Enhancing Acceleration Capabilities in Professional Women’s Football Players: A Comparative Analysis of Game-Based Versus Resisted Sprint Trainings

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Both integrated game-based and resisted sprint training methods effectively enhance acceleration in professional women’s football. Incorporating high-speed actions into training significantly improves sprint performance across distances, offering a practical solution during competitive periods.

Abstract

The recognition of high-speed demands in football has led elite academies to prioritize acceleration capabilities for player selection and promotion, particularly given their fundamental role in the motor skills of professional players and their impact on goal-related opportunities. This study explored the effectiveness of game-based versus resisted sprint training methods in enhancing the acceleration abilities of professional women’s football players. Over the entire competitive period, the training load of 26 athletes (24.2 ± 3.7 years) was assessed using GPS devices, and sprint capabilities were evaluated through four 30-m acceleration tests spaced six weeks apart. Linear mixed models (LMMs) analyzed physical load parameters, including distance covered at high speeds, speed events, and maximum speed, with periods and players as fixed and random effects, respectively. Significant sprint performance improvements were observed across all intervals, particularly when high-intensity distance volumes were combined with resisted sprint training. Conversely, high-intensity running without additional stimuli also led to performance gains, albeit to a lesser extent. Both game-based and resisted sprint training methods were effective in enhancing acceleration capabilities, while the absence of specific sprint focus did not significantly alter sprint performance. These findings support the inclusion of tailored sprint training in athletic programs to optimize acceleration in women’s football players.

1. Introduction

In contemporary women’s football, high-intensity conditional demands have increased significantly [1], mirroring trends in their male counterparts [2,3]. Such demands play a crucial role in talent recruitment, especially in elite academies, with acceleration capabilities becoming pivotal criteria for player selection and promotion [4,5,6].

Acceleration is not only integral to a football player’s skill set but also crucial for athletes in team-based sports, particularly in performance-driven scenarios [7,8,9,10]. In football, acceleration plays a fundamental role in winning duels, defending, and creating goal-scoring opportunities [7]. Sprinting, as one of the most frequent actions leading to goals, highlights its importance in these decisive moments [7,10]. The ability to accelerate effectively provides players with a competitive edge in game-critical situations, reinforcing the necessity of developing this skill as part of an athlete’s overall performance profile. Moreover, these abilities have been found to differentiate athletes based on their sporting levels [11,12]. This recognition has increased interest among both practitioners and researchers, leading to more studies on improving individual acceleration [13,14,15,16]. However, some experts suggest that selecting naturally faster players may be more effective than trying to improve players’ acceleration capabilities [17].

To address this objective, different approaches have targeted the specific enhancement of these capabilities. Sprint-specific methodologies adopt a decontextualized approach, prioritizing targeted athlete stimulation over game-related contextual factors. This allows for precise performance improvements without the interference of in-game variables [14,15,16,18]. Among these methodologies, resisted sprint training has gained popularity in team sports for its effectiveness in improving early acceleration under diverse overloading conditions [18]. In contrast, free and assisted sprinting are more suited to phase-specific adaptations, particularly in optimizing force application at high speeds [15]. Collectively, these sprint-specific interventions—whether resisted, free, or assisted—have been shown to improve sprint performance across various sports and competition levels [19,20,21]. Yet, the significance of exerting effort in relevant contexts is likely to contribute to the growing adoption of game-based methods [22]. Among these, small-sided games (SSGs) have gained prominence for their emphasis on technical–tactical skills and frequent short, high-intensity bursts. Nevertheless, although they are effective at improving cardiovascular fitness and mimicking official match acceleration–deceleration patterns, some studies indicate they might not optimally increase high-speed running or mirror the most intense competition periods [23,24]. Conversely, recent research suggests that adjustments in factors such as the number of players, individual player space, and space organization can potentially bridge the disparities in demands associated with high-speed activities [25,26,27,28].

