2012 – Page 7 – The Sport Journal

Throwing Techniques for Ultimate Frisbee

Evan Winograd, Jack R. Engsberg

### Abstract

The goal of this study was to determine if certain throwing techniques for the sport of Ultimate Frisbee were advantageous relative to other techniques. The defense can attempt to force a thrower to utilize a specific throw; knowing the advantages of different throws can influence a defender’s decision to force the thrower to use a certain throw.

Motion capture was used to monitor the flight of a disc (Discraft Ultrastar 175g) for three throwing techniques. The two main groups of throws were backhand (BH) and forehand (FH) throws, with the forehand throws divided into a closed forehand grip (CF) and a split forehand grip (SF). Sixteen participants were recruited with experience ranging from 3 years to 8 years based on survey. Throws were analyzed with regards to linear velocity, angular velocity, precession, and accuracy. Players threw a total of 45 throws: five throws for all combinations of the three throwing techniques combined with three objectives: accuracy, maximum spin, and maximum velocity. The order of the nine throwing groups was randomized.

Throws were analyzed for linear velocity, angular velocity, precession, and accuracy. Linear velocity was calculated by measuring the distance traveled in the first 0.02 seconds of flight, and angular velocity was measured by calculating the time required for four unique points on the disc to complete one rotation. Precession was measured by calculating the average angular deviation from the average normal plane of the disc, and accuracy was measured by the distance between the center of the disc and the target at closest approach using a quadratic fit to the known flight path.

There was a very strong linear correlation between linear velocity and angular velocity. There was no difference in linear velocity between backhand and forehand throws, although the closed grip forehand had a higher linear velocity than the split grip forehand. Backhand throws had higher angular velocities than forehand throws for a given speed; there was no difference in angular velocity between closed grip and split grip forehand throws. Backhand throws had less precession than forehand throws, and there was no difference in precession between closed grip and split grip forehand throws. There were no statistically significant differences in accuracy between any of the throws.

These results show that backhand throws tend to have more spin and wobble less making the backhand a superior throw. Throws with less spin have greater instability; as a defender, forcing the thrower to utilize a forehand throw would result in a throw with less stability than a backhand throw. Forehand throws did not perform better than backhand throws for any category tested.

Additionally, new players are often taught that the split-grip forehand is a bad throw, and that the closed-grip forehand should be used instead. The results show that the split-grip forehand performs on par with the closed-grip forehand with the exception of maximum velocity. New players should not be discouraged from using a split-grip forehand while learning the mechanics of the forehand, as the only disadvantage is a slight decrease in maximum velocity.

**Key Words:** Forehand, Backhand, Flick, Frisbee

### Introduction

In the sport of Ultimate Frisbee, players use two primary throws: backhand and forehand. My hypothesis, from personal experience, is that backhand throws will wobble less, have less spin, be more accurate, and travel faster than forehand throws. The aim of this study was to determine if one throw had a comparative advantage with respect to linear velocity, angular velocity, precession, and accuracy. Also, the split-grip forehand is often thought of as an inferior throw relative to the closed-grip forehand. The closed-grip forehand is expected to outperform the split grip forehand.

Players must be able to utilize both throws as the defenders can force players to throw one way or the other by positioning their bodies on a certain side of the thrower. As a defender, knowing advantages and disadvantages of each throw can factor into defensive strategies to increase the chances of a disc being thrown with sub-optimal flight characteristics as a result of differing throwing techniques. This may cause a higher incidence of turnovers due to incomplete passes.

Previous research has shown that a disc thrown with less angular velocity will result in a throw with less stability (1). Therefore, whichever throw has higher angular velocities will be the more stable throw and will be more likely to reduce turnovers. The angular velocity of a disc in flight does not have a significant effect on lift and drag coefficients (2).

### Methods

#### Participants

Participants were recruited by open invitation to the St. Louis Ultimate Association and both the Washington University in St. Louis men’s and women’s club ultimate teams. Participants completed a questionnaire to determine experience and skill level. The skill level was a ten point scale with 1=Beginner, 4=Recreational, 7=Competitive college, and 10=Elite. Of the participants, 5 were placed in the ‘elite’ category and 11 were placed in the ‘non-elite’ category. Experience was divided into seven categories: 0-1 years, 1-2 years, 2-4 years, 4-5 years, 5-8 years, 8-15 years, and 15+ years (see Table 1). No participants were excluded from the study and all participants performed the same number of each test.

Testing occurred at the Washington University School of Medicine Human Performance Lab after the participant signed the IRB-approved informed consent form. No financial compensation was provided for participating in the study.

#### Data Collection

Data were collected using a Motion Analysis system consisting of six high-speed Eagle Digital Cameras at 250 Hz and Cortex software. Three cameras were located above and behind the thrower (relative to the direction of the throw) with one camera directly behind the thrower, and the other two cameras located a couple meters to either side. These cameras were focused on the latter portion of the throw. The other three cameras were located in the same arrangement, but above the target, and focused on the volume around the thrower. This set up provided the largest capture volume so the throw would be in view of at least two cameras at all times. Seven reflective markers were used. One marker was placed on the thumbnail of the throwing hand with one marker in the center of the disc and three markers placed approximately five inches from the center marker in a triangular formation. Additionally, one marker was placed adjacent to one of the perimeter markers to provide an asymmetric model. The final marker was placed on a target, used to evaluate accuracy. Participants stood approximately 2.5 meters from the target and net. Participants threw 45 total throws consisting of nine categories: Backhand Accuracy (BH_A), Backhand Spin (BH_S), Backhand Velocity (BH_V), Closed Forehand Accuracy (CF_A), Closed Forehand Spin (CF_S), Closed Forehand Velocity (CF_V), Split Forehand Accuracy (SF_A), Split Forehand Spin (SF_S), and Split Forehand Velocity (SF_V). The order was randomized for each participant prior to data collection. For each category, participants had two practice throws followed by three throws, which were used for analysis.

#### Variables

The following variables were calculated: linear velocity, angular velocity, precession, and accuracy. For each throw, data processing began when the marker on the disc closest to the thumb marker was 0.3 meters away from the thumb marker indicating the disc had left the thrower’s hand. Linear velocity was calculated by computing the distance traveled over the first 0.02 seconds (5 frames). Angular velocity was calculated by tracking the time required for five different pairs of markers to complete one cycle. A cycle began when one marker’s y-coordinate crossed the other marker’s y-coordinate. The cycle ends after the first marker’s y-coordinate crosses the second marker’s y-coordinate twice. The angular velocity was calculated by averaging the times for each of the five pairs. Precession was calculated by calculating the average angular deviation from the average plane of the disc. The angles were calculated by taking the cross product of two vectors defined by two of the perimeter markers and the center marker. Accuracy was calculated by measuring the closest approach of the projected flight path to the center of the target. The flight path projection was calculated using a quadratic fit to the known flight path.

#### Statistical Analysis

The data were analyzed using SPSS Statistics® and Microsoft Excel®. Two sample paired t-tests were used to compare different throws and two sample t-tests (assuming equal variance) were used to compare elite vs. non-elite players. The significant threshold employed was p < 0.05. Regression analysis was used to determine whether correlations existed between linear velocity and both angular velocity and precession. Since angular velocity varied by linear velocity, the ratio of angular velocity to linear velocity was used to determine which throw achieved the highest angular velocities.

### Results

#### Elite vs. Non-Elite

The only difference found between elite and non-elite players was the maximum speed of throws: elite players had higher maximum velocities than non-elite players. There was no difference in accuracy, precession, or angular velocity to linear velocity ratios. With the exception of maximum velocity, no differences were found between elite and non-elite players; as a result, throw comparisons included both elite and non-elite players (see Table 2).

#### Throw Comparison

No significant differences were found between backhand and closed grip forehand or backhand and split grip forehand velocities. Closed grip forehands were found to have higher maximum velocities than split grip forehands. Backhand throws had an average maximum velocity of 20.1 m/s, closed grip forehand throws had an average maximum velocity of 20.6 m/s, and split grip forehands had an average maximum velocity of 19.2 m/s.

Backhand throws were found to have a higher angular velocity / linear velocity ratio than both closed grip and split grip forehands by more than 4 RPM per meter per second. No differences were found in the angular velocity / linear velocity ratio for closed grip forehands vs. split grip forehands (see Figure 1).

When participants were instructed to throw for maximum spin, throws were found to have higher angular velocity to linear velocity ratios than throws for accuracy and velocity; differences of greater than 5.5 RPM per meter per second were found for all three grips (see Table 3).

No correlation was found between velocity and precession.

No differences were found in accuracy for backhand, closed grip forehand, or split grip forehand throws, with average distances varying by less than 0.03 meters (1.25 inches). Backhand throws were found to have less precession than both closed grip forehands and split grip forehands by more than 35%. No differences were found between closed grip forehands and split grip forehands (see Figure 2)

Strong linear correlations were found between angular velocity and linear velocity when considering throws for maximum velocity and accuracy. values of greater than 0.9 were found for all three categories.

### Discussion

This study has limits that should be taken into consideration. First of all, several of the subjects have learned their throwing techniques from the same group of players, so certain efficiencies or inefficiencies in technique may affect results. Secondly, all participants use a closed-grip forehand; closed-grip forehand throws have been practiced by the participants, whereas split-grip forehand throws have not been practiced. Additionally, participants were throwing in a room with expensive equipment; participants may have altered their throws to ensure they hit the net. Accuracy data may have been inconclusive because the target was located 2-3 meters from the thrower. Also, certain throws may be more accurate for shorter distances and less accurate for longer distances. Limitations of being in a confined space may have prevented any significant results related to accuracy. The cameras also had a difficult time of tracking the higher velocity throws (18+ m/s). As a result, flight paths had to be reconstructed from partial data.

Backhand throws appear to be superior to forehand throws due to the higher angular velocity (see Figure 1) and less precession (see Figure 2) than forehand throws. Morrison found that angular velocity increases the stability of the disc (1) as the angular momentum provides gyroscopic stability, so backhand throws should be more stable than forehand throws. There were no differences in maximum linear velocity or accuracy between backhand and forehand throws. The only difference between the two forehand throws was that closed-grip forehands were thrown faster than split-grip forehands (see Table 3). There were no differences in the angular velocity to linear velocity ratio, precession, or accuracy for split-grip and closed-grip forehands.

Angular velocity can be predicted accurately by knowing linear velocity and intent of throw (maximum linear velocity, angular velocity, or accuracy). No predictors of precession were found in the study.

No previous studies have compared flight characteristics of forehand and backhand throws.

### Conclusion

Based on the results obtained, it would be advantageous to force the opposing team to throw forehand throws. Doing so results in throws with less stability as a result of less angular momentum, and more precession. It is possible that lower angular velocity and higher precession could lead to a decrease in distance traveled and stability. Additionally, higher precession values could expose the disc to more drag, causing the wind to affect the throw more.

Based on the results for forehand throws, the only advantage to throwing with a closed grip is the maximum attainable velocity. By using a closed grip, participants did not show any improvement in angular velocity or precession. Thus, the only instance where a closed-grip forehand is advantageous relative to a split-grip forehand is when a player is trying to throw for distance.

The hypothesis that backhand throws would wobble less was shown to be true and that backhand throws would have less spin was shown to be false. The hypotheses that backhand throws would be more accurate and travel faster were not supported by any results.

Overall, it appears that it would be advantageous to force the offense to throw more forehand throws than backhand throws and new players should not be discouraged from learning to throw a split-grip forehand while learning throwing mechanics.

### Applications In Sport

From a strategic standpoint, teams can change defensive strategies to force the opposition to use an inferior throw. Additionally, new players can be taught advantages and disadvantages of different grips. New players are often taught that the split-grip forehand is inferior to the closed-grip forehand, although the only disadvantage of the split-grip forehand is the maximum speed of the throw. For new players, if the split-grip is more comfortable than the closed grip, they will achieve the same angular velocity and precession as a closed-grip throw.

### Acknowledgements

I would like to sincerely thank Dr. Jack Engsberg for making this research possible. He welcomed my research proposal with open arms, having nothing to gain from the study. Jack has a true passion for helping others and I am extremely fortunate to be one of the many persons he has helped. He has guided me through every step of the research process offering invaluable advice along the way. Jack, thank you for being an amazing mentor and great friend.

### REFERENCES

1. Morrison, V.R. (2005). The Physics of Frisbees. Mount Allison University Physics Department.
2. Hummel, Sarah Ann (2003). Frisbee Flight Simulation and Throw Biomechanics. Office of Graduated Studies of the University of California Davis.

### TABLES

#### Table 1
Participant Survey

Age	Years Played	Skill Level
22	4.6	8.1
(1.9)	(2.5)	(1.2)

**Note:** Standard Deviations appear in parentheses below the means.

#### Table 2
Comparison of Elite and Non-Elite Players

	BH		FH
	Elite	Non-Elite	Elite	Non-Elite
Maximum Velocity (m/s)	21.2 (3.0)**	17.7 (2.4)	20.7 (2.7)**	18.3 (1.5)
Accuracy (m)	0.24 (0.13)	0.33 (0.15)	0.25 (0.20)	0.32 (0.19)
Precession (degrees)	2.3 (1.5)	2.6 (1.1)	3.8 (2.0)	3.7 (1.8)
Angular Velocity to Linear Velocity Ratio (RPM per m/s)	44.1 (9.7)	48.0 (6.9)	38.9 (6.5)	38.1 (6.2)

**Note:** BH is backhand, FH is forehand.

** Denotes significantly different from non-elite (p < 0.05)

#### Table 3
Throw Comparison

	BH			CF			SF
	A	V	S	A	V	S	A	V	S
Maximum Velocity (m/s)	—	20.1 (3.2)	—	—	20.6 (2.6)^	—	—	19.2 (2.5)**	—
Accuracy (m)	0.30 (0.14)	—	—	0.28 (0.18)	—	—	0.27 (0.21)	—	—
Precession (degrees)	—	2.4 (1.3)**^	—	—	3.7 (1.7)	—	—	3.8 (2.2)	—
Angular Velocity to Linear Velocity Ratio (RPM per m/s)	40.2 (3.4)‡	40.4 (2.9)‡	47.8 (11.5)‡	36.5 (3.1)†‡	34.8 (2.1)‡	43.8 (11.3)†	37.3 (2.8)†‡	36.0 (2.5)‡	42.9 (7.7)†
	—	42.8 (7.9)**^	—	—	38.4 (7.8)	—	—	38.7 (5.7)	—

**Note:** BH is backhand, CF is closed grip forehand, and SF is split grip forehand. A is accuracy, V is velocity, and S is spin.

