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Do static-sport athletes and dynamic-sport athletes differ in their visual focused attention?
### Abstract
The goal of this study was to evaluate current attention tests in sport psychology for their practical use in applied sport psychology. Current findings from the literature suggest that measures of visual focused attention may show different performances depending on sport type and test conditions (33). We predicted differences between static- and dynamic-sport athletes (17) when visual focused attention is tested with random (unstructured) versus fixed (structured) visual search in two experimental conditions (quiet environment versus auditory distraction). We analyzed 130 nationally competing athletes from different sports using two measures of visual focused attention: the structured d2 test and the unstructured concentration grid task. Compared to static-sport athletes, dynamic-sport athletes had better visual search scores in the concentration grid task in the condition with auditory distraction. These findings suggest that the results of attention tests should be differentially interpreted if different sport types and different test conditions are considered.
**Key words:** d2 test, concentration grid task, auditory distraction
### Introduction
The study reported here was motivated by recent calls within the applied field of sport psychology for a broad diagnostic framework in the domain of talent selection (7,35) as well as the ongoing evaluation for professional standards of the techniques that are used by practicing sport psychologists (14).
An increasing number of researchers have argued that psychological variables remain often unnoticed within talent identification models (1). However, among a range of other physical and technical variables, psychological variables have been identified as a significant predictor of success (18,27,34). For instance, during athletic performance attention is seen as one of the most important psychological skills underlying success because of the ability to exert mental effort effectively is vital for optimal athletic performance (12,22,27).
In cognitive psychology, attention is seen as a multidimensional construct. According to different taxonomies of attention, at least three distinct dimensions of attention have been identified (21,28,39). The first is _selectivity_. It includes selective attention as well as divided attention. The second dimension of attention refers to the aspect of _intensity_, which can include alertness and sustained attention. The third dimension is _capacity_ and refers to the fact that controlled processing is limited to the amount of information that can be processed at one time.
Individuals’ attentional performance in one or more of the aforementioned dimensions can be assessed in several ways (3, for an overview see 39). The selectivity aspect can, for instance, be approached with tasks involving either focused or divided attention. In focused attention tasks there are usually irrelevant stimuli, which must be ignored. In divided attention tasks, all stimuli are relevant, but may come from different sources and require different responses (39). Intensity requirements can be approached with tasks involving different degrees of difficulty, or with tasks that have to be carried out over longer periods of time. Finally, dual-task procedures, memory span tests, or other processing tasks are used to approach the capacity aspect (26). Practicing sport psychologists most often use standardized tests, which are easily administered in a paper-pencil form and therefore are easy to use in the field.
However, several authors (38) as well as diagnosticians in youth talent diagnostic centers in Germany have expressed a number of subjective impressions concerning the performance of athletes on attention tests (e.g., influence of sport type, test context, or expertise level) that are insufficiently indicated by the existing test norms. Therefore, the goal of the present study was to examine the influence of two essential factors (sport type and environmental context) on athlete’s performance in two different attention tests.
Boutcher’s multilevel approach (3) integrates relevant aspects of research and theory on attention from different perspectives. In his framework, internal as well as external factors, like enduring dispositions, demands of the task, and environmental factors, interact with attentional processes during performance. These factors are thought to initially influence the level of physiological arousal of the individual, which in turn influences controlled and automatic processing. When performing a task, the individual either uses controlled processing, automatic processing, or both, depending on the nature and the demands of the task. An optimal attentional state can be achieved by reaching or attaining the exact balance between automatic and controlled processing, essential for a particular task (3).
A sudden external distraction (e.g., auditory noise) is expected to hamper performance because it may disrupt the current attentional state by causing the individual to reach a level of arousal such that an imbalance in controlled and automatic processing occurs. However, individual differences may exist regarding the effect of internal or external distractions on attentional state. For instance, a gymnast normally performs his or her routine in a quiet environment in competition whereas during a basketball game the player is confronted with auditory noise. Unexpected auditory distractions may disrupt the attentional state of the gymnast but not the state of the basketball player because he is used to it.
There has been extensive research on different aspects of attentional performance in athletes. For instance, researchers examined attentional differences between athletes and non-athletes (5,20,23), between athletes on different expertise levels (8), as well as with regard to other factors, such as athlete type, sport type and gender (17,19,24,33) by using a variety of attentional tasks. Athletes are able to distribute their attention more effectively over multiple locations and better able switch their attention rapidly among locations than non-athletes (25). Furthermore, attentional performance seems to vary with the kind and amount of training provided by a sports environment so that athletes trained in more visually dynamic sports show better attentional control than athletes trained in less visually dynamic sports (24).
When using specific tests to assess attention performance, one should expect differences in test performance between athletes that vary in one or more of the aforementioned factors. In this context, Lum et al. highlight the need to examine athlete’s visual attention by using a variety of visual attention tasks (17, see also 20). Furthermore, existing test norms should account for the aforementioned differences to provide athletes with a reliable feedback on their individual attention performance.
For instance, to evaluate the visual focused attention performance of athletes, two common tests are used in the field of applied sport psychology, the d2 test and the concentration grid test (3, 4). Visual focused attention is usually operationalized as visual search so that target stimuli have to be found in a field of distractor stimuli (39). For instance, in the d2 test, participants need to select “d” letters with two dashes above them in an array of “d” and “p” letters with zero, one, or two dashes over or under each letter. The structure of reading letters from left to right provides an environment in which relevant stimuli need to be selected and irrelevant stimuli need to be ignored. The gaze searches throughout the visual array not in a random way but rather in a structured fashion. In contrast, in the concentration grid task, participants see a block of randomly distributed numbers, in which they need to search for numbers in sequence, such as number 01, then 02, 03, and so on. The concentration grid task is often administered as a training exercise in the field of applied sport psychology, and it has been proposed, that it works by developing the athlete’s ability to scan a visual array for relevant information, and to ignore irrelevant stimuli (11).
Given the different demands of these two tasks and the empirical evidence so far, one may speculate that athletes who have experience performing visual searches for relevant cues and making decisions in dynamic environments (which is typical for team sport athletes), will do better on the concentration grid test than on the d2 test (29). Athletes from individual sports who are exposed to a mostly static environment with one or a small number of stimuli should do better on the d2 test than on the concentration grid test.
Maxeiner compared, for instance, 30 gymnasts and 30 tennis players in their performance on the d2 test and on a reaction time task in which they were asked to press a pedal with their foot as soon as a square appeared on a computer monitor (19). Participants were tested under either a single-task condition, such that only the d2 test or the reaction time task had to be performed, or a multiple-task condition, in which both the d2 test and the reaction time task had to be carried out simultaneously. Reaction times showed a significantly stronger increase under the multiple-task condition for the gymnasts (about 28%) whereas no differences between gymnasts and tennis players were found for single-task conditions. The author concluded from this result, that tennis players have a better distributive ability of attention than gymnasts. However, the total number of items worked on the d2 test as well as the error rates did not differ between gymnasts and tennis players in either the single-task or multiple-task condition.
Tenenbaum, Benedick, and Bar-Eli conducted a similar study and found opposing results (33). The authors compared 252 young athletes from different sports disciplines in their d2-test performance. All athletes performed the d2 test in a quiet classroom with no distractions. Results indicate that the number of d’s the subjects have crossed (quantitative capacity) differed significantly by type of sport in females. High quantitative capacity scores in the d2 test were found for female athletes from sports such as tennis or volleyball, but not for female athletes from gymnastics. A similar pattern of results was found in male athletes, although only showing a tendency for rejecting the null hypothesis (p = .06). The authors found an additional effect for type of sport on error-rate. The largest error-rates were found in tennis and volleyball players whereas the smallest error-rates were found in track and field athletes. The authors concluded that concentration is individual and sport-type dependent and state that “Concentration should be further investigated with relation to motor performance” (p. 311).
Maxeiner and Tenenbaum et al. found opposing results in athletes from different sport domains in the d2 test (19,33). First, the authors assessed different parameters of the d2 test. Maxeiner quantified the total number of items worked on the d2 test, whereas Tenenbaum et al. quantified the number of d’s the subjects have crossed. The number of items worked on the d2 test is a reliable criterion for working speed (4), whereas the number of crossed d’s is related to both working speed and working accuracy. Assessing different parameters in the d2 test could lead to different results, therefore masking possible differences between participants from different sport domains. Following the suggestions of Brickenkamp, the practitioner should assess the concentration-performance score (number of marked d’s minus the number of signs incorrectly marked) in the first instance, because this value is resistant to tampering, such that neither the skipping of test parts nor the random marking of items increases the value (4).
Furthermore, Tenenbaum et al. had participants from tennis, fencing, volleyball, team-handball, track and field, and gymnastics indicating an unequal distribution of participants with regard to other criteria like kind of training provided by a sports environment (33). As mentioned above, attentional performance seems to vary with the kind and amount of training provided by a sports environment (24); the question arises whether athletes should be classified according to kind of training provided by a sports environment, rather than sport discipline per se when assessing their attentional performance.
Greenlees, Thelwell, and Holder examined the performance of 28 male collegiate soccer players in the concentration grid exercise (13,15). The players were assigned to either a 9-week concentration grid training or a control condition. During three test sessions the athletes were asked to complete a battery of concentration tasks, including the aforementioned concentration grid test. The results showed a significant main effect for training condition but not for test session, indicating that the concentration training group was superior to the control group but did not exhibit any improvement during the 9-week training interval. However, Greenless et al. assessed only soccer players with a playing experience of 10.45 2.31 years, which indicates that they already possess substantial experience in performing visual searches for relevant cues in dynamic environments (13). This could at least in part explain why the participants of the concentration training group did not improve their performance on the concentration grid task as compared to the participants of the control group. Additionally, the two groups were not homogeneous in their concentration grid performance at the study onset, which may in part explain the main effect for training condition. The findings of Greenless et al. highlight the need for further research on the concentration grid test, especially examining the extent to which the task reflects sport-specific concentration skills and therefore support the need for ongoing evaluation of this technique in diagnostics and intervention.
Taken together, we can identify two main factors that need to be considered when assessing athletes’ visual focused attention. First, a broad application of attention tests that are sensitive to the athlete’s experience in different types of sports should be made. This means, in particular, recognizing that different sport environments (static vs. dynamic), encouraging different visual search and decision strategies (fixed or structured vs. random or unstructured), and realizing that the same tests do not necessarily capture both types of strategies. Second, the environmental context (with or without distraction) can increase or decrease performance, respectively.
We adapted the dichotomy of Lum et al. and hypothesized that static-sport athletes and dynamic sport-athletes would not differ in d2 scores but would differ in concentration grid scores due to their different perceptual experiences (17). This finding would not only help to clarify previous results (19,33) but would extend them to different concentration tasks (d2 test vs. concentration grid) following the conclusions of Greenlees et al. as well as Tenenbaum et al. (13,33). We furthermore hypothesized that auditory distraction would have a detrimental effect on performance in both the d2 test and the concentration grid test because it may disrupt the current attentional state (3). We therefore compared performances in the d2 test and the concentration grid test with and without auditory distraction.
### Method
#### Participants
A sample of 130 athletes (students of Sport Science, German Sport University) were recruited to participate in the study (n = 44 women, mean age = 22 years and n = 86 men, mean age = 22 years). Ages ranged from 19 to 33 years, with a mean age of 22 years (SD = 2.4 years). Of these, 66 students (n = 15 women and n = 51 men) competed in 6 different sports with a dynamic visual environment (i.e., soccer, volleyball) and 64 (n = 29 women and n = 35 men) competed in another 6 different sports with mostly static visual environment (i.e., track and field athletics, gymnastics). All students had been performing their sport for at least 7 years with 19.2% (n = 25) of them reporting national experience (German championships or national league) and 11.5% (n = 15) also reporting international experience. All participants were informed about the purpose and the procedures of the study and gave their written consent prior to the experiment. Participants reported to have no prior experience with either the d2 test or the concentration grid test.
We recruited an additional sample of n = 25 students of sport science in order to evaluate the reliability of the d2 test and the concentration grid test and to estimate the validity of the concentration grid test. This was necessary because, first, we applied modified versions of the original tests and second, there were no reliability or validity statistics available in the current literature for the concentration grid test.
#### Tasks and Apparatus
##### d2 Test of Visual Focused Attention.
The d2 test was used to assess visual focused attention (4,39). It is seen as a reliable and valid instrument, most commonly being used in the fields of cognitive, clinical, and sport psychology. In the standardized version of this task, 14 lines consisting of 47 letters each are presented to the participant. The letters can be a “p” or a “d” with zero, one, or two small dashes above or below it. The task is to process all items (letters) of a line in a sequential order and to mark every “d” with two dashes above or below. All other letters are to be left unmarked.
