{"id":2752,"date":"2015-05-20T16:56:06","date_gmt":"2015-05-20T21:56:06","guid":{"rendered":"http:\/\/thesportjournal.org\/?p=2752"},"modified":"2018-10-25T10:22:24","modified_gmt":"2018-10-25T15:22:24","slug":"swing-kinematics-described-in-division-i-female-softball-players","status":"publish","type":"post","link":"https:\/\/thesportjournal.org\/article\/swing-kinematics-described-in-division-i-female-softball-players\/","title":{"rendered":"Swing Kinematics Described in Division I Female Softball Players"},"content":{"rendered":"

Submitted by\u00a0Cassie Reilly-Boccia1*, Travis Ficklin2*, Robin Lund3*<\/p>\n

1*\u00a0\u00a0Director of Research and Development at Athletes Warehouse in Pleasantville, NY<\/p>\n

2*\u00a0Assistant Professor of Movement and Exercise Science at the University of Northern Iowa<\/p>\n

3*\u00a0Associate Professor of Movement and Exercise Science at the University of Northern Iowa<\/p>\n

Cassie Reilly-Boccia is a former member of the National Champion University of Alabama softball team and is the Director of Research and Development at Athletes Warehouse in Pleasantville, NY.\u00a0 Travis Ficklin is an Assistant Professor of Movement and Exercise Science at the University of Northern Iowa.\u00a0 Robin Lund is an Associate Professor of Movement and Exercise Science at the University of Northern Iowa.<\/p>\n

ABSTRACT<\/strong><\/p>\n

The purpose of this study was to describe basic kinematic variables of the swing and the relationships that exist between these variables in Division I female softball players.\u00a0 These variables included bat velocity (BV), bat quickness (BQ), and bat acceleration (BA). \u00a0Video data were collected for all swings during a 15-game softball tournament in which six NCAA Division I teams played.\u00a0 High-speed video cameras recording at 300 Hz were located along the first and third base lines recording every pitch. \u00a0Data from 1,099 swings were analyzed for bat velocity (BV), bat quickness (BQ), and bat acceleration (BA). \u00a0BQ and BV were calculated by video analysis and digitization.\u00a0 All swings were rank ordered by BA and assessed for relationships among BV, BQ, and BA. \u00a0Descriptive statistics (mean \u00b1 SD) were calculated for all swing kinematic variables.\u00a0 Pearson product moment correlations were used to examine relationships among the swing kinematic variables. \u00a0Alpha was set at (p<0.05) for all tests.\u00a0 Mean BV for all swings was 28.77 \u00b1 4.94 m\/s, mean BQ for all swings was 0.208 \u00b1 0.042 s, and mean BA for all swings was 144.39 \u00b1 38.44 m\/s2<\/sup>.\u00a0 When observing correlations of all swings, BV and BQ unexpectedly had an inverse relationship.\u00a0 When grouping swings into homogenous strata based on BA, BQ, and BV proved to have a significant positive correlation.<\/p>\n

Key words:<\/strong> softball, bat velocity, bat quickness, bat acceleration<\/p>\n

<\/p>\n

INTRODUCTION<\/strong><\/p>\n

Historically, one of the most difficult actions to complete in sports is to successfully hit a baseball or softball. \u00a0Athletes and coaches from both sports have searched for countless ways in order to gain supreme technical proficiency in this task. \u00a0Bat quickness (BQ, s) and bat velocity (BV, m\/s) are two aspects of a swing that can be evaluated in order to define a successful hitter.<\/p>\n