** Denotes significantly different from CF (p<0.05), ^ Denotes significantly different from SF (p<0.05)

† Denotes significantly different from V (p<0.05)

‡ Denotes significantly different from S (p<0.05)

### Figures

#### Figure 1
Graph of Angular Velocity vs. Linear Velocity

![Figure 1](/files/volume-15/453/figure-1.jpg)

#### Figure 2
Graph of Precession vs. Linear Velocity

![Figure 2](/files/volume-15/453/figure-2.jpg)

### Corresponding Author

Jack R. Engsberg, PhD
Washington University School of Medicine: Human Performance Laboratory
4444 Forest Park, Campus Box 8505
St. Louis, MO 63108
<engsbergj@wustl.edu>
314 – 286 – 1632

### Main Author

Evan Winograd
Washington University School of Engineering and Applied Science
6985 Snow Way Drive Box 6861
St. Louis, MO 63130
<ewinograd@go.wustl.edu>
713-805-8609

### Author Bios

#### Evan Winograd
Evan Winograd is an undergraduate student studying Mechanical Engineering at the Washington University in St. Louis School of Engineering and Applied Science.

#### Jack Engsberg
Jack Engsberg is a Professor of Occupational Therapy and Neurosurgery at Washington University School of Medicine in St. Louis. His work in the Human Performance Laboratory focuses on rehabilitation for persons with disabilities including cerebral palsy, stroke, scoliosis, spinal deformity, spinal cord injuries, and amputations using high-speed motion capture systems, force plates, electromyography, and an isokinetic dynamometer.

U.S. Sports Academy2013-11-22T22:54:13-06:00January 5th, 2012|Sports Coaching|Comments Off

The Lifestyle and Sport Activity of Secretaries

Jera Zajec, Mateja Videmšek, Maja Meško, Jože Štihec (assist. Jera Zajec, Ph.D., University of Ljubljana, Faculty of Education prof. Mateja Videmšek, Ph.D., University of Ljubljana, Faculty of Sport assist. Maja Meško, Ph.D., University of Primorska, Faculty of Management asoc. prof. Jože Štihec, Ph.D., University of Ljubljana, Faculty of Sport)

### Abstract

#### Purpose
The aim of the study was to analyse the sports activity and lifestyle of secretaries in Slovenia.

#### Methods
A questionnaire with 37 variables was completed by 104 secretaries from different places within Slovenia. We calculated the frequencies and contingency tables, whereas the statistical characteristics were determined on the basis of a 5% risk level.

#### Results
We established that 26% of the secretaries were obese; most of the time secretaries are sitting down, working with their fingers, and are in forced positions. 56% of the secretaries occasionally take medicines; most of their pain occurs in the neck region, of the back, the shoulder region and in the loins; other common problems include insomnia, emotional exhaustion, and headache. The majority of secretaries engage in sporting activities on the weekend and 2 – 3 times weekly; most of them practiced sport in an unorganized way, with their family or by themselves. A good 20% engaged in an organized sport in a sport club or society, where fitness can also be classified. A good 20% practiced sport in an unorganized way, with their friends. It was established that those secretaries who engaged in an unorganized sport activity were accompanied by their friends or family. Those practicing an organized sport were mainly alone.

#### Conslusion
Secretaries who are frequently active often have a lower Body Mass Index (BMI), take painkillers less often or never, and believe that sport has a great impact on their health.

#### Applications in Sports
Sports clubs and associations should prepare appropriate activities for secretaries which will fullfil their interest, health, and wellbeing.

**Key words:** working conditions, wellbeing, health.

### Introduction

Modern professions are completely different from those undertaken in the past. Cutting-edge technology, robotics, and computer science have disburdened the human labour force and thus caused an increase in the demand and supply of office workers (secretaries, administrators, clerks etc.) whose sedentary jobs are characterized by long hours in forced postures. It is clear to see that the working conditions have drastically changed. Besides that, the leisure time and leisure activity preferences have also changed. According to the results of the latest studies, sport and recreation activities are being promoted and are increasingly gaining ground (13). The effects were first seen with highly educated people as they are aware of the potential negative consequences of a sedentary lifestyle, which is why they include a suitable sport activity in their everyday life (7, 9, 10). The fact that Slovenia is among the top European Union (EU) member states in terms of the physical activity of the population is more than encouraging. However, the latest studies show that 37.91% of adult residents of Slovenia are physically inactive (11). Due to the pressure to achieve higher productivity at work, the desire to be promoted and the aspirations for a higher income there is simply not enough time to engage in sport (8). People of different professions find themselves constantly pressed for time.

The work of secretaries is highly specific. Secretaries spend most of their working time in forced postures, sitting in unventilated offices, looking at a computer monitor most of the time, memorising huge amounts of information, and this all burdens them psychically and physically. Due to the many positive impacts of sport on physical, emotional and mental well-being (the condition of being contented, healthy, or successful) and given the nature of their work, it is highly recommended that secretaries engage in a sport activity (12). Long hours of sitting in front of a computer in a bent posture are detrimental to the human body. An appropriate sport activity can alleviate or even eliminate problems caused by a sedentary job (6). What is meant by appropriate sport activity is a recreational physical activity which positively affects both health and well-being (mood, sleep and self-confidence) (1).

This study aimed to establish the correlation between the sport activity of secretaries and some selected healthy lifestyle factors. For this purpose, a sample of secretaries was surveyed to establish the correlation between secretaries’ sport activity and the characteristics of their living environment as well as between the state of their nutrition and the type of their sport activity. We also established the frequency of health problems which precondition secretaries’ active engagement in sport activities.

### Methods

#### Sample of subjects

The sample included 104 randomly selected secretaries from different parts of Slovenia. The sample was selected at the congress of secretaries. The subjects were aged 23 to 61 years, while their average age was 41. Their jobs included personal assistant, business secretary and administrator.

#### Sample of variables

The study was based on a survey questionnaire consisting of 37 questions which enquired about social, environmental and work factors, the frequency and type of sport activity, nutrition, health condition, and psychical well-being (14). The data acquisition process was carried out in compliance with the Personal Data Protection Act. Subject gave informed consent for this study. The study was approved from the Etics Commission.

#### Data-processing methods

The data were processed using the SPSS-15.0 statistical program at the Computer Data Processing Department at the Faculty of Sport in Ljubljana. The basic statistical parameters and contingency tables were calculated. The subprograms FREQUENCIES and CROSSTABS were used for the calculation. The probability of a correlation between the variables was tested by a contingency coefficient. The statistical significance of the differences was accepted at a two-way 5% alpha error level.

### Results

#### Body characteristics

Body weight and height were self-reported. BMI was calculated from those data. Average BMI for secretaries was 23.7, indicating that the secretaries participating in the study had a normal body weight.

#### Working conditions

The secretaries’ working conditions varied (Table 1): sitting, standing – straight, standing – bending, lots of walking, working with fingers, working with hands, frequent forced posture (head and neck, turn of the torso, deep bending posture). Most secretaries spend almost all day sitting on a chair, working with their fingers and are in a forced postures. 10% of them stated these three combinations and 10% the combination of sitting and working with fingers

#### Taking work home

Secretaries often take work home with them. Sometimes they have to finish assignments at home, at other times they bring home their stress, problems, and burdens. Nearly 70% of the secretaries confirmed they sometimes feel the pressures of their work when at home (Figure 1).

#### Secretaries’ current health condition and their taking of painkillers

Most secretaries (57.7%) assessed their health condition as good. As many as 56% of them occasionally take medicines. It is statistically characteristic that those secretaries who take medicines more frequently less frequently engage in a sport activity. We established that nearly 40% of the surveyed secretaries never take any painkillers. Occasional use was reported by 56% and frequent use by 5%.

#### Secretaries’ injuries in the past three months and health problems

91.3% of the secretaries reported no injuries had been sustained in the past three months. The most frequent pains occurred in the neck, shoulder girdle, and the lumbar part of the spine. Also frequently reported were insomnia, emotional exhaustion, and headache. Other pains occur less frequently.

#### Secretaries’ absences from work

We established that 75.5% of the secretaries had not been absent on sick leave in the past six months. In the same period, 17.6% of the secretaries were on sick leave for less than 14 days. The reasons for their sick leave mainly included respiratory diseases (53.3%), care for other family members (16.7%), and injury at work or outside work (6.7%).

#### Secretaries’ assessment of the impact of sport on their health

It was established that the secretaries were aware of the importance of sport activity for their health, as nearly one-half (45.6%) of them assessed the positive impacts of sport on their health as strong, whereas the rest (53.4%) assessed them as very strong.

#### Frequency of engaging in sport

Most of the secretaries engaged in sport on weekends and 2-3 times a week. Only 4.9% of them stated they never engaged in sport (Figure 2). The time most of the secretaries dedicate to sport ranges from 35 minutes to 2 hours.

#### Types of sport activities

It was established that the secretaries engaged in several different sports at a time. The most practiced sports include cycling, fast walking, mountaineering, and swimming; skiing is also popular. One-quarter of the secretaries practice racquet sports. These sports constitute a type of physical activity which one may adapt to one’s momentary well-being and general physical fitness and, what is more, they enable the venting of psychical tensions typical of a secretary’s work. Degenerative changes in the body are not an obstacle to practicing racquet sports.

#### Method of practicing sport

Most of the secretaries practice sport in an unorganized way, with their family or by themselves. A good 20% of them engage in an organized sport in a sport club or society and the same percentage practice sport with their friends in an unorganized way. Racquet sports are undoubtedly among those activities which require only a small financial input and can be practiced nearly everywhere due to the availability of sport facilities and grounds and the fact that they can be modified to suit individual needs. It was established that those secretaries who engaged in a sport in an unorganized way were accompanied by their friends or family. Those who practiced an organized sport were mainly doing it by themselves.

#### Sport inactivity and motives for sport activity and against it

The reasons for sport inactivity lie primarily in the lack of time, fatigue, and lack of motivation, as well as inadequate organization. The motives for sport activity relate to different reasons: practice sport means to relax, maintain and improve one’s health, maintain and improve one’s physical fitness, and have a good feeling from doing something for oneself.

#### Impact of sport activity on well-being

Most of the secretaries who practice sport are more self-confident and efficient in their work. A good mood and relaxation are typical indicators of well-being and the secretaries reported being full of vitality and energy. They also enjoy better sleep after a sport activity. They reported that their tenacity, strength, flexibility, and adroitness have improved. Most of them claimed they were able to better withstand psychological pressures. All but one agreed they were not tired more than usual after engaging in a sport activity. The same was true for pain in the legs. Only three of them thought that pain in their legs was due to sport activity.

#### Employers’ role in the secretaries’ sport activity

Most of the secretaries believed that sport and recreation belonged to the private sphere of each individual. 20% of them thought that their employer should support their sport activity at least morally. The same percentage of secretaries said their employer sponsored sports events and employees’ sport clubs. Only three secretaries wished for sport activities to be included in the work process (exercises in the workplace, recreational facilities in the company). The employers did not award their employees for sport achievements (Figure 3).

The selected variables (14) were cross-checked using contingency tables in the CROSSTABS subprogram of the SPSS statistical package and the results showed a statistically significant correlation between the BMI and frequency of engaging in sport (k = 0.644, p = 0.001). A more frequent engagement in sport conditioned a lower BMI. The differences between taking medication and a frequent engagement in sport were also statistically significant (k = 0.444, p = 0.034). The more physically active secretaries only rarely took painkillers or never. The assessed health condition and frequency of engaging in sport were also statistically significantly correlated (k = 0.490, p = 0.004). A more frequent engagement in sport preconditioned a good health condition. The secretaries’ opinion on the impact of sport on their health and the frequency of engaging in sport were also statistically significantly correlated (k = 0.593, p = 0.002). The physically active secretaries believed that sport had a strong impact on their health.

### Discussion

The World Health Organization (WHO) defines obesity as excessive fat accumulation that presents a risk to health (1977). Women generally have more body fat than men. Men and women whose fat exceeds 25% and 30%, respectively, are obese. The results of our study showed that 26% of the secretaries were obese. In an extensive study involving the adult population of Slovenia, Zaletel Kragelj and Fras (15) established that as many as 40.1% of the individuals surveyed were obese and 38.5% had a normal weight. This leads us to conclude that the surveyed secretaries had a lower BMI than the Slovenian average. With reference to the above, in the future it would be reasonable to establish the ratio between the muscle mass and fat mass.

Good working conditions are certainly an essential element of the better performance of an employee, which is why good employers always strive for a better working environment for their employees (12). It was established in our research that the secretaries mainly work in the following working conditions: sitting, standing – straight or bending, and lots of walking. The study results showed that the secretaries most frequently sit, work with fingers and in forced postures. Due to such working conditions they should do specific gymnastic exercises several times a day to compensate for their long maintained sedentary positions.

Another important finding of our study was the frequency of taking medication. It these research was established that as many as 56% of the secretaries occasionally take medicines. Other researchers have found similar findings (14). In their research was namely established that the majority of people (even 70%) suffer from various intestinal difficulties for several years as a result of taking painkillers such as ibuprofen. They reported taking painkillers all too often.

Our findings about the secretaries’ injuries in the previous three months are encouraging because as many as 91.3% of the secretaries had sustained no injuries in the said period. We established that 75.5% of the secretaries had not been absent on sick leave in the past six months. In the same period, 17.6% of the secretaries were on sick leave for less than 14 days. The reasons for their sick leave mainly include respiratory diseases (53.3%), looking after other family members (16.7%) and injury at work or outside work (6.7%). The predominant diseases in terms of the percentage of absences on sick leave were diseases of the skeleton and bone system and connective tissues, followed by injuries and infections outside work, with injuries and infections at work occupying third place. In women, frequent reasons for an absence include pregnancy and diseases in the prenatal and postnatal periods (2). This is also comparable with the findings of our research.

As regards the secretaries’ current health conditions, it can be concluded that they correspond with the Slovenian average; however, the latter is considerably higher than that in the EU. A comparison with a relevant EU study reveals that Slovenians are more burdened by health problems caused by work. Nearly every second employee reports pain in the back (45.9%), one-quarter (25.7%) complain about frequent headaches and four employees out of ten (38.2%) suffer from muscle pain. The EU averages are considerably lower (3, 5).

