Pathways for enhancing competitive ability of basketball players based on physical fitness training

Abstract

Basketball’s high-intensity intermittent demands impose substantial and position-specific physical requirements on players. This study employed a non-randomized quasi-experimental design to assess physical fitness profiles of 48 provincial-level male basketball players, quantifying causal pathways between physical weaknesses and competitive performance limitations (R²=0.742). Following a 16-week structured intervention, the Specialized Training (ST) group (n = 24) demonstrated significantly greater improvements than the Traditional Training (TT) group in squat 1RM (+ 18.3% vs. +7.2%, Cohen’s d = 1.24), Yo-Yo test distance (+ 22.1% vs. +9.8%, Cohen’s d = 1.06), and game scoring (+ 15.2% vs. +5.9%); all between-group differences were statistically significant (P < 0.001). A position-differentiated, movement pattern–energy metabolism dual-track training system was developed and validated, with a physical-to-competitive transformation prediction model achieving cross-validation accuracy of 83.7%. These findings offer an evidence-based “diagnosis-mechanism-intervention-verification” framework for the systematic enhancement of basketball players’ competitive ability.

Introduction

Modern basketball exhibits competitive characteristics of high-intensity confrontation, rapid offensive-defensive transitions, and continuous running and jumping. In a single game, athletes must complete 60–80 high-intensity sprints, 40–50 jumping movements, and 5,000–7,000 m of total running distance, imposing extremely high requirements on aerobic endurance, anaerobic explosive power, strength endurance, and agility-coordination abilities. However, domestic basketball players commonly experience declining scoring efficiency in the fourth quarter, insufficient continuous confrontation ability, and lagging movement speed. Physical fitness shortcomings have become a critical bottleneck constraining competitive level improvement.

Recent research has advanced understanding of basketball-specific physical training across several interconnected dimensions. Čaprić et al. [1] demonstrated that HIIT improves aerobic and anaerobic capacity in female basketball athletes, though effects are moderated by training design specificity; Domínguez et al. [2] showed that individualized strength training protocols yield gains in maximum strength and explosive power approximately 1.6-fold greater than standardized programs in male players; Arede et al. [3] proposed velocity-based differentiated repeated sprint training as a novel approach for youth basketball development. Critically advancing the field of assessment methodology, Flórez Gil et al. [4] established the discriminative, criterion, and longitudinal validity of small-sided games (SSGs) as tools for evaluating physical fitness in female basketball players, confirming that SSGs can serve as ecologically valid assessment instruments sensitive to performance-level differences under competitive conditions. Expanding on this evidence, Rodríguez-Fernández et al. [5] further characterized how court orientation and bout-ending mechanisms differentially modulate external and internal training loads during SSGs in female players, providing refined methodological parameters that more faithfully replicate the physiological demands of real competition. Zhao Huimin et al. [6] characterized the high-intensity intermittent load profile of competitive basketball; Wang et al. [7] explored the hormonal and performance effects of nutritional supplementation during sprint interval training.

Despite this growing evidence base, critical gaps persist that the present study addresses: (1) no integrated physical-to-competitive transformation framework grounded in multi-system physiological mechanisms currently exists; (2) causal pathways linking specific, position-dependent physical deficits to measurable competitive limitations have not been quantitatively modelled; (3) empirical studies capturing the full “diagnosis-intervention-verification” training cycle with adequate methodological rigor remain scarce. This research aims to: diagnose athletes’ physical fitness status and construct a quantified causal correlation model, elucidate multi-system adaptation mechanisms and integrate a transformation theoretical framework, construct a three-dimensional specialized physical training system, verify effectiveness through 16-week experiments and establish a prediction model, providing support for the scientification of basketball training.

This study was designed, conducted, and reported in strict accordance with the Transparent Reporting of Evaluations with Nonrandomized Designs (TREND) statement guidelines for non-randomized comparative studies. A fully completed TREND checklist covering all applicable items, including title, abstract, introduction, methods, results, and discussion, has been included as Additional File 1 and is submitted alongside this revised manuscript to facilitate full methodological evaluation by editors and reviewers.

Diagnosis of basketball players’ physical fitness status and analysis of competitive ability limiting factors

Research design and construction of multi-dimensional physical fitness testing system

Subject selection and grouping criteria

The study enrolled 48 male basketball players at provincial level or above (age 22.8 ± 2.3 years, training experience 8.5 ± 1.9 years). Based on G*Power 3.1 software calculation, with effect size d = 1.2, statistical power 0.90, and significance level α = 0.05, the minimum sample size was determined to be 20 per group, expanded to 24 per group after considering a 20% dropout rate. A stratified allocation scheme based on playing position and individual training program suitability was employed to assign subjects to a Specialized Training (ST) group and a Traditional Training (TT) group, ensuring balanced and comparable position distribution (guards, forwards, centers), baseline squat 1RM (ST group 118.6 ± 15.2 kg vs. TT group 116.8 ± 14.7 kg, P = 0.682), and VO₂max levels (ST group 52.3 ± 4.8 ml·kg⁻¹·min⁻¹ vs. TT group 51.7 ± 5.1 ml·kg⁻¹·min⁻¹, P = 0.705) between groups, providing methodological guarantees for the internal validity of subsequent comparative analyses.

