8th Annual Coaches College Papers - Final

8/12/2019 8th Annual Coaches College Papers - Final

1/36

8thAnnual Coaches and Sport Science College December 2013

1

Conference Papers from the

8thAnnual Coaches and Sport Science College

December 13-14, 2013

Johnson City, Tennessee


2/36


2

Editors: George Beckham, Anna Swisher

Review Committee: Dr. Michael Stone, Dr. William Sands, Dr. BradDeWeese

Special thanks to our committee of reviewers, and all those who

submitted papers

Distributed by:

The Department of Exercise and Sport SciencesClemmer College of EducationEast Tennessee State University

Johnson City, TN

and

Center of Excellence for Sport Science and Coach EducationEast Tennessee State University


3/36


3

We want to thank our sponsors:


4/36


4

Contents

A comparison of baseball and softball players bilateral strength asymmetry and its relationshipwith performance ............................................................................................................................ 5

Kinetic and kinematic symmetry during unloaded and loaded static jumps .................................. 8

The efficacy of partial squats on measures of strength and explosiveness: an exploratory study 11

The application of accelerometry to weightlifting: current challenges ........................................ 14

A comparison of muscle activation of the lower back and legs between a back squat and a rear footelevated split squat exercise .......................................................................................................... 17

The efficacy of catapult sports minmax gps metrics in relation to session rating of perceivedexertion ......................................................................................................................................... 20

Trends from NFL combine data may predict minimum performance values of future NFL draftpicks .............................................................................................................................................. 23

Relationship of sprint intervals to 60 m sprint performance in NCAA Division-I sprinters: anexploratory study .......................................................................................................................... 26

The sport performance enhancement group: a five-year analysis of interdiciplinary athletedevelopment .................................................................................................................................. 28

The effect of verbal instruction on lower body power development during various plyometrics 31

Using benefits-based models to manage sport performance enhancement groups ....................... 34


5/36


5

A COMPARISON OF BASEBALL AND SOFTBALL PLAYERS BILATERAL STRENGTHASYMMETRY AND ITS RELATIONSHIP WITH PERFORMANCE

Christopher A. Bailey1, Timothy J. Suchomel1, George K. Beckham1, Timothy C. McInnis1, Austin R.Driggers1, Cody T. Haun1, Kimitake Sato1, and Michael H. Stone1.

1East Tennessee State University, Department of Exercise and Sport Sciences, Johnson City, TN

INTRODUCTION: Strength asymmetry has been examined as a possible precursor of injury.Unfortunately, its effectiveness as an injury predictor is ambiguous (Bennell et al., 1998, Knapik et al.,1991, Nadler et al., 2001). More recently, the possible performance detriment associated with strengthasymmetry has been evaluated. Yoshioka et al. (2011) evaluated the effect of lower limb bilateral strengthasymmetry on jumping performance in a simulation study. Their study included two models that possessedequal levels of absolute strength, but the distribution of strength differed. The findings indicated that abilateral strength asymmetry of 10% between the lower limbs did not result in a difference in jump height.In contrast to the previous study, Bailey et al. (2013) demonstrated that a strength asymmetry, as measuredin an isometric mid-thigh pull, is related to detriments in both jump height and peak power during staticand countermovement jumps. To the authors knowledge, studies evaluating the relationship between mid-

thigh pull strength asymmetry and mid-thigh pull performance do not yet exist.There also seems to be a lack of studies examining differences in strength symmetry between sports

as well as between the sexes. Owens et al. (2011) produced findings showing female athletes fromunspecified sports had statistical differences in force values between dominant and non-dominant limbs,measured during an isometric mid-thigh pull, whereas male athletes did not show the same trend. This studyseparated athletes based on sex and not by sport, which may lead one to question whether the asymmetryis a result of sex or a byproduct of specific sport participation. Therefore, the purpose of this study was tocompare bilateral strength asymmetry between similar male and female sports to determine if sex plays arole. A secondary purpose of this study was to evaluate the relationship of strength asymmetry and mid-thigh pull performance.

METHODS: Fifty NCAA Division I athletes (baseball n=31, softball n=19) participated in this study after

reading and signing University Institutional Review Board approved informed consent documents. Prior totesting, athletes completed a standardized warm-up consisting of 25 jumping jacks, a set of five mid-thighpulls with a 20 kg bar, and three sets of five mid-thigh pulls with a 60 kg load for males or a 40 kg load forfemales.

A multi-joint maximal effort isometric contraction, the isometric mid-thigh pull (IMTP), was usedto evaluate bilateral strength asymmetry. Isometric mid-thigh pulls were completed in a custom designedpower rack and force values were collected via a dual force plate system (separate 91 cm x 45.5 cm forceplates, Roughdeck HP, Rice Lake, WI). Data were collected at 1,000 Hz. The apparatus and positioningwas consistent with previously published work from Haff et al. (1997). The bar heights were set specificallyfor each athlete, with bar height corresponding to a knee angle of 1255. Each athletes hands were heldin place with weightlifting straps and athletic tape to ensure a maximal effort could be delivered withoutthe risk of losing grip. During testing, athletes performed two warm-up and familiarization trials at 50 and

75% of perceived maximal effort. Following these trials, athletes underwent two maximal effort trials.Athletes were instructed to pull as fast and as hard as possible to ensure maximum force and rate of forcedevelopment.

Isometric mid-thigh pulls were analyzed to produce summated and separate values for each platefor the following variables: peak force (PF), rate of force development (RFD) (from 0-250ms), force at250ms (F250), and impulse at 250ms (I250). The magnitude of strength asymmetry was evaluated via asymmetry index (SI) score produced from the following equation previously used by Sato and Heise (2012):SI = (larger value smaller value) / (total value) * 100. The formula results in a percentage, where an SIscore of 0 represents perfect symmetry and values further away from zero represent increasing asymmetry.


6/36


6

Prior to comparison, Levenes tests were run to evaluate variance equality between groups for allSI variables. Comparison between sports was completed with independent samples t-tests. A Holm-Bonferroni sequential adjustment was applied as four separate t-tests were run and the initial statisticalsignificance was set at 0.05. Cohens d effect sizes were also calculated to estimate effect sizes. Therelationship of symmetry and performance was analyzed by bivariate Pearson correlation coefficientsbetween summed values and SI scores for each variable collected.

RESULTS: Descriptive results for summed values are displayed in Table 1 and SI scores are displayed inTable 2. Table 3 shows the results the results from the Levenes test, t-test, and Cohens d effect sizeestimates. Statistical significance was not observed in the Levenes test for any variable; therefore, equalvariances were assumed. No statistically significant differences were revealed from the t-tests and onlysmall Cohens d effect size estimates were observed. Table 4 shows the results of the bivariate correlations.Statistically significant correlations were noted in baseball and softball samples, but softball revealedstronger relationships. Only one statistically significant relationship was found when the samples werecombined (PF and peak force symmetry index (PFSI), r = -0.29).

Table 1. Descriptive data for the combined group (n=50), baseball players (n=31), and softballplayers (n=19) (Mean SD).

Performance Variable

PF RFD250 F250 I250

Combined 3644.3 988.4 6293.5 2160.0 2597.4 740.3 436.9 124.5Baseball 4233.0 664.2 7471.3 1688.5 3029.8 542.8 503.4 102.4Softball 2683.7 594.4 4371.9 1289.4 1891.9 391.9 328.4 68.3

PF = peak force; RFD250 = rate of force development at 250ms; F250 = force at 250ms; I250 = impulse at250ms

Table 2. Symmetry Index (SI) for the combined group (n=50),baseball players (n=31), and softball players (n=19) (Mean SD)

Symmetry Index (%) Variable

PFSI RFD250SI F250SI I250SI

Combined 6.9 5.8 8.0 6.8 6.5 4.9 6.2 4.4Baseball 6.0 5.4 7.3 5.8 6.2 4.7 5.9 4.7Softball 8.3 6.3 9.0 8.2 7.2 5.3 6.7 3.9

PF = peak force; RFD250 = rate of force development at 250ms;F250 = force at 250ms; I250 = impulse at 250ms

Table 3. Results from Levenes test for homogeneity, independentsamples t-test, and Cohens d effect size estimates for comparison

between baseball and softball SI scores for all variables.

Levene's Test t-test Cohen's d

PFSI 0.486 0.178 0.32

RFD250SI 0.196 0.387 0.21F250 SI 0.410 0.481 0.17

I250SI 0.671 0.503 0.17

* indicates statistical significance at the 0.05 alpha level, PF = peakforce; RFD250 = rate of force development at 250ms; F250 = forceat 250ms; I250 = impulse at 250ms, SI = symmetry index

Table 4. Correlations between SI variables and PF, RFD250, F250, and I250for combined, baseball, and softball samples.


7/36


7

Performance Variable

PF RFD250 F250 I250

Combined (n=50) -0.29* -0.18 -0.05 0.11Baseball (n=31) 0.08 0.19 0.29 0.39*Softball (n=19) -0.71** -0.63** -0.46* -0.21

* Correlation is significant at the 0.05 level

** Correlation is significant at the 0.01 level

DISCUSSION: The primary purpose of this study was to compare the bilateral strength asymmetry betweenNCAA Division I baseball and softball players. There were no statistically significant differences betweenthe groups for any of the tested variables. Furthermore, Cohens d effect size estimates revealed only trivialto small differences between groups. The finding that athletes participating in sports emphasizing repetitivemovements on the same side do possess strength asymmetries (combined variable values range: 6.18-7.96%) may still be of value, but comparisons with other sports still need to be made to determine therelative importance of these values.