The analysis of the secretaries’ opinions about the importance of sport, frequency, type and method of engaging in sport yielded the results presented in the continuation. We assess the secretaries’ opinion about the importance of sport activity as good. An opinion as such is not enough, but the findings show that the secretaries corroborate their views with concrete activities. Namely, 55.7% of them practice a sport between 35 minutes and two hours mainly two to three times a week. In view of the Slovenian average established by Doupona Topič and Sila (4), namely that the Slovenian active population engages in sport 3.25 hours a week on average, we realised that the secretaries can be classified among the physically active population of Slovenia. In terms of the chosen type of sport activity, with the most popular being cycling, fast walking, mountaineering and swimming, this can be compared to the Slovenian average, for women, where high percentages also represented morning gymnastics, equestrian sports and martial arts (4). Most of the secretaries practiced sport in an unorganized way, with their family or by themselves. A good 20% engaged in an organized sport in a sport club or society, where fitness can also be classified. A good 20% practiced sport in an unorganized way, with their friends. It was established that those secretaries who engaged in an unorganized sport activity were accompanied by their friends or family. Those practicing an organized sport were mainly alone. The results of the Slovenian average show that unorganized sport activities are still predominant in Slovenia as 40.2% of people practice sport in this way. Less than 25% of the population practice organized sports (4). We believe that an employee’s opinion about sport and their method of engaging in sport (unorganized) is also influenced by their employer. Most secretaries (59.3%) answered the question about their employer’s support of their sport activity by saying that the employer considered sport activity as a private sphere of life. 25.3% of employers support sport activity at least morally.

### Conclusion

It has been established that sport activity plays an increasingly important role in the everyday life of the secretaries. Due to specificity of their work which exerts psychical and physical pressure on them secretaries are engaging in sport more frequently. This positively affects their well-being, health, general fitness, and lifestyle. In our sample, the frequency of practicing a sport and the time of practice were comparable to and higher than the Slovenian average for adults of the same age. The type of sport activity was also comparable. In our opinion, more attention should be paid to the organization of sport activities as the majority of secretaries engage in an unorganized physical activity. It was also established that the secretaries hoped for some organized types of sport that would be provided by their employers. The latter insufficiently support their secretaries’ sport activity. Most of them believe that sport is a private sphere of life, not part of work. They support sport activity only morally as they mainly fail to award sport achievements, sponsor sport events or include sport activities in the work process.

### Applications In Sport

The secretaries are aware of their work, presumptions, and life. They proved this with their low rate of absences on sick leave. They should be offered more possibilities for engaging in organized sport activities and be supported by their employers financially, not only morally. Consequently, they will reduce their excessive use of painkillers and alleviate the pain in their neck, lumbar part of the spine and shoulder girdle, which are consequences of the frequent forced postures they must adopt. At the same time, they will also improve their psychical, physical, and social life.

### Acknowledgments

Authors agree that this research has non-financial conflicts or interest. This includes all monetary reimbursement, salary, stocks, or shares in any company.

### References

1. Backović Juričan, A., Kranjc Kušlan M., & Mlakar Novak, D. (2002). Slovenia on the move project – move to health. International conference: Promoting health through physical activity and nutrition. Radenci: 68-70.
2. Bolniški staž. [Sickness absence of the job]. Retrieved August 5, 2010, from Institute of Public Health of the Republic of Slovenia, Web site: <http://www.ivz.si/Mp.aspx?ni=78&pi=6&_6_id=52&_6_PageIndex=0&_6_groupId=2&_6_newsCategory=IVZ+kategorija&_6_action=ShowNewsFull&pl=78-6.0>
3. Dobre delovne razmere v Sloveniji ogrožata visoka stopnja delovne intenzivnosti in zdravstvene težave, ki jih povzroča delo. [Good working conditions in Slovenia threatens a high degree of labor intensity and health problems caused by work]. Retrieved May 17, 2009, from Eurofound, Web site: <http://www.eurofound.europa.eu/press/releases/2007/070917_sl.htm>.
4. Doupona Topič, M., & Sila, B. (2007). Oblike in načini športne aktivnosti v povezavi s socialno stratifikacijo [Types and methods of sport activity in relation to social stratification]. Šport, 3: 12-16.
5. Gibson, S., Lambert, J., & Neate, D. (2004). Associations between weight status, physical activity, and consumption of biscuits, cakes and confectionery among young people in Britain. Nutrition Bulletin, 4: 301.
6. Görner, K., Boraczyński, T., & Štihec, J. (2009). Physical activity, body mass, body composition and the level of aerobic capacity among young, adult women and men. Sport scientific and practical aspects, 2: 5-12.ž
7. Meško, M., Videmšek, M., Štihec, J., Meško Štok, Z., & Karpljuk, D. (2010). Razlike med spoloma pri nekaterih simptomih stresa ter intenzivnost doživljanja stresnih simptomov. [Gender differences in some symptoms of stress and intensity of experiencing stress symptoms] Management, 2: 149-161.
8. Mlinar, S., Štihec, J., Karpljuk, D., & Videmšek, M. (2009). Sports activity and state of health at the casino employees. Zdravstveno varstvo, 3: 122-130.
9. Mlinar, S., Videmšek, M., Štihec, J., & Karpljuk, D. (2009). Physical activity and lifestyles of Hit casino employees. Raziskave in razprave, 3: 63-88.
10. Morabia, A., & Costanza, M.C. (2004). Does walking 15 minutes per day keep the obesity epidemic away? American Journal of Public Health, 3: 437-440.
11. Sila, B. (2007). Leto 2006 in 16. študija o športnorekreativni dejavnosti Slovencev [Year 2006 and the 16th study on sport-recreational activity of Slovenians]. Šport, 3: 3-11.
12. Videmšek, M., Karpljuk, D., Meško, M., & Štihec, J. (2009). Športna dejavnost in življenjski slog oseb nekaterih poklicev v Sloveniji. [Sports activities and lifestyle of some employers in Slovenia]. Ljubljana: Faculty of sport, Institute for kineziology.
13. Videmšek, M., Štihec, J., Karpljuk, D. & Starman, A. (2008). Sport activity and eating habits of people who were attending special obesity treatment program. Collegium antropologicum, 3: 813-819.
14. Zajec, J. (2006). Povezanost športne dejavnosti tajnic z izbranimi dejavniki zdravega načina življenja. (Unpublished bachelor’s thesis). Ljubljana: Faculty of sport.
15. Zaletel-Kragelj, L., & Fras, Z. (2005). Stanje gibanja za zdravje pri odraslih prebivalcih v Sloveniji [The status of the exercise for health of adult population of Slovenia]. In: Expert conference ‘Exercise for Adults’ Health – status, problems, supportive environments. Ljubljana: Institute of Public Health of the Republic of Slovenia, 23-26.

### Tables

#### Table 1
Secretaries’ working conditions

Working conditions	Frequency	Percentage
Sitting	101	97.1
Standing – straight	11	10.6
Standing – bending	4	3.8
Lots of walking	28	26.9
Working with fingers	54	51.9
Working with hands	35	33.7
Frequent forced posture (head and neck, turn of the torso, deep bending posture)	40	38.5

#### Table 2
Types of sport activities

Sport	Frequency	Percentage
Cycling	53	57
Fast walking	47	50.5
Swimming	32	34.4
Mountaineering	32	34.4
Skiing	28	30.1
Racquet sports	25	26.9
Dancing	22	23.7
Rollerblading	18	19.4
Aerobics	17	18.3
Morning gymnastics	13	14
Yoga	8	8.6
Volleyball	7	7.5
Pilates	4	4.3

### Figures

#### Figure 1
Percentage of feeling the pressures of work at home

![Figure 1](/files/volume-15/452/figure-1.jpg)

#### Figure 2
Percentage of engaging in sport

![Figure 2](/files/volume-15/452/figure-2.jpg)

### Corresponding Author

assist. Jera Zajec, Ph.D.
University of Ljubljana
Faculty of Education
Kardeljeva ploščad 16, 1000 Ljubljana, Slovenia, Europa
<jera.zajec@pef.uni-lj.si>
gsm: 0038640757335

Jera Zajec, Ph.D. is the assistant professor in Faculty of Education in Ljubljana. She is a member of sport cathedra. Her bibliography contains article all over the word. Her interests in researching are wilde and contains development in motopedagogic for preschool children to adults.

U.S. Sports Academy2013-11-22T22:54:24-06:00January 5th, 2012|Contemporary Sports Issues, Sports Exercise Science, Sports Studies and Sports Psychology, Women and Sports|Comments Off

Acute Effects of Combined Elastic and Free-weight Tension on Power in the Bench Press Lift

Sarah Prejean, Lawrence W. Judge, PhD, CSCS, Tiffany J. Patrick, David Bellar, PhD, CSCS

### Abstract

The present study investigated the acute effects on power following the bench press exercise with a combination of elastic band and free-weights vs. free weight only. Eight college-aged males and females participated in this study. All 8 subjects were college track and field athletes that participated in throwing events. The participants performed two bench press training sessions that consisted of three sets of five repetitions. One session used a combination of elastic band (15% of total resistance) and free-weight exercise (85% of total resistance), while the other session consisted only of a free-weight exercise (100%). Power was measured twice at 50% of their one repetition maximum (1 RM) at the conclusion of each lifting session. Analysis via repeated measures Ancova (Treatment by Time covaried for gender) revealed a significant effect for Time (F= 5.951, p=0.05) and a significant two way interaction for Treatment*Time (F=54.093, p<0.001). The present investigation demonstrated an initial power measurement that was greater for the combined group rather than the free-weight only group. This information is potentially beneficial for many different groups of trainee’s.

**Key Words:** Elastic tension, Strength Training, Acute Training Effect

### Introduction

Recently, there have been a number of investigations that have assessed the impact of combined elastic band and free-weight exercise. These bands have been shown to provide predictable variable resistance when applied to free weight exercises such as the back squat and bench press (5,7). Exercise professionals are continually trying to discover novel ways to increase strength and power gains. Wallace et al. (12) demonstrated that power was acutely increased in the back squat exercise with the addition of elastic tension. It was suggested from this research that an 80% free-weight/20% elastic tension ratio might be optimal. Stevenson et al. (10) also found that the combination of elastic band and free-weight exercise during the back squat can significantly increase rate of force development. Experienced power lifters and strength and conditioning professionals have claimed elastic band resistance combined with traditional training produces strength gains for several years (4,8,9). Anderson et al. (1) demonstrated an increase in the bench press and squat exercise strength after training with the addition of elastic tension for an athletic population. In this study, the back squat 1-RM improvement was nearly three times higher for the combined group. In addition, the bench press increase was doubled for the combined group. Furthermore, the combined group’s lower body average power increase was nearly three times better than the free-weight only group. Anderson et al. (2008) used the 80/20 ratio that was suggested by earlier studies. Anderson’s study demonstrated that combined elastic band and free-weight exercise was a viable option to use to train experienced lifters. That study also demonstrated that the group using the combination exercise experienced slightly less resistance at the bottom of the movement when the joints may be under maximal stress in free-weight training. Thus, band training may also provide reduced risk in back squat and bench press exercises.

Triber et al. (11) concluded that the combination of elastic and free-weight exercise provided beneficial effects on strength and functional performance in college-level tennis players. The experimental group experienced significant gains in both internal and external rotation torque. That same study concluded that an elastic band training program strengthened the rotator cuff muscles of collegiate baseball pitchers (11). Band training has the unique ability to target specific muscles, which can be beneficial for numerous sports teams. Using a combination of elastic band and free-weight exercise can also mimic the strength curve of most muscles better. A muscle’s strength curve denotes the alteration in strength of that muscle during the entire range of motion in a certain movement (13). Along these lines, it has been reported that combined elastic and free-weight exercises provided greater force during the first 25 percent of the eccentric phase and last ten percent of the concentric phase of a lift as compared to free-weights alone (3).

Elastic tension has also been reported to impact the neuromuscular performance. Page and Ellenbecker (6) claim that elastic band exercise imparts a higher neuromuscular control resulting in improved balance, gait and mobility. As stated, the gains resulting from the combination of elastic band and free-weight exercise are abundant and the use of this treatment is growing among professionals; though the acute effects on power have yet to be documented. Therefore, the purpose of the present investigation was to determine how if at all, combined elastic tension applied to a normal bench press training session affects power.

### Methods

The present investigation was approved by the local institutional review board and employed a within subjects design, with random assignment. The participants gave informed consent prior to participating and included: four male (age: 20.5±2.1yrs, height:1.82±0.07m, weight: 112.68±15.03kg) and four female (age: 19.9±1.7yrs, height: 1.76±0.05m, weight: 100.78±28.47kg) college track and field athletes involved in the throwing events (shot put, discus, hammer). The participants performed in a counterbalanced within-subjects design, two bench press training sessions that consisted of 3 sets of 5 repetitions at 85% of their 1-RM. The athletes had recently undergone a 1-RM assessment as part of practice; which was supervised by the research team and the weight selected for the treatment was based on this assessment.

One session consisted solely of resistance provided by a standard Olympic barbell with plates, which equated to 85% of the athletes previously determined one repetition maximum, the second session consisted of combined resistance where 85% of 1 RM was derived from 85% tension provided by an Olympic barbell with plates and 15% provided by Elastic Bands (Jump Stretch Inc., Youngstown, OH.). The 85% free weight and 15% elastic tension treatment was based upon previous research performed in our laboratory that suggested that this was an appropriate split for effective training between the isotonic tension provided via free weight and variable resistance by the elastic bands (2).

Immediately after the training sessions, the participants were asked to bench press 50% of 1RM at maximum velocity, in order to generate the greatest amount of watts possible. The participants performed two lifts at 50% of 1RM after each treatment, separated by a rest period of 90 seconds. The two sessions were separated by a 72 hour wash out period as to avoid undo fatigue affecting the results. The order of treatment was randomized so that half the participants lifting under the combined elastic band and free weight condition went first, with the other half lifting in the free weight only condition went first. During the second visit the participants lifted under the other treatment.

Instruments

Power was measured twice, with a minimum of 90 sec rest between measurements at 50% of 1-RM, following the conclusion of both lifting sessions, using a Max Factor tether type potentiometer (Max Rack Inc, Columbus, OH.). This instrument demonstrated reliability in pilot testing with Intraclass correlations of greater than 0.99 on repeated measures testing.

Statistical Analysis

Results of the present investigation were analyzed via a treatment (Combined free-weight and elastic tension vs. free weight only) by time (attempts 1,2) repeated measures Ancova (covaried for gender). The inclusion of the covariate was necessary based upon the natural differences in strength that existed between the male and female athletes in the present investigation. All statistical tests were performed with the use of a modern statistical software package (SPSS ver 17.0 for Macintosh). The criteria for statistical significance was set a priori at alpha <0.05.