Optimizing these abilities remains a priority in sports research and practice. This is not only due to their significant impact on player performance in sports but also due to the growing connections to the most frequent football injury, the hamstring tear [29,30,31,32,33,34]. We hypothesize that (i) the acceleration capabilities of female professional football players can be optimized during the competitive period, and (ii) both approaches can improve these capabilities, provided that the stimulus is delivered correctly. This study aimed to assess the effectiveness of integrated and resisted sprint training protocols in improving acceleration ability in professional women’s football.

2. Materials and Methods

2.1. Subjects

Twenty-six professional female football players from a single Spanish first division team, with an average age of 24.2 years (±3.7), a height of 1.69 m (±0.07), and a top speed of 27.2 km/h (±2.8), volunteered for this study. These athletes engaged in a regular weekly exercise routine that consisted of five football practices (approximately 8 h in total), five strength/power exercise sessions, and one competitive match per weekend. Notably, none of the players had prior experience with sprint-specific training. Their distribution across positions was as follows: 8 defenders, 8 midfielders, 6 wingers, and 4 forwards. Goalkeepers were excluded from the study due to distinct variations in their preparatory periods compared to outfield players. A priori power analysis was conducted to ensure the study’s ability to detect significant changes in sprint performance (0–30 m) over time. A linear mixed model (LMM) was fitted, with testing period (T1–4) as a fixed effect and random intercepts for each player (‘player ID’), accounting for within-subject variability. Based on 100 simulations with the current sample size of 26 players and 127 total observations, the power was estimated at 87.0% (95% CI [81.3, 92.4]), confirming that the sample size was sufficient to detect significant differences between periods. Prior to commencing the investigation, written informed consent was obtained from the players, and where needed, from their parents. This study protocol, in accordance with the principles of the Declaration of Helsinki, was granted approval by the research ethics committee at the Universidad Rey Juan Carlos (Ref: 0102202306623).

2.2. Study Design and Procedures

A longitudinal quasi-experimental design was employed to understand the effects of different approaches to stimulating acceleration capabilities at various points during the regular season. Participating players underwent three 6-week training periods during the regular season. These periods were divided into (i) regular football training (integrated) supplemented with resisted sprint-specific training (IRSST); (ii) integrated training without a focus on high-speed actions (INT); and (iii) integrated training with a focus on high-speed actions (INTHS). Regular football practice involved 1 active recovery session (or complementary training for players who participated less than sixty minutes in the previous match); 1 passive recovery session (no training at all); and 3 optimization training sessions characterized by (i) short, high-intensity events and neuromuscular actions through relatively dense (<100 m² per player) football drills with a lower number of players; (ii) tactically oriented football drills characterized by larger individual areas (>200 m² per player) and a greater number of players involved; and (iii) low-volume, high-intensity training sessions where high-speed events are interspersed with long rest periods. Lastly, pre-match training consisted of high-intensity, short-distance, high work-to-rest drills. The schematic representation of the experimental design is illustrated in Figure 1.

2.3. Sprint Training

Two training protocols were designed to complement game-based approaches and were present throughout the 3 intervention periods. The structure and characteristics of the game-based methodology were kept constant across the different intervention periods. The training for period 1 (P1) consisted of sprint-specific training characterized by a progressive overload in both intensity and volume of training. A detailed description can be found in

Table 1. Resisted training program training.

Session/Week	Training		Volume
1	3 × 20 m 20% BM	2 × 20 m 25% BM	100 m
2	4 × 20 m 30% BM	2 × 20 m 25% BM	120 m
3	5 × 20 m 40% BM	2 × 20 m 50% BM	140 m
4	6 × 20 m 60% BM	2 × 20 m 50% BM	160 m
5	7 × 20 m 70% BM	2 × 20 m 75% BM	180 m
6	8 × 20 m 80% BM	2 × 20 m 75% BM	200 m

Abbreviations: BM—body mass; and m—meters.

