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Rowing Ergometer Physiological Tests do not Predict On-Water Performance

U.S. Sports Academy2013-11-25T14:48:40-06:00January 2nd, 2012|Sports Exercise Science, Sports Management, Sports Studies and Sports Psychology|

### Abstract

Many studies have examined the relationship between 2000m rowing ergometer performance and physiological variables, often suggesting that rowing ergometer performance models can be used to predict on-water performance. While studies have examined the kinematic, oxygen consumption, and electromyography similarities between rowing ergometry and on-water rowing, this is the first study to examine the relationship between physiological variables measured on the ergometer and 2000m performance on the water.

Nineteen elite heavyweight male rowers (26.2 ± 3.6 years, 92.2 ± 4.3 kg, 192.2 ± 4.5 cm) participated in the study. All testing was done over a two-week period. A 2000m (2K) on-water pursuit time trial in single scull boats, where the athletes started 30 seconds apart and competed over a 2K course for time, and a 2K ergometer performance test, were conducted on consecutive days in the first week. A progressive continuous incremental ergometer VO2 max and a modified 45s rowing Wingate test, to measure peak (Peak 45) and average power (Ave 45), were performed a week later. Ventilatory threshold (VT) was determined from a plot of VE/VO2. All ergometer testing was done on a Concept II model C rowing ergometer.

While Pearson correlations showed that VO2 max (r = -0.55, p< 0.05), Peak 45 (r = -0.43, p< 0.05), and Power at VO2 max (r= -0.733, p< 0.05) were significantly related to 2K ergometer performance, there was no correlation between any of the measured variables and 2K on-water performance (Height, r=0.273; Weight, r=0.373; VO2 max, r=0.049; VT, r=0.043; Peak 45, r= -0.229; Ave 45, r= -0.200; Power at VO2 max, r= -0.292). Additionally, there was no correlation between 2K ergometer performance scores and 2K on water performance scores (r= 0.120). The data suggest that physiological and performance tests performed on a rowing ergometer are not good indicators of on water performance. While it is a common practice for many, rowing coaches and sport scientists should be cautious when using the rowing ergometer to predict on water performance or select rowing crews.

**Key Words:** Rowing, physiological tests, ergometer, on water

### Introduction

The rowing ergometer (erg) has become an important tool for training and physiological monitoring of rowers. The erg has allowed sport scientists and researchers to overcome environmental factors such as current, water temperature, and wind, that can make physiological monitoring and research on rowers difficult. As a result researchers have been able to study and describe the relationship between a variety of physiological variables and rowing ergometer performance (2,10,11,13,16 ).

The repeatability of results within a brand and between models of a brand of ergometer (12,15) is quite good but there are differences in the physiological responses to rowing on different ergometers (3,6,7). While the ergometer has aided in the advancement of the body of knowledge on rowing, rowers criticize the “feel” of the ergometer compared to rowing on water. During the recovery phase of the rowing stroke on water, the mass of the boat slides underneath the rower (7). On most brands of rowing ergometer the opposite occurs, the mass of the rower must move up and down the slide bar during recovery and leg drive (7).

Other than the single study where Urhausen, Weiler, and Kindermann (14) examined the differences in the heart rate-lactate relationship between on-water and ergometer rowing and found that, for a given level of lactate, heart rate values were significantly higher on the water, the relationship between rowing ergometer physiological data to on-water performance has not been studied extensively. The purpose of this study was to examine the relationship between rowing ergometer physiological and performance data to 2000m on-water rowing performance.

### Methods

#### Participants

Nineteen heavyweight male rowers (26.2  3.6 years, 92.2  4.3 kg, 192.2  4.5 cm) who were part of a Canadian National Team training camp participated in the study. Sixteen of the subjects went on to compete at the World Championships. All testing was done over a two-week period. All athletes agreed to participate in physiological monitoring as part of their training program. All procedures were approved by the Rowing Canada Sports Science and Medicine Committee.

#### Rowing time trial (2K row)

A 2000m time trial in single scull boats was performed on the first day of the investigation. Athletes reported to the lake at 7:00 am and were given 60 minutes to prepare for the time trial. Each athlete performed a self-selected warm-up similar to what they would use before racing. Following the warm-up, athletes reported to the start line and were sent off 30s apart. The time trial was also being used for ranking in the team selection process; increasing the athlete’s motivation to perform well. All athletes were familiar with the technique involved in sculling, having raced or trained in sculling boats in the previous six months.

#### Maximal 45s sprint test

A modified Wingate sprint test on the Concept II Model C rowing ergometer was performed 90 minutes following the maximal oxygen uptake test. Subjects performed a self-selected warm-up for 10 minutes. After the warm-up the ergometer was programmed for a 45s trial and the damper was set to provide a “drag factor” of 200, the maximum that is normally attained on a used ergometer. Because dust, worn parts and other factors can affect the amount of resistance provided by each stop in the resistance control dial, the “drag factor” is the method used on the Concept II Model C ergometer for standardizing the resistance setting between ergometers.

Participants performed an all-out 45s effort with verbal encouragement. Participants were asked to row full strokes on each stroke of the test rather than use the partial strokes that are often incorporated at the start of a race. Power (W) for every stroke was calculated and displayed on the Concept II computer and recorded by the investigators. Peak power (Peak 45) was the highest power obtained on any individual stroke. Mean power (Mean 45) was the average of the individual stroke power over the 45s trial as calculated by the Concept II computer.

#### Ergometer time trial (2K erg)

On the second day of the investigation all subjects completed a 2000m rowing ergometer time trial. Subjects reported to the ergometer centre at 7:00 am and were given 60 minutes to prepare for the time trial. They followed similar warm-up procedures to those they did for the on-water trial the previous day. All subjects started the time trial at the same time to create a competitive atmosphere. Prior to the start of the time trial each rowing ergometer was calibrated to a “drag factor” of 120, which is the drag factor that was in use for all selection-based testing.

#### Maximal oxygen uptake (VO2 max)

One week after the ergometer time trial, maximal oxygen uptake was measured using a Parvomedics True One metabolic cart (Parvomedics Inc, Park City Utah). Subjects performed a continuous incremental test on the Concept II Model C rowing ergometer. All subjects started at 290 watts, increasing wattage by 30 watts every three minutes throughout the test. The test was stopped when the subjects reached volitional fatigue or were unable to complete a stage within five watts of the intended wattage. Power at VO2 max (VO2 power) was determined as the average power for the final stage of the test as calculated by the Concept II computer.

#### Anaerobic Threshold (AT)

Anaerobic threshold (AT) in L/min of oxygen and power at anaerobic threshold (AT power) were determined from a plot of VE/VO2 using the procedure described by Caiozzo et al (1982).

#### Statistical Analysis

Pearson correlation coefficients (r) were calculated to establish the relationship between rowing ergometer physiological parameters and on-water rowing performance. A Student T-test was used to determine whether a difference existed between on-water rowing 2K time and 2K ergometer time. Statistical significance was determined using a probability level of p<0.05.

### Results

The 2K ergometer times (6:05.4± 5.5s) were significantly faster than the 2K row times (7:35.7 ±11.4s) (p<0.05). There was no correlation between 2K ergometer performance scores and 2K row performance scores (figure 1).

The mean VO2 max score was 5.9 ±0.4 L/min or 63.7±4.1 ml/kg/min. Power at VO2 max averaged 442.5 ±25.5 Watts. Both VO2 power (figure 2) and VO2 max (table 1) were significantly correlated to 2K erg but not to 2K row.

Anaerobic threshold (AT) occurred at 4.9 ± 0.3 L/min or oxygen which was 83.4± 4 percent of VO2 max. AT power ranged from 332-418 watts with a mean of 368.5 ± 21.3. AT power, but not AT, was correlated to 2K erg performance (Table 1). Neither was correlated with 2K water performance.

Peak 45 values ranged from 770-1134 Watts with a mean of 927.6 ± 95.3. There was a significant correlation between Peak 45 and 2K erg times but not with 2K row (table 1). Mean45 values ranged from 737-896 watts with a mean of 796.9 ± 74.2. There was no correlation between Mean45 and either 2K erg or 2K row.

### Discussion

Monitoring training and changes in physiological parameters is a challenge in rowing. Changes in weather and water conditions between tests can make it difficult to compare data and draw valid conclusions. Rowing ergometers were originally designed so that rowers in colder climates could continue to train in a fashion similar to their sport during periods when they could not be on the water. The ability to perform a rowing movement in a controlled environment made the rowing ergometer an attractive tool for monitoring changes in physiological variables. The findings of the present study are consistent with others (10,11) that have found significant correlations between rowing ergometer 2000m performance and VO2 max, AT and maximal power from a modified Wingate test performed on the same type of ergometers. The relationship between VO2 max and 2K erg performance in the current study is lower than that seen by Ingham et al. (4) and Cosgrove et al. (2), who found r = 0.88 and r = 0.85, respectively. Cosgrove (2) studied club level college-aged males of varying ability and while the Ingham study examined World Championship finalists both males and females as well as lightweight and heavyweight rowers were included in the analysis, creating a more heterogeneous group. The subjects in the current study were more homogenous in respect to their 2K erg times compared to other studies; 5:59-6:12 in the current study compared to ranges of 7:32.9- 8:07 in Riechman et al. (10), 6:20-7:26-in Russel et al. (11) and 6:30-7:45 in Cosgrove et al (2). Because a sport performance is multifaceted, with physiological, biomechanical, technical and psychological factors all playing roles in the final outcome, as the group performance becomes more homogenous it is less likely that any single physiological variable will be a strong distinguisher of performance.

One of the main purposes of determining the relationship between different physiological variables and rowing performance is to identify those variables that need to be trained to maximize performance (10). The current study demonstrates that although there may be a relationship between some physiological variables and rowing ergometer performance there is no relationship between physiological variables measured on a rowing ergometer and on-water performance in a group of elite heavyweight male rowers. This is the first study to directly compare the relationship between physiological variables determined on a rowing ergometer to on-water performance. Juimae et al. (5) examined the relationships among anthropometric variables, ergometer, and on-water performance, finding that only muscle mass correlated to on-water single scull performance while almost all anthropometric variables were related to ergometer performance. The lack of relationship between physiological or anthropometric predictors of ergometer performance and on-water performance is not surprising given that the relationship between on-water performance scores and ergometer performance scores can vary greatly across boat classes and levels of competition.