Although baseball and softball players did not differ in asymmetry magnitude, the relationships ofasymmetry with strength testing performance do seem to differ. In softball players, PF and RFD had strongnegative relationships with their SI scores (PF r=-0.71, RFD r=-0.63), while a moderate negative

relationship was seen between F250 and its SI score (r=-0.46). The relationship between I250 and its SIscore was small (r=-0.21), but it was still negative. The trend of negative relationships among the softballplayers indicates that strength testing performance increases as asymmetry decreases. The only performancevariable that produced a statistically significant correlation with its corresponding SI score in the baseballsample was I250 (r=0.39).

It appears that baseball players are able to perform better than the softball players in the includedperformance tests in spite of similar asymmetry. This finding may be consistent with Yoshioka andcolleagues (2011) jump simulation study where the stronger limb made up the difference in strength of theweaker limb. The results from the softball players correlations do not share this trend. Baseball playerswere statistically stronger than their softball counterparts for all variables with effect size estimates rangingfrom 2.02.4. This may indicate that absolute strength plays a role on the influence of asymmetry onperformance. Future researchers should focus on the role strength plays in asymmetry-related performance

detriments.

REFERENCES:

Bailey, C. A., Sato, K., Alexander, R. P., Chiang, C. Y., & Stone, M. H. (2013). Isometric force production symmetry and jumpingperformance in collegiate athletes.Journal of Trainology,2(1):1-5.

Bennell, K. Wajswelner, H., Lew, P. et al. (1998). Isokinetic strength testing does not predict hamstring injury in Australian Rulesfootballers.British Journal Sports Medicine, 32:309-314.

Haff, G., Stone, M.H., OBryant, H., et al. (1997). Force-time dependent characteristics of dynamic and isometric muscle actions.Journal of Strength and Conditioning Research, 11(4):269-272.

Knapik, J. J., Bauman, C. L., Jones, B. H., Harris, J. M., & Vaughan, L. (1991). Preseason strength and flexibility imbalancesassociated with athletic injuries in female collegiate athletes. The American Journal of Sports Medicine, 19(1):76-81.

Owens, E., Serrano, A., Ramsey, et al. (2011). Comparing lower-limb asymmetries in NCAA D-I male and female athletes.Journalof Strength and Conditioning Research, 25(s1):44-45.

Nadler, S., Malanga, G., Feinberg, J., et al. (2001). Relationship between hip muscle imbalance and occurrence of low back painin collegiate athletes: a prospective study.American Journal of Physical Medicine and Rehabilitation,80:572-577.

Sato, K., & Heise, G. D. (2012). Influence of weight distribution asymmetry on the biomechanics of a barbell back squat. Journalof Strength and Conditioning Research, 26(2), 342-349.

Yoshioka S., Nagano, A, Hay, D., et al. (2011). The effect of bilateral asymmetry of muscle strength on the height of a squat jump:a computer simulation study.Journal of Sports Sciences, 29:867-877.


8/36


8

KINETIC AND KINEMATIC SYMMETRY DURING UNLOADED AND LOADED STATIC JUMPS

Chris A. Bailey1, Kimitake Sato1, Brian Johnston1, William A. Sands1, Angus Burnett2, and Michael H.Stone1.

1East Tennessee State University, Department of Exercise and Sport Sciences, Johnson City, TN2Chinese University of Hong Kong, Department of Sports Science and Physical Education, Shatin, N.T.,Hong Kong

INTRODUCTION: Research on the topic of strength symmetry and asymmetry has been gaining popularity(Bailey et al., 2013, Benjanuvatra et al., 2013, Seibert et al., 2013). Much of the research has targetedpossible relationships between strength asymmetry and injury, but the results remain unclear (Bennell etal., 1998, Knapik et al., 1991, Nadler et al., 2001).

Others have analyzed the relationship between symmetry and performance (Bailey et al. 2013,Yoshioka et al. 2011). In a simulation study, Yoshioka and colleagues found that bilateral strengthasymmetry would not cause substantial alterations in jump height (JH) as the stronger leg compensated forthe weaker leg. Even if the JH is unaffected, the direction of the center of mass displacement (COMd) maybe altered. Many assume a strictly vertical direction for COMd, but an asymmetrical force production may

result in increases in mediolateral or anteroposterior COMd. Unlike Yoshioka, Bailey et al. (2013) showedmoderate to strong negative relationships between strength asymmetry and both jump height and peakpower.

There also seems to be a paucity of research in the area of joint symmetry from a kinematicstandpoint and its relationship with performance. Recently, Dai et al. (2013) found that ground reactionforce asymmetry during stop-jumps could predict peak and average knee joint moment asymmetry insubjects who recently underwent ACL reconstruction, but the study did not report further on kinematic data.Thus, there is a need for further research examining the relationship between force production symmetry,kinematic symmetry and jumping performance. The purpose of this study was to determine if forceproduction symmetry is related to measures of kinematic symmetry at the hip, knee and ankle. A secondarypurpose was to determine if relationships exist between force production symmetry and COMd.

METHODS: Sixteen NCAA Division I baseball players participated in this study. All subjects read andsigned informed consent documents approved by the University Institutional Review Board. Prior toactivity, the subjects performed a standardized warm-up which consisted of 25 jumping jacks, one set offive mid-thigh pulls with 20 kg, and three sets of five mid-thigh pulls with 60 kg. Athletes then completedunloaded and lightly-loaded (20 kg bar) static jumps (SJ). Prior to each jump condition, athletes performedfamiliarization trials at 50% and 75% of perceived effort. Approximately one minute of rest was allowedbetween jumps and two jump trials were completed at each weight. A PVC pipe was used during theunloaded SJ and its weight was considered negligible. There was no arm swing in either of the jumpconditions as either the PVC pipe or 20 kg bar was held by the hands just below the 7thcervical vertebrae.During both jump conditions the athlete descended to a position that was previously measured at 90 kneeflexion and jumped once the command was given. Trials were considered successful if there was nodetectable countermovement.

Along with the kinematic data, concurrent kinetic data were collected by two portable force plates(0.36 m x 0.36 m, PASCO Scientific PS-2142, Roseville, CA) collecting at 1,000 Hz. Variables derivedfrom kinetic measurement included maximum propulsive force (MPF) and MPF symmetry index score(MPF-SI). The symmetry index score was calculated with the formula shown previously by Sato and Heise(2012), where SI = (larger value smaller value)/total value * 100. The result was a percentage where 0%represented perfect symmetry and asymmetry increased with values away from zero. Kinematic data wascollected via a six camera infrared 3D motion capture system during all jumps (Vicon Nexus, ver. 1.85,Centennial, CO). Prior to testing, reflective markers were placed on each athlete according to the full-bodyPlug-in-Gait model. Kinematic variables included joint (hip, knee, and ankle) range of motion (ROM),


9/36


9

peak angular velocities (PV) and peak angular accelerations (PA), joint positions at which PV and PAoccurred (deg@PV, deg@PA) and SI scores for all of the aforementioned kinematic variables. Data werecollected at 200 Hz and raw position data were smoothed with a Woltring filter with the cut-off frequencyestablished by an optimization method programmed into the motion capture software (Woltring 1985).

Pearson correlations were used to evaluate the relationships between kinetic and kinematicsymmetry measures and to evaluate the relationship between force production symmetry and COMd in the

mediolateral or anteroposterior directions. Statistical significance was set at p0.05.

RESULTS: Results from the Pearson correlations of both jump conditions are in Tables 1 and 2 below.Table 1 displays correlation values between MPF SI (0 kg and 20 kg conditions) and kinematic variable SIscores. Table 2 displays correlation values between MPF-SI and COMd in either the anteroposterior ormediolateral directions.

Table 1: Correlation Matrices between MPFSI and kinematic variables SI scores

MPF 0kg SI MPF 20kg SI

Hip Kinematics ROM 0.06 0.17

PV -0.19 0.08

deg@PV -0.17 0.06PA -0.03 -0.32

deg@PA 0.06 0.35

Knee Kinematics ROM -0.26 -0.01

PV -0.28 -0.13

deg@PV 0.49* 0.39

PA -0.28 0.13

deg@PA -0.32 0.03

Ankle Kinematics ROM -0.11 0.51*

PV -0.1 -0.07

deg@PV -0.26 -0.14PA 0.04 0.11

deg@PA 0.24 -0.15

Table 2. Correlations between center of mass displacement and maxpush force symmetry index score

A/P COMd M/L COMd

MPF 0kg SI 0.12 -0.01

MPF 20kg SI -0.12 -0.16A/P = anteroposterior, M/L = mediolateral, * indicates statistical significance(p0.05)

DISCUSSION: This study primarily sought to evaluate the relationship between selected kinematicvariables, SI scores and ground reaction force symmetry during static jumps. Results showed a statisticallysignificant and strong correlation between MPF SI and ankle sagittal plane ROM (r=0.51) in the 20 kg jumpcondition, but not in the 0 kg condition. One should be cautioned when considering the strength of thisrelationship, as a scatter plot (below) reveals one large grouping and three outliers which are likely inflatingthe magnitude of the relationship. The data for these three subjects only appear as outliers in thisrelationship, thus, they were not removed.


10/36


10

A statistically significant moderate relationship (r=0.49) was observed between MPF SI and kneedeg@PV for the 0 kg jump condition, while a moderate relationship (r=0.39) that did not reach statistical

significance was observed in the 20 kg condition. It appears that the symmetry of the position at which PVoccurs is the only variable related to MPF SI.

