Correlation of pitching velocity with anthropometric measurements for adult male baseball pitchers in tryout settings

Jyh How Huang; Szu-Hua Chen; Chih Hui Chiu

doi:10.1371/journal.pone.0265525

. 2022 Mar 17;17(3):e0265525. doi: 10.1371/journal.pone.0265525

Correlation of pitching velocity with anthropometric measurements for adult male baseball pitchers in tryout settings

Jyh How Huang ¹, Szu-Hua Chen ², Chih Hui Chiu ^3,^*

Editor: Kei Masani⁴

PMCID: PMC8929570 PMID: 35298532

Abstract

Several studies have investigated factors influencing baseball pitching velocity. However, some measurements require expensive equipment, and some tests need familiarity to perform well. In this study, we adopted field tests executed using affordable equipment in a tryout event for a professional baseball team in Taiwan, 2019. We use half day to test 64 players, and the result of measurement are used to develop a model for predicting pitching velocity of amateur adult pitchers (age: 23.9 ± 2.8 years; height: 180.3 ± 5.9 cm; weight: 81.4 ± 10.9 kg). The measurements and tests in tryout settings should be easy to implement, take short time, do not need high skill levels, and correlate to the pitching velocity. The outcome measures included maximum external shoulder rotation, maximum internal shoulder rotation, countermovement jump (CMJ) height, 20-kg loaded CMJ height, 30-m sprint time, height, age, and weight tests. Multiple regression indicated a moderate correlation between these tests and pitching velocity (adjusted R² = 0.230, p = 0.0003). Among the measures, the ratio of loaded CMJ to CMJ, ratio of first 10-m sprint time to 30-m sprint time, and height were significant contributors to pitching velocity. Overall, these measures explained 23% of the variance in the predicted pitching velocity. These field tests can be adopted in tryout events to predict a prospect’s potential and to identify underestimated players. Coaches can obtain an expectation of a pitcher’s performance by comparing his pitching velocity with the predicted value derived from the statistical model presented herein, and the room of growth by comparing his current strength to average strength growth after being drafted and trained with professional coaches.

Introduction

Pitching velocity is a key indicator of pitching performance. To determine strategies for improving pitching velocity, numerous studies have investigated the effects of elbow and shoulder kinetic parameters [1,2]. However, whether improvements in elbow and shoulder kinetic parameters can increase pitching velocity is unclear. Oi et al. suggested that the pitching velocity of US pitchers was faster than that of Japanese pitchers; however, such a finding might be affected by anthropometric measures because the Japanese pitchers demonstrated larger shoulder horizontal adduction torque when the parameters were normalized to body weight and height [3]. Therefore, elbow and shoulder kinetic parameters during pitching movement are not the only factors influencing pitching velocity.

Pitching velocity has been widely explored. It could depend on several factors, including talent, mechanics, and strength and conditioning. Studies have reported that better temporal parameters and kinematic parameters often infer to faster pitching velocity [4,5]. Research also revealed that resistance training can increase pitching velocity [6]. Additionally, a study investigating the correlation between lower-extremity ground reaction forces and pitching mechanics determined that such forces are correlated with pitching velocity [7,8]. Lehman et al. performed lower-body field tests and correlated the results with pitching velocity [9]. Studies exploring predictors of the pitching velocity of pitchers have reported different findings. For example, a study investigated predictors of pitching velocity by recruiting participants aged 14.7 ± 2.6 years; the significant predictive factors were age, height, weight, body mass index, shoulder external rotation, and total arc of shoulder rotation, and significant kinematic factors were maximum knee height, stride length, knee flexion, foot angle, lead hip flexion, lateral trunk tilt, and hip–shoulder separation [10]. Mercier et al. conducted a thorough review of factors associated with baseball pitching performance and observed that body weight, age, lateral-to-medial jump, medicine ball scoop, standing long jump, 10-m sprint, and grip strength were significantly associated with pitching velocity [11]. For physical tests, the pitching kinetic energies were significantly correlated with height (R = 0.870), standing long jump (R = 0.850), sprint time (R = -0.759) and grip strength (R = 0.906 for right, and 0.894 for the left hand) in 164 youth baseball players aged 6.4–15.7 years[12]. Hoffman et al. compared anthropometric and performance variables in professional baseball players and the regression analysis showed that performance measures accounted for 25–31% of the variance in baseball batting performance[13]. Watanabe et. al. measured female baseball players’ physical fitness and found the that lower-limb power contributes to baseball hitting outcomes at a low to moderate level (R² = 0.144–0.315) [14], which is close to our finding for pitchers(R² = 0.230). Priest et al. conducted modified 60-yard run shuttle in their NCAA baseball tryouts, which is closest to our research, but did not correlate the measurements with pitching performance [15].

Only few studies have focused on the use of affordable, inexpensive tests to quantify potential pitching velocity in tryout events. I.e., how accurately can we predict a pitcher’s pitching velocity with affordable field test equipment in short period of time, with easy to execute tests? From past researches, we picked several tests which are quick and easy to implement. Past study has found the ratio of CMJ and loaded CMJ correlates to an athlete’s ability to produce higher peak and instantaneous forces [16], and is verified as one of the variables that correlates to pitching velocity in our research. Sprinting time of different distances, mostly 10 meters, 30 meters, and 60 yards, to the performance of baseball players have be discussed in several researches [12,17,18]. We chose 30-meter sprint for the tryout event, while first 10-meter’s time is also measured, and ratio of 10 to 30 meters calculated. Deriving an equation for predicting pitching velocity by using anthropometric measurements and inexpensive field tests in tryout settings can enable professional teams, coaches, and scouts a quick and easy way to identify underestimated pitchers who might not demonstrate fast pitching velocity due to poor mechanics or pichers with very good mechanics but lack of adequate strength training and conditioning.

