Differences in external loads of different pitch types in Chinese male college baseball players

Background: In baseball, monitoring player load solely through pitch count and innings overlooks the variability introduced by different pitch types. This study analyzed differences in external load, measured as Player Load, across various pitch types using a GPS-based wearable device. Additionally, the relationship between Player Load and ball velocity was examined.

Methods: Data from 320 baseball pitches were collected for experimental analysis. External loads were collected for four pitch types, fastball, changeup, curveball, and slider, using a wearable sensor device that recorded six variables: Maximum Player Load (Max PL), Maximum Rotation (Max Rot), Pitching Hourly Velocity, and Percentage of Acceleration Change in the Three Axes (Up Load %, Side Load %, and Fwd Load %). A one-way ANOVA test was used to assess differences across pitch types, while stepwise multiple regression analyzed the impact of Player Load on pitching speed.

Results: Significant differences were observed among pitch types for Max PL, Max Rot, Hourly Speed, Up Load %, and Fwd Load % (p < 0.05), while Side Load % did not show a significant difference (p > 0.05). Stepwise multiple regression indicated that pitching speed was influenced by Max PL, with the equation: mph = 78.816 + 7.001 Max PL, R² = 0.192, suggesting that Max PL accounted for 19.2% of the variability in pitching speed.

Discussion: External training loads in pitching vary by pitch type, with variable-speed pitches generating higher peak external loads, whereas fastballs are associated with greater ball speeds.

Introduction

Quantifying an athlete's training load is essential for improving performance and managing fatigue (1–4). In baseball, there is limited knowledge about the key factors that contribute to an accurate assessment of pitching training load (5, 6). Variables such as pitch count, pitch type, and pitching mechanics may influence training load (5). Pitch count or innings pitched is commonly used as a proxy for training load in pitchers, but it does not correlate well with performance prediction or provide a precise measure of actual workload (5, 6). Furthermore, relying on pitch count alone has proven less effective in preventing overuse injuries (7, 8). Another limitation is that pitch counts fail to account for velocity variations between pitch types, which can lead to different levels of joint loading stress (9, 10). This indicates that each pitch type may impose distinct demands on the body, and relying solely on pitch count may not accurately capture the cumulative workload.

The contribution of each pitch type to a pitcher's overall training load may be reflected in the external training load associated with each pitch (11). External training load can be quantified by analyzing the series of movements an athlete performs during training or competition (12). This includes parameters such as distance covered, velocity, acceleration, deceleration, and impulse (12–14), among other factors. These movements can be further categorized into peak and cumulative external training loads (12). Peak external training load is defined as the instantaneous measurement of these bodily movements (15), whereas cumulative external training load represents the total sum of an athlete's movements over a training session or competition (12). Global Positioning System (GPS)-based monitoring systems provide a means to quantify external training load using a metric known as Player Load (2). This metric is derived from changes in acceleration across three axes: vertical, anterior-posterior, and lateral (16). Research has demonstrated that these systems exhibit high inter-device reliability and strong convergent validity (2, 15, 17). Due to their reliability, validity, and portability, these devices have been widely adopted in field sports such as football, Australian rugby, and rugby to monitor external training loads during both training sessions and competitions (12–14).

Sensor-based biomechanical monitoring has become increasingly common in baseball (18), with a primary focus on analyzing the mechanical characteristics of the shoulder and elbow joints in relation to pitching performance, including ball velocity and accuracy, as well as injury risk (18–20). However, research on the application of GPS-based wearable technology in baseball remains limited. To date, only one study has reported higher peak and cumulative Player Load in fastballs compared to curveballs, sliders, and changeups (11), suggesting a potential relationship between increased Player Load and greater ball speed. Additionally, variations in pitch types may result in differing triaxial acceleration loading patterns due to differences in pitching mechanics. Analyzing the peak Player Load associated with various pitch types, along with differences in triaxial acceleration, may provide a deeper understanding of how different external loads impact pitchers. This information could enable coaches to more accurately evaluate overall pitching loads, which may help optimize performance while managing fatigue.