Construction principles of physical fitness testing indicator system

The testing system was constructed following three principles: sport-specificity, comprehensiveness, and reliability-validity. According to basketball’s energy supply characteristics of 60% aerobic, 30% glycolysis, and 10% phosphagen, the Yo-Yo Intermittent Recovery Test (ICC = 0.95) was selected to assess sport-specific endurance, with test distance strongly correlated with high-intensity running in games (r = 0.83, P < 0.001). The strength dimension used squat 1RM (ICC = 0.97) and bench press 1RM (ICC = 0.95) to reflect lower and upper body pushing power levels, with the former showing a correlation coefficient of 0.81 with vertical jump height (P < 0.001) and the latter correlating r = 0.68 with shooting distance (P < 0.01). Explosive power testing selected force plate-recorded countermovement jump (CMJ) flight time, converting to jump height through the formula h = t² × 1.226/8, with this indicator significantly correlating with game rebounds (r = 0.75, P < 0.001). Agility assessment employed the T-agility test (ICC = 0.92) and 505 change-of-direction test (ICC = 0.90), quantifying multi-directional change ability and 180° turning efficiency respectively, forming a 15-indicator system covering five dimensions: strength, explosive power, speed, endurance, and agility (see Table 1).

Table 1.

Core parameters of physical fitness testing indicator system

Physical Dimension	Testing Indicator	Reliability (ICC)	Validity (r)	Sport-specific Correlation
Maximum Strength	Squat 1RM	0.97	0.81***	Vertical jump height
Explosive Power	CMJ height	0.93	0.75***	Rebounds
Sport-specific Endurance	Yo-Yo distance	0.95	0.83***	High-intensity running
Agility	T-test	0.92	0.71**	Change-of-direction frequency

Validity r is the correlation coefficient between testing indicators and competitive performance

***P < 0.001, **P < 0.01

Position-differentiated physical characteristics profile and competitive demand matching analysis

Through SportsVU motion tracking system data collection from 10 games, significant differentiation in physical demands across positions was revealed: guards’ high-intensity running distance (1850 ± 180 m) was 89% higher than centers (980 ± 120 m) (P < 0.001), with change-of-direction frequency (142 ± 15 times vs. 76 ± 9 times) 87% higher, highlighting the core demands for speed endurance and agility; centers’ physical confrontation frequency (56 ± 8 times) was 2.24 times that of guards (25 ± 4 times) (P < 0.001), with jumping frequency (52 ± 7 times vs. 38 ± 5 times) 37% higher, reflecting the dominant position of maximum strength and explosive power. Physical fitness testing showed guards’ Yo-Yo distance (1680 ± 215 m) significantly superior to centers (1120 ± 185 m, P < 0.001), but squat 1RM relative body weight ratio (1.42 ± 0.18 vs. 1.68 ± 0.21) lagged by 15%. Constructing a demand-ability matching index (measured value/demand threshold × 100%), guards’ speed endurance matching was only 78%, while centers’ maximum strength reached 92%, with position-specific shortcomings constraining competitive performance improvement potential.

Construction of physical weakness-competitive ability limitation causal correlation model

Structural equation modeling (SEM) was employed to analyze the causal pathways of physical weaknesses on competitive performance (Fig. 1). The model construction included 4 latent variables: physical fitness level (composed of 4 observed variables: strength, explosive power, endurance, agility), physiological adaptation (VO₂max, lactate threshold, testosterone/cortisol ratio), sport-specific ability (shooting accuracy, breakthrough success rate, confrontation intensity), and competitive performance (scoring, efficiency value, high-intensity running).

Fig. 1.

Physical weakness-competitive ability limitation causal correlation model

Maximum likelihood estimation showed the physical fitness → physiological adaptation path coefficient β = 0.76 (P < 0.001), physiological adaptation → sport-specific ability β = 0.68 (P < 0.001), sport-specific ability → competitive performance β = 0.81 (P < 0.001), with a total effect of 0.419. Model fit indices were excellent (χ²/df = 2.34, CFI = 0.94, RMSEA = 0.065), with R²=0.742 indicating that physical weaknesses can explain 74.2% of the variance in competitive performance. Bootstrap testing confirmed that the mediating effect of physiological adaptation accounted for 52% (95%CI: 0.38–0.67), revealing that physical fitness improvement must undergo physiological system reconstruction to transform into competitive advantages, providing quantitative basis for precise intervention [8].