. The training for period 2 (P2) consisted of its usual training dynamics, characterized by a game-based methodology. Finally, the training for period 3 (P3) maintained the same structure as that present in P2, complemented with game-based tasks that emphasized high-speed situations through larger relative areas per player (>200 m² per player) or the inclusion of specific tasks aimed at recreating transition situations.

2.4. Sprint Performance Testing

The acceleration capabilities of the athletes were assessed using a 30-m maximum acceleration test starting from stationary. These capabilities were calculated based on the mean values of two 30-m sprint tests. Assessments were conducted at four time points throughout the season: T1 (after the pre-season), T2 (post-resisted intervention), T3 (post-control period), and T4 (post-integrated intervention). Data were collected using timing gates (Witty Microgate, Microgate, Bolzano, Italy): Dual-beam timing gates were placed on the track 1 m above ground level at 0, 20, and 30 m from the starting line to monitor training sessions of free sprinting. The starting position was located 0.5 m behind the first timing gate. A visual representation of a sprint testing session is shown in Figure 2. All tests were preceded by a standardized warm-up (6 min running at 8–10 km/h with general conditioning exercises and dynamic stretching) and a specific warm-up including wall drills, progressive speed runs, and resisted and flying runs. Likewise, all tests were performed at the same time of day for each player in order to avoid any negative effects of circadian rhythms. Verbal encouragement was provided by the research staff during all tests.

2.5. External Load Monitoring

Daily external load (EL) records were collected using GPS units at a rate of 18 Hz (GPEXE Pro2, Exelio SRL, Udine, Italy, firmware version 0.13) and analyzed for the 6 weeks leading up to each assessment (with the exception of the pre-season measurement). Sprint-related variables, such as (i) the number of meters covered at high (>21 km/h) and (ii) very high speeds (>24 km/h); (iii) the number of speed events (actions involving > 1 s runs above 21 km/h); and (iv) the quantity of accelerations (>3 m/s²), were considered both in absolute terms and relative to the practice time during the three periods between measurements: (a) post pre-season (T1) to post-resisted intervention (T2); (b) post-resisted intervention (T2) to post-control period (T3); and (c) post-control period (T3) to post-integrated intervention (T4). Along with these, the maximum capability of (v) acceleration and the (vi) peak speed were included in the analysis. Variables such as the distance covered by walking or jogging were excluded from the analysis due to their limited significance in optimizing these capabilities.

2.6. Statistical Analysis

Statistical analyses were conducted using R-Studio for Windows (version 4.4.1; Posit, PBC, Boston, MA, USA, 2020) with the lme4 package, as well as with jamovi (Version 2.3) [Computer Software]. The Kolmogorov–Smirnov test and Levene’s test were employed to assess the normality of residuals and homogeneity of variance, respectively. Both the performance-related and EL data exhibited a normal distribution and homogeneous variance. We employed LMMs, estimated using REML and the nloptwrap optimizer, to understand the relationship between sprint performance across different distances and periods. For predictions related to 0–30 m, 0–20 m, and 20–30 m distances based on the period (represented respectively by the formulas: 0–30 m~1 + period, 0–20 m~1 + period, and 20–30 m~1 + period), we incorporated ‘player ID’ as a random effect to account for individual player variability. The same was replicated for EL parameters with absolute and relative time distances above 21 and 24 km/h, speed events, and maximum speed for the defined periods in order to estimate the average workload per session during the analyzed period. The fixed factors comprised 6-week periods (P1–3) for EL sprint-related parameters and testing sessions (T1–4) for distinct assessment time points. Random intercepts were assigned to individual players in both analyses. The confidence level was established at 95%, and values of p < 0.05 were considered statistically significant. Upon determining the significance of the fixed factor, Bonferroni post hoc pairwise comparisons were employed.