This study aimed to examine differences in peak Player Load and triaxial acceleration loads across different baseball pitch types in collegiate male pitchers. The study also sought to explore the relationship between Player Load and pitch velocity. It was hypothesized that significant differences would be observed in peak Player Load and triaxial acceleration loads across distinct pitch types, and that higher peak Player Load would be associated with increased pitch velocity.

Materials and methods

Sample

Data from 320 baseball pitches were collected for experimental analysis. All athletes were national-level competitors, right-handed pitchers, and had no major injuries or illnesses within the past six months. They were fully informed about the experimental procedures and voluntarily agreed to participate.

Study design

Before the experiment, the GPS module was activated and securely fastened to the middle of the scapula using a vest. Each athlete completed a structured dynamic warm-up, which included jogging and stretching exercises. Following the warm-up, athletes were given the opportunity to throw an unlimited number of pitches across four pitch types, fastball, slider, changeup, and curveball, to ensure they were fully prepared for testing.

During the experiment, each pitch was thrown from the mound to a catcher positioned 18.44 m (60.5 ft) away. Pitchers were instructed to throw each pitch with maximum effort while the timing of each pitch was recorded. Pitch velocity was measured using a radar gun operated by surveyors positioned behind the catcher. There were no restrictions on pitch selection; before each pitch, pitchers verbally indicated the type of pitch they intended to throw. The order of pitch types was randomized for each athlete to minimize potential effects of accumulated fatigue on Player Load and velocity measurements. Each athlete completed 20 pitches per pitch type, resulting in a total of 320 recorded pitches from four athletes across four pitch types (changeup, curveball, slider, and line drive).

Experimental equipment

Data collection was conducted using a GPS-based wearable device, the Catapult Vector S7 (Catapult Innovations, Melbourne, Australia), with each athlete assigned a unique module to prevent data overlap. Before each test, all subjects wore upper-body garments, with microsensors positioned between the scapulae. These microsensors recorded data at 100 Hz using IMU devices (Figure 1). External training load was quantified through a three-dimensional accelerometer-based formula, known as accumulated Player Load (PL), as provided by the manufacturer. This formula determines PL by calculating the square root of the sum of the squares of the instantaneous rate of change in acceleration across three orthogonal planes, anterior-posterior, medial-lateral, and vertical, divided by a scaling factor of 100, with results reported in arbitrary units (AU). The calculation formula is as follows. Given the frequent and rapid changes in activity and direction characteristic of basketball, existing research suggests that Player Load effectively measures the instantaneous rate of change in acceleration across three planes of motion (1).

Figure 1

Figure 1. Catapult Vector S7.

Player Load is used to quantify the load experienced by a pitcher on each pitch. It represents a synthetic vector of the instantaneous rate of change in triaxial acceleration across different directions, divided by a scaling factor (16). The calculation follows the formula:

$\mathrm{Player}\mathrm{Load}=\sqrt{{\displaystyle \frac{(\mathrm{\Delta }{a}^2)+(\mathrm{\Delta }{b}^2)+(\mathrm{\Delta }{c}^2)}{100}}}$

where Δa represents the instantaneous change in front-back acceleration (Fwd Load), Δb corresponds to the instantaneous change in medial-lateral acceleration (Side Load), and Δc denotes the instantaneous change in vertical acceleration (Up Load) (16). Pitching speed (km/h) was measured using a radar gun (Stalker Sport 2, USA). The test metrics analyzed in this study included maximum Player Load (Max PL), maximum body rotation (Max Rot), percentage of triaxial acceleration change in different directions (Fwd Load%, Side Load%, and Up Load%), and pitching speed.

Data analysis

Descriptive statistics were presented as mean ± standard deviation (Mean ± SD). The variability of pitch type across six parameters—Max PL, Max Rot, Hourly Speed, Up Load%, Side Load%, and Fwd Load—was analyzed using the one-way ANOVA test. Stepwise multiple regression was used to assess the impact of pitching speed, with statistical significance set at p < 0.05. All statistical analyses were conducted using SPSS 25 (IBM SPSS Statistics, USA).