Physiological adaptation mechanisms of physical training promoting competitive ability enhancement

Energy metabolism system and basketball-specific energy supply characteristics

Basketball exhibits typical mixed energy supply mode, with energy metabolism system recruitment characteristics directly determining athletes’ performance capabilities in different competitive contexts (Table 2). According to SportsVU motion tracking data analysis, high-intensity sprints (> 85% V̇O₂max) in a single game account for 12–18% of duration, moderate-intensity movement (60–85% V̇O₂max) accounts for 38–45%, and low-intensity activity with intermittent recovery accounts for 40–48%. This intermittent load characteristic requires the aerobic system to provide a continuous energy supply foundation, while the glycolytic and phosphagen systems need rapid ATP resynthesis capability [9]. Laboratory testing shows that elite athletes’ lactate threshold speed reaches 14.8 ± 1.2 km/h, 19% higher than ordinary athletes, enabling blood lactate concentration to be 2.3 mmol/L lower at the same intensity, delaying fatigue onset. Mitochondrial biogenesis is the core adaptation mechanism of aerobic training, with PGC-1α upregulation promoting 40–60% increase in mitochondrial density and enhanced oxidative enzyme activity increasing fat oxidation energy supply proportion from 15% at rest to 35% during moderate-intensity exercise, conserving glycogen reserves to support high-intensity efforts in later stages.

Table 2.

Energy supply system contribution rates of basketball players at different exercise intensities

Exercise Intensity Range	Phosphagen System (%)	Glycolytic System (%)	Aerobic Oxidation System (%)	Typical Scenario
Maximum Sprint (< 10s)	85–90	10–15	0–5	Fast break sprint
High-intensity Confrontation (10–60 s)	15–20	60–70	15–20	Continuous breakthrough
Moderate-intensity Movement (1–3 min)	5–8	30–40	55–65	Positional offense
Full-game Average	10	30	60	Overall energy supply

Neuromuscular system adaptation mechanisms to physical training

Neuromuscular system remodeling is the physiological foundation for strength and explosive power improvement, with its adaptation process following a “neural priority-structural reconstruction” temporal pattern [10]. In the initial 4–6 weeks of training, motor unit recruitment synchronization and firing frequency optimization dominate strength gains, with surface electromyography (sEMG) integrated values increasing 35–42% while muscle cross-sectional area increases only 3–5%, reflecting rapid improvement in central nervous drive efficiency. High-load resistance training reconstructs corticospinal tract plasticity through descending pathways, lowering motor cortex excitation threshold and enhancing type IIa motor neuron recruitment capability, increasing activation rate during maximum voluntary contraction from 82% to 91%, fully releasing muscle strength potential[^8]. At the structural adaptation level, myofibrillar protein synthesis rate peaks 48 h post-training (2.8 times baseline metabolism), with mTOR signaling pathway activation promoting 18–25% increase in type IIx fast-twitch fiber cross-sectional area, and optimized sarcomere serial arrangement enhancing muscle force generation in optimal length ranges. The unique value of explosive power training lies in enhancing stretch reflex sensitivity, with rapid stretch stimulating muscle spindle Ia afferent fibers, shortening electromechanical delay (EMD) to 42 ± 6 ms, causing ground reaction force peak to appear earlier—this adaptation directly translates to improved vertical jump takeoff velocity and change-of-direction braking efficiency.

Endcrine system regulatory role on training load

The endocrine system, as the core regulatory network of body homeostasis, has hormone secretion patterns that decisively influence the quality and direction of training adaptation (Fig. 2). Testosterone, as the dominant anabolic hormone, increases 23–35% immediately after high-intensity resistance training of large muscle groups, continuously activating satellite cell proliferation and differentiation to repair micro-damaged muscle fibers, with long-term training increasing resting testosterone levels by 12–18%, creating a hormonal environment conducive to muscle growth. The catabolic effect of cortisol requires precise control; excessive training load causes chronic elevation, inhibiting protein synthesis and promoting muscle glycogen breakdown. The testosterone/cortisol ratio (T/C ratio) is considered a sensitive marker of training stress status; when this ratio falls below 0.35, it indicates overtraining risk requiring immediate load adjustment.

Fig. 2.

Training load-endocrine response-adaptation outcome feedback regulation diagram

Growth hormone exhibits “pulsatile” surges after intermittent high-intensity training, with peak concentrations reaching 8–12 times resting levels. Its lipolytic effect optimizes body composition while coordinating with insulin-like growth factor-1 (IGF-1) to promote chondrocyte proliferation, supporting bone and skeletal system development in young athletes. Thyroid hormones regulate basal metabolic rate and substrate oxidation efficiency by upregulating mitochondrial biogenesis gene expression, with T3/T4 ratio changes reflecting the body’s adaptation to endurance training, providing biochemical monitoring basis for periodized load arrangement.