Accordingly, the objective of this study was to examine whether pitching velocity would be correlated with the results of anthropometric measurements and field tests that can be conducted quickly with affordable equipment. By analyzing data collected during a tryout event for a professional baseball team in Taiwan in 2019, we obtained predictive models that can be used to identify pitchers with potentially high pitching velocities.

Methods

On the basis of procedures described by previous studies, we selected maximum external shoulder rotation, maximum internal shoulder rotation [5,19], age, height, weight [20], 30-m sprint time, and first 10-m sprint time [9,21] as the outcome measures based on significance, ease of execution, and cost of testing equipment. The ability to produce a relatively high peak force (PF), instantaneous force (IF), and isometric rate of force development (IRFD) was related to smaller differences between 20-kg loaded and unloaded jump heights [9]. Both loaded and unloaded countermovement jumps (CMJs) were significantly correlated with PF, peak power, and net impulse [22]. Accordingly, we included loaded and unloaded jump tests to assess player PF, IF, and IRFD and to predict pitching velocity.

Participants

A total of 64 Asian adult male pitchers (age: 23.9 ± 2.8 years; height: 180.3 ± 5.9 cm, weight: 81.4 ± 10.9 kg) were recruited during a tryout event in 2019 for a professional baseball team. During this event, the pitchers underwent several anthropometric measurements and field tests. This study was approved by the Antai Tian-Sheng Memorial Hospital Ethics Committee (IRB reference number: 19-024-B). All participants were informed of the study purpose and data handling and provided written consent before participating in the study. Nine of the participants were excluded because they demonstrated a pitching velocity of less than 130 km/h.

This study was designed to determine whether the selected anthropometric measurements and performance in field tests were correlated with pitching velocity. The participants were familiarized with the testing protocols and underwent a standardized 10-min dynamic warm-up process, including walking knee lift, walking over and under, leg cradle, lunge walk, lunge with a twist, high-knee walk, skip, and run. The anthropometric measurements of interest were body height, body weight (both measured with Tanita WB-380H; height output scale: 1 cm; weight graduation: 0.1 kg), and the passive range of motion (ROM) of internal and external shoulder rotation. The field tests comprised CMJ, loaded CMJ, and 30-m sprint tests.

ROM measurement

A joint protractor was used to measure the passive internal and external rotation of the shoulder joints on the pitching side of the participants when lying [23]. Each movement was measured twice, and the movement was tested thrice if the difference between the two results was greater than 5°. Of the three test results, the two results within a 5° range were averaged for further analysis. The rotation on the nonpitching side was not measured.

Loaded and unloaded CMJ

The loaded and unloaded CMJ tests were conducted using Gymaware (GymAware Lite v2.10; distance resolution: 0.3 mm; variable rate sampling base rate: 115,200 Hz) [24]. For the unloaded CMJ, each of the participants was instructed to keep their hands on the waist, squat until their thighs were parallel to the floor, and then explode off the ground to maximum height without pausing. A harness was attached to the waist of the participant during the test. The loaded CMJ involved a similar protocol to the unloaded CMJ, but the participant was asked to carry a 10-kg dumbbell in each hand (total weight 20 kg) and have their hands hang on the sides of their body. The loaded and unloaded CMJ protocols were executed twice, with 1 min of rest between them. A third test was conducted only if the difference between the results of the two tests was greater than 5 cm. The measured heights were averaged as the final result.

Speed test

A timing gate system (Witty Wireless Training Timer; resolution: 1/25,000 s) [25] was used to measure performance in the 30-m sprint test, in which 0–10- and 0–30-m sprint times were recorded. During the test, the participants were asked to keep their feet behind the starting line and then sprint for 30 m at full intensity. All participants use standing start, and each participant must complete two sprint tests, of which the faster sprint time was used for further analysis.

Velocity

During the tryout, pitching velocity was measured using Rapsodo Pitching 2.0. Rapsodo Pitching has been adapted by all Major League Baseball teams for measuring pitching velocity, movement, spin axis, and spin rate [26]. The Rapsodo unit is setup according to its manual, and was not moved or changed during the tryout. Participants warm up in a manner analogous to a game depending to their personal habits, including stretching and other drills. They know their try out ID number, and the numbers are called in order, so they can throw warm-up pitches based on their personal habits to try to perform best during the test. During the test, they are asked to throw 5 to 10 fastballs, and then some more breaking balls upon the coaches’ and scouts’ requests. The measurements and field test results are listed in Table 1.

Table 1. Anthropometric, physical parameters and field test results.

	Mean	SD
Age (yrs)	23.9	2.8
Height (cm)	180.3	5.9
Weight (kg)	81.4	10.9
ER_ROM (°)	107.7	7.8
IR_ROM (°)	52.5	11.3
Sprint₁₀ (sec)	1.82	0.08
Sprint₃₀ (sec)	4.05	0.17
Sprint_ratio	0.45	0.02
CMJ_loaded (cm)	35.49	5.29
CMJ_loaded (cm)	42.97	2.83
CMJ_ratio	0.83	0.07

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Statistical analysis

Descriptive values are expressed as mean (M) ± SD. A linear stepwise multiple regression analysis was performed using the R with step function in {stats} package to investigate the correlations of the five anthropometric measurements, three physical parameters, and various field test performance results with pitching velocity and to determine an optimal set of parameters for predicting pitching velocity. These parameters included body height, body weight, age, maximum international rotation, external rotation of throwing shoulder, 30-m sprint time (sprint₃₀), first 10-m sprint time (sprint₁₀), unloaded CMJ (CMJ_unloaded), and loaded CMJ (CMJ_loaded). The ratio of sprint₁₀ to sprint₃₀ (sprint_ratio) and the ratio of CMJ_loaded to CMJ_unloaded (CMJ_ratio) were calculated. The variance inflation factor (VIF) was used to test for multicollinearity. Results are expressed with adjusted R², residual standard error (RSS), and regression equations. The alpha level was set to .05.