Bat quickness is defined by the time it takes to complete the swing (9). \u00a0To calculate BQ, the number of frames between swing onset and bat contact with the oncoming ball were counted and multiplied by 1\/300s, which is the time of one frame. \u00a0Bat velocity is measured in m\/s and is measured at the instant the bat makes contact with the ball. \u00a0Theoretically, if a hitter can complete the swing in a shorter amount of time (decreased BQ), this will allow the hitter more time to evaluate the incoming pitch thus make a better decision about whether or not to commit to the incoming pitch (3).\u00a0 An increase in BV will potentially lead to an increase in ball velocity off the bat, thus increase the chance of the batted ball being recorded as a hit (5). \u00a0Previously, a study found that a higher BV would require more time thus a higher value for BQ. \u00a0Conversely, a quicker swing that was completed in less time (lower BQ), would have a slower BV (9).<\/p>\n

An effective swing is dependent on executing the phases of the swing via a kinetic chain occurring in a sequential order. \u00a0Proper technique theoretically leads to a hitter optimizing BV and BQ. \u00a0A novel way of evaluating the relationship between BQ and BV is to consider bat acceleration (BA). \u00a0Bat acceleration is an average acceleration of the bat head calculated by dividing the BV in m\/s of any swing at contact by the swing BQ time in seconds. \u00a0Greater BA values come from some combination of maximizing BV and minimizing BQ.<\/p>\n

Previously, of the swing research that exists, most studies have been conducted with male athletes in baseball, with very few done with female athletes in softball. \u00a0Specifically, Stellar and his colleagues (1993) (9) evaluated BQ and BV in Major League Baseball (MLB) professional male athletes. \u00a0Despite the obviously physiological differences that exist between males and females, the constraints of baseball and softball should demand similar swing kinematics from hitters in both sports. \u00a0Despite the pitching velocities being significantly different between the two sports, due to the pitching distance in softball being shorter than that of baseball, the reaction times for hitters are also similar. \u00a0However, no such study evaluating BQ, BV (measured from the end of the bat head), or BA of in game hitting performance has been completed in softball. \u00a0Therefore, the purpose of this study is to describe the swing kinematics (BQ, BV, and BA) and the relationships that exist between them in Division I female softball players. \u00a0<\/strong><\/p>\n

METHODS<\/strong><\/p>\n

Subjects<\/em><\/p>\n

Subjects for this study were members of six NCAA Division I softball teams, ranging in Ratings Percentage Index (RPI) at the time of data collection from 1 to 217.\u00a0 The teams in this study were ranked at the following RPI: 1, 59, 71, 73, 161, and 217.\u00a0 The participants in this study were between the ages of 18 and 23 years old. \u00a0All data collection procedures were approved by the university Institutional Review Board.<\/p>\n

Instrumentation<\/em><\/p>\n

Video data were collected for all pitches during a 15-game softball tournament in which the six NCAA Division I teams played.\u00a0 For every pitch of the tournament, video cameras (JVC GC-PX1, Tokyo, JP) shooting at 300 Hz were used to capture any swing made.\u00a0 Video was converted to the AVI format for use in Maxtraq (Innovision Systems, Inc., Columbiaville, MI) software for digitizing.\u00a0 Digitized data were exported in comma separated value files and analyzed using custom Matlab software (Mathworks, Natick, Massachusetts).<\/p>\n

Procedures<\/em><\/p>\n

Cameras were positioned on the first-base and third-base sides of the field with their optical axes aligned with the front edge of home plate.\u00a0 The first-base-side camera was used to capture any swings made by right-handed hitters, while the third-base-side camera was used to capture any swings made by left-handed hitters.\u00a0 Video was recorded for every pitch thrown using the camera pertaining to each hitter.\u00a0 This resulted in over 5000 video recordings.\u00a0 For analysis, only videos made of full swings were used.\u00a0 Left-handed slap attempts, balls or strikes taken (not swung at) by the batter and all pitches that hit the batter were excluded.\u00a0 Additionally, if contact was made in front of the batter box or behind the back corner of the plate, the swing was excluded from analysis due to the out-of-plane motions of the bat head, and therefore unreliable positional data, that resulted.\u00a0 Based on these criteria, 1,099 swings made by 80 separate players were analyzed.<\/p>\n