Multi-system integration perspective on physical-competitive transformation theoretical framework

Synergistic action network of energy-neural-endocrine systems

The transformation of physical fitness to competitive ability depends on the nonlinear coupling of three major physiological systems, with their synergistic network exhibiting “trigger-amplify-stabilize” cascade characteristics [11]. The nervous system, as a rapid response unit, initiates motor programs through motor cortex α-wave synchronization (8–13 Hz), with spinal interneurons integrating proprioceptive feedback to achieve coordinated muscle group recruitment within 0.08–0.12 s—this time window determines the precision of change-of-direction braking and takeoff force generation. The energy metabolism system provides ATP resynthesis rate support, with the phosphocreatine shuttle system establishing an energy bridge between mitochondria and myofibrils. When creatine kinase reaction rate reaches 180 µmol·g⁻¹·min⁻¹, explosive power output can be maintained for 8–12 s without attenuation. The endocrine system regulates adaptation direction through hormone signals, with acute catecholamine release enhancing fat mobilization efficiency, increasing plasma free fatty acid concentration to 1.2 mmol/L during intervals, pre-storing energy for the next high-intensity bout. The three-system interaction forms a positive feedback loop: neural drive efficiency improvement → ATP hydrolysis rate acceleration → metabolic product stimulation of hormone secretion → myofibril contractile protein expression upregulation, constituting the physiological foundation for continuous advancement of physical qualities.

Three-level transformation pathway from physical qualities to sport-specific technical abilities

The transformation of physical qualities into competitive performance undergoes a progressive process of “basic ability-movement quality-tactical execution,” with transformation efficiency at each level dominated by sport-specific training design (Fig. 3). First-level transformation focuses on kinetic chain integration, with isolated muscle group strength migrating to functional force generation patterns through closed-chain movements (e.g., hex bar deadlift). When hip joint explosive torque reaches 3.2 N·m·kg⁻¹, vertical jump height breaks the 0.65-meter threshold, meeting the biomechanical requirements for rebounding.

Fig. 3.

Three-level transformation pathway from physical qualities to sport-specific competitive ability

Second-level transformation emphasizes technical movement stability, with core muscle group isometric contraction ability (side plank hold time > 90 s) reducing trunk rotational inertia, controlling body sway angle to within 4° during jump shots, improving shooting accuracy by 12% points. Third-level transformation points to decision execution in complex contexts, with neuromuscular fatigue threshold delay enabling fourth-quarter physical retention rate above 75%, ensuring tactical positioning speed and defensive movement quality do not deteriorate due to lactate accumulatioⁿ[12]. The nonlinear characteristics of transformation pathways manifest as: each 0.1 increase in lower limb relative strength (strength/body weight ratio) increases breakthrough success rate by 8–15%, but when this ratio exceeds 2.0, marginal effects diminish, requiring transition to speed-strength composite training to break through adaptation plateaus.

Physiological and training factors influencing transformation efficiency

The transformation efficiency of physical qualities to competitive performance is constrained by multi-dimensional factors, with physiological-level neural plasticity windows and training-level sport-specific design jointly determining adaptation quality. Individual difference analysis shows that athletes with type IIa muscle fiber proportion of 52–58% have 27% higher explosive power training responsiveness than type I-dominant athletes, with ACTN3 gene RR-type carriers showing 1.4 times the strength gains of XX-type, suggesting genotype screening has predictive value for training program individualization. Training specificity index (movement pattern similarity × metabolic characteristic matching) must reach above 0.75 for efficient transformation. Traditional squats improve maximum strength, but single-leg squats combined with rotation (simulating defensive sliding) increase change-of-direction speed improvement from 9% to 18%. Load arrangement temporal effects are equally critical; embedding technical training during the neural excitability window (4–6 h) post-strength training promotes motor memory consolidation, with this strategy improving shooting movement consistency coefficient by 0.14. In fatigue management, when heart rate variability (HRV) between training units recovers to above 95% of baseline, high-quality sessions are conducted, avoiding excessive sympathetic nerve activation interfering with technical refinement, ensuring each stimulus produces positive adaptation rather than cumulative damage [13].

Construction of sport-specific physical training system based on movement patterns and energy metabolism

Theoretical framework and innovative characteristics of training system construction

The sport-specific physical training system centers on “movement pattern-energy metabolism” dual-track integration, breaking through traditional isolated quality development paradigms (Fig. 4). The movement pattern dimension constructs a functional training matrix based on six basic kinetic chains in basketball competition: push, pull, squat, hinge, rotation, and anti-rotation, ensuring direct transfer of strength qualities to sport-specific techniques [14].

Fig. 4.

Basketball-specific physical training system framework

The energy metabolism dimension designs load intensity ranges and interval recovery cycles based on phosphagen, glycolytic, and aerobic oxidation energy supply proportions, making training stimuli precisely match game metabolic characteristics [15]. Innovation manifests in three-dimensional periodization architecture: macro-cycles adopt “foundation-transformation-competition” three-stage progression, meso-cycles implement undulating load patterns (3:1 or 2:1 stress-recovery ratio), micro-cycles embed sequential sessions of neural activation-strength development-metabolic regulation. Position-differentiated load matrices allocate strength, speed, and endurance training proportions differentially based on guards’, forwards’, and centers’ physical demand-ability gap indices, with guards emphasizing speed endurance and centers emphasizing maximum strength. The system integrates TrainingPeaks platform for dynamic monitoring, controlling training stress-fatigue balance through acute training load (ATL) and chronic training load (CTL) ratio, with optimal adaptation efficiency when ATL/CTL is between 0.8 and 1.3.

