Athletic Directors – Page 5 – The Sport Journal

The Athletic Hurdles to Prestige: A Case Report

John L. Stump, Michael R. Allen, Robert J. Saxon, Jr., Vincent McConnell

### Abstract

This case report presents a history, diagnosis, prognosis and treatment of a young tennis athlete on scholarship to Florida State University. He sustains an acute ankle injury while in a tournament in the last month of high school that jolts him into realizing the injury hurdles that may lie ahead in the college athletic world. He and his parents choose alternative and complementary sports medicine rather than traditional methods and procedures for the health care of the young athlete. This case report details the procedures used to manage the acute ankle injury – Chiropractic, Acupuncture, Cold Laser – and the latest athletic training methods for sports rehabilitation are given in the integrative sports injury care given this athlete.

**Key Words:** Alternative, Complementary, Integrative, Chiropractic, Acupuncture, Cold Laser

### Introduction

Leonardo da Vinci called the human foot, which contains nearly 25 percent of the human’s bones and an elegantly functional arrangement of ligaments, tendons and fascia, a marvel of bioengineering and a work of art. Centuries after that observation by da Vinci, researchers continue to discover more about how the feet work, what can go wrong with motility maneuvers, and why (1).

Since 1990, Anderson Reed, a Daphne, Alabama resident and standout student at Bayside Academy in Daphne, Alabama, had been aspiring to greater heights in his chosen athletic endeavor of tennis (2). This year he graduated at or near the top of his class to go on to the University of his choice. He had a wide choice of colleges to select from by his sophomore year in high school when he became the top ranked tennis player in the State of Alabama. Reed played in tournaments across the United States through his high school career and accomplished a top 10 national ranking in tennis.

The young athlete narrowed his field of schools down to Georgia Tech, Vanderbilt, Louisiana State University, University of Alabama and Florida State University. After visiting the schools, talking with the administration, players, coach, and coaching staff at each college he felt informed enough to make a decision. His selection turned out to be Florida State University in Tallahassee.

His academic performance had been as good as his tennis record over the last four years, and it was reflected in the colleges and universities that sought Reed for their student body. Reed had decided on his collegiate career based on the academics of the school and the tennis program of Florida State University. Florida State has competed in the National Collegiate (NCAA) Sweet Sixteen finals each year for the past five years. They have top participants in the professional ranks, so he knew the coaching was going to be some of the best and – another plus – it was fairly close to home for him.

### Athlete and Injury

Last year at a state tournament in Mobile, Alabama, Reed was returning a hard volley and came down from a jump in the air, as he had done a thousand times before for a return shot. This time, however, he went down to the court in pain. He couldn’t move without severe pain. He had to forfeit the match and the game that day. His father and mother happened to be there and brought him home. They immediately called the practitioner they had depended on to keep Reed in good playing health for the last fifteen years. They all came together at the Integrative Medicine Centre office. The young athlete was taken to an examination room. It didn’t take long to determine that he had indeed sustained a bad ankle contusion with strain and sprain. The doctor thought he should be taken to the hospital for radiographs (X-rays), imaging (MRI), or both.

At the hospital the ER physician examined and followed up with radiographs and an MRI. It was determined he had sustained a grade III strain/sprain to the ankle (3). The radiologist had pointed out a couple of stress lines that he felt in most individuals would have been fractures (4). The diagnosis was a strain/sprain of the right ankle. Reed was to be out of action for the first time in his athletic career with an ankle injury. He had suffered mild shoulder, neck, wrist, and low back strains (5) over the past ten years, but nothing that kept him out of action more than a few days. This time was going to be different.

The team of Integrative Sports Medicine Specialists had seen hundreds of these injuries and had taken care of some of the best athletes in the world. They saw this as a good opportunity to illustrate the unified professional cooperation of the group.

![Reed’s Right Ankle](http://thesportjournal.org/files/)
Figure 1 Lateral View – Reed’s Right Ankle

### Biomechanical Analysis

The injury stunned Reed, his family and friends. Seeing him hobble around on crutches for weeks was just not what they were used to. But there were three components to this injury they had to understand. Because of the excessive flexibility of his body, especially the foot, over-pronation could easily have caused a fracture to occur. The three components that were involved, any one of which could have resulted in the injury were: a) too-rapid pronation (a turning-in of the foot); b) too extensive a degree of pronation; and c) pronation for too long a time.

When jumping, the athlete maintains pronation from the time of ground contact all the way into what’s called the propulsion phase. (At the propulsion phase, the foot should be very rigid to propel the body forward.) With a late-phase over-pronation, the foot is hypermobile (loose) and in danger of injury (6). Simply put, the body is applying a high level of force against the ground to propel the body forward while the foot is also excessively rolling inward. This inward foot rotation is transferred up the kinetic chain, alternating joint function. The uncontrolled load results in a high impact foot strike (6). So the athlete has reacted too fast, too excessively, and too long. The most difficult part for most people to understand is that he was just doing what his mind directed his body to do with the shot he was returning (6).

While traditional treatment methods for muscle, tendon and ligament injury have always emphasized rest, ice, compression and elevation (RICE), the team felt Reed should start functional treatment right away in order to retard scar tissue development (7).

### Methods and Materials

The team discussed the treatment plan after the consultation and examination, and determined that the athlete required a minimum of eight weeks of therapy, to consist of a physical examination (orthopaedic and neurological) (8,9), applied kinesiology or manual muscle testing to determine the weakened structures (10), acupuncture for quick pain control (10,11), and chiropractic for pelvic and low back compensation correction (6,8,10,12) as well as sports therapy procedures (13,14).

One new innovation in sports therapy, the Laser Therapy, a 500 mW cold laser (15,16), was to be utilized for the innovations provided by photobiostimulation technology within the last few years of Sports Medicine application and research (16). Medical Laser Systems has been working with doctors on a number of laser investigations, and this therapy seemed to be a good integrative approach to use. The laser utilizes acupuncture points that have been used in foot and ankle injuries in martial arts for hundreds of years with safety and efficiency (17).

Rehabilitative help from athletic training procedures and expertise was invaluable (18). The role of the Athletic Trainer’s (AT) involvement with the rehabilitation began early. Having worked with athletes for years, the AT was in tune with the mind and body needs of Reed’s injury from the start. There was the obvious need for some immobilization with an injury such as this, but the vital benefit of movement to promote healing in the affected area was not to be ignored (18).

The initial rehab session involved testing the ankle for range of motion (ROM) in relation to pain. The athlete needed to work in ranges where discomfort was 1-4 on a scale of 10 (19). This assessment was done in open chain fashion as the affected ankle tested in every possible position to locate the primary hindrances to healthy ROM.

What was found was there were a few positions in ROM (i.e. dorsiflexion) that caused greater degrees of pain than others. Once located, “pain-free zones” of ROM were used to work the ankle in those zones with resistance bands in a seated position. Some pain-free ROM with light resistance in the injured ankle progressed into the areas where discomfort was evident. The stimulus was kept passive as opposed to forcing any ROM that was not compliant. It must be noted here that the non-injured ankle was trained with the same resistance and workload. Well-established research literature indicates that working a non-injured limb results in strength improvement in the injured contralateral side. This is referred to as “cross-transfer” and even in immobilization situations reports show 10-77% (of healthy side) strength increases (20).

The athlete’s progress in ROM was quite amazing. He was seen 3-4 days per week through the rehab process in combination with his acupuncture and chiropractic treatments. Stability drills were added to the program. With the ankle, a healthy joint must have both mobility and stability (20). The ankle can be quite an uncooperative joint for an athlete, since if either the stability or mobility is compromised the other attribute suffers. Being a tennis player means the demands of repeated acceleration, deceleration, and change of direction are inherent. Proficiency in these athletic skills requires high degrees of mobility in the ankle joint. However, once that mobility threshold has been violated (as was the case with this injury), stability is the number one priority. Mobility cannot be restored to the athlete’s ankle without the presence of stability. Stretching of the lower leg was done with precision and care, to locate the ROM for that day as opposed to being competitive and forcing progress.

Though “ankle stability” was the focus, the vital fact is that the human body works in kinetic chains, meaning no specific area of the body (i.e. ankle) is an island unto itself. For example, the restoration of the ankle is intimately affected by mobility and stability in other key areas such as the hip and knee (21,22). We did not want our athlete performing closed chain movements on the injured ankle that would compromise ipsilateral hip function through compensations to “protect” the ankle. Standing exercises were implemented that required a “regulated stimulus” to the injured ankle. Reed performed these drills barefoot on a cushioned surface that required him to flex his feet into the surface.

After he developed the necessary stability in the ankle, it was time to implement lunge type drills with assistance from a resistance band around the waist. The bands are used in this case to _unload_ the drill so that less of the athlete’s bodyweight is placed on the injured ankle. The progression was to go into controlled horizontal force drills where the subject would move laterally while attached to the resistance band. This gradually reintroduced the ankle to deceleration forces as well as change-of-direction demands. These drills, and others similarly performed with trunk rotation, re-educated the kinetic chain that includes the hips and other core muscles as well as the shoulders.

Figure 2 Dr. Mike Allen, Dr. John Stump, Sports Medicine Specialist, with Anderson Reed

Reed followed the treatment and rehab plan exactly as was suggested and established a routine that had him back in competitive condition within the predicted 8 weeks.

It is reported, “Reed has been a natural athlete from the beginning of his career; not taking any medications, steroids or athletic enhancements has been his prerogative.” There was no question or desire for anti-inflammatory therapy during the treatment or rehabilitation phase. The athlete understood the theory that medication may give him short-term benefit but nothing permanent (23). He followed the daily grind of the exercises and the muscle therapy explained to him each week. The manual muscle testing (AK) showed the progress being made each week. His speed, strength and agility were there as before the injury. The relationship between the force of movement and the velocity of movement was well understood (24,25).

### Conclusion

Beginning high-competitive athletics at an early age, this young man just experienced what every athlete has to face, human frailty and lack of total control, the fact that athletic injury hurdles come up suddenly, unannounced and as quickly as moguls down a ski slope.

The sports injury team had worked with athletes from elementary to professional and Olympic levels during their career. They knew there are times when this happens to the best of athletes; it’s part of the price that each athlete has to pay climbing to the top of their athletic endeavor. Some athletes take it in stride and know and understand, but the knowledge is difficult for others. They just can’t understand why the body doesn’t always respond as quickly and as efficiently as it should to a mental command and when it tries, sometimes the communication breaks down.

This athlete has a great future ahead. He crossed this injury hurdle just as he had all other hurdles put in front of him, with hard work and patience. He took on his treatment and rehabilitation as if it was part of the challenge of the game, and it is a very important part. Our athletic staff would not be surprised at all to see him at Wimbledon in the near future if he continues to follow the work ethic he has set up for himself in the early stages of his athletic career. We want to thank the Physicians and staff of Thomas Hospital for their contribution and help with the imaging of the ankle.

Please address any questions, comments or suggestions to the authors at the following email address: [email protected] or visit www.alternative-concepts.com

### Applications in Sport

This article was written for the coaches, trainers and other sports health related personnel not familiar with the benefits of working in an Integrative Sports Healthcare facility. In this type of facility there are chiropractic, acupuncture, laser, nutrition and many non-traditional clinical applications that can speed an athlete’s injury toward recovery, in addition to the traditional approach in Sport Medicine.

### Acknowledgements

The authors wish to thank the radiology staff at Thomas Hospital, Fairhope, Alabama, especially the physicians who consulted with us in this case.

### References

1. Keele, KD with a commentary by Carlo Pedretti, Corpus of the anatomical works in the collection of her Majesty the Queen, New York: Johnson, 1979-1981. 3 vol. See also his fundamental study, Leonardo da Vinci’s elements of the science of man, New York: Academic Press, 1983.
2. American Academy of Orthopaedic Surgeons The Young Athlete New York, NY, July 2009.
3. Hole JW, Human Anatomy & Physiology, Wm C Brown Brothers, Oxford 1995 pages 172-200.
4. Fore, David and Radiology Staff, Thomas Hospital, Fairhope, AL. May 2009.
5. Gibble, M and Ashton, J. Young Athletes Fight Sports Injury www.CBS.Com June 2009.
6. Schafer RC. Clinical Biomechanics Musculoskeletal Actions and Reactions. Williams & Wilkins, Baltimore, 1998, pages 579-582.
7. Hammer, W. New Trends in Treating Muscle Injury. Dynamic Chiropractic, March, 2009.
8. Jenkins, DB Functional Anatomy of the Limbs and Back W.B. Saunders Company, Philadelphia, 1991.
9. Cyriax, J Orthopaedic Medicine Vol I & II Bailliere Tindall, London, 1984.
10. Micozzi M Fundamentals of Complimentary and Alternative Medicine Saunders Elsevier, 2006 pp 223-225.
11. Ibid pp255-73
12. Mayor DF Electroacupuncture Churchill Livingstone, London 2007 pp 191-195.
13. Oschman JL Energy Medicine: The Scientific Basis Churchill Livingstone London 2000 pp 165-193.
14. Stump JL Neuroma Pain of the Foot Successfully Managed with Laser Therapy Practical Pain Management, May 2009 pp 47-51.
15. Medical Laser Systems, Brandford, CT
16. White J and Kaesberg-White K Laser Therapy and Pain Relief. Dynamic Chiropractic. October 1994. 12(21).
17. Deadman P, AL- Khafaji M, and Baker K. A Manual of Acupuncture Journal of Chinese Publications. East Sussex, England. 2001.pp 10-20.
18. Konin JG Clinical Athletic Training SLACK Inc., Publishers, Thorofare, NJ 1996.
19. Irvin RL Classification of Chronic Pain. Pain. Supplement 3. 395-396.
20. Muscolino JE. The Muscular System Manual. Elsevier Mosby, St Louis, 2005.
21. Liebenson, C. Building Speed and Agility. Dynamic Chiropractic, June 2009.
22. Miller, John P. and Croce, Ronald V. (2007). “Analysis of Isokinetic and Closed Chain Movements for Hamstring Reciprocal Coactivation”. Journal of Sport Rehabilitation (16): 319–325.
23. Mishra DK, Friden J, Schmitz MC, et al. Anti-inflammatory medication after muscle injury. A treatment resulting in short-term improvement but subsequent loss of muscle function. J Bone Joint Surg Am, 1995; 77(10): 1510-9.
24. Munn, J., Herbert, R., & Grandevia, S. (2004). Contralateral effects of unilateral resistance training: a meta-analysis. Journal of Applied Physiology, 96, 1861-1866.
25. Lee, M., & Carrol, T. (2007). Cross Education: Possible mechanisms for the contralateral effects of unilateral resistance training. Sports Medicine, 37, 1-14.