Results

In the stepwise multiple linear regression analysis, sprint₁₀, weight, ER_ROM, IR_ROM, sprint₃₀, and age were eliminated in this order. Of the remaining variables, CMJ_loaded, CMJ_unloaded, CMJ_ratio, sprint_ratio, and height were selected on the basis of the sum of the squares.

The variance inflation factor was used to test for multicollinearity, and CMJ_unloaded and CMJ_loaded were eliminated in the process. Therefore, the regression indicated an overall significant correlation of height, CMJ_ratio, and Sprint_ratio with pitching velocity (F_{(4, 59)} = 7.26, p < .001, adjusted R² = .23), indicating that these parameters could explain approximately 23% of the variance in pitching velocity. Furthermore, examining individual predictors revealed that height (t = 2.82, p = .007), sprint_ratio (t = 2.98, p = .004), and CMJ_ratio (t = 2.35, p = .022) were significant predictors in the model. The following model was thus derived for predicting pitching velocity:

Velocity = 53.22 + 0.21 (height) + 14.3 (cmjWtoNW) + 75.03 (sprint10to30) + ϵ, adjusted R² = 0.230, RSS = 3.57, p = 0.0003113

The coefficients with p values for each parameter of the equation are listed in Table 2.

Table 2. Coefficients with p values for parameters.

Estimate	Std.	Error	t	p-value	Pr(>\|t\|)
(Intercept)	11.489	32.137	0.358	0.722
height	0.212	0.076	2.787	0.007	**
CMJ_ratio	67.141	33.513	2.003	0.050	*
CMJ_unloaded	1.118	0.698	1.602	0.115
CMJ_loaded	-1.290	0.807	-1.599	0.115
Sprint_ratio	66.332	26.531	2.500	0.015	*

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Discussion

This study investigated the correlation of anthropometric measurements and field test performance with pitching velocity using tests easy to implement, and derived an optimal set of parameters for predicting pitching velocity. We elected to not use kinematic or kinetic parameters because the required equipment is expensive and the corresponding measurements are usually conducted in laboratory settings [27]. Furthermore, kinematic and kinetic parameters represent pitching mechanics with little interpretative value for identifying a player’s potential through physical tests. The outcome measures included in this study were shoulder ER_ROM and IR_ROM, CMJ_loaded, CMJ_unloaded, sprint₃₀, age, weight, and height tests. These tests are easy to implement and were executed several times in different baseball stadiums. A group of 60 players could complete all tests within 2 hours, and the total cost of the equipment we used was less than USD $4,500. It is possible to run the accessments with equipment under USD $1,000 total,inclding My Jump 2 APP [28,29], and Dashr timing system [30],. Our results were determined to be correlated with pitching velocity (adjusted R² = 0.230, p < 0.001). Among age, height, weight, ER_ROM, IR_ROM, sprint₃₀, sprint_ratio, CMJ_loaded, CMJ_unloaded, and CMJ_ratio, only height, sprint_ratio, and CMJ_ratio were correlated with pitching velocity. Height is an inherent contributor to pitching velocity because taller pitchers have longer arms and can thus generate greater leverage power. Additionally, a greater height affords a larger room/path during the arm acceleration phase.

A positive CMJ_ratio was derived in this study. This signifies that pitchers carrying a 20-kg load could jump to a similar height to that achieved when jumping without such a load. Kraska et. al. studied the relationship between strength characteristics and CMJ_ratio, and found the ability to produce higher peak and instantaneous forces and IRFD is related to jump height and CMJ_ratio. One of their findings is that stronger athletes have smaller decrements in vertical jump heights associated with weighted jumps compared with weaker athletes[16]. The study conducted by Aagaard et. al., showed that training produced increases in neural drive (IRFD) is associated with adaptations in the contractile strength of skeletal muscle [31]. Thus, pitchers with better CMJ_ratio would demonstrate a relatively higher neural drive in the contractile skeletal muscle and lead to faster pitching velocity. Moreover, the sprint_ratio coefficient was found to be positive; a higher sprint_ratio value indicates a faster pitching velocity.A higher sprint_ratio value actually indicates a longer first 10-meter, and shorter 10 to 30-meter sprint time. While 10-meter sprint is commonly tested for baseball players [13,32], there does not seem to be a consensus of which distance is the best one to test, thus 60-yard, 40-yard, and 20-yard have all been used by the MLB teams [33]. We chose 30-meter test so that in addition to the first 10 meters, 10 to 30-meter sprint time can also be measured to look at transitional acceleration. Coleman and Amonette found the time from Home-plate to First-Base is most affected by acceletration pure acceleration [34]. Initially, sprinting time of 10-meter, 30-meter, and the sprint_ratio are all included in our equation. But only sprint_ratio is picked by the stepwise multiple linear regression analysis. This does not mean the first 10-meter sprint time is not important, but that for this group of athletes, transitional acceleration shows more significant correlation to pitching velocity. Maćkała et al. [32] reported that 10-m sprint time was significantly correlated with the stride index (SI, defined as sprint stride length/leg length); the SI has been suggested to be correlated with baseball pitching velocity [10,35]. However, sprint₃₀ was noted to be correlated with peak sprint velocity, maximum springing frequency, and SI [32]. In this study, sprint_ratio was selected in the model because of its higher correlation with pitching velocity than 10-m and 30-m sprint time. This can be explained as transitional acceleration is more important to pitching velocity than pure acceleration, but we need more tests to verify this. Also we would like to test 10-meters and 30-meter saperately in the future, to make sure full effort spent from the athletes in the 10-meter test. Finally, the model revealed a moderate yet significant correlation between the field tests and pitching velocity for the group of amateur players recruited in this study. Accordingly, we suggest using the model only for players whose age and playing level are close to those included in this study.

