The Digital Revolution Impact to Olympic Education

### Overview

1. Definition “Digital Media” and “Digital Revolution”
2. Empirical studies on the development and distribution of digital media
3. Phenomenological observations
4. Theses on the assessment of the digital revolution in terms of Olympic Education
5. References

### Definition of Digital Media and Digital Revolution

The term “digital media” refers to all electronic media, which operate on the basis of digital information and communication technology (Reimann/Eppler 2008). Their opposites are analog media. Digital media have become a communication media that functions on the basis of digital information and communication technology. On the other hand they represent technical equipment for digitizing, calculation, recording, storage, etc. of digital content (Wikipedia 2008).

“Revolution” generally means an overthrow or downfall. In our context, digital revolution describes the rapid and profound changes that have been coming along with the digital media in the last 30 years.

Describing scientifically the situation in Germany should help in obtaining a closer look at the digital revolution, where one can assume that the situation in other countries proceeded or will proceed very similarly, maybe a few years earlier or later.

Sociologists characterize a “modernization” in Germany in the 1970s and 1980s by television. Many researchers link this medium with far-reaching cultural and social changes, using phrases like “Television childhood” and the “loss of childhood” (maybe someone remembers the title of the famous book by the American sociologist Neil Postman). They feared considerably poorer conditions for the development of their children. To date, parents are insecure and researchers discuss the question of whether young people are negatively affected by television in the development of their personality. (Fölling-Albers 2001, 4).

In the 1990s, especially in the late 1990s—I’m talking about the past 10 years—a further development became apparent, which may be called the “second wave of modernization.” Beyond television, CD players, etc. there are now computers, mobile phones and the Internet, which gained importance at least in terms of older children and young people. On the one hand they did so as status symbols, on the other hand, as modified and enhanced media capabilities of information gathering and communication: Internet, e-mails, SMS etc. (Fölling-Albers 2001, 34), Facebook, etc.

For the first time in history, the American Internet store Amazon has sold more e-books than printed books—a fact that clearly shows how much the world is influenced by digital media (SZ 05./06.03.2011).

Furthermore, the newest developments presented on the “CEBIT”—the arguably largest computer convention in the world—make clear, that the technical development of digital media is not yet complete. Mobiles are not only used for calling, they serve as organizers, calculators, cameras, VCRs, small computers with Internet access which can download movies. In a recent development they are even used for creating and playing movies in 3-D format without glasses!

And again: parents, teachers, scientists are scared by the running development and the running market of digital media and their influence on adolescents.

Some empirical studies are sought to support the current distribution and importance of digital media for young people.

### 2.0 Empirical studies on the development and distribution of digital media

2.1 Due to the variety of data, we focus our analyses to adolescents between 14 and 18 years, since this age group is taking part in the Youth Olympic Games. It was a special demand of the IOC, that—in context of the cultural part of the official program of the YOG—young people from all over the world should be taught how to work with digital media.

#### 2.2 Some facts from empirical studies

The following facts are taken from the “Hans-Bredow-Institut” in Hamburg which gathers information about the use of media worldwide. The information is updated every year. The last edition is the 28th edition from the year 2009. Some more of the following facts are taken from the “Media Education Research Association Southwest” (= mpfs). They go back to the surveys in 2010, in which 1,208 young people between 12 and 19 years (51% boys and 49% girls) were interviewed by telephone between May and July 2010. These researches focus media in general—and within this digital media, too.

#### 2.2.1 Digital media in German households in which adolescents live in 2010
![Digital media in German households in which adolescents live in 2010](/files/olympic-edition/2011/table1.jpg)
(JIM-Study 2010, 6)

Figure 1 shows that nearly all German households do have mobiles, computers/laptops and Internet access.

#### 2.2.2 Spread of the Internet in Tunisia

2002 2003 2004 2005 2006 2007 2008
Number of Internet Subscribers 76,711 91,787 121,000 150,220 179,440 253,149 281,257
Number of Internet Subscribers /1000 Inhabitants 7.8 9.24 12.12 14.9 17.6 24.66 27.15
Number of Internet Users 505,500 630,000 835,000 953,770 1,294,910 1,722,190 2,800,000
Number of Internet Users /1000 Inhabitants 50.9 63.5 83.66 94.57 127.07 167.75 270.25
Number of Websites 898 1,622 1,775 4,028 4,930 5,796 4,467

Reference: Ministère des Technologies de la Communication, www.infocom.tn/index.php?id=26
(Hans-Bredow-Institut 2009, 1208)

Table 1 shows the rapid increase in the spread of the Internet use (in many respects between 4 to 6 times in 6 years).

#### 2.2.3 Media equipment in Indonesian households

1998 2005
inhabitants 220.56
TV 49
satellite antennas 3.5
mobile phones 46.91
conventional telephones 12,772

(Hans-Bredow-Institut 2009, 918)

Table 2: According to the data about Indonesia, the spread of Internet access and mobiles still seems to be near the beginning.

#### 2.2.4 Media equipment in Kenyan households (in %)

2000 2005
inhabitants in Mio. 30.2 33.4
radio 22.1
TV 2.6
PC (incl. notebook) 0.5
Internet access 1.09 4.50
Internet hosts 11,645
Internet users in Mio. 1.5
Conventional telephones (total) 309,379 299,300
Mobiles (in Mio.) 0.14 7.3

Research from: CCK 2005, APC Africa
(Hans-Bredow-Institut 2009, 988)

Table 3: In Kenya there are signs of a similar development as in Indonesia. Note in particular the increase concerning Internet access and mobiles.