Results

Figure 2 illustrates the relationship between maximum Player Load (Max PL) and pitch speed across four baseball pitch types: changeup, curveball, slider, and line drive. The horizontal axis represents Max PL, while the vertical axis denotes pitch velocity (km/h). Each data point corresponds to a specific pitch type, differentiated by distinct colors and shapes. Fastballs exhibited high Max PL values, typically ranging from 4.00 to 5.00, and recorded the greatest pitch velocities, generally between 120.00 and 130.00 km/h. This suggests that fastballs require greater physical exertion to generate higher velocities. Sliders demonstrated slightly lower Max PL values, ranging between 3.50 and 4.50, with pitch speeds between 100.00 and 110.00 km/h, indicating that sliders are moderate-intensity pitches. Curveballs had even lower Max PL values, typically between 3.00 and 4.00, with pitch velocities generally between 90.00 and 100.00 km/h. This indicates that curveballs impose a lower physical load on the body but result in correspondingly reduced speeds. Changeups recorded the highest Max PL values, usually ranging from 4.50 to 5.50, yet produced the lowest pitch velocities, typically between 80.00 and 90.00 km/h. Despite the greater physical load, the reduced pitch speeds suggest that changeups require higher exertion to create deception rather than velocity.

Figure 2

Figure 2. Maximum player load and speed for four different pitch types of baseballs.

Table 1 presents the descriptive statistics for all measured variables across the four pitch types.

Table 1

Table 1. Descriptive statistics of variables under different pitching types (Mean ± SD).

Table 2 displays the differences in Side Load % across pitch types. According to Table 2, no significant differences were observed in Side Load % among the different pitch types (p > 0.05). However, significant differences were found in Max PL, Max Rot, Hourly Speed, Up Load %, and Fwd Load % (p < 0.05), indicating that these parameters varied across pitch types. The specific median differences are detailed in Table 2.

Table 2

Table 2. Differences in external loads under different pitching types.

The stepwise multiple regression fit equation is: mph = 78.816 + 7.001Max PL, R² = 0.192, with Max Player Load explaining 19.2% of the variability in mph.

Discussion

Traditional methods of assessing a baseball pitcher's workload, such as pitch counts and innings pitched, have limitations in accurately reflecting total pitching load (6, 21). Understanding the variations in external training loads across different pitch types may provide better insight into overall pitching demands. This study aimed to analyze differences in external load, specifically Player Load, and examine the relationship between peak Player Load and pitching velocity among collegiate male baseball players using GPS-based wearable devices. The findings revealed significant differences in Max PL, Max Rot, Hourly Velocity, Up Load %, and Fwd Load % across the four pitch types. Additionally, Max PL had a significant effect on Pitching Hourly Velocity, accounting for 19.2% of its variability. Due to its high velocity demands, the fastball requires a substantial physical load from the pitcher. This pitch type is typically employed to challenge batters quickly but necessitates a high level of strength and explosive power from the pitcher.

The slider falls between the fastball and curveball in terms of speed and physical load. It is a commonly used off-speed pitch that effectively disrupts batters’ timing. The curveball, on the other hand, requires less physical exertion, but its slower speed and curved trajectory make it a deceptive pitch for batters. Although the fastball generates higher maximum loads, its reduced velocity characteristics make it a strategic off-speed pitch that can deceive batters by altering velocity and movement. To explain the “high load-low velocity” phenomenon observed in the changeup, it is important to consider the biomechanical demands of this pitch. The changeup is designed to deceive the batter by mimicking the arm speed and delivery motion of a fastball while achieving significantly lower ball velocity (22). This is primarily accomplished through specific grip and release techniques that reduce the efficiency of energy transfer from the arm to the ball, such as increased pronation or altered finger pressure. However, to maintain the deceptive similarity in motion, pitchers may need to exert additional muscular control and coordination in the trunk and core. This could involve applying “braking” or counteractive forces to absorb or redirect some of the proximal energy that would otherwise be fully transferred distally in a fastball. Such efforts may lead to greater instantaneous changes in acceleration at the trunk level, as captured by the torso-mounted sensor between the scapulae, resulting in higher Max PL despite the lower pitch velocity (10). This interpretation aligns with the need for enhanced neuromuscular control to modulate the kinetic chain without compromising deception.

