Monitoring and managing athlete workloads has received considerable attention in recent years. The demands of competition represent an important reference point when establishing training loads (Owen et al., 2017; Stevens et al., 2017), since we can express the load imposed in the training based on the proportion of the match's demand. In addition, the weekly workload is strongly influenced by the activity of players during competition, with match-play demands typically the highest load performed during the week (White and MacFarlane, 2015).
Time motion studies in field hockey competition have shown that there are significant differences in the distance covered at high-speed running (HSR) and sprint running (SR) between positions, with forwards (FW) spending a greater percentage of time at HSR and SR (Jennings et al., 2012; Lythe and Kilding, 2011). Jennings et al. (2012) found that FW covered 10.1[+ or -]7.4% more distance in HSR than midfielders (MD) (FW: 1896.6+368 m; MD: 1778.6[+ or -]387 m) and 26.6[+ or -]8.2% more than backs (BS). MD players perform more HSR than fullbacks and halfbacks, and also more SR than fullbacks as a percentage of total playing time (Jennings et al., 2012). These findings are in agreement with the only other research presenting data from the highest national level (Australia), where MD players were observed to cover more HSR than BS (Jennings et al., 2012). Spencer et al. (2004) documented the nature of motion analysis in field hockey and reported that high-intensity running accounted for 5.6% of the total match time, and this was composed of 4.1% striding and 1.5% SR. The results obtained were lower than other studies reported due to variation in the classification of motion activities (Lythe and Kilding, 2011; Spencer et al., 2004).
The use of generic, arbitrarily defined speed thresholds (km x [h.sup.-1]) is a common practice in time-motion studies in team sports (Cummins et al., 2013). However, inconsistency in these thresholds, particularly in hockey (Gabbett, 2010; Jennings et al., 2012; Macutkiewicz and Sunderland, 2011; Sunderland and Edwards, 2017), makes it difficult to compare different research studies even within a single sport (Cummins et al., 2013; Sweeting et al., 2017). Dwyer et al. reported a method to standardize protocols for time motion analysis recommending sport-specific velocity ranges (Dwyer and Gabbett, 2012). More recently, Sweetings et al. (2017) reviewed generic speed thresholds within team sport and the results of their study suggested that they do not account for differences between individual players. For example, results may be influenced by the peak speed of each player. Thus, in players with higher peak speed, the speed thresholds used to categorize an action as HSR or SR, represent a lower percentage of that player's maximum, thereby having greater ability to reach those thresholds.
In other team sports the use of individual rather than generic speed thresholds increases the activity at HSR of slower players while reducing the activity of the faster players (Buchheit et al., 2013; Gabbett, 2015; Murray et al., 2017). In adolescent soccer players, younger players recorded more repeated sprint sequences when using individual speed thresholds (> 61 % of individual peak running velocity) compared with the older players, whereas when employing generic speed thresholds (> 19 km x [h.sup.-1]), older players performed more repeated sprint sequences than younger players (Buchheit et al., 2010). Furthermore, the use of individual speed thresholds reduces individual and position variability during competitive matches in soccer with the result that these measures are more stable indicators of high-speed activity than generic arbitrarily defined thresholds (Carling et al., 2016). Finally, it has recently been postulated that achieving a higher percentage of individual speed (95% vs. 85%) during the training week has a protective effect, decreasing the probability of injury (Malone et al., 2017). In addition, there is a U-shaped relationship between the number of weekly exposures to individual maximum speed and the likelihood of injury (Malone et al., 2017), which justifies the use of these individual speed thresholds.
Therefore, the aim of the current research was to compare the running demands of professional field hockey players using generic and individualized speed thresholds. Furthermore, we aimed to determine if differences were more pronounced in slow, moderate or fast players while also examining whether these speed thresholds were position specific.
Sixteen male field hockey players from the same club participated in the study (age: 25.5 [+ or -] 2.9 years; body mass: 74.6 [+ or -] 5.5 kg; stature 1.77 [+ or -] 0.05 m). Players were categorized based on three positional lines of play, six backs (BS), five midfielders (MD) and five forwards (FW). All players where classified as slower (29.2-30.2 km x [h.sup.-1]), moderate (30.7-31.5 km x [h.sup.-1]) or faster (32.2-33.7 km x [h.sup.-1]) based on peak speed for each player. The slower group consisted of 3 BS, 1 MD and 1 FW, the moderate group comprised 3 BS, 2 MD and 1 FW, and the faster group contained 2 MD and 3 FW (Table 1). The participants played in the Spanish Hockey League Premier Division and had a hockey playing experience in these league of 6.5 [+ or -] 1.8 years. The players trained, on average, 4 times per week and played one official match every weekend. These data arose from the daily player monitoring in which player activities were routinely measured over the course of the season. The study conformed to the recommendations of the Declaration of Helsinki and players gave their informed written consent for participation in the research study.
Activity profiles were recorded in 16 outfield players competing for a single club in the Spanish Hockey League Premier Division in the 2014-2015 and 2015-2016 seasons. As determined by the league fixtures, matches were separated by a minimum of 6 days and there was a 2-month break over the Christmas period during both seasons. Ten players were present during both seasons. A total of 17 matches were analyzed, with a mean of 9.1 [+ or -] 4.4 matches per player. All matches were 60 minute in duration (4 x 15 minute quarters). All competitive matches...