Matthew A. Grant and Paul G. Schempp
Researchers sought to identify and analyze the actions of elite swimmers on a competition day that the athletes believed were critical to their success, and to understand the meaning the athletes assigned to each of these activities. The present study describes the competition-day routines of the elite swimmers by presenting the athletes’ actions, meanings, segments, and preparations within a substantive grounded theory. To this end, five U.S. Olympic medal-winning male swimmers from the 2008 Beijing Summer Olympic Games participated in a three-stage data collection: an initial interview during a two-day training visit, a competition observation at an elite meet, and a follow-up interview via telephone. In addition, each participant’s coach was interviewed. Utilizing constructivist grounded theory (Charmaz, 2006), a substantive theory of a competition-day routine for elite swimmers emerged. Results suggested that athletes understood all their actions during a competition day as one routine, and research of competitive routines should include both the ostensive (i.e., plan) and performative (i.e., enactment) aspects of routines (Feldman & Pentland, 2003).
Matthew A. Grant, Gordon A. Bloom, and Jordan S. Lefebvre
The purpose of this study was to examine mentor and mentee perceptions of the viability of a pilot e-mentoring programme for U.S. lacrosse (USL) coaches. Twelve mentees and 12 mentors were paired into dyads, met at a national coaching convention, and were directed to continue their mentoring relationship for up to 6 months via an online platform. Semistructured postprogramme interviews were conducted with four mentors and six mentees at the conclusion of the mentoring relationships. Interviews were transcribed verbatim and analysed via thematic analysis. Results showed that mentors and mentees experienced many of the benefits, barriers, and advantages found in traditional mentoring and e-mentoring relationships. Of interest were three key findings in which trust and respect was quickly experienced by participants, equity within the relationship created collegiality, and technology barriers limited effective teaching methods. Based on the results, practical implications for e-mentoring programmes are presented.
Paul G. Schempp, Bryan A. McCullick, Matthew A. Grant, Cornell Foo, and Kelly Wieser
The purpose of this study was to analyze the relationship between coaches’ professional playing experience and their professional coaching success. The sample (n = 134) included coaches who had the equivalent of three full seasons of head coaching experience in either Major League Baseball (MLB) (n = 46), the National Basketball Association (NBA) (n = 38) or the National Football League (NFL) (n = 50) as determined by the total number of games coached between the years 1997-2007. ANOVAs revealed no significant differences between coaches with more or less professional playing experience and professional coaching success as determined by professional winning percentage. Further, no significant relationship was found between professional playing experience and professional coaching success in MLB (r = -0.16), NBA (r = -0.05) or NFL (r = 0.00). It was concluded that professional playing experience was not a predictor of professional level coaching success. These findings support the notion that sources of knowledge other than playing experience may be necessary and useful in developing coaching expertise.
Sean Williams, Grant Trewartha, Matthew J. Cross, Simon P.T. Kemp, and Keith A. Stokes
Numerous derivative measures can be calculated from the simple session rating of perceived exertion (sRPE), a tool for monitoring training loads (eg, acute:chronic workload and cumulative loads). The challenge from a practitioner’s perspective is to decide which measures to calculate and monitor in athletes for injury-prevention purposes. The aim of the current study was to outline a systematic process of data reduction and variable selection for such training-load measures.
Training loads were collected from 173 professional rugby union players during the 2013–14 English Premiership season, using the sRPE method, with injuries reported via an established surveillance system. Ten derivative measures of sRPE training load were identified from existing literature and subjected to principal-component analysis. A representative measure from each component was selected by identifying the variable that explained the largest amount of variance in injury risk from univariate generalized linear mixed-effects models.
Three principal components were extracted, explaining 57%, 24%, and 9% of the variance. The training-load measures that were highly loaded on component 1 represented measures of the cumulative load placed on players, component 2 was associated with measures of changes in load, and component 3 represented a measure of acute load. Four-week cumulative load, acute:chronic workload, and daily training load were selected as the representative measures for each component.
The process outlined in the current study enables practitioners to monitor the most parsimonious set of variables while still retaining the variation and distinct aspects of “load” in the data.
Matthew J. Cross, Sean Williams, Grant Trewartha, Simon P.T. Kemp, and Keith A. Stokes
To explore the association between in-season training-load (TL) measures and injury risk in professional rugby union players.
This was a 1-season prospective cohort study of 173 professional rugby union players from 4 English Premiership teams. TL (duration × session-RPE) and time-loss injuries were recorded for all players for all pitch- and gym-based sessions. Generalized estimating equations were used to model the association between in-season TL measures and injury in the subsequent week.
Injury risk increased linearly with 1-wk loads and week-to-week changes in loads, with a 2-SD increase in these variables (1245 AU and 1069 AU, respectively) associated with odds ratios of 1.68 (95% CI 1.05–2.68) and 1.58 (95% CI 0.98–2.54). When compared with the reference group (<3684 AU), a significant nonlinear effect was evident for 4-wk cumulative loads, with a likely beneficial reduction in injury risk associated with intermediate loads of 5932–8651 AU (OR 0.55, 95% CI 0.22–1.38) (this range equates to around 4 wk of average in-season TL) and a likely harmful effect evident for higher loads of >8651 AU (OR 1.39, 95% CI 0.98–1.98).
Players had an increased risk of injury if they had high 1-wk cumulative loads (1245 AU) or large week-to-week changes in TL (1069 AU). In addition, a U-shaped relationship was observed for 4-wk cumulative loads, with an apparent increase in risk associated with higher loads (>8651 AU). These measures should therefore be monitored to inform injury-risk-reduction strategies.