### Authors

John Stump did his undergraduate work in biology at the University of Maryland and a Master’s and Doctorate in Sports Medicine at the United States Sports Academy. In addition he accomplished a doctorate in Chiropractic from Palmer College in Davenport, Iowa. He went on to do postdoctoral work in Oriental Medicine and Acupuncture in Japan, China and Korea. In addition he holds black belts in Judo, Karate, and Kempo.

Dr. Stump is armed with a unique perspective on health care from an eastern and western scientific view. Because of this Dr. Stump was asked to be a team doctor for the South Korean government in 1986 for the Asian Games and 1988 Seoul Olympics. He is the author of numerous scientific articles, and has coauthored 4 textbooks. The latest textbook publication Stump contributed to being Electroacupuncture, edited by David Mayor, published by Elsevier 2007. Later that year he released a non-fiction account of the tragic stroke he survived (“A Stroke of Midnight” Alternative Concepts Publishing, 2007.) John is now writing a unique east-west anatomy text for McGraw-Hill to be released in 2011. He is a National Faculty member of the United States Sports Academy.

Mike Allen did his undergraduate work at the University of Tennessee at Knoxville and graduated in 1999. He did post-graduate studies in Sports Medicine at the United States Sports Academy and Athletic Training at the University of Mobile in Alabama. He is presently assistant Clinic Director at Southwest College of Acupuncture, and attends patients at a clinic in Denver, Colorado. In addition he is Consultant in Acupuncture to the Integrative Medicine Centre, Fairhope, Alabama since 2005.

Bob Saxon did his undergraduate work biology at Loch Haven University in Pennsylvania. He graduated from New York Chiropractic College with his DC degree in 2000. He has worked at the Integrative Medicine Centre for the past three years as Assistant Clinic Director, Chiropractic Department. He is also certified in Acupuncture by the International College of Acupuncture. In addition he teaches Anatomy and Kinesiology for Blue Cliff College in Mobile, Alabama.

Vince McConnell is a certified fitness trainer and athletic preparation specialist. Coach McConnell has been working with private clients, as well as high school, collegiate and professional athletes. He has written numerous articles for various fitness magazines and is often a guest on TV and Radio programs. He owns and operates McConnell’s Athletics in Fairhope, Alabama.

### Corresponding Author

John L. Stump, DC, PhD, EdD
Integrative Medicine Centre
315 Magnolia Avenue
Fairhope, AL 36532
<[email protected]>
251-990-8188

John Stump did his undergraduate work in biology at the University of Maryland and took his Master’s and Doctorate in Sports Medicine at the United States Sports Academy. In addition he accomplished a doctorate in Chiropractic from Palmer College in Davenport, Iowa. He went on to do postdoctoral work in Oriental Medicine and Acupuncture in Japan, China and Korea. He also holds black belts in Judo, Karate, and Kempo.

Dr. Stump is armed with a unique perspective of health care from an eastern and western scientific view. Because of this Dr. Stump was asked to be a team doctor for the South Korean government in 1986 for the Asian Games and 1988 Seoul Olympics. He is the author of numerous scientific articles, and has coauthored 4 textbooks. The latest textbook publication Stump contributed to was Electroacupuncture, edited by David Mayor, published by Elsevier in 2007. Later that year he released “A Stroke of Midnight” (Alternative Concepts Publishing, 2007), a non-fiction account of the tragic stroke he survived. John is now writing a unique east-west anatomy text for McGraw-Hill to be released in 2011. He is a National Faculty member of the United States Sports Academy.

U.S. Sports Academy2013-11-25T15:32:30-06:00August 8th, 2011|Sports Coaching, Sports Exercise Science, Sports Management, Sports Studies and Sports Psychology|Comments Off

Usefulness of Bioelectrical Impedance in the Prediction of VO2max in Healthy Men and Women

Jordan R. Moon, Vincent J. Dalbo, Michael D. Roberts, Chad M. Kerksick, and Jeffrey R. Stout

### Abstract

VO₂max is an invaluable measure for the assessment of aerobic fitness; however, to yield accurate results direct assessment requires costly equipment, trained investigators, and that the participant produce a maximal effort to volitional fatigue. The majority of VO₂max prediction equations have attempted to predict aerobic capacity without considering physiological variables other than age and body composition. As a result, a majority of VO₂max prediction equations have been found to be invalid. A recent study proposed an equation accounting for additional physiological variables known to influence aerobic capacity, including blood volume, fat-free mass, urinary creatine excretion, and total body potassium. Therefore, this investigation sought to evaluate the validity of novel non-exercise prediction equations, which utilize bioelectrical impedance analysis (BIA) to obtain an estimate of blood volume and skeletal muscle mass as predictor variables in an attempt to increase the accuracy of non-exercise VO₂max prediction equations. VO₂max was assessed using indirect calorimetry. Healthy male (30.9 ± 6.0 y, 179.0 ± 4.3 cm, 94.1 ± 19.5 kg; n = 23) and female (32.0 ± 6.1 y, 167.8 ± 7.9 cm, 72.0 ± 9.6 kg; n = 25) participants completed a VO₂max test and a physical activity survey (PA-R) and were analyzed using bioelectrical impedance. Results indicated that each equation resulted in a significant (p ≤ 0.025) underestimation of VO₂max. These outcomes suggest that the use of BIA to estimate blood volume and skeletal muscle mass does not improve the accuracy of VO₂max prediction equations. Coaches and trainers will not benefit from the inclusion of BIA in an equation to predict aerobic fitness. Currently, the best methods to estimate aerobic fitness require submaximal and maximal exercise testing. Predicting aerobic fitness using non-exercise equations does not appear to be practical or valid.

**Keywords:** maximal, aerobic capacity, prediction, gender-specific

### Introduction

The rate of maximal oxygen consumption (VO₂max) has practicality in research and field settings as a measure of aerobic fitness, in order to prescribe exercise intensities and to assess exercise training responses following an intervention (19). An acceptable standard for VO₂max determination is the direct measure of expired gas samples obtained while an individual is performing maximal exertion exercise (2). From a research perspective reliable non-exercise VO₂max prediction equations could prove to be beneficial, as experimenters could obtain an immediate, valid measure of the aerobic fitness of an individual without maximal exercise testing. Additional advantages of non-exercise VO₂max prediction equations include the ease and cost associated with test administration and use in participants who are unable to perform a treadmill test, as VO₂max tends to be underestimated with other modes of exercise (19). However, the greatest advantage of an accurate VO₂max prediction equation is the practicality of use in research laboratories that do not possess the necessary equipment to access VO₂max and for coaches and trainers looking to evaluate several athletes and/or an entire team. Due to the disadvantages associated with VO₂max testing numerous submaximal (1,8,18,23) and non-exercise prediction equations (4,5,10,17,21,24,25) have been developed to reduce the necessity of direct VO₂max assessment.

Previous non-exercise prediction equations have been developed but the need to improve the accuracy of these equations has been suggested in previous literature (4,16,17,21). However, due to known deviations in VO₂max values determined from varying modes of exercise (bike, treadmill walking, treadmill running, and arm ergometry), the use of VO₂max prediction equations are dependent on the task. For example, a prediction equation for VO₂max during a treadmill run may not be accurate for predicting VO₂max during cycle ergometry. In addition, another primary shortcoming of non-exercise VO₂max prediction equations is the limited ability to account for genetic variability in VO₂max (21). According to Stahn et al. (21), the primary physiological determinants measured at rest to predict VO₂max are blood volume, which has been found to account for up to 80% of the variance in VO₂max, and a group of variables including fat-free mass, urinary creatine excretion, and total body potassium, which have been proposed to be related to skeletal muscle mass. Additional evidence supporting this claim was provided by Sananda et al. (20) who found total skeletal muscle mass to be highly correlated (r = 0.92, p < 0.001) with VO₂max (20).

Stahn et al. (21) sought to obtain an estimate of blood volume and skeletal muscle mass using bioelectrical impedance analysis (BIA). Previous work has suggested BIA to have a strong correlation with blood volume (r = 0.89, SEE = 9.0%) using the impedance index of height squared divided by impedance (22) and skeletal muscle mass, as compared to magnetic resonance imaging (r = 0.927, SEE = 9.0%) (11). As a result Stahn et al. (21) developed a non-exercise VO₂max prediction equation, which utilizes BIA to estimate resting levels of blood volume and skeletal muscle mass as predictor variables. However, the equation by Stahn et al. (21) has yet to be validated by an independent laboratory, and the benefits of utilizing BIA for predicting VO₂max have not been established. Therefore, the purpose of this study was to validate treadmill VO₂max predictions using the recently published BIA equation of Stahn et al. (21). It was hypothesized that the BIA equations would produce accurate VO₂max predictions due to the relationship between VO₂max, BIA, skeletal muscle mass, and blood volume.

### Methods
#### Subjects

Sixty participants chose to participate in this study, but 12 were eliminated for not reaching VO₂max (n = 48; Table 1). All testing was conducted after the participant signed the IRB-approved informed consent and completed comprehensive medical history questionnaires. Participants were excluded if they: 1) had a history of metabolic, hepatorenal, musculoskeletal, autoimmune, or neurological disease; 2) were currently taking androgenic medications; or 3) had consumed nutritional supplements that may affect metabolism [i.e., over 100 mg•d-1 of caffeine, ephedrine alkaloids, etc.] and/or muscle mass [i.e. creatine, protein/amino acids, androstenedione, dihydroepiandrosterone (DHEA), etc.] within three months of starting the study; 4) were unable to reach at least two of the three stated criteria for reaching VO₂max.

Table 1. Participant characteristics of validated equations

	Stahn et al. (21)			Current Validation Participants
	N	Males	Females	N	Males	Females
N	66	33	33	48	23	25
Age (yr)	24.0 (4.0)	25.0 (4.0)	23.0 (4.0)	31.5 (6.0)	30.9 (6.1)	32.1 (6.1)
Height (cm)	174 (6)	180 (5)	168 (6)	173 (9)	179.0 (4)	169 (8)
Weight (kg)	68.4 (7.6)	74.9 (8.3)	61.8 (6.8)	82.6 (18.7)	94.1 (19.5)	72.0 (9.6)
PA-R	6.6 (1.1)	6.6 (0.9)	6.3 (1.3)	2.9 (1.9)	3.4 (2.3)	2.4 (1.5)
VO₂max (mlkgmin^-1)	53.6 (5.0)	59.6 (5.5)	47.6 (4.4)	43.9 (13.4)	42.4 (14.4)	45.2 (12.6)

#### Non-Exercise VO₂max Prediction Equations

The equations selected for validation were developed by Stahn et al. (21) and are presented in Table 2.

Table 2. Submaximal VO₂max prediction equations

2MF	Stahn et al. (21)	VO₂max (DF50) = 14.29 · H²/Z + 104.14 · PA-R – 440.79 • Gender (M = 1, F = 0) + 489.47
2M	Stahn et al. (21)	VO₂max (DF50) = 14.29 • Height/Z + 104.14 • PA-R– 440.79 • Gender (M = 1) + 489.47
2F	Stahn et al. (21)	VO₂max (DF50) = 14.29 • Height2/Z + 104.14 • PA-R – 440.79 • Gender (F = 0) + 489.47

∗ All values from prediction equations were converted to ml•kg•min-1
H = Height (cm)
Z = Impedance (Ohm)
PA-R = Physical activity rating scale
M = Male
F = Female

#### Experimental Design

Testing was performed between 9:00 a.m. and 3:00 p.m. in a temperature-controlled laboratory maintained at 21.6 ± 0.7oC and 28.2 ± 5.5% relative humidity. Prior to testing, each subject was instructed to avoid the consumption of alcohol, refrain from heavy exertion for 48 hours, and avoid smoking and caffeine consumption the day of testing. Subjects were also instructed to consume 2 liters of water the day before testing in an effort to promote normohydration.

#### Anthropometry and Physical Activity Assessment

After voiding their bladders, subjects changed into minimal clothing and removed footwear for measurement of body mass and height, conducted on a calibrated scale and stadiometer (Detecto, Webb City, MO). Body mass was measured to the nearest 0.2 kg and height was assessed to the nearest 0.5 cm. The PA-R was used to assess the average weekly physical activity patterns of each participant in the 6 months prior to testing (7).

### Bioelectric Impedance Measurement

Whole-body impedance measurements were performed using a single frequency (50 kHz) bioelectrical impedance analyzer (IMPTM DF50, ImpediMed Inc, Queensland, Australia). Each morning prior to testing, the bioelectrical impedance device was calibrated following the manufacturer’s guidelines. Measurements were taken from the right side of the body using a tetrapolar electrode arrangement following the standard procedures used by Stahn et al. (21). Prior to testing each subject was asked remove jewelry and excess clothing before being instructed to lie in a supine position for 10 minutes with arms and legs abducted from the body at 10˚ and 20˚ respectively, allowing body fluids to stabilize. Following identification of electrode placement, body hair was removed with a razor before the skin was cleaned with alcohol and allowed to dry. Current-inducing electrodes (575 mm2: 25 mm x 23 mm) (ImpediMed Electrodes, Queensland, Australia) were placed 1 cm below the phalangeal-metacarpal joint in the middle posterior surface of the hand and 1 cm below the transverse (metatarsal) arch on the dorsum of the foot. Detector electrodes of the same type were placed on the lateral epicondyle of the humerus and the lateral condyle of the femur according to the guidelines of Stahn et al. (21). Interclass and intraclass correlation coefficients for within and between days using this technology vary between 0.960 and 0.997 (6,21), while interindividual within-day reliability measures are commonly 1.3-2.0% (13,15,21).