A limitation of this study is that we analyzed only Asian adult male amateur players, and there are only limited number of tests we can try due to the time limit of the tryout. The generalizability of our findings to players of differet age, sex, playing level is unclear. We thus suggest that future studies further investigate possible tests those can predict players’ potential and suitable for tryout events, and also try to apply the tests on players at other levels. As demonstrated in this study, quick field tests can be conducted to predict pitchers’ potential. Pitchers who pitch at a velocity slower than the prediction can work on their pitching mechanics, and pitchers who outperform the model can focus on higher peak and instantaneous forces and isometric rates of force development to gain more velocity.

8 players were drafted after the tryout, and we track their measurements and field tests for the following year, along with their pitching velocity. We want to see how much room there is for players to grow with proper strength and conditioning training, both in range of motion and explosive power. We found some positive intra-individual correlations between the growth of these measurement results and the pitching velocity, and will keep following. Tryouts are very important for finding diamonds in the rough, but yet there are few researches on what are the best measurements and tests to be conducted in tryout events. We hope to work on this topic not only the year of the tryout, but keep tracking the growth years after the players are drafted, to help more unpolished dimonds to be found, and shine.

Supporting information

S1 Dataset

(CSV)

Click here for additional data file.^{(3KB, csv)}

Data Availability

The dataset is uploaded and shared publicly on Zenodo https://zenodo.org/record/5796004#.YcHpkdBByUk DOI: 10.5281/zenodo.5796004 https://doi.org/10.5281/zenodo.5796004.

Funding Statement

This work is sponsored by Ministry of Science and Technology, Taiwan Award Number: 109-2622-H-028-001 | Recipient: JyhHow Huang The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS One. doi: 10.1371/journal.pone.0265525.r001

Decision Letter 0

Kei Masani

27 May 2021

PONE-D-21-08727

Correlation of pitching velocity to the anthropometric measurements for male adult baseball players

PLOS ONE

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Reviewer #1: Partly

Reviewer #2: No

**********

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Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #2: No

**********

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Reviewer #2: No

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Reviewer #1: The authors prevent a valuable contribution to the fields of scouting, performance and coaching. That being said, I do feel these results have been produced elsewhere (like at Driveline baseball). There could be much more thorough review of literature, and tempered recommendations at the end of the paper. I have some suggestions for the authors to further investigate, primarily, including their 9 removed athletes that didn't meet the professional standard. I would like to see the author's data as well.

Line Abstract - Pitching, not Pithing

Line 3-5: I understand the author’s intention - to find the relationship between mechanics and velocity, but this is so insufficient when it comes to understanding the kinematics associated with velocity. In fact, only one paper is referenced regarding mechanics, when it is widely studied how workload, fatigue, mechanics, and anthropometry/skill contribute to velocity. Recommend citing a review paper such as Seroyer, S. T., Nho, S. J., Bach, B. R., Bush-Joseph, C. A., Nicholson, G. P., & Romeo, A. A. (2010). The kinetic chain in overhand pitching: its potential role for performance enhancement and injury prevention. Sports health, 2(2), 135-146.

Line 11 - “the extent to pitching velocity has not been fully explored” - please re-construct sentence. This does not make sense.

Line 13 - identify what changes in mechanics would cause change in velocity

Line 16 - Furthermore, Driveline Baseball did a replication study on vertical jumps and the correlation to velocity. You do cite some other non-academic work, and I would recommend this being cited here.

https://www.drivelinebaseball.com/2016/09/examining-the-relationship-between-vertical-jump-force-and-velocity/

Line 22 - you are referencing Mercier here, could you please highlight the strength of the relationship in question? What was the r2 value?

Line 28 - what is your definition of “inexpensive field tests”?

Line 38 - would like to know which previous studies are cited for each test

Line 54 - Nine of the participants that were excluded from this study - did you have complete data for these athletes? Irrespective of their threshold for a professional standard, this could be a good data set to show the sensitivity of your model. If these athletes had very poor tests and the model predicted a very low velocity, this would only strengthen your model. Recommend these data be included.

Line 66 - was it the same experimenter recording ROM for each participant? If not, what was the inter-rater reliability?

Line 75 - see above comment. What is your definition of an inexpensive field test? How much was the Gym Aware hardware?

Line 93 - reference location of Rapsodo company

Line 99 - R step{stats} - is this an error, or is this how the Stats package is referenced?

Line 106 - may relationships in biological systems are non-linear. Did you explore any non-linear terms in your regression modelling? Why not?

Line 109 - many other studies have included BMI - would recommend this also be included in your model. While not perfect, it would give some context to large, muscular athletes.

Line 104 and Line 112 - Define VIF before using acronym

Line 132 - Total COST of the equipment at 4500, not COSE

Line 132 - 4500 USD is definitely less expensive than a full motion capture lab, but it isn’t inexpensive in my opinion - particularly if you’re looking at youth athletes.

Line 148 - what is the “first move when pitching”?

Line 160 - it sounds like you could erase the limitation of “not professional” by included the other 9 athletes who were not at the professional standard? Would like to see these data.

Line 162 - I think this section of the paper is very important for the layman, particularly in an open source journal. However, I think the examples you have given here are requiring more context. For example, your model has an RSS of 3.57. Your two examples have an error of 2 and 3 km/h. This is very close to the model error, and I don’t know if it’s a strong enough of an example, in the lack of other data on mechanics for you to make your claims. A generic example of a “mechanical tweak” is misleading - what kind of mechanical tweak are we considering? Furthermore, extensive research exists on the influence of fatigue on pitching velocity, as well as grip and the influence on spin rate, efficiency and velocity. These are all things that could lead to changes in velocity and are not controlled in this study.

Reviewer #2: The aim of study was to predict the outcomes that explain the pitching velocity in adult baseball pitchers. The measurements were age, height, weight, and some field tests including counter movement jump (CMJ), 20 kg-loaded CMJ, 10 m and 30 m sprint time, and maximum internal/external rotation angle of the throwing shoulder. The authors revealed that height, the ratio of the sprint time (10 m / 30 m), and the ratio of CMJ (unloaded / loaded) could predict pitching velocity. Pitching velocity is one of key factors to put out a butter for baseball pitchers and it is important to take the measure of the ability to throw a fastball for coaching staff, scout, and trainer and so on.