The visual search pattern in the d2 test is guided by the structure of the stimulus field (fixed visual search). To avoid ceiling effects, there is a temporal restriction of 15 seconds to process each line. After 15 seconds there is a verbal instruction to proceed to the next line. Norms are available for age groups between 9 and 60 years. Reliability coefficients of the test range from r = .84 to r = .98 (4).
In the present study, 7 lines of the d2 test had to be dealt with under each experimental condition with each line consisting of 47 letters. This test reduction was applied for practical reasons, particularly to match the working time of the concentration grid task. Prior to the study, we analyzed d2-test results of 7 lines (Version A) and 14 lines (Version B) in a test–retest design with a temporal delay of 1 week. The results indicate a significant product–moment correlation between the two versions of the test in a sample of 25 students of sport science (r = .80; p < .05). Therefore, we believed that the use of 7 instead of 14 lines should be adequate for the purposes of this study. From the performance of each participant in the d2 test, two parameters were obtained: a concentration-performance score and the error rate. The concentration-performance score is the number of d letters the subject marked minus the number of signs (dashes) incorrectly marked. The error rate is the number of signs incorrectly marked plus the number of correct signs missed.
##### Concentration Grid Task
Two versions of the concentration grid test were used as a second measure of visual focused attention, and in particular, visual search (15,21). They were modified from the concentration grid exercise, which can be found in Harris and Harris (1984). The first version (CG1) used in this study consisted of 7 horizontal and 7 vertical squares arranged in a grid of 49 squares altogether. A unique two digit-number (from 00 to 49) was placed randomly in the center of each square. The second version (CG2) of the concentration grid was identical to the first except for a different placement of the numbers. To ensure comparability, the relative distance from each number to the following number was the same in the two grids. We also examined the reliability of the concentration grid task. In a test–retest design with a temporal delay of a 1-week interval, a significant product-moment correlation of r = .79 (p < .05) was found in a sample of 25 students of sport science.
In the concentration grid task the participants were instructed to mark as many consecutive numbers (starting from 00) as possible within a 1-min period under each experimental condition. The resultant number of correctly processed items was used for further data analysis. In comparison to the d2 test, the participants’ visual search pattern in the concentration grid is not entirely guided by the structure of the stimulus field; instead, the participant is advised to scan the grid (random visual search). We calculated the product-moment correlation between the concentration grid scores and the d2 test results in the aforementioned sample of 25 students of sport science to estimate the construct validity of the concentration grid. The analysis revealed a non-significant product-moment correlation of r = .10 (p = .62), indicating that the concentration grid test captures a different aspect of visual focused attention than the d2 test.
#### Procedures
A trained research assistant introduced the experimental tasks to each individually tested participant. The participant was given a practice trial of 20 seconds for the concentration grid exercise (altered version of the original CG1) and a practice trial of two lines for the d2 test to become familiarized with the two experimental tasks. The participant had to perform each of the two tasks under two different experimental conditions, that is, in different environmental contexts (for a total of four experimental phases: d2 test and concentration grid task under normal and auditory distraction conditions, respectively). In one condition no sensory distractions were present. The participant completed the tasks in the quiet laboratory environment. In the other condition an auditory distraction was present. The participant wore headphones that enclosed the whole ear. A mixture of distracting, sport-specific environmental sounds was played back at 90 dB. We used ambient sound recordings of the audience and the players from the last 3 minutes of two first division basketball matches in which both teams played head to head until the end of the match. We compiled the sound recordings to fit the two 1-min periods for the auditory distraction condition (d2 test and concentration grid task) in such a way that the played back sound recording comprised the audience’s and the player’s sounds of three offense and three defense situations. In all tasks the participant sat at a worktable with a head–table distance of 40 cm. The test order was counterbalanced for the participants and the experimental tasks required approximately 20 minutes to complete.
### Results
A significance criterion of α = .05was established for all results reported (9). Prior to testing the main hypothesis, moderating effects of age, sex, and experimental sequence were assessed. We conducted separate analyses of variance on the dependent variables, first, with sex as categorical factor (male versus female), second, with age as continuous predictor, and third, with experimental sequence as categorical predictor (auditory distraction following no distraction versus no distraction following auditory distraction). There were no significant effects of sex, age, or experimental sequence on any of the dependent variables (p < .05).
A correlation analysis indicated that there was no significant product–moment correlation between the concentration-performance score of the d2 test and the number of correctly processed items in the concentration grid task (r = -.01; p = .68), nor between the concentration-performance score and the error rate in the d2 test (r = -.02; p = .47). To assess differences in the dependent variables, we conducted 2 × 2 (Environmental Context × Sport Type) univariate analyses of variance (ANOVAs) with condition being the repeated measure. Post hoc analyses were carried out using the Tukey HSD post hoc test. Cohen’s f was calculated as an effect size for all analyzed F values higher than 1 (6). Additionally, we conducted single sample t-tests to compare our study sample to the age matched normative sample. This was done for each participant’s d2 test performance (concentration-performance score and error rates) but not for the concentration grid task, because norms were available only for the d2 test. Cohen’s d was calculated as an effect size for all analyzed t values higher than 1.
#### d2 Test of Visual Focused Attention
Descriptive statistics for the concentration-performance scores and the error rate of the d2 test are shown in Table 1. First, we assumed that d2 scores would not differ between the two groups reflecting static-sport athletes and dynamic-sport athletes. A 2 × 2 (Sport Type × Environmental Context) ANOVA with repeated measures on the second factor was conducted, taking the concentration-performance score as the dependent variable. The results showed that the two groups did not differ in their concentration-performance scores, F(1, 128) = .004, p = .94, achieved power = .94. Our second assumption was that auditory distraction would have a detrimental effect on concentration performance. To our surprise, the ANOVA revealed a significant main effect for environmental context, F(1, 128) = 66.02, p < .05, Cohen’s f = 0.72, reflecting higher concentration-performance scores for the auditory distraction condition for both dynamic-sport and static-sport athletes (see Table 1). The effect size indicates a large effect (6). Furthermore there was no significant interaction effect for Sport Type × Environmental Context, F(1, 128) = .01, p = .76, achieved power = .98.
To determine if participants from our study sample differed from the general population in concentration performance, we calculated single sample t-tests. The results show that in the normal condition, neither static-sport athletes, t(63) = 1.56, p = .12, Cohen’s d = 0.19, nor dynamic-sport athletes, t(65) = 1.81, p = .07, Cohen’s d = 0.22, differed in their concentration performance from the normative sample’s mean. However, in the auditory distraction condition both groups differed significantly from the normative sample’s mean (static-sport athletes, t(63) = 3.17, p = .002, Cohen’s d = 0.39; dynamic-sport athletes, t(65) = 3.37, p = .001, Cohen’s d = 0.42).
Second, a 2 × 2 (Sport Type × Environmental Context) ANOVA with repeated measures on the first factor was conducted, taking the error rate in the d2 test as the dependent variable. There were no significant main effects, neither for sport type, F(1, 128) = 3.71, p = .06, Cohen’s f = 0.17, achieved power = .61, nor for environmental context, F(1, 128) = 1.50, p = .22, Cohen’s f = 0.11, achieved power = .75. In addition, the interaction effect Sport Type × Environmental Context showed no statistical significance, F(1, 128) = 2.02, p = .16, Cohen’s f = 0.13, achieved power = .95. Dynamic-sport athletes did not make more mistakes on the d2 test in comparison to static-sport athletes, neither in the normal nor in the auditory distraction condition.
To determine if participants from our study sample differed from the general population in error rate, we calculated single sample t-tests. The results show that in the normal condition, dynamic-sport athletes, t(65) = -2.88, p = .005, Cohen’s d = 0.35, but not static-sport athletes, t(63) = -1.41, p = .16, Cohen’s d = 0.17, made on average fewer mistakes than the participants from the normative sample. The same pattern of results was found for participant’s error rates in the auditory distraction condition (static-sport athletes, t(63) = 0.36, p = .71, Cohen’s d = 0.05; dynamic-sport athletes, t(65) = -3.17, p = .002, Cohen’s d = 0.39).
#### Concentration Grid Task
We assumed that concentration grid scores would differ between the two groups reflecting static-sport athletes and dynamic-sport athletes. The second assumption was that auditory distraction would have a detrimental effect on concentration performance. A 2 × 2 (Sport Type × Environmental Context) ANOVA with repeated measures on the second factor was conducted, taking the concentration grid score as the dependent variable. The ANOVA revealed no significant main effects for either sport type, F(1, 128) = 1.40, p = .24, Cohen’s f = 0.11, or environmental context, F(1, 128) = 0.27, p = .60. We assume that we can rely on the two findings because of a test power greater than .90. To our surprise the interaction effect Environmental Context × Sport Type showed statistical significance, F(1, 128) = 4.54, p = .04, Cohen’s f = 0.19. Post hoc analysis revealed that participants in the dynamic-sport group scored higher in the concentration grid task under the auditory distraction condition, whereas participants in the individual-sport group scored lower under the auditory distraction condition, compared to the normal condition (see Figure 1).
### Discussion
The goal of this study was to evaluate two attention tests in sport psychology in terms of their application in athletes who are trained in more visually dynamic sports compared to athletes trained in visually less dynamic sports with regard to different environmental contexts. Visual focused attention was examined with random (concentration grid task) versus fixed (d2 test) visual search in a quiet environment and under auditory distraction (4,15).
The results extend current findings on attention performance of athletes with regard to sport type, environmental context, and task dependency. Dynamic-sport athletes did not differ in their concentration performance from static-sport athletes, neither in the d2 test nor in the concentration grid task under quiet laboratory environmental conditions. This result confirms our first hypothesis with regard to the d2 test and supports the findings of Maxeiner (19). We assume that the different perceptual experience of dynamic-sport athletes does not account for their visual search performance in the d2 test. On the one hand, this implies a fairly stable underlying ability to focus attention in simple tasks when a fixed (structured) visual search is a constraint of the task. On the other hand, it can be speculated that attention abilities manifest themselves in a sport-specific way on a more strategic level when integrating basic (attention) abilities in different skills that are not assessed by the d2 test.
Our second hypothesis was that auditory distraction would have a detrimental effect on attention performance in both the d2 test and the concentration grid task. To our surprise the results of the d2 test indicate higher concentration performance scores for the auditory distraction condition for dynamic-sport athletes as well as static-sport athletes. The scores were not only higher when compared between both experimental conditions but also when compared with the corresponding normative sample of the d2 test. This finding supports the assumptions of Tenenbaum et al. and Wilson, Peper, and Schmid, that visual search performance in unstructured contexts is task dependent, especially under auditory distraction conditions (33,38).
From the viewpoint of Boutcher’s multilevel approach to attention, it seems possible that in the auditory distraction condition the participants’ attentional states were optimized (3). This optimization helped the participants achieve higher scores in the relatively simple d2 test, regardless of their sport type. However, whether the supposed optimization was due to changes in arousal level, changes in controlled or automatic processing, or both, cannot be concluded from our results. In addition, the results of the concentration grid task (where an unstructured visual search is an inherit component of the task) show that participants in the dynamic-sport group scored higher in the auditory distraction condition in comparison to the participants in the static-sport group. Changes in arousal level and therefore in attentional state are known to influence visual control (16,32). It is reasonable that an increased amount and/or increased amplitude of saccades, when scanning the concentration grid, can lead to ignoring the actual target or finding it later than under normal conditions. This could explain the decrease in performance in the concentration grid task for static-sport athletes, because they are normally not trained to deal with such a situation in their sport. To further examine the gaze behavior in performing different attention test, eye-tracking methodology should be integrated into the experimental design.
The increase of the concentration grid scores of the dynamic-sport athletes in the auditory distraction condition could also be explained by differences in information processing. Dynamic-sport athletes seem to be able to allocate their attention capacity to more crucial aspects of the task (37). When scanning the concentration grid they could, for instance, pre-cue remaining numbers in specific areas of the grid in advance, in order to find these numbers faster at a later point in time. However, this aspect is open for further investigation. We assume that dynamic-sport athletes benefit from their sport-specific perceptual experience especially in the concentration grid task under auditory distraction conditions.
We are aware of some critical issues in our design that need to be taken into account in further experiments, and want to highlight three specific aspects. First, the differentiation of dynamic- versus static-sport athletes could be more closely specified. This could be done by examining athletes from different sport disciplines that have different sport-specific structures (e.g., coactive vs. interactive sports). One can, for instance, hypothesize that athletes in coactive sports such as bowling or rowing may differ in their attention ability from athletes of interactive sports such as basketball or soccer due to different task demands. Subsequent analyses could also focus on different team positions, especially in interactive sports. For instance, it is likely that a goalkeeper differs in concentration ability from a playmaker (30,31).