The adoption of this analysis approach is recommended over more traditional designs, such as repeated measures ANOVA, due to its ability to handle incomplete designs and account for correlations from repeated observations across various microcycles. LMMs also effectively manage missing data without requiring imputation or case exclusion, thus retaining all available data, minimizing bias, and enhancing the reliability of results. This comprehensive approach ensures the dataset’s integrity and provides a more robust analysis, as supported by recent sports science research [35].

3. Results

3.1. Sprint Performance

For the 0–30 m prediction, the model demonstrated a significant overall explanatory power, with a conditional r² of 0.89 and a marginal r² of 0.12. The intercept, when the period equals zero, was at 4.86 (95% CI [4.76, 4.96], t(59) = 96.70, p < 0.001). Similarly, for the 0–20 m prediction, the model displayed a conditional r² of 0.85, a marginal r² of 0.08, and an intercept at 3.46 (95% CI [3.39, 3.54], t(59) = 92.03, p < 0.001). Finally, the model predicting the 20–30 m range showcased a conditional r² of 0.84, a marginal r² of 0.22, and an intercept at 1.40 (95% CI [1.37, 1.43], t(59) = 83.00, p < 0.001).

Significant main effects were observed for the testing session across three intervals: 0–30 m (beta = −0.07, t(59) = −7.64, p < 0.001), 0–20 m (beta = −0.04, t(59) = −5.17, p < 0.001), and 20–30 m (beta = −0.03, t(59) = −8.38, p < 0.001). These findings suggest that the effect size was consistently negative across all intervals, with standardized beta values indicating the following magnitudes: for 0–30 m, Std. beta = −0.35, 95% CI [−0.44, −0.26]; for 0–20 m, Std. beta = −0.28, 95% CI [−0.39, −0.17]; and for 20–30 m, Std. beta = −0.46, 95% CI [−0.57, −0.35] (see Figure 3).

Specifically, post hoc analysis revealed significant differences between T1 and the other testing sessions (p < 0.001) across all analyzed distances (0–30 m, 0–20 m, and 20–30 m). Additionally, T2 and T3 were found to differ significantly from T4. However, no statistical differences were observed between the control period (T2 vs. T3) (see

Table 2. Comprehensive post hoc analysis of sprint performance across testing sessions.

	Sprint Performance
	1st Test	2nd Test	3rd Test	4th Test	Post Hoc
0–20	3.44 ± 0.03	3.36 ± 0.04	3.35 ± 0.03	3.3 ± 0.03	T1–T2: p < 0.001; Diff: 0.08; t: 4.82
					T1–T3: p 0.002; Diff: 0.08; t: 7.67
					T1–T4: p < 0.001; Diff: 0.14; t: 7.67
					T2–T4: p 0.005; Diff: 0.06; t: 3.43
					T3–T4: p 0.019; Diff: 0.06; t: 3.02
0–30	4.81 ± 0.05	4.66 ± 0.05	4.69 ± 0.04	4.57 ± 0.04	T1–T2: p < 0.001; Diff: 0.15; t: 7.77
					T1–T3: p < 0.001; Diff: 0.13; t: 5.33
					T1–T4: p < 0.001; Diff: 0.24; t: 11.61
					T2–T4: p < 0.001; Diff: 0.09; t: 4.74
					T3–T4: p < 0.001; Diff: 0.11; t: 5.00
20–30	1.37 ± 0.01	1.31 ± 0.01	1.32 ± 0.02	1.27 ± 0.02	T1–T2: p < 0.001; Diff: 0.07; t: 7.19
					T1–T3: p < 0.001; Diff: 0.05; t: 4.17
					T1–T4: p < 0.001; Diff: 0.10; t: 10.63
					T2–T4: p < 0.001; Diff: 0.04; t: 4.07
					T3–T4: p < 0.001; Diff: 0.06; t: 5.31

Notes: T1, T2, T3, and T4 represent the first, second, third, and fourth testing sessions, respectively; Diff—absolute difference(s) between tests; and p—p-value.

).