In two separate studies that examined the relationship between World Championship ranking or Junior World Championship ranking and 2000m ergometer performance Mikulic et al. (8) and Mikulic et al. (9) found significant correlations in 10 of 13 World Junior events and 17 of 23 World Championship events, but the standard errors were too large to establish accurate ranking predictions for any of the events. The highest correlations (r=0.92) were seen in the junior women’s single scull event, followed by the junior men’s single scull (r=0.80) and the junior women’s double scull (r=0.79). In contrast to the r = 0.12 of the current study, the senior men’s single scull had an r = 0.72. Some of the difference in results of the Mikulic studies (8,9) compared to the current study may be due to the nature of the variables correlated. In both Mikulic studies (8,9) the correlations were with final World or Junior World Championship rankings, whereas the present study looked at the relationship between actual times rowing on the water versus rowing the ergometer. In the Mikulic studies (8,9), the highest correlations were seen in sculling boats, particularly the singles. Athletes competing at a World Championship in sculling boats are normally specialists in that discipline. In the current study although all athletes were familiar with sculling and spent some of their time training in single sculls, 14 of the 19 were not sculling specialists. The lowest correlations in Mikulic’s work (8,9) were also seen in sweep rowers competing in larger boats r = 0.47 for the heavyweight men’s eight and r = 0.21 for the lightweight men’s eight. Ergometer rowing is technically more similar to sculling than to sweep rowing, the technical differences between the sculling specialists and non-specialists may explain the difference in correlations seen in the current study. This clearly supports the notion that there are differences between rowing ergometer performance and on-water rowing performance, particularly for sweep rowing athletes, and that physiological variables determined on the rowing ergometer may not be good predictors of performance on the water.

### Applications In Sport

This study reinforces what many coaches already know; there is more to a rowing performance than physiological test results or rowing ergometer performance scores. Ergometer rowing requires less skill than on-water rowing (10). Rowing technique on the water is a complex skill that requires balance, efficiency, and maintaining the boat speed during the recovery phase. These factors cannot be measured on an ergometer. This makes the rowing ergometer a good tool for studying and tracking physiological changes that occur during a rowing movement and can help coaches identify those athletes who have a large discrepancy between ergometer and on-water performances that may be technique related. However, caution needs to be exercised when trying to extrapolate rowing ergometer performance and physiological scores to on-water performance.

### Tables

#### Table 1

* p< 0.05

### References

1. Caiozzo, V.J., Davis, J.A., Ellis, J.F., Azus, J.L., Vandagriff, R., Prietto, C.A., & McMaster, W.C. (1982). A comparison of gas exchange indices used to detect the anaerobic threshold. J Appl Physiol. 53, 1184–1189.
2. Cosgrove, M., Wilson, J., Watt, D., & Grant, S. (1999). The relationship between selected physiological variables of rowers and rowing performance as determined by a 2000m ergometer test. J. Sports Sci. 17, 845-852.
3. Hahn, A.G., Tumilty, D.M., Shakespear, P., Rowe, P., & Telford, R.D. (1988). Physiological testing of oarswomen on Gjessing and Concept II rowing ergometers. Excel. 5, 19-22.
4. Ingham, SA., Whyte, GP., Jones, K., & Nevill, A.M. (2002). Determinants of 2000m rowing ergometer performance in elite rowers. Eur. J. Appl. Physiol. 88, 243-246.
5. Jurimae, J., Maestu J. Jurimae T. & Pihl, E. (2000) Prediction of rowing performance on single sculls from metabolic and anthropometric variables, J. Hum. Mov. Stud. 38, 123-36.
6. Lormes, W., Buckwitz, R., Rehbein, H., & Steinacker, J.M.(1993). Performance and blood lactate on Gjessing and Concept II rowing ergometers. Int J Sports Med. 14(Suppl 1), S29-S31.
7. Mahony, N., Donne, B., & O’Brien, M. (1999). A comparison of physiological responses to rowing on friction-loaded and air-braked ergometers. J Sports Sci. 17, 143-149.
8. Mikulic, P., Smoljanovic, T., Bojanic, I., Hannafin, JA., & Pedisic, Z, (2009). Does 2000m rowing ergometer performance correlate with final rankings at the World Junior Rowing Championship? A case study of 398 elite junior rowers. Journal of Sports Sciences. 27, 361-366.
9. Mikulic, P., Smoljanovic, T., Bojanic, I., Hannafin, JA., & Matkovic, B, (2009). Relationship between 2000m rowing ergometer performance times and World Rowing Championships rankings in elite standard rowers. Journal of Sports Sciences. 27, 907-913.
10. Reichman, S., Zoeller, R., Balasekaran, G., Goss, F., & Robertson, R. (2002). Prediction of 2000m indoor rowing performance using a 30s sprint and maximal oxygen uptake. J. Sport Sci. 20, 681-687.
11. Russell, A., Le Rossignol, P., & Sparrow, W. (1998). Prediction of elite schoolboy 2000m rowing ergometer performance from metabolic, anthropometric and strength variables. J. Sport Sci. 16, 749-754.
12. Soper, C., & Hume, P.A.(2004). Reliability of power output during rowing changes with ergometer type and race distance. Sports Biomech. 3, 237-224.
13. Tachinaba, K., Yashiro, K., Miyazaki, J., Ikegami, Y., & Higuchi, M. (2007). Muscle cross sectional area and performance power of limbs and trunk in the rowing motion. Sports Biomechanics. 6, 44-58.
14. Urhausen, A., Weiler, B., & Kinderman, W. (1993). Heart rate, blood lactate, and catecholamines during ergometer and on-water rowing. Int. J. Sports Med. 14, Suppl 1, 20-23.
15. Vogler, A., Rice, A., & Withers, R. (2007). Physiological responses to exercise on different models of the Concept II rowing ergometer. Int. J. Sports Physiol and Perf., 2, 360-370.
16. Womack CJ. Davis SE. Wood CM. Sauer, K., Alvarez, J., Weltman, A., & Gaesser, G. (1996). Effects of training on physiological correlates of rowing ergometry performance. J. Strength Cond. Res. 10, 234-238.

### Coresponding Author

Ed McNeely
560 Proudfoot Lane #1012
London, Ontario
N6H 5C9
Canada
613-371-8913
<e.mcneely@rogers.com>

### Author Bio

Ed McNeely is the senior physiologist at the Peak Centre for Human Performance and a partner in StrengthPro Inc. a Las Vegas based sport and fitness consulting company he is also a National Faculty member of the United States Sports Academy

Psychological and Physiological Effects of Aquatic Exercise Program Among the Elderly

U.S. Sports Academy2013-11-25T14:48:57-06:00January 2nd, 2012|Sports Coaching, Sports Management, Sports Studies and Sports Psychology|

### Abstract

The purpose of this study was to investigate the effectiveness of a 3-week daily physical activity program in outdoor spring hot water on joint mobility and mood state in 31 healthy elderly people aged between 60 and 82. The variables comprising mood state were positive engagement revitalization, tranquillity and physical exhaustion whereas joint mobility focused on shoulder flexibility. Subjects were allocated to one exercise group (n= 20) and one control group (n=11). The exercise group participated in a 45-minute-per-day aquatic exercise program in hot water for 20 consecutive days whereas the control group didn’t participate in any kind of organized exercise. Subjects were pre- and post-tested for the variables of mood state and shoulder flexibility. The results indicate that the elderly people who participated in the outdoor aquatic exercise program had significant improvements in positive engagement (z=2.4, p<.05), revitalization (z=2.8, p<.05), tranquillity (z=2.8, p<.05), physical exhaustion (z=2.7, p<.05), and shoulder flexibility (t=9.25, p<.05). No significant changes in these variables were observed in the control group. The results indicate that an aquatic exercise program is an alternative training method for improving psychological state and functional fitness performance in healthy elderly people.

**Key Words:** elderly, aquatic training program, mood state, joint mobility.

### Introduction

Evidence shows that increase of age is associated with the decline of many motor functions, (1,18,8,17) and the subsequent disenabling of performance of basic daily requirements. In addition, as individuals progress beyond 60 years of age, there are also tendencies for increased prevalence of mood disturbance; i.e., increased negative effect and decreased positive effect; (6). Past research on activity, aging and psychological well-being has concluded that exercise has a positive effect on psychological well-being (12). Exercise prescribed for the elderly differs from that of younger individuals in the method in which it is applied. Since an elderly person is more fragile and has to overcome more physical and medical limitations in comparison to younger individuals, training methods should not include high impact activities, and possibly a more gradual training progression (2).

Exercising in water has become widely prominent, and it has been reported that water exercise, especially in hot water, is therapeutically beneficial for elderly individuals (3).Water exercise is also a viable form of conditioning for those who are suffering orthopaedic problems (20). Training in water provides buoyancy and a required resistance for training, resulting in a training regimen that provides high levels of energy expenditure with relatively low impact on the joint extremities (21). Furthermore, this method of training is more motivating for overweight individuals because their bodies are not exposed to other participants (9). The authors of the present study hypothesized that participation of the elderly in a daily physical activity program in hot water, would improve their physiological and psychological status. Specifically, the purpose of this study was to investigate the effect of a daily physical activity program in an outdoor hot water spring, on joint mobility and mood state in older men and women.

### Methods

#### Subjects

Subjects in this study were 31 independently living elderly volunteers (6 males, 25 females) ranging in age from 60 to 82 years old (M = 71, SD = 5), with body weights between 63kg and 86kg (M=75.7, SD=5.5), and heights between 154cm and 163cm (M=156, SD=3). Subjects were recruited from a resort community in Edipsos, Greece during the summer of 2009. None of the elderly had been involved in any physical activity for at least 6 months before the exercise program began. They were assigned randomly into one experimental group (n=20) and one control group (n=11). Participants were graduates of elementary education (55.1%) and the majority of them were retired (71.5%). Their previous profession was 31.3% civil servants, and 42.3% free professionals. The majority of them (79.4%) were married and living with their spouses (65.4%). The greater part of the participants (65.5%) had a moderate daily mobility level according to the AAHPERD exercise consent form for adults (16). The subjects also had similar health status. Specifically, the participants of this study did not suffer from serious cardiovascular problems (coronary illness, infarction) respiratory or neurological diseases or serious orthopaedic problems. The more prominent health problems that they faced were of orthopaedic nature (34.4%) as well as high blood pressure (31.5%), which did not constitute obstacles to their participation in the research. Therefore, no subject was excluded for medical reasons. Subjects who missed more than four exercise sessions were excluded from the analysis.

#### Procedures

The experimental procedure was 20 days in duration, with 1 day of pretesting and 1 day of post-testing. The pre- and post-exercise assessments were performed by the same person for both groups. In an effort to ensure maximum compliance with the program, the same instructor conducted the intervention program in all groups. The intervention program took place in an outdoor swimming pool consisting of 100 % spring water at 34 ºC located in the Revitalization Club. The 12-item Exercise-Induced Feeling Inventory (7) was employed to assess the responses of positive engagement (enthusiastic, happy and upbeat), revitalization (refreshed, energetic and revived), tranquility (calm, relaxed and peaceful), and physical exhaustion (fatigued, tired and worn-out) that arise as a result of exercise participation. On a 5-point scale, subjects were asked to indicate how strongly they had experienced each feeling state immediately after one hour of exercise. The scale ranged from 0 (do not feel) to 4 (feel very strongly). Internal consistency exceeded .70 for each subscale (11). Flexibility measurement focusing on the shoulder was based on the Senior Fitness Test. This test was done in the standing position. The subject placed one hand behind the head and back over the shoulder, and reached as far as possible down the middle of the back, with palm were touching the body and the fingers directed downwards. They placed the other arm behind their back, palm facing outward and fingers upward and reached up as far as possible attempting to touch or overlap the middle fingers of both hands. An assistant directed the subjects so that their fingers were aligned, and measured the distance between the tips of the middle fingers. If the fingertips touched then the score was zero. If they did not touch, the distance between the fingers tips was measured (a negative score). A positive score was measured by how far the fingers overlapped. Subjects practiced two times, and tested two times. The best score to the nearest centimeter was recorded. (18).