However, I have concerns on the followings; (1) a lack of novelty (previous studies have already reported similar results), (2) the use of low costs equipment and the ease of set up are insufficient as a claim of this study, (3) the rationale for the selection of measurements is insufficient, (4) the methodological descriptions of each measurement were not written in detail, and (5) the superficial discussion, especially about the ratio of loaded CMJ/ CMJ.

General comments

1. The authors’ selection on these measurements to predict pitching velocity are not justified: This manuscript does not mention the rationale that these measurements were appropriate to predict pitching velocity among field tests; and most of these measurements were already examined in the previous studies. Furthermore, the value of adjusted R2 was low, which does not fully support the authors’ conclusion. Therefore, unfortunately, it is difficult to find the novelty and relevance of this manuscript.

2. I suggest that this manuscript should examine whether each CMJ parameters (or speed test parameters) can explain pitching velocity. For example, as mentioned in the methods, CMJ and loaded CMJ were good indicator for the ability of explosive power exertion relate to pitching velocity. If each CMJ parameter relate to pitching velocity, CMJ and loaded CMJ tests were good assessment for pitching velocity. Thus, authors should examine the relationship between CMJ and pitching velocity in detail.

3. There are lack of clarities in descriptions of methods. For example, it is not clear whether the shoulder ROM was active or passive, and whether the posture during measurements was sitting or lying. Furthermore, the information about Rapsodo setup and number of pitches during trial or warming up are not described. Please revise the method section to provide much more details about the experiment.

Minor comments

1. The authors should space between values and unit.

2. The “throwing velocity” should be changed to “pitching velocity”.

3. References should be cited as “xxx et al. (20xx)…”.

4. Title: The title is not supported by results.

5. Intro: L15 lower extremity → upper extremity or throwing arm?

6. Methods: L49 The participants demographics such as their age, height, and weight were not provided.

7. Results: The results of two ratios about CMJ and sprint time were important factors that explain the pitching velocity. Thus, authors should describe the detailed analysis of CMJ and sprint time (e.g. height, time, and correlation between the values of ratio and pitching velocity).

8. Discussion: L132 cose → cost

9. References: 17 The journal is Res Q Exerc Sport?

**********

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Reviewer #1: Yes: Mike Sonne

Reviewer #2: No

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PLoS One. 2022 Mar 17;17(3):e0265525. doi: 10.1371/journal.pone.0265525.r002

Author response to Decision Letter 0

18 Aug 2021

PONE-D-21-08727

Correlation of pitching velocity to the anthropometric measurements for male adult baseball players

PLOS ONE

Dear Dr. Chiu,

Please emphasize the novelty of the study, which both reviewers pointed. Please improve the clarify in methods/results. Thorough literature review as well as discussion are suggested as well.

Please include the following items when submitting your revised manuscript:

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We look forward to receiving your revised manuscript.

Kind regards,

Kei Masani

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Partly

Reviewer #2: No

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

3. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: No

Reviewer #2: No

4. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: No

Reviewer #2: No

5. Review Comments to the Author

Line Abstract - Pitching, not Pithing

Corrected, thank you. We’ve also asked for help from professional English editing company and this manuscript is revised for proper style.

Citation added, thank you.

Line 11 - “the extent to pitching velocity has not been fully explored” - please re-construct sentence. This does not make sense.

The sentence is re-constructed, thanks for reminding.

Line 13 - identify what changes in mechanics would cause change in velocity

We apologize for the overstatemen. The cited research is inter-individual, not intra-individual. Thank you for pointing it out.

The sentence is revised to “Studies have reported that better temporal parameters and kinematic parameters often infer to faster pitching velocity”

https://www.drivelinebaseball.com/2016/09/examining-the-relationship-between-vertical-jump-force-and-velocity/

We do follow Driveline’s research and appreciate all the R&D done there. Citation added, thank you so much.

Line 22 - you are referencing Mercier here, could you please highlight the strength of the relationship in question? What was the r2 value?

Mercier gives no R2 value in the paper. That review is mainly about which research might be biased, and which parameters might truly be significant.

We’ve added another reference, Relationship Between Performance Variables and Baseball Ability in Youth Baseball Players Nakata, Hiroki (2013), which has more detailed correlation R value for physical tests.

Line 28 - what is your definition of “inexpensive field tests”?

We were hoping that the total cost is under USD $500.

My jump2 APP for measure CMJ is USD $18, and shows good correlation.

Line 38 - would like to know which previous studies are cited for each test

Citations added after each test accordingly, thank you.

The raw data, including the 9 excluded participants, is uploaded and can be downloaded at

https://drive.google.com/file/d/1YO4NC2gGXitzGmlJaU3AhW2fMB6YSVvw/view?usp=sharing

We re-run the model with 9 excluded participants and answer it together in the Line 160 question.

Line 66 - was it the same experimenter recording ROM for each participant? If not, what was the inter-rater reliability?

To finish all the tests in 2 hours, there were 3 experimenter recording ROM. Though we haven’t tested the inter-rater reliability, but all of experimenter are trained by Taiwan Athletic Trainers’ Society, by the same instructor and passed the certification exam.

Line 75 - see above comment. What is your definition of an inexpensive field test? How much was the Gym Aware hardware?

GymAware is USD $1,995. We were thinking that, comparing to Kistler force plate which costs over $40,000, under 2k is not expensive. It is still not cheap, we agree.

We agree that “inexpensive” is probably not the best word to describe it, “affordable” is used instead, plus option for under $1000 for all is added in the paper.

Line 93 - reference location of Rapsodo company

Reference updated, thank you.

Line 99 - R step{stats} - is this an error, or is this how the Stats package is referenced?