### Results

Intraclass correlation analysis suggested good reliability on all measures for the present investigation (>0.99). Analysis performed via repeated measures Ancova (Treatment by Time covaried for gender) revealed a significant main effect for Time (F= 5.951, p=0.05) and a significant two way interaction for Treatment*Time (F=54.093, p<0.001).

The subjects initial measurements of power immediately following the training session was higher in the combined elastic treatment (437.5+34.89 watts) as compared to the free-weight only condition (391.88+41.01 watts). (see Table 2)

### Discussion

The current study extended previous studies by using both male and female participants that were college track and field athletes. All 8 subjects were involved in throwing events and therefore trained regularly with resistance exercises such as a bench press with the involvement of both elastic and free-weight training. The present investigation revealed a differential response in power following training sessions that utilized combined elastic and free weight tension as compared to free weight only.

Affects have been seen with a combination of elastic band and free-weight tension in the past. Bellar et al. (2011) reported around a 5lbs increase in 1RM bench strength after only 3wks of training with a combination of elastic bands and free weights. Anderson et al. (2008) reported changes in power production with athletes who utilized a combination of elastic and free-weight tension. The current study builds upon these findings and notions by experts in the field (Mannie 2005, Simmons, 2007) who suggest adding elastic tension can have acute effects. Based upon these data, during the course of an upper body lifting session it appears that athletes are able to maintain more power when training with a combination of elastic tension and free-weights.

The recorded power was notably different between the sessions that used a combination of an Olympic barbell and an elastic band and those that only used an Olympic barbell. The difference between the two separate 50% 1-RM power assessments for the combination group was only 1 watt, while the difference between the free-weight only group was close to 46 watts. This finding is notable as the attempts post combined training were essentially identical, whereas the first attempt under the free weight only treatment was lower than the second by 46 watts. This suggests that the free weight only treatment may have acutely resulted in a reduction in power production capability that was washed out by the second attempt. The first power output between the two treatments differed by almost 35 watts. After the 90 second rest, the second power output of each group was extremely close, differing by 10 watts. The initial measurement of power following the training was higher for the group that performed the bench press with the combination of the elastic band and the free-weight, but the two different groups seemed to retain the same amount of power at the end. The overall results of the study suggest that in the immediate period following bench press training, athletes who use combined elastic and free weight tension will be better suited to activities that rely on greater power production, such as throwing a shot put. This finding is important as coaches often pair activities in complex training schemes.

### Conclusions

The present investigation has shed light onto the acute affects of combining elastic tension with free-weight exercise on power production in athletes. Further research should continue to explore the effects of power, strength, rate of force development, velocity, eccentric activity and neuromuscular stimuli when performing combination activities with both elastic band and free-weight exercises. It is plausible that given the data from the present investigation, chronic adaptations to training with elastic resistance in combination with free-weights may have been caused by lesser reductions in power during acute training sessions. If this acute effect does manifest in this fashion, then it would have ramifications as to the training volumes athletes utilize with this modality to gain maximum adaptations. The current research on the topic of combining elastic and free weight training is very limited and mostly focused on the back squat and bench press. Hence, investigations and applications on diverse exercises should be considered in forthcoming research.

### Applications In Sport

Based upon the present investigation, it would immediately appear at the conclusion of a training session that athletes retain more power production post combined elastic and free-weight training as compared to free-weight training alone. This information is potentially beneficial to professionals who work with athletes, as complex training is often incorporated into the program design. This form of training often involves the performance of a skill related activity post-resistance training bout.

### Tables

#### Table 1
Participant characteristics given in Means ± SD.

Gender	Age (yrs)	Height (m)	Weight (kg)
Male (n=4)	20.5 ± 2.1	1.82 ± 0.07	112.68 ± 15.03
Female (n=4)	19.9 ± 1.7	1.76 ± 0.05	100.78 ± 28.47

#### Table 2
Watts Produced by Treatment and Attempt given in Means ± SD.

Treatment	Attempt 1 (Watts)	Attempt 2 (Watts)
Combined Elastic and Free-weight	426.5 ± 257.0	427.5 ± 229.2
Free-weight Only	391.9 ± 206.3	437.5 ± 242.6

### References

1. Anderson, C.E., Sforza, G.A., Sigg, J.A. (2008) The effects of combining elastic and free weight resistance on strength and power in athletes. Journal of Strength and Conditioning Research, 22(2), 567-574.
2. Bellar, D., Muller, M., Ryan, E.J., Bliss, M.V., Kim, C-H, Ida, K Barkley, J.E., Glickman, E.L. (2011) The Effects of Combined Elastic and Free Weight Tension vs Free Weight Tension on 1 RM Strength in the Bench Press. Journal of Strength and Conditioning Research, 25(2), 459-463.
3. Israetel, M.A., McBride, J.M., Nuzzo, J.L., Skinner, J.W., Dayne, A.M. (2010) Kinetic and kinematic differences between squats performed with and without elastic bands. Journal of Strength and Conditioning Research, 24(1): 190-194.
4. Mannie K. Strike up the band training, the benefits of variable resistance. (2005) Coach Athletic Director, 75, 8-13.
5. Neelly, K., Carter, S.A., Terry, J.G. (2010) A study of the resistive forces provided by elastic supplemental band resistance during the back squat exercise: a case report. Journal of Strength and Conditioning Research, in press. Epub ahead of print retrieved June 20, 2011, from <http://journals.lww.com/nscajscr/Abstract/2010/01001/A_Study_Of_The_Resistive_Forces_Provided_By.119.aspx>
6. Page, P., & Ellenbecker, T. S. (2005). Strength Band Training. In Strength Training with Elastic Resistance [Excerpt]. Retrieved from Farnsworth Group website: <http://www.champaign411.com/sports_fitness/excerpts/strength_training_with_elastic_resistance>
7. Shoepe, T.C., Ramirez, D.A., Almstedt, H.C. (2010) Elastic band prediction equations for combined free-weight and elastic band bench presses and squats. Journal of Strength and Conditioning Research, 24(1), 195-200.
8. Simmons, L. (2007, March 5). Advanced programs for beginners. In Elite Fitness Systems [Article]. Retrieved March 22, 2011, from Elite Fitness Systems website: <http://totalphysiqueonline.com/2007/03/05/advanced-program-for-beginners/>
9. Simmons, L. (2009, July 15). Training athletes vs. full meet powerlifters [Web log post]. Retrieved from <http://www.wannabebig.com/training/powerlifting-and-functional-strength-for-athletics/q-a-with-westside-barbells-louie-simmons/>
10. Stevenson, M. W., Warpeha, J. M., Dietz, C. C., Giveans, R. M., & Erdman, A. G. (2010). Acute effects of elastic bands during the free-weight barbell squat exercise on velocity, power, and force production. Journal of Strength and Conditioning Research, 24(11), 2944-54.
11. Treiber, F. A., Lott, J., Duncan, J., Slavens, G., & Davis, H. (1998, July). Effects of theraband and lightweight dumbbell training on shoulder rotation torque and serve performance in college tennis players. Am J Sports Med, 26(4), 510-15.
12. Wallace, B.J., Winchester, J.B., McGuigan, M.R. (2006) Effects of elastic bands on force and power characteristics during the back squat exercise. J. Strength Cond. Res., 20(2), 268-27.
13. Woodrup, J. (2008). Band Training for Explosive Vertical Gains. In Vertical jumping [Article]. Retrieved March 22, 2011, from Vertical Jumping website: <http://www.verticaljumping.com/band_training.html>

### Corresponding Author

David Bellar
225 Cajundome Blvd
Department of Kinesiology
University of Louisiana Lafayette
<dmb1527@louisiana.edu>

### Author Bios

#### Sara Prejean

Sarah Prejean is an undergraduate student studying exercise science in the department of kinesiology at the University of Louisiana at Lafayette

#### Lawrence Judge

Lawrence Judge is an associate professor and coordinator of the graduate coaching program at Ball State University. Dr. Judge has a long-established background in coaching track and field athletes and an extensive research background in coaching behavior, moral issues, and competitiveness versus participation in athletics, specifically in youth sports.

#### Tiffany Patrick

Tiffany Patrick is an undergraduate student studying exercise science in the department of kinesiology at the University of Louisiana at Lafayette

#### David Bellar

David Bellar is an assistant professor and director of the human performance lab in the department of kinesiology at the University of Louisiana at Lafayette. Dr. Bellar has a background in coaching track and field athletes, and researching performance attributes within this population.

U.S. Sports Academy2013-11-22T22:55:06-06:00January 4th, 2012|Contemporary Sports Issues, Sports Coaching, Sports Facilities, Sports Management|Comments Off

NBA Gambling Inefficiencies: A Second Look

William Compton, Kevin Sigler

### Abstract

Our study used the log likelihood ratio methodology proposed by Even and Noble (2) to test the market efficiency of both point spread betting and totals betting for consecutive National Basketball Association (NBA) seasons from 2000–01 to 2007–08. It was motivated by recent contradictory evidence that both support and reject opportunities to exploit inefficiencies in NBA gambling by Paul and Weinbach (9, 11) as well as other evidence suggesting that these opportunities fade as the market responds to new information (12).

Based on the results of over 10,000 games in eight consecutive NBA seasons, betting the over on the total points per game is a fair bet, indicating an efficient market. For the higher totals (totals 211-220), the winning percentage on betting the over was above 52.38% (the percentage necessary to cover commissions) in eight of 10 cases, but the null hypothesis of a fair bet could not be rejected. The results for point spread betting also showed strong support for an efficient market in NBA gambling, with one exception: betting the home underdog was profitable for underdogs of 10 points or more. However, this was only true for a very small sub-sample and the inefficiency fades in the most recent sample period.

The few cases of big home underdogs beating the spread are consistent with the model of spread betting where bookmakers exploit the uninformed investor’s home favorite bias, shade the point-spread and maximize profits by betting on the underdog (7,6). Informed bettors may also bet the underdog but will not drive the point spread to the true value but only to the point where the probability of winning is no more than 52.38% (11). While bookmaker’s point shading activity is constrained by the action of informed bettors, the persistence of profit opportunities in a very small sub-sample can be explained by betting market constraints such as low limits on bets and the relative volume of bets placed by informed and uninformed bettors (9).

**Key Words:** point spreads, totals, National Basketball Association, NBA, gambling

### Introduction

Studies of market efficiency in sport betting are similar to those in the financial markets for good reason. Both markets involve many market participants and large sums of money, both involve informed and uninformed traders, market frictions, asymmetric information, and, as the weight of the evidence shows, both are heavily influenced by market psychology. In both markets, however, claims of abnormal returns and profitable strategies always raise a red flag. Like the anomalies literature in financial markets, claims of exploitable inefficiencies must be validated with out-of-sample tests to confirm that these inefficiencies are not confined to specific periods, or are driven by a few outliers in the data, or are simply artifacts of extensive data mining. Sport betting provides a unique test for market efficiency since the payoffs are known with certainty in advance of the outcome and the final outcome is determined when the game is played. This is not the case with equity investing (1).

The market for sports betting consists of a market maker, called a bookmaker or sports book, and a bettor. The bookmaker establishes the lines at which betting commences and then moves the line as bets are wagered on both sides of the line. Bettors typically pay the bookmaker $11 to win $10, providing the bookmaker a commission profit if money on both sides of the bet are balanced. Because of this commission, commonly called the “vig” or “juice”, bettors must win 52.38% of their bets to break even. A winning percentage greater than 52.38% insures a profit for the bettor. Recent evidence using data on dollars wagered has rejected the claim that bookmakers strive to balance the dollar on both sides of a wager and lends support to the argument that bookmakers attempt to set the line to accurately reflect actual game outcomes (6,7,11).

In the sports gambling world, an over/under or totals wager is a bet that is won or lost depending upon the combined score of both teams in a game. A bookmaker will predict the combined score of the two teams and bettors will bet that the actual number of points scored in the game will be higher or lower than that combined score. For example, in an NBA game of the Miami Heat versus the San Antonio Spurs the over/under for the score of the game was set at 195. A bet on the under wins the wager if the combined score at the end of the game is 194. If the combined score is 196 or more, then the over bet wins. If the combined score equals 195, then it is a tie and the bettor’s money is returned.

### Data And Methodology

This study was designed to test for the presence of exploitable inefficiencies in NBA sport gambling. Recent research in NBA gambling has produced evidence of over betting the over in totals betting, and over betting the favorite by uninformed bettors in point spread betting. The research also claims that there are profitable opportunities in betting the big underdog. This study tests those claims by examining both totals betting and point spread betting using updated data.

The data for studying the totals and point spread markets for National Basketball Association games was taken from the Gold Sheet, a well-known handicapping company, for eight NBA seasons 2000-01 through 2007-08. The data included all games from these years, both regular season and playoffs, except for games where totals or point spreads were not posted. Table 1 shows the summary statistics for the 10,325 games included in the sample. Five of the games had no line posted for the over/under and 175 games were ties. The average or mean actual total score for our sample of NBA games was 192.72 points and the average or mean over/under total for the sample was 192.27 total points per game.

The log likelihood ratio methodology proposed by Even and Noble (2) was used to test for market efficiency for the over/under betting market in the NBA. From the perspective of the over bettor, the value of the unrestricted log likelihood function (Lu) takes the form

> Lu = n[ln(q)] + (N – n)ln(1 – q) (1)

where N is the total number of NBA games where the over bettor or under bettor won the bet. The n is the number of games where the over covers the bet, and q is the proportion of games where the over covers the bet. If the betting market is efficient and a fair bet, then q = 0.5.

This creates the restricted log likelihood function (Lr), which was obtained by substituting 0.5 for q in Equation 2. The log likelihood ratio statistic for the null hypothesis that q = 0.5 is

> 2(Lu – Lr) = 2{n[ln(q) – ln(0.5)] + (N – n)[ln(1 – q) – ln(0.5)]} (2)

where q is the actual percentage of overs winning the over/under bet from our sample. To test for profitability, where the bettor must win enough to offset the commission or vigorish of the bookmaker, the test ratio changes into

> 2(Lu – Lr ) = 2{n[ln(q) – ln(0.524)] + (N – n)[ln(1 – q) – ln(0.476)]}. (3)

### Results And Discussion

#### Totals Betting

In a 2004 study, covering the seven NBA seasons from 1995-96 through 2001-2002, Paul et al.(8) found that, for all games, a bet on the underdog won about 50% of the time, as is expected in an efficient market. However, for the high scoring games (games above 200), they found a pattern of over betting the over, and this pattern increased as game totals increased. For every one point increase from 200 to 210, the winning percentage of the under bet was greater than 50%. In eight of those totals the winning percentage was greater than 52.38%, enough to cover the vigorish, and in five of those totals, the null hypothesis of a fair bet was rejected. However, none of the totals in their study produced a result that rejected the null of no profitability when accounting for commissions. Taking the contrarian bet, and betting against market sentiment, was not profitable. In a later study, using data on actual dollar amounts wagered, Paul and Weinbach (11) found that overs received a much higher percentage of bets compared to unders, but here again it was shown that informed bettors pushed the total to where it was not profitable to bet the under.