#### VO₂max Assessment

VO₂max testing was performed on a calibrated Quinton treadmill (Q65 Series 90, Bothell, WA) according to Stahn et al. (21). Participants began the test with a 4-minute warm-up at 1.5 m·s-1 at a 1% gradient. Following warm-up, 3-minute testing periods began at speeds of 2.0 m·s-1 for women and 2.5 m·s-1 for men. Completion of each stage resulted in a speed increase of 0.5 m·s -1 until volitional fatigue despite verbal encouragement.

Maximal heart rate, respiratory exchange ratio (RER), and VO₂max were measured with a calibrated metabolic cart (ParvoMedics TrueOne® 2400 metabolic measuring system, Sandy, UT). The system was calibrated 15 minutes prior to testing according to manufacturer specifications. Mean oxygen uptake (VO2), carbon dioxide output (VCO2), and pulmonary ventilation (VE) were computed for each breath and averaged over 15-second intervals. Heart rate was monitored during testing using a heart rate monitor (Polar F6, Lake Success, NY). The test was considered maximal if two of the following criteria were obtained: 1) a plateau of VO2 occurred, defined as an increase of less than 150 ml·min-1 despite increasing speed, 2) Respiratory exchange ratio (RER) was ≥ 1.15, and 3) maximal heart rate was within 10 beats of age-predicted maximal heart rate (21).

#### Data analysis

Validity of VO₂max estimates were based on an evaluation of predicted values versus the criterion value from direct treadmill VO₂max assessment by calculating the constant error (CE = actual VO₂max – predicted VO₂max), r value (Pearson product moment correlation coefficient), standard error of estimate and total error (9,14). The mean difference (CE) between the VO₂max prediction equations and the direct measure of VO₂max was analyzed using dependent t-tests with the Bonferroni alpha adjustment (12). The method of Bland and Altman (3) was used to identify the 95% limits of agreement between actual VO₂max values and predicted VO₂max values.

### Results

Demographic information of participants in the Stahn et al. (21) study and the current investigation are presented in Table 1. To optimize the accuracy of the prediction equations, results of the validation analysis are presented in two groups: male- and female-specific equations (Table 3). Each sex-specific equation produced a significantly different VO₂max value from the direct measure (p<0.05). TE values were greater than 13.2 ml•kg•min-1, SEE values were greater than 9.1 ml•kg•min-1 and r values were less than 0.75.

Table 3. Validity of non-exercise prediction equations for estimating VO₂max ml•kg•min-1

Method	VO₂max ± (x SD)	CE	r	Slope	Y-intercept	SEE	TE
Direct VO₂M	42.4 (14.4)
Male	33.3 (8.3)	9.1*	0.74	1.2	-0.5	9.9	13.3
Direct VO₂F	45.2 (12.6)
Female	34.0 (6.5)	11.2*	0.70	1.3	-0.78	9.2	14.5

### Discussion

The sex-specific equations analyzed in this investigation produced predicted VO₂max values that were significantly below the actual VO₂max (p<0.05). Using the predicted VO₂max values to produce exercise prescriptions would yield exercise intensities underestimated by an equivalent amount.

The aim of the Stahn et al. (21) study was to demonstrate the viability of using BIA for the non-exercise prediction of VO₂max. The authors attempted to account for the influence of physiological variables on aerobic performance by indirectly accounting for blood volume, fat-free mass, urinary creatine excretion and total body potassium with a time efficient assessment of blood volume and skeletal muscle mass using a BIA device. Results from the Stahn et al. (21) study appeared promising as their equation was reported to account for 88.7% of the variance in VO₂max in an athletic population, and the authors postulated the equation would be more effective in a more diverse population. However, in the current investigation the equations developed by Stahn et al. (21) were found to be invalid in a population of healthy men and women. Errors in the equations were most likely introduced by using predicted values of blood volume and skeletal muscle mass (via BIA). In essence, predicted variables were used to predict another predictor, VO₂max. The validity of the equations developed by Stahn et al. (21) may be improved by using a more accepted and still cost-effective measure of skeletal muscle mass, such as a multiple-site skinfold, as was used in VO₂max prediction equations developed by Jackson et al. (10).

### Conclusions

The equation developed by Stahn et al. (21) may have been effective at predicting VO₂max in the athletic population used in the original investigation but appears to significantly underestimate VO₂max in a representative sample of healthy young men and women. Future prediction equations should include percent body fat and physical activity rating scales, as these variables appear to have the greatest predictive power in the estimation of non-exercise VO₂max prediction equations. Although the prediction equations developed by Stahn et al. (21) were not found to be valid in this investigation, non-exercise VO₂max prediction equations should attempt to increase their predictive power by accounting for physiological factors that are known to influence VO₂max, namely skeletal muscle mass. Furthermore, future research should examine the accuracy of the equations developed by Stahn et al. (21) in an athletic population and determine the viability of using a BIA device in the prediction of VO₂max.

### Applications in Sport

An athlete’s aerobic fitness is a crucial component of performance regardless of the sporting event. Aerobic athletes and coaches/trainers can benefit from accurate measurements of aerobic fitness through VO₂max testing. However, direct VO₂max testing requires expensive equipment and is not practical in the field. Many prediction equations have been developed in an attempt to find an easy way to predict VO₂max in the field. However, results from this investigation suggest that using BIA in a non-exercise VO₂max equation may not be appropriate or valid in healthy men and women. Specifically, the Stahn et al. (21) BIA VO₂max equations underpredicted VO₂max, resulting in significantly lower VO₂max values, giving the impression of an individual who is less aerobically fit. Therefore, it is suggested that coaches and trainers utilize either submaximal or maximal VO₂max prediction equations for their athletes and clients, as non-exercise prediction equations may not provide valid information.

### Acknowledgements

The authors would like to thank all the participants for their willingness to participate in this investigation.

### References

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3. Bland, J. M., & Altman, D. G. (1986). Statistical methods for assessing agreement between two methods of clinical measurement. Lancet, 1(8476), 307-310.
4. Davis, J. A., Storer, T. W., Caiozzo, V. J., & Pham, P. H. (2002). Lower reference limit for maximal oxygen uptake in men and women. Clin Physiol Funct Imaging, 22(5), 332-338.
5. Fairbarn, M. S., Blackie, S. P., McElvaney, N. G., Wiggs, B. R., Pare, P. D., & Pardy, R. L. (1994). Prediction of heart rate and oxygen uptake during incremental and maximal exercise in healthy adults. Chest, 105(5), 1365-1369.
6. Fornetti, W. C., Pivarnik, J. M., Foley, J. M., & Fiechtner, J. J. (1999). Reliability and validity of body composition measures in female athletes. Journal of Applied Physiology, 87(3), 1114-1122.
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8. Golding, L. A. (2000). YMCA Physical Fitness Testing and Assessment Manual (4 ed.). Champaign: Human Kinetics
9. Heyward, V. H., & Wagner, D. R. (2004). Applied Body Composition Assessments. Champaign, IL: Human Kinetics.
10. Jackson, A. S., Blair, S. N., Mahar, M. T., Wier, L. T., Ross, R. M., & Stuteville, J. E. (1990). Prediction of functional aerobic capacity without exercise testing. Med Sci Sports Exerc, 22(6), 863-870.
11. Janssen, I., Heymsfield, S. B., Baumgartner, R. N., & Ross, R. (2000). Estimation of skeletal muscle mass by bioelectrical impedance analysis. J Appl Physiol, 89(2), 465-471.
12. Keppel, G. a. T. D. W. (2004). Design and Analysis: A Researchers Handbook (4th ed.). Upper Saddle River, NJ: Prentice Hall.
13. Kushner, R. F., & Schoeller, D. A. (1986). Estimation of total body water by bioelectrical impedance analysis. Am J Clin Nutr, 44(3), 417-424.
14. Lohman, T. G. (1996). Human Body Composition. Champaign, IL: Human Kinetics.
15. Lukaski, H. C., Johnson, P. E., Bolonchuk, W. W., & Lykken, G. I. (1985). Assessment of fat-free mass using bioelectrical impedance measurements of the human body. Am J Clin Nutr, 41(4), 810-817.
16. Malek, M. H., Berger, D. E., Housh, T. J., Coburn, J. W., & Beck, T. W. (2004). Validity of VO₂max equations for aerobically trained males and females. Med Sci Sports Exerc, 36(8), 1427-1432.
17. Matthews, C. E., Heil, D. P., Freedson, P. S., & Pastides, H. (1999). Classification of cardiorespiratory fitness without exercise testing. Med Sci Sports Exerc, 31(3), 486-493.
18. Pare, G., Noreau, L., & Simard, C. (1993). Prediction of maximal aerobic power from a submaximal exercise test performed by paraplegics on a wheelchair ergometer. Paraplegia, 31(9), 584-592.
19. Ross, R. M. (2003). ATS/ACCP statement on cardiopulmonary exercise testing. Am J Respir Crit Care Med, 167(10), 1451; author reply 1451.
20. Sanada, K., Kearns, C. F., Kojima, K., & Abe, T. (2005). Peak oxygen uptake during running and arm cranking normalized to total and regional skeletal muscle mass measured by magnetic resonance imaging. Eur J Appl Physiol, 93(5-6), 687-693.
21. Stahn, A., Terblanche, E., Grunert, S., & Strobel, G. (2006). Estimation of maximal oxygen uptake by bioelectrical impedance analysis. Eur J Appl Physiol, 96(3), 265-273.
22. Stahn, A., Terblanche, E., & Strobel, G. (2004). Relationships between bioelectrical impedance and blood volume. Proceedings of the 11th Pre-Olympic Congress, 219-220.
23. Storer, T. W., Davis, J. A., & Caiozzo, V. J. (1990). Accurate prediction of VO₂max in cycle ergometry. Med Sci Sports Exerc, 22(5), 704-712.
24. Wier, L. T., Jackson, A. S., Ayers, G. W., & Arenare, B. (2006). Nonexercise models for estimating VO₂max with waist girth, percent fat, or BMI. Med Sci Sports Exerc, 38(3), 555-561.
25. Williford, H. N., Scharff-Olson, M., Wang, N., Blessing, D. L., Smith, F. H., & Duey, W. J. (1996). Cross-validation of non-exercise predictions of VO2peak in women. Med Sci Sports Exerc, 28(7), 926-930.

### Corresponding Author

Jordan R. Moon, PhD
Department Head
Department of Sports Fitness and Health
United States Sports Academy
One Academy Drive
Daphne, AL 36526

### Author Affiliations

Jordan R. Moon, PhD
Department of Sports Fitness and Health
United States Sports Academy
One Academy Drive
Daphne, AL 36526

Chad M. Kerksick, PhD and Jeffrey R. Stout, PhD
Department of Health and Exercise Science
University of Oklahoma
1401 Asp Ave.
Norman, OK 73019

Vincent J. Dalbo, PhD
School of Medical and Applied Sciences
Institute of Health and Social Science Research
Central Queensland University
Rockhampton, Australia

Michael D. Roberts, PhD
University of Missouri-Columbia
Department of Biomedical Science
Veterinary Medicine Building
Columbia, MO 65211

U.S. Sports Academy2016-04-01T09:13:13-05:00July 27th, 2011|Contemporary Sports Issues, Sports Exercise Science, Sports Management, Sports Studies and Sports Psychology|Comments Off

Do static-sport athletes and dynamic-sport athletes differ in their visual focused attention?

Dr. Thomas Heinen

### Abstract

The goal of this study was to evaluate current attention tests in sport psychology for their practical use in applied sport psychology. Current findings from the literature suggest that measures of visual focused attention may show different performances depending on sport type and test conditions (33). We predicted differences between static- and dynamic-sport athletes (17) when visual focused attention is tested with random (unstructured) versus fixed (structured) visual search in two experimental conditions (quiet environment versus auditory distraction). We analyzed 130 nationally competing athletes from different sports using two measures of visual focused attention: the structured d2 test and the unstructured concentration grid task. Compared to static-sport athletes, dynamic-sport athletes had better visual search scores in the concentration grid task in the condition with auditory distraction. These findings suggest that the results of attention tests should be differentially interpreted if different sport types and different test conditions are considered.

**Key words:** d2 test, concentration grid task, auditory distraction

### Introduction

The study reported here was motivated by recent calls within the applied field of sport psychology for a broad diagnostic framework in the domain of talent selection (7,35) as well as the ongoing evaluation for professional standards of the techniques that are used by practicing sport psychologists (14).

An increasing number of researchers have argued that psychological variables remain often unnoticed within talent identification models (1). However, among a range of other physical and technical variables, psychological variables have been identified as a significant predictor of success (18,27,34). For instance, during athletic performance attention is seen as one of the most important psychological skills underlying success because of the ability to exert mental effort effectively is vital for optimal athletic performance (12,22,27).

In cognitive psychology, attention is seen as a multidimensional construct. According to different taxonomies of attention, at least three distinct dimensions of attention have been identified (21,28,39). The first is _selectivity_. It includes selective attention as well as divided attention. The second dimension of attention refers to the aspect of _intensity_, which can include alertness and sustained attention. The third dimension is _capacity_ and refers to the fact that controlled processing is limited to the amount of information that can be processed at one time.

Individuals’ attentional performance in one or more of the aforementioned dimensions can be assessed in several ways (3, for an overview see 39). The selectivity aspect can, for instance, be approached with tasks involving either focused or divided attention. In focused attention tasks there are usually irrelevant stimuli, which must be ignored. In divided attention tasks, all stimuli are relevant, but may come from different sources and require different responses (39). Intensity requirements can be approached with tasks involving different degrees of difficulty, or with tasks that have to be carried out over longer periods of time. Finally, dual-task procedures, memory span tests, or other processing tasks are used to approach the capacity aspect (26). Practicing sport psychologists most often use standardized tests, which are easily administered in a paper-pencil form and therefore are easy to use in the field.

However, several authors (38) as well as diagnosticians in youth talent diagnostic centers in Germany have expressed a number of subjective impressions concerning the performance of athletes on attention tests (e.g., influence of sport type, test context, or expertise level) that are insufficiently indicated by the existing test norms. Therefore, the goal of the present study was to examine the influence of two essential factors (sport type and environmental context) on athlete’s performance in two different attention tests.