These video clips were trimmed and converted to the AVI format, then transferred to the Maxtraq program for digitizing.\u00a0 Digitizing and calculations were executed using a method that has been shown to yield valid velocity data for swings recorded from the camera orientation used (4).\u00a0 eference points were digitized at the front inside corner of both batters boxes and the back corner of home plate.\u00a0 To mark the onset of the swing, the head of the bat was digitized in the frame at which the swing started.\u00a0 The onset of the swing was judged to happen in the frame where the bat head began initial trajectory toward contact with the ball.\u00a0 Then, the bat head was digitized at contact or, in the case of a miss, in the frame at which it had attained the same horizontal position as the ball.\u00a0\u00a0 \u00a0The bat head was also digitized four frames prior to the frame of contact.\u00a0 Data were exported in a comma separated value file containing the coordinates of the reference points, the location of the bat head at contact, the location of the bat head five frames prior to contact, and the location of the bat head at the onset of downswing.<\/p>\n

All calculations were carried out using custom software written in Matlab.\u00a0 To make position and velocity calculations, a reference distance was calculated.\u00a0 The centroid of the batters box corners was calculated, and based upon the known distance from the back corner of the plate to this centroid (1.4351m), a scale factor for each trial was calculated for conversion from pixels to meters.\u00a0 To calculate BV, the horizontal displacement of the bat head between contact and five frames prior to contact was converted to meters and divided by 1\/75s, which is the time spanned by four frames.\u00a0\u00a0 \u00a0This time span was chosen in order to get a valid velocity at contact while minimizing the percent error of digitizing that could occur if only single frame spans were to be used (4).\u00a0\u00a0 To calculate BQ the number of frames between swing onset and contact were counted and multiplied by 1\/300s, which is the time of one frame.\u00a0 BA was calculated by dividing BV by BQ to yield acceleration in m\/s\/s.<\/p>\n

Statistical Analysis<\/em><\/p>\n

Pearson product moment correlations were used to determine the strength of the relationships in question.\u00a0 Alpha was set at p<0.05 for all tests.\u00a0 SPSS 21 (International Business Machines Corp, USA) was used for all analyses.<\/p>\n

RESULTS<\/strong><\/p>\n

Descriptive statistics of swing kinematic variables for all swings can be found in Table 1.\u00a0 The mean BV for all swings was 28.77 m\/s (\u00b1 4.94 m\/s).\u00a0 The mean BQ for all swings was 0.21 s (\u00b1 0.042 s).\u00a0 The mean BA for all swings was 144.30 m\/s2 <\/sup>(\u00b1 38.44 m\/s2<\/sup>).\u00a0 The descriptive statistics of the swing kinematic variables collapsed for all players can be found in Table 2.\u00a0 The mean BV for all players was 29.12 m\/s (\u00b1 2.19 m\/s).\u00a0 The mean BQ for all players was 0.20 s (\u00b1 0.03 s).\u00a0 The mean BA for all players was 148.54 m\/s2 <\/sup>(\u00b1 22.37 m\/s2<\/sup>).\u00a0\u00a0 As expected, BA had significant relationships with both BV (0.65) and BQ (-0.85) since BA was calculated from these variables.<\/p>\n

Correlations between all swing kinematic variables for the collapsed averages calculated for each player can be found in Table 3.\u00a0 No significant relationship between BV and BQ existed.\u00a0 The correlation between BV and BQ for collapsed swings was<\/p>\n

(-0.19).<\/p>\n

DISCUSSION<\/strong><\/p>\n

Due to the findings of the current study, it is evident that the basic swing kinematic variables of a Division I female softball player and the relationships that exist between these variables have considerable applications to the sport.\u00a0 The variables described for Division I softball players can now be compared to those of previous studies completed with MLB hitters.\u00a0 These findings can continue to improve the way athletes in both sports are coached and developed.<\/p>\n