Second, the type of distraction could be more differentiated. Athletes have to deal with different distractions in competition such as comments from the coach and other athletes, or different forms of either expected or unexpected noise. These distractions could have different effects on attention performance. One could, for example, examine the impact on attention performance of different distractions with different structures, such as visual versus auditory distraction with a sport-specific structure versus no structure. One can hypothesize that structured distractions of a sport-specific nature would have no impact on concentration performance at all, because athletes are normally habituated to such distractions. In our study we speculated that the impact of the auditory distraction on the attentional state of the athletes would be to enhance their performance in the d2 test. To control this aspect, measurements of arousal level (e.g., heart rate or galvanic skin response) should be integrated into further studies.
Third, we adopted the concentration grid test as a measure for visual focused attention, because visual focused attention is usually operationalized as visual search (39). Research suggests a close link between working memory capacities and the selectivity dimension of attention (10). We acknowledge that when performing the concentration grid test, a participant could potentially optimize his or her visual search by selectively memorizing the position of stimuli that have to be found after preceding stimuli have been marked. However, participants were not instructed to memorize the position of the stimuli but rather to actively scan the grid and mark as many consecutive numbers (starting from 00) as possible within a 1-min period. Subsequent studies could compare participant’s performance in working memory tests (10), as well as in other tests of visual attention (39), with their concentration grid test scores to evaluate if the concentration grid is more a measure of visual focused attention or working memory.
### Conclusions
The findings of the current study suggest that the results of attention tests should be differentially interpreted if different sport types and different test conditions are considered in the field of applied sport psychology or applied sport science. Their predictive power for sport-specific attention skills, however, may only be seen with regard to different factors such as sport type, environmental context, and task.
### Applications in Sport
There are some practical consequences and implications of this study. First, non-specific concentration tests only seem to be able to differentiate between athletes from more visually dynamic sports and athletes from more visually static sports when they mimic a sport-specific environmental context together with sport-specific demands of the task. Therefore, one may need more specific tests for specific sports to diagnose not only fundamental aspects of attention, but attention abilities on a more strategic level (2). These tests should then be integrated in a systematic talent diagnosis with test norms for specific sports (7). In a talent diagnostic, however, psychological variables remain often unnoticed (1), even if they have been identified as significant predictors of success (27). They could serve as an intrapersonal catalyst in the developmental process of talented youngsters (35). However, their impact on performance may change throughout the development process of the individual. When administering attention tests, this development needs to be taken into account. It is, for instance, questionable whether young gymnasts can be compared to young soccer players in their ability to focus attention, because of different attentional demands in both sports. Second, it would be very useful to conduct longitudinal or to combine analysis of performance in tests with analysis of performance criteria (33). A final issue that should be addressed is the impact of specific interventions on attention performance, especially if attention training is used that is similar to the structure of the concentration test itself (13, 38).
### Acknowledgments
The author thanks Mr. Konstantinos Velentzas and for assistance with data collection and Mrs. Lisa Gartz for her critical and helpful comments on the manuscript.
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### Tables and Figures
#### Table 1
Means (M) and standard deviations (SD) for the concentration-performance scores and the error rate of the d2 test with regard to environmental context and sport type (n=130). The terms of static and dynamic refer to the visual environment in which the athletes from different types of sport usually perform.
| Environmental context | ||||
|---|---|---|---|---|
| Normal | Auditory distraction | |||
| M | SD | M | SD | |
| Concentration-performance score | ||||
| Static sports | 137.28* | 69.26 | 153.05*+ | 73.93 |
| Dynamic sports | 138.59* | 66.34 | 153.23*+ | 71.05 |
| Error rate | ||||
| Static sports | 11.21 | 8.79 | 13.26 | 10.72 |
| Dynamic sports | 9.52+ | 9.16 | 9.36+ | 8.72 |
* p < .05 (according to Tukey HSD post hoc test).
+ p < .05 (according to single sample t-test between the study sample and the corresponding normative sample, cf., 4).
#### Figure 1
Mean concentration grid performance as a function of sport type and environmental context (error bars represent the standard error of the mean; * = significant difference at p < .05 between experimental and control group according to Tukey HSD post hoc analysis).
### Corresponding Author
Dr. Thomas Heinen
German Sport University Cologne
Institute of Psychology
Am Sportpark Müngersdorf 6
50933 Cologne
GERMANY
Tel. +49 221 4982 – 5710
Fax. +49 221 4982 – 8320
Email: <t.heinen@dshs-koeln.de>
### Author’s Affiliation and Position
German Sport University Cologne, Institute of Psychology
A Study on the Self-Efficacy of Elite Coaches Working at the Turkish Coca-Cola Academy League
### Abstract
As defined by Bandura, self-efficacy is an individual’s belief about her/his ability to perform well in a given situation. The purpose of this study was to determine the levels of self-efficacy amongst elite professional Turkish soccer coaches. One-hundred twenty-three coaches from 41 professional soccer clubs in four different regions of Turkey, training U14 and U15 age groups voluntarily participated in this study. This study used the Coaching Efficacy Scale (CES) comprising four specific efficacies (motivation (ME), game strategy (GSE), teaching technique (TTE) and character building (CBE). According to the total coaching efficacy scale, results suggested that participating coaches’ self-belief in efficacy was at highest levels (M=8.26, SD=.49). Coaches’ self-belief in the sub-scale of character development efficacy was at highest (M=8.60, SD=.54), whereas self-belief in game strategy was at lowest levels (M=8.03, SD=.61). One of the most important findings of the study was that coaches’ self belief in the sub-scale of motivation efficacy differed according to the category in which they work (t=2.049, p<.05). Game strategy efficacy differed significantly according to marital status (t=2.417, p<.05); and type of coaching certificate (t= 2.186, p<.05). A higher degree of self-belief regarding motivation efficacy amongst coaches training young teams compared to professional-level coaches was due to the athletes they worked with. In many cases, it is easier to motivate young players rather than professionals. Coaches’ self-improvement in motivation will definitely have a decisive impact on their success in professional sports.
**Key words:** coaching efficacy, elite coaches, professional sport, soccer
### Introduction
Extensive research about the behavior exhibited by individuals throughout their lives suggests the existence of many factors influencing human behavior. One of these factors is self-efficacy (4,5). The social cognitive theory focuses on how the individual learns new information and behaviors by observing, imitating an individual or by taking the individual as a model (1). This theory suggests that one of the most important roles in the individual expression of personal behavior is the individual’s level of self-efficacy.
First mentioned by Bandura (4), the concept of self-efficacy is defined as one’s belief in his or her own ability to perform a certain type of task. Self-efficacy is specific to a certain task and is dynamic (10,14). In other words, it is open to change over time with new information, experience and learning (14). The individual makes a comparison between expected performance and his or her own capacity (12). In the scope of the concept of self-efficacy, the need for a high degree of self-belief to be successful in a specific behavior stands out as one of the most important factors in exhibiting that behavior.
Sometimes knowledge and skill might not be adequate for successful behavior. On most occasions people may know the correct course of action, yet be unable to act accordingly. Self-efficacy stands out as an important bridge between knowledge and behavior. Personal level of self-efficacy influences an individual’s perspective and behavior toward the action. Positive or negative feedback received by the individual in response to his or her abilities and competence results in the strengthening or weakening of the individual’s own belief in his or her self-efficacy (18). Studies suggest that individuals with high self-efficacy tend to be more resilient in the face of obstacles to accessing sports activities (6). They also have heightened levels of social skills (2) and are more eager to take bigger risks (16,17).
Performance build-up in soccer requires long periods of time. What constitutes the fundamental elements required by soccer training throughout this long process is a topic of enduring discussion (3). The most important issues in this context are accurate organizational structures; correct training models; adequate club facilities; environmental conditions and, maybe more than anything, coaching efficacy. It is stated that the athlete’s learning process becomes much more rapid, efficient and thorough, if the format of competitions and training participated in by children are developed with consideration to their mental, psychological and motor abilities (24). At this point, while it is fundamental for a coach to believe in his or her self-efficacy in the context of building up athlete performance (20), this characteristic demands constant enhancement (19).
Based on the notion that coaches can be perceived as teachers, the Coaching Efficacy Scale (CES), developed by Feltz, Chase, Moritz & Sullivan (8), is the only published scale to date that is used frequently in studies on coaching efficacy (11,16,17). D.L. Feltz, et al., (8) define coaching efficacy as coaches’ self-belief in their capacity to influence an athlete’s level of performance and learning. Consisting of 24 items and four sub-scales, the psychometric characteristics of the scale are supported by exploratory and confirmatory factor analysis (8).
The majority of studies on the topic have been conducted on individuals in the United States. Others include Tsorbatzoudis, Daroglou, Zahariadis & Grouios’s study (22) on professional team coaches in Greece and Gencer, Kiremitci & Boyacioglu’s study (9) on Turkish coaches in the disciplines of basketball, soccer, tennis and handball. This latter concludes validity and reliability findings coherent with Feltz et al.’s study (8). The present study addresses significance in terms of CES examining the self-efficacy levels of Turkish elite professional soccer coaches.
### Method
#### Participants
The study group consisted of 123 coaches working for the U14 and U15 age groups within the Turkish Coca-Cola Academy Leagues, founded in the 2008-2009 soccer season. Coaches actively work for 41 professional soccer clubs distributed amongst five regions established for this league; all participated voluntarily in the study. The sample group participating in the study consisted of males only, with ages varying between 22 and 60 (M=38.6, SD=7.9).
#### Coaching Efficacy Scale (CES)
Data for the study was collected using the Coaching Efficacy Scale (CES) developed by Feltz et.al. (8). Total Coaching Efficacy (TCE) consists of 24 items within four sub-scales including: (a) Motivation Efficacy (ME – 7 items), (b) Game Strategy Efficacy (GSE – 7 items), (c) Teaching Technique Efficacy (TTE – 6 items), and (d) Character Building Efficacy (CBE – 4 items). Items were scored on a 10-point Likert scale ranging from 0 (not at all confident) to 9 (extremely confident), and each item was preceded with a prefix, “How confident are you in your ability to …” The scale contains items such as “How confident are you in your ability to motivate your athletes?” identified by ME; “How confident are you in your ability to understand competitive strategies?” identified by GSE; “How confident are you in your ability to detect skill errors?” identified by TTE; and “How confident are you in your ability to instill an attitude of fair play among your athletes?” identified by CBE.
Scale validity and reliability for the sample of Turkish coaches has been conducted by Gencer et. al. (9). Exactly identical to the original, the Turkish adaptation of the scale, grouped under four sub-scales, reached significantly similar results to the original scale (8) with a variance rate of 59.8%. Although the Cronbach’s alpha coefficients for factors creating the scale were relatively coherent (between .80 and .87) with original scale values, the Cronbach’s coefficient for the entire scale was exactly identical. Values (x2=468,21, df=238, normed chi-square (NC, x2/df)=1.97, p<.05; RMSEA=0.069, S-RMR=0.062, GFI=0.84, AGFI=0.80, CFI=0.91, NNFI=0.89) obtained from confirmatory factor analysis of the scale indicate that the model adapts to data at admissible levels.
### Procedure
Using a face-to-face interview method, researchers personally presented coaches with AYÖ, the Turkish version of the Coaching Efficacy Scale and the scale forms containing questions collecting information on coaches. Researchers provided detailed information to participating coaches about the purpose of the study and how the questionnaire should be completed, although this information was delivered in writing on the documents. Researchers distributed questionnaires on the third day of a training seminar and collected them the same day.
### Data Analysis
Obtained data was subject to t-test using the SPSS 15.0 program in order to clarify whether there was a statistically significant difference between the Total Coaching Efficacy (TCE) and its sub-scales: Motivation Efficacy (ME), Game Strategy Efficacy (GSE), Teaching Technique Efficacy (TTE), and Character Building Efficacy (CBE), or differences among it and age groups, marital status, education level, athletic career, coaching certificate, coaching level and years in coaching. Coaches’ ages, sporting backgrounds and coaching backgrounds were divided in to two groups after taking sample group averages.
### Results
Sample group average age was considered for data analysis and samples were gathered under two age groups, age 39 and less, and age 40 and over. Pursuant to this grouping, 78 (63.4%) of participant soccer coaches were age 39 and under and 45 (36.6%) were age 40 and over. A total of 100 (81.3%) soccer coaches were married and 23 (18.7%) were single. An investigation on coaches’ levels of education indicated that the majority of participating coaches were university graduates (n=77, 62.6%). (Table 1)
All coaches participating in the study played soccer as licensed athletes in their past sports careers. While 47 (38.2%) of the coaches played at an amateur level, 76 (61.8%) of them played at a professional level. An investigation on coaching certificates showed that 87 (70.7%) of the coaches hold UEFA B Licenses while 36 (29.3%) hold UEFA A Licenses. A majority of coaches work for the youth teams of professional soccer clubs (n=95, 77.2%).