Boutcher’s multilevel approach (3) integrates relevant aspects of research and theory on attention from different perspectives. In his framework, internal as well as external factors, like enduring dispositions, demands of the task, and environmental factors, interact with attentional processes during performance. These factors are thought to initially influence the level of physiological arousal of the individual, which in turn influences controlled and automatic processing. When performing a task, the individual either uses controlled processing, automatic processing, or both, depending on the nature and the demands of the task. An optimal attentional state can be achieved by reaching or attaining the exact balance between automatic and controlled processing, essential for a particular task (3).

A sudden external distraction (e.g., auditory noise) is expected to hamper performance because it may disrupt the current attentional state by causing the individual to reach a level of arousal such that an imbalance in controlled and automatic processing occurs. However, individual differences may exist regarding the effect of internal or external distractions on attentional state. For instance, a gymnast normally performs his or her routine in a quiet environment in competition whereas during a basketball game the player is confronted with auditory noise. Unexpected auditory distractions may disrupt the attentional state of the gymnast but not the state of the basketball player because he is used to it.

There has been extensive research on different aspects of attentional performance in athletes. For instance, researchers examined attentional differences between athletes and non-athletes (5,20,23), between athletes on different expertise levels (8), as well as with regard to other factors, such as athlete type, sport type and gender (17,19,24,33) by using a variety of attentional tasks. Athletes are able to distribute their attention more effectively over multiple locations and better able switch their attention rapidly among locations than non-athletes (25). Furthermore, attentional performance seems to vary with the kind and amount of training provided by a sports environment so that athletes trained in more visually dynamic sports show better attentional control than athletes trained in less visually dynamic sports (24).

When using specific tests to assess attention performance, one should expect differences in test performance between athletes that vary in one or more of the aforementioned factors. In this context, Lum et al. highlight the need to examine athlete’s visual attention by using a variety of visual attention tasks (17, see also 20). Furthermore, existing test norms should account for the aforementioned differences to provide athletes with a reliable feedback on their individual attention performance.

For instance, to evaluate the visual focused attention performance of athletes, two common tests are used in the field of applied sport psychology, the d2 test and the concentration grid test (3, 4). Visual focused attention is usually operationalized as visual search so that target stimuli have to be found in a field of distractor stimuli (39). For instance, in the d2 test, participants need to select “d” letters with two dashes above them in an array of “d” and “p” letters with zero, one, or two dashes over or under each letter. The structure of reading letters from left to right provides an environment in which relevant stimuli need to be selected and irrelevant stimuli need to be ignored. The gaze searches throughout the visual array not in a random way but rather in a structured fashion. In contrast, in the concentration grid task, participants see a block of randomly distributed numbers, in which they need to search for numbers in sequence, such as number 01, then 02, 03, and so on. The concentration grid task is often administered as a training exercise in the field of applied sport psychology, and it has been proposed, that it works by developing the athlete’s ability to scan a visual array for relevant information, and to ignore irrelevant stimuli (11).

Given the different demands of these two tasks and the empirical evidence so far, one may speculate that athletes who have experience performing visual searches for relevant cues and making decisions in dynamic environments (which is typical for team sport athletes), will do better on the concentration grid test than on the d2 test (29). Athletes from individual sports who are exposed to a mostly static environment with one or a small number of stimuli should do better on the d2 test than on the concentration grid test.

Maxeiner compared, for instance, 30 gymnasts and 30 tennis players in their performance on the d2 test and on a reaction time task in which they were asked to press a pedal with their foot as soon as a square appeared on a computer monitor (19). Participants were tested under either a single-task condition, such that only the d2 test or the reaction time task had to be performed, or a multiple-task condition, in which both the d2 test and the reaction time task had to be carried out simultaneously. Reaction times showed a significantly stronger increase under the multiple-task condition for the gymnasts (about 28%) whereas no differences between gymnasts and tennis players were found for single-task conditions. The author concluded from this result, that tennis players have a better distributive ability of attention than gymnasts. However, the total number of items worked on the d2 test as well as the error rates did not differ between gymnasts and tennis players in either the single-task or multiple-task condition.

Tenenbaum, Benedick, and Bar-Eli conducted a similar study and found opposing results (33). The authors compared 252 young athletes from different sports disciplines in their d2-test performance. All athletes performed the d2 test in a quiet classroom with no distractions. Results indicate that the number of d’s the subjects have crossed (quantitative capacity) differed significantly by type of sport in females. High quantitative capacity scores in the d2 test were found for female athletes from sports such as tennis or volleyball, but not for female athletes from gymnastics. A similar pattern of results was found in male athletes, although only showing a tendency for rejecting the null hypothesis (p = .06). The authors found an additional effect for type of sport on error-rate. The largest error-rates were found in tennis and volleyball players whereas the smallest error-rates were found in track and field athletes. The authors concluded that concentration is individual and sport-type dependent and state that “Concentration should be further investigated with relation to motor performance” (p. 311).

Maxeiner and Tenenbaum et al. found opposing results in athletes from different sport domains in the d2 test (19,33). First, the authors assessed different parameters of the d2 test. Maxeiner quantified the total number of items worked on the d2 test, whereas Tenenbaum et al. quantified the number of d’s the subjects have crossed. The number of items worked on the d2 test is a reliable criterion for working speed (4), whereas the number of crossed d’s is related to both working speed and working accuracy. Assessing different parameters in the d2 test could lead to different results, therefore masking possible differences between participants from different sport domains. Following the suggestions of Brickenkamp, the practitioner should assess the concentration-performance score (number of marked d’s minus the number of signs incorrectly marked) in the first instance, because this value is resistant to tampering, such that neither the skipping of test parts nor the random marking of items increases the value (4).

Furthermore, Tenenbaum et al. had participants from tennis, fencing, volleyball, team-handball, track and field, and gymnastics indicating an unequal distribution of participants with regard to other criteria like kind of training provided by a sports environment (33). As mentioned above, attentional performance seems to vary with the kind and amount of training provided by a sports environment (24); the question arises whether athletes should be classified according to kind of training provided by a sports environment, rather than sport discipline per se when assessing their attentional performance.

Greenlees, Thelwell, and Holder examined the performance of 28 male collegiate soccer players in the concentration grid exercise (13,15). The players were assigned to either a 9-week concentration grid training or a control condition. During three test sessions the athletes were asked to complete a battery of concentration tasks, including the aforementioned concentration grid test. The results showed a significant main effect for training condition but not for test session, indicating that the concentration training group was superior to the control group but did not exhibit any improvement during the 9-week training interval. However, Greenless et al. assessed only soccer players with a playing experience of 10.45  2.31 years, which indicates that they already possess substantial experience in performing visual searches for relevant cues in dynamic environments (13). This could at least in part explain why the participants of the concentration training group did not improve their performance on the concentration grid task as compared to the participants of the control group. Additionally, the two groups were not homogeneous in their concentration grid performance at the study onset, which may in part explain the main effect for training condition. The findings of Greenless et al. highlight the need for further research on the concentration grid test, especially examining the extent to which the task reflects sport-specific concentration skills and therefore support the need for ongoing evaluation of this technique in diagnostics and intervention.

Taken together, we can identify two main factors that need to be considered when assessing athletes’ visual focused attention. First, a broad application of attention tests that are sensitive to the athlete’s experience in different types of sports should be made. This means, in particular, recognizing that different sport environments (static vs. dynamic), encouraging different visual search and decision strategies (fixed or structured vs. random or unstructured), and realizing that the same tests do not necessarily capture both types of strategies. Second, the environmental context (with or without distraction) can increase or decrease performance, respectively.

We adapted the dichotomy of Lum et al. and hypothesized that static-sport athletes and dynamic sport-athletes would not differ in d2 scores but would differ in concentration grid scores due to their different perceptual experiences (17). This finding would not only help to clarify previous results (19,33) but would extend them to different concentration tasks (d2 test vs. concentration grid) following the conclusions of Greenlees et al. as well as Tenenbaum et al. (13,33). We furthermore hypothesized that auditory distraction would have a detrimental effect on performance in both the d2 test and the concentration grid test because it may disrupt the current attentional state (3). We therefore compared performances in the d2 test and the concentration grid test with and without auditory distraction.

### Method

#### Participants

A sample of 130 athletes (students of Sport Science, German Sport University) were recruited to participate in the study (n = 44 women, mean age = 22 years and n = 86 men, mean age = 22 years). Ages ranged from 19 to 33 years, with a mean age of 22 years (SD = 2.4 years). Of these, 66 students (n = 15 women and n = 51 men) competed in 6 different sports with a dynamic visual environment (i.e., soccer, volleyball) and 64 (n = 29 women and n = 35 men) competed in another 6 different sports with mostly static visual environment (i.e., track and field athletics, gymnastics). All students had been performing their sport for at least 7 years with 19.2% (n = 25) of them reporting national experience (German championships or national league) and 11.5% (n = 15) also reporting international experience. All participants were informed about the purpose and the procedures of the study and gave their written consent prior to the experiment. Participants reported to have no prior experience with either the d2 test or the concentration grid test.

We recruited an additional sample of n = 25 students of sport science in order to evaluate the reliability of the d2 test and the concentration grid test and to estimate the validity of the concentration grid test. This was necessary because, first, we applied modified versions of the original tests and second, there were no reliability or validity statistics available in the current literature for the concentration grid test.

#### Tasks and Apparatus

##### d2 Test of Visual Focused Attention.

The d2 test was used to assess visual focused attention (4,39). It is seen as a reliable and valid instrument, most commonly being used in the fields of cognitive, clinical, and sport psychology. In the standardized version of this task, 14 lines consisting of 47 letters each are presented to the participant. The letters can be a “p” or a “d” with zero, one, or two small dashes above or below it. The task is to process all items (letters) of a line in a sequential order and to mark every “d” with two dashes above or below. All other letters are to be left unmarked.

The visual search pattern in the d2 test is guided by the structure of the stimulus field (fixed visual search). To avoid ceiling effects, there is a temporal restriction of 15 seconds to process each line. After 15 seconds there is a verbal instruction to proceed to the next line. Norms are available for age groups between 9 and 60 years. Reliability coefficients of the test range from r = .84 to r = .98 (4).

In the present study, 7 lines of the d2 test had to be dealt with under each experimental condition with each line consisting of 47 letters. This test reduction was applied for practical reasons, particularly to match the working time of the concentration grid task. Prior to the study, we analyzed d2-test results of 7 lines (Version A) and 14 lines (Version B) in a test–retest design with a temporal delay of 1 week. The results indicate a significant product–moment correlation between the two versions of the test in a sample of 25 students of sport science (r = .80; p < .05). Therefore, we believed that the use of 7 instead of 14 lines should be adequate for the purposes of this study. From the performance of each participant in the d2 test, two parameters were obtained: a concentration-performance score and the error rate. The concentration-performance score is the number of d letters the subject marked minus the number of signs (dashes) incorrectly marked. The error rate is the number of signs incorrectly marked plus the number of correct signs missed.

##### Concentration Grid Task

Two versions of the concentration grid test were used as a second measure of visual focused attention, and in particular, visual search (15,21). They were modified from the concentration grid exercise, which can be found in Harris and Harris (1984). The first version (CG1) used in this study consisted of 7 horizontal and 7 vertical squares arranged in a grid of 49 squares altogether. A unique two digit-number (from 00 to 49) was placed randomly in the center of each square. The second version (CG2) of the concentration grid was identical to the first except for a different placement of the numbers. To ensure comparability, the relative distance from each number to the following number was the same in the two grids. We also examined the reliability of the concentration grid task. In a test–retest design with a temporal delay of a 1-week interval, a significant product-moment correlation of r = .79 (p < .05) was found in a sample of 25 students of sport science.

In the concentration grid task the participants were instructed to mark as many consecutive numbers (starting from 00) as possible within a 1-min period under each experimental condition. The resultant number of correctly processed items was used for further data analysis. In comparison to the d2 test, the participants’ visual search pattern in the concentration grid is not entirely guided by the structure of the stimulus field; instead, the participant is advised to scan the grid (random visual search). We calculated the product-moment correlation between the concentration grid scores and the d2 test results in the aforementioned sample of 25 students of sport science to estimate the construct validity of the concentration grid. The analysis revealed a non-significant product-moment correlation of r = .10 (p = .62), indicating that the concentration grid test captures a different aspect of visual focused attention than the d2 test.

#### Procedures

A trained research assistant introduced the experimental tasks to each individually tested participant. The participant was given a practice trial of 20 seconds for the concentration grid exercise (altered version of the original CG1) and a practice trial of two lines for the d2 test to become familiarized with the two experimental tasks. The participant had to perform each of the two tasks under two different experimental conditions, that is, in different environmental contexts (for a total of four experimental phases: d2 test and concentration grid task under normal and auditory distraction conditions, respectively). In one condition no sensory distractions were present. The participant completed the tasks in the quiet laboratory environment. In the other condition an auditory distraction was present. The participant wore headphones that enclosed the whole ear. A mixture of distracting, sport-specific environmental sounds was played back at 90 dB. We used ambient sound recordings of the audience and the players from the last 3 minutes of two first division basketball matches in which both teams played head to head until the end of the match. We compiled the sound recordings to fit the two 1-min periods for the auditory distraction condition (d2 test and concentration grid task) in such a way that the played back sound recording comprised the audience’s and the player’s sounds of three offense and three defense situations. In all tasks the participant sat at a worktable with a head–table distance of 40 cm. The test order was counterbalanced for the participants and the experimental tasks required approximately 20 minutes to complete.

### Results

A significance criterion of α = .05was established for all results reported (9). Prior to testing the main hypothesis, moderating effects of age, sex, and experimental sequence were assessed. We conducted separate analyses of variance on the dependent variables, first, with sex as categorical factor (male versus female), second, with age as continuous predictor, and third, with experimental sequence as categorical predictor (auditory distraction following no distraction versus no distraction following auditory distraction). There were no significant effects of sex, age, or experimental sequence on any of the dependent variables (p < .05).