The results of the current study indicated that Division I softball players had similar swing kinematic variables to MLB professional hitters.\u00a0 Despite the physiological differences that exist, there can be several reasons to explain why there are similarities.\u00a0 The characteristics of the bat differ greatly in both sports; the average weight of a fast pitch softball bat is much less compared to that of an MLB bat.\u00a0 The range for a collegiate softball bat weight and length is 23-28 ounces and 32-34 inches.\u00a0\u00a0 \u00a0A profession baseball bat weight and length is 32-34 ounces and 31-34 inches (6).\u00a0 6However, the length and weight of the bat alone do not make the bat more or less difficult to swing.<\/p>\n

The moment of inertia of a bat is dependent on the mass of the bat and how that mass is distributed with respect to the pivot point (8).\u00a0 The further the mass of the bat is distributed from the pivot point – approximately at the mid-grip of the bat as the bat is held in the hands – the larger the moment-of-inertia will be.\u00a0 The larger the moment-of-inertia, the more difficult it is to swing the bat.\u00a0 The way in which the weight is distributed for a softball bat compared to a baseball bat leads to a softball bat feeling easier to swing compared to the bats used by MLB (8).\u00a0 Additionally, a study found a direct relationship between the speed of the \u2018sweet-spot\u2019 (the point of the bat that is most advantageous to make contact with the ball) and the speed of the bat center of mass.\u00a0 This would conclude that if a hitter had the sufficient strength and swing technique to handle a greater moment of inertia, this could result in a larger bat velocity via improved bat acceleration for the hitter (2).\u00a0 Because of these differences in length, weight, and moment-of-inertia, the similar swing parameter values found in this study can be explained despite a lack of female collegiate player strength compared to that of the major league counterpart.<\/p>\n

Total reaction time given to hitters (the time between the release of the ball by the pitcher to the instant at which contact needs to be made) is comparable between elite-level baseball and softball hitters.\u00a0 For example, a 95-mph fastball pitched from a 60 ft 6 in distance in baseball has comparable travel time to that of a 66 mph softball pitched from 43 ft in softball making the reaction time for hitters in each sport to be approximately 0.40 s (1).\u00a0 It is therefore reasonable that the BQ values for the subjects in the present study would need to be similar to those of elite baseball hitters in previous studies (1,8).\u00a0 Because of the reduced mass and moment of inertia of the softball bats, it is reasonable to witness similar BV values generated during the BQ times of swing.\u00a0\u00a0 \u00a0Indeed, many coaches do not distinguish between a baseball or softball swing.\u00a0 Instead, one swing is taught that is viable for both sports (7).\u00a0 There is nothing that limits the female player from having identical swing mechanics to those of a male hitter.\u00a0 The biological strength differences are then minimized by the differences in bat properties and the rules of each game.<\/p>\n

As noted previously, the study conducted by Stellar, House, DeRenne, and Blitzblau (1993) (9) reported significant relationships between BV and BQ, namely that the two variables are directly related.\u00a0\u00a0 \u00a0That is, as BQ increases, there is more time for the hitter to generate a higher BV.\u00a0 As BQ decreases, the hitter is left with a smaller amount of time, and thus generates a smaller BV.\u00a0 In the current study, this relationship did not materialize (Figure 1).\u00a0 Instead, a significant inverse correlation was observed between BV and BQ.\u00a0 In other words, the greater BV was associated with shorter BQ, meaning the best velocities were being generated in the least time.<\/p>\n

This unexpected result was most likely due to the heterogeneous nature of the population of swings that were examined.\u00a0 As mentioned, the range in RPI for this convenience sample was 1-217, meaning the data included kinematic swing variables from some of the best, but also from some of the worst teams in Division I softball.\u00a0 This variety in talent may explain why the best velocities in BV were associated with the smallest swing times in BQ (likely better players on better teams) and vice versa (worse players on worse teams).<\/p>\n