Coaches participating in the study had been working in this profession between 1 and 23 years (M=7.87, SD=5.88). The sample group’s average years in the career were considered for data analysis and samples were gathered under two groups; eight years and fewer, and nine years and more. According to this grouping 78 coaches (63.4%) with less than eight years experience, and 45 (36.6%) with more than nine years experience, participated in the study (Table 1).
Coaches’ average belief in self-efficacy was determined to be M= 8.26, SD=.49. The level of Character Building, one of the sub-scales rendering beliefs on self-efficacy, was found to be at highest levels (M=8.6, SD=.54). The Character Building sub-scale was respectively followed by Teaching Technique (M= 8.22, SD= .58), Motivation (M= 8.17, SD= .57) and Game Strategy (M= 8.03, SD= .61) (Table 1).
The t-test results obtained from the study reveal that the efficacy and efficacy-related sub-scales of coaches participating in the study did not differ by age group, level of education, athletic career or years in soccer coaching. However, coaches’ belief in efficacy, when related to the strategy sub-scale, revealed significant difference by marital status (t= 2.417, p=.021) and coaching license (t=2.186, p=.032). Similarly, belief in efficacy when related to the motivation sub-scale differed significantly as well by the category coaches worked in (t= 2.049, p=.046) (Table 1).
Table 2 presents the correlations between total coaching efficacy (TCE) and coaching efficacy sub-scales. Correlations among dimensions of coaching efficacy ranged from 0.46 to 0.80, and correlations of TCE with dimensions of coaching efficacy ranged from 0.75 to 0.92 (Table 2). These relationships are coherent with the hierarchical structure suggested by previous studies (8,16).
### Discussion
Studies have shown that there is a positive relation between individuals’ increasing level of education and occupational efficiency, and that an individual’s contribution to the society was directly proportionate to the level of education. Based on population, Turkey ranked 15th in the world for level of education (7). Approximately 62.6% of coaches participating in our study were university graduates, suggesting that the education levels of these coaches were considerably above the national average.
Besides the high level of education among coaches participating in the study, the fact that most of them (61.8%) had previously played soccer at a professional level, along with the fact that 70.7% held a UEFA B License and 29.3% held a UEFA A License, was perceived as the reason for a considerably high degree of self-efficacy (M=8.26, SD=.49). In 2008, the Turkish Soccer Federation started an initiative to update certificates in accordance with UEFA (Union of European Football Associations) criteria and with this objective gave priority to developing the competence of coaches joining the Turkish Coca-Cola Academy League. Being informed on latest updates and receiving relevant training has contributed positively to the self-efficacy of coaches comprising our study group, and, in comparison with other studies (8,15,23), they presented a higher level of self-efficacy.
When compared to other sub-scales that constitute coaches’ belief in self-efficacy, Character Building was found to be at the highest levels (M=8.6, SD=.54). This finding is supportive of findings from other studies (8, 11, 15, 16, 23) conducted on coaching efficacy. One of the fundamental purposes of establishing the Coca-Cola League was exemplified by the slogan “Good Individual, Good Citizen, Good Athlete.” Bearing this slogan in mind, and considering the group coaches work for, highest levels of perceived self-efficacy in this sub-scale was highly significant. As a matter of fact, Lidor (13) underlined the necessity for ensuring the execution of plans and procedures directed at character-building within sports activities. Considered from a social perspective, character-building is undoubtedly very significant.
The Character Building sub-scale was respectively followed by Teaching Technique (M=8.22, SD=.58), Motivation (M=8.17, SD= .57), and Game Strategy (M=8.03, SD=.61). Mean values determined for these three sub-scales were calculated to be higher than those given in other related studies (8, 11, 15, 16, 23). The positive values, classified under these four sub-scales as the positive values which successful coaches are expected to have, were valuable in terms of their contribution to athletes. Game Strategy-related self-efficacy perception of coaches was identified to be lower than other sub-scales, which is important in regard to game strategy, being a decisive factor in game results.
Obtained t-test results revealed that the efficacy and efficacy-related sub-scales of coaches participating in the study did not differ by age group, level of education, sports career or years in soccer coaching. These findings are unsupportive of Tsorbatzoudis et al.’s finding (22) that, unlike inexperienced coaches, experienced coaches perceive themselves to be technically more competent in terms of coaching experience. However, this condition could be explained by the fact that coaches participating in our study had a higher level of experience. Teams joining the Turkish Coca-Cola League are some of the most elite clubs in Turkey, and these clubs are rigorous in choosing coaches. These two factors were considered to be the reason for such a result.
Coaches’ belief in efficacy related to the GSE revealed significant differences by marital status (t=2.417, p=.021) and coaching certificate ownership (t=2.186, p=.032) (Table 1). Familial responsibilities of married coaches might lead them to believe that they are more competent than do single coaches in the strategy sub-scale. In fact, strategy is very closely related to experience. That coaches with UEFA A License have further experience in the game of soccer than UEFA B License holders might help explain the difference emerging once again in the strategy development sub-scale.
It is interesting to note that belief in efficacy related to the motivation sub-scale differed significantly by the category coaches worked in (t=2.049, p=.046) (Table 1). Youth team coaches having more self-efficacy than professional team coaches in the motivation sub-scale is completely relative to experiences coaches have with soccer players. It is perhaps easier to motivate youth team players aspiring to become professionals for upcoming games than it is to motivate those who have already reached the professional level. Concepts of fame and money that engage in professional sports, after a while, cause a gradual sense of fulfillment, and this presents itself as coaches having difficulty in motivating players. More so, compared with youth team coaches, professional team coaches face further difficulties due to various other responsibilities and diversifying interests of older players. Therefore, considering experiences, it appears logical that youth team coaches perceive themselves to be more competent in terms of motivation than do professional team coaches.
### Conclusion
Besides being well educated, elite soccer coaches participating in the study also had good careers as athletes and coaches, explaining the high degree of self-efficacy among them. It was interesting to see that the degree of GSE, the capacity of directing the team during a game, was higher amongst married coaches than those who were single. It was logical to see a higher degree of GSE in coaches holding a UEFA A certificate compared to UEFA B certificate holders. The most interesting result from the study was the varying degree of motivation among coaches depending on their position. This suggests coaches’ need for knowledge and experience about the concept of motivation increased parallel to the significance of the league they worked for.
### Applications in Sport
Self-efficacy is an effective structure demanding improvement for efficiency from the coach. The fact that this effective structure transforms over time in light of newly acquired information and experiences demonstrates the need for meticulously organized coach training programs and even coach appointments. Respective federations and/or organizations have a great deal of responsibility in this matter.
### Acknowledgments
The author wishes to express his sincere thanks to Assistant Professor Dr. Melih Balyan for his support and cooperation in this study.
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### Tables
#### Table 1. Descriptive statistics of coaches and t-test results related to the Coaching Efficacy Scale
| Motivation Efficacy | Game Strategy Efficacy | Teaching Technique Efficacy | Character Building Efficacy | Total Coaching Efficacy | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| n | % | M | SD | M | SD | M | SD | M | SD | M | SD | |
| Age | ||||||||||||
| 39 & less | 78 | 63.4 | 8.19 | .58 | 8.01 | .62 | 8.28 | .60 | 8.57 | .54 | 8.26 | .50 |
| 40 & over | 45 | 36.6 | 8.15 | .56 | 8.06 | .60 | 8.13 | .53 | 8.64 | .54 | 8.24 | .48 |
| t-value | .420 | -.480 | 1.439 | -.763 | .171 | |||||||
| Marital Status | ||||||||||||
| Married | 100 | 81.3 | 8.21 | .56 | 8.1 | .6 | 8.26 | .54 | 8.61 | .55 | 8.29 | .48 |
| Single | 23 | 18.7 | 8.03 | .61 | 7.76 | .6 | 8.08 | .72 | 8.52 | .51 | 8.1 | .53 |
| t-value | 1.264 | 2.417* | 1.121 | .759 | 1.627 | |||||||
| Education Level | ||||||||||||
| High school & lower | 46 | 37.4 | 8.17 | .57 | 8.1 | .58 | 8.23 | .56 | 8.63 | .54 | 8.28 | .49 |
| University & higher | 77 | 62.6 | 8.17 | .57 | 8 | .62 | 8.22 | .59 | 8.58 | .54 | 8.24 | .49 |
| Sporting Background | ||||||||||||
| Amateur | 47 | 38.2 | 8.17 | .56 | 7.98 | .58 | 8.18 | .62 | 8.63 | .49 | 8.24 | .47 |
| Professional | 76 | 61.8 | 8.18 | .58 | 8.07 | .63 | 8.25 | .55 | 8.58 | .57 | 8.27 | .50 |
| t-value | -.062 | -.777 | -.593 | .537 | -.295 | |||||||
| Coaching Certificate | ||||||||||||
| UEFA B | 87 | 70.7 | 8.15 | .59 | 7.96 | .62 | 8.19 | .61 | 8.57 | .55 | 8.21 | .50 |
| UEFA A | 36 | 29.3 | 8.23 | 53 | 8.21 | .55 | 8.31 | .48 | 8.66 | .50 | 8.35 | .45 |
| t-value | -.731 | 2.186* | -1.171 | -.883 | -1.451 | |||||||
| Coaching Level | ||||||||||||
| Youth | 95 | 77.2 | 8.23 | .57 | 8.03 | .61 | 8.26 | .57 | 8.64 | .51 | 8.29 | .48 |
| Professional | 28 | 22.8 | 7.98 | .55 | 8.04 | .61 | 8.1 | .60 | 8.45 | .60 | 8.14 | .50 |
| t-value | 2.049* | -.009 | 1.258 | 1.540 | 1.389 | |||||||
| Coaching Background | ||||||||||||
| 8 years & less | 78 | 63.4 | 8.18 | .57 | 8 | .62 | 8.24 | .60 | 8.57 | .57 | 8.25 | .50 |
| 9 years & more | 45 | 36.6 | 8.17 | .58 | 8.1 | .58 | 8.19 | .54 | 8.64 | .49 | 8.28 | .47 |
| t-value | .087 | -.944 | .484 | -.796 | -.337 | |||||||
| Total | 123 | 100 | 8.17 | .57 | 8.03 | .61 | 8.22 | .58 | 8.6 | .54 | 8.26 | .49 |
* p < .05
#### Table 2. Pearson correlations between dimensions of coaching efficacy and total coaching efficacy
| Game Strategy Efficacy | Teaching Technique Efficacy | Character Building Efficacy | Total Coaching Efficacy | |
|---|---|---|---|---|
| Motivation Efficacy | 0.80 | 0.74 | 0.60 | 0.92 |
| Game Strategy Efficacy | 0.71 | 0.46 | 0.88 | |
| Teaching Technique Efficacy | 0.75 | |||
| Character Building Efficacy | 0.75 | |||
| Total Coaching Efficacy | – |
p < .001
### Corresponding Author
**R.Timucin Gencer, PhD**
University of Ege
School of Physical Education and Sports
Bornova, Izmir, Turkey, 35100
<timucin.gencer@ege.edu.tr>
+90 232 3425714 (office)
+90 532 3030610 (mobile)
### Author Bio
R.Timucin Gencer, PhD, is an assistant professor in the Department of Sport Management at the University of Ege. He played basketball as a professional from 1990-1997. He was also the assistant coach of the Turkish National Basketball Team U-16 men who won the European Championship Title in 2005.
Imagery Use and Sport-Related Injury Rehabilitation
Submitted by Matthew L. Symonds1* and Amanda S. Deml2*
1* Associate Professor, Department of Health and Human Services, Northwest Missouri State University
2* Intramural Sports Coordinator, University of Oregon
Amanda Deml is the Intramural Sports Coordinator at the University of Oregon. She earned both her BS and MS Ed degrees from Northwest Missouri State University in Maryville, Missouri. Matthew Symonds is an Associate Professor in the Department of Health and Human Services at Northwest Missouri State University and also serves as Department Chair.
ABSTRACT
This study sought to investigate mental imagery use among college athletes during the rehabilitation process, specifically examining the use of three functions of imagery – motivational, cognitive, and healing. The Athletic Injury Imagery Questionnaire-2 (AIIQ-2) was administered to varsity athletes representing 12 varsity sports at public, regional, Masters I institutions in the Midwestern United States. From the convenience sample, survey respondents included 61 males and 82 females. The study examined imagery use by: (a) sport and gender of current varsity athletes at the institution, and (b) between groups of respondents self-reporting as injured on uninjured. Results indicated that motivational imagery was more commonly employed than cognitive and healing imagery in the rehabilitation process. In addition, males used each function of imagery more than females. Furthermore, differences among sports concerning cognitive and healing imagery existed. No significant differences among injured and uninjured athletes and imagery use were found. The results of this study provided insight and additional perspective as to imagery use in the rehabilitation process. We recommend athletes, coaches, and athletic training personnel develop and implement imagery practices to improve athletic performance and the effectiveness of the injury rehabilitation process.