A correlation analysis indicated that there was no significant product–moment correlation between the concentration-performance score of the d2 test and the number of correctly processed items in the concentration grid task (r = -.01; p = .68), nor between the concentration-performance score and the error rate in the d2 test (r = -.02; p = .47). To assess differences in the dependent variables, we conducted 2 × 2 (Environmental Context × Sport Type) univariate analyses of variance (ANOVAs) with condition being the repeated measure. Post hoc analyses were carried out using the Tukey HSD post hoc test. Cohen’s f was calculated as an effect size for all analyzed F values higher than 1 (6). Additionally, we conducted single sample t-tests to compare our study sample to the age matched normative sample. This was done for each participant’s d2 test performance (concentration-performance score and error rates) but not for the concentration grid task, because norms were available only for the d2 test. Cohen’s d was calculated as an effect size for all analyzed t values higher than 1.

#### d2 Test of Visual Focused Attention

Descriptive statistics for the concentration-performance scores and the error rate of the d2 test are shown in Table 1. First, we assumed that d2 scores would not differ between the two groups reflecting static-sport athletes and dynamic-sport athletes. A 2 × 2 (Sport Type × Environmental Context) ANOVA with repeated measures on the second factor was conducted, taking the concentration-performance score as the dependent variable. The results showed that the two groups did not differ in their concentration-performance scores, F(1, 128) = .004, p = .94, achieved power = .94. Our second assumption was that auditory distraction would have a detrimental effect on concentration performance. To our surprise, the ANOVA revealed a significant main effect for environmental context, F(1, 128) = 66.02, p < .05, Cohen’s f = 0.72, reflecting higher concentration-performance scores for the auditory distraction condition for both dynamic-sport and static-sport athletes (see Table 1). The effect size indicates a large effect (6). Furthermore there was no significant interaction effect for Sport Type × Environmental Context, F(1, 128) = .01, p = .76, achieved power = .98.

To determine if participants from our study sample differed from the general population in concentration performance, we calculated single sample t-tests. The results show that in the normal condition, neither static-sport athletes, t(63) = 1.56, p = .12, Cohen’s d = 0.19, nor dynamic-sport athletes, t(65) = 1.81, p = .07, Cohen’s d = 0.22, differed in their concentration performance from the normative sample’s mean. However, in the auditory distraction condition both groups differed significantly from the normative sample’s mean (static-sport athletes, t(63) = 3.17, p = .002, Cohen’s d = 0.39; dynamic-sport athletes, t(65) = 3.37, p = .001, Cohen’s d = 0.42).

Second, a 2 × 2 (Sport Type × Environmental Context) ANOVA with repeated measures on the first factor was conducted, taking the error rate in the d2 test as the dependent variable. There were no significant main effects, neither for sport type, F(1, 128) = 3.71, p = .06, Cohen’s f = 0.17, achieved power = .61, nor for environmental context, F(1, 128) = 1.50, p = .22, Cohen’s f = 0.11, achieved power = .75. In addition, the interaction effect Sport Type × Environmental Context showed no statistical significance, F(1, 128) = 2.02, p = .16, Cohen’s f = 0.13, achieved power = .95. Dynamic-sport athletes did not make more mistakes on the d2 test in comparison to static-sport athletes, neither in the normal nor in the auditory distraction condition.

To determine if participants from our study sample differed from the general population in error rate, we calculated single sample t-tests. The results show that in the normal condition, dynamic-sport athletes, t(65) = -2.88, p = .005, Cohen’s d = 0.35, but not static-sport athletes, t(63) = -1.41, p = .16, Cohen’s d = 0.17, made on average fewer mistakes than the participants from the normative sample. The same pattern of results was found for participant’s error rates in the auditory distraction condition (static-sport athletes, t(63) = 0.36, p = .71, Cohen’s d = 0.05; dynamic-sport athletes, t(65) = -3.17, p = .002, Cohen’s d = 0.39).

#### Concentration Grid Task

We assumed that concentration grid scores would differ between the two groups reflecting static-sport athletes and dynamic-sport athletes. The second assumption was that auditory distraction would have a detrimental effect on concentration performance. A 2 × 2 (Sport Type × Environmental Context) ANOVA with repeated measures on the second factor was conducted, taking the concentration grid score as the dependent variable. The ANOVA revealed no significant main effects for either sport type, F(1, 128) = 1.40, p = .24, Cohen’s f = 0.11, or environmental context, F(1, 128) = 0.27, p = .60. We assume that we can rely on the two findings because of a test power greater than .90. To our surprise the interaction effect Environmental Context × Sport Type showed statistical significance, F(1, 128) = 4.54, p = .04, Cohen’s f = 0.19. Post hoc analysis revealed that participants in the dynamic-sport group scored higher in the concentration grid task under the auditory distraction condition, whereas participants in the individual-sport group scored lower under the auditory distraction condition, compared to the normal condition (see Figure 1).

### Discussion

The goal of this study was to evaluate two attention tests in sport psychology in terms of their application in athletes who are trained in more visually dynamic sports compared to athletes trained in visually less dynamic sports with regard to different environmental contexts. Visual focused attention was examined with random (concentration grid task) versus fixed (d2 test) visual search in a quiet environment and under auditory distraction (4,15).

The results extend current findings on attention performance of athletes with regard to sport type, environmental context, and task dependency. Dynamic-sport athletes did not differ in their concentration performance from static-sport athletes, neither in the d2 test nor in the concentration grid task under quiet laboratory environmental conditions. This result confirms our first hypothesis with regard to the d2 test and supports the findings of Maxeiner (19). We assume that the different perceptual experience of dynamic-sport athletes does not account for their visual search performance in the d2 test. On the one hand, this implies a fairly stable underlying ability to focus attention in simple tasks when a fixed (structured) visual search is a constraint of the task. On the other hand, it can be speculated that attention abilities manifest themselves in a sport-specific way on a more strategic level when integrating basic (attention) abilities in different skills that are not assessed by the d2 test.

Our second hypothesis was that auditory distraction would have a detrimental effect on attention performance in both the d2 test and the concentration grid task. To our surprise the results of the d2 test indicate higher concentration performance scores for the auditory distraction condition for dynamic-sport athletes as well as static-sport athletes. The scores were not only higher when compared between both experimental conditions but also when compared with the corresponding normative sample of the d2 test. This finding supports the assumptions of Tenenbaum et al. and Wilson, Peper, and Schmid, that visual search performance in unstructured contexts is task dependent, especially under auditory distraction conditions (33,38).

From the viewpoint of Boutcher’s multilevel approach to attention, it seems possible that in the auditory distraction condition the participants’ attentional states were optimized (3). This optimization helped the participants achieve higher scores in the relatively simple d2 test, regardless of their sport type. However, whether the supposed optimization was due to changes in arousal level, changes in controlled or automatic processing, or both, cannot be concluded from our results. In addition, the results of the concentration grid task (where an unstructured visual search is an inherit component of the task) show that participants in the dynamic-sport group scored higher in the auditory distraction condition in comparison to the participants in the static-sport group. Changes in arousal level and therefore in attentional state are known to influence visual control (16,32). It is reasonable that an increased amount and/or increased amplitude of saccades, when scanning the concentration grid, can lead to ignoring the actual target or finding it later than under normal conditions. This could explain the decrease in performance in the concentration grid task for static-sport athletes, because they are normally not trained to deal with such a situation in their sport. To further examine the gaze behavior in performing different attention test, eye-tracking methodology should be integrated into the experimental design.

The increase of the concentration grid scores of the dynamic-sport athletes in the auditory distraction condition could also be explained by differences in information processing. Dynamic-sport athletes seem to be able to allocate their attention capacity to more crucial aspects of the task (37). When scanning the concentration grid they could, for instance, pre-cue remaining numbers in specific areas of the grid in advance, in order to find these numbers faster at a later point in time. However, this aspect is open for further investigation. We assume that dynamic-sport athletes benefit from their sport-specific perceptual experience especially in the concentration grid task under auditory distraction conditions.

We are aware of some critical issues in our design that need to be taken into account in further experiments, and want to highlight three specific aspects. First, the differentiation of dynamic- versus static-sport athletes could be more closely specified. This could be done by examining athletes from different sport disciplines that have different sport-specific structures (e.g., coactive vs. interactive sports). One can, for instance, hypothesize that athletes in coactive sports such as bowling or rowing may differ in their attention ability from athletes of interactive sports such as basketball or soccer due to different task demands. Subsequent analyses could also focus on different team positions, especially in interactive sports. For instance, it is likely that a goalkeeper differs in concentration ability from a playmaker (30,31).

Second, the type of distraction could be more differentiated. Athletes have to deal with different distractions in competition such as comments from the coach and other athletes, or different forms of either expected or unexpected noise. These distractions could have different effects on attention performance. One could, for example, examine the impact on attention performance of different distractions with different structures, such as visual versus auditory distraction with a sport-specific structure versus no structure. One can hypothesize that structured distractions of a sport-specific nature would have no impact on concentration performance at all, because athletes are normally habituated to such distractions. In our study we speculated that the impact of the auditory distraction on the attentional state of the athletes would be to enhance their performance in the d2 test. To control this aspect, measurements of arousal level (e.g., heart rate or galvanic skin response) should be integrated into further studies.

Third, we adopted the concentration grid test as a measure for visual focused attention, because visual focused attention is usually operationalized as visual search (39). Research suggests a close link between working memory capacities and the selectivity dimension of attention (10). We acknowledge that when performing the concentration grid test, a participant could potentially optimize his or her visual search by selectively memorizing the position of stimuli that have to be found after preceding stimuli have been marked. However, participants were not instructed to memorize the position of the stimuli but rather to actively scan the grid and mark as many consecutive numbers (starting from 00) as possible within a 1-min period. Subsequent studies could compare participant’s performance in working memory tests (10), as well as in other tests of visual attention (39), with their concentration grid test scores to evaluate if the concentration grid is more a measure of visual focused attention or working memory.

### Conclusions

The findings of the current study suggest that the results of attention tests should be differentially interpreted if different sport types and different test conditions are considered in the field of applied sport psychology or applied sport science. Their predictive power for sport-specific attention skills, however, may only be seen with regard to different factors such as sport type, environmental context, and task.

### Applications in Sport

There are some practical consequences and implications of this study. First, non-specific concentration tests only seem to be able to differentiate between athletes from more visually dynamic sports and athletes from more visually static sports when they mimic a sport-specific environmental context together with sport-specific demands of the task. Therefore, one may need more specific tests for specific sports to diagnose not only fundamental aspects of attention, but attention abilities on a more strategic level (2). These tests should then be integrated in a systematic talent diagnosis with test norms for specific sports (7). In a talent diagnostic, however, psychological variables remain often unnoticed (1), even if they have been identified as significant predictors of success (27). They could serve as an intrapersonal catalyst in the developmental process of talented youngsters (35). However, their impact on performance may change throughout the development process of the individual. When administering attention tests, this development needs to be taken into account. It is, for instance, questionable whether young gymnasts can be compared to young soccer players in their ability to focus attention, because of different attentional demands in both sports. Second, it would be very useful to conduct longitudinal or to combine analysis of performance in tests with analysis of performance criteria (33). A final issue that should be addressed is the impact of specific interventions on attention performance, especially if attention training is used that is similar to the structure of the concentration test itself (13, 38).

### Acknowledgments

The author thanks Mr. Konstantinos Velentzas and for assistance with data collection and Mrs. Lisa Gartz for her critical and helpful comments on the manuscript.

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### Tables and Figures

#### Table 1
Means (M) and standard deviations (SD) for the concentration-performance scores and the error rate of the d2 test with regard to environmental context and sport type (n=130). The terms of static and dynamic refer to the visual environment in which the athletes from different types of sport usually perform.

	Environmental context
	Normal		Auditory distraction
	M	SD	M	SD
Concentration-performance score
Static sports	137.28^*	69.26	153.05^*⁺	73.93
Dynamic sports	138.59^*	66.34	153.23^*⁺	71.05
Error rate
Static sports	11.21	8.79	13.26	10.72
Dynamic sports	9.52⁺	9.16	9.36⁺	8.72

^* p < .05 (according to Tukey HSD post hoc test).
⁺ p < .05 (according to single sample t-test between the study sample and the corresponding normative sample, cf., 4).

#### Figure 1
![Mean concentration grid performance as a function of sport type and environmental context](/files/volume-14/415/figure1.jpg)
Mean concentration grid performance as a function of sport type and environmental context (error bars represent the standard error of the mean; * = significant difference at p < .05 between experimental and control group according to Tukey HSD post hoc analysis).

### Corresponding Author

Dr. Thomas Heinen
German Sport University Cologne
Institute of Psychology
Am Sportpark Müngersdorf 6
50933 Cologne
GERMANY
Tel. +49 221 4982 – 5710
Fax. +49 221 4982 – 8320
Email: <[email protected]>

### Author’s Affiliation and Position
German Sport University Cologne, Institute of Psychology

U.S. Sports Academy2013-11-25T16:23:44-06:00June 3rd, 2011|Contemporary Sports Issues, Sports Coaching, Sports Management, Sports Studies and Sports Psychology|Comments Off

A Study on the Self-Efficacy of Elite Coaches Working at the Turkish Coca-Cola Academy League

R. Timucin Gencer

### Abstract

As defined by Bandura, self-efficacy is an individual’s belief about her/his ability to perform well in a given situation. The purpose of this study was to determine the levels of self-efficacy amongst elite professional Turkish soccer coaches. One-hundred twenty-three coaches from 41 professional soccer clubs in four different regions of Turkey, training U14 and U15 age groups voluntarily participated in this study. This study used the Coaching Efficacy Scale (CES) comprising four specific efficacies (motivation (ME), game strategy (GSE), teaching technique (TTE) and character building (CBE). According to the total coaching efficacy scale, results suggested that participating coaches’ self-belief in efficacy was at highest levels (M=8.26, SD=.49). Coaches’ self-belief in the sub-scale of character development efficacy was at highest (M=8.60, SD=.54), whereas self-belief in game strategy was at lowest levels (M=8.03, SD=.61). One of the most important findings of the study was that coaches’ self belief in the sub-scale of motivation efficacy differed according to the category in which they work (t=2.049, p<.05). Game strategy efficacy differed significantly according to marital status (t=2.417, p<.05); and type of coaching certificate (t= 2.186, p<.05). A higher degree of self-belief regarding motivation efficacy amongst coaches training young teams compared to professional-level coaches was due to the athletes they worked with. In many cases, it is easier to motivate young players rather than professionals. Coaches’ self-improvement in motivation will definitely have a decisive impact on their success in professional sports.

