The Relationship Between Aerobic Capacity, Lactate Clearance, and Heart Rate Recovery in Ice Hockey Players

Abstract

This study investigated the influence of aerobic capacity on lactate clearance rate and heart rate recovery during ice hockey matches. Considering the growing intensity and anaerobic demands of modern ice hockey, the ability to recover quickly between high-intensity shifts is essential for optimal performance. Thirty-eight amateur ice hockey players (age: 35 ± 5 years; VO₂max: 48.93 ± 3.88 mL·min⁻¹·kg⁻¹) from the Silesian Amateur Hockey League underwent laboratory ramp tests to determine VO₂max, followed by on-ice repeated sprint tests and heart rate monitoring during matches. The results demonstrated significant positive correlations between VO₂max and lactate clearance (ΔLa4–8min [mmol/L]= 2.55 ± 0.58 mmol·L⁻¹; rho = 0.545; p < 0.001), as well as heart rate recovery (Δ%HRmax = 25.88 ± 2.09%; rho = 0.682; p < 0.001). Players with higher VO₂max exhibited a faster reduction in heart rate during recovery periods between shifts and maintained better sprint performance (rho = –0.877; p < 0.001). These findings confirm that higher aerobic capacity enhances both metabolic and autonomic recovery processes, enabling players to sustain high-intensity efforts more effectively during the game. The study highlights the importance of developing aerobic fitness in hockey training to improve recovery efficiency and match performance.

1. Introduction

The training regimen of hockey players places a strong emphasis on the development of anaerobic capacity [1], the results of which have increased significantly in recent years, thereby confirming that the game is becoming faster and more intense [2]. The heightened aerobic capacity of players enables expeditious post-workout recovery during brief rest periods of approximately three minutes, particularly in the context of playing four formations [3]. During a game, the coach deploys 3–4 formations of players, with each formation comprising two defensemen and three forwards, in addition to two goalkeepers [4]. Players spend approximately 30 s to 1 min on the ice, with 2 min of rest allocated between shifts [5]. The intensity of the game necessitates rapid substitutions, with no interruption to the action, maintaining maximal level during the shift. Peak heart rates during a shift on the ice exceed 90% of HR max with average on-ice values of about 85% of HR max [6].

A number of authors have reported that maximal oxygen uptake (VO₂max) values for hockey players should be at least in the 50–60 mL/kg/min range [7,8,9,10,11]. A series of studies carried out in the world’s leading league, the National Hockey League (NHL), have indicated that the maximum rate of oxygen consumption of hockey players applying to the NHL was higher than the levels reported by other researchers, ranging from 54 to 62 mL/kg/min [12]. During the match, 69% of the hockey player’s effort is based on anaerobic metabolism, during which the hockey player incurs an oxygen debt of 8.5–9.6 L and there is a significant increase in lactate concentration to levels of 8–14 mmol/L [7]. The remarkable capacity of athletes to execute interval exercise on the ice, which is characterized by repeated high-intensity efforts, is largely determined by the rapid restitution processes, during which aerobic metabolism continuously contributes to the resynthesis of energy stores in muscle tissue—even as anaerobic pathways predominate during short, maximal bouts [9,13].

The rate of restitution is directly proportional to the rate of utilization of glycogen breakdown products [14], and a reduction in VO₂max can impede an athlete’s capacity for expeditious recovery [15]. In the long term, this can hinder an athlete’s ability to sustain high-intensity effort during a match and to execute the tactical objectives outlined by the coach during a match [16]. The most common test used to analyze the aerobic capacity of hockey players is the VO₂max ramp test on a cycloergometer, in which the load increases by 0.5 W per second [17,18]. The ramp test is frequently employed to avoid local fatigue issues when testing hockey players on cycloergometers, or when specific tests have been performed on ice [19].

The objective of this study was to ascertain whether the level of aerobic capacity influences the rate of restitution during players’ work on the ice during a game. To this end, the correlations between VO₂max and the rate of lactate utilization as well as the rate of heart rate reduction during the rest period of the game were analyzed. Additionally, it was aimed to recruit amateur athletes to demonstrate that in-game recovery is dependent on VO₂max, considering the high heterogeneity observed in amateur sports. We hypothesized that ice hockey players with higher VO₂max would exhibit (a) a larger decline in capillary blood lactate from 4 to 8 min following a repeated-sprint protocol, with a moderate-to-large positive association, and (b) a faster relative decrease in heart rate during inter-shift rest periods on ice.