##### Preprogram procedures

Prior to enrollment in the training program, all subjects who wanted to participate in the study were required to provide a signed letter of clearance from their personal general physician regarding their participation in the program. At the onset of the program, individuals were informed that they would be participating in a 45-minute-per-day aquatic exercise program for 20 consecutive days, and were given a brief demonstration of the program content. Information forms were then distributed to all individuals volunteering to participate in the investigation.

Once informed consent forms were read and signed by all subjects, a preprogram questionnaire packet was distributed. During the first day, both experimental and control groups completed the Revised Physical Activity Readiness Questionnaire (22) and a short demographic questionnaire assessing age, height, weight, and mobility level (16). Finally, before the training program began, each participant completed the Exercise-Induced Feeling Inventory (EFI), and participated in shoulder flexibility measurements.

##### Intervention Program

The experimental group participated in a 45-minute aquatic exercise program for 20 consecutive days. The control group was not involved in the exercise program but participated in spring water bath therapy. The exercise program was based on the Long Term Physical Activity Workshop (4), and consisted of 15 minutes of warm-up and callisthenic exercises for the improvement of flexibility, 10 minutes of resistance exercise, 10 minutes of endurance-type exercise (walking and dancing), and 10 minutes of cool-down exercise and leisure activities for the reinforcement of self-esteem and self confidence. The exercise intensity recommended by the American Heart Association varied from 50% to 75% of the subject’s maximum heart rate, as determined by a pilot study. However, no heart rates were recorded during the study. Instead subjects were taught to monitor their pulse rate according to perceived exertion (4). During exercise, the Borg Scale (6 – 20) was used to monitor perceived exertion relative to exercise intensity. Self-monitoring how hard their body was working helped them adjust the intensity of the activity by speeding up or slowing down their movements. The elderly exercisers were working in the Moderate (12-14) exertion range. Also, subjects were able to speak in their normal voices and tones during the exercise, in order to maintain a consistent heart rate and exercise intensity.

##### Post-program Procedures

At the conclusion of the aquatic exercise program, on the 21st day, each participant once again completed the EFI and shoulder flexibility measurement.

##### Stastistical Analysis

All data analyses were performed using SPSS, version 14.0. The normality of the distribution and the equality of variances for all variables were checked with the Kolmogorov-Smirnov test for each group. Bartlett-Box and Cochran’s C tests were used to identify differences among groups of the selected items. From the pretest, there were no differences beyond the .05 level of significance between any of the two groups. Wilcoxon test for two related samples was used to compare differences of means scores between the initial and final measurements of both the experimental and control groups in the mood state variables. Comparisons of means scores between the initial and final measurements of two groups in the shoulder flexibility parameter were performed using a paired t-test analysis.

### Results

The results revealed significant differences between pre- and post- measures for the experimental group regarding the four subscales of mood state (Table 1). In contrast, there were no changes in mood state for the control group at pre- and post- measures on any of the 4 subscales. As shown in table 1, after a 45-minute-per-day aquatic exercise program for 20 consecutive days, there was a marked increase in reported variables of mood state for the experimental group while the control group showed no changes during the same period of time.

The aquatic exercise program induced significant improvement in shoulder flexibility. In particular, the t-test for paired groups analysis revealed that shoulder flexibility had significantly improved in the experimental group (t=9.25, p<.05), while no significant difference was observed in the control group (t=0.89, p>.05). Scores for the pre- and post-tests for both groups on the selected variable are shown in figure 2.

### Discussion

The results reveal that a 45-minute-per-day aquatic exercise program for 20 consecutive days produced significant improvements in mood state as well as in shoulder flexibility of sedentary elderly people. The lack of improvement for the subjects of the control group gives additional support to the idea that the program applied was responsible for the improvement of the experimental group. It seems that even a 20-day aquatic exercise program is capable of producing significant changes in basic physiological and psychological variables similar to the ones in the present study. Significant improvements in the elderly in a number of physical abilities after following a training program have been reported by researchers. Takeshima et al., (21), reported significant improvements in 45 elderly women (60-75 yrs. of age) who had participated in a 12-wk supervised water exercise program, 70 minutes per day, 3 days per week, in cardiovascular fitness, muscle strength and power, flexibility, agility, and subcutaneous fat. Additionally, the exercising group demonstrated an improvement in pulmonary function and blood lipids. In 2006, Tsourlou et al. (23), reported significant improvements in a number of physical abilities (maximal isometric torque of knee extensors and knee flexors, grip strength and dynamic strength during chest press, knee extension, lat pull down, and leg press, jumping performance functional mobility, and trunk flexion) in 22 healthy women over 60 years of age, after their participation in a 24-week aquatic training program.

Furthermore, these results are consistent with the conclusions of previous studies reporting changes in elements of psychological well-being in terms of physical activity. These changes are referred to as enhanced perceptions of mastery (11), improved life satisfaction (14), and mood (15,5,10) as well as reduced negative affect of psychological state. Moreover, similar results were found in a 12-week investigation by Whitlatch et al. (24). In addition, Moore and Blumental’s narrative review (13) with older adults, focusing on specific elements of mood, supported the positive role of aerobic exercise in reducing negative affect.

### Conclusions

The results of the present study indicate that water-based exercise elicits significant improvement in psychological well-being and joint mobility in the elderly. Specifically, a 45-minute–per-day aquatic exercise program in hot water for 20 consecutive days can result in considerably better positive engagement, revitalization, and tranquillity, as well as joint mobility focused on shoulder flexibility, in older men and women. Moreover, it may provide additional benefits by reducing negative mood in terms of physical exhaustion. Therefore, water-based exercise is one of the most potent alternative training methods for improving basic elements of their psychological and physiological health.

### Applications In Sport

Overall, the findings of the present investigation should be adopted by public and private institutes that offer water-based exercise programs for older men and women. Elderly people’s participation in a 45-minute aquatic exercise regimen for 20 consecutive days with various enjoyable activities results in significant improvements to general shoulder range of motion, facilitating their performance at common activities of daily living and allowing them to maintain independent lifestyles. Besides, their participation in this kind of program makes them familiar and sociable persons. This suggests that water-based exercise may be a valuable short- term strategy for the self regulation of mood in older people. Finally, practical exercise prescriptions from instructors must take into account the special interests and needs of the elderly, inducing happiness, tranquillity, pleasant tiredness and, at the same time, initiating progressive improvement in general physical and psychological health.

### Acknowledgments

We acknowledge the participants for their voluntary involvement in this study.

### Tables

#### Table 1
Means, Standard deviations and Wilcoxon test for mood state variables in the pretest and post-test measurements for elderly people in experimental and control groups.

### Figures

#### Figure 2
Pre-test and Post-test shoulder flexibility in older men and women in both experimental and control groups.


### References

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### Corresponding Author

Matsouka Ourania
Lecturer
Department of Physical Education & Sport Sciences
University of Thrace
Komotini, 69100
Greece
<oumatsou@phyed.duth.gr>

Comparison of Two Different Training Methods for Improving Dribbling and Kicking Skills of Young Football Players

U.S. Sports Academy2016-10-12T15:02:12-05:00December 30th, 2011|Sports Exercise Science, Sports Management, Sports Studies and Sports Psychology|

### Abstract

The purpose of this study was to examine the effectiveness of two different training methods on the performance improvement of dribbling and kicking technical skills of young football players (8-11 years old). The sample consisted of 90 boys aged 8 to 11 years old. Participants were randomly assigned to three practice groups of 30 participants each group, that is, two experiment groups (experiment group A, experiment group B) and one control group. Practice according to training method A included a 20 min warm-up period, 20 min of practicing technical football skills, 20 min game and a 5min cool-down period. Practice according to training method B included a 20min warm-up period followed by 45 min of technical skills’ training with no time given for a cool-down period. Control group participants followed the physical education program according to school’s curriculum. Three measurements were conducted to evaluate technical skills. Initial measurement took place prior the onset of the program, whereas the second measurement conducted after the eighteen (18) weeks of training followed by the final measurement four (4) weeks after the end of the program to assess maintenance of skills attained. Results showed improvement in dribbling and kicking skills of the two experiment groups, whereas no statistically significant differences were noted in the control group for the specific skills measured. Furthermore, it was noted that benefits of training method B maintained for a longer period of time compared to training method A. Future studies using larger samples, in different age ranges, and sports are needed in order to further verify probable advantage of training method B compared to other methods.

**Keywords:** training methods, comparison, children’s football, dribble, kick.

### Introduction

In European football, two training models dominate the field during the last decades on which all training choices that are available to coaches are based. Training model A (15) follows a specific training implementation procedure in football. That is, a training unit, that includes a warm-up period consisted of exercises with or without the use of a ball, the main part aiming to improve performance of technical football skills with no previous fatigue present as no physical condition program precedes followed by a football game adapted to the objectives of the main part. Last, a cool-down period concludes the training unit and signals the end of practice.

In training model B (16) the procedure followed during a training unit in football includes a warm-up period that is adapted to the objectives of the main part with ball use, followed by the main part where only technical training takes place. This pattern is in effect until the onset of the microcycle involving technique application, as from this point and beyond more football game is used. In this training model no cool-down period exists on the contrary to training model A. According to Lehnertz (14), technical training should constitute the final piece of a training unit as dynamic registrations that are created definitely require a consequent phase of consolidation (13).

In summary, differences that are observed between the two training methods during the six microcycles could be described as follows: Training method A includes a 20 min warm-up period, 20 min performance improvement of technical football skills, 20 min football game and 5 min cool-down period. In training method B, a 20 min warm-up period takes place, followed by 45 min for improving performance of technical football skills. According to training method B, no cool-down period is needed and no particular importance and time is given for such purpose.

Planning, guidance, and application of training according to these two models have led previously to successes as well as to failures. Up to now, it is not possible only through the examination of results (success or failure) to say with certainty which is the most advisable method in football training.

Although it is extremely difficult to locate relative studies concerning the comparison of complete and different training methods emphasizing football technique, many researches use football ability tests to measure pass accuracy, ball control and dribbling (6,20,5). Van Rossum and Wijbenga (21) reconstructed Kuhn’s (12) technique tests for football players and applied them in Dutch children teams. Overall, six technique tests are considered by the researches as reliable and valid for football practice: 1. Kick accuracy, 2. Ground pass with accuracy, 3. High pass with accuracy, 4. Dribble, 5. Controlling ball on air using one leg, and 6. Controlling ball on air using two legs.

French and Thomas (10) research conducted to young basketball players aged 8-10 and 11-12 years (high level athletes, beginners, and no-athletes) to examine the importance of knowledge element on the development of basketball skills showed that high level athletes of both age ranges exhibited a better performance in dribbling and shooting skills. Correlation analysis showed that knowledge relevant to sport was related to choice of answer, whereas kicking and dribbling skills were in relation to the motor elements of control and execution. Furthermore, it was noted that the development of relative to sport knowledge plays an important role in the high performance of 8-12 years old children, with cognitive development occurring faster compared to motor development (10).