Regarding the practical implications of these findings, particularly in light of the trunk-mounted sensor's limitations in directly assessing distal joint loads, coaches and athletes should interpret the higher peak external loads in off-speed pitches like the changeup with caution. While the elevated Max PL suggests greater trunk involvement, biomechanical evidence indicates that changeups impose lower stresses on the shoulder and elbow compared to fastballs, potentially distributing energy more proximally and reducing injury risk at distal segments through the kinetic chain (9). For instance, changeups exhibit reduced elbow varus torque, shoulder horizontal adduction torque, and proximal forces during key pitching phases, making them a safer alternative for varying pitch selection without excessively loading the arm. Therefore, rather than limiting changeups as “high-risk” due to trunk load, coaches may encourage their use to complement fastballs, promoting pitch diversity that could mitigate overuse injuries associated with high-velocity pitches. However, to optimize injury prevention, this should be integrated with comprehensive monitoring, including arm-specific sensors where possible, and individualized workload management based on factors like pitch volume and fatigue.

Compared to previous research on external load differences among baseball pitch types, our findings reveal a broader range of variations. Earlier studies either found no significant differences in Max PL across pitch types (8) or reported that Max PL was only significantly higher for the fastball (4.0 ± 0.9) compared to the changeup (3.8 ± 0.9) (11). In contrast, our study identified significant differences across all four pitch types. The highest Max PL was observed in the changeup (4.67; 4.2, 5.0), followed by the fastball (4.32; 3.6, 4.7), curveball (4.30; 4.0, 4.7), and slider (3.65; 3.3, 4.5). Additionally, the changeup exhibited high levels of Max Rot (2.15; 1.9, 2.3; p = 0.016). Significant differences (p < 0.05) were also observed in the percentage of acceleration along the longitudinal and anterior-posterior axes among the four pitch types. However, when examining pitch velocity, the fastball had a significantly higher hourly velocity (p < 0.001). Furthermore, Max PL had a significant positive effect on pitching velocity (R² = 19.2), aligning with previous findings that higher pitch speeds are associated with increased external loading on the upper limb (23–25). However, since Max PL explains only 19.2% of the variability in pitch velocity, the remaining 80.8% may be influenced by other key biomechanical factors, including the efficiency of energy transfer through the kinetic chain from the lower body to the upper extremities, timing and velocity of pelvis and trunk rotations, arm angular velocity at ball release, and specific kinematic parameters such as stride length, lead knee flexion, and maximum shoulder external rotation. This underscores the multifactorial nature of pitch velocity and highlights avenues for future research to integrate these variables for a more comprehensive model.

There is no consensus on the differences in external load, specifically Player Load, across various pitch types. One factor contributing to the variability in findings may be the placement of sensors (8, 11). Previous studies have examined Player Load at three locations, the torso, upper arm, and forearm, across different pitch types, with significant differences observed only in the forearm. Variations in acceleration-based devices, sampling frequencies, and definitions of pitch onset and completion further complicate comparisons. Additional research is needed to better understand how external load differs by pitch type. Bullock et al. (8) proposed using torso-mounted sensors as an alternative to pitch counts for identifying potential risk factors for pitching-related injuries (11). However, this approach does not appear to be a reliable substitute for assessing loads on the shoulder and elbow (8). In terms of trunk mechanics, all four pitch types are considered to follow similar movement patterns (11). However, differences emerge at the forearm and wrist. Curveballs generate greater forearm rotation than sliders and fastballs (10), while both curveballs and sliders result in less wrist extension (10). A systematic review by Grantham et al. (19) found that curveballs produce lower proximal force and elbow torque compared to fastballs (26). Consequently, using torso-mounted sensors may reduce the ability to detect mechanical load differences at the shoulder, elbow, and wrist across pitch types. Optical motion capture analyses have demonstrated that higher torso velocities are linked to reduced elbow torque (27–29). The kinetic chain in the throwing motion functions by sequentially transferring energy along the body from proximal to distal segments, allowing the high output generated by the trunk to be converted into lower mechanical stress on the arm (8). However, the relationship between trunk acceleration and mechanical stresses at the shoulder, elbow, and wrist remains unclear (8). Wearable devices utilizing GPS, accelerometers, and gyroscopes are typically positioned on the back, approximately below the seventh cervical vertebra and between the scapulae. Because Player Load calculations primarily reflect movements of the body's center of mass, where large fluctuations in instantaneous acceleration result in greater muscle contractions and energy expenditure, this metric is more commonly applied in whole-body running sports such as football and rugby (30). In contrast, its applicability to unilateral kinetic chains, such as throwing, may be more limited.