A secondary purpose of this study was to evaluate the relationship of COMd and force production

symmetry. Neither direction of COMd (anteroposterior or mediolateral) produced meaningful relationshipswith MPF SI. It appears that MPF SI was not related to alterations in COMd during either jump condition.

The lack of statistically significant relationships observed may indicate that either force productionsymmetry is not related to measures of kinematic symmetry and alterations in COMd, or it may indicate

that peak force SI may not be a sufficient measure of symmetry during the propulsive phase of jumping inhealthy populations. Future investigations should focus on variables that are not instantaneous force values,such as rate of force development, impulse or time to peak force, in order to get a more detailed view of

overall jump symmetry.

REFERENCES:

Bailey, C. A., Sato, K., Alexander, R. P., Chiang, C. Y., & Stone, M. H. (2013). Isometric force production symmetry and jumpingperformance in collegiate athletes.Journal of Trainology,2(1):1-5.

Benjanuvatra, N., Lay, B. S., Alderson, J. A., & Blanksby, B. A. (2013). Comparison of ground reaction force asymmetry in one-

and two-legged countermovement jumps.Journal of Strength and Conditioning Research, 27(10), 2700-2707.Bennell, K. Wajswelner, H., Lew, P. et al. (1998). Isokinetic strength testing does not predict hamstring injury in Australian Rules

footballers.British Journal of Sports Medicine, 32:309-314.Dai, B., Butler, R., Garrett, W., et al. (2013). Using ground reaction force to predict knee kinetic asymmetry following anterior

cruciate ligament reconstruction. Scandinavian Journal of Medicine and Science in Sports, Epub Ahead of print (October

2013 acceptance).Horak, F. B. (1987). Clinical measurement of postural control in adults.Physical Therapy, 67(12), 1881-1885.Knapik, J. J., Bauman, C. L., Jones, B. H., Harris, J. M., & Vaughan, L. (1991). Preseason strength and flexibility imbalances

associated with athletic injuries in female collegiate athletes.The American Journal of Sports Medicine, 19(1), 76-81.

Nadler, S., Malanga, G., Feinberg, J., et al. (2001). Relationship between hip muscle imbalance and occurrence of low back painin collegiate athletes: a prospective study.American Journal of Physical Medicine and Rehabilitation,80:572-577.

Sato, K., & Heise, G. D. (2012). Influence of weight distribution asymmetry on the biomechanics of a barbell back squat. Journalof Strength and Conditioning Research, 26(2), 342-349.

Seibert, R., Marcellin-Little, D. J., Roe, S. C., DePuy, V., & Lascelles, B. D. (2012). Comparison of body weight distribution, peakvertical force, and vertical impulse as measures of hip joint pain and efficacy of total hip replacement. VeterinarySurgery, 41(4), 443-447.

Woltring, H. (1985). On optimal smoothing and derivative estimation from noisy displacement data in biomechanics. HumanMovement Science, 4(3), 229-245.

Yoshioka S., Nagano, A, Hay, D., et al. (2011). The effect of bilateral asymmetry of muscle strength on the height of a squat jump:a computer simulation study.Journal of Sports Science, 29:867-877.


11/36


11

THE EFFICACY OF PARTIAL SQUATS ON MEASURES OF STRENGTH AND EXPLOSIVENESS:AN EXPLORATORY STUDYCaleb D. Bazyler1, Kimitake Sato1, Craig A. Wassinger, Hugh S. Lamont, Michael H. Stone1

1: Department of Exercise and Sport Science, East Tennessee State University, Johnson City, TN, USA2: Department of Physical Therapy, East Tennessee State University, Johnson City, TN, USA3: Department of Exercise Science, California Lutheran University, Thousand Oaks, CA, USA

INTRODUCTION: Partial-lifts are often incorporated into strength and conditioning programs (Clark et al.2011, Harris et al. 2000). The proposed benefits of partial lifts include improved strength at the terminalrange of motion (ROM) and weak portions of a movement, a substitute for full ROM exercise duringrehabilitation, injury prevention, enhanced metabolic adaptations, increased training volume, trainingvariation, and enhanced sport performance (Clark et al. 2011, Massey et al. 2004). Only a few studiesdirectly examine the efficacy of partial lifts in improving maximal strength, and the findings are conflicting(Massey et al. 2004, Pinto et al. 2012). Additionally, some studies used untrained subjects while evidenceindicates that partial-lifts, if effective, would benefit lifters with previous training experience (Clark et al.2011, Massey et al. 2004). Observations by the authors indicate that many strength-power athletes,including weightlifters and powerlifters, integrate partial movements into their training. The purpose of this

study was to examine the effects of two different training modalities, full ROM training (F) and full ROMplus partial ROM training (FP). Our hypotheses were: (1) both groups would improve from pre to post-intervention on all dynamic and isometric variables measured; (2) FP would improve to a greater extentthan F at measurements associated with maximum effort at the terminal ROM (1-RM partial-squat, 120isometric squat peak force, RFD and impulse scaled).

METHODS: Subjects recruited were 18 recreationally trained college-aged males with at least one year ofresistance training experience on the squat (1.3 * body mass). Pre-intervention squat 1-RM was similar toprevious studies: 146.8 23.0 kg (Harris et al. 2000). Nine subjects in the F group (age 20.8 2.0 years,height 176.4 6.3 cm, body mass (BM) 84.9 10.9 kg) and nine subjects in the FP group (age 20.7 1.9years, height 177.6 8.1 cm, BM 86.1 8.9 kg) completed the study. Prior to participating, all subjectscompleted a health history questionnaire and signed an informed consent (East Tennessee State Universitys

Institutional Review Board). Subjects were matched according to absolute and relative 1-RM squat frompre-intervention testing and assigned to a group. One group performed full ROM squats, whereas the otherperformed full and partial ROM squats at 100 knee angle (Table 1). Prior to the intervention, subjects werefamiliarized with partial-squats and isometric squats to minimize learning effects and to record bar heightsand knee angles for subsequent testing and training. Subjects were required to complete >80% of theprogrammed volume load to be included in the data analysis.

Dynamic warm-up followed anthropometric measurements. The 1-RM squat was tested firstfollowed by 1-RM partial-squat at 100 knee angle (measured in the sagittal plane using the greatertrochanter, the lateral condyle of the femur, the head of the fibula, and the lateral malleolus as bonylandmarks). Back squat depth was determined as the top of the leg at the hip joint being below the knee.The bar was set on safety pins at a height corresponding to 100 of knee flexion for partial squats asdetermined during the familiarization sessions. Subjects performed the concentric portion of the squat to

lockout then lowered the bar back down to the safety pins.Kinetic variables were measured on 0.45 m x 0.91 m dual force platforms affixed side by side (Rice

Lake, WI) sampling at 1,000 Hz. Testing was performed at 90 and 120 with the bar placed across theback in the same position used in training. Following warm-up, the tester instructed subjects to push as fastand as hard as possible for at least two maximal attempts. Subjects were tested at the same time of the dayfor both test days. The force-time curve data were smoothed using an 11-point moving average (all datapoints equally weighted) and analyzed with Labview software (ver. 2010, National Instruments, Austin,TX, USA). A 2x2 (group by time) repeated measures ANOVA, paired sample t-test and one-way ANOVAwere run to determine within and between group differences for all dependent variables. Due to the paucity


12/36


12

of research on partial ROM training the present study was exploratory and a Bonferonni adjustment wasnot used to decrease the possibility of committing a type II error (Huberty & Morris, 1989). SPSS softwareversion 20 was used to perform all statistical analyses (IBM Co., NY, USA).

RESULTS: The repeated measures ANOVA revealed a statistical time effect; F(1,15)=72.28, p


13/36


13

DISCUSSION: These findings demonstrate that partial plus full ROM training can be an effective trainingmodality for improving maximal strength. However, further research is needed to ascertain whethercombined training is more effective than full ROM training alone. Although the subjects in the present

study were not athletes, their strength level was comparable to previous research with athletes (Clark et al.2011, Harris et al. 2000). Findings of the present study suggest that combined training may be more

effective than full ROM training alone for improving early force-time curve characteristics. The larger

effect sizes for IPFa 120 and impulse scaled with 120 in the FP group have implications for strength-power athletes. For example, the contact time for an elite sprinter is ~90 ms, the percent change for impulse

scaled at 90 ms at a 120 knee angle was 17.4% vs. 5.1% in FP and F, respectively. For an elite sprinter,producing larger forces in that narrow time window may be the difference in winning versus losing.

REFERENCES:

Clark, R. A., Humphries, B., Hohmann, E., & Bryant, A. L. (2011). The influence of variable range of motion training onneuromuscular performance and control of external loads. The Journal of Strength & Conditioning Research, 25(3), 704-711.

Harris, G. R., Stone, M. H., O'Bryant, H. S., Proulx, C. M., & Johnson, R. L. (2000). Short-term performance effects of high power,

high force, or combined weight-training methods. The Journal of Strength & Conditioning Research, 14(1), 14-20.Huberty, C. J., & Morris, J. D. (1989). Multivariate analysis versus multiple univariate analyses.Psychological bulletin, 105(2),

302.

Massey, C. D., Vincent, J., Maneval, M., Moore, M., & Johnson, J. T. (2004). An analysis of full range of motion vs. partial rangeof motion training in the development of strength in untrained men. The Journal of Strength & Conditioning Research, 18(3),518-521.

Pinto, R. S., Gomes, N., Radaelli, R., Botton, C. E., Brown, L. E., & Bottaro, M. (2012). Effect of range of motion on musclestrength and thickness. The Journal of Strength & Conditioning Research, 26(8), 2140-2145.