Progressive development pathway of strength training

Strength quality development follows a four-stage progressive logic of “stability foundation-maximum strength-explosive power-sport-specific strength,” with fundamentally different neuromuscular adaptation mechanisms at each stage [16]. The stability phase (preparation period 1–3 weeks) employs closed-chain movements such as single-leg Romanian deadlifts and anti-rotation presses to activate deep stabilizer muscles and establish kinetic chain integration ability. When single-leg standing eyes-closed time reaches 45 s and side plank hold reaches 90 s, core stability is deemed adequate, laying a movement foundation for subsequent heavy-load training. The maximum strength phase (foundation period 4–8 weeks) implements traditional protocols at 85–95% 1RM loads, 3–5 repetitions per set, completing 4–6 sets with 3–5 min inter-set recovery to promote neural drive optimization. This stage increases motor unit recruitment synchronization and electromyographic signal integration, with squat 1RM target values set at 1.8–2.2 times body weight, adjusted according to position characteristics. The explosive power transformation period (transformation period 9–12 weeks) adopts complex training mode, with squat heavy-load exercises immediately followed by vertical jump movements, utilizing post-activation potentiation effect (PAPE) to increase nervous system excitability, improving jump height by 5–7 centimeters compared to isolated training—this adaptation stems from sustained activation of high-threshold motor units [17]. The sport-specific strength phase (competition period 13–16 weeks) embeds basketball movement patterns, such as single-leg squat with ball followed by turn and breakthrough, with loads reduced to 60–75% 1RM but maximum execution speed required, strengthening neural pattern transfer from strength to technical movements. Motor cortex and cerebellar synergistic activation patterns are optimized, ultimately achieving significant squat relative strength improvement and transforming into enhanced in-game physical confrontation and rebounding capabilities.

Neural-dominant strategies for speed and agility training

Speed and agility quality improvement is essentially the result of central nervous system processing speed and motor program optimization, with training design strictly following neural freshness principles to avoid fatigue interference. Linear speed development employs short-distance sprints (10–30 m) for 6–10 sets, with inter-set recovery until heart rate drops below 120 bpm, ensuring each start is in a state of complete phosphagen system recovery. Phosphocreatine resynthesis requires 3–5 min to reach above 95% of initial levels. Velocity-based training (VBT) monitoring implements real-time feedback; when sprint speed declines by more than 5%, the training unit is terminated. This threshold control strategy enables continuous improvement in 10-meter acceleration time from baseline levels, with neural conduction velocity and muscle contraction coordination simultaneously optimizing [18]. Agility training emphasizes cognition-decision-execution integration. Reactive agility uses visual or auditory random signal-triggered changes of direction (505 test, T-test), with stimulus-response latency time targets controlled within 0.25 s. Change-of-direction agility embeds basketball tactical contexts, such as choosing sliding or turning based on ball-handler movements in 2-on-1 defense, forcing athletes to switch movement schemes within 0.3–0.5 s decision windows. Neural plasticity training enhances attention allocation efficiency through dual-task interference (dribbling combined with visual tracking), strengthening functional connectivity between prefrontal cortex and motor cortex, significantly improving change-of-direction success rate in complex contexts, achieving effective transformation of speed-agility qualities into tactical execution capabilities.

Hiearchical progressive model of endurance training

Basketball-specific endurance presents a three-layer structure of aerobic foundation support, lactate tolerance capacity, and interval recovery efficiency. The training model must match metabolic targets at different levels to achieve system adaptation [19]. The aerobic foundation layer (preparation period) implements moderate-intensity continuous training (MICT), with heart rate controlled at 65–75% of maximum heart rate for 20–30 min, promoting capillary density increase and mitochondrial biogenesis. Enhanced mitochondrial oxidative enzyme activity significantly improves V̇O₂max levels, providing metabolic support for high-intensity interval exercise [20]. The lactate threshold improvement layer (foundation period) adopts tempo run training, with intensity locked at lactate threshold speed (corresponding to blood lactate 4 mmol/L), interval design of 5-minute work × 4–6 sets with 2-minute recovery. This intensity stimulates monocarboxylate transporter (MCT) expression upregulation, increasing lactate clearance rate from 0.18 to 0.26 mmol·L⁻¹·min⁻¹, delaying acidosis onset during high-intensity exercise and maintaining muscle contraction function [21]. The sport-specific endurance layer (transformation period) implements basketball-specific interval training, simulating game characteristics of 15-second high-intensity (90–95% HRmax) running alternating with 30-second active recovery, total training volume 1,200-1,500 m. Yo-Yo interval test distance improves by over 20% from baseline, corresponding to fourth-quarter high-intensity running distance retention rate increasing from 68% to 84%, achieving direct transformation of sport-specific endurance into late-game competitive ability, ensuring technical-tactical execution quality does not decline due to physical exhaustion at critical moments.

