Methodological Considerations for Furthering the Understanding of Constraints in Applied Sports

Browne, Peter; Sweeting, Alice J.; Woods, Carl T.; Robertson, Sam

doi:10.1186/s40798-021-00313-x

Sports Medicine - Open

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]

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