To explore this hypothesis, a secondary analysis of the data was performed.\u00a0 In an attempt to group like swings with one another, all swings were sorted by BA and placed into ten strata of similar size based on percentiles.\u00a0 Bat quickness and BV correlations for each strata can be found in Table 5.\u00a0 As hypothesized, the relationship between BV and BQ became positive within a stratus of similar swings.\u00a0 However, the strength of these relationships within some of the strata was unexpected.\u00a0 BQ and BV correlations produced coefficients that ranged from 0.379 \u2013 0.975.\u00a0 However, these correlations were found to be weakest in the top percentile group and lowest percentile group.\u00a0 The strength of the relationship between BV and BQ is clear in the middle eight strata.\u00a0 This strength of relationship begins to deteriorate in the extreme strata enough so that these swings disrupted the overall expected model for BV and BQ.\u00a0 These relationships can also be observed in Figure 2.<\/p>\n

Figure 3 displays data of BQ vs. BV with the top and bottom 10th<\/sup> percentiles removed from the data set.\u00a0 By doing so, a positive significant correlation of 0.136 is observed.\u00a0\u00a0 Figure 4 displays data of BQ vs. BV with the top and bottom 20th<\/sup> percentiles removed from the data set.\u00a0 By doing so, an even stronger positive significant correlation of 0.428 is observed.\u00a0 This shows that when homogenous swings are grouped with one another, the expected direct relationship between BV and BQ materializes.\u00a0 This also suggests that within this current sample, the best swings in terms of BV were also executed in the shortest times.\u00a0 These were swings probably executed by stronger, higher level players, and future research should examine the strength training implications for these kinematic variables in executing a swing.<\/p>\n

CONCLUSIONS<\/strong><\/p>\n

In conclusion, the swing kinematic variables described in this study clearly show that similarities exist between Division I softball hitters and MLB baseball hitters.\u00a0 The likeness in the swing parameters between both males and females despite obvious differences in relative strength can be explained by the bat properties and the constraints of each game.<\/p>\n

Interesting relationships materialized when evaluating correlations within the swing kinematic variables.\u00a0 Unexpectedly, an overall negative relationship between BQ and BV became evident when analyzing all swings.\u00a0 \u00a0However, when these swings were percentile ranked and each swing grouped with other like swings, there was a clear positive correlation between BV and BQ as expected.\u00a0 It appears the heterogeneous nature of the convenient sample contributed to the overall negative correlation between these swing kinematic variables.\u00a0 Future studies should take a look at the predictive power of these swing kinematic variables on in game statistical performance variables.<\/p>\n

APPLICATIONS IN SPORT<\/strong><\/p>\n

In coaching baseball and softball athletes, coaches should resist the urge to evaluate bat velocity alone.\u00a0 Being careful that a hitter does not use too much swing time in maximizing velocity will aid the hitter in pitch selection and becoming a more consistent hitter.\u00a0 It is evident that the combination of both BV and BQ plays a significant role in a swing, and future research should be conducted to verify this.<\/p>\n\n\n\n\n\n\n\n
Table 1.\u00a0\u00a0 Descriptive Statistics for All Swings<\/em><\/td>\n<\/tr>\n
Variable<\/td>\nN<\/td>\nMean<\/td>\nSD<\/td>\n<\/tr>\n
Bat velocity (m\/s)<\/p>\n

Bat quickness (s)<\/p>\n

Bat acceleration (m\/s2<\/sup>)<\/td>\n

1,099<\/td>\n28.77<\/td>\n4.94<\/td>\n<\/tr>\n
1,099<\/td>\n0.21<\/td>\n0.042<\/td>\n<\/tr>\n
1,099<\/td>\n144.39<\/td>\n38.44<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n\n\n\n\n
Table 2.\u00a0\u00a0 Descriptive Statistics for All Players<\/em><\/td>\n<\/tr>\n
Variable<\/td>\nN<\/td>\nMean<\/td>\nSD<\/td>\n<\/tr>\n
Bat velocity (m\/s)<\/p>\n