The results found the opposite of the 2004 study (8) for the high scoring games. For all games in the eight seasons from 2000-01 through 2007-08, a bet on the underdog still won about 50% of the time. However, a bet on the over won more often than a bet on the under for high scoring games. The game results, and the log likelihood test of efficiency, are reported in Table 2. For game totals between 200 and 210, the winning percentage of the over bets hover right around 50%, indicating an efficient market. When we extended the testing to higher totals (211-220) the percentage of over winners was more than the commission breakeven point (52.38%) for eight of the 10 totals. However, in no instance was the log likelihood ratio large enough to reject the null hypothesis of a fair bet.

Point Spread Betting and Betting the Underdog

When an NBA gambler bets the point spread of an NBA game he is not interested in who wins the game, only the final score. For example, if the point spread for a National Basketball Association game reads

> Heat -4 Pacers +4

The (-) before the 4 indicates that the Heat is the point spread favorite. The (+) indicates that the Pacers are the point spread underdog. If one bets on the Heat, the Heat would have to win by a total of five points for the bettor to win. If one bets on the Pacers, the Pacers would have to win outright or lose by no more than three points for the bettor to win. A four point victory by the Heat (four point loss by the Pacers) would equal a tie and the money bet by the NBA gambler is returned to him.

Prior evidence suggests that there are systemic bettor misperceptions in the NBA point spread gambling market. In a 2005 study Paul and Weinbach (9) presented evidence from the 1995-96 through 2001-2002 seasons that favorites are over bet by uninformed bettors. In that study, a strategy of betting big underdogs rejected the null hypothesis of a fair bet, and betting big home underdogs not only rejected a fair bet was also profitable. Levitt (7) provides us with a model where bookmakers do not attempt to balance the dollars wagered, but rather they shade the point spread to exploit uninformed bettor bias and then take positions on the opposite side, betting the big underdog. Informed bettors may attempt to exploit this inefficiency by also betting the big underdog but will only bet to the point where it is profitable to do so, meaning that they may bet on the underdog and push the point spread only to where there is no less than 52.38% chance of winning the bet. Other studies (6, 11), using data on actual dollars wagered, have found that a majority of dollars are wagered on the stronger or favorite team by uninformed bettors.

This study examined the NBA betting market on point spreads for the seasons 2000-01 through 2007-08 to see if this underdog anomaly persists. It used the closing line on point spreads for NBA games for the same seasons that we examined in the over/under analysis performed in the previous section of the paper. For the market to be efficient the actions of the informed bettors should offset any bias shown by uninformed bettors and the bookmakers closing line should equal the actual game score outcome. Recent studies have shown that the betting public removes biases in sport book’s opening lines in NBA betting by game time (3-5).

Table 3 is a summary of the data for point-spread betting. The sample contained 10,325 games with five of the games posting no closing line to bet on and 90 games posting a closing line of zero. This is called a push and these games were not included when betting favorites and underdogs. There were 141 ties which indicate that the difference in the score (underdog – favorite) was equal to the closing point spread. The average closing line based on the favorite score minus the underdog score was 5.89 and actual difference in score in the NBA games in the sample was 5.38. For the entire sample of games the underdog won 49.86% of the games, indicating that a strategy of betting the underdog was a fair bet, based on the log likelihood ratio test.

The results in Table 4 indicate that the betting public appears to over bet the heavy favorite by a slight margin, but, unlike the study by Paul and Weinbach (9), we found that the winning percentage of betting the big underdog (10 points or more) hovered around 50% and thus we failed to reject the null hypothesis of a fair bet. The same result occurred for the sub-sample of games for seasons 2000-01 through 2003-04 and for the sub-sample of games for seasons 2004-05 through 2007-08. In all of these cases the null hypothesis of a fair bet could not be rejected.

The results for the small sample of games involving the home underdog of 10 points or more had significant results for both a fair bet and profitability. For the entire sample of games (50 games over the entire seasons) the null hypothesis of a fair bet was rejected at a 10% significance level. For the small sample of games in the earlier sub-period (25 games) we found that a bet on the home underdog also rejected the null hypothesis of no profitability.

### Conclusion

This study found that gambling markets for both point spread betting and totals betting for NBA seasons spanning from 2000–01 to 2007–08 are efficient. Based on the results of over 10,000 games in eight consecutive NBA seasons, betting the over on the total points per game is a fair bet. Although for higher totals (211-220) the winning percentage on betting the over was above 52.38% (the percentage necessary to cover commissions), in eight of 10 cases the null hypothesis of a fair bet could not be rejected. The results for point spread betting also showed strong support for an efficient market in NBA gambling, with one exception: betting the home underdog was profitable for underdogs of 10 points or more. However, this was only true for a very small sub-sample and the inefficiency fades in the most recent sample period.

### Applications In Sports

Many fans enjoy wagering on their favorite sport whether it is NBA basketball or another sport. Gambling can be fun and can enhance the excitement of the game by adding a financial component. The evidence suggests that the average bettor is biased toward high scores and prefers betting on the favorite. However, utilizing this knowledge and betting on the underdog will probably not be a profitable strategy for a fan wagering on NBA games because of the actions of informed (professional) gamblers. The informed gambler will bet on the underdog until it is not profitable for him to do so. This activity drives the point spread to a level where a fan cannot make a profit on an underdog bet after accounting for commission. Therefore, the average gambler should focus on having fun and not count on making a profit when gambling on NBA games.

### Tables

#### Table 1
NBA Seasons 2000-01 Through 2007-08 Summary Statistics for Over/Under Betting for All NBA Games

	Totals	Actual game
Mean	192.27	192.72
Median	191	192
Total games		10,325
Games with no line		5
Ties		175
Over wins		5,059
Under wins		5,086
Winning % for betting overs		49.87%
Log likelihood		0.07

#### Table 2
Winning Percentages for Betting the Overs

Point level	Over/Under winners	Winning % of betting the over	Log likelihood ratio for fair bet
200	1252-1234	50.36	0.13
201	1139-1131	50.18	0.03
202	1022-1027	49.88	0.01
203	919-914	50.14	0.01
204	801-796	50.16	0.02
205	699-695	50.14	0.01
206	621-625	49.84	0.01
207	542-547	49.77	0.02
208	470-474	49.79	0.02
209	415-401	50.86	0.24
210	66-339	51.91	0.52
211	321-290	52.54	1.57
212	282-246	53.41	2.46
213	239-214	52-76	1.38
214	210-183	53.43	1.86
215	186-156	54.39	2.63
216	162-136	54.39	2.63
217	139-127	52.26	0.54
218	114-102	52.78	0.67
219	93-88	51.38	0.14
220	80-71	52.98	0.53

Note. The log likelihood test statistics have a chi-square distribution with one degree of freedom.

Critical values are 2.706 (for an α = 0.10), 3.841 (for an α = 0.05), 6.635 (for an α = 0.01).

* is significant at 10%.

** is significant at 5%.

*** is significant at 1%.

#### Table 3
Closing Line Betting Seasons 2000-01 Through 2007-08

Total games	10,325
Average closing line (favorite – dog)	5.89
Average actual score difference (favorite – dog)	5.38
Games with no point spread line	5
Ties	141
Pushes	90
Neutral sites	2
Favorite wins	5,058
Underdog wins	5,029
Winning % for underdog	49.86
Log likelihood ratio	0.01

#### Table 4
Betting the NBA Underdog Seasons 2000-01 Through 2007-08

Seasons	Wins for underdog	Winning %	Log likelihood ratio fair bet	Log likelihood ratio no profitability
Point spread betting for all games
2000-01 thru 2007-08	5029	49.86	0.08	NA
2000-01 thru 2003-04	2448	49.62	0.28	NA
2004-05 thru 2007-08	2581	50.08	0.01	NA
Betting underdog by +10 points or more
2000-01 thru 2007-08	689	52.08	2.28	NA
2000-01 thru 2003-04	319	51.45	0.52	NA
2004-05 thru 2007-08	370	52.63	1.95	NA
Betting home underdog by +10 points or more
2000-01 thru 2007-08	50	59.52	3.07*	1.72
2000-01 thru 2003-04	25	69.44	5.59**	4.33**
2004-05 thru 2007-08	25	65.79	0.08	NA
Betting road underdog by +10 points or more
2000-01 thru 2007-08	639	51.57	1.23	NA
2000-01 thru 2003-04	294	50.34	0.03	NA
2004-05 thru 2007-08	345	52.67	1.87	NA

Note. The log likelihood test statistics have a chi-square distribution with one degree of freedom.

Critical values are 2.706 (for an α = 0.10), 3.841 (for an α = 0.05), 6.635 (for an α = 0.01).

* is significant at 10%.

** is significant at 5%.

*** is significant at 1%.

NA – not applicable

### References

1. Brown, W., Sauer, R. (1993). Fundamentals or noise? Evidence from the professional basketball betting market. Journal of Finance, 48, 1193–1209.
2. Evan, W. E., & Noble, N. R. (1992). Testing efficiency in gambling markets. Applied Economics, 24, 85-88.
3. Gandar, J., Zuber, R., O’Brien, T., & Russo, B. (1988). Testing rationality in the point spread betting market. Journal of Finance, 43, 995-1007.
4. Gandar, J., Dare, W., Brown, C., Zuber, R. (1998). Informed traders and price variations in the betting market for professional basketball games. Journal of Finance, 53, 385–401.
5. Gandar, J, Zuber, R. & Lamb, R. (2000). The home field advantage revisited: a search for the bias in other sports betting markets. Journal of Economics and Business, (53) 4, 439-453.
6. Humphreys, B. (2010). Point spread shading and behavioral biases in NBA betting market. Rivista Di Diritto Economia Dello Sport, 13-26.
7. Levitt, S. (2004). Why are gambling markets organized so differently? The Economics Journal, 114, 223-246.
8. Paul, R., Weinbach, A., Wilson, M. (2004). Efficient markets, fair bets, and profitability in NBA totals 1995–1996 to 2001–2002. The Quarterly Review of Economics, 44, 624–632.
9. Paul, R. J. & Weinbach, A. P. (2005). Bettor misperceptions in the NBA, Journal of Sports Economics, (6) 4, 390-400.
10. Paul, R. J. & Weinbach, A. P. (2007). Does Sportsbook.com set pointspreads to maximize profits? Tests of the Levitt model of sportsbook behavior. Journal of Prediction Markets, (1) 3, 209-218.
11. Paul, R. J. & Weinbach, A. P. (2008). Price setting in the NBA gambling market: Tests of the Levitt model of sportsbook behavior. International Journal of Sports Finance, (3) 3, 2-18.
12. Wever, S., & Aadland, D. (2010). Herd Behavior and the Underdogs in the NFL. Applied Economics Letters, (forthcoming).

### Corresponding Author

Kevin Sigler, PhD
601 S. College Road
Cameron School of Business
University of North Carolina-Wilmington
Wilmington, NC 28403
<siglerk@uncw.edu>
910-962-3605

William Compton is Associate Professor of Finance in the Cameron School of Business, UNCW Kevin Sigler is Professor of Finance in the Cameron School of Business, UNCW

U.S. Sports Academy2013-11-22T22:55:25-06:00January 4th, 2012|Contemporary Sports Issues, Sports Studies and Sports Psychology|Comments Off

Coach Effectiveness and Personality Assessments: An Exploratory Analysis of Thin Slice Interpersonal Perceptions

Jennifer L. Knight, Daniel R. Czech, Barry Joyner, Tyler McDaniel, Alan D. Zwald, Trevor Egli, Larry D. Bryant and Trey Burdette; Georgia Southern University

### Abstract

Gordon Allport (3) suggested that people are able to form accurate perceptions of others from mere glimpses of their behavior. The concept of interpersonal perception accuracy based solely on thin slices has been brought to mainstream attention by the popular book Blink by Malcolm Gladwell (35). Gladwell (35) proclaims that “decisions made very quickly can be as good as decisions made consciously and deliberately” (p. 14). Research suggested that expressive behaviors (movement, speech, gesture, facial expressions, posture) contribute to impressions made about the target (8). With that said, coaching research has identified behaviors that elicit positive perceptions from athletes towards coaches (63, 78). This research examined accuracy, consensus, and self-other agreement of personality assessments and coaching effectiveness based on thin-slice judgments of 30-second video clips of 9 recreation level coaches. Naïve raters (N=206) viewed the clips and rated the targets on coaching effectiveness and personality attributes. Ratings of coaching effectiveness were correlated with expert ratings of effectiveness to measure accuracy. The ratings of attributes were correlated with expert ratings of the same attributes to measure consensus. Gender, race, and level of sport participation of naïve raters was subjected to independent samples t-tests and one-way analyses of variance (ANOVA) to determine if they moderated thin-slice judgments. Results indicated that naïve raters as a group were not accurate in assessment of coaching effectiveness, nor were there significant correlations on consensus or self-other agreement. There were significant differences between levels of sport participation groups on two of the fourteen attributes: competence and confidence.

**Key Words:** Thin-slicing, Coaching Effectiveness, Consensus, Accuracy

### Introduction

In 1937, Gordon Allport (3) introduced this idea that people are able to form accurate perceptions of others from mere glimpses of their behavior. Making judgments from so called “thin slices” of behavior has become very popular in contemporary social psychological research (6-9). Interpersonal perception accuracy is based on thin slices, which was brought to mainstream attention by the popular book Blink by Malcolm Gladwell (35). This concept suggests that most people can thin-slice with surprising success, so that “decisions made very quickly can be as good as decisions made consciously and deliberately (p. 14).” Gladwell provides examples from academic research to support his overall premise, including that of Ambady and Rosenthal (9). Thin-slices are brief excerpts of expressive behavior less than five minutes sampled from the behavioral stream (6).

Ambady and Rosenthal (8) suggested that expressive behaviors (movement, speech, gesture, facial expressions, posture) contribute to impressions made about the target. Early researchers were interested in the link between expressive behaviors as the indicators of personality (3,4). The cues that are projected by expressive behavior have been shown to be interpreted accurately in as little as a 2-second nonverbal clip of a target (9).