Das and Benerjee (7) investigated effectiveness of periodicity according to the duration of a football training program applied to young football players. Participants (10-12 years old children) were examined with the use of motor and technical skills’ tests in the beginning of the training process and after four (4), six (6), and eight (8) weeks of training. Motor skill tests included evaluation of 30m speed, explosive force, 4 times of 40m agility, flexibility, and 8 min continuous running. Technique tests included measurement of successive goal shots, controlling ball on air using legs, distant pass accuracy, throw-in distance, and dribbling in specific time-period. Regarding motor skill tests results showed that four weeks were enough to improve agility and aerobic capacity and six weeks of training were necessary to improve muscle power and speed, whereas no improvement was noticed concerning flexibility in eight weeks time. In technical skills testing, after four weeks of football practice an improvement in shooting towards target, controlling ball on air and distant pass was noticed, whereas in throw-in and dribbling skills improvement was evident after six weeks. All technical skills were significantly improved after eight weeks of training (7). According to the results of (7) study, a six weeks training program could be considered sufficient to produce evident training results.

Venturelli, Trentin and Bucci (22), evaluated three training methods for improving muscle power of young football players after eight weeks of practice. Twenty one high level football players formed three groups (A1, B1, C1) of 7 individuals each group and followed different training programs. Α1 group followed a free weight-lifting program, B1 practiced according to a specific exercise program and C1 group followed a combined training program. Results showed that the combined training program (weight lifting for thorasic muscles along with jump and speed exercises) produced better results compared to the other two training programs. Fontes et al. (2007), evaluated maximum cardiac frequency and anaerobic threshold of 10 professional A Division category Brazilian football players on three measurements taking place during training that included “technique”, “tactics”, “game with terms” and “regular game”. According to the results, technique had a lower intensity compared to other analyzed types of training, whereas no differences were noted during training regarding “tactics”, “game with terms” and “regular game”. Furthermore, “game with terms” seemed to offer higher intensity compared to other training conditions. In conclusion, although decrease of field dimensions increases, in turn, ball contact, intensity seems to lessen due to the reduction of high in energy actions of football players.

Taxildaris (19), using Heidelberger Basketball Test (4) in adolescent age girls (11-14 years old), examined improvement of football skills’ technique level between a control group of female athletes that did not follow any exercise program and two experiment groups following a “general” exercise program and a “specialized” exercise program. Results showed that group of “specialized” practicing exhibited a statistically significant difference compared to group following the “general” exercise program in terms of pass and shooting technical skills measured. Furthermore, although not statistically significant a tendency in favor of the specialized training group was noticed in dribbling and penetration & shooting skill, whereas control group did not present any progress.

As it is shown, motor and technical football tests have been widely applied by many researches, with special attention given to kicking and dribbling abilities as the most important skills that each football player should possess to the highest degree. Dribbling virtuosity of the footballer who has the ability to control the ball with his legs and surpasses his opponents, is the most effective and fascinating technique that is always admired by every athlete and spectator (3). Dribble use allows possession of ball throughout the football court until a player is free of guard in a favorable position to receive the ball and score.

According to (11), dribble as well as ball guidance presumes the covering of ball with the body, eye-contact with the team-mates, peripheral sight, avoidance of the opponents’ attack and surpassing of opponents using manoeuvres. Furthermore, although dribbling style of each football player is unique, one can recognize the short dribble, the dribble of ball between opponent’s legs, dribble of pretending going left and then heading to the opposite (right) direction, ball transport to the left and then dribbling to the right, speed dribble, dribble with turn and “scissors” dribbling (1,2,8,11,18).

Dribble technique requires high concentration and energy and a relaxed body in perfect balance, whereas it also demands continuous control of the game, placement of the opponents in the football court and transition of team-mates. In real game situations in modern football, defenders are always more that the offending players, taking systematically position in front of their goalkeeper’s post. Therefore, in order to penetrate this defensive “wall” and apart from other technical or tactical inventions, dribbling ability of players is a very important skill for the outcome of the game that is added to the overall strategy of the team (2).

The objective goal in football is scoring, thus, scoring ability with legs, head or with the ball stopped is of vital importance for every football team that requires the best possible technique aiming to send the ball to the opponent goal-net. All other skills in football are of little importance in case players do not take advantage of their opportunity to kick the ball and score. Kicking the ball constitutes the final expression of the game since every time a player attempts to score that doesn’t mean he will succeed, however, without performing this skill is very difficult to accomplish anything. A good scoring ability requires the player to be capable to kick the ball in narrow spaces under the pressure of the opponent, plus, team-mates’ contribution is needed to provide the opportunity for attempting to score under the best possible conditions (11).

The development of such important technical skills in football should start from a young age. In case a proper selection of training method during practicing and acquisition of these skills takes place, the young athlete will develop his technique at his full potential as well as his effectiveness to perform successfully during training and official football games. However, reviewing the literature it seems that no researches are conducted examining the effectiveness of training methods A and B on the development of such important skills.

The purpose of this study was to compare the effectiveness of the two training methods according to model A (17, 15) and model B (16) in the performance improvement of basic football skills of young football players (8-11 years old).

### Methodology

#### Sample

Random selection was used to choose children from the 3rd to 6th school classes first by selecting nine (9) out of thirty-three (33) primary schools of Volos city. In each school researchers provided information to children and their legal guardians about the purpose of the study.

The initial sample of children showing interest to participate was 98 children who were also informed that their participation was voluntary. Individuals were also aware that all the information provided was confidential and they were free to withdraw at any point without prejudice. Overall, the legal guardians of two children did not sign the consent form whereas 6 children decided not to participate prior the initiation of the first training session. As a final result, 90 boys, aged 8 to 11 years old, all primary school students constituted the final sample of this study. Participants were separated randomly in three (3) groups (experiment A, experiment B and control) of 30 participants each group (See Table 1). Written informed consent was obtained from all participants and their legal guardians prior participation. All experimental procedures were approved by the Institutional Review Board of Ethics Committee for investigations involving human subjects.

#### Training program

Overall duration of research was 22 weeks. Initial measurements of all participants along with participants’ separation in three groups (experiment group A, experiment group B and control group) were conducted prior the beginning of the training program (at the end of October. See Table 2). Next, the training period lasting 18 weeks followed with the final measurement taking place at the end of the training period (beginning of March). An additional measurement four (4) weeks after the end of the training program was also conducted to assess maintenance of skills attained.

Experiment groups followed different training programs focusing on the development of six technical skills (transition of ball, dribbling, pass, ball control, head and kick) with special attention given to the development and monitoring of dribbling and kicking ability. Experiment group A practiced according to training method A and experiment group B followed the training method B, whereas control group individuals did not participate in any football program and simply followed the physical education program according to their school’s curriculum, that included track and field events, general team sports’ practice (in basketball, volleyball, handball, and football) and dance.

The weekly program of the two experiment groups included two training units of 65 min each one (every Tuesday and Thursday) where technical skills were practiced and one training unit playing a 60 min football game (every Saturday). In each training unit, practice for the experiment group A according to training method A included a warm-up period with or without the ball, the main part of practicing technical football skills, a football game and a cool-down period. Practice according to training method B for the experiment group B included a warm-up period with the ball followed by 45 min of technical skills’ training with no time given for a cool-down period. In particular: Table 2.

Furthermore, training programs A and B for the participants of experiment group A and B respectively were divided in three middle-term cycles of six weeks duration each one, with each middle-term cycle including 6 micro-term cycle training units. In each middle-term cycle emphasis was given to the two out of the six skills practiced, whereas the other four skills were practiced as well but to a less quantity (See Table 3).

In addition, the six micro-cycles within each middle-term cycle were structured in pairs according to the aims pursued to achieve in every two training units. Exercises used in micro-cycles were the same so much in training method A as much as in training method B (See Table 4).

All training units were accomplished by two physical education (PE) teachers who were also qualified football coaches with specialty in football. Prior the beginning of research, the two PE teachers were informed and practiced on the application of each training program followed, that is, training program A applied by the 1st coach and training program B applied by the 2nd coach respectively.

#### Testing procedures

Russell (18) tests regarding dribbling and kicking skills were selected for the purpose of this research, as they are immediate, easy to measure and dynamic with no “static” exercises included such as kicking a still ball or controlling the ball on air. Furthermore, they have been devised and recorded by the English Football Federation and evaluated for a long period of research time by the Social Statistics Department of Southampton University, exhibiting a high level (0.90) of reliability (18). The advantage of Russell’s tests is that they are performed in real game conditions (5).

In each measurement its participant performs two skills during testing. The purpose of the kicking test was to gain the highest number of point by aiming to shoot the ball in to the half of the goal furthest away from the player shooting (18).

In dribbling test each individual was asked to imagine the markers as defenders and to dribble the ball as quickly as possible in front and away from markers, ABCD from the start of the course to the finish (18)

The coding system for the number of successful kicking efforts and dribbling in relation to time of skill execution was as follows (See Table 5).

These tests could also be used in the form of football exercises during training or combined in one more complex activity. In this way, the coach could assess technical skills of his players not only through testing but also through practice.

Technical skills’ evaluation of the participating children was conducted by four (4) physical education teachers who were also football coaches trained by an experienced instructor. Video tape recording to assess reliability of each coach as well as between coaches judging the results was used through the application of tests on 20 children other than those participating in the program. In particular, 20 children performed the two skills in question prior the onset of the program, their performance was videotaped and PE teachers were then asked to watch the video and evaluate each test two times. Intra-class k correlation factor to assess reliability between coaches was equal to 0.71 (k = 0.71) whereas for each coach, k intra-class correlation factor was 0.80 (k = 0.80).

Final measurement was limited in students who followed training programs consistently, three times per week for eighteen (18) weeks without absences. All students in each group loosing three or more training units were automatically excluded from the study.

#### Statistical analysis

Planning application included a 3×3 measurement (initial, final, maintenance) x Group (experiment group A, experiment group B and control group C) with repeated measurements (ANOVA repeated measures) used to locate possible differences existing among the training methods in terms of performance in dribbling and kicking skills. The importance of differences between the means of cells was examined with the application post hoc test of multiple comparisons Scheffe. The importance of difference between the medium averages of performance was examined using Scheffe post hoc test. Level of statistical significance was set at p0.05.

### Results

Cronbach a analysis, the most widely accepted technique of indicating reliability, was used to examine reliability of skills’ measurements in each group (See Table 6).

Pre-test measures showed that all 3 groups started from the same point of reference in term of base line performance prior the beginning of the intervention (dribbling F(2,87)=2.526, p=.086, and kicking F(2,87)=2.769, p=.068 skills).

Observing means of dribbling technical skill for the experiment groups A and B (See Table 7), an increased performance is noticed from the first (October) up to the third measurement (April). In the control group, an ascendant course of mean is observed in the second measurement (March) as well as a mean stabilization at the third measurement.

Performance difference between the three training methods in dribbling skill was checked with the application of two-way ANOVA, from which one was with repeated measurements. Mauchly’s test showed that the three groups exhibited homogeneity in their variance (p >.050).

The interaction between the two factors (skills and training methods) in relation to time was not found to be statistically significant (See Table 8).