Bat quickness (s)<\/p>\n

Bat acceleration (m\/s2<\/sup>)<\/td>\n

63<\/td>\n29.12<\/td>\n2.19<\/td>\n<\/tr>\n
63<\/td>\n0.20<\/td>\n0.03<\/td>\n<\/tr>\n
63<\/td>\n148.54<\/td>\n22.37<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n\n\n\n\n
Table 4.\u00a0\u00a0 Correlations of Swing Kinematics for Collapsed Swings<\/em><\/td>\n <\/td>\n<\/tr>\n
 <\/td>\nN<\/td>\nBV<\/td>\nBQ<\/td>\nBA<\/td>\n<\/tr>\n
BV (m\/s)<\/td>\n63<\/td>\n1.00<\/td>\n-0.19<\/td>\n\u00a0\u00a0\u00a0 0.65**<\/td>\n<\/tr>\n
BQ (s)<\/td>\n63<\/td>\n-0.19<\/td>\n1.00<\/td>\n\u00a0 -0.85**<\/td>\n<\/tr>\n
BA (m\/s2<\/sup>)<\/td>\n63<\/td>\n\u00a0\u00a0\u00a0\u00a0 0.65**<\/td>\n\u00a0\u00a0\u00a0 -0.85**<\/td>\n1.00<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Table 5.\u00a0\u00a0 BQ\/BV Correlated by Percentile Strata<\/em><\/td>\n<\/tr>\n
Strata<\/td>\nr<\/td>\n<\/tr>\n
1.0-0.9<\/td>\n0.596**<\/td>\n<\/tr>\n
0.9-0.8<\/td>\n0.961**<\/td>\n<\/tr>\n
0.8-0.7<\/td>\n0.966**<\/td>\n<\/tr>\n
0.7-0.6<\/td>\n0.975**<\/td>\n<\/tr>\n
0.6-0.5<\/td>\n0.970**<\/td>\n<\/tr>\n
0.5-0.4<\/td>\n0.974**<\/td>\n<\/tr>\n
0.4-0.3<\/td>\n0.966**<\/td>\n<\/tr>\n
0.3-0.2<\/td>\n0.953**<\/td>\n<\/tr>\n
0.2-0.1<\/td>\n0.938**<\/td>\n<\/tr>\n
0.1-0.0<\/td>\n0.379**<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u00a0<\/strong><\/p>\n

Figure 1<\/em>.\u00a0\u00a0 \u00a0BQ vs.\u00a0\u00a0 BV means for every swing executed during the tournament.<\/p>\n

\"Figure<\/p>\n

Figure 2.\u00a0\u00a0 <\/em>BV and BQ Relationships Stratified by Deciles<\/p>\n

\"Figure<\/p>\n

Figure 3.\u00a0\u00a0 <\/em>BV vs.\u00a0\u00a0 BQ Relationships for Swings in 10th<\/sup>-90th<\/sup> Percentile<\/p>\n

\"Figure<\/p>\n

Figure 4.\u00a0\u00a0 <\/em>BV vs.\u00a0\u00a0 BQ Relationships for Swings in 20th<\/sup>-80th<\/sup> Percentile<\/p>\n