Ambady and Rosenthal (8) also suggested that the accuracy of thin-slice judgments have practical applications in fields that are interpersonally oriented. When thin slice ratings predict criterion variables, they can be used, for example, to target biased teachers or gauge expectancies of newscasters. They also suggest that thin slice judgments can be used in the selection, training, and evaluation of people in fields where interpersonal skills are important. Accuracy of thin-slice judgments of coaches could be very useful in selection, training, and evaluation of coaches.

Accuracy in personality and social psychology research can be defined in three ways: the degree of correspondence between a judgment and a criterion, interpersonal consensus, and a construct possessing pragmatic utility (49). These definitions fall into two approaches within the field. The pragmatic approach defines a judgment as accurate if it predicts behavior. This approach looks at personality judgments as necessary tools for social living and evaluates their accuracy in terms of their practical value (31). The constructivist approach focuses on consensus between raters. This approach looks at all judgments as perceptions and evaluates their accuracy in terms of agreement between judges (31). Kenny (45) further explained that target accuracy is broken into three categories: Perceiver, generalized, and dyadic. Generalized target accuracy is the correlation between how a person is generally seen by others and how that person generally behaves. Target accuracy can be defined in thin-slice research as the correspondence between participants’ judgments of a target individual and well-defined external criterion (6,8,9).

Thin-slice judgments have been shown to produce similar judgments to ecologically valid criterion. Ecologically valid criteria are characterized by pragmatic utility in that they are used in everyday decisions about people as an external outcome of observed behavior (9). Support for congruence in this relationship has been shown by significant positive correlations between naïve judgments and outcomes, such as predicting judgments of candidates in job interviews and effectiveness of teachers (7).

The target accuracy and consensus of naïve raters given thin-slices of information appears to be moderated by characteristics of the raters, traits assessed, and characteristics of the targets. Studies show that individual differences of raters can affect judgments based on thin-slices of information including gender and ethnicity (6,7,29,73). Previous research is equivocal regarding the accuracy of judgments based on gender. Some research suggests that females are more accurate judges of non-verbal behavior (40), while other research found no difference in judgments of non-verbal behavior based on gender (8). Researchers have found that raters judge targets of a different ethnicity more negatively than targets of the same ethnicity (73).

Another bias can involve the dimensions being rated. One study found accuracy at zero acquaintance for judgments of extraversion, but not conscientiousness (47). Another study found similar correlations for extraversion as well as a relationship between zero acquaintance ratings of conscientiousness, but not for agreeableness, emotional stability, and culture (14). John and Robins (42) suggest that differences in ratings on traits depend on evaluativeness and observability. Traits that are less evaluative (neutral) and more observable reach greater consensus and accuracy (42). They define observability by the degree to which behaviors are relevant to the trait can be easily observed. They define evaluativeness by the degree to which a trait is relatively neutral.

Limitations are also present on the persons being judged. Persons who possess extraversion and good mental health are simpler to judge at first glance than targets who possess introversion or poor mental health, as Flora (28) denotes “exterior behavior mimics their internal view of themselves. What you see is what you get” (p. 66). Social context can also play a role depending on personality types. Expressive behaviors were limited by individuals with a high self-monitoring in social situations, therefore making judgments on their mood more difficult.

Ambady and Rosenthal (9) researched intuitive judgments on teacher

effectiveness. It was determined that thin-slice evaluations by naive raters of 30 seconds, 5 seconds, and 2 seconds were congruent with evaluations by students and principals who observed the teacher for a semester. It is suggested that the accuracy of the thin-slice judgments can be attributed to raters’ years experience in classroom situations; therefore, within the coaching context, amount of sport experience may also be an individual difference that moderates interpersonal perception accuracy. Ambady and Rosenthal (8) measured judgments on fourteen personality attributes: Accepting, active, dominant, empathic, enthusiastic, honest, likable, optimistic, professional, supportive, and warm. Teaching is an interpersonal field, as well as coaching. Due to similarities in the fields the same attributes were chosen in this study.

The teaching and coaching environment may have parallels and crossover applications. Often cited in coaching and teaching lore is John Wooden, who was one of the most successful collegiate basketball coaches. Wooden pointed out that coaches are teachers first and profiled ten criteria needed for a successful teacher; Among them, knowledge and warm personality and genuine consideration of others (79).

Research in the teaching profession highlights attributes of successful teaching. The list includes a teacher’s enthusiasm and positive attitude, approachability, an environment that is positive, cooperative, and clear-cut, specific objectives, as well as appropriate feedback (20,52,62). Wooden’s (79) coaching philosophy includes all of the aforementioned in his pyramid of success. Bloom (13) explains that coaching, like teaching, can perhaps best be viewed as an interpersonal relations field, which rests primarily on effective communication and interaction among various participants.

Coaching research has identified behaviors that elicit positive perceptions from athletes towards coaches (63,78). Behaviors include positive reinforcement, technical instruction, encouragement, and structuring fun practices. It is theorized that coachs behaviors plays a significant role in the psychological development of young athletes (64). Youth sport research highlights the positive relationship between specific coaching behaviors and self-esteem, satisfaction, and enjoyment in children (64,67). This has led to a recent theoretical model (19) that emphasizes how coaching behaviors impact youth psychosocial outcomes which emphasizes the role of athletes’ perceptions.

A recent study explored the characteristics of expert university level coaches and found several personal attributes that these coaches possessed: Commitment to learning; learning from past mistakes; knowledgeable; open-minded; balanced; composed; caring; and genuinely interested in their athletes (72).

Previous research targets the importance of increasing self-awareness of coaches’ referencing personal behavior while coaching (63,65,74). In a study that coded coaches’ behaviors, the athletes were significantly more successful than the coaches’ in the recall of those behaviors (63). This same research determined that youth athletes’ interpretation of coaches’ behaviors are of even greater impact than the actual behaviors in psychosocial outcomes. At the recreation level, game outcomes bear little significance in psychosocial outcomes (reaction to coach, enjoyment, and self-esteem) for the athletes. The measurement of psychosocial outcomes showed a significant relationship between coaches’ behavior and aforementioned outcomes. Earlier research (13) indicated that the coach is central to the development of expertise in a sport.

Nonverbal behavior can be very significant in an environment where high levels of stress and decision-making are concerned. Perceptions can cause shifts in confidence.

Research supports that the self-efficacy of athletes who judged opponents non-verbal behavior was directly related to those perceptions (39). As outcome expectations may be influenced by perceptions of sporting opponents, and have been shown to influence performance levels (24,26,76).

The purpose of this study is to examine the relationship between naïve ratings of thin-slices of coaching and ecologically valid criterion measures, which are end of the season evaluations by supervisors, as well as self measures of coaching attributes and effectiveness. This research will also include the demographic background of the naïve raters and explore the differences among evaluations based on gender, race, and level of sport participation. The following nine research questions are explored: What is the naïve raters’ accuracy in their assessment of coaching effectiveness; What is the consensus between naïve raters and experts on each attribute; What is the self-other agreement between naïve raters and coaches on each attribute; Is there a significant difference in accuracy between male and female raters; Is there a significant difference in consensus between male and female raters; Is there a significant difference in accuracy between races of raters; Is there a significant difference in consensus between races of raters; Is there a significant difference in accuracy between raters’ level of sport participation; Is there a significant difference in consensus between raters’ level of sport participation?

### Methods

#### Participants

There were two samples of participants in this study. Sample A consisted of 206 naïve raters recruited from undergraduate healthful living classes. Raters ranged from 18 to 55 years old (M = 19.6; SD = 4.4) and included 115 men and 91 women. Raters included African-Americans (n = 47), Caucasians (n = 147), Hispanics (n = 6), and other races (n = 4). The naïve raters indicated the highest level of sport in which they participated: none (n = 26); recreation (n = 46); junior varsity (n = 16); varsity/elite (n = 91); and college (n = 20). Sample B consisted of nine coaching students (eight men, one woman) from an undergraduate level coaching course at a southeastern university. There were eight Caucasian coaches and one African-American coach. The average age of the coaches was 20.2 years old (SD = 1.4).

#### Instrumentation

Coach attributions. Naïve raters, coaches, and supervisors rated each coach using an attributional survey (9) which included the following subscales: accepting, active, dominant, empathic, enthusiastic, honest, likable, optimistic, professional, supportive, and warm. Each coach was rated three times for each attribute on a 9-point Likert scale ranging from not at all (1) to very (9). The reliability in previous research of the mean of the judges’ ratings of the sum of the mean ratings of the 14 nonverbal variables was .80, assessed by an intraclass correlation (9).

Coach effectiveness. In addition, overall effectiveness of the coach was rated on a 5-point Likert scale: “Overall, how would you rate this coach?” Respondents could answer from very poor (1) to very good (5). Coaches and supervisors completed evaluations with the attributional survey and overall effectiveness questions at the end of the evaluation tool.

#### Procedures

Permission was obtained to use videotapes of coaching sessions by nine students in an undergraduate coaching class, who, as part of their course, were filmed for a practice session to be evaluated by their professor. The students coached recreation level youth football (n = 5) and soccer (n = 4) teams which ranged in competition level from under six to under fourteen. Consistent with Ambady and Rosenthal’s (9) previous research, three 10 second silent video clips were used from each coach’s session from the beginning, middle, and end; the clips feature the coach alone, consistent with previous research to control for the effects of interaction effects in the environment of the target (9).

All of the coach’s clips were arranged in one videotape in a randomized Latin-square design (8). The final tape consisted of 27 clips: 3 clips for each of the 9 coaches.

Each coach rated him/herself on the attribution scale and effectiveness item.

Supervisors completed the attribution scale and overall effectiveness item on each coach as part of their formal evaluation of the coach. Evaluations were delivered by the supervisors to the professor and picked up by the researcher.

Raters completed a demographic questionnaire and observed the video of the twenty seven 10-second video clips. Following each clip, raters completed the attributional scale and overall effectiveness question. End-of-the season evaluations by the recreation department supervisors, as well as self-evaluations were used for comparison with the raters’ scores on each of the 14 attributes.

#### Data Analysis

Given that each naïve rater rated each of nine coaches on three occasions, a within-rater mean across three occasions was computed for each coach for each attribute as well as effectiveness. To create an individual difference variable representing target accuracy, 206 correlations between each rater’s mean effectiveness scores and supervisor effectiveness scores (df = 7) were calculated. To create an individual difference variable representing consensus, 206 correlations between each rater’s mean scores and supervisor scores (df = 7) were calculated for each attribute. To create an individual difference variable representing self-other agreement, 206 correlations between each rater’s mean scores and self scores (df = 7) were calculated for each attribute and for effectiveness.

Inferential statistics were utilized to examine moderators of target accuracy, consensus, and self-other agreement. Means were compared using independent sample t-tests for gender comparisons and one-way ANOVAs for comparisons between races and sport participation groups. Post hoc comparisons using Fisher’s LSD were conducted on any significant results ascertained from ANOVAs (p < .01).

Individual correlations between each naïve rater’s score on effectiveness and the supervisor’s score on effectiveness for each coach were calculated and a mean consensus score was obtained. This provided an individual difference variable representing accuracy accuracy.

Individual correlations between each naïve rater’s attributional ratings across nine

coaches observed and the supervisors’ attributional ratings of these coaches were calculated and a mean correlation was determined to provide an individual difference variable representing consensus. Individual correlations between each naive rater’s attributional ratings across nine coaches observed and the actual coach were calculated and a mean correlation was determined to provide an individual difference variable representing self-other agreement.

Means were compared using independent sample t-tests for gender comparisons and one-way ANOVAs for comparisons between races and sport participation groups.

Post hoc comparisons using Fisher’s LSD were conducted on any significant results ascertained from ANOVAs: (p < .01).

### Results

The mean correlations between the naïve raters’ effectiveness ratings and the supervisors’ effectiveness ratings were calculated to estimate target accuracy of the thin slice judgments by the naïve raters (see Table 1).

The mean correlations between the naïve raters’ ratings on each of the fourteen attributes with the supervisors’ ratings on each of the fourteen attributes were calculated to estimate consensus, as well as other results regarding self-other agreement (see table 1). Independent samples t-tests were run based off of means generated on male and female raters to determine differences between the two groups on accuracy. There were no differences found on accuracy between groups (see Table 2). Independent samples t-tests run on differences on consensus between genders found significant differences (p < .01) on one of the fourteen variables: likeability. Female raters were higher on means consensus than male raters on likeability (see Table 2).

Due to the small sample size of Hispanic, Asian, and Other, these categories were not included in analyses on race differences. Independent samples t-tests run on differences between Caucasian and African-American raters found no significant differences on accuracy or consensus (p > .01) (see Table 3).

In addition, a one-way ANOVA showed no significant differences between levels of sport participation on accuracy (p > .01) (see Table 4). However, there were significant differences (p < .01) between level of sport participation groups on consensus on two of the fourteen variables: Competence and confidence (see Table 4). Fisher’s LSD post hoc tests indicated that naïve raters who participated in collegiate athletics showed significantly more consensus with supervisor ratings on competence than all other categories of level of sport participation raters. College raters also showed significantly more consensus with supervisor ratings on confidence than two other sport participation groups: no participation and varsity/elite participation.

### Discussion

There were several constructs of accuracy measured in this study. The first research question examined the target accuracy of the naïve raters. Due to the lack of correlation between the naïve raters’ judgments and the supervisors’ evaluations, the naïve raters as a group were not accurate in their assessments of coaching effectiveness. There are several explanations why this may have occurred. The nine coaches varied across two sports and four age levels. They were not observed directly with the athletes so differences in coaching behaviors due to varying age and sport contexts may have caused some of the variability. Thin-slice judgments in the sport context may have more variables that need to be controlled for than thin-slicing in classroom settings or social settings that have been previously examined. Modeling the Ambady and Rosenthal (8) study, the coaches were presented on muted video clips without athletes present. Ambady and Rosenthal (8) presented teachers alone in the clips they showed to naïve raters to control for biases to the reactions from students being taught. The coaching context requires adaptations to lessons as well as more frequent feedback. There may be a need for more frequent transactions whereas teaching may include more directive communication. Observations of a coach may require this interaction to accurately assess coaching effectiveness. The design of this study did not allow naïve raters to observe direct interactions between the coach and players.