Figure 2: Percent changes in impulse scaled at 120.*p


14/36


14

THE APPLICATION OF ACCELEROMETRY TO WEIGHTLIFTING: CURRENT CHALLENGES

George K. Beckham1, Kimitake Sato1, Timothy J. Suchomel1, Chieh-Ying Chiang1, Benjamin H. Gleason1,William A. Sands1, Christopher A. Bailey1, Michael H. Stone1.

1Department of Exercise and Sport Sciences, Center of Excellence in Sport Science and Coach Education,

East Tennessee State University, Johnson City, TN.

INTRODUCTION: Many devices have been used to improve our understanding of the snatch and cleanand jerk. This equipment helps us to monitor training and provide feedback to the coach and lifter at agreater resolution and accuracy than coaching observation alone. Qualitative video analysis allows foraccuracy and important information, but does not provide quantitative information important for evaluationof execution, progress, and comparison. Quantitative analysis methods, such as the V-Scope TM,potentiometer/encoder pairs, and 2D/3D motion capture have been used in the past, and provide higherquality information; however, these tools are often expensive, require significant scientific or engineeringexpertise and custom analysis, and/or are unrealistic for use in competition. Typically, these methods alsodelay the time between collection and data return to the coach and/or athlete. Recently, accelerometers havebeen introduced as a potential tool for evaluating and monitoring weightlifting performance (Sato,

Fleschler, & Sands, 2009a; Sato, Sands, & Stone, 2012; Sato, Smith, & Sands, 2009b). These devices haveseveral advantages that may address some of the concerns with other devices. More recent iterations ofaccelerometers are smaller and lighter than previous versions, wireless for real-time data collection andanalysis, and are less expensive (approximately $150-500).

Several recent studies have laid the groundwork for the use of accelerometers in weightlifting,evaluating both the validity and reliability of a certain model of telemetered accelerometers, and measuringresultant acceleration (Sato, et al., 2012; Sato, et al., 2009b). These studies have focused solely on peakacceleration (PA), which occurs during the execution of the second pull. Given the importance of the secondpull to weightlifting performance (Garhammer & Gregor, 1992), variables measured in this time periodvery likely have a strong relationship to the proper execution of the lift. The purpose of this paper is todiscuss insights and potential problems of accelerometers, potential applications, and the future hurdles tobe cleared.

LIFTER AND EXERCISE ISSUES:Accelerometer work in our lab has only been conducted with fullsnatches thus far. One of the issues we have discovered through observation is that when monitoringsubmaximal loads (especially with


15/36


15

between attempts with similar PAs that are not easily explained by PA alone. For example, Figure 1 showsthree separate attempts, with relatively similar PA values. However, it is evident that there are differencesin the pattern of acceleration through the transition phase (the circled region). Exactly why this differencehappened is unclear and measurement of PA alone would not have shown this difference in the accelerationpattern. Similarly, we also do not know the substantive differences between lifts with different accelerationvalues and acceleration profiles. In Figure 1, we can form hypotheses as to the reason for the acceleration

profiles in the transition phase and subsequent second pull, but we must do more work to discover what theacceleration patterns during each of the phases mean. While extensive work on bar path and other kinematicanalysis has been conducted with other measurement methods, very little work on bar acceleration has beencompleted.

Figure 1: Example snatch from a lifter, using the same weight for three single attempts. Phases of the liftare estimates of the phases the lifter is going through.

There is also the factor of kinematic symmetry of the bar motion. So far, accelerometer researchhas focused only on one side of the bar, without consideration of the possible acceleration differencesbetween sides. While the research is mixed on whether there is substantial asymmetry in lifter and barmovement during the lifts (Lake, Lauder, & Smith, 2010, 2011; Rossi et al., 2007), it must still beconsidered.

TECHNICAL ISSUES: The spatial orientation of an accelerometer is important to measurement ofacceleration in any single axis. If a Z-direction accelerometer is not oriented exactly in that direction, itwill not measure the full acceleration in that direction. In an extreme example, incorrect orientation ofninety degrees in a vertical accelerometer will result in missing the entirety of the vertical acceleration (seeFigure 2). Due to rotation of the bar, a substantial source of error is introduced during execution of the lift.In turning the bar over during the catch, the amount of rotation throws acceleration measurement off

substantially, thus it is difficult to evaluate anything after the catch, (i.e. the jerk). Using triaxialaccelerometers allows for measurement of resultant acceleration. However, the direction of theaccelerations is unknown if rotation is unaccounted for; thus, using a gyroscope to account for this rotationis warranted.

In order to protect the accelerometer from the forces and vibration from the bars impact with theground after the lift, the accelerometer must be encased in some kind of damping material. Thus far,researchers have been using a custom-made dense foam case. As these are custom made, other researchersmay be unable to obtain similar foam encasings, and may face possible problems with different densities


16/36


16

and stiffnesses of materials. What must be answered is what effect, if any, does the characteristics of thecasing material have on the accelerometer measurements?

Figure 2: Demonstration of an improper orientation of a vertical accelerometer. The black linerepresents the vertical direction, while the dashed arrow represents the measurement direction of the

accelerometer.

CONCLUSION:

The measurement of barbell acceleration is a promising research area, given the plethoraof unanswered questions. Unfortunately, many of these questions relate to problems that must be assessedbefore extensive application of this methodology can be accomplished. However, with more understandingof the application of these devices, they may provide us with a great deal of information, allow fast returnof measurements, and do so with a relatively low cost.

REFERENCES:

Garhammer, J., & Gregor, R. (1992). Propulsion forces as a function of intensity for weightlifting and verticaljumping.Journal of Strength & Conditioning Research, 6(3),129-134.

Lake, J. P., Lauder, M. A., & Smith, N. A. (2010). The effect that side dominance has on barbell power symmetryduring the hang power clean.Journal of Strength and Conditioning Research, 24(11),3180-3185.

Lake, J. P., Lauder, M. A., & Smith, N. A. (2011). Does side dominance affect the symmetry of barbell end kinematics

during lower-body resistance exercise?Journal of Strength and Conditioning Research, 25(3),872-878.Rossi, S. J., Buford, T. W., Smith, D. B., Kennel, R., Haff, E. E., & Haff, G. G. (2007). Bilateral comparison of barbell

kinetics and kinematics during a weightlifting competition. International Journal of Sports Physiology andPerformance, 2(2),150-158.

Sato, K., Fleschler, P., & Sands, W. (2009a). Barbell acceleration analysis on various intensities of weightlifting.Paper presented at the Annual Conference of the International Society for Biomechanics in Sports, Limerick,Ireland.

Sato, K., Sands, W. A., & Stone, M. H. (2012). The reliability of accelerometry to measure weightlifting performance.Sports Biomechanics, 11(4), 524-531.

Sato, K., Smith, S. L., & Sands, W. A. (2009b). Validation of an accelerometer for measuring sport performance.Journal of Strength and Conditioning Research, 23(1),341-347.


17/36


17

A COMPARISON OF MUSCLE ACTIVATION OF THE LOWER BACK AND LEGS BETWEEN ABACK SQUAT AND A REAR FOOT ELEVATED SPLIT SQUAT EXERCISE

Christopher Bellon1, Steven Leigh2, Timothy Suchomel1

1Center of Excellence for Sport Science and Coach Education, Department of Exercise and Sport Sciences,

East Tennessee State University, Johnson City, TN2Department of Exercise and Physical Education, Montclair State University, Montclair, NJ

INTRODUCTION: Strength and conditioning professionals often prescribe squatting exercises, bothbilateral and unilateral, as a means to increase muscular strength (Comfort, Haigh, & Matthews, 2012).Previous studies have compared bilateral and unilateral squat variations with respect to muscle activity(McCurdy, OKelley, Kutz, Langford, Ernest, Torres, 2010) and improvements in muscular strength andpower (McCurdy, Langford, Doscher, Wiley, & Mallard, 2005). However, discrepancies exist in previousliterature with respect to comparisons of muscle activity of the legs between unilateral and bilateral squatexercises. Additionally, no known research investigation has compared the muscle activation of the lowerback musculature between unilateral and bilateral squat variations. Therefore, the purpose of this study wasto compare the muscle activation of the low back and legs between a barbell back squat (BS) and a rear

foot elevated split squat (RFESS). These particular exercises were selected due to the similar closed kineticchain movements.

METHODS:Sixteen recreationally trained male participants were included in this study. Participants were20.0 + 2.1 years; 175.0 + 6.6 cm in height, and their body mass was 77.4 + 12.2 kg. All participants had aminimum of two years of resistance training experience and had performed both squat exercises within twomonths of their testing session. All participants completed a screening session in which they demonstratedtheir competency to safely perform a BS and a RFESS with 50% and 25% of their BS 1-repetition maximum(1RM), respectively. Back squat 1RM was calculated from previous training records. These screening testswere observed by a Certified Strength and Conditioning Specialist to ensure each potential participantpossessed adequate technique and squat depth in the exercises. Adequate squat depth for the BS and RFESSconsisted of the inguinal fold reaching a position level with the apex of the patella. If participants could not

adequately perform either of the exercises in a safe and controlled fashion, they were excluded from thestudy. Participants read and signed the required informed consent documents pertaining to testingprocedures in accordance with the guidelines of the Montclair State University Institutional Review Board.