#### 2.2.5 Average number of digital media per household
![Average number of digital media per household](/files/olympic-edition/2011/table2.jpg)
(JIM-Study 2010, 7)

Figure 2: In many German households digital media are to be found several times. On average there are 4 mobile phones, 2.7 computers and 2.4 televisions per household. In other words, over 50% of households own three or more computers and 42% do own at least 3 televisions. More than 88% of households possess 3 or more phones (JIM-Study 2010, 7f.)

#### 2.2.6 Digital media owned by young people in 2010
![Digital media owned by young people in 2010](/files/olympic-edition/2011/table3.jpg)
(JIM Studies 2010, 8)

Figure 3: 97% of young German people between 12 and 19 years have their own mobile phone, 79% have their own computer or their own laptop, and more than 50% have their own Internet access. These data are similar for girls and boys (JIM 2010, 7f).

#### 2.2.7 Leisure time use of digital media in 12-19 year olds in Germany
![Leisure time use of digital media in 12-19 year olds in Germany](/files/olympic-edition/2011/table4.jpg)
(JIM Studies 2010, 12)

Figure 4: In terms of daily use, mobiles rank first. However, these findings do not surprise as mobile phones more and more turn into small portable computers.

2.2.8 Content related distribution of Internet use
![Content related distribution of Internet use](/files/olympic-edition/2011/table5.jpg)
(mpfs 2008, 16)

Figure 5 is of particular interest as it reflects the high proportion of Internet communication. Especially girls (56%) spend significantly more time online in comparison to their male peers (42%).

### Conclusion

Summing up this overview, there is no doubt that the digital revolution in adolescents occurs worldwide and that it influences our reality. Whether we like it or not, we will definitely not be able to stop it.

Similarly is the finding that adolescents often do handle those new media much easier than adults and that the new media considerably changed everyday life, leisure time and thus the life of adolescents (Fölling-Albers 2001, 38).

### 3.0 Phenomenological observations

#### 3.1 Some fundamental comments on digital media

The digital media can be viewed as one of the pillars of the globalized world. An almost unlimited access to all kinds of information is possible within the shortest time almost everywhere around the world.

It also seems important to note that access to the desired entertainment (e.g. movies) or information is immediately possible at any time. Digital media provide the possibility of directly participating in events happening in politics, business, sports etc. It is no longer necessary to wait for the newspaper on the next day to get information about the newest developments. How fast Internet groups form, could recently be observed in the affair about the German minister of defence zu Guttenberg. Even before the largest newspaper in Germany was able to barrack with zu Guttenberg, tens of thousands of PHD-students had already formed to a powerful opposition in the web. Since then, experts consider the Internet as the fifth power in the state (next to the judical, executive etc.).

Not only the speed of digital media, but their almost unlimited amount of data must be noted. Without doubt: This is positive. But this is also associated with problems: On the one hand there is the problem of information overload and the danger of losing oneself in it. The distinction between important and unimportant contents is absolutely necessary for the users of digital media. Especially when you look at adolescents it is doubtful whether they can always meet this distinction sufficiently.

On the other hand, it is often discussed whether in fact all information should be accessible to everyone or not. The recently published WikiLeaks-revelations about US-American assessments of politicians around the world is just one example.

Another observation has already been said to be the major cause for the loss of childhood in our present time by Neil Postman: children and young people have access to all information and pictures of the adult world. These images range from images of horror after natural disasters or from war zones, to glorification of violence, to pornography.

Internationally recognized brain researchers point out that the human brain is always learning. It continuously learns and stores the results of what is being offered to it. There are a number of studies from the U.S., demonstrating a direct link between aggressive content of media (TV, Internet) and aggressive behaviour of the consuming people (Spitzer 2010; Kölner call; and references to Bedenk 2010, 11). The effectiveness especially of the role model of aggressive simulation games is regarded as problematic if there are “aggressive tendencies as a result of experienced psychosocial attention deficits in childhood or because of previously experienced success of their own aggression” (Mogel 2008, 206).

Their prevalence is as unclear as the question of whether in post-modern societies, such as through changes in family structures, they may increase or not. Basically, this problem can be cut right to the chase whether everything should and can actually be accessible for everyone. Even if one denies this question, the question of how to block non-desired contents still remains. Think of the area of child pornography.

In addition, the information in words and pictures are of political power; it crucially determines the public perception of an event: This was clearly the case in the Iraq war, in Germany with Stuttgart 21, or is currently happening in the states of North Africa. It seems only logical that those in power try to control the information spread by the mass media. Especially in the current political situation in the North African states it is getting obvious that digital media play a vital and important role concerning the people´s communication options. It seems as if the race between the suppression of free digital communication and the removal of corresponding blocks was of decisive meaning for the outcome of the political events.

#### 3.2 Isolation by use of digital media

Without a doubt, the technically innovative design of digital media has a challenging character for adolescents. Especially with regard to the computer games (on- or offline), their attractiveness rises by showing more perfect, more varied and more diverse games in bursts and by a variety of ways to involve the players. Sometimes, it seems like adolescents disregard their personality development. Is this really true?