In R programming, the name of the package is enclosed by {}. To better deliver the message, the text has been modified accordingly. “A linear stepwise multiple regression analysis was performed using R with step function in {stats} package to investigate……….”

Line 106 - may relationships in biological systems are non-linear. Did you explore any non-linear terms in your regression modelling? Why not?

The ways to fit a nonlinear model are infinite. Without knowing a strong foundation of the starting values for the nonlinear algorithm from the literature, we preferred taking a conservative approach and fitting a linear model, which gives an ability to better interpret the R square and p value of each dependent variable.

Line 109 - many other studies have included BMI - would recommend this also be included in your model. While not perfect, it would give some context to large, muscular athletes.

Thank you for the suggestion. We ran an additional statistical analysis with BMI included in the regression model. The overall model fitting was not improved (R square = 0.223 versus 0.230 in the original model), thus we prefer keeping the model as it is (few predictors and higher R square value).

Fig1. The statistical result of the model presented in this study. (without BMI)

Fig2. The statistical result of the model with BMI included

Line 104 and Line 112 - Define VIF before using acronym

Thanks. We have edited the text accordingly. “Variance inflation factor (VIF) value was used to examine the multicollinearity”

Line 132 - Total COST of the equipment at 4500, not COSE

Corrected, thank you.

Line 132 - 4500 USD is definitely less expensive than a full motion capture lab, but it isn’t inexpensive in my opinion - particularly if you’re looking at youth athletes.

GymAware USD $1,995, can be replace by My Jump2 APP, which is USD $18 and has been validated by some research. https://147.197.131.104/handle/2299/21027.

A DIY laser gate measuring sprinting time costs around USD $120 can be used to measure sprinting time instead of Witty timing system. https://create.arduino.cc/projecthub/Pablerdo/wireless-laser-gate-timing-system-for-track-and-field-ba8cd9

Dashr timing system costs USD $350 https://www.dashrsystems.com/products

Line 148 - what is the “first move when pitching”?

Sentence is revised as follows, “In contrast to batting, the force does not have to be developed in the initial wind up move in pitching.”.

Line 160 - it sounds like you could erase the limitation of “not professional” by included the other 9 athletes who were not at the professional standard? Would like to see these data.

When drawing the tryout pitchers’ max velocity on a chart, there is a gap shown.

It looks like a good idea to make a cut at either 128km/h or 130km/h, since the data under 130km/h is very sparse.

Anyways, we still re-run the model with 9 removed pitchers added to the dataset, and here is the result.

Adjusted R2 is 0.161. And with the 9 low velo pitchers removed

Adjusted R2 is 0.230, with all the coefficients being significant.

Yes, we agree that grip, fatigue, and lots of other factors affects the pitching velocity. We used too many assertions. After lots of discussions, we decided to remove the “practical applications” subsection and feel the quality of the paper will be better doing so.

General comments

Yes, we agree most measurements were examined in previous studies, however, most previous studies are done in the lab setting with expensive equipment which not everyone has access to. We try to find the field tests possible to perform with mobile, affordable equipment in the short period of time so can be performed in most tryouts. The tests we performed can be done in a baseball field or any gym with 30 meters length and examine 60 athletes in 2 hours. R2 is mild, but p = 0.0003, so confidence level is very high, and the model is reliable.

In the study “Relationships between ball velocity and throwing mechanics in collegiate baseball pitchers” by Werner et. al, they conclude that “Body mass and 9 temporal and kinematic parameters related to pitching mechanics combine to account for 68% of the variance in ball velocity for a collegiate population of athletes.” That also says there are some factors affecting pitching velocity yet to be explored for pitching velocity. Thus, our model with inputs from field tests explains 23% of the variance in pitching velocity, seems reasonable and helps solve part of the puzzle.

We did. It was explained in the Result section. Loaded CMJ and CMJ were still in the model after stepwise multi-regression. But they were eliminated in the multicollinearity testing process, meaning that they are highly correlated with the loaded CMJ/CMJ ratio and will boost the R2 falsely if included.

Shoulder ROM was passive, and posture was lying. The setup for Rapsodo and more warmup procedure are added to the Velocity sub-section, thank you for reminding.

Minor comments

1. The authors should space between values and unit.

Revised, thanks for pointing it out.

2. The “throwing velocity” should be changed to “pitching velocity”.

Throwing velocity is changed to pitching velocity, thank you.

3. References should be cited as “xxx et al. (20xx)…”.

References updated, thank you.

4. Title: The title is not supported by results.

There is no one dominant factor in baseball pitching, A lot of factors add up to a pitcher’s velocity. We present that field tests correlate to a pitcher’s velocity at 23%, and the confidence level of the model is very high.

5. Intro: L15 lower extremity → upper extremity or throwing arm?

It is lower extremity we refer to, and it is very important to pitching velocity. There were research on correlation of lower extremity and pitching velocity before and we cited two papers as well.

[7] J. A. J. Guido and S. L. Werner, “Lower-Extremity Ground Reaction Forces in Collegiate Baseball Pitchers,” The Journal of Strength & Conditioning Research, vol. 26, no. 7, pp. 1782–1785, Jul. 2012, doi: 10.1519/JSC.0b013e31824e1211.

[8] “Relationship Between Vertical Jump Force and Pitching Velocity,” Driveline Baseball, Sep. 08, 2016. https://www.drivelinebaseball.com/2016/09/examining-the-relationship-between-vertical-jump-force-and-velocity/ (accessed Jun. 22, 2021).

6. Methods: L49 The participants demographics such as their age, height, and weight were not provided.

Since Taiwan is not very international regarding the athletes living here, participants in this tryout are very close in age, height, and weight. We’ve added that they are all Asian male in the sentence, thanks for reminding.