Statistically significant differences were observed between the groups (See Table 9) in dribbling performance [F(2.87) =5.159, (p<.001)], that is, the effect of training methods was different concerning the development of dribbling skill in total measurements.

Application of Scheffe post hoc test showed the following statistically significant differences: a) between the second and third measurement (Scheffe value = 1.80 (p<.050) of training method B and control group program (See Table 7), b) on the third measurement between training method B and control group program (Scheffe value = 2.20 (p<.001) and c) on the third measurement between training method Α and control group program (Scheffe value = 1.70 (p<.050).

No statistically significant differences were noticed between training method A and training method B in dribbling performance (See Table 9), with a Scheffe value on the second measurement equal to.60 (p>.050), whereas on the third measurement Scheffe value was .50 (p>.050). Finally, no statistically significant differences were observed between training method A and control group program on the second measurement (Scheffe value= 1.20, p>.050).

Observing means of kicking skill for the experiment group A (Table 7), an increased performance is noticed from the first up to the second measurement followed by a decrease on the third measurement. In the experiment group B an increased performance from the first up to the second measurement is also observed, followed by a stabilization of performance on the third measurement. Control group exhibited a slight increased course of means from the first up to the third measurement.

As in dribbling skill, performance difference between the three training methods in kicking skill was checked with the application of two-way ANOVA, from which one was with repeated measurements, with Mauchly’s test indicating a homogeneity in groups’ variance (p >.050).

The interaction between the two factors (skills and training methods) in relation to time was not statistically significant (See Table 10).

Statistically significant differences were observed between the groups in kicking performance [F(2.87)=14.533, p<.001], that is, the effect of training methods was different regarding the development of kicking skill in total measurements (See Table 9).

Scheffe post hoc test application revealed the following statistically significant differences in kicking skill performance: a) on the second measurement (Scheffe value = 1.20 (p<.050) between training method B and training method A (See Table 9), b) on the third measurement between training method B and training method A (Scheffe value = 1.67, p<.001), c) on the second measurement between training method B and control group program (Scheffe value = 2.00, p<.001) and d) on the third measurement between training method B and control group program (Scheffe value = 2.40, p<.001).

Finally, no statistically significant differences were noticed between training method A and control group program in kicking performance with a Scheffe value on the second measurement equal to 1.20 (p>.050) whereas on the third measurement Scheffe value was .50 (p>.050).

Overall, comparison of means revealed statistically important differences existing as regards to performance improvement of dribbling and kicking football skills in total measurements resulting from the application of different football training methods.

### Discussion

The improved performance of dribbling skill recorded in this study as a result of 18 weeks of training is in agreement with (7) stating that a training program of at least six weeks duration can be considered as fair enough to produce positive training results in technical skills’ development.

Both training methods seemed to produce positive results related to an improved performance in dribbling skill up to a point where no differences were located. As a result, comparison between training method A and training method B revealed no statistically significant differences in final and maintenance measurement, although a superiority tendency of training method B compared to training method A was also noticed.

In particular, no statistically important differences in performance were noted between training method A and control group program apart from maintenance measurement. However, between training method B and control group program such differences do exist not only in maintenance results but in final measurement also, a finding that indicates the greater influence of training method B to improve dribbling.

As for kicking skill, comparison of means showed also a statistically significant improved performance resulting from training method B application. The improved performance in kicking skill recorded only for the participants of training group B is not in agreement with (7) study that in general a training program of at least six weeks duration is enough to produce positive results in technical skills’ development. In this study, findings suggest that after 18 weeks of training this was the case for dribbling but not for kicking skill improvement. Thus, it seems that not only duration of program according to (7) notion is important, but also the “nature” of skill itself. In addition measurements between groups during, point out an influence of the training methods on kicking performance that is different according to group.

Comparison between training method A and training method B illustrated statistically important differences in final and maintenance measurement. More specifically, individuals following training method B improved their kicking skill performance according to final measurement and sustained this performance until maintenance measurements. On the contrary, participants of training group A improved slightly their performance on final measurement whereas on maintenance measurement returned to their initial performance level. As an overall result, individuals practicing according to training method B exhibited a greater improvement of kicking skill performance compared to A group individuals up to a point that this improvement was statistically significant.

Furthermore, results showed that training method A and control group program did not produce performance differences in kicking ability as no differences were noted between the two methods in final and maintenance measurement. On the contrary, such differences were evident between training group B and control group in the same measurements. The fact that training method B significantly improved kicking skill performance compared to training method A and control program, indicates clearly the superiority of training method B in order to improve the specific skill measured.

Overall, results of this study showed that the use of training method B helped young players to improve performance more according to skill and to maintain training results for a longer period of time. Thus, findings suggest that when it comes to young players, football coaches should use more the training method B suggested by (16) compared to any other method (17, 15).

Future studies should examine whether the application of the two training methods produce different results in all fundamental football skills taught, apart from dribbling and kicking, that is, transition of ball, pass, head, and ball control. In this way, it would be ascertained whether training method B is more proper for teaching basic football skills to young players throughout their whole developmental period. Moreover, future studies should use larger samples, in different age ranges and sports (e.g. basketball, volleyball, handball etc) in order to further verify the probable advantage of training method B.

### Applications In Sport

Improvement of fundamental football technical skills influences to a great extent the progress of young footballers in their later athletic course and future (Van Rossum & Kunst, 1993). The findings of this study suggest that when it comes to technical skills’ improvement, football coaches of young players should use more training method B during the initial microcycles of each training session compared to any other method so as to maximize performance and achieve high dexterity levels of crucial football skills such as dribbling and kicking.

### Tables

#### Table 1
Mean age of groups

#### Table 2
Training unit of groups

#### Table 3
Middle-term cycle structure

Training Program (18 weeks)

#### Table 4
Structure of microcycles in pairs in each middle-term cycle

#### Table 5
Skills scoring system

#### Table 6
Cronbach’s α reliability analysis of results

#### Table 7
Arithmetic means, standard deviations and Schefe’s post hoc tests of significant differences between groups on the dribbling and kicking test

* pairs of groups between whom significant differences have been detected

#### Table 8
ANOVA means in dribbling and training methods according to time

#### Table 9
Independent Groups ANOVA Comparing Mean dribbling and kicking

#### Table 10
ANOVA means in kicking and training methods according to time

### Figures

#### Figure 1
Performance diagram of dribbling skill


#### Figure 2
Performance diagram of kicking skill


### Acknowledgments

I would like to express my gratitude to all young footballers and their parents who made this research possible with their willingness to participate. Also, I would like to thank the Ethics Committee of the University of Thessaly for its guidance throughout the whole research procedure.

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21. Van Rossum J.H.A., & Wijbenga, D. (1993). Soccer skills technique tests for youth players: construction and implications. In T. Reilly, J. Clarys and A. Stibbe (Eds), Science and Football II, (pp. 313-318). London: Spon.
22. Venturelli, M., Trentin, F., & Bucci, M. (2007). Strength training for young soccer players. Journal of Sports Science and Medicine Supplement (10), 80-81.

### Corresponding Author

Christos Plainos
Leivaditi 5
University of Thrace /Department of Physical Education and Sport Science
Nea Ionia 38446
Volos, Greece
<chplainos@yahoo.gr>
(697) 773-8230

Ticket Price Comparison of Double-A and Triple-A Affiliate Baseball Leagues

U.S. Sports Academy2013-11-25T14:51:21-06:00December 2nd, 2011|Contemporary Sports Issues, Sports Facilities, Sports Management|

### Abstract

As the economy continues to decline, sport managers realize that discretionary spending is limited. As such, sport managers are giving more consideration to price strategies within their own marketing mix as well as their comparison to other sport teams. The purpose of this study was to conduct a cross-sectional pricing investigation of individual teams by region within a Class-AAA and Class-AA league from the minor league baseball system. Data were obtained for ticket prices and fees from baseball team websites and phone interviews. Multivariate analysis of variance was examined for both Double-A and Triple-A leagues divided into regions. This study found no significant F (1,6) = .09, p = .77 differences for the highest ticket prices, F (1,6) = .09, p = .78, or the lowest ticket prices, and F (1,6) = .07, p = .80 for the groups within the Double-A Affiliate Texas League. However, a significance F (2,13) = 8.08, p = .00 was found in lowest ticket price within the Triple-A Affiliate Pacific Coast League, unlike highest ticket prices and fees which were not measurably different. Most minor league sport managers could consider this advantageous for promoting their entertainment as a good economic value.

**Key Words:** Baseball, Ticket Prices, Minor League

### Introduction

In light of recent economic times, sport organizations are faced with the challenge of maintaining a competitive marketplace while keeping a close eye on the bottom line. At the same time, the economic market watch (9) indicates consumers are becoming more selective with discretionary spending. Since sport consumption is not a fundamental cost of living, sport organizations have had to take a hard look at their strategic placement in the market. Pricing is a fundamental component of the marketing mix (6). Economic strategists have recommended complex formulas to establish pricing structures (2), while many sport organizations are opting for simplicity (6). In fact, Mullin, Hardy, and Sutton (6) said “the core issues in any pricing situation are cost, value, and objectives” (p. 215). In keeping with the simple pricing strategies that are the focus today, many sport franchises have utilized price comparisons as a simple and effective method of determining where a sport organization “fits” in the regional and league markets.

#### Price Comparisons

Determining the best fit in the sport consumer marketplace and how pricing strategies align with peer teams has become an emphasis for sport managers within the minor league baseball industry. As baseball ticket prices increase (1) and pricing strategies become complex (7), baseball consumers may look to alternative discretionary spending investments. Price comparison consumption behaviors have increased exponentially with the convenience of the Internet (5). Websites like Pricerunner, Amazon, and Shop.com have allowed potential consumers to price shop merchandise with several companies at the same time. Sport organizations have not considered price comparisons as a major influence on strategic pricing, due to the uniqueness offered in sport consumption. For example, sporting events occur sometimes great distances apart whereby a potential consumer may traditionally only be willing to travel 30 miles (4) and therefore do not offer a competitive risk to the local sport organization. However, as seen in recent articles (e.g., 3, 8) with a click of a button, price comparisons are made. In today’s tumultuous economy, many sport organizations have elected to market their event as a “value” within the discretionary spending category. This marketing technique is not only being utilized in relation to their direct sport competition, but also with discretionary spending businesses in general (e.g. cinema, concerts, other types of sporting events).

It was hypothesized that there was a significant (p < .05) difference between Texas and Non-Texas regions when comparing ticket pricing (highest price, lowest price, and ticketing fee) for minor league baseball Class-AA Texas League, as well as a significant (p < .05) difference among West, South, and Central regions when comparing ticket pricing (highest price, lowest price, and ticketing fee) for minor league baseball Class-AAA Pacific Coast League. Additionally, it was hypothesized that there was a significant (p < .05) difference between Double-A and Triple-A affiliate leagues when comparing ticket pricing (highest price, lowest price, and ticketing fee). This study examined price comparisons of minor league professional baseball teams segmented by league and region. A selection criterion was based upon geographic region of the minor league baseball teams as well as a comparison between Class-AA and Class-AAA organizations. Tables 1 and 2 represent the teams included in the study organized by league and region.

