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Table 1 A selection of constraints and contextual factors from team sports unless otherwise specified, which can currently be measured, or could be better measured through improvements in technology

From: Methodological Considerations for Furthering the Understanding of Constraints in Applied Sports

Group Constraint category Constraint/context Constraint examples in the literature How technology can improve measurement of this constraint  Application of technology in other disciplines
Match events Task Location and type of match event Kempton et al. [43], O’Shaughnessy [44] Automated ball tracking through computer vision  
Task Sport specific events, e.g. Australian football—kick type (drop punt, snap, etc.); hockey—hit type Slade [45], Hughes and Franks [46] Automated detection of events via computer vision or device on athlete/equipment (i.e. ball or stick) Traffic event detection [47]
Task Shot location
- Angle/distance of goal face visible
Pocock et al. [48], Goldsberry [49] Player and ball tracking aligned with game logs  
Task Time in possession
- Individual possession length
- Length of possession chain
- Team split of previous 10 mins
Higham et al. [50], Robertson et al. [32] Player and ball tracking aligned with game logs  
Task/individual Shot trends: ‘hot hand fallacy’
- Team
- Individual
Skinner [51], Bar-Eli et al. [52] Player and ball tracking aligned with game logs  
Individual Disposal efficiency
- In game
- History
Pocock et al. [48], Reich et al. [53] Player and ball tracking aligned with game logs paired with analytics  
Task Available space
- Physical pressure
- No. of players between ball and goal
- Ratio of attackers to defenders
Rein et al. [54], Alexander et al. [55] Player and ball tracking paired with improved analytics
Proximity sensor
Emotional response in crowds [56]
Task Kick distance Blair et al. [57, 58] Ball tracking
Automated measurement through computer vision
Automated detection of distances in cars [59]
Individual/task Physical output
- Game time played
- Time between efforts
- High speed metres
Almonroeder et al. [60], Sarmento et al. [61] Player and ball tracking paired with match events  
Task Ball weight Nimmins et al. [62], Fitzpatrick et al. [63] Computer vision Computer vision to estimate weight of livestock [64]
Individual Task Coaching
- Technique/feedback
- Game style
- Enable self-regulation
Wulf and Lewthwaite [65], Wrisberg [66] Recording and natural language processing
Speech to text software
Military detection of keywords [67]
Individual Dominant side, e.g. preferred foot Cust et al. [68], Ball [69] Automated detection through computer vision  
Individual Heart function (heart rate, oxygen saturation) Klusemann et al. [70], Dong [71] Sensors in uniforms Sensors built into clothing [72]
Individual Mental components, e.g. mental fatigue, brain activity levels, motivation, resilience, confidence, decision-making skill, emotional state Russell et al. [73], Joshi et al. [39] Portable brain electrical activity machines Health sector development of portable EEG (electroencephalogram) [74]
Individual Player characteristics, i.e. physical characteristics, experience, playing position Piette et al. [75], Sarmento et al. [61]   
Environment Social
• Cultural
• Interactions
Anshel et al. [76], Davids et al. [77] Proximity sensors Social proximity using Bluetooth [78]
Individual Recovery (training load) Halson [79], Gastin et al. [80] Ubiquitous monitoring through 24/7 sensors Health sector monitoring at-risk patients [81]
Individual Sleep Juliff et al. [82], Halson and Juliff [83] Improvements in sleep tracking technology Validation of non-invasive sleep technology [84]
Match context Task Difference in team quality Robertson and Joyce [85], Franks et al. [86]   
Task Defensive style/intent Tan et al. [87], Wang et al. [88] Player and ball tracking aligned with match log  
Environment Opposition characteristics, i.e. physical characteristics, experience, playing position Franks et al. [86], Milanese et al. [89]   
Task Scoreboard
- Margin/scoring trends
Goldman and Rao [90], Pocock et al. [48] Automation through computation  
Environment Time in season, fixture type Dellal et al. [91], Soroka and Lago-Peñas [92]   
Environment Playing Surface
- Material, i.e. grass (hard, soft, wet, dry)
Bartlett et al. [93], Crowther et al. [94] Racetrack penetrometer
Clegg-Hammer
Magnetic layer detection
Magnetic layer detection to measure top soil density [95]
Task Pitch dimensions, i.e. area, depth of pockets Klusemann et al. [70], Kelly and Drust [96]   
Environment Weather (rainfall, wind, sun position) Thornes [97], Ely et al. [98] Wireless sensor network Weather impact on air traffic management [99]
Environment Venue—crowd, stadium type (roof, open), distance travelled, noise Gama et al. [100], Goldman and Rao [90] Computer vision to monitor crowd emotion Emotion tracking for city planning [101]
Task Time: elapsed/remaining in
- Period
- Game
Pettigrew [102], Sandholtz and Bornn [103] Automation through computational timing  
Task Time elapsed since last
- Foul
- Stoppage
- Turnover
- Score
Andrienko et al. [104], Skinner [51] Automation through computational timing  
Task Team synergy Araújo and Davids [105], Araújo et al. [106] Ball and player tracking aligned with match log
Facial expression extraction
Emotion tracking for city planning [101]