**Key words:** coaching efficacy, elite coaches, professional sport, soccer

### Introduction

Extensive research about the behavior exhibited by individuals throughout their lives suggests the existence of many factors influencing human behavior. One of these factors is self-efficacy (4,5). The social cognitive theory focuses on how the individual learns new information and behaviors by observing, imitating an individual or by taking the individual as a model (1). This theory suggests that one of the most important roles in the individual expression of personal behavior is the individual’s level of self-efficacy.

First mentioned by Bandura (4), the concept of self-efficacy is defined as one’s belief in his or her own ability to perform a certain type of task. Self-efficacy is specific to a certain task and is dynamic (10,14). In other words, it is open to change over time with new information, experience and learning (14). The individual makes a comparison between expected performance and his or her own capacity (12). In the scope of the concept of self-efficacy, the need for a high degree of self-belief to be successful in a specific behavior stands out as one of the most important factors in exhibiting that behavior.

Sometimes knowledge and skill might not be adequate for successful behavior. On most occasions people may know the correct course of action, yet be unable to act accordingly. Self-efficacy stands out as an important bridge between knowledge and behavior. Personal level of self-efficacy influences an individual’s perspective and behavior toward the action. Positive or negative feedback received by the individual in response to his or her abilities and competence results in the strengthening or weakening of the individual’s own belief in his or her self-efficacy (18). Studies suggest that individuals with high self-efficacy tend to be more resilient in the face of obstacles to accessing sports activities (6). They also have heightened levels of social skills (2) and are more eager to take bigger risks (16,17).

Performance build-up in soccer requires long periods of time. What constitutes the fundamental elements required by soccer training throughout this long process is a topic of enduring discussion (3). The most important issues in this context are accurate organizational structures; correct training models; adequate club facilities; environmental conditions and, maybe more than anything, coaching efficacy. It is stated that the athlete’s learning process becomes much more rapid, efficient and thorough, if the format of competitions and training participated in by children are developed with consideration to their mental, psychological and motor abilities (24). At this point, while it is fundamental for a coach to believe in his or her self-efficacy in the context of building up athlete performance (20), this characteristic demands constant enhancement (19).

Based on the notion that coaches can be perceived as teachers, the Coaching Efficacy Scale (CES), developed by Feltz, Chase, Moritz & Sullivan (8), is the only published scale to date that is used frequently in studies on coaching efficacy (11,16,17). D.L. Feltz, et al., (8) define coaching efficacy as coaches’ self-belief in their capacity to influence an athlete’s level of performance and learning. Consisting of 24 items and four sub-scales, the psychometric characteristics of the scale are supported by exploratory and confirmatory factor analysis (8).

The majority of studies on the topic have been conducted on individuals in the United States. Others include Tsorbatzoudis, Daroglou, Zahariadis & Grouios’s study (22) on professional team coaches in Greece and Gencer, Kiremitci & Boyacioglu’s study (9) on Turkish coaches in the disciplines of basketball, soccer, tennis and handball. This latter concludes validity and reliability findings coherent with Feltz et al.’s study (8). The present study addresses significance in terms of CES examining the self-efficacy levels of Turkish elite professional soccer coaches.

### Method
#### Participants

The study group consisted of 123 coaches working for the U14 and U15 age groups within the Turkish Coca-Cola Academy Leagues, founded in the 2008-2009 soccer season. Coaches actively work for 41 professional soccer clubs distributed amongst five regions established for this league; all participated voluntarily in the study. The sample group participating in the study consisted of males only, with ages varying between 22 and 60 (M=38.6, SD=7.9).

#### Coaching Efficacy Scale (CES)

Data for the study was collected using the Coaching Efficacy Scale (CES) developed by Feltz et.al. (8). Total Coaching Efficacy (TCE) consists of 24 items within four sub-scales including: (a) Motivation Efficacy (ME – 7 items), (b) Game Strategy Efficacy (GSE – 7 items), (c) Teaching Technique Efficacy (TTE – 6 items), and (d) Character Building Efficacy (CBE – 4 items). Items were scored on a 10-point Likert scale ranging from 0 (not at all confident) to 9 (extremely confident), and each item was preceded with a prefix, “How confident are you in your ability to …” The scale contains items such as “How confident are you in your ability to motivate your athletes?” identified by ME; “How confident are you in your ability to understand competitive strategies?” identified by GSE; “How confident are you in your ability to detect skill errors?” identified by TTE; and “How confident are you in your ability to instill an attitude of fair play among your athletes?” identified by CBE.

Scale validity and reliability for the sample of Turkish coaches has been conducted by Gencer et. al. (9). Exactly identical to the original, the Turkish adaptation of the scale, grouped under four sub-scales, reached significantly similar results to the original scale (8) with a variance rate of 59.8%. Although the Cronbach’s alpha coefficients for factors creating the scale were relatively coherent (between .80 and .87) with original scale values, the Cronbach’s coefficient for the entire scale was exactly identical. Values (x²=468,21, df=238, normed chi-square (NC, x²/df)=1.97, p<.05; RMSEA=0.069, S-RMR=0.062, GFI=0.84, AGFI=0.80, CFI=0.91, NNFI=0.89) obtained from confirmatory factor analysis of the scale indicate that the model adapts to data at admissible levels.

### Procedure

Using a face-to-face interview method, researchers personally presented coaches with AYÖ, the Turkish version of the Coaching Efficacy Scale and the scale forms containing questions collecting information on coaches. Researchers provided detailed information to participating coaches about the purpose of the study and how the questionnaire should be completed, although this information was delivered in writing on the documents. Researchers distributed questionnaires on the third day of a training seminar and collected them the same day.

### Data Analysis

Obtained data was subject to t-test using the SPSS 15.0 program in order to clarify whether there was a statistically significant difference between the Total Coaching Efficacy (TCE) and its sub-scales: Motivation Efficacy (ME), Game Strategy Efficacy (GSE), Teaching Technique Efficacy (TTE), and Character Building Efficacy (CBE), or differences among it and age groups, marital status, education level, athletic career, coaching certificate, coaching level and years in coaching. Coaches’ ages, sporting backgrounds and coaching backgrounds were divided in to two groups after taking sample group averages.

### Results

Sample group average age was considered for data analysis and samples were gathered under two age groups, age 39 and less, and age 40 and over. Pursuant to this grouping, 78 (63.4%) of participant soccer coaches were age 39 and under and 45 (36.6%) were age 40 and over. A total of 100 (81.3%) soccer coaches were married and 23 (18.7%) were single. An investigation on coaches’ levels of education indicated that the majority of participating coaches were university graduates (n=77, 62.6%). (Table 1)

All coaches participating in the study played soccer as licensed athletes in their past sports careers. While 47 (38.2%) of the coaches played at an amateur level, 76 (61.8%) of them played at a professional level. An investigation on coaching certificates showed that 87 (70.7%) of the coaches hold UEFA B Licenses while 36 (29.3%) hold UEFA A Licenses. A majority of coaches work for the youth teams of professional soccer clubs (n=95, 77.2%).

Coaches participating in the study had been working in this profession between 1 and 23 years (M=7.87, SD=5.88). The sample group’s average years in the career were considered for data analysis and samples were gathered under two groups; eight years and fewer, and nine years and more. According to this grouping 78 coaches (63.4%) with less than eight years experience, and 45 (36.6%) with more than nine years experience, participated in the study (Table 1).

Coaches’ average belief in self-efficacy was determined to be M= 8.26, SD=.49. The level of Character Building, one of the sub-scales rendering beliefs on self-efficacy, was found to be at highest levels (M=8.6, SD=.54). The Character Building sub-scale was respectively followed by Teaching Technique (M= 8.22, SD= .58), Motivation (M= 8.17, SD= .57) and Game Strategy (M= 8.03, SD= .61) (Table 1).

The t-test results obtained from the study reveal that the efficacy and efficacy-related sub-scales of coaches participating in the study did not differ by age group, level of education, athletic career or years in soccer coaching. However, coaches’ belief in efficacy, when related to the strategy sub-scale, revealed significant difference by marital status (t= 2.417, p=.021) and coaching license (t=2.186, p=.032). Similarly, belief in efficacy when related to the motivation sub-scale differed significantly as well by the category coaches worked in (t= 2.049, p=.046) (Table 1).

Table 2 presents the correlations between total coaching efficacy (TCE) and coaching efficacy sub-scales. Correlations among dimensions of coaching efficacy ranged from 0.46 to 0.80, and correlations of TCE with dimensions of coaching efficacy ranged from 0.75 to 0.92 (Table 2). These relationships are coherent with the hierarchical structure suggested by previous studies (8,16).

### Discussion

Studies have shown that there is a positive relation between individuals’ increasing level of education and occupational efficiency, and that an individual’s contribution to the society was directly proportionate to the level of education. Based on population, Turkey ranked 15th in the world for level of education (7). Approximately 62.6% of coaches participating in our study were university graduates, suggesting that the education levels of these coaches were considerably above the national average.

Besides the high level of education among coaches participating in the study, the fact that most of them (61.8%) had previously played soccer at a professional level, along with the fact that 70.7% held a UEFA B License and 29.3% held a UEFA A License, was perceived as the reason for a considerably high degree of self-efficacy (M=8.26, SD=.49). In 2008, the Turkish Soccer Federation started an initiative to update certificates in accordance with UEFA (Union of European Football Associations) criteria and with this objective gave priority to developing the competence of coaches joining the Turkish Coca-Cola Academy League. Being informed on latest updates and receiving relevant training has contributed positively to the self-efficacy of coaches comprising our study group, and, in comparison with other studies (8,15,23), they presented a higher level of self-efficacy.

When compared to other sub-scales that constitute coaches’ belief in self-efficacy, Character Building was found to be at the highest levels (M=8.6, SD=.54). This finding is supportive of findings from other studies (8, 11, 15, 16, 23) conducted on coaching efficacy. One of the fundamental purposes of establishing the Coca-Cola League was exemplified by the slogan “Good Individual, Good Citizen, Good Athlete.” Bearing this slogan in mind, and considering the group coaches work for, highest levels of perceived self-efficacy in this sub-scale was highly significant. As a matter of fact, Lidor (13) underlined the necessity for ensuring the execution of plans and procedures directed at character-building within sports activities. Considered from a social perspective, character-building is undoubtedly very significant.

The Character Building sub-scale was respectively followed by Teaching Technique (M=8.22, SD=.58), Motivation (M=8.17, SD= .57), and Game Strategy (M=8.03, SD=.61). Mean values determined for these three sub-scales were calculated to be higher than those given in other related studies (8, 11, 15, 16, 23). The positive values, classified under these four sub-scales as the positive values which successful coaches are expected to have, were valuable in terms of their contribution to athletes. Game Strategy-related self-efficacy perception of coaches was identified to be lower than other sub-scales, which is important in regard to game strategy, being a decisive factor in game results.

Obtained t-test results revealed that the efficacy and efficacy-related sub-scales of coaches participating in the study did not differ by age group, level of education, sports career or years in soccer coaching. These findings are unsupportive of Tsorbatzoudis et al.’s finding (22) that, unlike inexperienced coaches, experienced coaches perceive themselves to be technically more competent in terms of coaching experience. However, this condition could be explained by the fact that coaches participating in our study had a higher level of experience. Teams joining the Turkish Coca-Cola League are some of the most elite clubs in Turkey, and these clubs are rigorous in choosing coaches. These two factors were considered to be the reason for such a result.

Coaches’ belief in efficacy related to the GSE revealed significant differences by marital status (t=2.417, p=.021) and coaching certificate ownership (t=2.186, p=.032) (Table 1). Familial responsibilities of married coaches might lead them to believe that they are more competent than do single coaches in the strategy sub-scale. In fact, strategy is very closely related to experience. That coaches with UEFA A License have further experience in the game of soccer than UEFA B License holders might help explain the difference emerging once again in the strategy development sub-scale.

It is interesting to note that belief in efficacy related to the motivation sub-scale differed significantly by the category coaches worked in (t=2.049, p=.046) (Table 1). Youth team coaches having more self-efficacy than professional team coaches in the motivation sub-scale is completely relative to experiences coaches have with soccer players. It is perhaps easier to motivate youth team players aspiring to become professionals for upcoming games than it is to motivate those who have already reached the professional level. Concepts of fame and money that engage in professional sports, after a while, cause a gradual sense of fulfillment, and this presents itself as coaches having difficulty in motivating players. More so, compared with youth team coaches, professional team coaches face further difficulties due to various other responsibilities and diversifying interests of older players. Therefore, considering experiences, it appears logical that youth team coaches perceive themselves to be more competent in terms of motivation than do professional team coaches.

### Conclusion

Besides being well educated, elite soccer coaches participating in the study also had good careers as athletes and coaches, explaining the high degree of self-efficacy among them. It was interesting to see that the degree of GSE, the capacity of directing the team during a game, was higher amongst married coaches than those who were single. It was logical to see a higher degree of GSE in coaches holding a UEFA A certificate compared to UEFA B certificate holders. The most interesting result from the study was the varying degree of motivation among coaches depending on their position. This suggests coaches’ need for knowledge and experience about the concept of motivation increased parallel to the significance of the league they worked for.

### Applications in Sport

Self-efficacy is an effective structure demanding improvement for efficiency from the coach. The fact that this effective structure transforms over time in light of newly acquired information and experiences demonstrates the need for meticulously organized coach training programs and even coach appointments. Respective federations and/or organizations have a great deal of responsibility in this matter.

### Acknowledgments

The author wishes to express his sincere thanks to Assistant Professor Dr. Melih Balyan for his support and cooperation in this study.