Key words: imagery, injury, rehabilitation
A New Test of the Moneyball Hypothesis
### Abstract
It is our intention to show that Major League Baseball (MLB) general managers, caught in tradition, reward hitters in a manner not reflecting the relative importance of two measures of producing offense: on-base percentage and slugging percentage. In particular, slugging is overcompensated relative to its contribution to scoring runs. This causes an inefficiency in run production as runs (and wins) could be produced at a lower cost. We first estimate a team run production model to determine the run production weights of team on-base percentage and team slugging. Next we estimate a player salary model to determine the individual salary weights given to these same two statistics. By tying these two sets of results together we find that slugging is overcompensated relative to on-base percentage, i.e., sluggers are paid more than they are worth in terms of contributing to team runs. These results suggest that, if run production is your objective, as you acquire talent for team rosters more attention should be paid to players with high on-base percentage and less attention to players with high slugging percentage.
**Key words:** Moneyball, strategy, quantitative analysis, economics
### Introduction
It is our intention to show the Major League Baseball (MLB) general managers did not immediately embrace the new statistical methods for choosing players and strategies that are revealed in the 2003 Michael Lewis Moneyball book. In particular we will show that three years after the Moneyball publication, a player’s on-base percentage is still undercompensated relative to slugging in its contribution to scoring runs. This contradicts a study by two economists (3) who claim Moneyball’s innovations were diffused throughout MLB only one season after the book’s publication.
#### Background
In the 2003 publication of _Moneyball_, Michael Lewis (4) describes the journey of a small-market team, the Oakland Athletics, and their unorthodox general manager, Billy Beane. This team was remarkable in its ability to attain high winning percentages in the American League despite the low payroll that comes with the territory of being a small-market team. Lewis followed the team around to discover how they managed to utilize its resources more efficiently than any other MLB team. Moneyball practice included the use of statistical analysis for acquiring players and for evaluating strategies in a way that was allegedly not recognized prior to 2003 by baseball players, coaches, managers, and fans. Central to this statistical analysis is determining the relative importance of on-base percentage versus slugging percentage. By buying more undervalued inputs of on-base percentage, Billy Beane could put together a roster of hitters that would lead them to more wins on the field while still meeting its modest payroll. Although there are many other aspects of Moneyball techniques discussed in the book (e.g. scouting, drafting players, and game strategy), in this paper we will focus on whether a team can increase its on-field performance for a given budget by sacrificing some more expensive slugging performance for more, but less expensive, on-base performance. This is what we will call the Moneyball test: efficiency in the use of resources requires the equality of productivity per dollar for on-base percentage versus slugging percentage.
Hakes and Sauer (3) were the first researchers to use regression analysis to demonstrate at the MLB level just what Beane and Lewis had suggested: 1) slugging and on-base-percentage (more so than batting average) are extremely predictive in producing wins for a team, 2) players before the current Moneyball era (beginning around 2003) were not paid in relation to the contribution of these performances. In particular, on-base percentage was underpaid relative to its value. They used four statistics to predict team wins: own-team on-base percentage, opposing-team on-base percentage, own-team slugging percentage, and opposing-team slugging percentage. The regression coefficients for the team on-base percentage and slugging percentage assign the weight each factor has in determining team wins. A second regression for player salaries assigns a dollar value to each unit of a hitter’s on-base percentage (OBP) and slugging percentage (SLUG). The following statistics were used in player salary equation: OBP, SLUG, fielding position, arbitration and free agent status, and years of MLB experience. They estimated salary models each year for the four MLB seasons prior to the release of the _Moneyball_, and the first season after. The regression coefficients of OBP and SLUG assign the weight each factor has on player salary. By comparing the salary costs of OBP versus SLUG with the effect each factor has on wins the authors determined whether teams are undervaluing OBP relative to SLUG. Their results showed that in the years before the _Moneyball_ book, managers/owners undervalued on-base percentage in comparison to slugging average. In other words, a team could improve its winning percent by trading some SLUG inputs for an equivalent spending on OBP inputs. However, the year after the publication of the _Moneyball_ book, Hakes and Sauer report that on-base percentage was suddenly no longer under-compensated. A team could no longer exploit the higher win productivity per dollar of OBP because now the ratio of win productivity to cost was the same for both OBP and SLUG factors. They concluded that this aspect of Moneyball analysis was diffused throughout MLB.
The speed of this diffusion is surprising, and it does raise questions as to their methodology. For example, what if this test of the Moneyball hypothesis is misdirected? Hitters are paid to produce runs, not wins. A mis-specified statistical model can lead to erroneous conclusions. In this paper we propose a more direct test of the Moneyball hypothesis: comparing the run productivity per dollar of cost for both OBP and SLUG factors. In other words, will an equivalent dollar swap for a small increment of slugging percentage in return for a small increment of on-base percentage lead to the same increase in runs scored? If this is not the case, then a team can exploit this difference and score more runs for the same team payroll by acquiring more units of OBP in place of SLUG units. On the other hand, if the ratios are equal, MLB is in equilibrium with respect to the run productivity for the last additional units of OBP and SLUG.
### Methods
This study differs from Hakes and Sauer in three ways: 1) the focus is on run production rather than win production, 2) the designated hitter difference between the National League and the American League will be controlled, and 3) more recent data from the MLB website is used.
#### Team Run Production Model
An MLB general manager should attempt to gain the most effective combination of the on-base and slugging attributes given the amount of money the MLB team is able to spend. This will maximize the team’s run production subject to its budget constraint. The run production model on a team basis will be of the form:
RPSit = β1 + β2OBPit + β3SLGit + β4NL + eit
– RPSit = **number of runs produced by team i in season t.** This takes the total number of runs by each team for the 162 games in a season. If fewer than 162 games are played, this number is adjusted to make it equivalent to a 162 games season.
– OBPit = **on-base percentage of team i in season t.** This is found by taking the total number times the hitters reached base (or hit a homerun) on a hit, walk, or hit batsman and dividing this by the number of plate appearances (including walks and hit batsmen) for the season. This proportion is then multiplied by 1,000 in order to make it more relatable. For example, a team that reached base 350 times per one thousand plate appearances would have a 350 “on-base percentage.”
– SLGit = **slugging percentage of team i in season t.** This is the number of bases (single, double, triple, or home run) that a team achieves in a season divided by the number of at bats (excluding walks and hit batsmen). This proportion is multiplied by 1,000 in order to make it more relatable. For example, a team that achieved 175 singles, 40 doubles, 5 triples and 35 homeruns per 1000 at bats would have 410 bases per 1000 at bats and therefore a 410 “slugging percentage.”
– NLi = **dummy variable = 1 if team i is in the National League, 0 otherwise.** The American League and National League do not have exactly the same set of game rules. One difference is the American League Designated Hitter rule that allows a non-fielding hitter to bat for the pitcher.
– eit = **random error for team i in season t.** This component allows for the fact that runs produced cannot be perfectly predicted using the above variables.
#### Player Salary Model
The second regression will show how much each of the two statistics, on-base percentage and slugging percentage for individual players, is rewarded by team management for their proficiency in each category. Position dummies were employed but only the catcher and the shortstop had statistically significant increases in pay due to their contributions to fielding. The other dummy variables for position were dropped. The other factor that is included is player experience as measured by lifetime MLB game appearances. The experience factor will appear in quadratic form to allow for diminishing returns toward the end of the player’s career. This model follows the economic literature on salary models starting with Mincer (1974):
Mj = β1 + β2Gj + β3G2j + β4OBPj + β5SLGj + β6CTj + β7SSj + ei
– Mj = **salary of player j.** 2006 MLB salary in thousands of dollars.
– Gj = **MLB career games played by player j.** This measures the improvement in a player due to experience.
– Gj2 = **MLB career games squared.** In conjunction with G, a negative coefficient for G2. This will allow for a diminishing rate of improvement as more and more experience is achieved, and will even permit a decline in performance at the end of a player’s career.
– OBPj = **on-base percentage of the player.** This is compiled as an average of the 3 MLB seasons prior to the beginning of the season in which the player’s salary is put into effect (2003-2005).
– SLGj = **slugging percentage of the player.** This is compiled as an average of the 3 MLB seasons prior to the beginning of the season in which the player’s salary is put into effect (2003-2005).
– CTj = **dummy variable = 1 if the player is a catcher, 0 otherwise.** This variable is included to see if any special value is attributed to this fielding skill position.
– SSj = **1 if the player is a shortstop, 0 otherwise.** This variable is included to see if any special value is attributed to this fielding skill position.
– NLi = **dummy variable = 1 if player j is in the National League, 0 otherwise.**
– ei = **random error.** This component allows for the fact that player salaries produced cannot be perfectly predicted using the above variables.
#### Sample Selection
For the team run production, five seasons of data (2002-2006) are collected for each of the MLB teams, for a total sample size of 150 observations. Descriptive statistics for five years of 16 National League teams and 14 American League teams are given in Table 1. The mean runs scored per team during this time period is 765 per season, or 4.7 per game. The standard deviation is 76 runs, which is saying that from one team to the next the typical difference in runs per season is 76 or about 0.5 runs per game. Of particular note are the means and standard deviations of on-base percentage and slugging percentage. The mean team OBP is 334, with a typical change from one team to another of 12. For SLUG the mean is 423 and the standard deviation is 23.5.
Batting statistics from players are averaged over the course of the last three MLB seasons in order to match recent performance and salary more closely. To be selected as a player in the salary regression, the athlete must play in at least two of the last three MLB seasons (2003-2005) and play in at least 100 games each season. Another important restriction was that all players in the sample needed to have played at least six seasons at the Major League level. Before six seasons, MLB players are unable to become free agents, a very important concern for their salary. As free agents, players are permitted to seek employment from any team, commonly resulting in competitive bidding for the player’s services and a free market determination of wages. With this we have our sample of 154 hitters (free agent eligible starting players). The 2006 salaries of players and their three year MLB performance averages (prior to 2006) are given in Table 2. The highest salary in the sample is $25,681,000 and the lowest is $400,000. The mean salary is $6.2 million with a standard deviation from one player to the next of $4.89 million. The mean OBP for the players is 347, with a typical change of 34 from one player to the next. The average SLUG is 450 with a standard deviation of 65.5.
### Results and Discussion
#### Team Run Production Model
Applying ordinary least squares, the following team runs regression was estimated for the five seasons:
RPS = -908 + 2.85 OBP + 1.74 SLG – 23.0 NL + e
In Table 3 the more statistical details for the above equation (Model 1) and other versions of the run production model are shown. Model 1 is the one used in the Moneyball hypothesis, and it explains 92 percent of the variance in team runs scored. This verifies that team OBP and SLUG are extremely predictive of team runs scored. It should also be noted that the runs scored equation fit is better than the one Hakes and Sauer have for their winning equation. Model 2 drops the dummy for the National League and Model 3 adds interaction terms of NL with OBP and SLG. The differences from the first model are small. This sensitivity analysis confirms that Model 1 is the most appropriate.
We will now interpret each slope coefficient in Model 1, holding the other included factors constant. A 10 unit change in team OBP (e.g., going from 330 to 340), brings an additional 10(2.85) =28.5 team runs scored per season, on the average. A 10 unit change in SLUG brings a 10(1.74) = 17.4 more runs, on the average. Each regression coefficient, including the one for NL, is statistically significant at a 1% level. This identifies the relative importance of each hitting factor. For an incremental 10 unit change, getting on base more frequently has a bigger impact on scoring runs than getting more bases per hit. What is needed now is a determination of what these factors cost the team in salary.
#### Player Salary Model
Applying ordinary least squares the following player salary regression was estimated for the 156 starting free agent players in 2006:
SAL = -30164 + 10.28 G – 0.00321 G2 + 37.05 OBP + 36.98 SLG + 1748.1 CT + 2024.87 SS – 876.96 NL + e
In Table 4 the more statistical details for the above equation (Model 4) and other versions of the player salary model are shown. In Model 4 we see the estimated coefficients from the player salary model—the one used in the subsequent test for the Moneyball hypothesis. This model explains 55% of the variance in salaries, roughly the same as the salary equations for Hakes and Sauer. In Model 5 the NL dummy is removed, and in Model 6 the position dummies are removed. There were only small changes in the remaining coefficients compared to Model 4. This sensitivity analysis confirms that Model 4 is the most appropriate.