The height of each participant was measured using a stadiometer and was recorded to the nearest0.1 cm. Body mass was determined using a standard laboratory scale and was recorded to the nearest 0.1kg. The length of the participants shank was measured from the lateral epicondyle of the tibia to the floor.These measurements were collected with a soft measuring tape. Mean activation of the right erector spinae(RES), left erector spinae (LES), right gluteus maximums (RGM), right biceps femoris (RBF), right vastuslateralis (RVL) and right vastus medialis (RVM) were collected during both squat conditions. These datawere recorded and analyzed using electromyography acquisition and analysis software (Myomonitor IVElectromyography Acquisition and Analysis, Delsys Incorporated, Natick, MA). Maximum VoluntaryIsometric Contractions (MVIC) for each muscle were collected prior to performing either squat condition.

The investigator manually resisted participants during isometric contractions to facilitate an MVIC for eachmuscle. While this approach was used in a previous investigation (Gullett, Tillman, Gutierrez, & Chow,2009), the method was still a limitation in this study, as the manually resisted MVIC was not task-specific.Accordingly, the MVIC reported may not represent true isometric values for the muscles examined during

the squat exercises performed during the testing session. Following the testing session, the EMG data forall MVIC and squat testing sets were rectified and filtered. A Butterworth filter with a 0.125 ms windowwas used during the analysis. The maximum activation was the only measure analyzed for the MVIC testsets for each muscle. For the squat testing sets however, mean activation was analyzed for only the 3rdrepetition of every heavy testing set. This repetition was chosen because it was performed with the highest


18/36


18

quality and stability during pilot testing for most subjects. These measures were then normalized to arelative percentage of the MVIC.

Participants performed a light warm-up set and heavy test set of each squatting exercise. Loadsof 50% and 75% of the participants BS 1RM were used during the BS warm-up and work sets, respectively.

Loads of 25% and 37.5% of the participants BS 1RM were used during the RFESS warm-up and worksets, respectively. During the RFESS, a measure of 2.5 times the length of the participants tibia was used

to standardize the distance between the edge of the bench and the toes of the right foot. This measure wasdetermined to be the position at which participants obtained the most stability during the pilot study. The

participants completed five repetitions for every set in both squat exercises. A rest period of three minutes

was allotted between sets and eight minutes between squat conditions. The order in which squat exerciseswere performed was randomized and counterbalanced. The experimental design can be found in Figure 1.

A 2 (BS and RFESS) by 6 (muscles) repeated measures factorial ANOVA was used to determine

if the differences in mean muscle activation between the squatting exercises were statistically different.Mauchlys test of sphericity was violated in the ANOVA procedure, which resulted in the use of

Greenhouse-Geiser adjusted values.

Figure 1. Basic Experimental Design

RESULTS: There was no main effect for exercise (F(1,15) = 0.118, p= 0.736, p2= 0.008, c = 0.062).

There was a statistically significant main effect for muscles (F= 23.88,p= 0.001, df = 1,5,p2= 0. 614, c= 1.00). There was no interaction effect between exercise and muscle (F (2.37, 36.6) = 2.712,p= 0.071,

p2= 0.153, c = .546).

Table 1. Mean EMG Activation between Squat Conditions (Mean SD; n = 16). Mean Percentage of MVIC

Characteristic Bilateral Squat Unilateral Squat

Left Erector Spinae 41.86 + 17.29 22.71 + 13.21Right Erector Spinae 40.91 + 9.49 28.83 + 14.17

Right Gluteus Maximus 31.61 + 15.18 43.51 + 21.94Right Biceps Femoris 25.28 + 23.97 31.98 + 24.57

Right Vastus Lateralis 101.99 + 53.26 108.88 + 59.33Right Vastus Medialis 83.37 + 27.47 95.42 + 60.09

DISCUSSION:Key findings of this investigation revealed no main effect for exercise and no interactioneffect between exercise and muscle. Accordingly, no conclusions can be drawn to validate the utility of one

squat variation versus the other. The differences in muscle activity in the legs and the lower back betweena BS and a RFESS, if any, remain unclear following this experiment. The absence of a task-specific MVIC

protocol could have played a role in reaching this conclusion. Future research may compare EMG of the


19/36


19

lower back and legs during other squat variations, such as a BS and a dumbbell rear foot elevated splitsquat.

REFERENCES:

Comfort, P., Haigh, A., & Matthews, M. J. (2012). Are changes in maximal squat strength during preseason training reflected in

changes in sprint performance in rugby league players? Journal of Strength and Conditioning Research, 26(3),772-776.Gullett, J. C., Tillman, M. D., Gutierrez, G. M., & Chow, J. W. (2009). A biomechanical comparison of back and front squats inhealthy trained individuals. Journal of strength and conditioning research, 23(1),284-292.

McCurdy, K. W., Langford, G. A., Doscher, M. W., Wiley, L. P., Mallard, K. G. (2005). The effects of short-term unilateral andbilateral lower-body resistance training on measures of strength and power. Journal of Strength and Conditioning Research,19(1),9-15.

McCurdy, K. W., O'Kelley, E., Kutz, M., Langford, G., Ernest, J., Torres, M. (2010). Comparison of lower extremity EMG betweenthe 2-leg squat and modified single-leg squat in female athletes.Journal of Sport Rehabilitation, 19,57-70.


20/36


20

THE EFFICACY OF CATAPULT SPORTS MINMAX GPS METRICS IN RELATION TO SESSIONRATING OF PERCEIVED EXERTION

Aaron B. Casey1, Garett E. Bingham1, Matt I. Sams1, Kevin M. Carroll1, Christopher B. Taber1, and RyanP. Alexander1

1Department of Exercise and Sport Sciences, East Tennessee State University, Johnson City, TN, USA,

INTRODUCTION: Monitoring the stresses incurred by athletes is of paramount importance to sportsscientists. Intermittent sports such as soccer can be particularly challenging to monitor effectively. CatapultSports claims their GPS system is an effective means of monitoring the exact activities of field playersin soccer. Impellizeri et al., (2004), and Alexiou & Coutts, (2008), have suggested session Rating ofPerceived Exertion (Session RPE), as a useful measure of player training load. More recently Metabolicenergy (kJ/kg) has been suggested as a metric to evaluate energy expenditure during sprinting (di Prampero,Fusi, Sepulcri, Morin, Belli, & Antonutto 2005). The proposed kJ/kg metric equates accelerating on flatground, to running at a constant pace on an incline, owing to the angle of the torso in relation to the runningsurface (di Prampero et al., 2005). Subsequently the kJ/kg metric has been suggested as a method forevaluating energy expenditure in soccer matches (Osgnach, Poser, Bernardini, Rinaldo, & di Prampero,

2010). Catapult Sports includes literature in its help files that briefly describe the calculations forgenerating metabolic energy costs. According to Catapult Sports (2013) help manual, metabolic energyis computed with the following equation:

EC=fn(equivalent slope)x(equivalent mass)xKT

Equivalent Slope and Equivalent Mass are both explained in detail by Osgnach et al, (2010). To paraphrase,equivalent slope is an estimate of the energy cost of acceleration on flat ground by a comparison to runningat a constant pace on an inclined slope. Equivalent mass is described as the additional force (g) requiredto overcome acceleration. (Catapult Sports, 2013).

PURPOSE STATEMENT: This study seeks to investigate the relationship between Session RPE, and

Catapult Sports GPS variables of total distance (Odo) and Metabolic Energy (kJ/kg).

METHODS: Twenty-one Division 1 female soccer athletes, (Height (cm): 166.30 7.18; Mass (kg): 72.61 11.11; Body fat (%): 20.93 4.78)) participated in the 2013 NCAA competitive season. All athletes wereasked to wear Catapult Sports MinMax S4 GPS transmitters (South Melbourne, Victoria, Australia)during games, and provided Session-RPE data to sports science staff post-game (1-10 rating of difficultyof warmup, game and cooldown). Session-RPE rating was multiplied by the minute duration of games,including warm-up, and cool-down to quantify the Session RPE training load. Total distance covered andMetabolic Energy were obtained from GPS data. Relationships between variables were tested with aPearson Correlation. Data was collected as part of the ongoing ETSU long-term athlete monitoringprogram. Athletes read and signed written informed consent in accordance with the guidelines of EastTennessee State Universitys Institutional Review Board.

RESULTS: Athletes covered an average distance of 9,290 (3,046) meters. The average reported MetabolicEnergy was 41.75 (13.92) kJ/kg, and reported an average Session RPE training load of 823 (263). SessionRPE training load had strong correlations to both ODO (r=0.736) and kJ/kg (r=0.754). ODO and kJ/kgshowed a near perfect correlation (r=0.981). Figures 1 and 2 show the correlation between Session RPEand ODO, and Session RPE and kJ/kg, respectively.


21/36


21

Figure 1: Scatterplot of kJ/kg vs. Session RPE training load.

Figure 2: Scatterplot of Odometer vs. Session RPE training load.

DISCUSSION: It is unsurprising that the odometer and metabolic energy provided by Catapult Sportsshow a near perfect correlation to each other. It is a mathematical necessity for greater work to be done tocover a greater distance in the same amount of time. However, there is currently only one study thatinvestigates the metabolic energy metric, from Osgnach, et al., (2010), and this appears to be the sole basisfor Catapult Sports calculation. The data presented by Catapult Sports is most hampered by assumedand derived athlete body masses. Without valid and reliable measurements, it is impossible to accept scaledmetrics as accurate. The strong relationship between both of these variables and Session RPE training loadsuggests these variables may be important to player monitoring. Further research should investigate the

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

0 200 400 600 800 1000 1200 1400 1600

Odo

meter(m)

Session RPE training load

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

0 500 1000 1500

kJ/kg

Session RPE Training Load


22/36


22

differences between positional demands of metabolic energy, and correlations to heart rate-based trainingloads.