If one follows the theory that toys can be seen as witnesses of their age and we see our time unmistakably characterized by computers, it is logical that video and computer games expand. Since many computer-games are for being played alone in front of the screen, this circumstance pokes the fear that adolescents play alone too often and too long. Especially in single children there is an increased risk of lack of social contacts and additional social isolation. On the other hand it is suggested that “multi-player games” are suitable for promoting social contacts with each other. It is also observed that adolescents spend entire afternoons and nights to beat high scores (Mogel 2008, 192ff.) being linked to each other in LAN sessions. Under the label of e-sports major national and international communities have come together to play their digital games within regular events and championships in an organized form of competition—some with prizes exceeding € 100,000 (eg. EPS Finals from June 13-14, 2009) (Wiemeyer 2009, 127). Therefore, M. Bedenk sees the image of some popular “lonely” computer players, ever since the development of online multi-player games, as outdated. The Internet offers both, significant opportunities to play online with and against each other and to communicate during the game and after. Of course, it has to be noted that social exchange taking place here is media-mediated and does not take place through a direct encounter. On the one hand, this leads to the fact that for example the communication partners are not able to respond to facial expressions or gestures, so that information is lost. On the other hand, it is easy to meet with new players and conversation partners from other countries or cultures (Bedenk 2010, 51f.).

The question whether children become isolated by the intensive use of digital media is, therefore, answered differently by experts. However, it is of concern, that playing computer games limits the meaning of experiences in the visual and acoustic sense, whereas the so-called “secondary experience” prevail and the “primary experience” gets lost (Horn 2010).

Although many games are now constructed in a very realistic way, such as flight simulators, the concept of reality, e.g. in the game “Need for Speed,” in which accidents can happen over and over again without any consequences, remains questionable. Also, a canoeing-trip in the computer game can—despite the many dangers that come along from time to time—not be compared to a real canoeing-trip. Not to mention the correspondence to reality of so called “shooters” in which as many people as possible must be killed without any real consequences.

Of course, one could argument, that the reference of the player who is playing in fictional and illusory worlds, is completely real (Mogel 2008, 196ff) comparable to the role-playing games that are an integral part of the child’s game development. In contrast, the neuroscientist M. Spitzer considers that the human brain is constantly changing with its use and therefore the use of digital media does have an impact on the growth of individuals. M. Spitzer summarizes these effects by the loss of the holistic learning and the negative impact on emotional and socio-psychological processes (Spitzer 2010).

#### 3.3 Hypoactivity through digital media

The typical movement character of games mostly comes short in the use of digital media. By using mouse and keyboard, motor processes are limited to fine motor skills and therefore to a minimum. However, it can not be said, that gamers actually move less. A global review of studies on computer use and physical activity (eg, Maaz 2005; Brettschneider & Naul 2004; Lorber 2006; Marshall 2004; Schneider, Dunton, Cooper 2007; Koezuka 2006, etc. – and references to Wiemeyer 2009, 123ff) documents a heterogeneous situation. A general negative impact of digital games is – if any – weak (Wiemeyer 2009, 125).

The above-mentioned periods of use of digital media and the increase in time in front of the TV in Germany – German adults in 2010 watched TV at an average of 223 minutes per day (MB 04.01.2011) – suggest that the use of modern media could contribute to a lack of exercise. Obviously also the computer game industry has recognized the call for action. Thus, increasingly, digital games are offered, which require the activity of the whole-body, e.g. Dance Revolution or Wii. However, anyone who has ever played “tennis” on Wii will probably agree that this has only very little in common with real tennis or whole-body activity. Also, studies show that energy expenditure – under appropriate intensity and the involvement of large muscle groups – may rise to 8 kcal / minute. However, to reach the generally recommended health-related physiological threshold of 1000 kcal / week, we would have to spend more than 2 hours a week playing. The declining motivation, which is adjusted in these games relatively quickly (eg Madsen et al., 2007), suggests that the lack of physical activity of adolescents in Europe and the U.S. can not be adequately met by playing these games (Wiemeyer, 2009, 124ff. ). Also the statement that digital games could be specifically used “to convey techniques or disciplines” (Wiemeyer 2009, 126) in sports must currently be considered as illusory.

#### 3.4 Changing communication through digital media

The innovative aspect in the communications by phone and computer is that both communication partners are accessible at anytime and anywhere. The desire to tell someone, even about trivia, does not need to be delayed. Similar to the access to information, also the desired communication partners can be reached immediately, regardless of their staging.

Computers and the Internet offer a platform for self-expression and self-presentation, as everyone is and would be appreciated to be seen. This possibility fits the need of post-modern societies, in which broken predetermined roles, traditions and self-understandings are substituted by the need to find their individuality and embody themselves (Bette 1999; 2008, 361; Wetz 2008). Facebook, for example, offers a global platform for this purpose which allows you to publish pictures and information about yourself that you would like to make available to the public. The fact that some people allow a closer look into their private lives, and that they give personal information to other people in other contexts (eg. job applications) are not necessarily advantageous of the “glass man.” The variety of friendships certainly provides the possibility to find old friends and/or make new friends. Who ultimately gets you by which attention appears problematic especially in the adolescence. A good thing is the possibility to reject or terminate “friendships.” Another new aspect of those media is that you can terminate friendships even without facing each other.

#### 3.5 Multi-tasking

Finally, there are three developments by the digital media which accelerate the existing trends on television. First, there is a loss of a prior choice what you want to see or what one wants to deal with. Digital media offer you ideal conditions to surf and then, if something seems interesting, to stick with it. In addition, here you may encounter the phenomenon of “zapping.” You do not want to see anything in particular; therefore, you are searching the almost unlimited possibilities for what could be interesting. Finally, the internet more and more invites you for “multi-tasking”: research information on the Internet, listen to music and communicate at the same time. Such behavior is in clear contrast to the traditional philosophical or educational positions demanding “concentration.” Here, however, also brain research warns, which emphasizes that the human brain can do only one thing. Thus, it is shown that Multi-tasking just does not enhance hiding distracting stimuli and switching between tasks (Spitzer 2010). M. Wolf also expressed in her book “The reading brain”—where she writes about changes in the brains of the users by digital media—that “more” and “faster” does not necessarily mean “better” (Spitzer 2010). A causal relationship between lack of concentration, attention deficits, etc., and multi-tasking is obvious, although the variety of studies in this regard are another matter.