\"Figure<\/p>\n

REFERENCES<\/strong><\/p>\n

    \n
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  2. Bahill, A.T. (2004). The ideal moment of inertia for a baseball or softball bat. IEEE\u00a0<\/em>Transactions On Systems, Man, and Cybernetics \u2013 Part A: Systems and Humans.\u00a0<\/em>University of Arizona, Tuscon AZ.<\/li>\n
  3. DeRenne, C. (1993) High-tech hitting<\/em>. Laguna Hills, CA: West Pub Co.<\/li>\n
  4. Ficklin T.K., and Lund R. (2014). Validation of a two-dimensional video method for measuring in-game softball bat velocity. World Congress on Biomechanics. (Boston, MA).<\/li>\n
  5. \u00a0Fortenbaugh, D.M. (2011). The biomechanics of the baseball swing<\/em> (Doctoral dissertation). University of Miami Library, Miami, FL.<\/li>\n
  6. Noble, L., and Eck, X. (1985). Empirical determination of the axis of percussion\u00a0of softball and baseball bats. Biomechanics<\/em> Champaign, IL. pp. 516-520.<\/li>\n
  7. \u00a0RightView Pro. (2009). Baseball and softball training with Mike Candrea [Video \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 file]. Retrieved from http:\/\/www.rightviewpro.com\/about-us\/founders-\u00a0\u00a0\u00a0\u00a0 staff\/candrea.<\/li>\n
  8. \u00a0Smith, L., Broker, J., & Nathan, A. (2003). A study of softball player swing speed.\u00a0Sports Dynamics Discovery and Application<\/em>.\u00a0 RMIT University, Melbourne, Australia, pp. 12-17.<\/li>\n
  9. Stellar, T., House, T., DeRenne, C., & Blitzblau, A. (1993). The absolutes of hitting:\u00a0<\/em>Dynamic balance, kinetic links, axis of rotation, bat lag. <\/em>(Motion Picture) United States: BioKinetics Inc.<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