2. Material and Methods

2.1. Study Design

Testing was conducted in December 2023. Laboratory and on-ice assessments lasted two days for each hockey player. Prior to the exercise testing, anthropometric assessments—including bioimpedance analysis and height measurement—were performed under standardized conditions. Three hours after breakfast, each hockey player completed a ramp test on a cycloergometer to determine VO₂max. All participants performed the test under laboratory conditions, having abstained from exercise and medication for 48 h prior to testing, with ambient temperature maintained at 21 °C throughout. After 48 h of rest from the laboratory test, on-ice assessments were conducted: a 6 × 30 m speed-endurance test (six 30 m sprints with 2 min of passive recovery) was used to simulate in-game efforts [13], during which sprint times were recorded to the nearest 0.01 s with photocells and capillary blood samples for lactate concentration were taken at 4 and 8 min of recovery. Additionally, heart rates were monitored at 5 s intervals during two playoff games using chest-strap transmitters synchronized to video recordings; these data were used to calculate peak and second-minute recovery heart-rate values (%HRmax) and corresponding deltas for each shift and period.

2.2. Participants

A priori power analysis was conducted using G*Power v3.1.9.6 to determine the required sample size for a repeated measures ANOVA (within-subjects design). Based on an assumed medium effect size (ES = 0.30), an alpha level of 0.05, and a desired statistical power of 0.95, the analysis indicated that a minimum total sample size of 31 participants would be necessary. The calculated actual power for the design was 0.956. Prior to testing, each tested participant underwent a medical examination to rule out the presence of a cold, fever, or any other contradiction identified by physician that could serve as an exclusion criterion for the study. All the athletes possessed up to date medical examinations confirming proper health status and the ability to perform high-intensity exercise. The study ultimately included 38 men who played in two teams of the Silesian Amateur Hockey League under the aegis of the Polish Hockey Association; goalies did not participate in on-ice testing due to the significantly different physical demands of their playing position. Participants were recruited via direct invitations sent to all eligible team members within the league, with minimal highest division experience of 5 years required. To minimize potential selection bias, inclusion criteria were clearly communicated, and all athletes who met these criteria were invited to participate. The tests were conducted after the end of the regular season and before the playoffs in December 2023. The participants had a mean age of 35 ± 5 years, height of 177.70 ± 4.22 cm, and body mass of 83.36 ± 7.25 kg. Body composition analysis showed an average fat-free mass of 66.25 ± 4.70 kg, body fat mass of 17.11 ± 3.90 kg, and total body water of 48.58 ± 3.39 L. The mean body mass index (BMI) was 26.38 ± 1.80 kg/m² and the mean percentage of body fat was 20.35 ± 3.32%. Participants were advised about the study’s possible risks and benefits, and their consent to participate was received. Each participant was asked to get a whole night’s sleep of at least 8 h before testing, to abstain from any ergogenic substances for 48 h before testing [20], and not to perform any physical activity that might affect their performance. The tested athletes were informed about the purpose and methods of the research and about the possibility of withdrawing from the research at any stage. The research was conducted with the consent of the Ethics Committee for Scientific Research at the Jerzy Kukuczka Academy of Physical Education in Katowice (29/2023) of 9 March 2023. All procedures were carried out according to the Declaration of Helsinki.

2.3. Anthropometric Measures

Body composition was estimated using an 8-electrode bioimpedance analyzer (InBody 720; Biospace Co., Tokyo, Japan). All anthropometric measurements were conducted by a certified MEDfitness representative (the exclusive InBody distributor in Poland) under standardized procedures. Height was measured to the nearest 0.1 cm using a wall-mounted stadiometer; participants stood barefoot with heels together, back straight, and head in the Frankfort plane. Body mass was measured between 09:00 and 10:00, two hours after an easy-to-digest breakfast; participants wore light athletic clothing, removed shoes and socks, and emptied pockets. A calibrated digital scale (±0.05 kg accuracy) was used, and each athlete’s mass was recorded twice—if the two readings differed by more than 0.1 kg, a third measurement was taken and the median value retained. The intraclass correlation coefficients (ICC) for repeated InBody 720 body composition analyses ranged from 0.86 to 0.97.

