This test provides an individualized analysis (analysis by team, by playing position, or both) of movement. In the test (Figure 2), a player individually covers a certain distance, using the type of movement designated by one of the team's physical trainers (e.g., see definition of movement in youth basketball in Table 3), whereas a collaborator records the result. The distance between the photoelectric cells should be established according to the mean distance of the movements of the particular sport. Moreover, 3 additional meters should be added before the first photoelectric cell and after the second photoelectric cell, so that the player's start will be a flying start and he or she will not have to slow down before reaching the last pair of photoelectric cells.
This phase of the procedure can be performed by means of 2 techniques: observation and software. The filming and analysis will differ depending on the technique. Observation requires training one or more observers and for them to record the types of movements they see on a recording instrument, based on pre-established sets of types of movements. Observation has the advantages of low cost and being easy to perform. Its main disadvantage is the amount of time required to perform the observations. In contrast, software simplifies the analysis, depending on the type: (a) track performance system (33) and (b) video tracking system (35). This type of software is highly reliable, and it requires less time to obtain the data, but it has the disadvantages of its high cost and difficulty to install and use during the game.
Independent of the technique (observation or software), analysis provides information about the frequency of movement, duration of each movement, total duration, speed, distance of each movement, total distance, and work-rest ratio. Taking into account the data of all the players or of a specific position, physical trainers can design training plans by establishing a work range for the entire team or for specific positions (21). This type of planning favors training specificity.
For example, after performing TMA, youth basketball players may demonstrate the need for short but intense work intervals. In these training tasks, high-intensity movements (running, sprinting, and sport-specific movements) should be interspersed with recovery periods (standing, walking, and jogging). A work-rest ratio should be determined; for example, 1:1.5. This would mean that for each minute of work, players should have 1 minute and 30 seconds of recovery.
The information provided by TMA can be very useful to develop specific training plans based on players' physical patterns (39). The information provided by the work-rest ratio, sprint distances, the duration of recovery periods, as well as the amount of changes in the direction and specific movements could allow the coach to prepare a plan specifically suited to the athletes.
The procedure described aims to facilitate athletes' performance, providing data that allows for workloads that are appropriate for the physical capacities of the players of a specific team. The procedure should be implemented at the beginning of the season for teams that will use TMA to quantify game demands. Procedures such as this are particularly necessary in youth sports if the information provided in the scientific literature for planning workouts is insufficient for a sport.
1. Abdelkrim N, El Fazaa S, El Ati J. Time-motion
analysis and physiological data of elite under-19-year-old basketball players during competition. Br J Sports Med 41: 69–75, 2007.
2. Abdelkrim N, Castagna C, Jabri I, Battikh T, El Fazaa S, El Ati J. Activity profile
and physiological requirements of junior elite basketball players in relation to aerobic-anaerobic fitness. J Strength Cond Res 24: 2330–2342, 2010.
3. Ali A, Farrally MA. Computer-video aided time motion analysis technique for match analysis. J Sports Med Phys Fitness 3: 82–88, 1991.
4. Ali A, Foskett A, Gant N. Validation of a soccer skill test of use with females. Int J Sports Med 29: 917–921, 2008.
5. Bangsbo J, Norregaard L, Thorso F. Activity profile
of competition soccer. Can J Sport Sci 16: 110–116, 1991.
6. Barbero-Álvarez JC, Barbero-Álvarez V, Granda-Vera J. Match analysis and heart rate of futsal players during competition. J Sports Sci 26: 63–73, 2008.
7. Bloomfield J, Polman R, O'Donoghue P. Physical demands
of different position in FA Premier League Soccer. J Sports Sci Med 6: 63–70, 2007.
8. del Vecchio FB. A review of time-motion
analysis and combat development mixed martial arts matches at regional level tournaments. Percept Mot Skills 112: 639–648, 2011.
9. Bradley P, Carling C, Archer D, Roberts J, Dodds A, Di Mascio M, Pablo D, Gómez A, Peart D, Krustrup P. The effect of playing formation on high-intensity running and technical profiles in English FA Premier League soccer matches. J Sports Sci 29: 821–830, 2011.
10. Burton AW, Greer NL, Wiese DM. Changes in overhand throwing patterns as a function of ball size. Pediatr Exerc Sci 4: 50–67, 1992.
11. Cánovas M, Arias JL, García P, Yuste JL. Velocity
test in mini-basketball: Pilot study. Movimiento Humano 3: 27–34, 2012.
12. Casamichana D, Castellano J. Time-motion
, heart rate, perceptual and motor behaviour demands in small-side soccer games: Effects of pitch size. J Sports Sci 28: 1615–1623, 2010.
13. Castagna C, D'Ottavio S, Abt G. Activity profile
of young soccer players during actual match play. J Strength Cond Res 17: 775–780, 2003.