A total of 64 Asian male pitchers (age: 23.9 ± 2.8 years; height: 180.3 ± 5.9 cm, weight: 81.4 ± 10.9 kg)

We did explain it in the discussion section. On the other hand, we do not want to overstate it. We did present the result objectively with some hypothesis.

8. Discussion: L132 cose → cost

Corrected, thank you.

9. References: 17 The journal is Res Q Exerc Sport?

Corrected, thanks for pointing it out.

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Mike Sonne

Reviewer #2: No

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PLoS One. doi: 10.1371/journal.pone.0265525.r003

Decision Letter 1

Kei Masani

8 Nov 2021

PONE-D-21-08727R1Correlation of pitching velocity to the anthropometric measurements for male adult baseball playersPLOS ONE

Dear Dr. Chiu,

==============================

ACADEMIC EDITOR: Please follow the reviewer 2's comments 1 and 3, emphasizing the novelty and deepen the discussion. Also, please justify your method with considering the reviewer 2's comment 2.

==============================

Please submit your revised manuscript by Dec 23 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

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Kei Masani

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #2: No

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #2: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #2: General comments

1. The purpose of this study was to verify anthropometric measurements and field tests with inexpensive equipment to predict pitching velocity. However, this study is a lack of novelty because some previous studies (reference no. 9-12) already found the variables to predict pitching velocity, including height, jump performance, and sprint time. Indeed, although these previous studies examined the physical tests in the laboratory setting, but these measurements (excluding sprint test) can mostly execute in any places (e.g. laboratory, gymnasium, baseball field, and so on). Furthermore, some measurements in the previous studies (e.g. lateral to medial jump, medicine ball throw, and grip strength which could predict pitching velocity) were lower price than the measurements in this manuscript (e.g. Gymaware). Therefore, your claim that found the field tests with inexpensive equipment to predict pitching velocity is weak and a lack of novelty.

2. If the counter movement jump (CMJ) is a good indicator for predicting pitching velocity, authors should shift the direction of this study from to find variables that can predict pitching velocity using affordable device at field toward to verify how explosive power during CMJ contributes to the ball velocity in baseball pitchers. Although CMJ is different whole-body movement to baseball pitching, the ability of explosive force exertion during CMJ movement would relate to generate ball velocity in baseball pitchers. If so, authors can provide new methods to predict pitching velocity using only CMJ that previous studies did not yet demonstrate. Thus, authors should verify in detail the relationship the elements of explosive power during CMJ (and loaded CMJ) and pitching velocity (e.g. the rate of force development, EMG volume, kinematics, or kinetics).

3. Discussion of the current study is extremely cheap, and thus, authors should discuss more detailed, comparing to the finding of previous studies. For example, why did authors include passive shoulder internal/external rotation which was shown to not be a good predictor in the previous studies? Why did you choice the CMJ and not the lateral to medial jump which is considered a good predictor? Furthermore, why do differences appear in variables to predict pitching velocity among different age group (especially adolescent/youth baseball pitchers)? I would like you to properly discuss these things and the difference with the results of previous studies and meaning of selection of measurements in this study.

Minor comments

Introduction:

1. (L2) pitcher performance → pitching performance

2. (L11) thatbetter → that better

3. (L.56) Why did authors exclude the pitchers below 130 km/h? Authors should explain the reason why this exclusion criteria why you only included analysis above that. If authors included these subjects, the findings of this study might have shown different results.

4. (L88) Were subject's posture at the start of the sprint test uniform among all subjects? Because the time of first 10 m is strongly affected in acceleration at start, authors need to describe about this.

Discussion:

5. L141 The field test results → Our results

6. L163 velocitythan → velocity than

Table:

7. Is Table 2 going to be inserted somewhere within the manuscript?

**********

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Reviewer #2: Yes: Hirofumi Kobayashi

PLoS One. 2022 Mar 17;17(3):e0265525. doi: 10.1371/journal.pone.0265525.r004

Author response to Decision Letter 1

21 Dec 2021

Reviewer #2: General comments

Thank you for your comments. We made some major revisions and changed the paper title to "Correlation of pitching velocity with anthropometric measurements for adult male baseball pitchers in tryout settings". The purpose of this study is to find the suitable anthropometric measurements and field tests for time limited tryout events attended by several tens of players to find their potential of playing in the professional baseball league. Although most tryouts have some anthropometric measurements and field tests, but to our best knowledge, this study is the first one to report the correlation between the measurements, field tests and pitching velocity in the tryout settings. There are differences implementing measurements/tests in a lab setting and in tryouts, e.g., medicine ball throw. Most players are used to throw it to the wall at the angle parallel to the ground, but not shooting it for distance. It takes some practice throws for the athletes to find the optimal angle and posture but that takes time, which is luxury during tryouts while in the lab setting we might have time to instruct them, plus give them some time to practice. We did try medicine ball throw couple of times in other occasions, but found no significant correlation between medicine ball throw and pitching velocity without practice throws. We believe it will be a good test when most athletes are familiar with the move, but there really isn't enough time for the athletes to find the best posture and throwing angle during the tryout. We definitely agree lateral to medial jump is a good candidate test, but choose CMJ over it for the same reason, because of athletes' familiarities. Most athletes here are not familiar with lateral to medial jump, and for the first couple of attempts, some use their legs crossed/trunk twisted better than others to to gain some extra distance. So some players need practice jumps to be fair, which we didn't have time to do. We love the lateral to medial jump test and think it will be good test to implement in, say monthly tests on a pro team. But in a tryout, we picked CMJ for now for ease of implementation. Is lateral to medial jump is a better choice? We don't know until tested in tryouts. Grip strength was measured in the paper "Relationship Between Performance Variables and Baseball Ability in Youth Baseball Players" and found to be one of the significant predictors, but the age group is 6.4–15.7 years old, very different from ours.

We appreciate your comments, which remind us to emphasize the value of our paper. We added "The measurements and tests in tryout settings should be easy to implement, take short time, do not need high skill levels, and correlate to the pitching velocity." in the abstract, along with other modifications and the changing of title.