2.4. VO₂max

Each athlete performed a ramp protocol on an Excalibur Sport cycloergometer (Lode T30×1), beginning at a resistance of 30 W·min⁻¹ with a pedal cadence maintained between 70 and 80 rpm. Workload was increased continuously at a rate of 0.5 W·s⁻¹ until volitional exhaustion to determine VO₂max. During the T30×1 test, breath-by-breath measurements of HR, minute ventilation (VE), oxygen uptake (VO₂), carbon dioxide output (VCO₂), respiratory exchange ratio (RER), and breathing frequency (BF) were recorded using a MetaLyzer 3B-2R system (Cortex, Isernhagen, Germany). All gas-exchange data were averaged in 15 s epochs to identify an oxygen-uptake plateau (ΔVO₂ ≤ 100 mL·min⁻¹ at peak). VO₂max was confirmed when both of the following criteria were satisfied: (1) a plateau in VO₂ despite increasing workload and (2) an RER ≥ 1.15 (100% of participants). Prior to each test, the ergometer’s saddle height and handlebar position were individually adjusted to optimize comfort and biomechanical alignment.

2.5. Specific Endurance Test

After 48 h of rest, on-ice tests were performed with the subjects wearing full ice hockey equipment. Special on-ice tests were performed to obtain information on the athlete’s speed endurance: a 6 × 30 m speed endurance test repeated 6 times with a 2 min rest interval. This was designed to simulate the effort during the period including change in direction, similar to shift duration (40 s) and rest time, while performing on maximal intensity as during the game. After the last repetition of the 6 × 30 m test, capillary blood samples were taken to determine lactate concentrations at the 4th and 8th minute of rest. Using these data, ΔLa4–8min [mmol/L] was calculated. Microgate photocells (Bolzano, Italy) recorded the times of each sprint with an accuracy of 0.01 s.

2.6. Heart Rate Measurement

On-ice HR acquisition and synchronization with video. During two playoff games, HR was recorded every 5 s using the Polar Team system (Polar Electro Oy, Kempele, Finland). Prior to warm-up, the time-of-day on the Polar base unit (tablet controlling the Team system) and the Panasonic Full HD camera was synchronized to the same network time source. After the devices were time-matched, we performed a fine alignment between the exported HR time series and the video time-code using an event-based procedure: (1) we time-stamped the first bench entry after the opening face-off (frame-accurate at 50 Hz; 0.02 s resolution) and the first bench exit to define an on-ice/off-ice step function from video; (2) we computed the cross-correlation between that step function and the first-difference in HR (to capture the characteristic HR increase when returning to play and drop at benching), and selected the lag that maximized correlation as the residual offset; (3) we repeated this procedure at the start of each period to check for clock drift and, if present, applied a period-specific offset. Because HR samples are available every 5 s, the effective timing precision for extracting “immediately after coming down” and “2 min” values was defined a priori as the nearest sample within 0–5 s of bench entry and the sample at 120 ± 2.5 s, respectively. For plotting only, HR was linearly resampled to 1 s, but all metrics were computed on the native 5 s grid. This pipeline yields alignment within one HR sample (±2.5 s) with video-frame identification at 0.02 s, while correctly acknowledging the HR sampling constraint. Polar chest-strap sensors are validated against ECG with minimal bias (0 ± 1 bpm) and ~1–3 ms agreement in R-R intervals during exercise, supporting Polar Team’s use for on-ice monitoring [21].

2.7. Lactate Measurement

A resting-baseline capillary blood sample was obtained from the athlete’s fingertip after a 10 min seated rest (baseline), and subsequent post-exercise samples were collected at the 4th and 8th minute of passive recovery following the final repetition of the 6 × 30 m speed-endurance test. For each sampling, the fingertip was first cleaned with an alcohol swab and allowed to air-dry. The first drop of blood was wiped away, and a second drop (~50 µL) was drawn into a heparinized capillary tube. Within 30 s of collection, 10 µL of whole blood was transferred onto a pre-warmed Biosen C-Line enzymatic-amperometric test strip (EKF Diagnostics GmbH, Barleben, Germany). The analyzer had been calibrated at the start of each testing, with verification via a 10 mmol·L⁻¹ quality-control solution. Each sample was measured in duplicate at ambient room temperature (21–23 °C), and if the two readings differed by more than 0.3 mmol·L⁻¹, a third measurement was performed; the median value was recorded. Baseline lactate ([La]rest) and post-effort values at 4 min (La₄) and 8 min (La₈) were documented, and the change in lactate concentration (ΔLa) was calculated as La₄–La₈.