### References

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2. Balyan, M. (2009). The comparison of primary school 2nd level and elementary school students’ attitudes towards physical education, social skills and self efficiency levels. Unpublished doctoral dissertation, University of Ege, İzmir.

3. Balyan, M., Vural, F., Arıkan, N., Tunçer, Y. (2009, January). Analysis of some technical and tactical data of the U13 – U14 matches which are played in different field sizes. Poster Session Presented At The Third Soccer and Science National Congress, Antalya.

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5. Bandura A. (1986). Social Foundations of Thought and Action: A Social Cognitive Theory. NJ: Prentice Hall.

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8. Feltz, D.L., Chase, M.A., Moritz, S.E. & Sullivan, P.J. (1999). A conceptual model of coaching efficacy: preliminary investigation and instrument development. Journal of Educational Psychology, 91 (4), 765-776.

9. Gencer, R.T., Kiremitci, O. & Boyacioglu, H. (2009). Psychometric properties of coaching efficacy scale (CES): a study on Turkish coaches. E-Journal of New World Sciences Academy, Sport Sciences, 4 (2), 143-153.

10. Haverback, H.R. & Parault, S.J. (2008). Pre-service reading teacher efficacy and tutoring: a review. Educational Psychology Review. 20, 237-255.

11. Kent, A. & Sullivan, P.J. (2003). Coaching efficacy as a predictor of university coaches’ commitment. International Sports Journal, 7(1), 78-88.

12. Korkmaz, İ. (2002). Sosyal öğrenme kuramı. In B. Yeşilyaprak (Ed.), Gelişim ve öğrenme psikolojisi (pp 197-220).Ankara: Pegem Yayıncılık.

13. Lidor, R. (1998). Development of character through sport activities. International Journal of Physical Education, 35 (3), 91-99.

14. Luthans, F. & Peterson, J.J. (2001). Employee engagement and manager self-efficacy-Implications for managerial effectiveness and development. Journal of Management Development. 21, (5), 376-387.

15. Marback, T.C., Short, S.E., Short, M.W. & Sullivan, P.J. (2005). Coaching confidence: an exploratory investigation of sources and gender differences. Journal of Sport Behavior. 28 (1), 18-34.

16. Myers, N.D., Vargas-Tonsing, T.M. & Feltz, D.L. (2005). Coaching efficacy in ıntercollegiate coaches: sources, coaching behavior, and team variables. Psychology of Sport & Exercise, 6, 129-143.

17. Myers, N.D., Wolfe, E.W. & Feltz D.L. (2005). An evaluation of the psychometric properties of the coaching efficacy scale for coaches from the United States of America. Measurement in Physical Education and Exercise Science, 9 (3), 135-160.

18. Ors, E., Koruç Z. & Kocaekşi S. (2006). Takım Sporlarında Öz-Yeterlik ve Kaygının Cinsiyet İle İlişkisinin Belirlenmesi. Proceeding of The International Sport Science Congress, Turkey, 944-949.

19. Popper, M. & Lipshitz, R. (1992). Coaching on leadership. Leadership & Organization Development Journal, 13(7), 15-18.

20. Tams, S. (2008). Constructing Self-efficacy at work: a person-centered perspective. Personnel Review, 37 (2), 165-183.

21. Toker, H. & Helvacıoğlu, E. (2000). Futbolun sırrı. Bilim ve Ütopya 72, 14-30.

22. Tsorbatzoudis, H., Daroglou, G., Zahariadis, P. & Grouios, G. (2003). Examination of coaches’ self efficacy: preliminary analysis of the coaching efficacy scale. Perceptual and Motor Skills, 97, 1297-1306.

23. Vargas-Tonsing, T.M., Warners, A.L. & Feltz, D.L. (2003). The predictability of coaching efficacy on team efficacy and player efficacy in volleyball. Journal of Sport Behavior. 26 (4), 396-407.

24. Wein, H. (2001). Developing youth soccer, Champaign IL: Human Kinetics.

### Tables

#### Table 1. Descriptive statistics of coaches and t-test results related to the Coaching Efficacy Scale

			Motivation Efficacy		Game Strategy Efficacy		Teaching Technique Efficacy		Character Building Efficacy		Total Coaching Efficacy
	n	%	M	SD	M	SD	M	SD	M	SD	M	SD
Age
39 & less	78	63.4	8.19	.58	8.01	.62	8.28	.60	8.57	.54	8.26	.50
40 & over	45	36.6	8.15	.56	8.06	.60	8.13	.53	8.64	.54	8.24	.48
t-value			.420		-.480		1.439		-.763		.171
Marital Status
Married	100	81.3	8.21	.56	8.1	.6	8.26	.54	8.61	.55	8.29	.48
Single	23	18.7	8.03	.61	7.76	.6	8.08	.72	8.52	.51	8.1	.53
t-value			1.264		2.417^*		1.121		.759		1.627
Education Level
High school & lower	46	37.4	8.17	.57	8.1	.58	8.23	.56	8.63	.54	8.28	.49
University & higher	77	62.6	8.17	.57	8	.62	8.22	.59	8.58	.54	8.24	.49
Sporting Background
Amateur	47	38.2	8.17	.56	7.98	.58	8.18	.62	8.63	.49	8.24	.47
Professional	76	61.8	8.18	.58	8.07	.63	8.25	.55	8.58	.57	8.27	.50
t-value			-.062		-.777		-.593		.537		-.295
Coaching Certificate
UEFA B	87	70.7	8.15	.59	7.96	.62	8.19	.61	8.57	.55	8.21	.50
UEFA A	36	29.3	8.23	53	8.21	.55	8.31	.48	8.66	.50	8.35	.45
t-value			-.731		2.186^*		-1.171		-.883		-1.451
Coaching Level
Youth	95	77.2	8.23	.57	8.03	.61	8.26	.57	8.64	.51	8.29	.48
Professional	28	22.8	7.98	.55	8.04	.61	8.1	.60	8.45	.60	8.14	.50
t-value			2.049^*		-.009		1.258		1.540		1.389
Coaching Background
8 years & less	78	63.4	8.18	.57	8	.62	8.24	.60	8.57	.57	8.25	.50
9 years & more	45	36.6	8.17	.58	8.1	.58	8.19	.54	8.64	.49	8.28	.47
t-value			.087		-.944		.484		-.796		-.337
Total	123	100	8.17	.57	8.03	.61	8.22	.58	8.6	.54	8.26	.49

^* p < .05

#### Table 2. Pearson correlations between dimensions of coaching efficacy and total coaching efficacy

	Game Strategy Efficacy	Teaching Technique Efficacy	Character Building Efficacy	Total Coaching Efficacy
Motivation Efficacy	0.80	0.74	0.60	0.92
Game Strategy Efficacy		0.71	0.46	0.88
Teaching Technique Efficacy				0.75
Character Building Efficacy				0.75
Total Coaching Efficacy				–

p < .001

### Corresponding Author

**R.Timucin Gencer, PhD**
University of Ege
School of Physical Education and Sports
Bornova, Izmir, Turkey, 35100
<[email protected]>
+90 232 3425714 (office)
+90 532 3030610 (mobile)

### Author Bio

R.Timucin Gencer, PhD, is an assistant professor in the Department of Sport Management at the University of Ege. He played basketball as a professional from 1990-1997. He was also the assistant coach of the Turkish National Basketball Team U-16 men who won the European Championship Title in 2005.

U.S. Sports Academy2013-11-25T16:26:52-06:00May 25th, 2011|Sports Coaching, Sports Facilities, Sports Management, Sports Studies and Sports Psychology|Comments Off

A New Test of the Moneyball Hypothesis

Anthony Farrar and Thomas H. Bruggink

### Abstract

It is our intention to show that Major League Baseball (MLB) general managers, caught in tradition, reward hitters in a manner not reflecting the relative importance of two measures of producing offense: on-base percentage and slugging percentage. In particular, slugging is overcompensated relative to its contribution to scoring runs. This causes an inefficiency in run production as runs (and wins) could be produced at a lower cost. We first estimate a team run production model to determine the run production weights of team on-base percentage and team slugging. Next we estimate a player salary model to determine the individual salary weights given to these same two statistics. By tying these two sets of results together we find that slugging is overcompensated relative to on-base percentage, i.e., sluggers are paid more than they are worth in terms of contributing to team runs. These results suggest that, if run production is your objective, as you acquire talent for team rosters more attention should be paid to players with high on-base percentage and less attention to players with high slugging percentage.

**Key words:** Moneyball, strategy, quantitative analysis, economics

### Introduction

It is our intention to show the Major League Baseball (MLB) general managers did not immediately embrace the new statistical methods for choosing players and strategies that are revealed in the 2003 Michael Lewis Moneyball book. In particular we will show that three years after the Moneyball publication, a player’s on-base percentage is still undercompensated relative to slugging in its contribution to scoring runs. This contradicts a study by two economists (3) who claim Moneyball’s innovations were diffused throughout MLB only one season after the book’s publication.

#### Background

In the 2003 publication of _Moneyball_, Michael Lewis (4) describes the journey of a small-market team, the Oakland Athletics, and their unorthodox general manager, Billy Beane. This team was remarkable in its ability to attain high winning percentages in the American League despite the low payroll that comes with the territory of being a small-market team. Lewis followed the team around to discover how they managed to utilize its resources more efficiently than any other MLB team. Moneyball practice included the use of statistical analysis for acquiring players and for evaluating strategies in a way that was allegedly not recognized prior to 2003 by baseball players, coaches, managers, and fans. Central to this statistical analysis is determining the relative importance of on-base percentage versus slugging percentage. By buying more undervalued inputs of on-base percentage, Billy Beane could put together a roster of hitters that would lead them to more wins on the field while still meeting its modest payroll. Although there are many other aspects of Moneyball techniques discussed in the book (e.g. scouting, drafting players, and game strategy), in this paper we will focus on whether a team can increase its on-field performance for a given budget by sacrificing some more expensive slugging performance for more, but less expensive, on-base performance. This is what we will call the Moneyball test: efficiency in the use of resources requires the equality of productivity per dollar for on-base percentage versus slugging percentage.

Hakes and Sauer (3) were the first researchers to use regression analysis to demonstrate at the MLB level just what Beane and Lewis had suggested: 1) slugging and on-base-percentage (more so than batting average) are extremely predictive in producing wins for a team, 2) players before the current Moneyball era (beginning around 2003) were not paid in relation to the contribution of these performances. In particular, on-base percentage was underpaid relative to its value. They used four statistics to predict team wins: own-team on-base percentage, opposing-team on-base percentage, own-team slugging percentage, and opposing-team slugging percentage. The regression coefficients for the team on-base percentage and slugging percentage assign the weight each factor has in determining team wins. A second regression for player salaries assigns a dollar value to each unit of a hitter’s on-base percentage (OBP) and slugging percentage (SLUG). The following statistics were used in player salary equation: OBP, SLUG, fielding position, arbitration and free agent status, and years of MLB experience. They estimated salary models each year for the four MLB seasons prior to the release of the _Moneyball_, and the first season after. The regression coefficients of OBP and SLUG assign the weight each factor has on player salary. By comparing the salary costs of OBP versus SLUG with the effect each factor has on wins the authors determined whether teams are undervaluing OBP relative to SLUG. Their results showed that in the years before the _Moneyball_ book, managers/owners undervalued on-base percentage in comparison to slugging average. In other words, a team could improve its winning percent by trading some SLUG inputs for an equivalent spending on OBP inputs. However, the year after the publication of the _Moneyball_ book, Hakes and Sauer report that on-base percentage was suddenly no longer under-compensated. A team could no longer exploit the higher win productivity per dollar of OBP because now the ratio of win productivity to cost was the same for both OBP and SLUG factors. They concluded that this aspect of Moneyball analysis was diffused throughout MLB.

The speed of this diffusion is surprising, and it does raise questions as to their methodology. For example, what if this test of the Moneyball hypothesis is misdirected? Hitters are paid to produce runs, not wins. A mis-specified statistical model can lead to erroneous conclusions. In this paper we propose a more direct test of the Moneyball hypothesis: comparing the run productivity per dollar of cost for both OBP and SLUG factors. In other words, will an equivalent dollar swap for a small increment of slugging percentage in return for a small increment of on-base percentage lead to the same increase in runs scored? If this is not the case, then a team can exploit this difference and score more runs for the same team payroll by acquiring more units of OBP in place of SLUG units. On the other hand, if the ratios are equal, MLB is in equilibrium with respect to the run productivity for the last additional units of OBP and SLUG.

### Methods

This study differs from Hakes and Sauer in three ways: 1) the focus is on run production rather than win production, 2) the designated hitter difference between the National League and the American League will be controlled, and 3) more recent data from the MLB website is used.

#### Team Run Production Model

An MLB general manager should attempt to gain the most effective combination of the on-base and slugging attributes given the amount of money the MLB team is able to spend. This will maximize the team’s run production subject to its budget constraint. The run production model on a team basis will be of the form:

RPS_it = β1 + β₂OBP_it + β₃SLG_it + β₄NL + e_it

– RPS_it = **number of runs produced by team i in season t.** This takes the total number of runs by each team for the 162 games in a season. If fewer than 162 games are played, this number is adjusted to make it equivalent to a 162 games season.
– OBP_it = **on-base percentage of team i in season t.** This is found by taking the total number times the hitters reached base (or hit a homerun) on a hit, walk, or hit batsman and dividing this by the number of plate appearances (including walks and hit batsmen) for the season. This proportion is then multiplied by 1,000 in order to make it more relatable. For example, a team that reached base 350 times per one thousand plate appearances would have a 350 “on-base percentage.”
– SLG_it = **slugging percentage of team i in season t.** This is the number of bases (single, double, triple, or home run) that a team achieves in a season divided by the number of at bats (excluding walks and hit batsmen). This proportion is multiplied by 1,000 in order to make it more relatable. For example, a team that achieved 175 singles, 40 doubles, 5 triples and 35 homeruns per 1000 at bats would have 410 bases per 1000 at bats and therefore a 410 “slugging percentage.”
– NL_i = **dummy variable = 1 if team i is in the National League, 0 otherwise.** The American League and National League do not have exactly the same set of game rules. One difference is the American League Designated Hitter rule that allows a non-fielding hitter to bat for the pitcher.
– e_it = **random error for team i in season t.** This component allows for the fact that runs produced cannot be perfectly predicted using the above variables.