3.2. External Load Analysis

LMMs were fitted to predict different metrics using the period variable, consistently treating ‘player ID’ as a random effect. The model targeting distance/Z5(m) showed a conditional r² of 0.13, a marginal r² of 4.61 × 10⁻³, and an intercept of 38.45 (95% CI [26.02, 50.88], t(1725) = 6.07, p < 0.001). Whereas for distance/Z5(m)′, the conditional r² was 0.03, the marginal r² was 0.03, and the intercept was −0.12 (95% CI [−0.49, 0.25], t(1725) = −0.64, p = 0.521). In the model predicting distance/Z6(m) using the formula distance/Z6(m) ~ 1 + period, the model displayed moderate explanatory power (conditional R² = 0.14) and a marginal r² of 4.89 × 10⁻³. Its intercept, corresponding to period = 0, was valued at 10.98 (95% CI [2.06, 19.90], t(1725) = 2.41, p = 0.016). In contrast, the model for distance/Z6(m)′ revealed a conditional r² of 0.03 and a marginal r² of 0.02, with an intercept at −0.24 (95% CI [−0.53, 0.05], t(1725) = −1.61, p = 0.108). Predicting “max speed” (km/h) led to a model with a conditional r² of 0.12, marginal r² of 4.22 × 10⁻³, and an intercept of 23.10 (95% CI [22.52, 23.68], t(1725) = 78.01, p < 0.001). For “speed events”, the model’s explanatory power was moderate (conditional R² = 0.17), with a marginal r² of 7.31 × 10⁻³, and an intercept of 15.02 (95% CI [11.73, 18.30], t(1725) = 8.97, p < 0.001). Lastly, the model pertaining to “speed events′” exhibited a conditional r² of 0.06, a marginal r² of 0.04, and an intercept of 0.03 (95% CI [−0.05, 0.12], t(1725) = 0.81, p = 0.419).

The period variable had significant effects on the z5 distance (beta = 5.73, t(1725) = 2.80, p = 0.005) and on the z5′ distance (beta = 0.54, t(1725) = 6.61, p < 0.001), followed by pronounced effects on the z6 distance (beta = 4.14, t(1725) = 2.90, p = 0.004) and on the z6′ distance (beta = 0.34, t(1725) = 5.36, p < 0.001). This illustrates that the effect size was consistently positive across these distances, with standardized beta values providing further clarity: for z6, Std. beta = 0.07, 95% CI [0.02, 0.12]; for z6′, Std. beta = 0.13, 95% CI [0.08, 0.18]; for z5, Std. beta = 0.07, 95% CI [0.02, 0.12]; and for z5′, Std. beta = 0.16, 95% CI [0.11, 0.21] (see Figure 4).

For high-speed running distances, post hoc analysis found statistically significant differences in absolute volumes between P1 and P3. Similarly, differences in relative volumes were significant for both the P1–P3 and P2–P3 intervals (p < 0.001) (see

Table 3. Comprehensive post hoc analysis of absolute and relative workload parameters at high intensity, alongside principal workload indicators by period.