We will now interpret each slope coefficient of Model 4, holding the other included factors constant. A 10 unit change player’s OBP for increases 2006 salary on average by 37.05(10) = 370.5 ($370,500), and 10 unit increase in a player’s SLUG increases salary on average by 36.98(10) = 369.8 ($369,800). The coefficients for G and G2 show that experience increases salary at a decreasing rate. Both the catchers and shortstops earn higher salaries, holding OBP and SLUG constant, than the other fielding positions. The experience and hitting coefficients are statistically significant at a 1% level. The position dummies are statistically significant at a 5% level. The NL dummy is statistically significant at a 10% level.
#### The Moneyball Hypothesis
In the _Moneyball_ book small market teams like the Oakland Athletics can compete against larger market teams if they can acquire run production factors that provide more runs per dollar spent. This occurs when OBP is undervalued relative to SLUG. To see if this is the case in 2006 we will compare the two main models (Models 1 and 4). A 10 unit increase in team OBP is brings an additional 28.5 runs and a 10 unit increase in team SLUG yields an additional 17.4 runs. The salary equation reveals that a 10 unit increase in individual OBP costs $370,500, and a 10 unit increase in individual SLUG costs $369,800. At essentially the same increase in team salary (at the player level) an increase in OBP brings in 11.1 more runs than SLUG. This means that teams can achieve a higher run production at essentially the same cost by swapping 10 units of SLUG for 10 units of OBP. The ratio of run production to cost favors OBP. The Moneyball hypothesis of slugging percentage being overvalued relative to on-base percentage remains in effect three seasons after the _Moneyball_ book.
Why did our results differ from Hakes and Sauer, who argue that slugging was no longer overvalued one season after the _Moneyball_ book? We repeat our differences in methodology here: 1) using a run production model instead of a winning production model because players are paid to produce runs, not wins; 2) including a variable to differentiate the National League from the American League; and 3) using more recent data.
### Conclusions
In this paper we propose a new test of the Moneyball hypothesis using team run production in place of team wins. We clearly show that in producing runs baseball managers continue to overpay for slugging versus on-base percentage. In the 2006 MLB season, for the same payroll, a team could generate more runs by trading some SLUG for OBP. The question is, why don’t general managers recognize these results in their roster and payroll decisions? We propose several possible reasons:
1. Only small revenue market teams need to be efficient in their labor decisions.
2. Sluggers are paid for more than just their ability to score runs.
3. Moneyball techniques will take time before all teams adopt them.
Each of these answers will now be discussed. Large-revenue market teams are profligate partly in response to the pressure they feel by the fan base to produce a winner at whatever cost. By acquiring well-known free agents at high cost rather than bargain free agents who are not recognized by home fans seems a safe way to operate, even if it cuts into some profits. These well-known players tend to be the sluggers. The second reason for slugger overcompensation is that they are crowd-pleasers, and it may be more profitable (higher gate attendance and television viewership) to have more homerun hitters. This study does not attempt to measure this alternative hypothesis. Finally, Hakes and Sauer believed equilibrium between OBP and SLUG in the player market occurred in just one year after the _Moneyball_ book was published, but it is doubtful such innovation can spread throughout MLB so quickly.
> “Given the A’s success, why hasn’t a scientific approach come to dominate baseball? The answer, of course, is the existence of a deeply entrenched way of thinking….Generally accepted practices have been developed over one-and-a-half centuries, practices that are based on experience rather than analytical rigor.” (1, p. 80)
The behavioral patterns in MLB change slowly. For example, it took twelve years after Jackie Robinson joined the Brooklyn Dodgers before every team in MLB acquired African-American players on their roster, despite the large pool of talent in the Negro Leagues. The slow pace of diffusion can also be claimed for the more recent immigration of Asian players in MLB. And more to the point, batting average still receives more attention than on-base percentage in the evaluation of talent.
Finally, the adoption of Moneyball is not limited to baseball. General managers in hockey (6), basketball (8), football (5), and soccer (2) are beginning to see the same advantages in using statistical analysis to supplement or replace conventional wisdom in making decisions on personnel and strategy. Despite the Oakland Athletics’ more recent lackluster performance, Moneyball is here to stay.
### Applications in Sport
The increased use of quantitative analysis in the coaching and management of sports teams allows colleges and professional teams to make decisions based more on data driven results rather than merely tradition. “Moneyball” is often the term used to convey this decision-making apparatus, particularly when money resources, if allocated efficiently, can improve on-field performance (scoring, wins) on a limited budget.
The advantage of adopting Moneyball techniques before your rival teams may be short term, however, widespread adoption eliminate opportunities (e.g., acquisition of under-rated players) that are not also seen by other teams in your sport. But this study shows that the diffusion of Moneyball techniques is taking place slowly, creating advantages for managers who are open to this approach.
### References
1. Boyd, E. A. (2004). Math works in the real world: (You just have to prove it again and again). Operations Research/Management Science, 31(6), 81.
2. Carlisle, J. (2008). Beane brings moneyball approach to MLS. ESPNsoccer. Retrieved from <http://soccernet.espn.go.com/columns/story?id=495270&cc=5901>
3. Hakes, J. K., and R. D. Sauer (2006). An economic evaluation of the moneyball hypothesis. Journal of Economic Perspectives, 20, 173-185.
4. Lewis, M. (2003). Moneyball: the art of winning an unfair game. New York: W.W. Norton & Company.
5. Lewis, M. (2008) The blind side. New York: W.W. Norton & Company.
6. Mason, D. S. and W. M. Foster (2007). Putting moneyball on ice? International Journal of Sport Finance, 2, 206-213.
7. Mincer, J. (1974). Schooling, experience, and earnings. New York: Columbia University Press.
8. Ostfield, A. J. (2006). The moneyball approach: basketball and the business side of sport. Human Resource Management, 45, 36-38.
### Tables
#### Table 1. Descriptive Statistics for the Team Run Production Sample
| RPS | OBP | SLG | NL | |
|---|---|---|---|---|
| Mean | 765.04 | 332.927 | 423.27 | 0.53 |
| Median | 760.34 | 332.000 | 423.00 | 1 |
| Standard Deviation | 76.43 | 12.168 | 23.52 | 0.50 |
| Range | 387.00 | 63.000 | 123.00 | 1 |
| Minimum | 574.00 | 300.000 | 368.00 | 0 |
| Maximum | 961.00 | 363.000 | 491.00 | 1 |
| Count | 150 | 150 | 150 | 150 |
#### Table 2. Descriptive Statistics for the Player Salary Sample
| G | OBP | SLG | NL | CT | SS | |
|---|---|---|---|---|---|---|
| Mean | 1146.1 | 347.3 | 450.0 | 0.552 | 0.130 | 0.12 |
| Median | 1070.5 | 346.5 | 446.5 | 1 | 0 | 0 |
| Standard Deviation | 462.1 | 34.0 | 65.5 | 0.499 | 0.337 | 0.322 |
| Range | 2345.0 | 237.9 | 432.0 | 1 | 1 | 1 |
| Minimum | 385.0 | 276.1 | 310.7 | 0 | 0 | 0 |
| Maximum | 2730.0 | 514.0 | 742.7 | 1 | 1 | 1 |
| Count | 154 | 154 | 154 | 154 | 154 | 154 |
#### Table 3. Coefficients for the Team Run Production Models
| MODEL 1 | MODEL 2 | MODEL 3 | ||||
|---|---|---|---|---|---|---|
| Variable | Coefficient s | t Stat | Coefficient s | t Stat | Coefficient s | t Stat |
| Intercept | -908.00*** | -17.16 | 941.72*** | -18.46 | -861.67*** | -13.73 |
| OBP | 2.85*** | 11.21 | 2.69*** | 13.22 | 2.86*** | 10.26 |
| SLG | 1.74*** | 15.42 | 1.92*** | 15.30 | 1.62*** | 10.37 |
| NL | -23.00*** | -6.26 | -134.3* | -1.34 | ||
| (NL)(OBP) | 0.275 | 1.06 | ||||
| (NL)(OBP) | 0.241 | 0.06 | ||||
| Adj. R-Squared | 0.921 | 0.900 | 0.923 | |||
| F | 568.9 | 661.6 | 343.3 | |||
*** .01 level ** .05 level * .10 level
#### Table 4. Coefficients for the Player Salary Models
| MODEL 1 | MODEL 2 | MODEL 3 | ||||
|---|---|---|---|---|---|---|
| Variable | Coefficient s | t Stat | Coefficient s | t Stat | Coefficient s | t Stat |
| Intercept | -30164*** | -9.38 | -30952*** | -9.67 | -27182.6*** | -8.73 |
| G | 10.28*** | 4.21 | 10.24*** | 4.18 | 9.75*** | 3.95 |
| G2 | -0.00321*** | -3.67 | -0.00323*** | -3.68 | -0.00304*** | -3.42 |
| OBP | 37.05*** | 3.32 | 38.08*** | 3.39 | 35.30*** | 3.10 |
| SLG | 36.98*** | 6.47 | 37.01*** | 6.43 | 33.58*** | 5.88 |
| CT | 1748.10** | 2.14 | 1798.21** | 2.19 | ||
| SS | 2024.87** | 2.34 | 2048.73** | 2.35 | ||
| NL | -876.96* | -1.65 | -929.14* | -1.71 | ||
| Adj. R-Squared | 0.557 | 0.552 | 0.532 | |||
| F | 28.48 | 32.39 | 44.44 | |||
*** .01 level ** .05 level * .10 level
### Corresponding Author
#### Thomas H. Bruggink, Ph.D.
Department of Economics
Lafayette College
Easton PA 18042
<bruggint@lafayette.edu>
610-330-5305
### All Authors
#### Anthony Farrar
Brinker Capital
Berwyn, PA
#### Thomas H. Bruggink
Lafayette College
Easton, PA
Information Technology and Sports: Looking Toward Web 3.0
### Abstract
From the founding of the Olympic movement in the late 19th century at the height of the Industrial Revolution through the beginning of the Information Age in the 1970s, channels of media distribution evolved from primarily written tracts in publications to electronic broadcasting. The changes in the mode of information distribution and the underlying technology over time caused the message content being promulgated to similarly change. As there were comparatively few channels available for the distribution of content during this period, a relative few individuals served as “gatekeepers” on the flow of information. These gatekeepers, such as editors and producers, exercised extraordinary control over what information entered the public domain through a process that was largely autocratic. The Information Age has changed the paradigm of information dissemination, and in so doing, has democratized the process of sharing information. The participation of the public-at-large in the development and dissemination of information that shapes humanistic ideas has grown in scale to a size unprecedented in human history. Since the advent of the Internet, this human discourse has changed over time driven both by the application of new technologies together with the exponential growth in that portion of the population that has access to them. Perhaps the most significant development in this movement was the development of the World Wide Web (the web). As the web has moved from comparatively static Web 1.0 content through the development of Web 2.0 social media applications to the beginning of Web 3.0 practices, there have been significant changes in how humans use computer technology to interact with one another. Despite the positive changes that have been brought about by the development of these technologies, such as a democratization of the information sharing process, there are still negative aspects to social media applications. There will also be significant challenges ahead in the development of new communication technologies that must be overcome before the full promise of the Internet can be realized by all.
**Key words:** Olympic movement, social media, Internet, web, technology, humanistic ideas
### Introduction
Human play, as embodied in sports, is one of the most important expressions of human culture. It can be said that the games people play in a society are a reflection of the society as a whole. It can also be said that communication is the one dominant attribute that distinguishes human beings from every other species on the planet. Thus, the intersection of communication and sports in the human experience is an important one.
The Olympic movement is considered to be one of the largest social movements in human history. Nowhere else do the countries of the world gather in one place as they do during the Summer Olympic Games. While the peaceful gathering of the world’s youth for sports competition is the embodiment of that intersection of sport and communication, this fact underscores the importance of the media in conveying Olympic values and ideals. In many respects, it is a relationship between the Olympic community and the media that allows the Games to be conducted on the scale that they are.
This presentation will briefly examine the evolution of this relationship from the founding of the Olympic movement at the height of the Industrial Revolution to the dawning of the Information Age. The discussion of the early days will necessarily be brief as the primary focus of this presentation is on the ways that technology, and more specifically the Internet, is driving the communications process and with it the dissemination of the human ideals. There will be a discussion of some of this new media and the presentation will conclude with some of the challenges before us, as we look to the future being wrought through technological change.
#### Evolution of Media
As has already been noted, the Olympic movement was founded at the height of the Industrial Revolution in the late 19th century. The founder of the Olympic movement, Baron Pierre de Coubertin, authored many articles arguing for the establishment of a modern Olympic Games. An example of this effort was the publication of an essay in the “Review de Paris” in June 1894 – on the very eve of the first Olympic Congress – setting out his vision for the establishment of a modern Olympic Games (Guttman, 1992).