2.8. Statistical Analysis

Prior to inferential analyses, basic descriptive statistics—median and interquartile range (IQR)—were calculated for all variables to characterize the sample, and normality was assessed using the Shapiro–Wilk test. As normality was violated for several variables, nonparametric methods were applied: correlations between variables were analyzed using Spearman’s rho, with magnitudes interpreted as trivial (|rho| ≤ 0.10), small (0.10 < |rho| ≤ 0.30), moderate (0.30 < |rho| ≤ 0.50), large (0.50 < |rho| ≤ 0.70), very large (0.70 < |rho| ≤ 0.90), and nearly perfect (|rho| > 0.90 [22]. Repeated measures differences were evaluated using the Friedman test, with effect size expressed as Kendall’s W and interpreted as trivial (W < 0.10), small (0.10 ≤ W < 0.30), moderate (0.30 ≤ W < 0.50), and large (W ≥ 0.50) [22]. In the event of a significant Friedman’s ANOVA, Durbin–Conover pairwise comparisons were carried out for post hoc analysis, with effect size for each comparison calculated as r = ∣Z∣/N and interpreted as trivial (r < 0.10), small (0.10 ≤ r < 0.30), moderate (0.30 ≤ r < 0.50), and large (r ≥ 0.50) [23]. Coefficient of variation (CV) was provied to assess VO₂max variability. Statistical significance was set at α ≤ 0.05, and all analyses were conducted using Jamovi (2.6.44.0, Sydney, Australia) and STATISTICA 13.1 (TIBCO Software Inc., Palo Alto, CA, USA).

3. Results

Analyses began by presenting basic descriptive statistics. Detailed results are presemted in

Table 1. Basic descriptive statistics.

Variable	Me (IQR) Min–Max	Variable	Me (IQR) Min–Max
WR [W]	324.00 (44.38) 261–382	Lar 4′ [mmol/L]	12.56 (1.90) 9.44–15.72
HR [1/min]	180.25 (10.75) 167–199	Lar 8′ [mmol/L]	10.16 (2.28) 6.10–13.54
VO2max [mL/min/kg]	48.10 (4.35) 40.65–64.43	ΔLa4–8min [mmol/L]	2.48 (0.95) 1.51–3.68
VE [l/min]	129.13 (21.20) 83.40–188.40	Δ%HRmax I	29.57 (4.22) 26.32–34.38
BF [1/min]	51.85 (10.42) 37.40–70.20	Δ%HRmax II	25.78 (1.97) 22.61–30.29
6 × 30 m-I [s]	34.63 (2.48) 33.02–40.40	Δ%HRmax III	22.22 (4.35) 17.49–25.79
6 × 30 m-VI [s]	36.11 (2.58) 34.11–42.34	Δ%HRmax	25.79 (3.01) 21.78–30.10

WR = work rate; HR = heart rate; VO₂max = maximal oxygen uptake; VE = ventilation; BF = breathing frequency; 6 × 30 m-I = time of the first 30 m sprint set; 6 × 30 m-VI = time of the sixth 30 m sprint set; Larest 4′ = blood lactate concentration after 4 min; Larest 8′ = blood lactate concentration after 8 min; ΔLa4–8min = change in lactate concentration from 4 to 8 min; Δ%HRmax = percentage decrease in maximal heart rate; 1st, 2nd, and 3rd periods = consecutive recovery stages; Me—median; IQR–interquartile range; Min—minimal value; Max—maximal value.

.