REFERENCES:

Alexiou, H., & Coutts, A. J. (2008). A comparison of methods used for quantifying internal training load in women

soccer players.International Journal of Sports Physiology and Performance, 3, 320-330.di Prampero, P.E., Fusi S., Sepulcri, L., Morin, J.B., Belli A., & Antonutto G. (2005). Sprint running: a new energetic

approach.Journal of Experimental Biology 208(14),2809-2816.Impellizzeri, F. M., Rampinini, E., Coutts, A.J., Sassi, A., & Marcora, S.M. (2004). Use of RPE-based training load

in soccer.Medicine and Science in Sports & Exercise, 36(6),10421047.Osgnach, C., Poser S., Bernardini R., Rinaldo R., & Di Prampero, P. E. (2010). Energy cost and metabolic power in

elite soccer: a new match analysis approach. Medicine and Science in Sports & Exercise 42(1), 170178.


23/36


23

TRENDS FROM NFL COMBINE DATA MAY PREDICT MINIMUM PERFORMANCE VALUES OFFUTURE NFL DRAFT PICKS

Keith A. Leiting and Ben Gleason.

East Tennessee State University, Department of Exercise and Sport Sciences, Johnson City, TN 37601

INTRODUCTION: The National Football League (NFL) Combine is used as part of a process to determinea players draft status and future promise. The Combine tests are used as a form of talent identification(Hendricks, DeBrock, & Koenker, 2001; Sierer, Battaglini, Mihalik, Shields, & Tomasini, 2008) helpingreassure scouts, coaches, and general managers that a given individual has the requisite physical qualitiesto play for that team. A successful draft pick for coaches and general managers would be a player thatperforms well during NFL game play. In contrast, for some athletes, just getting drafted is considered asuccessful first step.

The ability to predict physical characteristics of NFL draftees would be of great benefit to athletesand coaches to ensure quality players are drafted. Kuzmits & Adams, (2008) assessed Combineperformance in comparison to draft pick, three year salary, games played, and position specific data (NFLCombine) for quarterbacks, running backs, and wide receivers. Kuzmits & Adams, (2008) observed no

consistent relationship between Combine tests and performance, except the sprint tests of the running backs,where weak to moderate correlations were found.

McGhee and Burkett investigated 326 NFL Combine datasets from the 2000 draft to determineregression equations for all running backs, wide receivers, offensive linemen, defensive linemen, defensivebacks, linebackers, and quarterbacks, in relation to salary, draft selection, and performance. Regressionanalysis was useful for a single year, but only minimally applicable for comparison with ensuing draftclasses. One limitation of NFL Combine studies are the intangible characteristics: such as ability to read adefense, memorize a large playbook, injury, or incompatible style of play, which can all affect the salary,draft selection number, and performance.

Rather than assessing relationships or using regressions this study uses historical trends ofperformance characteristics on the NFL Combine with the goal of predicting the performancecharacteristics of the 2014 NFL draftees. Therefore, strength and conditioning coaches will have minimum

performance values for their athletes to achieve during the NFL Combine, which may increase the chanceof draft selection.

METHODS: All subjects were invited to compete in the NFL Combine and their data are available to thepublic. All NFL Combine data was scraped from: http://nflCombineresults.com/nflCombinedata.php,and all draft pick history was scraped from: http://www.nfl.com/draft/history/fulldraft?position onNovember 27, 2012, (Table 1). These data were retrieved and analyzed with permission of the InstitutionalReview Board of East Tennessee State University. Data were retrieved on November 27, 2012 and included13 years of NFL Combine data and all draft picks for those 13 years (1999-2012). All data were analyzedusing Microsoft ExcelTM2010, (Version 2010, Redmond, WA, USA). All NFL draft picks and their draftpick rank were matched with their NFL Combine data from the respective year of the athletes draft andCombine results. Combine participants that were not drafted did not have a draft ranking with which to

compare, therefore non-drafted participants combine data were not used for analysis.

Table 1. Number of Study Participants from 1999-2012 by Position

Offensive

Linemen

Tight

Ends

Running

Backs

Wide

Receivers

Quarter

backs

Defensive

Linemen Linebackers Cornerbacks Safeties

1999-2012 382 141 184 322 146 383 284 257 76


24/36


24

Trend analyses are an alternative statistic commonly used in business (Alwan & Roberts, 1988),health (Sohn, Czarnecki, & Farrar, 2000), and research of elite athletes (Kinugasa, Cerin, & Hooper, 2004)

and is applied to the analysis of groups. Trends were established by taking the average of each performancevalue, player position and each years data, plotting it and applying a line of best fit which was extrapolated

to 2015. Statistical process control (SPC) was applied to the line of best fit, (Figure 1). Statistical processcontrol is a business or industrial method of identifying outliers as a method of quality control (Alwan &

Roberts, 1988) or any data that are collected over time with an interest in determining trends and variability.To determine the minimum performance values for each year the upper (timed events) or lower (all otherevents) 2SEE were used. Performance values outside of the 2SEE were considered outliers. Further

evaluation of this model included plotting of the individual data against the 2012 minimum performancevalue. The ratio of athletes that achieved the minimum performance values compared to athletes that didnot achieve the minimum performance values is identified as a success percentage, (Figure 2).

Figure 1. Figure 2.

RESULTS: The results for position and event have a high variability for success percentage ranging from44-100, (Table 2). There is also large variability within each position suggesting that not all events are ofequal importance. Likewise, there is large variability within each event suggesting that no one event is more

important than the others. The average success percentage is relatively high across all positions, rangingfrom 69-84 percent, (Table 2). Table 3 shows the predicted minimum performance values for draftees to

achieve in the 2014 NFL Combine that may increase draft success.

Table 2. Success Percentage of SPC and Non-Overlapping Data in determining NFL Draft Selection

DISCUSSION: The results suggest certain events of the NFL combine may be more applicable to specific

positions and this method of analysis may provide a relatively accurate way to determine minimumperformance values for future NFL Combines.

The success percentage may indicate that certain performance tests are better predictors of NFL

draft selection. The higher the success percentage implies greater importance of that event on draft

Physical Characteristics/ EventQuarterbacks

Running

Backs

Wide

ReceiversTight Ends

Offensive

Linemen

Defensive

LinemenLinebackers Cornerbacks Safeties

Height 74 67 63 63 72 70 78 47 44

Weight 74 57 63 63 78 60 85 70 89

Wonderlic 89 * * * * * * * *

40 Yard Dash 71 57 54 50 41 79 59 60 63

Bench Press * 50 88 86 59 75 65 81 100

Verical Jump 100 71 88 83 96 80 84 84 88

Broad Jump 100 100 86 50 71 63 68 80 100

Shuttle Drill 83 100 94 100 62 83 94 100 100

Three Cone Drill 83 100 74 100 54 87 73 80 71

Peak Power 86 71 73 67 89 80 60 94 88Average Success Percentage 84 75 76 74 69 75 74 77 83

All values are represented as a percentage, * indicates that position player did not perform event


25/36


25

selection. For example: The running backs have a success percentage of 100 for broad jump, shuttle drill,and three cone drill and only a 57 for the 40 yard dash. This suggests that there is a large demand for runningback to possess a high level of acceleration- broad jump, and change of direction skills- shuttle drill andthree cone drill. It may be less important for a running back to run fast in a straight line- 40yard dash, asindicated by the large percentage of players that did not attain the minimum performance values set by the2SEE control limit.

Table 3. Predicted Minimum Performance Value for 2014 NFL Draft Selections by Position and Event

Looking at the results collectively; the average success percentage of this predictive model for 2012supports the use of trend analysis with linear 2SEE used to set minimum performance valuesand percentof non-overlapping data as a method of determining performance values of future draft picks. Therefore,using the NFL Combine data, this method of analysis may be able to identify the NFL Combine minimumperformance values of NFL draft picks for 2014. The authors suggest that potential NFL draft picks of 2014should meet or exceed the minimum performance standards presented to improve their chance of beingdrafted.

One limitation of this study is that the researchers were unable to evaluate NFL combine performersthat were not draft selections. It is possible that undrafted players achieved the minimum performancevalues, but were undrafted for other reasons. Also, it needs to be noted that peak power is derived frombody mass and jump height with arm swing. This approach overestimates peak power. Thus, the verticaljump is reliable but not valid for comparison to other testing methods of peak power.

It is recommended that results from this article be used by strength and conditioning coaches to setminimum standards for NFL Combine invitees, as a target for their performance. Achieving values greaterthan the minimums presented may help set the athletes up for draft pick success.

REFERENCES:lwan, L. C., & Roberts, H. V. (1988). Time-series modeling for statistical process control. Journal of Business &

Economic Statistics, 6(1), 8795.Hendricks, W., DeBrock, L., & Koenker, R. (2001). Uncertainty, hiring and subsequent performance: The NFL draft.

Available at SSRN 289265.Retrieved from http://papers.ssrn.com/sol3/papers.cfm?abstract_id=289265Kinugasa, T., Cerin, E., & Hooper, S. (2004). Single-subject research designs and data analyses for assessing elite

athletes conditioning. Sports Medicine, 34(15),10351050.Kuzmits, F. E., & Adams, A. J. (2008). The NFL combine: does it predict performance in the national football league?

Journal of Strength & Conditioning Research, 22(6),17211727.Sierer, S. P., Battaglini, C. L., Mihalik, J. P., Shields, E. W., & Tomasini, N. T. (2008). The national football league

combine: performance differences between drafted and nondrafted players entering the 2004 and 2005 drafts.Journal of Strength & Conditioning Research, 22(1),612.