#### 3.6 Conclusion

At all mentioned points, findings are contradictory. Rejectors and supporters are equally distributed. For further research, it is imperative to involve on the one hand, both the digital media and the person using it and the particular situation of use (Bedenk 2010, 31). On the other hand it´s important to involve the many scientific disciplines that deal with the “new” media, in an integrative approach (Bedenk 2010, 11).

### 4. Evaluation of the Digital Revolution in Terms of Olympic Education

The assessment of a case, for example the digital media always happens within the interactions between perception, explanation, and values of the evaluator (Bedenk 2010, 24). The consideration of the first two issues has shown that the current globalized world is essentially determined by the digital media. Those who want to participate in the current “world society,” must have access to digital media and have the know-how of its use. As a further development an even wider use of digital media is to be expected in the future, especially adolescents are affected by this development: thus, the world of digital media is increasingly becoming the world of young people. The above-mentioned third aspect—the values—is necessarily subjective. Here, it is therefore made in the form of theses, which should serve as a basis for discussion:

**Thesis 1:** The information-presentation and dissemination of the Olympic idea and the Olympic ideals must be presented to the young people by the media they use. Since these are primarily digital media, Olympism has to represent the Olympic movement by using that media if Olympism wants to reach the young people around the world without being redundant.

**Thesis 2:** Opportunities for access to and know-how of the use of digital media is to be regarded as an extended condition of understanding in our contemporary world. A division of the world into a (majority) part of digital media and a (smaller) part of non-digital-media would mean a further injustice that would be against the peace idea of Olympism. This is the case when “peace” within the meaning of the German philosopher Immanuel Kant is seen as a just (world) order, which includes far more than a silence of weapons (Kant 1999).

**Thesis 3:** As digital media involve certain risks, it would be irresponsible to let young people alone with dealing with the digital media and the commercial interests of suppliers who deliver them. In contrast, an education for meaningful use of digital media in general and in the spirit of Olympism is essential. The Olympic ideal of perfection of the individual was certainly possible without digital media. But now that they have become an integral part of our world an educational mission regarding the development of the personality is connected with them. This also includes the global sport in its exercise, its media presentation and a critical assessment. Fair play, especially in highest level sports (which media are interested in and where everyone is almost condemned to success), still plays a major role here as an ethical scale.

**Thesis 4:** As digital media disseminate the sedentary world, it is important to show young people again and again the usefulness of physical activity, to educate them about exercise, sports and games and to give them the joy of sports competition in order to communicate fairness and mutual respect to themselves and to others. To move, play games and do sports so that it enriches the lives, that it contributes to well-being and satisfaction and that it provides a sense of achievement and happiness – this is a part of the Olympic education, especially in the world of digital media (Horn 2009).

**Thesis 5:** If the YOG really wants to create a new understanding of Olympism for young people it is not enough to just set another international sport event for them. It is necessary to try new ways. And one way can be to educate them as it is intended in a CEP and to include the understanding and responsible use of digital media.

### References

1. Bedenk, M. (2010). Computerspielen verstehen.Marburg: Tectum Verlag.
2. Bette, K.-H. (1999). Systemtheorie und Sport. Frankfurt: Suhrkamp.
3. Bette, K.-H. (2008). Soziologie des Abenteur- und Risikosports. In K. Weis und R. Gugutzer (Hrsg.). Handbuch Sportsoziologie. 358 – 367. Schorndorf: Hofmann.
4. Fölling-Albers, M. (2001). Veränderte Kindheit – revisited. Konzepte und Ergebnisse sozialwissenschaftlicher
5. Kindheitsforschung der vergangenen 20 Jahre. In: M. Fölling-Albers, S. Richter, H. Brügelmann , A. Speck-Hamdan (Hrsg.). Jahrbuch Grundschule III. Fragen der Praxis – Befunde der Forschung. 10 – 51. Seelze/Velber: Kallmeyersche Buchhandlung.
6. Hans-Bredow-Institut (Hrsg.). (2009). Internationales Handbuch Medien. 28. Auflage. Baden-Baden: Nomos.
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10. Kant, I. (1972). Zum ewigen Frieden. Stuttgart: Reclam.
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12. Mangfall-Bote vom 04. 01.2011, 1.
13. Medienpädagogischer Forschungsverbund Südwest (mpfs) (2008). Computer- und Consolenspiele. Download am 27.12.2010 unter www.mpfs.de/Computer_Consolen_JIMKIM08.pdf (= mpfs).
14. Medienpädagogischer Forschungsverbund Südwest (mpfs) (2010). JIM-Studie 2010.Jugend, Information, (Multi-)Media. Basisuntersuchung zum Medienumgang 12 – 19-Jähriger. Landesanstalt für Kommunikation Baden-Württemberg, Thomas Rathgeb, Reinsburgstr. 27, 70178 Stuttgart (= JIM-Study).
15. Mogel, H. (2008). Psychologie des Kinderspiels. 3. Aufl. Heidelberg: Springer.
16. Postman, N. (1983). Das Verschwinden der Kindheit. Frankfurt: Fischer.
17. Reimann, G./Eppler, M. (2008). Wissenswege. Bern. <Http://www.persoenliches-wissensmanagement.com/content/definition-digitale-medien>.
18. Spitzer. M. (2010).Im Netz. SZ vom 22.09.2010, 8.
19. SZ vom 05./06. 03. 2011, Wochenendbeilage V2/1.
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21. Wiemeyer. J. (2009). Digitale Spiele. (Kein) Thema für die Sportwissenschaft?! Sportwissenschaft 2/2009, 120 – 128.