Participants demonstrated a median work rate (WR) of 324.00 W (IQR 44.38), with a median peak heart rate (HR) of 180.25 bpm (IQR 10.75). Median VO₂max was 48.10 mL/min/kg (IQR 4.35; CV = 7.93%), median ventilation (VE) was 129.13 L/min (IQR 21.20), and median breathing frequency (BF) was 51.85 breaths/min (IQR 10.42). In the repeated sprint test, the median time for the first sprint (6 × 30 m-I) was 34.63 s (IQR 2.48), and for the sixth sprint (6 × 30 m-VI) it was 36.11 s (IQR 2.58). Blood lactate concentration at 4 min (Lac 4′) had a median of 12.56 mmol/L (IQR 1.90), and at 8 min (Lac 8′) it was 10.16 mmol/L (IQR 2.28), resulting in a median lactate reduction (ΔLa_4–8min) of 2.48 mmol/L (IQR 0.95). Heart rate recovery showed a median decrease of 29.57% (IQR 4.22) in the first period (Δ%HRmax I), 25.78% (IQR 1.97) in the second period (Δ%HRmax II), and 22.22% (IQR 4.35) in the third period (Δ%HRmax III), with an overall median recovery (Δ%HRmax) of 25.79% (IQR 3.01).

The correlation analysis using Spearman’s rho revealed significant associations between VO₂max and several performance variables (Figure 1). A significant negative correlation was observed between VO₂max and 6 × 30 m-VI (rho = −0.397, p = 0.014). A very strong negative correlation was found between VO₂max and Δ6 × 30 m (rho = −0.877, p < 0.001). Moreover, VO₂max showed a strong positive correlation with ΔLa4–8min (rho = 0.545, p < 0.001). No significant association was observed between VO₂max and the first sprint (6 × 30 m-I, rho = −0.102, p = 0.541).

Further revealed significant positive relationships between VO₂max and all heart rate adaptation variables (Figure 2). Specifically, VO₂max correlated positively with Δ%HRmax-I (rho = 0.528, p < 0.001), Δ%HRmax-II (rho = 0.483, p = 0.002), Δ%HRmax-III (rho = 0.617, p < 0.001), and the overall Δ%HRmax (rho = 0.682, p < 0.001).

The non-parametric Friedman’s ANOVA based on ranks revealed a significant effect across the compared conditions (χ²(2, N = 38) = 76.00, p < 0.0001; W = 1). Subsequent multiple comparison testing using the Durbin-Conover test indicated significant differences between all pairs of measurements: ∆%HR_max-I vs. ∆%HRmax-II (r ≈ 1; p < 0.001), ∆%HRmax-I vs. ∆%HRmax-III (r ≈ 1; p < 0.001), and ∆%HR_max-II vs. ∆%HR_max-III (r ≈ 1; p < 0.001). Results are presented below (Figure 3).

4. Discussion

The purpose of this study was to determine whether the level of aerobic capacity influences the rate of restitution during on-ice performance in amateur ice hockey players. Specifically, the investigation focused on evaluating the correlations between maximal VO₂max and recovery indices such as lactate clearance (ΔLa_4–8 min) and heart rate reduction (Δ%HR_max) measured over successive recovery periods. The results showed that higher VO₂max values were significantly associated with a greater reduction in resting heart rate and a smaller decline in repeated sprint performance. These findings suggest that superior aerobic conditioning enhances both metabolic and autonomic recovery processes following high-intensity intermittent exercise.

The values of VO₂max observed in the present study (48.93 ± 3.88 mL·min⁻¹·kg⁻¹) align with the broad range reported in the literature for hockey players at various levels of proficiency. Analyses of NHL players have revealed VO₂max values between 54 and 62 mL·min⁻¹·kg⁻¹ [24]. Such discrepancies in aerobic capacity across different hockey populations may explain why our correlations are particularly pronounced in the amateur group, where greater heterogeneity in physical fitness enables a clearer detection of the relationship between VO₂max and recovery parameters. From a statistical perspective, greater variability in VO₂max increases the likelihood of detecting a stronger positive relationship between VO₂max and both Δ%HR_max and ΔLa.

From a practical perspective, the observed associations indicate that aerobic capacity contributes meaningfully to between-shift recovery in ice hockey. The positive correlation between VO₂max and the reduction in heart rate during bench rest reached a large magnitude, meaning that players with higher aerobic fitness can restore cardiac autonomic balance more rapidly between high-intensity bouts. Similarly, the positive relationship between VO₂max and post-exercise lactate clearance reflects the physiological role of enhanced oxidative capacity in accelerating removal and oxidation of lactate, thereby facilitates repeated high-intensity efforts. Conversely, the negative correlation between VO₂max and sprint performance decrement across the 6 × 30 m test points that athletes with greater aerobic capacity are better able to maintain speed over repeated sprints, a quality directly transferable to competitive play. Although correlation does not imply causation, effect magnitudes in the moderate-to-large range are considered operationally relevant in applied sport science, particularly given that they exceed typical measurement error reported for HR recovery and lactate assessments. This observation is consistent with established evidence that individuals with superior aerobic fitness exhibit faster heart rate normalization after intense exercise [25].