Sohn, H., Czarnecki, J. A., & Farrar, C. R. (2000). Structural health monitoring using statistical process control.Journal of Structural Engineering, 126(11),13561363.

Height (cm) Weight (kg) Wonderlic 40 Yard Dash (s) Bench Press (reps) Vertical Jump (cm) Broad Jump (cm) Shuttle Drill (s) Three Cone Drill (s) Peak Power (w)

Offensive Lineme 193 139 * 5.35 23 63 252 4.86 7.81 8141

Tight Ends 193 114 * 4.77 19 81 287 4.56 7.18 7950

Wide Receivers 183 91 * 4.52 11 83 297 4.41 6.97 7125

Running Backs 177 94 * 4.58 19 84 284 4.41 7.39 7367

Quarterbacks 190 101 26 4.90 * 69 269 4.51 7.13 7187

Defensive Lineme 191 130 * 5.02 27 69 270 4.71 7.63 8259

Linebackers 186 107 * 4.74 22 79 282 4.44 7.17 7708

Cornerbacks 179 86 * 4.55 15 83 300 4.35 6.94 6975

Safeties 181 92 * 4.58 15 79 296 4.53 7.3 7143

* indicates that position players did not perform this test


26/36


26

RELATIONSHIP OF SPRINT INTERVALS TO 60 M SPRINT PERFORMANCE IN NCAA DIVISION-I SPRINTERS: AN EXPLORATORY STUDY

Zhan Xin Sha1, Kimitake Sato1, H. Stamps2, Chris A. Bailey1, Ryan P. Alexander1, Tim C. McInnis1, BrianJohnston1, Michael Ramsey1, Meg E. Stone1, Michael H. Stone1.

1: Department of Exercise and Sport Sciences, East Tennessee State University, Johnson City, TN, USA.2: Department of Athletics, East Tennesee State University

INTRODUCTION: Assessing sprint time intervals over specific distances or phases such as 10 m to 40 mhas been used in evaluating sprint ability of soccer players, football players and rugby players (Bucheit,Simpson, and Villanueva., 2012; Young, Russell, Burge, Clarke, Cormack, Stewart., 2008). Differentdistance intervals reflect related qualities from biomechanical, muscle activation, and strengthrequirements. However, there are few studies that focus on evaluating sprinters performance at differentphases of sprinting. Participants in previous studies were either football players or recreational athletes(Bucheit et al., 2012; Delecluse, Coppenolle, Willems, Leemputte, Diels., 1995; Young et al., 2008). Thus,the purpose of this study was to reevaluate relationships of different phases of the 60 m sprint in NCAADivision I sprinters.

METHODS: Nine male NCAA Division I sprinters (East Tennessee State University Track Team)participated in the study as part of a sport monitoring program. Athletes read and signed Universityapproved informed consent documents, prior to participation in this study.

After warm up and dynamic stretching, two full effort 60 meter sprint trials were measured. A tenminutes rest period between trails was given. All sprinters used a standing start position. The sprint timeswere measured by an electronic timing gate system (Brower Timing, Draper, UT, USA). Electronic timinggates were placed at the starting line, and at 10 m intervals until the 60 m finish line. The average of thetrials was used for further analysis. Relationships between each 10 m running interval and the final sprint(60 m) running times were evaluated with Pearson correlation coefficients. Strength of relationships werebased on recommendations by Hopkins (1997), where 0.0-0.1 is trivial; 0.1-0.3 is low; 0.3-05 is moderate;0.5-0.7 is strong; 0.7-0.9 is very strong; 0.9-1.0 is nearly perfect.

RESULTS: Means standard deviations (SD) are shown in Table 1. There was a strong relationshipbetween the first 10 m interval and t 30(20-30 m) interval (r=0.63). The first 10 m interval showed a verystrong correlation with final sprint time (r=0.76). The t 20 (10-20 m) interval had a strong relationship withthe t 40 (30-40 m) interval (r=0.66). The t 30 (20-30 m) interval showed a nearly perfect relationship withfinal sprint times (r=0.93). Additionally, the t 30 interval also had a strong relationship with the t 50 (40-50m) interval (r=0.62). T 60 (50-60 m) interval which represents the maximal speed phase, had a very strongrelationship with the t 30 interval (r=0.73). The t 60 (50-60 m) interval also had a strong relationship withfinal sprint time (r=0.65).

Table 1. Descriptive Statistics (N=9)

Mean (s) Std. Deviation (s)

t 10 1.56 0.11t 20 1.19 0.03

t 30 1.09 0.03

t 40 1.06 0.03

t 50 1.06 0.03

t 60 1.08 0.03

Final sprint time 7.04 0.18


27/36


27

DISCUSSION: Typically coaching logic indicates that the acceleration phase plays an important role for60 m sprint performance. This logic is consistent with the findings of the current study, which showed thatthe first 10m interval very strongly related to final sprint time. This study confirmed the results of previousstudies (Bucheit, Simpson, and Villanueva., 2012; Delecluse et al., 1995; Young et al., 2008). Agreeingwith these previous studies, the current study showed that the first 10 m acceleration phase was somewhat

unique as it did not correlate well with the maximal speed phase t 60 (50-60 m). However, the t- 10 timeshowed a strong correlation with t 30 (20-30 m) interval. This suggests that the first 10 m may be importantfor continuous acceleration. In order to achieve an optimal performance in the 10 m; the athlete should haveexplosive and powerful muscles to generate as much force in a short amount of time as possible to propelthe body from a stationary position to high speed. Additionally, athletes could better utilize a forward-leaning body position to apply more horizontal force to the ground (Kugler and Janshen; 2009).

The t 30 (20-30 m) interval showed a nearly perfect relationship with final running time and themaximum speed phase in the current study, which also agrees with previous studies (Bucheit et al., 2012;Delecluse et al., 1995; Young et al., 2008). Thus, the 30 m interval may also be a valuable assessment toolfor sprinters 60 m maximum speed performance. Future researchers should focus on the effect of bodyposition on the acceleration phase of sprinters performance.

CONCLUSION: The current study indicates that the t10 m (acceleration phase) is somewhat unique andindependent compared to the other 10 m intervals. Thus, the 10 m sprint may be used to test and trainathletes independently. Furthermore, based on the current data, t30 (20-30 m) running performance appearsto be related to maximal speed and with the 60 m sprint performance for Division-I sprinters. Future studyshould explore these results for sprinters with different backgrounds.

REFERENCE:

Young, W., Russell, A., Burge, P., Clarke, A., Cormack, S., Stewart, G., (2008). The use of sprint tests for assessmentof speed qualities of elite Australian rules footballers. International Journal of Sports Physiology andPerformance, 3(2),199-206.

Buchheit, M., Simpson, E., Villanueva, A.M., (2012) Assessing maximal sprinting speed in highly trained youngsoccer players.International Journal of Sports Physiology and Performance, 7(1),76-78.

Delecluse, C., Coppenolle, H.V., Willems,E., Leemputte, M. V., Diels, L.R., (1995). Influence of high resistance andhigh velocity training on sprint performance.Medicine and Science in Sports and Exercise 27(8),1203-1209.

Kugler, F., Janshen, L., (2010) Body position determines propulsive forces in accelerated running, Journal ofBiomechanics, 43(2), 343-348.


28/36


28

THE SPORT PERFORMANCE ENHANCEMENT GROUP: A FIVE-YEAR ANALYSIS OFINTERDICIPLINARY ATHLETE DEVELOPMENT

Christopher J. Sole1, Ashley A. Kavanaugh1, Jacob P. Reed1, Michael A. Israetel2, LindseyE. Devine3, Michael W. Ramsey, William A. Sands1, and Michael H. Stone1.

1Department of Exercise and Sport Science, Center of Excellence for Sport Science and Coach Education,East Tennessee State University, Johnson City, TN.2Department of Nutrition and Kinesiology, University of Central Missouri, Warrensburg, MO.3Department of Athletics, East Tennessee State University, Johnson City, TN.

INTRODUCTION/PURPOSE:A sport performance enhancement group (SPEG) is an interdisciplinary andcollaborative approach to enhancing athlete performance and development. Within a SPEG sport coaches,sport scientists, strength and conditioning, sport medicine, and other service providers pool their individualexpertise to maximize athletic potential. A SPEG works to achieve this objective by implementingscientifically-based training programs, performing regular athlete monitoring, managing fatigue, andimproving overall efficiency of the training process. The purpose of this investigation was a retrospectiveanalysis of five years of SPEG involvement with an NCAA Division I womens volleyball team. Given the

interdisciplinary and collaborative approach of a SPEG, this investigation will evaluate team performancefrom various perspectives.

METHODS: The following areas were examined: incidence of injury, muscular strength, total traininghours, allotment of training hours, and on-court performances. Specific variables included were based onthe availability and reliability throughout the five-years of this investigation. Injury was defined as anydamage to a body region, incurred during volleyball activities, weight training, and other conditioning,which interfered with training and/or competition. In order to make comparisons between years, injury ratewas normalized for number of athletes relative to exposures to injury (i.e. hours). This investigationexpressed injury rate as injuries per 100 exposure hours. Strength was measured using an isometric mid-thigh pull (IMTP). Athletes performed the IMTP in a custom built power rack equipped with a force plate(Rice Lake, Rice Lake, WI) sampling at 1000Hz. This apparatus and standardized pulling position were

based on previously published research (Haff et al., 2005). All data collection and analysis were performedusing custom software (LabVIEW, National Instruments, Austin, TX, USA). To obviate body massdifferences, allometrically scaled peak force (IPFa) and force at 250 milliseconds (IPF@250a) werereported for the team members. Reliabilities were IPFa (ICC 0.91, CV 7.7%) and IPF@250a (ICC 0.85, CV 13.1%). Training time (hours) was totaled for each academic year and by activity (practice,competition, strength and conditioning). On-court performance (overall winning percentage) was calculatedfor each academic year. The scope and methodology of this investigation was reviewed and approved bythe East Tennessee State University Institutional Review Board for the Protection of Human Subjects.