2016-04-01T09:33:41-05:00June 30th, 2011|Contemporary Sports Issues, Sports Coaching, Sports Management|Comments Off on The Digital Revolution Impact to Olympic Education

Medicine and the Olympic Games of Antiquity

Mr President of the International Olympic Academies, Distinguished Directors, Ladies and Gentlemen; it is a distinct honor and a great pleasure indeed to return to the magic of Ancient Olympia on the occasion of the 11th International Session for Directors of the National Olympic Academies.

I am grateful to President Kouvelos for the invitation to speak on “Medicine and the Olympic Games of Antiquity.”

I shall discuss today athleticism and the profound influence sport exerted on the evolution of the healing arts of classical Greece.

I shall also argue that the unique ethical, philosophical and clinical profile of ancient Hellenic Medicine is not a random event in the history of civilisation but the direct consequence of a culture that indulges in nature, excels in competitive sport, cultivates reason and respects the individual.

Imagine now that you are visitor to the city of Athens in the year 380 B.C. the year of the 100th Olympiad about to take place on the very grounds that we stand today; the year when Xenophon of Aigai – of the Royal city of Macedon – will be crowned with the olive wreath for his victory in the pankration.

Imagine for a moment that on a crisp spring morning you are standing on the Acropolis. In the distance you can see Plato’s Academy, the famous gymnasium of Athens, where the youth of the day have begun their training in preparation for the forthcoming Olympic Games. You turn south and in the distance you see the glittering Aegean Sea, the witness of the battle of Salamis, when democracy triumphed over despotism; and a few streets away an orator is putting the final touches to his speech to be delivered shortly at Olympia. This is what he writes:

> _“…now the founders of our great festivals are justly praised for handing down to us a custom by which, having proclaimed a truce and resolved our pending quarrels, we come together in one place, where, as we make our prayers and sacrifices in common, we are reminded of the kinship which exists among us and are made to feel more kindly towards each other for the future, reviving our old friendships and establishing new ties…”_ – Isocrates (in Panegyricos)

Written in 380 B.C., the ideals of Isocrates’ (436-338 B.C.) speech are still reverberating at the opening ceremonies of contemporary Olympiads and are as appealing and elusive to humanity today, as they were two millennia ago, to the Hellenes congregating at Elis for the greatest celebration of their world. Isocrates’ _Panegyricos_, although in praise of Athens, captures also the political dimension of the Olympiad as a Pan-Hellenic institution in the conscience of Hellas.

Aware of the repercussions of an Olympic victory, Philip of Macedon competes in the equestrian events and erects the _Φιλίππειον_ to commemorate his victory; a valuable instrument of his political and dynastic ambitions for hegemony over the rest of Greece. The ruins of this building can still be seen by the modern visitor of ancient Olympia.

### The Sporting Ethos

Perhaps no other passage of Greek literature reflects the ethos of sportsmanship and the values of Ancient Greece than Homer’s account of Odysseus’ involvement in the Phaeacian games.

> “…One can see you are no sportsman, your mind is on profit…”

This is how Prince Euryalus talks to Odysseus who, exhausted from his sea voyage, declines the invitation to join the athletic games of the Phaeacians. Insulted, Odysseus leaps to his feet, picks up the biggest discus of all, a huge weight, and throws it overshooting all other marks. It is this spirit of sportsmanship and an aversion to profit – pecuniary or otherwise – that is the core of the Olympic ideal and so central to the culture of ancient Greece. Homer, of course, has good reasons to describe this episode in these colors; he is the Educator of Hellas.

A natural environment that permits outdoor activities throughout the year facilitates sportsmanship that becomes an essential element in the life of the Ancient Greek.

A society developing – in the words of Hippocrates – _in privileged climatic conditions_, learns to respect the individual, becomes increasingly detached from theosophy and superstition and cultivates reason; this passionately naturalist culture, enjoys a liberal religion of gods with human weaknesses and humor and cares largely for excellence on earth and little for afterlife.

Excellence develops with the athletic and intellectual pursuits of the youth in the gymnasia of the _polis_ and is ultimately glorified in Pan-Hellenic festivals, the most celebrated of which was held at Olympia. Medicine emerges in parallel and in the service of these activities.

### Philosophy and Sport

Originally the gymnasia were places where the young men would exercise in athletics naked (_γσμνοί_). This, in fact, is the derivation of the word for the modern gymnast exercising on bars. Gradually, as the symmetrical and harmonious training of body and mind became the educational concern of the state, the gymnasia became places of learning and intellectual pursuit.

The _Academy_ and _Lyceum_ in Athens where **Plato** (427-347 BC) and **Aristotle** (384-322 BC) taught were the two most famous gymnasia that influenced in a profound way the whole of the Greek civilisation.

**Aristotle** is known in our universities as a philosopher and naturalist, not as a doctor. He is however familiar with medicine through his father **Nicomachos**, the Royal Physician to **Philip of Macedon** and he is interested in the anatomy and function of living organisms in broad biological terms.

From Aristotle and the lesser known _Hippias of Elis_ we have the early catalogues of the names of Olympic victors. **Koroibos of Elis** was the first man to win the stadion race at the first Olympiad in 776 BC. His name has been associated with the beginning of the Olympic Games.