		Period 1	Period 2	Period 3	Post Hoc
Absolute Workload	distance/Z5 (m)	45.0 ± 5.45	48.9 ± 5.14	56.1 ± 5.21	P1–P3: p: 0.024; Diff: −11.1; t: −2.65
	distance/Z6 (m)	18.4 ± 3.95	15.1 ± 3.75	25.1 ± 3.79	P2–P3: p < 0.001; Diff: −10; t: −4.19
	speed events	16.4 ± 1.47	19.1 ± 1.41	20.2 ± 1.43	P1–P2: p: 0.021; Diff: −2.66; t: −2.71; P1–P3: p < 0.001; Diff: −3.75; t: −3.72
	acc events	2.09 ± 0.31	1.96 ± 0.30	3.64 ± 0.30	P1–P3: p < 0.001; Diff: −1.55; t: −7.36; P2–P3: p < 0.001; Diff: −1.67; t: −9.66
Relative Workload	distance/Z5 (m)′	0.66 ± 0.13	0.64 ± 0.11	1.65 ± 0.11	P1–P3: p < 0.001; Diff: −0.99; t: −5.99 P2–P3: p < 0.001; Diff: −1.0; t: −7.0
	distance/Z6 (m)′	0.29 ± 0.11	0.20 ± 0.09	0.90 ± 0.09	P1–P3: p < 0.001; Diff: −0.61; t: −4.72; P2–P3: p < 0.001; Diff: −0.7; t: −6.31
	speed events’	0.229 ± 0.03	0.243 ± 0.03	0.486 ± 0.03	P1–P3: p < 0.001; Diff: −0.26; t: −7.33; P2–P3: p < 0.001; Diff: −0.24; t: −8.11
	acc events’	0.03 ± 0.01	0.02 ± 0.01	0.09 ± 0.01	P1–P3: p < 0.001; Diff: −0.06; t: −6.95; P2–P3: p < 0.001; Diff: −0.06; t: −8.91
Intensity Indicators	max speed (km/h)	23.3 ± 0.25	23.6 ± 0.24	23.9 ± 0.24	P1–P3: p: 0.023; Diff: −0.52; t: −2.66
	max acc (m/s2)	3.91 ± 0.04	3.94 ± 0.04	4.05 ± 0.04	P1–P3: p < 0.001; Diff: −0.15; t: −4.09 P2–P3: p < 0.001; Diff: −0.11; t: −3.7
	max dec (m/s2)	−4.30 ± 0.06	−4.46 ± 0.05	−4.50 ± 0.05	P1–P2: p < 0.001; Diff: 0.17; t: 3.65; P1–P3: p < 0.001; Diff: 0.20; t: 4.23
	m/min	64.6 ± 4.80	65.6 ± 3.88	116.2 ± 3.92	P1–P3: p < 0.001; Diff: −51.6; t: −8.30; P2–P3: p < 0.001; Diff: −50.5; t: −9.16

).

Regarding very high-speed distances, there were significant differences between P2 and P3 in both absolute and relative terms (p < 0.001). While P1 and P3 showed variations in relative volumes, no such differences were observed between P1 and P2 for either absolute or relative very high-speed running measurements.

4. Discussion

This study primarily aimed to clarify the ongoing debate concerning the efficacy of integrated versus decontextualized sprint-specific strategies on acceleration in professional women’s football. Our findings suggest the potential for enhancing acceleration capabilities during competitive periods among trained players. Notably, both integrated and sprint-specific strategies, when emphasizing high-speed actions, exhibited positive impacts on sprinting ability across diverse distances.

In the context of team sports, professionals aim to balance player availability and performance [36]. Unlike individual sports, the relationship between fitness and performance in team sports is not straightforward [37]. This reality, compounded by dense competitive schedules, impacts loading strategies, often placing priority on player availability [38]. Notably, poor load management increases the risk of injury, particularly during periods of intense competition [39]. However, accepting a gradual decline or “plateau” in performance indicators such as acceleration over the course of a season can have negative effects on athletes. This is especially true since these conditions are not uniform among players throughout the season, so the stimulation to optimize such capabilities cannot be discarded.

Our results suggest that similar improvements in acceleration can be achieved using both sprint-specific methods and game-based approaches, provided the design of the game-based approaches allows for situations that necessitate high-intensity actions. Concerning resisted sprint training for female athletes, the literature still has a limited number of investigations on this topic. However, the results observed during this first period align closely with the findings of studies such as [40,41]. Both studies were of similar length, consisting of eight sessions, and despite the employment of different loads (12% BM and 40% BM), similar reductions in 30-m sprint times were reported. Specifically, reductions ranged from approximately 0.9 s for physically active women (T30: 5.14 s) to 0.16 s for highly trained handball players (T30: 4.81 s). In contrast, other studies focusing on analogous training methods have not provided detailed split times, choosing instead to highlight the impact on overall running velocity. Such studies observed minor enhancements during the later stages of acceleration without significant changes in the initial phases [42,43]. The variability in outcomes across these studies might stem from differences in intervention durations, participant characteristics, or specific loading dynamics, further complicating comparisons given the existing research gap [14,15].