Recovery mechanisms between shifts in hockey players have been examined in detail by comparing active versus passive recovery strategies. It was shown that light activity on the bench effectively increases cardiac output by an average of 2.5 ± 0.2 L/min (p = 0.03) at 45, 50, and 120 s post-exertion [26]. These changes were driven primarily by maintaining an elevated heart rate—approximately 12 beats/min higher (p = 0.05)—in conjunction with a physiologically meaningful, though not statistically significant, increase in stroke volume (11.6 mL, p = 0.06) [19]. Furthermore, investigations into lactate clearance confirmed that active recovery (bench stepping at a heart rate of 120 beats/min) resulted in significantly lower blood lactate concentrations (6.1 ± 2.2 mmol·L⁻¹) compared to passive recovery (8.1 ± 1.6 mmol·L⁻¹, p < 0.05) [27]. These mechanisms help explain why players with higher VO₂max values exhibit superior recovery: their aerobic system more efficiently supports oxidative processes during both exertion and subsequent recovery.

A well-developed endurance capacity likely enhances parasympathetic reactivation and diminishes sympathetic drive more rapidly after maximal efforts, as manifested by a greater drop in heart rate after the shift or period [28]. Consequently, VO₂max was inversely correlated with performance decrements in the 6 × 30 m sprint test. Players with greater aerobic fitness achieved better repeated sprint performance, as reflected by a lower velocity index (VI) or fatigue index over six 30 m sprints. Specifically, athletes with higher VO₂max maintained their sprint speed more consistently over successive 30 m efforts, whereas less aerobically fit athletes showed a greater decline in performance. This negative correlation suggests that high aerobic capacity mitigates fatigue during repeated anaerobic bursts. This finding is consistent with the notion that endurance-trained athletes are better able to sustain power output during repeated high-intensity bouts [29]. Previous studies in team sports have similarly reported that a higher VO₂max (or related measures such as lactate threshold) is associated with superior repeated sprint ability and faster recovery between sprints [30].

Notwithstanding the clear benefits of aerobic capacity, the efficiency of recovery was found to decrease over the course of the game. As the match progressed from the first to later periods, players showed signs of accumulating fatigue and slower recovery. Specifically, the Δ%HR between periods was smaller on average in the final period compared to earlier breaks, and the effects of fatigue on performance were more pronounced. Even the best-conditioned players experienced some attenuation of recovery by the third period, suggesting that the taxing nature of repeated high-intensity shifts eventually strains the body’s recovery mechanisms [31]. With each successive period, residual lactate from previous shifts may remain elevated and muscle glycogen stores become progressively depleted, leading to slower clearance and less energy available for recovery [14]. Cardiac autonomic recovery may also be blunted later in a match due to sustained sympathetic activation [32].

The present findings are consistent with the practical observation that peak skating speeds and work rates often decrease in the final periods of hockey games as fatigue increases [14]. It is likely that increased aerobic capacity raises the ceiling of recovery potential, mitigating but not completely preventing the late-game fatigue effect [25]. Recognizing this reduced recovery efficiency late in the game is important in interpreting performance results—for example, a slower sprint or shift in the third period may be as much a consequence of accumulated fatigue as it is of the player’s intrinsic ability [33].