### Function of the Officials

Aristotle tells us about the tasks of _gymnastai_ and _paidotribai_, the officials in the gymnasia, who were responsible for the training of athletes.

Other officials, the _ἀλείπται_ or _anointers_, were responsible for anointing with oil the athletes who were about to exercise. This initially simple task developed gradually into methodical massaging and eventually into a speciality that was concerned with many aspects of hygiene and athletic routine.

Thus the _ἀλείπται_ gradually became known as _ἰατραλείπται_ (healer-anointers), or doctors of hygiene _ὑγιεινοί ἰατροί_. These interesting paramedics – we shall call them _athliatroi_ – greatly promoted dietetics and the art of caring for orthopaedic injuries and other commonplace traumata in the gymnasia.

Among the best known _athliatroi_ are **Herodicos of Selybria** and **Ikkos of Taras**, men of broad education otherwise known as sophists, who were particularly concerned with athletic hygiene. Ikkos himself may have won the pentathlon in 444 BC at Olympia. Professional rivalries between _athliatroi_ and the more orthodox therapists of the Hippocratic and Galenic tradition were inevitable.

### Hippocrates

The Hippocratic corpus consists of 72 treatises; there are copious references within the Corpus to the words _gymnastics_, _exercise_, _diets_, _athletes_ etc. However no references were found to _Olympia_, _Olympiad_ or _Olympionices_ (Olympic victor).

**Hippocrates** (460 BC) distinguishes between gymnastics and medicine in the treatise, _On the places of man_ (ΠΔΡΙ ΣΟΠΩΝ ΣΩΝ ΚΑΣΑ ΑΝΘΡΩΠΟΝ) (***Γσμναζηική δὲ καὶ ἱηηρική ὑπενανηία πέθσκεν…***); “Gymnastics and medicine,” we read, “are by their nature opposite, for gymnastics have no need to cause changes [in the human body] but medicine has. For changes are not needed in the state of a healthy individual, but this is necessary in the patient.”

In the treatise _On joints_ (ΠΔΡΙ ΑΡΘΡΩΝ), Hippocrates makes a clear distinction between properly trained doctors, “iatroi”, and those “lesser experts,” as he puts it, who frequent the wrestling rings (***ηὸ ηοιοῦηο δὲ ποιῆζαι μεηρίως ἐπιηήδειος ἄν ηις εἴη ηῶν ἀμθί παλαίζηρῃ εἰθηζμένων***). Elsewhere in the same treatise he advises on a method of reducing a shoulder dislocation, “a method simple and useful in the palaistra” (***Αὗηαι δὲ αἱ ἐμβολαί πᾶζαι καηά παλαίζηρην εὔτρηζηοί εἰζιν.***)

### Special Diets

There are stories about Olympic athletes who achieved high performances and ultimately their victories on special diets. One athlete is known to have had a diet of dried figs and another gave up cheese for large quantities of meat. We do not know the reasons for this choice. In the treatise _On Ancient Medicine_ (ΠΔΡΙ ΑΡΥΑΙΗ ΙΗΣΡΙΚΗ) Hippocrates discusses extensively the impact of various foods on well being and we find an elaborate reference to the intolerance of cheese which can be “a wicked food” (***πονηρόν βρῶμα***) for some people, whereas others tolerate it well and for them can be an excellent nutrient.

### Galen

Some six centuries later, the celebrated Physician **Galen of Pergamum** (129 – 200 AD) and a scholiast of Hippocrates, is concerned with similar issues. The Olympic Games continued uninterrupted to his time and gymnastics, hygiene and athletics were still very much part of everyday life of the Hellenic and Roman world.

In a treatise with the title, “Is health a matter of medicine or gymnastics?” (ΓΑΛΗΝΟΤ ΠΡΟ ΘΡΑ ΤΒΟΤΛΟΝ ΒΙΒΛΙΟΝ, ΠΟΣΔΡΟΝ ΙΑΣΡΙΚΗ Η ΓΤΜΝΑ ΣΙΚΗ Δ ΣΙ ΣΟ ΤΓΙΔΙΝΟΝ) addressed to his friend Thrasyboulos, Galen cannot hide his distaste towards the athletes’ trainers. “The most unfortunate of the athletes,” he writes, “who never won a victory, suddenly decide to call themselves gymnastai. Even worse some of them attempt to write and argue about massage and wellbeing or health or exercises”. In another treatise, _Protrepticos_, an “Exhortation on the art,” (ΓΑΛΗΝΟΤ ΠΡΟΣΡΕΠΣΙΚΟ ΛΟΓΟ ΕΠΙ ΣΑ ΣΕΧΝΑ) he addresses the question, does the athlete’s life benefit himself or the state? He makes a case against the athletes and quotes **Euripides** who, in his usual tragic mood, calls the athletes “The worst evil of Greece”. In the same work Galen derides **Milon of Kroton**, a celebrated Olympic victor who allegedly won the olive wreath seven times.

This extraordinary athlete had an extraordinary end. He tried to cut open with his hands a tree trunk. The tree closed up and trapped his hands. He could not free himself and in the evening he was torn to pieces by wild beasts. “A silly man,” says Galen. “but what else can one expect from an athlete?” (Ἐδήλωζε δὲ καὶ ἡ ηελεσηή ηἀνδρός, ὅπως ἦν ἀνόηηος)

Galen is not an impartial witness. He is attacking the athletes probably because he despises their trainers, who interfere in medical matters. He is also unfair to Milon who, apart from his astonishing athletic achievements, was an educated man and a disciple of Pythagoras.