Writing in the 19th century was a lengthy process, meaning that 19th century writers faced a much longer period than happens today, between researching, writing, and receiving payment for their work. Only the best educated individuals, usually from privileged backgrounds, had the time, expertise, talent, inclination, and financial backing to undertake this effort (2). Illustrated news weeklies or monthlies were among the primary means of communication and dissemination of the news in the late 19th and early 20th centuries. This medium was also one that was particularly well suited to the audience that de Coubertin was trying to reach. The founders of the Olympic movement were well educated and well-to-do. Therefore, the message to this audience leant itself well to the tenets of the early games that they should only be open to amateurs; those who participated in sport as an avocation as opposed to a vocation (4).
However, the on-going Industrial Revolution was bringing about important society-wide changes that allowed sports to flourish. This included a population migration from rural to urban centers, increases in disposable income accompanying a rise in the middle-class and eventually, more leisure time that allowed more recreational activities, among them participation in and the viewing of sports events.
Concurrent with the rise in the middle class was a wider distribution of newspapers, many of which began to include sports coverage. Sports coverage did, in fact, become one of the ways that newspapers in larger metropolitan areas competed with each other. As interest in sports generally, and local teams particularly, began to appear in newspapers, the amount of space given over to this content expanded over time. As there were no broadcast media in these early days, the newspaper sports coverage of the day was largely descriptive play-by-play recaps of the sports events.
Eventually, however, broadcast media was introduced to the communications mix and began to usurp the role historically played by the newspapers. First radio, and later television, allowed the audience to experience the sport events as they occurred with their play-by-play broadcasts. Thus, the role of the newspapers and weekly or monthly sport-themed news magazines began to evolve from reporting the play-by-play, now done by the broadcast media, to more reporting of “behind the scenes” activities or analysis of the athletes, teams and events.
There are two lessons to be learned from this experience. First is that as technology evolved and new forms of communication emerged, message content carried in the channels of distribution changed as well. So, too, is this the case today; as technology evolves, so does the nature of the message content being distributed.
The second lesson concerns the role of “gatekeepers” such as editors or producers in the public communications process. During this early period, there were comparatively few media outlets. In Europe, countries might have one or two “national” newspapers, plus those in the metropolitan areas. In the United States, there was no general national newspaper until the advent of “USA Today” in 1982. While larger metropolitan areas may have as many as five news dailies, most of the country had smaller markets that could support no more than one or two. In terms of electronic broadcasting, the available air time for sports was typically limited, since most outlets aired a variety of content. Also in the early days of television in the United States, there were only three major television networks. Because of the limited availability of channels of distribution, editors in the newsroom or producers of over-the-air broadcasting wielded enormous power in determining what their audience would read or hear. The selection process for media was typically driven by market concerns; but in any case was decidedly autocratic.
#### The Information Age and Rise of the Internet
Human civilization has moved from the Age of Industry to the Information Age. While the general consensus is that the dawn of the Information Age is the 1970s, the changes wrought to society through technological change really accelerated with the creation of the World-Wide-Web (the web). As changes in technology changes channels of communication and message content, a brief discussion of the underlying technology is in order.
The early 1960s saw experimentation with computer technology that established the protocols for what became known as the Internet in 1969. This feat was followed by the development of Hypertext Mark-up Language (HTML) in 1989 that became the basis for the development of the web, though it was not until 1993 that the web was introduced to the public-at-large.
Most early websites were a series of static web pages connected by hyperlinks that could be internal, which provided structure to a website, or external leading to other websites based on whatever criteria the webmaster decided. The underlying computer technology such as processors, memory, and connectivity limited the content of these early web pages. Most hosts, or the site where the web content was posted, were initially personal computers (PCs) adapted for this purpose, although eventually specialized computer devices called “web servers” evolved. Over the years, the capability of these website servers has changed dramatically as has the role of the webmaster. Today, virtually all commercial or professionally developed websites are dynamic with the web content contained in a relational database called “the backend.” Most websites also have a variety of plug-in applications, such as secure financial transaction software for ecommerce, called “middleware,” and the front facing graphic interface that people see when they arrive at a website. Webmasters have evolved into web developers and the skills required for maintaining a website can vary significantly between those working the backend and those designing the frontend.
On the recipient’s end were similar technological limitations by PC’s that had their processor capability expressed in numbers such as the 286, 386, 486 and Pentiums. In terms of connectivity, bandwidth has increased exponentially with a succession of changes from dial-up modems to ISDN and now broadband. Thus, early on the limitations of technology necessarily limited the content; e.g. the message.
Over the past 30 years, society has experienced a fundamental change in the way information is created and disseminated. From its rudimentary early beginning, the interface between computer technology and users has evolved to a point where virtually anyone can create “media content” and post it to the web where it can be accessed and read by anyone in the world with access to the computer resources to do so. This has led to another fundamental and extraordinarily significant change: a process of democratization. No longer can gatekeepers such as the editors or publishers of the old media exert autocratic or monopolistic control over the flow of information into the public sphere. There are, however, both positives and negatives to this state of affairs as we shall see in our ensuing discussion of the evolution of the web.
#### Web 1.0 – The Inaugural Web
During the formative days of the web, strategies for the dissemination of information could be broadly classified as “push” versus “pull.” Push refers to the proactively sending out or distributing messages across the Internet most commonly by email from one user’s account to another. One of the ways in which email was used as a precursor to today’s Web 2.0 applications, such as blogs and social networking sites, was the listserv. A listserv was a group of individuals typically bound together by a common interest who signed onto an email list to receive messages on a topic of mutual interest. When an email was sent in bulk to the list, anyone in the group could respond to the sent message which subsequently went to everyone else in the group. In so doing, an online discussion and sharing of ideas would ensue.
Unfortunately, the widespread abuse of email has gradually restricted its utility as a medium of communication exchange beyond personal messages. Both marketers and criminals seized upon email as a means to try and sell their wares or dupe people into giving up money which gave rise to the spam phenomenon. Unfortunately, spam is still a plague on the Internet with an estimated 48.5 billion messages sent everyday largely through networks of compromised computers called botnets. In March 2011, one of the largest of these, the Rustock Botnet that was sending as many 13.82 billion spam emails each day, was finally taken down by the authorities (8). Partially as a consequence of this abuse, more and more people are seeking out alternative channels for the sharing of electronic communications, such as through the messaging capabilities of Facebook or Twitter.
The other concept is that of “pull” in which individuals actively seek out web content utilizing web browsers and devices such as search engines. The key to this strategy is to insure that this web content is properly optimized and has appropriate tags, so it becomes more visible on the web and easier to find.
Education is the most powerful vehicle for the transmission of human ideals. It is in the realm of education that the Internet has had a profound impact. The advent of the Internet and the worldwide web has fundamentally changed the paradigm of education; a paradigm that had essentially been unchanged since the 16th century. Early on, the Academy embraced this change and developed a distance education program that can be defined as asynchronous, transformational, and computer mediated. This means that the Academy’s students can pursue their studies across the Internet using computer resources at any time and from any place without the faculty and student needing to present online at the same time. While removing impediments to learning created by time and space, the institution has transformed the traditional educational experience of the lecturer in the classroom to learning activities distributed through the web in which learning outcomes and course objectives are satisfied.
There has been a lot of skepticism with respect to the efficacy of online education. The validity of the model has been validated by the Academy’s own research among which has been the comparison of comprehensive examination results between resident and online students. The institution’s accrediting agency, Southern Association of Colleges and Schools, reviewed and approved the Academy’s distance education program in 1996, and currently more than 85% of the Academy’s students report that they have learned as much or more through online education as they did in resident study. The Academy is also pleased that more than 96% of its students would recommend the Academy’s online education programs to friends or colleagues.
Illustrative of this approach to education is the Olympic Values Education Program (OVEP) that was prepared for distance learning delivery by the Academy under a grant from the International Olympic Committee (IOC) in 2008. Through the web, the OVEP program is available to anyone in the world who has access to the Internet, and further utilizing emerging technology, such as the Google Universal Translator, albeit with some inherent limitations, it can be accessed in any one of 52 different languages. The online OVEP course can be reached at students.ussa.edu/Olympic_values. I should also note that the Academy recently completed another such cross-cultural academic offering with the preparation of a bachelor’s degree course entitled the “Shaolin Philosophy of Kung Fu.” The basis for the course is a 1,500-year-old manuscript that was translated from the ancient to the modern version of Chinese and then into English. The Academy’s Department of Instructional Design then refined the English and placed it into an online course environment. In so doing, East meets West, the ancient meets the new and we come full circle insofar as the modern English course can be translated back into Chinese with the universal translator function built into the Academy’s Course Management System (CMS).
Very important in the supporting of student education and the dissemination of human values is access to libraries and research resources. In 1997, the Academy was among the first organizations to put online a peer-reviewed research journal – [_The Sport Journal_](http://www.thesportjournal.org). This Journal is provided subscription-free to the public and is accessed on average about 15,000 times per week. As a matter of interest, all of the papers from last year’s International Olympic Academy (IOA) were posted to The Sport Journal site in a special Olympic edition of the Journal. From the comfort of their own homes, the Academy’s students can use the Internet to access more than 57,000 libraries in 112 countries that have more than 70 million holdings and 270,000 unique journals through the institution’s library portal on its website. However, access to educational resources, such as libraries, are not restricted to students in universities. Very early in the development of the web, the Encyclopedia Britannica posted its entire body of work online and made it available on a subscription basis. Today, there are a myriad of libraries to which the public has access free-of-charge, such as the Alabama Public Online Library. Organizations such as Google are digitizing the holdings of entire research libraries with the ultimate intent of placing these online for ease of access; though inevitably at a price.
Web 2.0 – The Social Web
The rise of participatory information sharing through the Internet has truly revolutionized the dissemination of information using web 2.0 techniques. With the advent of the social web, the creation of content has evolved from the efforts of a comparative few in the media professions to a model that maximizes the contributions of the multitudes. With about 400 social media platforms available and an untold number of blogs being authored, the proliferation of communication channels, both public and professional, and private and amateur, allow for the contribution of millions of people sharing a public conversation unprecedented in the human experience. One of the most important consequences of the proliferation of these platforms available to virtually anyone with access to the Internet, is the democratization of media content. What people can see and hear has been taken out of the hands of the gatekeepers and placed into the hands of society at large.
It is not possible within the constraints of this presentation to cover all aspects of the social web, so the author has selected five representative examples beginning with a discussion of Wikipedia. If the Encyclopedia Britannica, long acknowledged as a definitive compendium of human knowledge, represents Web 1.0 technology in which content is simply posted and accessed by people through subscription, Wikipedia represents a web 2.0 application because of its collaborative nature insofar as anyone can submit articles for inclusion.
Ironically enough, I have turned to Wikipedia for a definition of itself, though I should note that at the Academy there is a prominent notice posted on the library portal that Wikipedia is not considered an appropriate source of citations for research papers for reasons that will be explained. By its own definition, Wikipedia is a free, web-based, collaborative, multilingual encyclopedia project supported by the non-profit Wiki Media Foundation. Its 18 million articles (over 3.6 million in English) have been written collaboratively by volunteers around the world, and almost all of its articles can be edited by anyone with access to the site. Wikipedia was launched in 2001, and has become the largest and most popular general reference on the Internet ranking seventh among all websites on Alexa.com (a web statistics reporting site) and boasting 365 million readers. (Wikipedia, 2011)
The reason that Wikipedia has not been widely accepted in academic research has its roots in its early days. The articles submitted at that time frequently were not carefully researched, often inaccurate, and sometimes posted with malicious intent. It is significant to note that many of these issues have been addressed through the use of anonymous reviewers who examine submissions from the general public for both accuracy and appropriateness. Nonetheless, it still remains a very important resource insofar as researchers, especially the youngest, still access Wikipedia as a point of departure in their research to give them ideas on where to go for additional information.
For those of you who have entries in Wikipedia, it is worth your time to periodically check the content to ensure that someone has not submitted inaccurate or even malicious information. Further, and especially given the reach of Wikipedia, it affords organizations the opportunity to promulgate their missions and activities. For example, in the entry on Olympia, the article posted there cites its role in the ancient Olympic Games and presents a chronology of the site by era to the present day. It does not, however, mention the IOA. A submission could be authored for consideration and inclusion on how Olympia serves as the site of the IOA along with a description of the IOA’s mission and function.
One of the true phenomena of the last few years in Web 2.0 technology is the rise of Facebook as suggested by Internet usage statistics posted on Alexa.com. In April 2011, more than 40% of all global Internet users visited Facebook on a daily basis, a rate of usage that has remained consistent over the past three months.
Facebook represents the power of social media as individuals sharing common experiences are provided a platform through which these experiences or interests can be shared. As friends beget friends, the media content on Facebook expands in ever increasing circles. This content is not limited to posts or messaging, but also includes YouTube video clips, decidedly unscientific opinion polls, and games. Additionally, the messaging function built into Facebook has, in many circles, replaced email as the preferred means of interpersonal electronic communication.