The strong correlations observed in this study support the role of recovery processes during high-intensity intermittent exercise. Hockey is characterized by short, intense shifts that rely heavily on anaerobic energy pathways, followed by brief periods of recovery [14]. Under these conditions, a well-developed aerobic system becomes essential to remove metabolic by-products and restore cardiorespiratory balance between bouts [34]. It should be emphasized that not only aerobic capacity but also the appropriate strength ratio between specific muscle groups plays an important role in movement efficiency and the ability to perform high-intensity efforts. Previous research has shown that maintaining an optimal strength balance between the gluteus medius and thigh muscles may affect muscle activation patterns and postural stability during functional exercises, which could be particularly relevant in team sports such as ice hockey [35]. Our findings that players with higher VO₂max had faster heart rate recovery and smaller declines in performance suggest that their bodies were more adept at rapid recovery—likely through enhanced lactate clearance and phosphocreatine resynthesis during rest intervals [36]. Increased aerobic capacity is known to accelerate the removal of lactate from the bloodstream by optimizing oxidative metabolism; muscle cells with greater mitochondrial density and enzyme activity can oxidize lactate more efficiently [37,38]. Molecular studies have confirmed the presence of lactate dehydrogenase (LDH) within the mitochondria of skeletal muscle, cardiac muscle, and liver, thereby enabling the direct utilization of lactate as an energy substrate [39]. Mitochondrial oxidation of lactate is especially significant because cytosolic lactate concentrations exceed pyruvate concentrations by an order of magnitude, making lactate the predominant monocarboxylate oxidized by mitochondria in vivo [39]. Athletes with higher VO₂max values exhibit greater mitochondrial density and increased activity of oxidative enzymes, as evidenced by seasonal studies in hockey players, which demonstrated an increase in succinate dehydrogenase activity (3.26 ± 0.31 vs. 3.91 ± 0.11 μmol·mg protein⁻¹·min⁻¹) and citrate synthase activity (7.26 ± 0.70 vs. 8.70 ± 0.55 μmol·mg protein⁻¹·min⁻¹) following the hockey season, what reduces residual fatigue and helps maintain muscle pH, preparing the athlete for the next training session [40]. At the same time, superior aerobic fitness contributes to faster cardiac recovery, as evidenced by the greater Δ%HR in fitter players. This likely reflects a more rapid re-engagement of vagal (parasympathetic) tone and a decrease in circulating catecholamines after exercise, phenomena typically seen in endurance trained individuals [41,42].

While the study yielded valuable insights, several limitations must be acknowledged. First, the sample was limited to male amateur hockey players, which restricts the generalizability of the findings. It is plausible that the physiological responses exhibited by elite professional players, who generally possess higher VO₂max and greater anaerobic capacity, may not demonstrate the same extent of correlations observed in our cohort. Consequently, our interpretations of the mechanisms remain speculative. For instance, we attribute improved recovery to lactate metabolism and PCr resynthesis based on known physiology [43], but without direct measurements, we cannot confirm the exact biochemical pathways at work in these players. Thirdly, the cross-sectional design of the study precludes any conclusions about causality. While an association between higher VO₂max and enhanced recovery was observed, a definitive assertion that elevating a player’s VO₂max invariably leads to improved between-period recovery remains unsubstantiated without longitudinal evidence. Additionally, confounding factors such as individual differences in pacing, tactical role (e.g., forwards vs. defense may have different exertion patterns), or prior training history that were not fully controlled may exist. Although our laboratory assessments (incremental VO₂max ramp test and repeated-sprint protocol) provided controlled and reliable indices of aerobic capacity and recovery physiology, they cannot fully replicate the complexity of an actual ice-hockey game. In the laboratory, environmental factors (e.g., temperature, ice friction, tactical constraints) and contextual elements (e.g., line changes, opponent pressure, psychological stress) are absent. Consequently, physiological responses observed under standardized laboratory conditions may underestimate the variability and cumulative fatigue experienced during competition. To address this limitation, we incorporated heart-rate measurements from competitive playoff games, thereby linking laboratory-based indices with recovery dynamics under true match demands. Nevertheless, future studies should further increase ecological validity by integrating additional in-game metrics (e.g., time-motion analysis, accelerometry, lactate sampling on ice) to capture the multifactorial nature of competitive performance. It is important to note that the testing was conducted within the context of a specific amateur league, and the intensity and environmental conditions of a match may vary in other settings. Our data were collected during the playoff phase, when accumulated match exposure and training stress are typically high. This seasonal context may have influenced absolute values of VO₂max, lactate clearance, and heart-rate recovery, and thus the strength of the observed associations. While our results indicate meaningful links under these high-demand conditions, the magnitude of such relationships could differ at other stages of the season (e.g., pre-season or mid-season).

Our sample comprised exclusively male amateur hockey players, which significantly restricts the generalizability of these findings. We intentionally recruited amateur athletes to capture the high heterogeneity inherent in non-professional populations and to demonstrate that recovery dynamics are dependent on VO₂max within this demographic (CV = 7.93%). However, physiological responses might not mirror those observed here with CV of VO₂max testing oscillating around 1–3% [44]. Thus, extrapolation of our results to female players, youth athletes, or professional men should be made with caution and warrants direct investigation in those subpopulations.