Galen refers to the Olympiad in his book on “Periods.” “Some early physicians,” he writes, “mention that paroxysms of certain diseases happen periodically, but they do not explain what the name period means.” He goes on to give a definition of the Olympic period relevant to medicine in chronological terms.

In another treatise, “On the composition of medicines” (ΠΔΡΙ ΤΝΘΔ ΔΩ ΦΑΡΜΑΚΩΝ ΣΩΝ ΚΑΣΑ ΣΟΠΟΤ ΒΙΒΛΙΟΝ Γ), he refers to “the brown medicament of the Olympionice, (Φαιὸν τὸ τοῦ Ὀλσμπιονίκοσ ἐπιγραφόμενον) that promptly relieves great pains and chemoses.” The prescription is obviously not his, because he eagerly states his modification by two additions to the previously described components. It was possible to resurrect Galen’s ointment at the Chelsea School of Pharmacy with the kind help of Dr Jolliffe and Mr Burt. The ointment contains cadmium? (***Καδμείας κεκασμένης καὶ πεπλσμένης δρατμὰς ή***), opium, antimony, zinc oxide, frankincense, aloe indica, saffron, myrrh and a raw egg.

Galen’s medicament had to be really good if it were to be of any use, for injuries in the Olympic Games, particularly in the body contact events, were serious. There were no silver or bronze medallists in those days. Only one of the contestants in each event could win, the rest were losers. The competition for the olive wreath among the athletes was fierce, and casualties frequent and occasionally fatal.

### Deaths and Injuries

We know of at least two boxers who were responsible for the death of their opponents-**Diognetos of Crete**, and **Cleomedes of Astypalaia** who subsequently went mad. The judges denied the latter his victory, not because he killed his opponent but because he broke the rules of the contest. Fatalities were recognised risks in sporting competitions and athletes who accidentally caused the death of their opponent during an Olympic contest were normally immune from prosecution.

Boxers tried to protect themselves during training by wearing ear-protectors called ἀμφωτίδες or ἐπωτίδες. However, these circular pieces of thick leather or metal, fastened around the head and jaw, were not allowed during the actual contest when the most punishing injuries were taking place. Fractured noses, cut eyes and torn ears were common. Derisory epithets of boxers such as “Cauliflower Ears” (Ωτοθλαδίας) have survived in the literature.

Yet, all was not ugly in boxing in those days. We hear of a certain **Melankomas** who was “as healthy and unmarked as a runner” because of his unique style and tactics. His biographer **Dio Chrysostomon** tells us that Melankomas, a favourite of the crowds, used to exhaust his opponents by continually changing position without ever receiving or striking a blow. His movements were simple, light and graceful. He won numerous competitions in various Pan-Hellenic festivals and may have won an Olympic victory during the 206th Olympiad (45 AD).

### The Pankration

Athletes suffered even more devastating injuries during the Pankration, an event combining wrestling and boxing. **Plato** comments on it “as a contest combining imperfect wrestling with imperfect boxing”. The only things that were forbidden during this contest were “biting and gouging”. We hear of **Arrichion of Phigaeleia**, a Pankatiast (the word means all-powerful), who won his victory posthumously. He was captured by his opponent in a terrible hold that was strangling him. In a desperate attempt to free himself, Arrichion seized the foot of his opponent and crashed it, dislocating the ankle. The other man, unable to bear the pain, raised his hand in the signal of a withdrawal, while Arrichion breathed his last at the same moment; he won the victory not because he died, but because his opponent gave up.

Injuries from spectacular falls during the popular horse and chariot races must have added to medical emergencies.

The soil of Olympia may have claimed several victims with tetanus. This disease was well recognised at the time of Hippocrates and is thoroughly described in the Corpus, but we have no written accounts of tetanus episodes relating to Olympic athletes.

Another possible cause of injuries may have been accidents from the throwing of javelins and the discus. Tradition has it that **Oxylos**, the founder of Elis, the Greek province where Olympia is, left his country because he accidentally killed his brother **Thermios** while throwing the discus.

### Sanitation and Medical Services During the Games

Heat, dust, a limited supply of water, rudimentary sanitation and those Mediterranean insects that are determined to spoil the enjoyment of ancient and modern visitors to Olympia, must have added to morbidity among the thousands of participants in the games. The overwhelming majority of visitors slept in the open air or in tents, and for food and drink depended on itinerant caterers.

**Pausanias**, a traveller and writer of the second century AD, gives us an idea of the problem with insects. “They say,” he writes, “that when Heracles was sacrificing at Olympia he was badly pestered by flies, so he invented or was taught by someone the sacrifice to Ζεύς Απομύιος [Zeus the averter of flies]. The Eleans are said to sacrifice to Zeus Apomyios in the same way to drive away the flies from Olympia.”

Zeus cannot have been very effective, however willing to help. The gastrointestinal nuisances, that even in our days can turn the vacations of the most sophisticated of travellers into a disaster, must have been common among the spectators and on occasions may have stolen the Olympic crown from the better man. Nevertheless we have no information about any major epidemics.

We know that among the officials at Olympia a doctor was included during the games. It is unlikely, however, that comprehensive medical services were available to cope with all emergencies; the place must have been a paradise for wandering quacks and healers who were prepared to offer their skills to a massive clientele, returning every four years for the most popular spectacle of the ancient Hellenic world. Under the punishing sun of Olympia the most common medical emergency was probably sunstroke. Philostratos wrote that athletes had to be strong enough “to endure and to be burnt”, implying that they should be able to withstand the great heat at Olympia.