### Methods

#### Procedures

The data were collected through a variety of methods. Most of the information was collected through individual team websites. Some information was obtained though cold-calling via landline phones, and remaining data were provided through personal interviews. Once the data were collected, they were entered into a Microsoft Excel spreadsheet and reserved for future reference. Collection of data occurred over several months during the 2009 baseball season.

#### Data Analyses

Descriptive statistics, specifically means and standard deviations, were initially reviewed and reported for the Leagues, regions, and individual teams. The data obtained for the purpose of determining the research hypotheses were analyzed using MANOVA statistical methods. The independent variables were regions within the Texas League (Texas and Non-Texas regions) and Pacific Coast League (West, South, and Central regions). The dependent variables were the ticket pricing (highest ticket price, lowest ticket price, and ticketing fee), concession pricing (draft beer and hot dogs), and price for a family of four. Data were analyzed using Statistical Package for the Social Sciences (SPSS) version 17.0.

### Results And Discussion

#### Descriptive Statistics

Descriptive statistics were reported on all the dependent variables for the team, region and league. Table 3 provides the means and standard deviations found for highest ticket price, lowest ticket price, and ticketing fees.

Further examination of the ticket pricing established by the respective franchises indicates that the Triple-A teams (i.e. Pacific Coast League) have the greatest price point means. Specifically, the West region is higher than all other regions examined across all three price point values $22.63, $7.56, and $3.66 respectively. Inversely, the Southern region within the same league offers considerably lower price points for the highest ticket ($12.00) and lowest ticket ($5.50). All regions studied included fees into their ticket prices, particularly when utilizing online purchasing websites. Most regions were consistently adding approximately $2.00 to the overall price of the ticket.

Figures 1 and 2 show graphical comparisons between the highest and lowest ticket prices for the Texas League teams and Pacific Coast League teams respectively. As shown in both the Texas League and the Pacific Coast League price comparisons, there is great variability among teams when comparing the highest ticket prices; however within both leagues all the franchises have a relatively similar low cost for tickets.

The aforementioned price points did not include additional fees traditionally included in ticket prices for sporting events. As an example of how prices fluctuate with fees included in the price, Figures 3 and 4 show the price increase for the highest ticket price per franchise within both the Texas League and Pacific Coast League. Previously noted within Table 3, the West region of the Pacific Coast League had the highest ticket prices and once again that is reflected in Figure 4 as the fees are also the greatest among several West coast baseball franchises.

#### MANOVA Hypotheses Testing

The three hypotheses were tested by applying MANOVA to the data with SPSS software. The first group analyzed was the Texas League regions (Texas and Non-Texas) as the independent variable and the ticket prices (highest, lowest, and fees) as the dependent variables. As indicated in Table 4, there were no significant F (1,6) = .09, p = .77 differences for the highest ticket prices, F (1,6) = .09, p = .78, and the lowest ticket prices, F (1,6) = .07, p = .80, or the ticketing fees between Texas teams and Non-Texas teams within the Double-A Affiliate Texas League baseball.

As shown in Table 5, the same statistical principles were applied to the Pacific Coast League. The three regions, West, South, and Central, were the independent variables and the ticket prices (highest, lowest, and fees) were the dependent variables. There was a significant F (2,13) = 8.08, p = .00 difference in lowest ticket price between Pacific Coast League when divided by region. A Scheffe post hoc analysis revealed that the South region was significantly different from both the West (p = .00) and the Central (p = .04) regions. The South region had the lowest of the low ticket prices with an average of $5.50 as compared to the West which was $7.56 and the Central at $7.25.

Table 6 shows the difference between the Double-A Texas League and the Triple-A Pacific Coast League ANOVA source table. As noted in the Table, there were no significant F (1,22) = 1.91, p = .18 differences for the highest ticket prices, F (1,22) = 4.11, p = .06, the lowest ticket prices, and F (1,22) = .66, p = .42, or the ticketing fees.

### Conclusions

As more sport franchises compete in this challenging economic market, the need to maintain a positive public image is imperative. Baseball ticket pricing has increased substantially (1) and complex ticket prices could potentially confuse the consumer (7). As a means of determining the best fit in the sport consumer marketplace and how pricing strategies align with peer teams, leagues are examining ticketing price points. This is a simple marketing approach in line with sport marketing professionals (6). Since the advent of the internet price comparison shopping, consumers are able to make buying decisions with a simple click of a button (3, 8). With that said, sport franchises are more conscious than ever of how their ticket prices compare to their competitors’. This research determined, through mainly website analysis, that most of the ticket prices within Double-A and Triple-A baseball affiliate leagues were similar to competition franchises located within their regions. The only exception was found in the Triple-A Pacific Coast League where the South region had substantially lower low-end ticket prices (more similar to that of the Double-A Texas League). As the consumer becomes savvier with online price comparisons, and as economic discretionary spending continues to decline (9), knowing where a team fits within the market offers a greater promotional advantage. Future research may consider examining the impact of how price comparisons can improve sport franchise marketing potential (e.g. illustrating the “value” of minor league entertainment) and measure spectator attitudes toward region price comparisons.

### Applications In Sport

As the present economy is depressed and the future market is unpredictable, discretionary spending on sport entertainment may continue to decline. As such, sport managers within the minor league structure are determining the best approach to continue financial feasibility. This study revealed a common price point for minor league baseball organizations with similar attributes. Most importantly, however, this study revealed that the lowest ticket price in most minor league venues is still relatively affordable. This offers a unique marketing perspective for the increased demand for discretionary spending and sport management organizations should capitalize on this marketing opportunity.

### Acknowledgments

The authors would like to thank graduate research assistant, Lindsey Eidner, and undergraduate research assistant, Nick Garcia, for their invaluable contributions to data collection and analyses of this research endeavor.

### Tables

#### Table 1

Texas League teams organized by region.

#### Table 2

Pacific Coast League teams organized by region.

#### Table 3

Descriptive statistics (mean ± standard deviation) for the baseball teams separated by region.

* Ticket prices are in US dollars
** Fees were team specific, examples included online convenience charges, facility improvement fees, and taxes

#### Table 4

MANOVA source table for the Texas League by region.

* p < .05

#### Table 5

MANOVA source table for the Pacific Coast League by region.

* p < .05

#### Table 6

MANOVA source table for the Double-A Texas League compared to the Triple-A Pacific Coast League.

* p < .05

### Figures

#### Figure 1

Texas League individual franchise highest and lowest ticket prices.


#### Figure 2

Pacific Coast League individual franchise highest and lowest ticket prices.


#### Figure 3

Texas League highest ticket price with franchise-specific fees included.


#### Figure 4

Pacific Coast League highest ticket price with franchise-specific fees included.


### References

1. Alexander, D.L. (2001). Major league baseball: Monopoly pricing and profit-maximizing behavior. Jounal of Sports Economics, 2, 341-355.
2. French, C.W. (2002). Jack Treynor’s ‘Toward a theory of market value of risky assets’. Social Science Research Network. Retrieved April 09, 2010, from <http://papers.ssrn.com/so13/papers.cfm>.
3. Henderson, Dan. (2007, December 13). Online or offline, price comparison tools help consumers shop smart. The Free Library. (2007). Retrieved April 09, 2010, from <http://www.thefreelibrary.com/Online or Offline, Price Comparison Tools Help Consumers Shop Smart-a01073766340>.
4. Jallai, T. (2008). Development of fan loyalty questionnaire for a Double-A minor league baseball affiliate (Master thesis, Texas A&M University-Kingsville, 2008).
5. Lake, C. (2006). Shopping comparison engines market worth £120m-£140m in 2005, says E-consultancy. UK & Global News Distribution. Retrieved April 09, 2010, from <http://www.ukprwire.com/Detailed/Computer_Internet_Shopping_Comparison_Engines_market_worth>.
6. Mullin, B.J., Hardy, S., & Sutton, W.A. (2007). Pricing Strategies. In Human Kinetics (3rd), Sport Marketing (pp. 213-230). Champaign, IL: Human Kinetics.
7. Rascher, D.A., McEvoy, C.D., Magel, M.S., & Brown, M.T. (2007). Variable ticket pricing in major league baseball. Journal of Sport Management, 21, 407-437.
8. Simonds, M. (2009, April 29). Online price comparisons: Easing off your shopping experience. Articlesbase. (2009). Retrieved April 09, 2010, from <http://articlesbase.com/shopping-articles/online-price-comparison-easing-off-your-shopping-experience>.
9. U.S. Bureau of Labor Statistics. (2008). The consumer expenditure survey: Thirty years as a continuous survey.

### Corresponding Author

Liette B. Ocker, Ph.D.
Department of Kinesiology
Texas A&M University – Corpus Christi
6300 Ocean Drive, Unit 5820
Corpus Christi, TX 78412-5820
O (361) 825-2670 F (361) 825-3708
<Liette.Ocker@tamucc.edu>

Using Team Efficacy Surveys to Help Promote Self-and-Team-Efficacy among College Athletes

U.S. Sports Academy2013-11-25T14:56:15-06:00December 2nd, 2011|Sports Management, Sports Studies and Sports Psychology|

### Abstract

The purpose of this study was to track and understand attitudinal changes and trends among 3 NCAA Division I intercollegiate teams at the United States Air Force Academy (USAFA). We wanted to see if surveys of team efficacy would help promote self-and-team efficacy with respect to team goals and outcomes. Measures of team efficacy and locus of control were measured throughout the season: preseason, mid-season and postseason. Even though the results varied slightly for each sport, common trends were found with respect to team efficacy and their perceived chances for success and team history. Team goals did not fluctuate much throughout the season. However, results from the survey showed a significant drop in team efficacy for both the baseball and women’s basketball teams from preseason to midseason for both internal locus of control: baseball, t(15) = 3.53, p = .003); women’s basketball, t(15) = 3.67, p = .002. A significant drop in the teams external locus of control was also observed for both baseball, t(15) = 4.43, p < .001 and women’s basketball, t(15) = 2.95, p = .010. However, for the hockey team, there was not a significant drop in internal locus of control, t(15) = 1.23, p = .237 or in external locus of control, t(15) = 1.10, p= .289. As the baseball and women’s basketball teams lost more games both their internal and external locus of control dropped. Accordingly, because the Hockey team did not lose as many games from midseason on their locus of control measures did not experience any drop-off.

**Key Words:** team efficacy, coaching, locus of control

### Introduction

In order to be able to contribute to a team, one must first be confident in one’s own ability to support the team framework. According to Bandura (1) self-efficacy describes the level to which an individual can successfully perform a behavior required to facilitate a specific outcome. Assuming that the individual possesses the skills required to perform the task, self-efficacy is hypothesized to positively influence performance (14). This positive relationship between success and self-efficacy is empirically supported in studies relating to human endurance (25), as well as in the sport of baseball (9). In research targeting task-specific efficacy, supportive evidence suggests that state or task-specific self-efficacy is related to job performance (24) which, in turn, suggests that self-efficacy may also correlate with job performance.