Our study also suggests that Max PL plays a significant role in influencing pitch velocity. Nissen et al. (29) reported that in youth pitchers, curveballs produced lower shoulder internal rotation moments and elbow valgus moments compared to fastballs, with these moments being influenced by both the magnitude of the applied force and the length of the force arm (31). Among collegiate pitchers, an increase in maximum shoulder external rotation leads to greater rearward elevation during the arm-cocking phase, which increases stored elastic energy and muscle stretch. This energy is then utilized to maximize acceleration forces applied to the ball during the subsequent acceleration phase, ultimately increasing pitch velocity (32–34). This transient interaction between force and the force arm may result in higher Player Load values in trunk movements, which in turn could contribute to greater pitch velocity.

This study was exploratory in nature and has certain research limitations. The small sample size of only four right-handed pitchers limits the generalizability of the findings to a broader population of collegiate pitchers. Individual differences among these athletes, such as variations in biomechanics, height, weight, and strength levels, may have influenced key metrics like Player Load, potentially introducing bias and reducing the representativeness of the results. While the total number of pitches (320) provides a substantial dataset, the reliance on a limited number of participants means that the observed differences may not fully capture the variability present in a larger, more diverse group of pitchers. A larger sample size and multiple repetitions of pitch collection would have provided a more detailed assessment of external load differences among pitch types. Additionally, previous research has shown that factors such as body weight, leg length, and pitching stride length also influence pitching velocity (35). Future studies should focus on larger sample sizes, including athletes of different competitive levels, while incorporating human morphology and biomechanical performance to increase the accuracy of analyses using torso-mounted sensors. Specifically, validating these findings with a larger and more diverse sample is recommended to enhance the generalizability and robustness of the results. Furthermore, investigating the effects of different pitch types and batters’ responses in actual game settings could provide insights that are more applicable to real-world baseball performance. Each pitch type—fastball, slide, curveball, changeup—imposes unique stresses on a pitcher's elbow and shoulder. Knowing which pitches are “high-cost” allows coaches and trainers to manage a pitcher's workload intelligently.

Conclusion

This study demonstrated that external training loads in pitching vary based on pitch type. The findings revealed that variable-speed pitches generate greater peak external training loads, while fastballs result in higher ball velocities. Additionally, pitch velocity was influenced by the maximum Player Load at the time of release. From a practical standpoint, these insights can assist coaches and athletes in optimizing baseball training and competition by enabling more nuanced workload management; for instance, incorporating a balanced mix of pitch types to distribute loads, prioritizing off-speed pitches like changeups for deception without over-relying on high-velocity fastballs that may increase fatigue or injury risk, and using wearable GPS devices to monitor real-time Player Load for personalized recovery protocols and pitch limits during practices or games. Future research should consider sensor placement, human morphology, and biomechanical factors in studies involving larger sample sizes across different competitive levels. Further investigations should also examine peak and cumulative load variations between in-season and off-season training, as well as the physiological responses associated with pitching.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.

Ethics statement

The studies involving humans were approved by Sports Science Experiments of Beijing Sport University. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

SQ: Writing – review & editing, Conceptualization, Writing – original draft, Software. DR: Resources, Supervision, Writing – review & editing. ZL: Methodology, Writing – original draft. BZ: Writing – original draft, Resources, Supervision, Conceptualization, Writing – review & editing.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. This study was supported by the National office for Philosophy and Social Sciences of China [23BTY002].

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declare that no Generative AI was used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher's note

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fspor.2025.1724517/full#supplementary-material

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