**Thales of Miletos**, one of the wise men of ancient Greece, is believed to have died at Olympia from sunstroke.

### An Honorable End

Intense emotion and heat must have contributed to the death of the famous boxer **Diagoras of Rhodes**. There is a moving story of how this popular athlete, three times Olympic victor, met his end.

He watched his two sons win the Boxing and Pankration events during the 83rd Olympiad. His victorious sons received their crowns and in a magnanimous gesture approached their father, placed the olive wreaths on his head, and carried him triumphantly on their shoulders around the stadium. No mortal could stand the overwhelming emotion of such glory and pride. Diagoras bent his head and died happily on the shoulders of his Olympian sons. This was in 448 BC.

By 261 AD, the last official record of the Olympic Games, times were different.

Soon there would be no place for athletics in the new ethos and social order that an austere monotheism was about to establish. An earthquake destroyed most of the buildings of ancient Olympia around 300 AD, and several decades later the edict of Emperor Theodosios banned all pagan cults and effectively put an end to the festivals at Elis.

The salvationist spirit of the new order was now marching on and the beautiful statues of Olympic gods and victors were soon to be replaced by the ascetic icons of Byzantium. The Olympic Games, and with them medicine, went into a long period of hibernation from which they were revived only in recent times.

### References and Further Reading

ΓΑΛΗΝΟΤ ΑΠΑΝΣΑ: Gottlob Carolus K, ed. Ιn KUHN MEDICI. Lipsiae, 1821-1829 All Volumes as cited in text.

Green RB, A translation of Galen’s Hygiene (De Sanite Tuenda). Springfield, Illinois: Charles C. Thomas, 1951

Finley MI, Pleket HW. The Olympic games – the first thousand years. Book Club Associates. London. 1976.

The Olympic games through the ages. Ekdotike Athenon SA Athens. 1976.

Sarton G. Galen of Pergamon. University of Kansas Press, 1954.

Gardiner EN. Athletics of the ancient world. Oxford: Clarendon Press, 1955.

ΙΠΠΟΚΡΑΣΗ΢ ΑΠΑΝΤΑ ΤΑ ΕΡΓΑ. Ποσρναρόποσλος Γ.Κ. Εκδ. Μαρηίνος Α. ΑΘΗΝΑΙ 1971. Με αναθορές ζηο κείμενο.

Ι΢ΣΟΡΙΑ ΣΟΤ ΕΛΛΗΝΙΚΟΤ ΕΘΝΟΤ. Κλαζζικός Ελληνιζμός Σόμοι Γ1 & Γ2 ΕΚΔΟΣΙΚΗ ΑΘΗΝΩΝ. ΑΘΗΝΑΙ 1972.

Homer The Odyssey Translated by E.V. Rieu

### Acknowledgements

I am grateful to Ekdotike Athenon SA for permission to quote passages from their book “The Olympic games through the ages,” particularly the translation of Isocrates’ _Panegyricos_. Also to Chatto and Windus Ltd for quotations from “The Olympic games – the first thousand years,” by M.I. Finley and H. W. Pleket.

My special thanks are due to the Department of Medical Illustration at Westminster Hospital for the preparation of the slides for this presentation and pictures from exhibits at the British Museum, included in earlier publications of this article.

Dr. Jolliffe and Mr. Burt of the Chelsea School of Pharmacy offered valuable help in resurrecting Galen’s “ointment of the Olympic victor.”

There have been several earlier versions of this article which was first published in the journal, _History of Medicine_, Vol. 9, No. 1, 1981 and subsequently in _The Greek Review_ (copyright 1982 – world rights reserved). Also in the Journal, UPDATE, June 1, 1983.

“Medicine and the Olympic Games of Antiquity” was the keynote address at the Opening Ceremony of the _1st International Medical Olympiad_ held in 1996 at the Asclepieion of Kos under the High Patronage of the President of The Hellenic Republic. This Olympiad was organized by Professor Spyros Marketos Editor of the Proceedings.

A version of this lecture was delivered at the Annual General Meeting of the Hunterian Society in London in 1997. The text is included in the Hunterian Society Transactions, Session 1996-1997; Volume LV: 117-125.

2018-01-24T07:56:00-06:00June 28th, 2011|Sports Coaching, Sports Exercise Science, Sports Studies and Sports Psychology|Comments Off on Medicine and the Olympic Games of Antiquity

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

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

#### d2 Test of Visual Focused Attention

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

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

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

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

#### Concentration Grid Task

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

### Discussion

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

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

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

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

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

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

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

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

### Conclusions

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

### Applications in Sport

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

### Acknowledgments

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

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### Tables and Figures

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

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

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

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

### Corresponding Author

Dr. Thomas Heinen
German Sport University Cologne
Institute of Psychology
Am Sportpark Müngersdorf 6
50933 Cologne
GERMANY
Tel. +49 221 4982 – 5710
Fax. +49 221 4982 – 8320
Email: <t.heinen@dshs-koeln.de>

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

2013-11-25T16:23:44-06:00June 3rd, 2011|Contemporary Sports Issues, Sports Coaching, Sports Management, Sports Studies and Sports Psychology|Comments Off on Do static-sport athletes and dynamic-sport athletes differ in their visual focused attention?

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.

2013-11-25T16:26:52-06:00May 25th, 2011|Sports Coaching, Sports Facilities, Sports Management, Sports Studies and Sports Psychology|Comments Off on A Study on the Self-Efficacy of Elite Coaches Working at the Turkish Coca-Cola Academy League

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

2013-11-25T16:28:11-06:00May 20th, 2011|Contemporary Sports Issues, Sports Coaching, Sports Management|Comments Off on A New Test of the Moneyball Hypothesis
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