Moreover, comparing the effectiveness of these methods with game-based strategies is challenging, mainly due to the lack of research focused on this objective. Most existing research on game-based interventions has demonstrated subpar outcomes in terms of enhancing acceleration when sport only is implemented [14], a finding reinforced by our study. Studies conducted by Djaoui et al. [44] or Kyprianou et al. [45] have examined game-based approaches, highlighting their inefficacy in reaching top speeds, which may be due to an overreliance on technical–tactical contexts or the intrinsic motivation of the athlete.

Performance improvements were noted in two conditions: (i) when specific sprint training was incorporated while maintaining a comparable overall workload to other intervals (e.g., P1 vs. P2); and (ii) when integrated training increased both absolute and relative workloads, particularly for key metrics (e.g., P1 vs. P3 and P2 vs. P3). In the periods characterized by integrated training that did not emphasize speed actions (P1 vs. P2), significant differences were only observed in variables such as “speed events” and “max dec”. Both variables showed higher average intensities during P2. However, despite recording equal or lower average intensities for the previously mentioned variables, performance improvements were only notable when the stimuli of that period (P1) were complemented with resisted sprint training. In contrast, during P3, the increase in maximum deceleration intensity was notable. These higher deceleration intensities likely reflect increased overall physical demands, which may be partly due to improved acceleration capabilities or elevated velocities preceding decelerations as a result of task design modifications. For example, extending the acceleration phase before a change of direction—such as accelerating over 15 m compared to 5 m—results in higher approach speeds (approximately 83% versus 54% of maximum velocity), which, in turn, requires a greater number of braking steps (around five to six steps versus three steps) [46], and also increases the deceleration load not in terms of training volume but in intensity [47]. Specifically, T1 0–20 recorded 3.44 ± 0.03, T2 0–20 had 3.36 ± 0.04, and T3 0–20 was at 3.35 ± 0.03, with differences ΔT1–T2 = 0.08 and ΔT2–T3 = 0.01. Similarly, T1 0–30 had 4.81 ± 0.05, T2 0–30 was 4.66 ± 0.05, and T3 0–30 recorded 4.69 ± 0.04, with differences ΔT1–T2 = 0.15 and ΔT2–T3 = −0.03. Lastly, T1 20–30 showed 1.37 ± 0.01, T2 20–30 was 1.31 ± 0.01, and T3 20–30 registered 1.32 ± 0.02, with variations ΔT1–T2 = 0.05 and ΔT2–T3 = −0.01. During the final phase, P3 (T3–T4), notable reductions in sprint times were observed across all distances. Specifically, the 0–20 m segment showed a difference ΔT3–T4 = 0.06 (p = 0.019), the 0–30 m segment showed a difference ΔT3–T4 = 0.11 (p < 0.001), and the 20–30 m segment had a difference ΔT3–T4 = 0.05 (p < 0.001). These reductions indicate further improvements in acceleration capability, particularly during the latter portions of the sprint. It is noteworthy that longitudinal observational studies [48,49] have reported enhancements in acceleration performance during the preseason and first stages of competitive periods. Remarkably, these gains were observed even without specialized training interventions targeting these capabilities, suggesting that sport practice during these phases might inherently foster the development of such capabilities.

Nevertheless, reductions in high-intensity training volumes during the latter phase of the season, as observed by Mara [49], combined with the absence of specialized sprint training noted by Jiménez-Reyes et al. [48], have been associated with a decline in sprint performance, particularly during the initial phases of acceleration. Conversely, the ability to reach maximal speeds appears relatively consistent with limited inter-individual variation, as noted by López-Sagarra et al. [50].