Training load distribution matrix for different positions

Analysis of physical training proportion based on position competitive demands

Position-differentiated training load distribution stems from fundamental differences in competitive demands, with SportsVU tracking data revealing significant differentiation in movement patterns and energy expenditure among guards, forwards, and centers (Fig. 5). Guards’ high-intensity running distance is nearly 90% higher than centers, with change-of-direction frequency exceeding 87%, making speed endurance and agility core qualities requiring training proportion emphasis toward these two aspects. Centers’ physical confrontation frequency is 2.24 times that of guards, with jumping frequency 37% higher, making maximum strength and explosive power occupy dominant positions. Forward positions combine characteristics of both ends, requiring balanced configuration between strength and speed. Physical demand-ability matching index analysis shows guards’ speed endurance matching is only 78% while centers’ maximum strength reaches 92%, suggesting training resources need precise allocation to position-specific shortcomings. Based on this, a three-position load distribution matrix is constructed: guards—endurance training 35%, speed-agility 30%, strength 25%, other 10%; forwards—endurance 29%, strength training 31%, speed-agility 25%, other 15%; centers—strength training 42%, endurance 23%, speed-agility 21%, other 14%. This matrix ensures tight coupling between training stimuli and competitive demands.

Fig. 5.

Physical training load content distribution proportions for different positions

Training load distribution scheme and periodization for guard position

Guard position training scheme centers on speed endurance and agility. The preparation period establishes aerobic foundation and movement stability, with moderate-intensity continuous running combined with core stability training, single-session duration controlled at 75–90 min, heart rate range maintained within appropriate levels of maximum heart rate. The foundation period introduces lactate threshold training and reactive agility exercises, with tempo run intensity locked at threshold speed, interval ratio designed at 2:1 work-to-recovery time. Simultaneously embed T-agility tests and visual signal-triggered change-of-direction training. This stage strength training focuses on functional movements such as single-leg squats and split squats, with loads controlled within reasonable ranges [22]. The transformation period implements basketball-specific intervals, simulating game characteristics of alternating high-intensity running and rapid changes of direction, with moderately increased training density, combined with speed-strength composite training to improve acceleration and braking capabilities. The competition period reduces total load volume but maintains intensity, with increased proportion of sport-specific endurance combined with tactical training, ensuring speed endurance and decision execution capabilities are fully expressed in game contexts.

Training load distribution scheme and periodization for forward position

Forward positions combine offensive-defensive transition speed and physical confrontation ability, with training schemes needing to establish balanced development pathways between strength and speed. The preparation period emphasizes movement pattern establishment and aerobic foundation construction, employing full-body strength training (e.g., hex bar deadlift, bench press) combined with moderate-intensity running, establishing kinetic chain integration ability and metabolic foundation (Table 3). The foundation period strength training enters maximum strength development phase, with squat and deadlift loads progressively increasing, synchronously developing explosive power. Box jump training and medicine ball throws enhance lower and upper body explosive power output. Endurance training adopts mixed mode, with tempo runs and basketball-specific intervals alternately arranged, both improving lactate threshold and strengthening interval recovery capacity. The transformation period implements complex training mode, with strength exercises immediately followed by jumping or sprinting, utilizing post-activation potentiation effect to optimize neuromuscular synergy. The competition period moderately adjusts training frequency, with deep integration of sport-specific strength and tactical training. Ball-handling breakthroughs and aerial confrontations become main training content, ensuring physical qualities efficiently transform in complex competitive contexts.

Table 3.

Periodized training load parameter settings for forward position

Training Period	Strength Training Load	Endurance Training Mode	Weekly Training Frequency	Core Training Objectives
Preparation Period	Moderate Load	Moderate-intensity Continuous Running	4–5 sessions	Movement Foundation and Aerobic Base
Foundation Period	Heavy Load	Tempo Running + Interval Running	5–6 sessions	Maximum Strength and Threshold Improvement
Transformation Period	Moderate-Heavy Load	Sport-specific Interval Training	5–6 sessions	Explosive Power and Sport-specific Endurance
Competition Period	Maintenance Load	Tactical Integration Training	4–5 sessions	In-game Ability Integration and Expression

Implementation of periodized training program and verification of competitive ability enhancement effects