14. Castellano J, Blanco-Villaseñor A, Álvarez D. Contextual variables and time-motion
analysis in soccer. Int J Sports Med 32: 415–421, 2011.
15. Coelho e Silva MJ, Moreira H, Conçalves CE, Fiqueiredo AJ, Elfrerink-Gemser MT, Malina R. Growth, maturation, functional capacities & sport-specific skills in 12-13 years old-basketball players. J Sports Med Phy Fitness 50: 174–181, 2010.
16. Cronbach LJ. Test “reliability”: Its meaning and determination. Psychometrika 12: 1–16, 1947.
17. Deutsch MU, Maw GJ, Jenkins D, Reaburn P. Heart rate, blood lactate and kinematic data of elite colts (under-19) rugby union players during competition. J Sports Sci 16: 561–570, 1998.
18. Deutsch MU, Kearney GA, Rehrer NJ. Time-motion
analysis of professional rugby union players during match-play. J Sports Sci 25: 461–472, 2007.
19. Di Salvo V, Gregson W, Atkinson G, Tordoff P, Drust B. Analysis of high intensity activity in premier league soccer. Int J Sports Med 30: 205–212, 2009.
20. Dobson BP, Keogh JW. Methodological issues for the application of time-motion
analysis research. J Strength Cond Res 29: 48–55, 2007.
21. Dogramaci SN, Watsford ML, Murphy AJ. The reliability and validity of subjective notational analysis in comparison to global positioning system tracking to assess athlete movement patterns. J Strength Cond Res 25: 852–859, 2011.
22. Dogramaci SN, Watsford ML, Murphy AJ. Time-motion
analysis of international and national level futsal. J Strength Cond Res 25: 646–651, 2011.
23. Duthie G, Pyne D, Hooper S. Time motion analysis of 2001 and 2002 super 12 rugby. J Sports Sci 23: 523–530, 2005.
24. Gray A, Jenkins D, Andrews M, Taaffe D, Glover M. Validity and reliability of GPS for measuring distance travelled in field-based team sports
. J Sports Sci 28: 1319–1325, 2010.
25. Hill-Haas SV, Dawson BT, Coutts A, Rowsell GJ. Physiological responses and time-motion
characteristics of various small-sided soccer game in youth players. J Sports Sci 27: 1–8, 2009.
26. Johnston T, Sproule J, McMorris T, Maile A. Time-motion
analysis and heart rate response during elite male field hockey: Competition versus training. J Hum Mov Stud 46:189–203, 2004.
27. Keane S, Feilly T, Hughes N. Analysis of work rates in Gaelic football. Aust J Sci Med Sport 25: 100–102, 1993.
28. Lupo C, Tessitore A, Cortis C, Ammendolia A, Figura F, Capranica L. A physiological, time-motion
, and technical comparison of youth water polo and Acquagoal. J Sports Sci 27: 823–831, 2009.
29. Macutkiewicz D, Sunderlandt C. The use of GPS to evaluate activity profiles of elite women hockey players during match-play. J Sports Sci 29: 967–973, 2011.
30. Mahar MT, Rowe DA. Practical guidelines for valid and reliable youth fitness testing. Meas Phys Educ Exerc Sci 12: 126–145, 2008.
31. Matthew D, Delextrat A. Heart rate, blood lactate concentration, and time-motion
analysis of female basketball players during competition. J Sports Sci 27: 813–821, 2009.
32. Mayhew SR, Wegner HA. Time-motion
analysis of professional soccer. J Hum Mov Stud 11: 49–52, 1985.
33. McInnes SE, Carlson CJ, Mckenna MJ. The physiological load imposed on basketball players during competition. J Sports Sci 10: 285–296, 1993.
34. McLellan CP, Loveli DI, Gass GC. Performance analysis of elite rugby league match play using global positioning systems. J Strength Cond Res 25: 1703–1710, 2011.
35. Pers J, Bon M, Kovacic S, Sibila M, Dezman B. Observation and analysis of large-scale human motion. Hum Mov Sci, 21: 295–311, 2002.
36. Regimbal C, Deller J, Plimpton C. Basketball size as related to children's preference, rated skill and scoring. Percept Mot Skills 75: 867–872, 1992.
37. Sáez E, González-Badillo JJ, Izquierdo M. Low and moderate plyometric training frequency produces greater jumping and sprinting gains compared with high frequency. J Strength Cond Res 22: 715–725, 2008.
38. Spencer M, Lawerence S, Rechichi C, Bishop D, Dawson B, Goodman C. Time-motion
analysis of elite field hockey, with special reference to repeated-sprint activity. J Sports Sci 22: 843–850, 2004.
39. Taylor J. Basketball: Applying time motion data to conditioning. Strength Cond J 25: 57–64, 2003.