospective cohort Player training session 952 23/13 55
RUDDY ET AL., 2018 [38] Occurrence of hamstring strain injury Age, previous hamstring strain injury, low levels of eccentric hamstring strength 362 Male professional players from the Australian Football League in Australia: 186 in 2013 (age 23.2 ± 3.6) and 176 in 2015 (age 25.0 ± 3.4) 2 seasons (2013, 2015) Prospective cohort Player 2013: 186
2015: 176
2013: NR/27
2015: NR/26
3 or 8
THORNTON ET AL., 2017 [41] Occurrence of Injury Training intensity 25 Male professional rugby players from Australian National Rugby League in Australia. Athletes were included in the dataset if they sustained more than 3 injuries in total 3 seasons (2013–2015) Prospective cohort Player days NR 156/25 NR
WHITESIDE ET AL., 2016 [46] Occurrence of ulnar collateral ligament reconstruction Demographic and pitching performance 208 Male professional baseball pitchers from the Major League Baseball in the USA and Canada: 104 cases (age 27.3 ± 3.8) and 104 controls (age 27.8 ± 3.7) 5 years (2010–2015) Matched Case–control Player 208 NR/NR 14
  1. a The for the analysis relevant number is put in bold. If the unit of observation is player, then the number of injured players is relevant, since one only detects if a player gets injured at least once. If there are multiple observations per player, the total number of injuries is relevant for the analysis