Another explanation to support the complexity of the sport context is the individual differences in perceptions of effective coaches. Previous research found a negative correlation between body size and perceptions of coaching effectiveness by female gymnasts, while no correlation was found for soccer players or basketball players (21). This study did not survey for particular sport participation so variation may be due mainly to perceptions of coaching effectiveness in a particular sport. Other research suggests that the personality of the athlete can effect coaching evaluations. Williams et al. (78) found that athletes with higher anxiety and lower self-confidence rated effectiveness of coaches more negatively. This study did not look at the personality makeup of the raters to determine if those attributes moderate accuracy.

Previous research also suggests that mood state can affect evaluations (6). Recent research shows that mood state of customers can effect evaluation of sales people (57). When customers were in a bad mood and the salesperson was perceived as happy the customer rated the salesperson negatively. Ambady and Gray (6) found that negative mood states affected accuracy of social perceptions.

Another possible explanation why there was not a relationship between naïve raters and coaches on coaching effectiveness is the lack of congruency between the present circumstances of the raters and the environment of the target. The targets were coaching at the recreation level and the raters were college students. If they had participated in recreation level athletics they were many years removed from the situation. Much of the previous research on thin-slicing has used blind raters who are within the context being evaluated. One example is Ambady and Rosenthal’s (8) study on teacher effectiveness. The naïve raters were college students and they were rating college instructors and their judgments were compared to other student evaluations. This current study used college aged naïve raters who evaluated other college student coaches in a youth sport context. Other studies look at social contexts that most people are familiar with on a current basis (7). It may be useful to preface the thin-slicing with the context being rated. The naïve raters were not aware they were judging recreation level coaches. It may have been more useful to use parents of children who are in the recreation level context.

Consensus between naïve raters and experts on attributes was not reached on thirteen of the fourteen attributes. Consensus was defined within this study as the agreement between the naïve raters and the expert on personality attributes. Overall significance was not reached on thirteen of the fourteen attributes. Overall consensus was not reached on thirteen of the fourteen attributes. Considering how many correlations were measured, it can be expected that one could reach significance solely by chance. Kenny (45) defines consensus as the agreement between two raters. This research treats the naïve raters as one and the expert as the second rater. Consensus operationalized this way shows if naïve raters view a target similarly to a person who has greater knowledge of the target.

This approach has limitations because the naïve raters are compared with only one knowledgeable rater. Previous research suggests that there is greater accuracy in judgments of a target when there are two are more evaluations from people who know the target (48). Consensus may have been higher if more than one judgment by knowledgeable others could have been averaged to determine consensus. Consensus in Ambady and Rosenthal’s (9) research was operationalized by intracorrelations of naïve raters’ judgments of attributes which were placed in a 15 X 15 matrix and subjected to a principle components analysis. It is possible that consensus between naïve raters was reached in this study, which means they could have viewed the target similarly. This is a research question that should be considered for future research.

In regards to consensus, there was a moderate relationship between naïve raters and supervisors on the attribute enthusiastic. Previous research on the Norman and

Goldberg’s (54) Big Five and zero acquaintance research found consensus on the extraversion factor of the Big Five (33,46,55). Characteristics suggested by the extraversion category include sociable and energetic. It is possible that enthusiastic may be very similar to, or an expression of, extraversion. It could be easier to observe than the other traits. Researchers (46) suggest that extraversion is processed very quickly. John and Robins (42) suggest that the observability and evaluativeness of the attributes can contribute to accuracy and agreement between raters. The more neutral (less evaluative) and observable an attribute is the greater the agreement between raters is about the target. For example talkativeness is observable and neutral, while arrogance could be viewed as negative and more difficult to observe. Most of the fourteen attributes in this study were positively charged and difficult to directly observe: Accepting, attentive, competent, confident, dominant, empathic, enthusiastic, honest, likable, optimistic, professional, supportive, and warm.

Little research has examined thin-slicing in the sport context. Potentially personal biases of raters could affect judgments of coaches’ attributes. Kenny (45) explains that “personal stereotypes”, such as whether a rater subscribes to a widely held view. An example would be “all professors are absent-minded”, which can be reflected in judgments, and does not necessarily change with increasing acquaintance. Current research shows that stereotypes are based on more than gender or race. Kenny (45) explains that appearance cues and nonverbal behaviors are associated with different personality traits.

There was not self-other agreement in this study between the naïve raters’ judgments and the coaches self judgments of personality attributes. Previous perception research found that self judgments were less accurate when assessing behavior than others (48,69). Robins and John (58) suggest that mood affects self judgments as well as the need to protect self-esteem. The coaches in this study were undergraduate college students with no previous coaching experience. Their own perceptions about their coaching may have entered into the answering of the survey questions. Coaching literature has found that coaches are unaware of how they present themselves and behave while coaching (63,65,74). It is possible that the coaches in this study are similar and unaware of their behaviors.

This study supports the research literature in which no significant differences were found between gender and target accuracy. This supports an earlier meta-analysis by Ambady and Rosenthal (8) that examined numerous studies and concluded that overall gender did not affect thin slicing or zero acquaintance judgments. It has been suggested that women are better judges of nonverbal behavior (40). Rosenthal and DePaulo (60) found that women are better judges when the information is presented in more controllable channels. Speech is considered the most controllable channel, while the voice is considered the least controllable (15). This study did not involve an auditory component so potential differences in gender may not have arisen because of the channels for the cues of nonverbal behavior.

There was a significant difference between male and female naïve raters on one of the fourteen attributes. The most only significant difference (p < .01) was for likeability attribute. Female raters were closer to consensus with supervisors than male raters. This may pertain to the different expectations by gender on participation in sport. Previous studies have shown that females emphasize friendship and social interaction over competition and achievement than males do (1,34,36,56). Dubois (22) found that the longer youth participate in sport the greater the divergence in values placed on the outcomes by gender, Experienced males place greater importance on outcomes, whereas females consistently place emphasis on social aspects of sport. Potentially female raters in this study may have been more attuned to characteristics that embody the outcomes they desire in a sport setting. The other two attributes in which females differed significantly from males were enthusiastic and optimistic. All three of the differences between variables could be explained by the greater emphasis females place on these attributes and potentially the greater awareness they have of these attributes.

Overall there were no differences between African-Americans and Caucasians on target accuracy or consensus. Little research has examined racial differences in perception of naïve raters. Previous research has found race of target to affect accuracy and consensus (17,37). This research shows that race of raters does not affect target accuracy or consensus. Perhaps the sport context is different due to the length of participation of different races in sport and public acceptance of different races in sport over other areas in society. Edwards (23) suggests that lack of opportunities in mainstream society due to discrimination has led a disproportionately high number of blacks to pursue sport. Bledsoe (12) highlighted the practice in which young blacks pursue sport because of the lack of successful black role models in other areas. Sport is an area that has provided opportunity for those lower on the socioeconomic ladder to gain recognition and money when other avenues were closed off to them. (18). This can be supported by statistics: Blacks make up 77% of the NBA, 64% of the WNBA, and 65% of the NFL, they are only 4.2% of our physicians, 2.7% of our lawyers and 2.2% of our civil engineers (16). In NCAA Division I athletics blacks comprise 23.5% of student athletes: black males = 29.5% of male athletes; black females = 14.2% of female athletes). Black males comprise 60% of basketball players and 51% of football players and 27% of track athletes, while black females constitute 35% of basketball players and 31% of track athletes (53).

Perceptions of the race of the coaches may have also played a role in the lack of significant differences between races. There were eight Caucasian coaches and one African American coach. Statistics show a disproportionate number of non-Latino white males in coaching positions in the professional leagues and NCAA (50). “Stacking” theories in sport studies suggest that blacks are placed in positions that require more speed and stamina but less cognitive processes. One result of this is less opportunity to coach for minorities because of the positions they played that required less understanding of the overall game (18). There is a pattern found in professional sports and college sports of a disproportionately high number of blacks playing on teams coached by whites (18).

Overall there were no differences among levels of sport participation of raters on consensus of effectiveness. There was no correlation with the criterion variable between sport participation groups. Eight of the nine coaches were rated by supervisors as a four or a five out of five on effectiveness. The ninth coach was rated a three. Naïve raters overall rated coaches less effective than the supervisors. This could be a function of expectations of effective coaches at different levels. These coaches are fulfilling a requirement of an undergraduate coaching course which meets 3 hours a week. These coaches may experience more instruction which affects their ratings by supervisors.

While there were not significant differences in most of the attributional categories, there were significant differences on two of the fourteen attributes among levels of sport participation of raters. The higher the level of sport participation the greater the consensus with the expert judge on the competence attribute: The raters with college participation were significantly different than raters with varsity/elite experience, junior varsity experience, recreation level experience, and no sport experience. The college level athletes had greater consensus than all the other groups. One explanation could be the greater participation of these raters in sport and their level of attunement to competence of coaches. These raters possibly had a greater exposure to a number of coaches and are more sensitive to competence. Millard (51) posits that the higher level an athlete pursues the greater the need for winning and the greater the need for technical instruction from a coach. She found that coaches who provided more instruction based feedback were perceived as more competent. High-experience coaches are noted to provide more technical feedback and less general encouragement than low-experience coaches (61). This difference could also account for the awareness of competence of the college level raters.

The college level raters were also significantly different than varsity/elite athletes and recreation level athletes on confidence. The college level raters showed more consensus with supervisors’ ratings. They could also be attuned to the confidence level of coaches. Research shows that male coaches are generally more confident in abilities than female coaches (51). This study used eight male coaches and one female coach. College level raters due to length involved in sport may be more attuned to the confidence level of a coach.

Researchers attempt to define the moderators surrounding the rater, the channel, the judgments, and the target that could affect accuracy. It is also valuable to learn in what scenarios judgments are not accurate. Evans (25) notes that it is more important to know in what contexts people do not make good decisions. Previous research suggests that the degree to which a judge cares about the judgment he or she is making can affect the accuracy and consensus (27,31). The environment observed may have also affected consensus on personality judgments. Previous research found that less structured situations yield greater correlations on personality (32,68). This research involved judgments of targets in a classroom setting observing video clips instead of directly observing the targets in the sport environment.

This research is promising because it is the first to examine thin-slicing in the sport environment. It suggests that the sport context may have more variables to control for when doing zero acquaintance research. Future research should attempt to control variables and look at particular sports and use naïve raters who have experienced that sport. Future research could also examine zero acquaintance situations at different levels, like the collegiate or elite level. Looking at moderators of consensus based on the demographics of the coach, like gender and race would be valuable. Qualitative studies could further understand personal biases that underscore perceivers’ views of effective coaches, whether gender, sport level and type, or race could affect that.

### Application in Sport

This thought of split second decision making about a coach could be very critical in developing the most cohesive team possible. With further research necessary based on the above suggestions, thin-slicing could potentially benefit the cohesion of the team. By reversing this idea, coaches might be able to more effectively choose players that fit their team when recruiting. Stats are very important, but if there were other intangible ways to ‘correctly’ choose athletes that fit the mold of their team, coaches might be able to more effectively choose a cohesive, talented team.

### Tables

#### Table 1
Descriptive Statistics

	M	SD	Skewness (SE = 0.17)	Kurtosis (SE = 0.34)	M	SD	Skewness (SE = 0.17)	Kurtosis (SE = 0.34)
	Target Accuracy
	Consensus				Self-Other Agreement
Effectiveness Attribute	-.27	0.25	0.65	0.69
Acceptance	-.33	0.28	0.65	0.65	.03	0.30	-0.57	0.33
Active	-.16	0.25	0.10	-0.44	.16	0.28	-0.08	0.30
Attentive	.23	0.27	-0.69	0.91	.11	0.28	-0.20	0.03
Competent	-.15	0.23	0.69	1.40	.19	0.28	-0.15	-0.19
Confidence	.15	0.25	-0.07	0.18	-.05	0.28	0.23	-0.12
Dominance	-.11	0.24	0.30	1.10	.27	0.25	-0.90	-0.05
Empathic	-.17	0.28	0.45	0.56	.42	0.32	-1.20	0.60
Enthusiastic	.45	0.24	-0.99	1.50	-.11	0.30	0.64	1.80
Honesty	-.07	0.27	0.25	-0.10	-.08	0.26	0.33	0.42
Likeability	.20	0.23	-0.22	0.21	.01	0.29	0.48	0.02
Optimistic	.00	0.23	0.13	0.15	.18	0.28	-0.59	0.43
Professional	-.09	0.25	0.02	-0.35	.22	0.27	-0.42	0.10
Supportive	-.17	0.25	0.35	0.11	.01	0.27	0.00	0.10
Supportive	-.17	0.25	0.35	0.11	.01	0.27	0.00	0.10
Warm	-.13	0.28	0.16	-0.10	-.09	0.29	0.23	-0.08

#### Table 2
Descriptive Statistics for Target Accuracy and Consensus Differentiated by Gender

	Gender
	Males		Females
Attributes	M	SD	M	SD
Effectiveness	-.28	0.28	-.26	0.23
Acceptance	-.33	0.30	-.33	0.26
Active	-.18	0.23	-.14	0.25
Attentive	.20	0.30	.25	0.24
Competent	-.14	0.24	-.16	0.23
Dominance	-.12	0.25	-.11	0.23
Empathic	-.18	0.33	-.17	0.24
Enthusiastic	.40	0.24	.48	0.23
Honesty	-.08	0.30	-.05	0.24
Likeability*	.14	0.23	.25	0.22
Optimistic	-.04	0.25	.03	0.22
Professional	-.13	0.26	-.07	0.24
Supportive	-.18	0.28	-.15	0.22
Warm	-.13	0.30	-.13	0.27

* p < .01

#### Table 3
Descriptive Statistics for Target Accuracy and Consensus Differentiated by Race

	Race
	African-Americans		Caucasians
Attributes	M	SD	M	SD
Effectiveness	-.26	0.25	-.30	0.25
Acceptance	-.31	0.29	-.39	0.24
Active	-.31	0.29	-.39	0.24
Attentive	.24	0.28	.20	0.22
Competent	-.15	0.22	-.13	0.26
Dominance	-.09	0.24	-.17	0.18
Empathic	-.17	0.28	-.19	0.29
Enthusiastic	.45	0.25	.42	0.23
Honesty	-.06	0.28	-.09	0.23
Likeability	.19	0.23	.26	0.24
Optimistic	-.02	0.23	.06	0.23
Professional	-.10	0.26	-.07	0.22
Supportive	-.18	0.25	-.15	0.25
Warm	.14	0.28	-.11	0.27