Collective-efficacy has been found to regulate how much effort a group chooses to exert in accomplishing certain tasks, and its persistence in the face of failure (2). Mischel and Northcraft (17) suggested that the cognition of “can we do this task?” is different from the cognition of “can I do this task?” Hodges and Carron (11) and Lichacz and Partington (16), using experimental laboratory tasks, found support for the hypothesis that teams with high collective-efficacy outperformed low-efficacy teams, and that performance failure resulted in lower collective-efficacy on successive performance trials. Prussia and Kinicki (20) also found that collective-efficacy was related to collective goals and performance. Spink (23) found support for a relationship between team cohesion and team-efficacy for elite athletic teams but not for recreational teams. Teams with high collective-efficacy were higher in team cohesion than were teams with low collective-efficacy. Similar studies have indicated that team-efficacy and potency are related positively to performance (10, 13).

Feltz and Lirgg (8) defined team-efficacy as the consensus among players’ perceptions of their personal capabilities to perform within the team. In order to study team-efficacy, Feltz and Lirgg followed one hundred sixty intercollegiate hockey players through the course of a season. They found that team victories increased team-efficacy and team defeats decreased team-efficacy to a greater extent than player efficacy beliefs. They also found a significant decrease in team-efficacy after losing competitions. This opened new doors in the study of team-efficacy because they compared the change in efficacy in times of both success and failure.

#### Attribution Theory

Attribution theory focuses on how people explain their success and failure. According to Weiner, Nierenberg, and Goldstein (26), success and failure are perceived as chiefly caused by ability, effort, the difficulty of the task, and luck. This view, popularized by Weiner, holds that thousands of outside influences for success and failure can be classified into two categories. The first of these categories is stability. Stability is a factor to which one attributes success or failure as fairly permanent or unstable. Factors such as ability, task difficulty, and bias are perceived as relatively stable, whereas other causes, such as luck, effort, and mood are subject to moment-to-moment, periodic fluctuations and are considered unstable.

#### Locus of Control

Locus of control distinguishes two types of individuals: internals, who perceive the likelihood of an event occurring as a product of their own behavior, and externals, who view events as contingent on luck, chance, or other people (22). Causes internal to an individual are ability, effort, and mood. External factors are task difficulty, luck, and bias (26). Team-efficacy includes factors such as team cohesion and the ability of individual players; both internal and stable. Because individuals judge their capabilities partly through social comparisons with the performance of others, it is reasonable to believe that teams will react in the same manner by comparing their collective competencies with their opponents (8,19). Therefore, if a team is comprised of members that ascribe performance success to stable causes, they will expect these outcomes to occur in the future. If team members attribute their success to unstable, external factors, team-efficacy will be much lower. In contrast to these beliefs, Barrick and Mount (5) found no relationship between job performance and stable factors, such as emotion.

In a recent study of NCAA division one baseball players, DeRohan and Nagy (7) found evidence, in support of this theory, suggesting that internal locus of control is more dependent on success and failure then vice versa. Our question centers on a team setting and probes the link between success/failure with locus of control and how they might regulate team-efficacy? The purpose of this study was to track and understand attitudinal changes and trends among 3 NCAA Division I intercollegiate teams at the United States Air Force Academy (USAFA) and see if team efficacy would help promote self-and-team efficacy with respect to team goals and outcomes.

### Methods

#### Study 1: Baseball

##### Participants

Twenty-five male division 1 baseball players and three coaches at the USAFA participated in this study. The player’s ages ranged from eighteen to twenty-four years. Two of the three coaches were in their second year at the Academy. The third coach was in his first year at the Academy. The players and coaches volunteered for this study and did not receive compensation for completing the surveys.

##### Surveys

Preseason, mid-season, and end-of-season surveys were administered to the players and coaches. Each participant took a core of 19-item survey. The surveys measured team-efficacy, attribution theory, locus of control, and demographic information before, during, and after the season of play. The surveys contained restricted-item questions based on six point Likert-type scales ranging from strongly disagree to strongly agree.

##### Procedures

The researchers administered the surveys when the team was all together (e.g., team meetings). The participants took the surveys in the presence of at least one researcher administering each administration of the survey. Upon completion, the researchers collected the surveys from each athlete and coach and placed them in his folder. A mid-season survey was administered the day before the first conference games were played. The same instructions were given as they appeared on the first survey. Upon completion, the administrator collected the surveys from each athlete and coach and placed them in his folder. The same protocols and instructions were followed for the third and final survey that was administered the day after the final conference game was played. Again, upon completion the researchers collected all the surveys and all the data was entered into spss. It is important to note that the coaches never had access to the players’ surveys and the players never had access to the coaches’ surveys throughout the study.

#### Study 2: Basketball

##### Participants

Sixteen female division 1 basketball players from the USAFA basketball team volunteered to participate in this study. One player was eliminated from the study as she did not participate throughout the entire season. Players in this study ranged from 18 to 23 years old. The players and coaches did not receive compensation for completing the surveys.

##### Surveys

The same protocols used for the baseball team were also used for the basketball team. The only difference was the number of surveys administered throughout their season. Whereas the baseball team only had three surveys during their season, the women’s basketball team took a total of six surveys throughout their season: a preseason, four during the season, and one post season survey.

##### Procedures

The same instructions, protocols, and procedures used with the baseball team were used for the women’s basketball team. Upon completion, the researchers collected all surveys for subsequent data compiling and analysis.

#### Study 3: Hockey

##### Participants

Twenty-seven division 1 hockey players and two coaches from the USAFA hockey team participated in this study. Participants ranged in age from 20 to 24 years. All players and coaches agreed to volunteer for this study and did not receive compensation for completing the surveys. All participants were treated according to the American Psychological Association’s ethical guidelines.

##### Surveys

The same core of 19-questions used in the baseball and women’s basketball surveys were also used to survey the hockey team. Like the baseball surveys, the hockey surveys were administered three times during their season: preseason, mid-season, and end-of-the season.

##### Procedure

The same protocols and procedures were used to administer the first two surveys to the hockey team. However, to convenience the hockey players at the end of their season, the researchers gave the postseason surveys to the hockey team captain who agreed to administer it to the team before their final practice leading up to their tournament. Each player took the survey, returned it to the team captain, who then gave them all to the researchers.

##### The Present Study

IRB approval was obtained prior to the start of our investigation. For each team: men’s baseball, women’s basketball, and men’s hockey, we developed two specific hypotheses to address the differences and trends for each team studied at the United States Air Force Academy (USAFA). For example, the baseball team and the women’s basketball team have not been very successful in recent team history. However, based on the trends of wins and losses, the women’s basketball team is on a slightly upward trend in wins whereas the baseball team has maintained a fairly constant trend in wins. The hockey team on the other hand has enjoyed more success in recent history, compiling a significantly higher percentage of wins than the other two teams under study. As a result, we expected to find differing results from these teams based on the team-efficacy levels and their histories of success

### Statistical Analysis

The researchers entered the data generated by the surveys into the Statistical Package for the Social Sciences (SPSS version 14.0) for analysis. Players were then organized according to the last four digits of their social security number. For testing “changes in team-efficacy” and “internal/external locus of control” we created separate constructs. These constructs included internal locus of control, external locus of control, and team-efficacy averages for each survey. In order to establish our efficacy scale, we aggregated the results from eight separate six-point Likert-type scales. Each question targeted team-efficacy individually, but together created the team-efficacy construct. This same process was repeated for the corresponding eight questions in every survey in order to create the efficacy construct for each team. All data were compiled and entered into SPSS for analysis.

#### Baseball

Our first hypothesis was that the margin of victory would be related to team-efficacy. The margin of victory average of each particular two week period was then paired with the respective team-wide team-efficacy score, and graphed as a linear regression (Figure 1). To test our second hypothesis, we ran a dependent samples t-test to measure changes in locus of control between surveys. This gave us 3 dependent samples t-tests for internal locus of control as well as 3 dependent samples t-tests for external locus of control (Figure 2). In order to compare locus of control with margin of victory, we averaged the locus of control scores of all the players to create a team-wide locus of control for each survey.

#### Basketball

Our first hypothesis was the same as for the baseball team: margin of victory would be related to team-efficacy. The margin of victory average of each particular two week period was then paired with the respective team-wide team-efficacy score. To test our second hypothesis we ran a dependent samples t-test to measure changes in players’ internal and external loci throughout the season. In the end, this gave us 15 dependent samples t-tests for internal locus of control as well as 15 dependent samples t-tests for external locus of control. Figure 3 shows the comparisons between internal and external locus of control. In order to compare locus of control with margin of victory, we averaged the locus of control scores of all the players and coaches to create a team-wide locus of control for each survey.

#### Hockey

Our first hypothesis was that winning percentage would be related to team-efficacy over the course of the season. To test this hypothesis, we ran a dependent samples t-test between the efficacy scores from the first survey and from the third survey (Figure 4). As a result the team posted a .472 winning percentage for the season, which was similar to the average for the previous five seasons. As a result, we expected to observe stable team-efficacy scores over the season. In order to test our next hypotheses we ran 3 dependent samples t-tests for internal locus of control, as well as 3 dependent samples t-tests for external locus of control (Figure 5).

### Results

#### Baseball

We found a strong correlation between margin of victory and team-efficacy (r = .907, n = 3). Between the first and second survey, the margin of defeat was 3 while the team-efficacy dropped significantly (t (16) = 5.939, p < .001). Between the second and third survey, however, the margin of defeat was 7 while the team-efficacy dropped only slightly (t (16) = 1.301, p = .212). We found a strong correlation between margin of victory and internal locus of control (r = .785, N = 3). There was also a strong correlation between margin of victory and external locus of control (r = .886, N = 3). Between the first and second survey, the margin of defeat was 3 while internal locus of control dropped significantly (t (16) = 3.526, p = .003) and external locus of control also dropped significantly (t (15) = 4.427, p < .001). Between the second and third survey, the margin of defeat was 7 while internal locus of control dropped only slightly (t (16) = 0.777, p = .448) and external locus of control dropped only slightly as well (t (14) = 1.818, p = .091).

#### Basketball

The correlation between margin of victory and team-efficacy was slightly inversed (r = -0.275, N = 6). Between the first and second surveys, the margin of defeat was 2.4 while the team-efficacy dropped significantly (t (17) = 4.065, p = .001). Between the second and third surveys, the margin of victory was .333 while the team-efficacy dropped just slightly (t (15) = 1.094, p = .291). Between the third and fourth surveys, the margin of defeat was 12.75 while the team-efficacy dropped significantly (t (16) = 3.772, p = .002). Between the fourth and fifth surveys, the margin of defeat was 18.75 while the team-efficacy dropped significantly (t (12) = 3.370, p = .006). Between the fifth and sixth surveys, the margin of defeat was 9.63 while the team-efficacy increased just slightly (t (11) = 0.526, p = .610). The correlation between margin of victory and internal locus of control was weak (r = .296, n = 6), as was the correlation between margin of victory and external locus of control (r = .160, n = 6). Between the first and second surveys, the margin of defeat was 2.4 while internal locus of control dropped significantly (t (17) = 2.50, p = .023) and external locus of control dropped only slightly (t (17) = 0.338, p = .740). Between the second and third surveys, the margin of victory was .333 while internal locus of control dropped significantly (t (15) = 3.674, p = .002) and external locus of control also dropped significantly (t (15) = 2.955, p = .010). Between the third and fourth surveys, the margin of defeat was 12.75 while internal locus of control dropped significantly (t (16) = 3.159, p = .006) and external locus of control also dropped significantly (t (16) = 3.040, p = .008). Between the fourth and fifth surveys, the margin of defeat was 18.75 while internal locus of control dropped only slightly (t (12) = 1.238, p = .239) and external locus of control dropped significantly (t (12) = 2.213, p = .047). Between the fifth and sixth surveys, the margin of defeat was 9.63 while internal locus of control dropped significantly (t (12) = 5.522, p < .001) and external locus of control also dropped significantly (t (12) = 2.243, p = .045).