Several uncontrolled variables may have influenced our associations. Individual training history (e.g., cumulative years of hockey-specific or cross-training), tactical role (forwards, defense, or special teams), and pacing strategies (e.g., a player deliberately modulating effort to conserve energy) could all affect both VO₂max and recovery kinetics. For example, a defenseman’s typical shift pattern—often characterized by more skating time but fewer high-speed sprints—differs from that of a forward, potentially altering lactate production and clearance. Similarly, athletes with a history of structured conditioning (e.g., off-season strength and conditioning programs) may demonstrate superior recovery independent of VO₂max per se. Although participants were instructed to maintain usual routines, future work should incorporate more detailed assessments—such as training logs, positional workload analyses, or wearable GPS tracking—to quantify and adjust for these confounders.

An important next step would be to conduct training intervention studies: for example, implementing an aerobic conditioning program for amateur players and tracking changes in VO₂max alongside changes in heart rate recovery and lactate clearance during games. Such studies could establish a causal relationship between improved aerobic fitness and improved restitution dynamics. It would also be informative to extend this research to different cohorts—including female hockey players and professional athletes—to determine if and to what extent the same correlations hold. Monitoring how restitution metrics evolve over the course of a competitive season, as players potentially improve fitness or accumulate fatigue, may provide insight into the temporal aspect of fitness and restitution.

In addition, the inclusion of more direct physiological measures in future studies would enhance mechanistic understanding. Techniques such as muscle biopsies or microdialysis could measure intramuscular lactate and phosphagen levels before and after exercise, while wearable near-infrared spectroscopy could non-invasively track muscle oxygenation and recovery kinetics in real time. Heart rate variability monitoring could complement post-exercise heart rate recovery as an index of autonomic function [45,46].

In light of the observed associations between VO₂max and both metabolic and autonomic recovery indices it is recommended that targeted aerobic conditioning be integrated into the weekly training prescription. Specifically, two to three high-intensity interval training (HIIT) sessions per week at approximately 90–95% of each player’s maximal heart rate—such as repeated 30 s on-ice sprints followed by 2 min active recoveries—should be implemented to elevate mitochondrial enzyme activity and improve lactate clearance [20,36]. Additionally, one longer, steady-state aerobic session (30–40 min of continuous skating or cycling at 65–75% HR_max) should be included to augment capillary density and oxidative capacity [29,43]. By combining short, near-maximal efforts targeting oxygen-delivery systems with moderate-intensity endurance work, phosphocreatine resynthesis and cardiac parasympathetic reactivation between shifts can be enhanced [36,41]. Over a 6–8-week mesocycle, interval volume may be progressively increased (e.g., adding two extra sprint repetitions per session) and recovery durations modestly reduced (e.g., decreasing passive rests from 2:00 to 1:30 min) to sustain adaptations and transfer improvements to on-ice performance [29,41].

5. Conclusions

Superior aerobic capacity emerged as a pivotal factor in sustaining repeated high-intensity efforts in amateur ice hockey, underpinning both more efficient lactate clearance and faster heart-rate recovery between shifts. Practically, this indicates that realistic, mesocycle-level gains in VO₂max are likely to yield perceptible competitive advantages—higher on-ice work rates, delayed fatigue, and fewer performance-related errors across periods—underlining the applied relevance of our findings. While the design is associative, the consistency of the relationships across recovery markers supports their utility in everyday practice. In programming practicioners should prioritize VO₂max development within a polarized framework to support adaptation while managing load. Our findings support monitoring aerobic capacity and between-shift recovery (e.g., 2 min HR decline, ΔLa_4–8) to inform individualized conditioning within existing team practices, while recognizing that the choice, dose, and periodization of training were not evaluated here and should be tailored to context. To strengthen causal inference and scope, studies should use longitudinal or randomized training interventions that target VO₂max and track in-game recovery across a season; test moderators (sex, age category, playing standard, seasonal phase, match congestion) and dose–response relationships; and incorporate mechanistic indices (HRV, near-infrared muscle oxygenation, and metabolic biomarkers) to elucidate pathways by which aerobic fitness accelerates restitution in intermittent sport.