#### Table 4
Analysis of Variance for Attributes between Levels of Sport Participation Groups

Attributes	df	F	p
Acceptance	4	0.85	0.50
Active	4	0.29	0.89
Attentive	4	0.96	0.43
Competent*	4	3.57	0.01
Confidence*	4	3.67	0.01
Dominance	4	0.31	0.87
Empathic	4	0.32	0.86
Enthusiastic	4	3.22	0.01
Honesty	4	0.70	0.59
Likeability	4	1.14	0.23
Optimistic	4	0.94	0.45
Professional	4	0.71	0.59
Supportive	4	1.51	0.20
Warm	4	1.45	0.22

* p < .01

### References

1. Alderman, R. B. (1988). Strategies for Motivating Young Athletes. In W. F. Straub (Ed.) Sport psychology: An analysis of athlete behavior (pp. 4961). Ithaca, NY: Movement.
2. Abromovitch, R. (1977). Children’s recognition of situational aspects of facial expressions. Child Development, 48, 459-463.
3. Allport, G. W. (1937). Personality: A psychological interpretation. New York: Holt.
4. Allport, G. W., & Vernon, P. E. (1933). Studies in expressive movement. New York: Haffner.
5. Ambady, N., Conroy, M., Mullins, J., & Tobia, A. (2001). Friends, lovers, and strangers: Judging dyadic relationships from thin slices. Manuscript submitted for publication.
6. Ambady, N., & Gray, H. M. (2002). On being sad and mistaken: Mood effects on the accuracy of thin-slice judgments. Journal of Personality and Social Psychology, 83, 947-961.
7. Ambady, N., Hallahan, M., Rosenthal, R. (1995). On judging and being judged accurately in zero-acquaintance situations. Journal of Personality and Social Psychology, 69, 518-529.
8. Ambady, N., & Rosenthal, R. (1992). Thin slices of expressive behavior as predictors of interpersonal consequences: A meta-analysis. Psychological Bulletin, 111, 256-274.
9. Ambady, N., & Rosenthal, R. (1993). Half a minute: Predicting teacher evaluations from thin slices of nonverbal behavior and physical attractiveness. Journal of Personality and Social Psychology, 64, 431-441.
10. Archer, D., & Akert, R. M. (1977). Words and everything else: Verbal and nonverbal cues in social interpretation. Journal of Personality and Social Psychology, 35, 443-449.
11. Bem, D. J. (1972). Self-perception theory. In L. Berkowitz (Ed.), Advances in experimental social psychology, (Vol.6, pp. 1-62).NewYork:Academic Press.
12. Bledsoe, T. (1973). Black dominance in sports: Strictly from hunger. The Progressive, 37, 16-19.
13. Bloom, B. (1985). Developing talent in young people. New York: Ballantines.
14. Borkenau, P., & Liebler, A. (1992). Trait inferences: Sources of validity at zero acquaintance. Journal of Personality and Social Psychology, 62, 645-657.
15. Brown, R. (1986). Social psychology: The second edition. New York: Free Press.
16. Bureau of Labor Statistics (1997). Retrieved June 6, 2006 from ftp://ftp.bls.gov/pub/special.requests/lf/aat11.txt
17. Chiao, J. Y., Heck, H. E., Nakayama, K., & Ambady, N. (2006). Priming race in biracial observers affects visual search for black and white faces. Psychological Science,17, 387-392.
18. Coakley, J. (2001). Sport in society: Issues and controversy. New York: McGraw-Hill.
19. Conroy, D. E., & Coatsworth, J. D. (2006). Coach training as a strategy for promoting youth social development. The Sport Psychologist, 20, 124-144.
20. Csikszentmihalyi, M., & McCormack, J. (1986). The influence of teachers. Phi Delta Kappan, 66, 415-419.
21. Cumming, S. P., Eisenmann, J. C., Smoll, F. L., Smith, R. E., Malina, R. M. (2005). Body size and perceptions of coaching behaviors by adolescent female athletes. Psychology of Sport and Exercise, 6, 693-705.
22. Dubois, P. (1990). Gender differences in value orientation towards sports: A longitudinal analysis. Journal of Sport Behavior, 13, 3-14.
23. Edwards, H. (1973). Sociology of sport. Dorsey Press: Homewood, IL.
24. Eyal, N., Bar-Elis, M., Tenenbaum, G., & Pie, J. S. (1995). Manipulated outcome expectations and competitive performance in motor tasks with gradually increasing difficulty. The Sport Psychologist, 9, 188-200.
25. Evans, J. St. B. T. (1984). In defense of the citation bias in the judgment literature. American Psychologist, 39, 1500-1501.
26. Feltz, D. L., & Riessinger, C. A. (1990). Effects of in vivo emotive imagery and performance feedback on self-efficacy and muscular endurance. Journal of Sport and Exercise Psychology, 12, 132-143.
27. Flink, C., & Park B. (1991). Increasing consensus in trait judgments through outcome dependency. Journal of Experimental Social Psychology, 27, 453-467.
28. Flora, C. (2004). The once-over can you trust first impressions. Psychology Today, 37, 60-65.
29. Fortunato, V. J., & Mincy, M. D. (2003). The interactive effects of dispositional affectivity, sex, and a positive mood induction of student evaluations of teachers. Journal of Applied Sport Psychology, 33, 1945-1968.
30. Funder, D. C. (1991). Global traits: A neo-Allportian approach to personality. Psychological Science, 2, 31-39.
31. Funder, D. C. (1995). On the accuracy of personality judgment: A realistic approach. Psychological Review, 102, 652-670.
32. Funder, D. C., & Colvin, C. R. (1991). Explorations in behavioral consistency: Properties of persons, situations, and behaviors. Journal of Personality and Social Psychology, 60, 773-794.
33. Funder, D. C., & Dobroth, K. M. (1987). Differences between traits: Properties associated with interjudge agreement. Journal of Personality and Social Psychology, 52, 409-418.
34. Gill D. L., Gross, J. B., Huddleston, S. (1983). Participation motivation in youth sports. International Journal of Sport Psychology, 14, 1-14.
35. Gladwell, M. (2005). Blink. New York: Little, Brown & Company.
36. Gould, D., Feltz, D., & Weiss, M. (1986). Motives for participating in competitive youth swimming. International Journal of Sport Psychology, 16, 126-140.
37. Gosling, S. D., Ko, S. J., Mannarelli, T., & Morris, M. E. (2002). A room with a cue: Personality judgments based on offices and bedrooms. Journal of Personality and Social Psychology, 82, 379-398.
38. Grahe, J. E., & Bernieri, F. J. (1999). The importance of nonverbal cues in judging rapport. Journal of Nonverbal Behavior, 23, 253-269.
39. Greelees, I., Bradley, A., Holder, T., & Thelwell, R. (2005). The impact of opponents’ non-verbal behavior on the first impressions and outcome expectations of table-tennis players. Psychology of Sport & Exercise, 6, 103-115.
40. Hall, J. A. (1984). Nonverbal sex differences: Communication accuracy and expressive style. Baltimore: Johns Hopkins University Press.
41. Hofstee, W. K. B. (1994). Who should own the definition of personality? European Journal of Personality, 8, 149-162.
42. John, O. P., & Robins, R. W. (1993). Determinants of interjudge agreement on personality traits: The Big Five domains, observability, evaluativeness, and the unique perspective of the self. Journal of Personality, 61, 521-551.
43. Jones, E. E., & Nisbett, R. E. (1971). The actor and the observer: Divergent perceptions of the causes of behavior. Morristown, NJ: General Learning Press.
44. Kahlbaugh, P. E., & Haviland, J. M. (1994). Nonverbal communication between parents and adolescents: A study of approach and avoidance behaviors. Journal of Nonverbal Behavior, 18, 91-113.
45. Kenny, D. A. (1994). Interpersonal perception: A social relations analysis. New York, New York: Guilford Publications. Retrieved March 16, 2006 from http://davidakenny.net/ip/assim.htm
46. Kenny, D. A., Albright, L., Malloy, T. E., & Kashy, D. A. (1994). Consensus and interpersonal perception: Acquaintanceship and the Big Five. Psychological Bulletin, 116, 245-258.
47. Kenny, D. A, Horner, C., Kashy, D. A., & Chu, L. (1992). Consensus at zero acquaintance: Replication, behavioral cues, and stability. Journal of Personality and Social Psychology, 62, 88-97.
48. Kolar, D. W., Funder, D. C., & Colvin, C. R. (1996). Comparing the accuracy of personality judgments by the self and knowledgeable others. Journal of Personality, 64, 311-337.
49. Kruglanski, A. W. (1989). The psychology of being “right”: The problem of accuracy in social perception and cognition. Psychological Bulletin, 106, 395-409.
50. Lapchick, R., & Mathews, K. (2000). 1998 Racial and gender Report Card. The Center for the Study of Sport in Society: Boston, MA.
51. Millard, L. (1996). Differences in coaching behaviors of male and female high school soccer coaches. Journal of Sport Behavior, 19, 19-32.
52. McGinnis, A. L. (1985). Bring out the best in people. Minneapolis, MN: Augsburg Publishing House.
53. NCAA (1998). Race demographics of NCAA member institutions’ athletics personnel. The National Collegiate Athletics Association: Overland Park, Kansas.
54. Norman, W. T., & Goldberg, L. R. (1966). Raters, ratees, and randomness in personality structure. Journal of Personality and Social Psychology, 4, 681-691.
55. Park, B., & Judd, C. M. (1989). Agreement of initial impressions: Differences due to perceivers, trait dimensions, and target behaviors. Journal of Personality and Social Psychology, 56, 493-505.
56. Petrie, B. M. (1971). Achievement orientations in adolescent attitudes toward play. International Review of Sport Sociology, 6, 89-101.
57. Puccinelli, N. M. (2006). Putting Your Best Face Forward: The Impact of Customer Mood on Salesperson Evaluation. Journal of Consumer Psychology, 16, 156-162.
58. Robins, R. W., & John, O. P. (1997). The quest for self-insight: Theory and research on accuracy and bias in self-perception. In R. Hogan, J. A. Johnson, & S. R. Briggs (Eds.), Handbook of personality psychology (pp. 649-679). San Diego, CA: Academic Press.
59. Rosenthal, R. (1987). Judgment studies: Design, analysis, and meta-analysis. New York: Cambridge University Press.
60. Rosenthal, R., & DePaulo, B. M. (1979). Sex differences in accommodation in nonverbal communication. In R. Rosenthal (Ed.), Skill in nonverbal communication. Cambridge, MA: Oelgeschlager, Gunn, & Hain.
61. Sherman, M. A., & Hassan, J. S. (1986). In M. Pieron, & G. Graham (Eds.), The 1984 Olympic scientific congress proceedings (Vol. 6) (pp. 103-108). Champaign, IL: Human Kinetics.
62. Slavin, R. E. (1980). Cooperative learning. Review of Educational Research, 50, 312-342.
63. Smith, R. E., & Smoll, F. L. (1997). Coaching the coaches: Youth sports as a scientific and applied behavioral setting. Current Directions in Psychological Science, 6, 16-21.
64. Smith, R. E., & Smoll, F. L. (1990). Self-esteem and children’s reaction to youth sport behaviors: A field study of self-enhancement processes. Developmental Psychology, 26, 987-993.
65. Smith, R. E., Smoll, F. L., & Curtis, B. (1978). Coaching behaviors in Little League baseball. In F.L. Smoll & R.E. Smith (Eds.), Psychological perspectives in youth sports (pp. 173-201). Washington, DC: Hemisphere.
66. Smith, R. E., Smoll, F. L., & Curtis, B. (1979). Coach Effectiveness Training: A cognitive-behavioral approach to enhancing relationship skills in youth sport coaches. Journal of Sport Psychology, 1, 59-75.
67. Smoll, F. L., Smith, R. E., Barnett, N. P., & Everett, J. J. (1993). Enhancement of children’s self-esteem through social support training for youth sport coaches. Journal of Applied Psychology, 78, 602-610.
68. Snyder, M., & Ickes, W. (1985). Personality and social behavior. In G. Lindzey & E. Aronson (Eds.), Handbook of social psychology (3rd ed., Vol. 2, pp. 883-948). New York: Random House.
69. Spain, J.S., Eaton, L.G, & Funder, D.C. (2000). Perspectives on personality: The relative accuracy of self versus others for the prediction of emotion and behavior. Journal of Personality, 68, 837-867.
70. Tickle-Degnan, L., & Rosenthal, R. (1987). Group rapport and nonverbal behavior. In C. Hendrick (Ed.), Group processes and intergoup relations. Review of personality and social psychology (Vol. 9, pp. 113-136). Beverly Hills, CA: Sage.
71. Tickle-Degnan, L., & Rosenthal, R. (1990). The nature of rapport and its nonverbal correlates. Psychological Inquiry, 4, 285-293.
72. Vallee, C. N., & Bloom, G. A. (2005). Building a successful university program: Key and common elements of expert coaches. Journal of Applied Sport Psychology, 17, 179-196.
73. Vrij, A., Dragt, A., & Koppelaar, L. (1992). Interviews with ethnic interviewee: Non- verbal communication errors in impression formation. Journal of Community & Applied Social Psychology, 2, 199-208.
74. Wandzilak, T., Ansorge, C.J., & Potter, G. (1988). Comparison between selected practice and game behaviors of youth sport soccer coaches. Journal of Sport Behavior, 2, 78-88.
75. Watson, D. (1989). Strangers’ ratings of the five robust personality factors: Evidence of a surprising convergence with self-report. Journal of Personality and Social Psychology, 57, 120-128.
76. Weinberg, T., Gould, D., & Jackson, A. (1979). Expectations and performance: An empirical test of Bandura’s self-efficacy theory. Journal of Sport Psychology, 3, 345-354.
77. Wiggins, J. S. (1973). Personality and prediction: Principles of personality assessment, Reading, MA: Addison-Wesley.
78. Williams, J. M., Jerome, G. J., Kenow, L. J., Rogers, T., Sartain, T. A., & Darland, G. (2003). Factor structure of the coaching behavior questionnaire and its relationship to athlete variables. Sport Psychologist, 17, 16-35.
79. Wooden, J. R. (1980). Practical modern basketball. New York: John Wiley and Sons.

### Corresponding Author

Dr. Daniel R. Czech, CC-AASP
Department of Health and Kinesiology
Box 8076
Georgia Southern University
Statesboro, Georgia 30460-8076
<drczech@georgiasouthern.edu>
(912) 478-5267

U.S. Sports Academy2013-11-22T22:55:49-06:00January 4th, 2012|Contemporary Sports Issues, Sports Coaching, Sports Management, Sports Studies and Sports Psychology|Comments Off