#### Hockey

From the first to the third survey, the average team-efficacy dropped just slightly from 4.84 to 4.68 (n = 15). Our dependent samples t-test showed us that the team-efficacy was indeed stable (t (14) = 1.451, p = .170). Internal locus of control dropped slightly between the first and second surveys (t (15) = 1.232, p = .237), though it increased slightly between the second and third surveys (t (10) = 1.232, p = .625). Over the entire season it dropped, but not quite enough to attain a significant level (t (13) = 2.042, p = .062). External locus of control increased slightly between the first and second surveys (t (15) = 1.100, p = .289), and dropped significantly between the second and third surveys (t (10) = 2.758, p = .020). Over the entire season it dropped significantly as well (t (13) = 2.842, p = .014).

### Discussion

#### Baseball

Data from the current study showed strong evidence in support of hypothesis one. We found very strong correlations between team-efficacy and margin of victory for each survey distribution. Furthermore, we also found significant differences in team-efficacy between the preseason survey and pre-conference survey. The baseball team had a fairly high level of team-efficacy before the season started. The mean team-efficacy was 4.05 out of 6 possible points. Overall the team as a whole believed they played well together and had the ability to compete successfully at the division one level. However, after winning just 4 games after the first 18, efficacy dropped significantly entering conference play, and remained low for the duration of the season.

The team’s internal locus of control dropped significantly between surveys. Interestingly, external locus of control dropped significantly as well, which we did not predict. Although the results showed no significant difference in changes in internal or external locus of control from the first to the second surveys, both internal and external locus of control dropped significantly when we compared the first and last surveys. We suspect this stems from overall poor performance throughout the season. Perhaps, as the season continued through conference play, the team settled into the reality of both internal factors, such as talent, and external factors, such as their difficult competition.

#### Basketball

We found a slight inverse relationship between the team’s efficacy and margin of victory or defeat for each survey distribution. Although there was not a significant relationship between team-efficacy and margin of victory or defeat, we did find a significant difference in team-efficacy over the season, particularly between the preseason survey and each mid-season survey. The basketball team had a fairly high level of team-efficacy during the preseason. The mean team-efficacy was 4.71 out of 6 possible points. Overall the team as a whole believed they played well together and had the ability to compete successfully at the division one level. However, after winning three of the first eight games, efficacy began to drop. Interestingly, in support of our hypothesis, as the season progressed, team-efficacy levels fluctuated somewhat consistently with the team’s performance, although not necessarily dependent on margin of victory/defeat. It is interesting to note that the freshmen basketball players dropped more significantly in team-efficacy than upper-class players from the preseason survey to the subsequent surveys. The reason behind this might be that the “newer” players were more overconfident before the season began than the upper-class players. It stands to reason that freshmen players entering college are surrounded by athletes better than those they have played with in high school. Because of this, they overestimate the performance potential of the team, and thus report higher levels of team-efficacy before the season begins. The upperclassmen, on the other hand, already have experience in playing at the division one level, and possess a more realistic view of the team’s potential.

As predicted in our second hypothesis, after poor performance between the preseason survey and the mid-season survey, internal locus of control dropped significantly, while external locus of control did not. We predicted a decrease in external locus of control, but not internal locus of control. Interestingly, despite a large margin of defeat between surveys three and four, the data reported significant increases in both internal and external locus of control. However, after taking a closer look at the results of the games, the increase in internal loci makes sense. Although the basketball team experienced two blowout games in a row during conference play, they won a conference game, and then lost the next game by a very close score. Team-efficacy was at its highest after these games, which further explains why they attributed their success to internal reasons.

#### Hockey

As predicted, the results showed that the team had high levels of team-efficacy and showed no significant difference during the season. The results showed a strong relationship between winning percentage and team-efficacy with respect to our linear regression graph. The team lost half of their games between the preseason and mid-season surveys which led to a significant decrease in team-efficacy. Interestingly, the team-efficacy actually increased slightly. Games early in the season are typically non-conference games, where the competition may not be quite as good as the conference teams. However, conference play typically presents a much greater challenge. Therefore, the team felt much more accomplished having won a quarter of the games. Finally, the third survey asked questions that target efficacy over the entire season. Because the questions were based on the whole season, team members apparently considered the season marginally successful.

The hockey team experienced a higher percentage of wins during the season and therefore internal locus of control remained constant throughout the season, which supports our second hypothesis. Despite slight ebbs-and-flows throughout the season, we found no significant difference in internal loci, whereas external loci decreased significantly over the course of the season. Although we predicted that external loci would remain constant, it makes sense that it decreased. Because the team was successful, they did not believe that their success was dependent upon external factors that they could not control.

### Conclusions

The three constructs this present study targeted were team-efficacy, attribution theory, and locus of control within the USAFA baseball, hockey, and women’s basketball teams. For the baseball and women’s basketball teams, the researchers used a margin of victory/defeat construct as a measure of success. Over the past few seasons, the women’s basketball and baseball teams have had very unbalanced win/loss records, which we assumed would not change during the research season. Furthermore, not a single player on the baseball and basketball teams had experienced a winning season, while the hockey team has experienced a higher percentage of wins throughout their seasons. Because of this, we needed something concrete to call “success” that seemed attainable for the teams. For this reason, we assumed that if the teams were losing, but were not continuously getting “blown out” by their opponents, team members would gage those losses as successful. Typically, players take a close loss to a strong opponent much better than a game with a huge point/run spread. In contrast, the hockey team has experienced successful seasons, so we used the team’s win/loss record. Our intent was to run a similar study to that of Feltz and Lirgg (8), and investigate a relationship between team-efficacy and performance. However, specific differences exist between the current study and that of Feltz and Lirgg. First, the current study targeted team efficacy with general questions about team efficacy, whereas Feltz and Lirgg asked statistic-based questions specific to hockey. Second, our results may differ due to the disparity in observations met in each study. Feltz and Lirgg evaluated the team’s level of efficacy after each game during the course of the season. They recorded the team’s efficacy after both wins and losses. Our study only assessed the level of team efficacy during discrete time periods, and we assumed that efficacy carried over between games. While this current study attempted to measure efficacy over the season as did Feltz and Lirgg, constraints in time and resources prevented data collection after each game. The differences in observation may have caused the differing results. Finally, the focus of Feltz and Lirgg’s study was finding that perceived self-efficacy was a strong predictor of performance whereas our study attempted to aggregate self-efficacy into “team efficacy” and performance.

A major limitation of this study was the low sample sizes for each team. Unfortunately, researchers may find this difficult to control because most teams carry fewer than thirty players. To solve this problem, researchers might try to look at multiple teams from the same sport, possibly from different schools (6,15). Future research in this area might find our results to be accurate regardless of our low n-values. As stated in the discussion section above, when a team experiences continual failure, like the baseball and basketball teams in our study, it makes sense that they would continue to regulate external factors for their lack of success, stop attributing their losses to internal factors, and drop in overall team efficacy. This being said, further research should also focus on how wins and losses affect internal locus of control, external locus of control, and team-efficacy. These changes would improve both internal and external validity. The world of sports provides a phenomenal databank of raw information that aids in discovering how people operate in team settings. Taking full advantage of the opportunity to discover as much as possible about the intricate workings of the team atmosphere provides a vital source for improving strategically developed teams in both the corporate and athletic worlds.

### Applications In Sport

Generally speaking, applying the results from these studies to sports seems to reveal that both coaches and players can use these surveys to help regulate their self-efficacy and team efficacy to help monitor their perceived beliefs, motivational levels, and goal attainment throughout the season. As a coach, one could simply use a simple survey to gauge where his or her players are with respect to team efficacy. As a player, one could use the survey to monitor trends in their perceptions of their team’s efficacy and ask themselves why any shifts in perceptions exist. Finally, with respect to locus of control, monitoring team efficacy has the potential to allow players and coaches the opportunity to reflect upon the internal and external influences and how they can change their behaviors to better correspond to their beliefs.

Another application to sport is that winning can indeed affect team-efficacy. Just as we hypothesized, the more a team wins, the higher the team efficacy. Not only does the outcome of winning affect a team’s efficacy, but the margin of victory can also play a significant role. In other words, the more a team wins by (i.e., points, runs, and goals) the greater the team efficacy. Among the coaches however, the drop-off in team efficacy isn’t affected as drastically as the players. Perhaps this has more to do with the “perceived” leadership and how the coaches’ attitudes need to be more even-keeled. In a similar fashion, coaches on winning teams need to maintain a more even-keeled efficacy and not allow themselves to inflate their perception of their team. These applications make good sense in the world of sports psychology with respect to leadership. Ever notice how coaches on championship teams tend to appear to be level-headed and are able to keep their emotions in check?

### Acknowledgments

The authors would like to acknowledge the head coaches and their assistants at the United States Air Force Academy for participating in the studies. Women’s Basketball: Head coach Ardie McInelly , assistant coaches Lisa Robinson, Angie Munger, and Holly Togiai; Men’s Baseball: Head coach Mike Hutcheon, assistant coaches Ryan Thompson, and Scott Marchand; Men’s Ice Hockey: Head Coach Frank Serratore, assistant coaches Mike Corbett, and Andy Berg. We would also like to acknowledge all the student-athletes on these teams who participated in these surveys throughout their respective seasons.

### Tables and Figures

#### Figure 1

Linear regression between margin of victory and team-efficacy for the baseball team.


#### Figure 2

Changes in internal and external locus of control for the baseball team between the three administrations of the survey over the course of the season (preseason, mid-season, and postseason).


#### Figure 3

Changes in internal and external locus of control for the women’s basketball team between the three administrations of the survey over the course of the season (preseason, mid-season, and postseason).


#### Figure 4

Linear regression between winning percentage and team-efficacy for the hockey team.


#### Figure 5

Changes in internal and external locus of control for the hockey team between the three administrations of the survey over the course of the season (preseason, mid-season, and postseason).


Legend:

1. Preseason team efficacy average
2. Mid-season team efficacy average
3. Postseason team efficacy average

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### Corresponding Author

Andrew D. Katayama, Ph.D.
Department of Behavioral Sciences and Leadership
United States Air Force Academy
2354 Fairchild Dr., Ste 6L101
USAF Academy, CO 80840-6228
<andrew.katayama@usafa.edu>
719-333-1313

Andy Katayama is a Professor of Psychology in the Department of Behavioral Sciences and Leadership at the United States Air Force Academy in Colorado Springs. Andy has also spent six years serving as an officer representative for the intercollegiate varsity baseball team.