Facebook can be a double-edged sword, as the most decorated Olympic athlete of all time found out much to his chagrin. This individual, who won a record eight gold medals in the 2008 Beijing Olympics, suffered the consequences of the posting of a photograph to Facebook of him consuming illegal recreational drugs. This incident sullied his image and reputation and cost him millions of dollars in endorsement revenue. The irony is that the picture posted was not posted on his personal Facebook page, but on that of another individual who happened to be at the same party. In this instance, the interconnectivity of the medium produced dire consequences for a sports hero and role model. This incident also underscores the need to be circumspect with what one posts to social media sites. A good guideline is not to post anything you would not want to see in a newspaper. It is not uncommon for prospective employers, among others, to search out Facebook pages in an effort to gain insights on a given individual.
Another extraordinarily popular site, and one already mentioned, is YouTube. Founded in February 2005, viewership on YouTube exceeded two billion views per day in May 2010. YouTube allows viewers to watch and share originally created videos and provides a forum for people to connect, inform, and inspire others across the globe and acts as a distribution platform for original content creators and advertisers large and small (YouTube, 2011). Alexa.com reported in April 2011 that YouTube is the third most visited global website receiving just over 26% of daily website visits over the past three months.
YouTube, whose web interface is available in 42 languages, can be accessed by anyone, although those individuals who want to post content on the site must be registered. For regular users, the time limit for any one post is 15 minutes. Posting video content there can be accomplished from a wide range of devices from computers to mobile phones. YouTube video posts spread across the entire Internet by appearing as links in emails, posts on other social media platforms, such as Facebook and in blogs. Periodically, a video on YouTube will “go viral,” which simply refers to a phenomenon in which the content captures the public’s imagination and is promulgated through a vast array of distribution channels.
However, sites such as YouTube pose a recognized threat to the business model of many sports organizations. The blogging and social media rules of the IOC specifically proscribe the posting of “moving images” or sound. While these guidelines can be enforced on accredited individuals to the Games, such as national delegations or the media, it is much harder to do with spectators seated in the stadium. Modern 3G or 4G phones can easily capture video of sporting events from the stadium seat, and the video can be uploaded to YouTube through a user’s account. While such activity violates the terms of service for registered account holders, the process for removing the content and terminating a user’s account can sometimes be a lengthy one. In the meantime, to the extent to which the video has been accessed and distributed through posts on other social media web sites or platforms, it can never be removed from the web in its entirety. Obviously, this is a major issue for media companies that may pay as much as billions of dollars for exclusive media rights to the event.
Another social media phenomenon is Twitter and, in fact, the Winter Games in Vancouver were cited as the first “Twitter Olympics” (5). The Twitter posts, called tweets, of the athletes provide insights into their physical and mental preparation for competition, their reactions to being in the Olympic Games and other aspects of the Olympic experience that simply were not possible in the past through traditional media outlets. Twitter allows for the sharing of the human experience with an unparalleled immediacy and intimacy with potentially vast audiences that is not tempered with the interference of a gatekeeper. Many tweets generated by Olympians at the Vancouver Winter Games can be found on the web by simply “Googling” Olympic athlete tweets.
However, as was the case with Facebook, Twitter can also be a double-edged sword. There have been instances where athletes have posted comments denigrating their competition, the officials, and even their teammates or coaches. These actions can create dissension on teams and when comments go viral, they can take on a life of their own and stir considerable controversy and unfavorable comment in the press. This has occurred frequently enough that some teams ban their athletes from using Twitter, while other teams such as that of the Australian Olympic Team provide their athletes with training on the appropriate use of the medium.
Lastly, I would like to touch on “blogging” as a medium for the dissemination of the human experience. A blog can be thought of as an online diary, open to the public, and onto which an author can write on any topic they choose and to which anyone who reads the post can, in turn, reply. These blogs typically focus on a particular topic such as politics or sports and there are blogs on virtually every topic imaginable. Taken altogether, these blogs are referred to as the “blogosphere.”
With all of the attention that this form of human endeavor engenders and the emotion that it evokes, sports are a common topic in the blogosphere. As one might expect, the blogging commentary related to sports can be both positive and negative. Frequently the authors of blogs do not have the professional or academic preparation to speak knowingly about which they write. The unfortunate thing about blog posts that are inaccurate is that they often carry more weight than they deserve. Illustrious of this situation is the phrase, “it must be true, I read it on the Internet.” The Academy is seeking to address this situation in some small measure through its decision to change one of our online publications, [_The Sport Digest_](http://thesportdigest.com), into a blog. Through this effort, Academy faculty and other well regarded individuals in the profession generate articles on a host of issues surrounding the sport profession. These posts have a basis in fact or are otherwise well-reasoned and as is the case with other blogs, afford the readers an opportunity to respond to the issues.
#### Web 3.0 – The Semantic Web
While the term Web 2.0 has entered the lexicon, Web 3.0 will be the next step in the evolution of the Internet. A common, agreed upon definition for Web 3.0 has yet to emerge but a consensus is building that it will be a combination of technology through which the entire web is turned into a database combined with the marshaling of human resources. New computer languages such as HTML5 will allow computers to read online content and so will facilitate the identification and indexing of the web, a process that will make content more accessible.
Beyond the changes in technology, renowned web futurist Clay Shirky argues that for the first time in history the web has provided the tools to harness society’s “cognitive surplus.” Essentially, the cognitive surplus is derived from the trillions of hours of free time that the residents of the developed world enjoy and that has steadily increased since World War II. Increases in gross domestic product, education, and life span have provided riches of free time but that prior to the Internet was squandered in non-productive pursuits. The Internet democratized the tools of production and distribution and the Internet made the benefits scalable: value comes from the combined cognitive surplus of millions of individuals connected to a network that allows collaboration. (1)
Shirky is an example of this dynamic at work. In the course of researching this paper, the author continuously came across references to Shirky and his theories of cognitive surplus. As more authors agreed with the concept than those that did not, it suggests that these theories are gaining traction and apparently have some merit. Through this process of review and debate, concepts and theory are continually refined adding to the body of knowledge through which the human condition can be enriched.
#### Challenges
With all of its potential to elevate human discourse and to assist in the dissemination of human ideals, many challenges remain. This can fall into three broad areas as follows:
The first is economic. There exists in a very real sense a digital divide in which a vast proportion of the worlds’ population remains without access to computers or the Internet. In many respects, the Internet still remains a world of the “haves and have nots.” In some respects we have almost come full circle to the human condition of when Olympic movement first began in the late 19th century in which access to information was the domain of the privileged few. This fact has been recognized and there are efforts to address this imbalance through the production of low-cost machines to allow the underserved populations without the necessary economic resources to gain access to the Internet.
A looming issue is a social one. Governments all over the world took note at the “Jasmine Revolution” in Tunisia and the events in Tahrir Square in Egypt and the role that Web 2.0 applications played in mobilizing the population to overthrow the political establishment. In the most populous country of the world, the two most globally accessed websites everyday cannot be reached at all. So in a very real sense, we could be headed to a world of two Internets; one in which the flow of information is free and unfettered, and another where access to information resources are tightly controlled or restricted to what the government believes to be “politically acceptable.” (7) In the West, the Internet has played a role in self-censorship resulting in societal fragmentation and polarization insofar as people have a tendency to seek out and read only that information that reinforces their points of view. If the ability to share information is deemed to be a strength, impediments to the free flow of information can only be deemed to be a detriment in a future of shared human values.
The last issue is technical. Computers as we know them – those bulky desktop machines and even portable laptops – are going away. What is going to occur in the future will be a proliferation of smaller devices such as tablet computers, iPhones, and Androids that provide access to the Internet, but where the information that they generate is stored on the Internet itself (also called the cloud). However, all of these devices require wireless connectivity and the amount of electromagnetic spectrum through which these connections are made is a finite resource. In June 2009, the U.S. Government took back that portion of the electromagnetic spectrum through which analog television signals were broadcast. This spectrum was subsequently auctioned off to telecommunication providers and others such as Google. But the fact remains that in the not-too-distant future this bandwidth will also be exhausted. All of this is setting the stage for a time in which data consumption will be metered as is any other utility and subject to the laws of supply and demand (3). Thus, if the digital divide was created by economic conditions, the situation can be exacerbated by “metered Internet access.”
The solution will be found both in the technical, such as content providers better streamlining their services, or through the creation of better means by which access is gained, such as twisting the wireless signals.
### Conclusions
Information technology has unquestionably changed human society in ways that can scarcely be imagined. From early experiments in the 1960s to today, the Internet, as embodied in the web, has over 171 million web hosts. Assuming an average 100 pages per website (the Academy website has more than 800 pages) would yield an estimated 17.1 billion pages of web content, the vast majority of which can be accessed by anyone. Research shows that the Internet, excluding the deep web, is growing by more than 10 million new static pages every day. (6) Thus, the Internet spans virtually the entire gamut of the human existence and can be a powerful medium for the conveying of humanistic ideas. It has provided a vehicle that can educate and entertain us and can serve to make society more cohesive. In so doing, it has created an environment for public discussion unequaled in human history but at the same time, it can also serve to isolate us from each other. People can immerse themselves in an environment where the virtual becomes reality and normal communication with others slowly becomes lost. In any case, the evolution of the Internet has brought about a democratization of media content and has created an environment in which all can participate. It is, as the title of a popular novel suggests, “A Brave New World.”
### Applications in Sport
On our way to Web 3.0, it is critical that we participate in this powerful medium and spread humanistic ideas and Olympic values across the world. The Internet has provided a vehicle that can educate and entertain us and can serve to make society more cohesive. However, despite the potential to elevate human discourse, challenges remain, such as the digital divide that prevents much of the worlds’ population from accessing the Internet, tightly controlled or restricted access by some governments, and technical obstacles that limit wireless connectivity. In any case, the evolution of the Internet has brought about an unprecedented democratization of media content and has created an environment in which all can participate and make a difference.
### References
1. Davis, P. (2010). Here Comes Everything: A Review of Clay Shirky’s Cognitive Surplus. Shareable: Work and Enterprise. <http://shareable.net/blog/here-comes-everything-a-review-of-clay-shirky%E2%80%99s-cognitive-surplus>. (13 July, 2010).
2. Harper, A. (2007). 19th Century Magazine – An Amazing Source of Public Domain Information. Ezinearticles. <http://ezinearticles.com/?19th-Century-Magazines—An-Amazing-Source-of-Public-Domain-Information&id=762208>. (3 October, 2007).
3. Gruman, G. and Kaneshige, T. (2008) Is Our Internet Future in Trouble? InfoWorld. <http://www.infoworld.com/d/networking/our-Internet-future-in-danger-715>. (11 November, 2008).
4. Guttmann, A. (1992). The Olympics; A History of the Modern Games. (2nd. Ed.). Champaign-Urbana: The University of Illinois Press. 13 Ibid. 14.
5. Mann, B. (2010). Olympians On Course Using Twitter. MarketWatch Blogs. <http://blogs.marketwatch.com/vancouverolympics/2010/02/10/olympians-on-course-using-twitter/> (10 February, 2010).
6. Metamend. (2011). How Big is the Internet? Metamend. <www.metamend.com/Internet-growth.html>. (14 April. 2011)
7. McMahon, R.; Bennett, I. (2011). U.S. Internet Providers and the Great Firewall of China. Council on Foreign Relations. <http://www.cfr.org/china/us-Internet-providers-great-firewall-china/p9856>. (23 February, 2011)
8. Slashdot. (2011). Spam Drops 1/3 After Rustock Botnet Gets Crushed. Slashdot IT Blog. <http://it.slashdot.org/story/11/03/29/1516241/Spam-Drops-13-After-Rustock-Botnet-Gets-Crushed>. (29 March, 2011).
9. Wikipedia (2011). Wikipedia. Wikipedia. <http://en.wikipedia.org/wiki/Wikipedia>. (24 March, 2011).
10. YouTube (2011). About YouTube. YouTube. <http://www.youtube.com/t/about_youtube>. (23 March, 2011).
### Corresponding Author
T.J. Rosandich, Ed.D
One Academy Drive
Daphne, Ala., 36526
<vicepres@ussa.edu>
251-626-3303
### Author Bio
Dr. T.J. Rosandich serves as Vice President for the United States Sports Academy, where he earned both his master’s and doctoral degrees. In addition to having oversight responsibility for the Academy’s administrative and financial functions, he also chairs the Technology Committee and is responsible for international programs. Dr. Rosandich rejoined the staff at the Academy’s main campus in 1994 after spending nine years in Saudi Arabia, where he was general manager of Saudi American Sports.