Exerimental design and 16-week intervention program implementation

The study adopted a non-randomized prospective comparative design, with 48 athletes stratified by position and allocated based on training program suitability to a Specialized Training (ST) group and a Traditional Training (TT) group. The ST group implemented a sport-specific physical training system based on movement pattern–energy metabolism dual-track integration. Training periods were divided into preparation (weeks 1–3), foundation (weeks 4–8), transformation (weeks 9–12), and competition (weeks 13–16), with load parameters at each period dynamically regulated according to ATL/CTL ratio, maintaining optimal stress range of 0.8–1.3. Each training session in the ST group was preceded by a standardized, three-phase warm-up protocol occupying 15–20 min of each session. Phase 1 (general activation, 5–8 min) consisted of progressive aerobic movement at 50–60% HRmax, including light jogging, dynamic lateral shuffles, and low-intensity court movement to elevate core temperature and heart rate. Phase 2 (dynamic mobility, 5–7 min) targeted basketball-relevant joint ranges through controlled movements emphasizing hip flexion and extension, ankle dorsiflexion, thoracic rotation, and shoulder complex activation. Phase 3 (sport-specific neural priming, 5–6 min) incorporated progressive sprint accelerations (10–15 m at increasing intensities up to 85% HRmax), reactive lateral steps, and position-specific movement previews. This structured multi-phase warm-up approach aligns with documented practices among experienced basketball strength and conditioning coaches, for whom systematic pre-session activation is integral to optimizing neuromuscular readiness and reducing injury risk [23]. The TT group performed standard pre-session warm-up routines as habitually implemented by their coaching staff, without structured modification or standardization across sessions.The TT group continued traditional isolated quality development mode, with strength, speed, and endurance training arranged independently. The intervention process real-time monitored heart rate variability, subjective fatigue ratings, and testosterone/cortisol ratio through TrainingPeaks platform. When T/C ratio fell below 0.35 or HRV recovery rate was insufficient at 95% baseline, loads were immediately adjusted, ensuring each training unit produced positive adaptation rather than cumulative fatigue damage.

Training effect analysis of physical indicators

Post-intervention, the ST group’s squat 1RM improved from baseline 118.6 ± 15.2 kg to 140.3 ± 17.8 kg (+ 18.3%, P < 0.001), with a large between-group effect size (Cohen’s d = 1.24, 95%CI: 0.73–1.74), substantially exceeding the TT group’s gain of 7.2% (P < 0.05); the significant inter-group difference (P < 0.001) confirms that the sport-specific training architecture produced neuromuscular adaptations qualitatively distinct from traditional volume-based approaches (Table 4). Relative strength ratio in the ST group reached 2.05 ± 0.26, breaking the sport-specific demand threshold identified in Sect.  "Position-differentiated physical characteristics profile and competitive demand matching analysis".

Table 4.

Comparison of physical indicator training effects between ST and TT Groups

Testing Indicator	Experimental Group Improvement	Control Group Improvement	Inter-group Difference
Squat 1RM	+ 18.3% (21.7 kg)	+ 7.2%	P < 0.001
Yo-Yo Interval Distance	+ 22.1% (371 m)	+ 9.8%	P < 0.001
CMJ Jump Height	+ 12.4% (6.8 cm)	+ 5.9% (3.2 cm)	P < 0.01
T-Agility Test	-8.7% (0.52s)	-3.1%	P < 0.01

The ST group significantly outperformed the TT group across four dimensions—maximum strength, sport-specific endurance, explosive power, and agility—confirming the systematic advantages of the comprehensive training program

Yo-Yo interval test distance in the ST group improved 22.1% (from 1,680 ± 215 m to 2,051 ± 268 m; Cohen’s d = 1.06, 95%CI: 0.57–1.55), compared to 9.8% in the TT group (P < 0.001 between groups), reflecting the superior adaptation of the intermittent energy supply system when training stimuli precisely match game metabolic demands. CMJ height in the ST group increased 6.8 cm (+ 12.4%; Cohen’s d = 0.89, 95%CI: 0.40–1.37), compared to 3.2 cm (+ 5.9%) in the TT group (P < 0.01), with explosive power output improvement confirming neuromuscular recruitment efficiency optimization (Fig. 6). T-agility test time in the ST group shortened by 0.52 s (8.7% improvement; Cohen’s d = 0.95, 95%CI: 0.46–1.43), compared to 3.1% in the TT group (P < 0.01), with the 505 change-of-direction test improving by 0.31 s; the magnitude of agility improvement is consistent with the decision-execution integration training load described in Sect.  "Neural-dominant strategies for speed and agility training", providing convergent validity for the neural plasticity mechanism proposed.

Fig. 6.

Dynamic change trends

Verification of sport-specific competitive ability enhancement

At the competitive performance level, the ST group’s game average scoring improved from baseline 16.8 ± 4.3 points to 19.3 ± 4.8 points (+ 15.2%, P < 0.001), while the TT group increased only 5.9%, with differences confirming transformation efficiency from physical fitness to technical execution. This magnitude of scoring improvement is notably larger than the 5–8% increments typically reported following isolated physical training interventions[1,2], suggesting that the integrated movement pattern–metabolism framework enables a more complete transfer of physical qualities to match performance than single-quality approaches. Fourth-quarter scoring efficiency retention in the ST group improved from 68% to 84%, TT group maintained at 72% (P < 0.01), with the 16-percentage-point improvement in late-game retention aligning with the lactate clearance rate gains documented in Sect.  "Hiearchical progressive model of endurance training", providing mechanistic coherence between training design and competitive outcome. Breakthrough success rate in the ST group increased 11.7% points (from 52.3% to 64.0%), TT group 4.2% (P < 0.001), reflecting effective transfer of explosive power and agility to sport-specific techniques. Defensive sliding speed in the ST group improved 0.34 m/s, rebounding success rate increased 8.6%, physical confrontation intensity rating improved 12.3%, confirming the complete pathway of maximum strength transforming through functional kinetic chains into in-game confrontation capabilities.