    Submitted by\u00a0Cassie Reilly-Boccia1*, Travis Ficklin2*, Robin Lund3* 1*\u00a0\u00a0Director of Research […]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"jetpack_publicize_message":"","jetpack_is_tweetstorm":false,"jetpack_publicize_feature_enabled":true,"jetpack_social_options":[]},"categories":[290],"tags":[591,590,589,506],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p4btio-Io","jetpack-related-posts":[{"id":2378,"url":"https:\/\/thesportjournal.org\/article\/kinematic-analysis-of-the-slap-hitting-technique-in-division-i-softball-players\/","url_meta":{"origin":2752,"position":0},"title":"Kinematic Analysis of the Slap Hitting Technique in Division I Softball Players","date":"March 17, 2015","format":false,"excerpt":"Submitted by Robin Lund1, Ph.D.*, Travis Ficklin2, Ph.D.* Mr. Johnathan Faga3*, Ms. Cassie Reilly-Boccia4* 1*\u00a0Assistant Professor of Physical Education\u00a0at University of Northern Iowa,\u00a0Cedar Falls, IA 50614 2* Assistant Professor\u00a0of\u00a0Physical Education\u00a0at\u00a0University of Northern Iowa,\u00a0Cedar Falls, IA 50614 3* B.A. in Movement and Exercise Science from the University of Northern Iowa. 4*\u00a0Director\u2026","rel":"","context":"In "Contemporary Sports Issues"","img":{"alt_text":"Table 1","src":"https:\/\/i0.wp.com\/thesportjournal.org\/wp-content\/uploads\/2015\/03\/Table1-Kin.jpg?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":184,"url":"https:\/\/thesportjournal.org\/article\/a-composite-softball-bat-revolution-why-the-pitcher-has-little-time-to-react-to-a-batted-ball\/","url_meta":{"origin":2752,"position":1},"title":"A Composite Softball Bat Revolution: Why the Pitcher has Little Time to React to a Batted-Ball","date":"January 4, 2005","format":false,"excerpt":"Submitted by: Mark McDowell, Ph. D., Michael V. Ciocco, Ph.D. & Bryan Morreale Abstract In the past few years, there has been a composite bat construction revolution in the softball bat industry. While composite material bats have enabled softball bat performance to increase much to the delight of hitters, they\u2026","rel":"","context":"In "Contemporary Sports Issues"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":1906,"url":"https:\/\/thesportjournal.org\/article\/temporal-description-of-the-stolen-base-in-high-school-softball\/","url_meta":{"origin":2752,"position":2},"title":"Temporal Description of the Stolen Base in High School Softball","date":"June 4, 2014","format":false,"excerpt":"Submitted by Robin Lund, Travis Ficklin and Cassie Reilly-Boccia ABSTRACT The purpose of this study is to describe the temporal factors that determine the outcome of a stolen base attempt in high school softball. Two hundred and sixty-eight high school softball players were videotaped using a high-speed video camera to\u2026","rel":"","context":"In "Contemporary Sports Issues"","img":{"alt_text":"lund table","src":"https:\/\/i0.wp.com\/thesportjournal.org\/wp-content\/uploads\/2014\/06\/Lund-Table1.jpg?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":5079,"url":"https:\/\/thesportjournal.org\/article\/perceptions-of-ncaa-division-i-athletes-on-strength-training\/","url_meta":{"origin":2752,"position":3},"title":"Perceptions of NCAA Division I Athletes on Strength Training","date":"May 25, 2017","format":false,"excerpt":"Authors: Joni M. Boyd, Ashley M. Andrews, Janet R. Wojcik, & Charles J. Bowers Corresponding Author: Joni M. Boyd, PhD Winthrop University 216L West Center Rock Hill, SC 29733 boydj@winthrop.edu 803-323-4936 Joni Boyd is an Assistant Professor of Exercise Science in the Department of Physical Education, Sport, and Human Performance\u2026","rel":"","context":"In "Sport Training"","img":{"alt_text":"Table 1","src":"https:\/\/i0.wp.com\/thesportjournal.org\/wp-content\/uploads\/2017\/05\/Table1.jpg?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":299,"url":"https:\/\/thesportjournal.org\/article\/eating-disorders-among-female-college-athletes\/","url_meta":{"origin":2752,"position":4},"title":"Eating Disorders Among Female College Athletes","date":"April 2, 2008","format":false,"excerpt":"Submitted By: Nikkie Smiley, Aberdeen Family YMCA, Aberdeen, S.D., Jon Lim, Minnesota State University, United States Sports Academy Doctoral Graduate Abstract The study examined attitudes about eating in relation to eating disorders, among undergraduate female student-athletes and non-athletes at a mid-size Midwestern NCAA Division II university. It furthermore examined prevalence\u2026","rel":"","context":"In "Sports Exercise Science"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":3438,"url":"https:\/\/thesportjournal.org\/article\/the-high-performance-management-model-from-olympic-and-professional-to-university-sport-in-the-united-states\/","url_meta":{"origin":2752,"position":5},"title":"The High Performance Management Model: From Olympic and Professional to University Sport in the United States","date":"February 4, 2016","format":false,"excerpt":"Authors: Jed Smith* (1), Peter Smolianov (2) (1) Head Strength and Conditioning Coach and an Instructor in the area of Movement and Exercise Science at the University of Northern Iowa and is currently a doctoral student at the United States Sports Academy (2) Sport Management Professor at Salem State University\u2026","rel":"","context":"In "Sports Management"","img":{"alt_text":"Smith Figure 1","src":"https:\/\/i0.wp.com\/thesportjournal.org\/wp-content\/uploads\/2016\/02\/Smith-4.jpg?resize=350%2C200","width":350,"height":200},"classes":[]}],"_links":{"self":[{"href":"https:\/\/thesportjournal.org\/wp-json\/wp\/v2\/posts\/2752"}],"collection":[{"href":"https:\/\/thesportjournal.org\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/thesportjournal.org\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/thesportjournal.org\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/thesportjournal.org\/wp-json\/wp\/v2\/comments?post=2752"}],"version-history":[{"count":6,"href":"https:\/\/thesportjournal.org\/wp-json\/wp\/v2\/posts\/2752\/revisions"}],"predecessor-version":[{"id":6140,"href":"https:\/\/thesportjournal.org\/wp-json\/wp\/v2\/posts\/2752\/revisions\/6140"}],"wp:attachment":[{"href":"https:\/\/thesportjournal.org\/wp-json\/wp\/v2\/media?parent=2752"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/thesportjournal.org\/wp-json\/wp\/v2\/categories?post=2752"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/thesportjournal.org\/wp-json\/wp\/v2\/tags?post=2752"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}