We didn't mean, and never said CMJ alone is a good indicator for predicting pitching velocity. CMJ is different movement to baseball pitching, just like grip strength test movement. But good grip strength and high amounts of lean body mass usually relate to jumping ability, which can hopefully be used to predict pitching velocity. The purpose of this study is to find the best combination tests to infer the potential of players, and we found that Height, CMJratio and SPRINTratio together can infer 23%. The stepwise multiple linear regression analysis actually picked CMJ weight to none weight ratio(CMJratio) over CMJ. The context is as following

"In the stepwise multiple linear regression analysis, sprint10, weight, ERROM, IRROM, sprint30, and age were eliminated in this order. Of the remaining variables, CMJloaded, CMJunloaded, CMJratio, sprintratio, and height were selected on the basis of the sum of the squares. The variance inflation factor was used to test for multicollinearity, and CMJunloaded and CMJloaded were eliminated in the process. Therefore, the regression indicated an overall significant correlation of height, CMJratio, and Sprintratio with pitching velocity (F(4, 59) = 7.26, p < .001, adjusted R2 = .23), indicating that these parameters could explain approximately 23% of the variance in pitching velocity. Furthermore, examining individual predictors revealed that height (t = 2.82, p = .007), sprintratio (t = 2.98, p = .004), and CMJratio (t = 2.35, p = .022) were significant predictors in the model. The following model was thus derived for predicting pitching velocity. Velocity = 53.22 + 0.21 (height) + 14.3 (cmjWtoNW) + 75.03 (sprint10to30) + ϵ , adjusted R2 = 0.230, RSS = 3.57, p = 0.0003113"

The R square is for the whole equation, not CMJ alone. We executed the measurements/tests on 64 athletes, and presented the result honestly(p = 0.0003113). Our attempt is to find the measurements/tests suitable to be implemented in tryout events. This might not be the perfect combination, but we are hoping to get it started.

Passive shoulder internal/external rotation test in included because we are not just trying to correlate anthropometric measurements and field tests with current pitching velocity, we want to know, e.g., can pitchers with good shoulder ROM but weak strength gain more velocity than others if they are taken care by professional SnC trainers? 8 players were drafted after the tryout, and we track their measurements and field tests for the following year. We want to see how much room there is for a player to grow with proper strength and conditioning training, both in range of motion and explosive power. We have some findings but case number is not large enough, so we need some more years, more cases until we can present the findings.

Thank you for suggesting adding "differences appear in variables to predict pitching velocity among different age group (especially adolescent/youth baseball pitchers)" to our discussion section. We checked couple other related research papers, including DEVELOPMENT OF A BASEBALL-SPECIFIC BATTERY OF TESTS AND A TESTING PROTOCOL FOR COLLEGE BASEBALL PLAYERS by KOHMURA et. al., and RELATIONSHIP BETWEEN PHYSICAL FITNESS AT THE END OF PRESEASON AND THE INSEASON GAME PERFORMANCE IN JAPANESE FEMALE PROFESSIONAL BASEBALL PLAYERS by WATANABE et. al. , and they focus on their participating group. None of them discuss the difference in variables for different age groups.

We are aware that the development of strength in the muscles, hypertrophy, agility, etc are still growing for adolescent/youth baseball pitchers, and thank for your suggestion to how to add depth to our discussion. We've added more in depth discussion for possible reasons why CMJratio and SPRINTratio are picked, and it should be a lot better now. So please take another look at it, really appreciate.

Minor comments

Introduction:

1. (L2) pitcher performance → pitching performance

Changed, thank you.

2. (L11) thatbetter → that better

Edited, thanks.

Those pitchers were not on the list. They were added last minute by some sponsors. Plus we do not see any pitchers who can survive with velocity below 130km/h in this league, except submarine pitchers.

Still, the raw data we uploaded for the paper have these 9 participants included should the readers are curious to try.

https://drive.google.com/file/d/1YO4NC2gGXitzGmlJaU3AhW2fMB6YSVvw/view?usp=sharing

All participants use standing start, added to that paragraph, thank you.

Discussion:

5. L141 The field test results → Our results

Modified, thank you.

6. L163 velocitythan → velocity than

That sentence is deleted during the revision process, thank you.

Thank you again for taking your time reviewing our paper, and Merry Christmas. :)

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PLoS One. doi: 10.1371/journal.pone.0265525.r005

Decision Letter 2

Kei Masani

4 Mar 2022

Correlation of pitching velocity with anthropometric measurements for adult male baseball pitchers in tryout settings

PONE-D-21-08727R2

Dear Dr. Chiu,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0265525.r006

Acceptance letter

Kei Masani

9 Mar 2022

PONE-D-21-08727R2

Correlation of pitching velocity with anthropometric measurements for adult male baseball pitchers in tryout settings

Dear Dr. Chiu:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

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Associated Data

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Supplementary Materials

S1 Dataset

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Data Availability Statement

The dataset is uploaded and shared publicly on Zenodo https://zenodo.org/record/5796004#.YcHpkdBByUk DOI: 10.5281/zenodo.5796004 https://doi.org/10.5281/zenodo.5796004.

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PERMALINK

Correlation of pitching velocity with anthropometric measurements for adult male baseball pitchers in tryout settings

Jyh How Huang

Szu-Hua Chen

Chih Hui Chiu

Roles

Abstract

Introduction

Methods

Participants

ROM measurement

Loaded and unloaded CMJ

Speed test

Velocity

Table 1. Anthropometric, physical parameters and field test results.

Statistical analysis

Results

Table 2. Coefficients with p values for parameters.

Discussion

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Kei Masani

Roles

Author response to Decision Letter 0

Decision Letter 1

Kei Masani

Roles

Author response to Decision Letter 1

Decision Letter 2

Kei Masani

Roles

Acceptance letter

Kei Masani

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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