Speed Comparisons Between Match Outcomes By Position In NCAA Women’s Soccer

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PURPOSE: The purpose of this study was to determine if performance measures differed dependent on game outcomes and field position during a full NCAA collegiate women’s soccer season. METHODS: Average speed [km·hr-1] was monitored in 89 female soccer athletes across 5 NCAA Division 1 teams (mean ± SD; age, 19.8 ± 1.1 y; body mass, 81.57 ± 32.66 kg; height 158.78, ± 20.34 cm) using GPS-enabled player tracking devices during the competitive season. Athletes were categorized into three groups, depending on field position (forwards (FWDs), midfielders (MIDs), and defenders (DEFs)). Within group comparison for wins, losses, and ties were determined using mean differences (MD) with 95% confidence interval (95% CI) and effect sizes (ES). This was assessed post-hoc with a Tukey HSD, with alpha set at 0.05 for all analysis. RESULTS: Average speed across all positions was 3.35 ± 1.17 km·hr-1 in wins, 3.06 ± 1.05 km·hr-1 in ties, and 3.5 ± 1.31 km·hr-1 in losses. Within group, FWDs, MIDs, and DEFs achieved a significantly greater average speed in games that resulted in a loss versus a tie (MD[95%CI]; FWDs=0.69[0.20,1.18] km·hr-1; ES=0.63, p=0.003; MIDs=1.18[0.62,1.74] km·hr-1; ES=0.82, p<0.001; DEFs=0.82[0.38,1.26] km·hr-1; ES=0.65, p<0.001). There was also a significantly greater average speed achieved in games that resulted in a win versus a tie for all positions (MD[95%CI]; FWDs=0.50[0.03,0.98] km·hr-1; ES=0.45, p=0.034, MIDs=0.78[0.23,1.32] km·hr-1; ES=0.62, p=0.003, DEFs=0.58[0.16,1.00] km·hr-1; ES=0.53, p=0.004). For all positions, average speed was greater in games that resulted in a loss versus a win, however this difference was only significant for MIDs=0.40[0.08,0.73] km·hr-1; ES=0.29, p=0.011 and not for FWDs (p=0.25) and DEFs (p=0.06). It should also be noted that the effect sizes between wins and losses were 0.17 (FWDs), 0.29 (MIDs), and 0.20 (DEFs). CONCLUSION: Average speed across all positions were greatest in games that result in a loss. This novel data can provide insights to coaches on how game results impact physiological demands by position. Tailored recovery strategies may be derived from this type of data to create a positional specific plan.