#### Player Salary Model

The second regression will show how much each of the two statistics, on-base percentage and slugging percentage for individual players, is rewarded by team management for their proficiency in each category. Position dummies were employed but only the catcher and the shortstop had statistically significant increases in pay due to their contributions to fielding. The other dummy variables for position were dropped. The other factor that is included is player experience as measured by lifetime MLB game appearances. The experience factor will appear in quadratic form to allow for diminishing returns toward the end of the player’s career. This model follows the economic literature on salary models starting with Mincer (1974):

M_j = β₁ + β₂G_j + β₃G²_j + β₄OBP_j + β₅SLG_j + β₆CT_j + β₇SS_j + e_i

– M_j = **salary of player j.** 2006 MLB salary in thousands of dollars.
– G_j = **MLB career games played by player j.** This measures the improvement in a player due to experience.
– G_j² = **MLB career games squared.** In conjunction with G, a negative coefficient for G2. This will allow for a diminishing rate of improvement as more and more experience is achieved, and will even permit a decline in performance at the end of a player’s career.
– OBP_j = **on-base percentage of the player.** This is compiled as an average of the 3 MLB seasons prior to the beginning of the season in which the player’s salary is put into effect (2003-2005).
– SLG_j = **slugging percentage of the player.** This is compiled as an average of the 3 MLB seasons prior to the beginning of the season in which the player’s salary is put into effect (2003-2005).
– CT_j = **dummy variable = 1 if the player is a catcher, 0 otherwise.** This variable is included to see if any special value is attributed to this fielding skill position.
– SS_j = **1 if the player is a shortstop, 0 otherwise.** This variable is included to see if any special value is attributed to this fielding skill position.
– NL_i = **dummy variable = 1 if player j is in the National League, 0 otherwise.**
– e_i = **random error.** This component allows for the fact that player salaries produced cannot be perfectly predicted using the above variables.

#### Sample Selection

For the team run production, five seasons of data (2002-2006) are collected for each of the MLB teams, for a total sample size of 150 observations. Descriptive statistics for five years of 16 National League teams and 14 American League teams are given in Table 1. The mean runs scored per team during this time period is 765 per season, or 4.7 per game. The standard deviation is 76 runs, which is saying that from one team to the next the typical difference in runs per season is 76 or about 0.5 runs per game. Of particular note are the means and standard deviations of on-base percentage and slugging percentage. The mean team OBP is 334, with a typical change from one team to another of 12. For SLUG the mean is 423 and the standard deviation is 23.5.

Batting statistics from players are averaged over the course of the last three MLB seasons in order to match recent performance and salary more closely. To be selected as a player in the salary regression, the athlete must play in at least two of the last three MLB seasons (2003-2005) and play in at least 100 games each season. Another important restriction was that all players in the sample needed to have played at least six seasons at the Major League level. Before six seasons, MLB players are unable to become free agents, a very important concern for their salary. As free agents, players are permitted to seek employment from any team, commonly resulting in competitive bidding for the player’s services and a free market determination of wages. With this we have our sample of 154 hitters (free agent eligible starting players). The 2006 salaries of players and their three year MLB performance averages (prior to 2006) are given in Table 2. The highest salary in the sample is $25,681,000 and the lowest is $400,000. The mean salary is $6.2 million with a standard deviation from one player to the next of $4.89 million. The mean OBP for the players is 347, with a typical change of 34 from one player to the next. The average SLUG is 450 with a standard deviation of 65.5.

### Results and Discussion

#### Team Run Production Model

Applying ordinary least squares, the following team runs regression was estimated for the five seasons:

RPS = -908 + 2.85 OBP + 1.74 SLG – 23.0 NL + e

In Table 3 the more statistical details for the above equation (Model 1) and other versions of the run production model are shown. Model 1 is the one used in the Moneyball hypothesis, and it explains 92 percent of the variance in team runs scored. This verifies that team OBP and SLUG are extremely predictive of team runs scored. It should also be noted that the runs scored equation fit is better than the one Hakes and Sauer have for their winning equation. Model 2 drops the dummy for the National League and Model 3 adds interaction terms of NL with OBP and SLG. The differences from the first model are small. This sensitivity analysis confirms that Model 1 is the most appropriate.

We will now interpret each slope coefficient in Model 1, holding the other included factors constant. A 10 unit change in team OBP (e.g., going from 330 to 340), brings an additional 10(2.85) =28.5 team runs scored per season, on the average. A 10 unit change in SLUG brings a 10(1.74) = 17.4 more runs, on the average. Each regression coefficient, including the one for NL, is statistically significant at a 1% level. This identifies the relative importance of each hitting factor. For an incremental 10 unit change, getting on base more frequently has a bigger impact on scoring runs than getting more bases per hit. What is needed now is a determination of what these factors cost the team in salary.

#### Player Salary Model

Applying ordinary least squares the following player salary regression was estimated for the 156 starting free agent players in 2006:

SAL = -30164 + 10.28 G – 0.00321 G² + 37.05 OBP + 36.98 SLG + 1748.1 CT + 2024.87 SS – 876.96 NL + e

In Table 4 the more statistical details for the above equation (Model 4) and other versions of the player salary model are shown. In Model 4 we see the estimated coefficients from the player salary model—the one used in the subsequent test for the Moneyball hypothesis. This model explains 55% of the variance in salaries, roughly the same as the salary equations for Hakes and Sauer. In Model 5 the NL dummy is removed, and in Model 6 the position dummies are removed. There were only small changes in the remaining coefficients compared to Model 4. This sensitivity analysis confirms that Model 4 is the most appropriate.

We will now interpret each slope coefficient of Model 4, holding the other included factors constant. A 10 unit change player’s OBP for increases 2006 salary on average by 37.05(10) = 370.5 ($370,500), and 10 unit increase in a player’s SLUG increases salary on average by 36.98(10) = 369.8 ($369,800). The coefficients for G and G2 show that experience increases salary at a decreasing rate. Both the catchers and shortstops earn higher salaries, holding OBP and SLUG constant, than the other fielding positions. The experience and hitting coefficients are statistically significant at a 1% level. The position dummies are statistically significant at a 5% level. The NL dummy is statistically significant at a 10% level.

#### The Moneyball Hypothesis

In the _Moneyball_ book small market teams like the Oakland Athletics can compete against larger market teams if they can acquire run production factors that provide more runs per dollar spent. This occurs when OBP is undervalued relative to SLUG. To see if this is the case in 2006 we will compare the two main models (Models 1 and 4). A 10 unit increase in team OBP is brings an additional 28.5 runs and a 10 unit increase in team SLUG yields an additional 17.4 runs. The salary equation reveals that a 10 unit increase in individual OBP costs $370,500, and a 10 unit increase in individual SLUG costs $369,800. At essentially the same increase in team salary (at the player level) an increase in OBP brings in 11.1 more runs than SLUG. This means that teams can achieve a higher run production at essentially the same cost by swapping 10 units of SLUG for 10 units of OBP. The ratio of run production to cost favors OBP. The Moneyball hypothesis of slugging percentage being overvalued relative to on-base percentage remains in effect three seasons after the _Moneyball_ book.

Why did our results differ from Hakes and Sauer, who argue that slugging was no longer overvalued one season after the _Moneyball_ book? We repeat our differences in methodology here: 1) using a run production model instead of a winning production model because players are paid to produce runs, not wins; 2) including a variable to differentiate the National League from the American League; and 3) using more recent data.

### Conclusions

In this paper we propose a new test of the Moneyball hypothesis using team run production in place of team wins. We clearly show that in producing runs baseball managers continue to overpay for slugging versus on-base percentage. In the 2006 MLB season, for the same payroll, a team could generate more runs by trading some SLUG for OBP. The question is, why don’t general managers recognize these results in their roster and payroll decisions? We propose several possible reasons:

1. Only small revenue market teams need to be efficient in their labor decisions.
2. Sluggers are paid for more than just their ability to score runs.
3. Moneyball techniques will take time before all teams adopt them.

Each of these answers will now be discussed. Large-revenue market teams are profligate partly in response to the pressure they feel by the fan base to produce a winner at whatever cost. By acquiring well-known free agents at high cost rather than bargain free agents who are not recognized by home fans seems a safe way to operate, even if it cuts into some profits. These well-known players tend to be the sluggers. The second reason for slugger overcompensation is that they are crowd-pleasers, and it may be more profitable (higher gate attendance and television viewership) to have more homerun hitters. This study does not attempt to measure this alternative hypothesis. Finally, Hakes and Sauer believed equilibrium between OBP and SLUG in the player market occurred in just one year after the _Moneyball_ book was published, but it is doubtful such innovation can spread throughout MLB so quickly.

> “Given the A’s success, why hasn’t a scientific approach come to dominate baseball? The answer, of course, is the existence of a deeply entrenched way of thinking….Generally accepted practices have been developed over one-and-a-half centuries, practices that are based on experience rather than analytical rigor.” (1, p. 80)

The behavioral patterns in MLB change slowly. For example, it took twelve years after Jackie Robinson joined the Brooklyn Dodgers before every team in MLB acquired African-American players on their roster, despite the large pool of talent in the Negro Leagues. The slow pace of diffusion can also be claimed for the more recent immigration of Asian players in MLB. And more to the point, batting average still receives more attention than on-base percentage in the evaluation of talent.

Finally, the adoption of Moneyball is not limited to baseball. General managers in hockey (6), basketball (8), football (5), and soccer (2) are beginning to see the same advantages in using statistical analysis to supplement or replace conventional wisdom in making decisions on personnel and strategy. Despite the Oakland Athletics’ more recent lackluster performance, Moneyball is here to stay.

### Applications in Sport

The increased use of quantitative analysis in the coaching and management of sports teams allows colleges and professional teams to make decisions based more on data driven results rather than merely tradition. “Moneyball” is often the term used to convey this decision-making apparatus, particularly when money resources, if allocated efficiently, can improve on-field performance (scoring, wins) on a limited budget.

The advantage of adopting Moneyball techniques before your rival teams may be short term, however, widespread adoption eliminate opportunities (e.g., acquisition of under-rated players) that are not also seen by other teams in your sport. But this study shows that the diffusion of Moneyball techniques is taking place slowly, creating advantages for managers who are open to this approach.

### References

1. Boyd, E. A. (2004). Math works in the real world: (You just have to prove it again and again). Operations Research/Management Science, 31(6), 81.
2. Carlisle, J. (2008). Beane brings moneyball approach to MLS. ESPNsoccer. Retrieved from <http://soccernet.espn.go.com/columns/story?id=495270&cc=5901>
3. Hakes, J. K., and R. D. Sauer (2006). An economic evaluation of the moneyball hypothesis. Journal of Economic Perspectives, 20, 173-185.
4. Lewis, M. (2003). Moneyball: the art of winning an unfair game. New York: W.W. Norton & Company.
5. Lewis, M. (2008) The blind side. New York: W.W. Norton & Company.
6. Mason, D. S. and W. M. Foster (2007). Putting moneyball on ice? International Journal of Sport Finance, 2, 206-213.
7. Mincer, J. (1974). Schooling, experience, and earnings. New York: Columbia University Press.
8. Ostfield, A. J. (2006). The moneyball approach: basketball and the business side of sport. Human Resource Management, 45, 36-38.

### Tables

#### Table 1. Descriptive Statistics for the Team Run Production Sample

	RPS	OBP	SLG	NL
Mean	765.04	332.927	423.27	0.53
Median	760.34	332.000	423.00	1
Standard Deviation	76.43	12.168	23.52	0.50
Range	387.00	63.000	123.00	1
Minimum	574.00	300.000	368.00	0
Maximum	961.00	363.000	491.00	1
Count	150	150	150	150

#### Table 2. Descriptive Statistics for the Player Salary Sample

	G	OBP	SLG	NL	CT	SS
Mean	1146.1	347.3	450.0	0.552	0.130	0.12
Median	1070.5	346.5	446.5	1	0	0
Standard Deviation	462.1	34.0	65.5	0.499	0.337	0.322
Range	2345.0	237.9	432.0	1	1	1
Minimum	385.0	276.1	310.7	0	0	0
Maximum	2730.0	514.0	742.7	1	1	1
Count	154	154	154	154	154	154

#### Table 3. Coefficients for the Team Run Production Models

	MODEL 1		MODEL 2		MODEL 3
Variable	Coefficient s	t Stat	Coefficient s	t Stat	Coefficient s	t Stat
Intercept	-908.00^***	-17.16	941.72^***	-18.46	-861.67^***	-13.73
OBP	2.85^***	11.21	2.69^***	13.22	2.86^***	10.26
SLG	1.74^***	15.42	1.92^***	15.30	1.62^***	10.37
NL	-23.00^***	-6.26			-134.3^*	-1.34
(NL)(OBP)					0.275	1.06
(NL)(OBP)					0.241	0.06
Adj. R-Squared	0.921		0.900		0.923
F	568.9		661.6		343.3

^*** .01 level ^** .05 level ^* .10 level

#### Table 4. Coefficients for the Player Salary Models

	MODEL 1		MODEL 2		MODEL 3
Variable	Coefficient s	t Stat	Coefficient s	t Stat	Coefficient s	t Stat
Intercept	-30164^***	-9.38	-30952^***	-9.67	-27182.6^***	-8.73
G	10.28^***	4.21	10.24^***	4.18	9.75^***	3.95
G2	-0.00321^***	-3.67	-0.00323^***	-3.68	-0.00304^***	-3.42
OBP	37.05^***	3.32	38.08^***	3.39	35.30^***	3.10
SLG	36.98^***	6.47	37.01^***	6.43	33.58^***	5.88
CT	1748.10^**	2.14	1798.21^**	2.19
SS	2024.87^**	2.34	2048.73^**	2.35
NL	-876.96^*	-1.65			-929.14^*	-1.71
Adj. R-Squared	0.557		0.552		0.532
F	28.48		32.39		44.44