Our findings emphasize the effectiveness of integrated training, especially when focused on high-speed activities, in enhancing acceleration capabilities across evaluated distances. Maintaining mean absolute and relative workloads through a game-centric integrated approach over an identical duration (6 weeks) was less effective in enhancing acceleration capabilities than when complemented with sprint-specific training (as evidenced by differences only in speed events and “max dec” between P1 and P3). However, the inclusion of high-speed metrics—such as distances covered at speeds exceeding 21 and 24 km/h, the number of speed events, or vigorous accelerations—led to significant performance improvements across all distances compared to integrated training alone.

Both individual studies [51,52] and systematic reviews [14,15] have found game-centric approaches like SSGs to be ineffective in enhancing sprinting capabilities. In the third phase of our study (P3), we prioritized strategies such as altering space [28], providing expansive play areas per player [25,53], and incorporating specific SSG variations termed ‘transition games’ [26,27,53] focused on counter-attacking scenarios with swift offensive and defensive transitions. Our results revealed improvements that surpassed those previously documented in the literature. While current evidence highlights the increased specificity of game-based approaches in elevating the frequency of high-intensity events, no studies have yet evaluated their long-term effects on in-season professional football players.

4.1. Limitations

Conducting research in elite athletic environments presents unique challenges. Throughout this study, the absence of players during evaluation sessions due to international duties was a notable limitation. Additionally, the dynamic nature of the team composition, influenced by transfer periods, further complicated our research. Given these challenges, we opted for an LMM approach, diverging from traditional analysis methods. This decision was driven by the difficulties in achieving a complete block design, underscoring the adaptability required in high-level research contexts. One limitation of the study is the extended time span between the second and third testing sessions, partly due to the inclusion of a transitional period. Although only the six weeks leading up to T3 were analyzed after the competitive regime resumed, and no significant workload differences were observed compared to the first intervention period, the variation in time intervals between testing sessions, particularly between T2 and T3, may have influenced the results.

4.2. Future Research Directions

While this study contributes valuable insights into the impact of integrated and sprint-specific training methods, further research is needed in several areas. First, more investigations are warranted on resisted sprint training in female athletes, as the current literature is still limited. Studies exploring optimal load configurations and their long-term impact on acceleration capabilities will help refine best practices. Additionally, further research on game-based approaches, particularly those incorporating specific high-intensity actions, is necessary to better understand their effects on sprint performance. Comparative studies examining the effectiveness of integrated strategies across various team sports and populations could provide a broader perspective on their utility.

4.3. Practical Implications

The findings of this study have important implications for the design of training programs in professional women’s football. Coaches and performance staff should consider incorporating both sprint-specific and speed-oriented game-based training into their regimens, particularly during competitive periods. When game-based training is used, special attention should be given to designing drills that recreate high-speed events. Maintaining these high-intensity stimuli throughout the season is critical to avoiding declines in sprint performance. Each intervention period showed approximately a one-tenth of a second improvement in 0–20 m and 0–30 m sprints, equating to a 30–50 cm advantage in 30 m sprints during one-on-one duels, which can be decisive in match situations. These results offer valuable information supporting different strategies that can be incorporated into varied training contexts to enhance sprint performance.

5. Conclusions

Both high-speed game-based approaches and resisted sprint training have been identified as effective methods for enhancing acceleration capabilities. Resisted sprint training, in particular, delivers substantial benefits, even when high-intensity events are not specifically targeted during regular football training. Introducing training elements that increase high-intensity volumes prevents the decline of sprint performance over the competitive season, yielding improvements similar to those seen in sprint-specific interventions. In contrast, periods of training without a sprint focus led to changes in performance that were not statistically significant. These findings highlight the importance of incorporating structured sprint-focused elements into football training to maintain and improve acceleration throughout the season.