RESULTS: Data from five consecutive academic years (2008-2009 through 2012-2013) were used in thisanalysis. A total of twenty-four female volleyball athletes participated, accounting for 1,811 totaltraining/competition hours. During these five academic years the team participated in a total of 163 matches

(626 sets). Practice hours totaled 936.5 (51.7% of total hours), competition hours totaled 474 (26.2%), andstrength and conditioning sessions accounted for 400.5 hours (22.1%). Strength measures were assessed onfifteen separate occasions (Figure 1). Mean IPFa and IPF@250a values were 188.90 13.57 and 128.63 11.57 N/kg0.67 respectively. There were 150 injuries recorded during the five-year span of thisinvestigation. Annual injury rate ranged from 0.43 to 1.16 injuries per 100 exposure hours (Figure 1).Themost frequently injured body regions were the knee accounting for 18.7% of total injuries followed by theankle (12.7%) and the shoulder (12.0%). The activity with the greatest number of injuries was practice with112 (74.7% of total) followed by competition with 31 (21.7%), and strength and conditioning sessions with7 (4.7%). Team overall winning percentage ranged from 37.5% (2008-2009) to 74.3% (2010-2011).


29/36


29

Figure 1. Time-series displaying injury rate (15-day resolution) and allometrically scaled isometric peakforce (IPFa) over five academic years. Injury rates are injuries per 100 exposure hours and peak

force values are means standard deviation.

Figure 2.Allotment of total training time (practice and strength and conditioning) and annual injury rateover five academic years.

DISCUSSION: The most frequently injured body regions (knee, ankle, and shoulder) were consistent with

the findings of previous investigations (Eerkes, 2012; Reeser, Verhagen, Briner, Askeland, & Bahr, 2006).Annual injury rate steadily decreased following a peak during the 2009-2010 academic year, and reachedits lowest point during 2012-2013; a reduction of 62.9%. It is important to note that at no time during this

three-year period were any specific injury prevention training interventions employed. All strength trainingwas aimed at enhancing bio-motor abilities requisite for volleyball (e.g. strength and power) (Cormie,

McGuigan, & Newton, 2010).Total training time and allotment of training time shifted notably during the five-year period.

Substantial changes in total practice time as well as shifts in allotment of training time were incorporatedfollowing the 2010-2011 academic year (Figure 2). When comparing 2008-2009 to the 2011-2012 and

76.6% 74.7% 67.4% 62.8% 64.4%

23.4% 25.3% 32.6% 37.2% 35.6%

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2008-2009 2009-2010 2010-2011 2011-2012 2012-2013

Injurie

sper100ExposureHours

%o

fTotalTrainingTime

Practice Strength and Conditioning Injury Rate


30/36


30

2012-2013 academic years total practice hours were reduced as much as 42.6% and 42.4%, respectively.During this same period, strength and conditioning hours increased from 23.4% of total training time to37.2% and 35.6%. Changes in training hours were implemented by the SPEG in the Spring of 2011 toimprove fatigue management through the prescription of daily practice intensities and durations. Note thateven though total practice hours were reduced substantially in the last three years, overall winningpercentage still remained higher than the years with the largest number of practice hours (2008-2009 and

2009-2010).The results of strength testing show potentially meaningful associations between IPFa, injury rates,

and allotment of total training time. The academic year (2008-2009) with the lowest mean IPFa value(156.20 25 N/kg0.67) and lowest annual mean IPFa value (169.26 18.48 N/kg0.67) was followed by theyear with the highest annual injury rate (2009-2010). Conversely, the year with the lowest annual injuryrate (2012-2013) was preceded by the year with the highest annual mean IPFa values (2011-2012: 208.30 21.48 N/kg0.67). These findings suggest that increasing strength may play a role in reducing injury, aconcept supported by several authors (Fleck & Falkel, 1986; Kennedy et al., 2012; Lauersen, Bertelsen, &Andersen, 2013; Radin, 1986) Additionally, the greatest mean IPFa (208.30 21.48 N/kg0.67) occurred inthe academic year (2011-2012) where the greatest number of strength and conditioning hours occurred.

In conclusion, the sport performance enhancement group (SPEG) can be a valuable paradigm forimproving athlete performance and reducing injury. A coordinated interdisciplinary effort to improve

performance may lead to more effective and efficient use of training time and resources. Optimization oftraining may result in a decreased incidence of injury, increased adaptation to training, and mostimportantly, on-court success. Unfortunately, few teams and organizations have adopted an approach suchas a SPEG. Coaches, administrators, strength and conditioning and sport science personnel should be awareof the potential benefits of a SPEG, and attempt to make steps towards employing this approach withintheir own teams (Sands, 1991; Stone, Sands, & Stone, 2004).

REFERENCES:

Cormie, P., McGuigan, M. R., & Newton, R. U. (2010). Adaptations in athletic performance after ballistic power versus strengthtraining.Med Sci Sports Exerc, 42(8),1582-1598.

Eerkes, K. (2012). Volleyball injuries. Current Sports Medicine Reports, 11(5),251-256.Fleck, S. J., & Falkel, J. E. (1986). Value of resistance training for the reduction of sports injuries. Sports Medicine, 3(1),61-68.

Haff, G. G., Carlock, J. M., Hartman, M. J., Kilgore, J. L., Kawamori, N., Jackson, J. R., et al. (2005). Force--Time CurveCharacteristics of Dynamic and Isometric Muscle Actions of Elite Women Olympic Weightlifters. Journal of Strength &Conditioning Research, 19(4),741-748.

Kennedy, M. D., Fischer, R., Fairbanks, K., Lefaivre, L., Vickery, L., Molzan, J., et al. (2012). Can pre-season fitness measurespredict time to injury in varsity athletes?: a retrospective case control study.BMC Sports Science, Medicine and Rehabilitation,4(1),26.

Lauersen, J. B., Bertelsen, D. M., & Andersen, L. B. (In Press). The effectiveness of exercise interventions to prevent sports injuries:a systematic review and meta-analysis of randomised controlled trials. British Journal of Sports Medicine, Accessed:http://bjsm.bmj.com/content/early/2013/10/07/bjsports-2013-092538.short.

Radin, E. L. (1986). Role of muscles in protecting athletes from injury.Acta Med Scand Suppl, 711, 143-147.Reeser, J. C., Verhagen, E., Briner, W. W., Askeland, T. I., & Bahr, R. (2006). Strategies for the prevention of volleyball related

injuries.Br J Sports Med, 40(7),594-600; discussion 599-600.Sands, W. A. (1991). Monitoring the elite female gymnast. Strength & Conditioning Journal, 13(4),66-72.Stone, M. H., Sands, W. A., & Stone, M. E. (2004). The downfall of sports science in the United States. Strength & Conditioning

Journal, 26(2), 72-75.


31/36


31

THE EFFECT OF VERBAL INSTRUCTION ON LOWER BODY POWER DEVELOPMENT DURINGVARIOUS PLYOMETRICS

1Timothy J. Suchomel, 2William P. Ebben, 3Luke R. Garceau, 1Alexander P. Harrison, 1Michelle I. Howe,1Jacob L. Grazer, 1Jacob Goodin, and 1Isaiah M. McBride

1Center of Excellence for Sport Science and Coach Education, Department of Exercise and Sport Sciences, EastTennessee State University, Johnson City, TN

2Department of Health, Exercise Science, and Sport Management, University of Wisconsin-Parkside, Kenosha, WI3Department of Physical Therapy, Program in Exercise Science, Marquette University, Milwaukee, WI

INTRODUCTION: Coaching strategies that may improve training stimuli and sport performance andtesting are often sought in the strength and conditioning field. Verbal instruction (VI) is a simple, cost-effective coaching practice that has the potential to improve training stimuli during various plyometricexercises (Arampatzis, Bruggemann, & Klapsing, 2001; Suchomel, Garceau, & Ebben, 2013). Verbalinstruction has been shown to reduce the duration of contact time during depth jump landings, resulting ina more optimal relationship between jump height and the time it takes to jump, ultimately creating a moreexplosive athlete (Arampatzis, et al., 2001). Similarly, Suchomel et al. (2013) indicated that by providing

VI to their participants before and during the countermovement jump (CMJ) and tuck jump (TJ), theparticipants were able to increase their reactive strength index-modified values (ratio of jump height andtime to takeoff) by 15.7% and 13.3%, respectively. By improving training stimuli where athletes can bemore explosive, it may be possible to improve their muscular power, leading to an enhanced performance.

A common method to improve the muscular power abilities of athletes is the incorporation ofplyometric exercises in their training programs (Stone, 1993). A recent study by Staub and colleagues(2013) indicated that verbal feedback following sets of maximal countermovement jumps resulted in astatistical enhancement of mean power outputs (5.4%) and peak power outputs (5.6%) on subsequentrepetitions and sets. The previous study examined the

Date post:	03-Jun-2018
Category:	Documents
Upload:	mehr6544
View:	214 times
Download:	0 times

8th Annual Coaches College Papers - Final

Documents