Physical-competitive transformation prediction model based on empirical data

Based on 16-week intervention data, a multivariate nonlinear regression model was constructed (Fig. 7), with squat relative strength, Yo-Yo distance, CMJ height, and T-test time as predictor variables, and competitive performance composite score as dependent variable.

Fig. 7.

Physical-competitive transformation prediction model framework

The model incorporated quadratic terms and interaction terms to capture nonlinear associations between variables. Stepwise regression screening showed that squat relative strength × Yo-Yo distance interaction term contributed most (β = 0.42, P < 0.001), revealing the amplifying effect of strength-endurance synergy on competitive ability. Model goodness of fit R²=0.837, cross-validation accuracy 83.7%, RMSE = 2.14 points. Sensitivity analysis indicated that when squat relative strength reaches 2.0 and Yo-Yo distance exceeds 2000 m, competitive performance predicted values break through elite athlete thresholds. This critical value provides quantitative basis for training goal setting, achieving closed-loop management of physical diagnosis-precise intervention-effect prediction.

Conclusion

The present study provides a systematic and quantitatively grounded investigation of the physical-to-competitive performance transformation pathway in male provincial-level basketball players. The core finding—that a movement pattern–energy metabolism dual-track training system produces significantly superior physical and competitive adaptations compared to traditional isolated training—extends the existing evidence base in several important directions.

The 18.3% squat 1RM improvement in the ST group is consistent with Domínguez et al. [2], who demonstrated that individualized strength programming outperforms standardized approaches in male basketball players, and the present study further shows this advantage translates directly into competitive indicators such as rebounding success rate and confrontation intensity. The 22.1% Yo-Yo distance improvement is in line with HIIT-driven aerobic capacity gains in basketball populations documented by Čaprić et al. [1], while the position-differentiated training loads introduced here add a layer of specificity not present in generalized HIIT protocols. The use of validated assessment frameworks—including SSG-based evaluation tools established by Flórez Gil et al. [4] and load parameters refined by Rodríguez-Fernández et al. [5]—supports the ecological validity of both assessment and training protocols in the present study. Notably, the enhancement of fourth-quarter performance retention (68%→84%) provides direct competitive evidence for the endurance-to-execution transfer chain, a pathway theoretically proposed but rarely empirically quantified in the prior literature.

The predictive model (R²=0.837, cross-validation accuracy 83.7%) advances beyond descriptive associations by offering a quantified tool for target-setting: when squat relative strength reaches 2.0 and Yo-Yo distance exceeds 2,000 m, predicted competitive performance crosses the elite athlete threshold. This type of conditional threshold model is practically applicable and represents a methodological contribution absent from comparable studies.

Limitations of the present study must be acknowledged. First, the non-randomized allocation—necessitated by practical coaching and team constraints—limits causal inference; although baseline comparability was statistically confirmed, residual confounding cannot be fully excluded. Future studies should employ stratified randomization where logistically feasible. Second, the sample consisted exclusively of provincial-level male basketball players, constraining generalizability to female athletes and youth populations; the growing evidence on female-specific physical fitness dynamics [4, 5] highlights the importance of sex-stratified investigations. Third, the 16-week intervention captures initial adaptive phases but does not address long-term retention, seasonal periodization effects, or detraining responses. Fourth, game performance metrics were drawn from regular-season matches with varying competitive contexts, introducing measurement variability that cannot be fully controlled. Future research should address these limitations through randomized designs, longitudinal follow-up across full competitive seasons, and investigation of female and youth cohorts.

Abbreviations

RCT

Randomized Controlled Trial

HIIT

High-Intensity Interval Training

CMJ

Countermovement Jump

1RM

One-Repetition Maximum

HRV

Heart Rate Variability

SEM

Structural Equation Modeling

ATL

Acute Training Load

CTL

Chronic Training Load

VBT

Velocity-Based Training

PAPE

Post-Activation Performance Enhancement

T/C ratio

Testosterone/Cortisol ratio

ICC

Intraclass Correlation Coefficient

EMD

Electromechanical Delay

sEMG

Surface Electromyography

ChiCTR

Chinese Clinical Trial Registry

Authors' contributions

Mingchen Xu: Conceptualization, methodology, writing—original draft. Xiaojing Zhang: Data curation, formal analysis, writing—review and editing. Hongcui Chi: Investigation, resources, data collection. Haixia Liu: Supervision, project administration, writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Data availability

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

Declarations

Ethics approval and consent to participate

This study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Review Board (IRB) of Weifang Engineering Vocational College (WEVC20240508). Written informed consent was obtained from all individual participants included in this study prior to their enrollment. Participants were fully informed of the study procedures, potential risks, and their right to withdraw at any time without consequence.

Consent for publication

Not Applicable. This manuscript does not contain any identifying images or other personal or clinical details of individual participants that could compromise their anonymity. Accordingly, no separate consent for publication from individual participants is required.

Competing interests

The authors declare no competing interests.