Sports History – Page 4 – The Sport Journal

A Modern Perspective of the Ancient Olympic Events

Submitted by: Dr. Richard Bell, Ed.D.

Today’s Modern (Summer) Olympic Games have 32 different categories of competitive events. When you consider that of these many, like track and field, have several events within the category and then break down further to men’s and women’s and team and individual competition, it is nearly impossible to keep track of the Games as they progress. Things were simpler in the old days. The Ancient Olympics had 13 events divided into 6 main categories. Of course they were for men only.

The main categories were boxing, equestrian events, pnkration, pentathlon, running and jumping. The Equestrian events were broken down into two sub-categories: chariot racing and riding. The Pentathlon was a combination of five events: discus, javelin, jump, running, and wrestling.

Boxing in ancient Greece had fewer rules than boxing today. There were no rounds and boxers fought until one of them was knocked out, or admitted he had been beaten. There was no rule that prevented a boxer from hitting an opponent when he was down. There was no weight class in either the men’s or boy’s divisions and the contestants were chosen randomly. The boxers did not wear gloves but wrapped their hands and wrists with leather straps called himantes. Their fingers were left free.

Equestrian events were divided into classes of chariot racing and riding. The chariot races consisted of both the 2-horse chariot and the 4-horse chariot and there were separate races for chariots drawn by foals. There was a race of carts included in this event that consisted of competition between carts drawn by teams of 2 mules. The length of the chariot races was 12 laps around the stadium track which was approximately 9 miles.

Riding was the other equestrian event and the course was 6 laps around the stadium track which equaled 4.5 miles. The jockeys rode without stirrups and the races were broken down into competition between foals and full-grown horses. Because it was so expensive to train, feed and equip the participants the owners were awarded the olive wreath of victory instead of the riders.

Probably the most physical event of the Ancient Olympic Games was the pankration. This grueling event consisted of both boxing and wrestling. The hands were not wrapped in the leather himantes. The only limitations on physical brutality were the rules against biting and gouging the opponent’s eyes, nose, or mouth with fingernails. Kicking in any part of the body was allowed. There were separate divisions for men and boys, but like in boxing there was no weight division and the opponents were chosen at random.

The pentathlon, like the modern event, consisted of a 5-event combination. The 5 events of the Ancient Olympic Games were discus, javelin, jumping, running and wrestling. The Greeks considered this the most beautiful of the contests because it combined the endurance of the race course and the bodily strength necessary for the other physical events. The discus was made of iron, stone, bronze, or lead and was shaped to resemble the discus of today. The sizes varied and the boys competed with a lighter weight than the men. The ancient Greeks thought the precision and rhythm of an athlete throwing the discus as important as his strength.

The javelin was a throwing event as in the modern games and like the discus the competition was based on the distance the object was thrown and in the case of the javelin the precision. The javelin was made of wood, with either a sharpened end or an attached metal point. The javelin had a thong for the throwers’ fingers that was attached close to the center of gravity of the instrument that increased the precision and distance of the throw.

The jump event was similar to the modern long jump but with a major exception. The jumpers carried stone or lead weights called halteres. These weights, shaped like telephone receivers, were carried out in front of the jumper when they jumped the weights were thrust backward and dropped during the descent to increase the distance of the jump.

Running was broken down into 4 types of races in the Ancient Olympic Games. The stadion was the oldest of the events and consisted of a sprint covering one stade (192 meters) which was the length of the stadium. Other races were the 2-stade race and the long distance run ranged from 7 to 24 stades. The most grueling of the races was the warrior race designed to build and test the speed and stamina Greek men needed for military service. The race was 2 to 4-stades in distance and was run by an athlete wearing armor. The standard armor of that time weighed approximately 50-60 pounds and of course included a helmet and shield.

Wrestling was similar to the modern sport in that the athlete was required to throw his opponent to the ground landing on a hip, shoulder, or back for a fair fall. To win a match required 3 fair falls or throws. Genital holds and biting were not allowed and breaking your opponent’s fingers was also not permitted.

The art and sculpture of ancient Greece is alive with the depictions of the Olympics and the events described in this article. One can feel the excitement and spirit of the Ancient Olympic Games in that art. In modern games the spirit of the Olympism of old is recreated in the ceremonies and competitiveness of the event.

U.S. Sports Academy2015-10-24T01:31:30-05:00February 13th, 2008|Contemporary Sports Issues, Sports History|Comments Off

Sports History? Sports Archives!

Submitted by: Cristina Bianchi, Archivist Olympic Museum

The scarcity of professional literature about sports archives confirms what I have noticed since I entered the world of sports through the International Olympic Committee and its Olympic Museum, both located in Lausanne, Switzerland. The awareness of the richness that sports and Olympic archives can bring to an institution, a sports club or an amateur organizing committee is only emerging nowadays, with all the gaps and losses it implies. The concept of archival obscurity that Richard Fagan uses for Australia can be applied to many countries in that respect and trying to compile information on sports archives is somewhat akin to hitting the metaphorical wall of the marathon runner 1. Relatively few sporting organizations have established archival programs; the personal records of individuals involved with sport do not appear to have a high priority among collection institutions; records of government sport administrative bodies seem to be scarcely represented, or mixed with other subjects, in governmental archival repositories. It is an area of society, maybe because of its connotation of leisure and recreation, that has not been adequately documented. The tendency is reversing now, incredibly media-conscious, but records usually focus on only the most popular athletes and sports. Broadcasting and archiving images of sporting events has, contrary to administrative records, become a powerful money-maker. Specialized institutions have therefore developed these recent years to collect and diffuse still and moving images to the world, with copyright attached to them. What can we do as a paper archivists working in small and large sports institutions to improve the situation of records and give a fair access of the available material to an ever-growing number of sports historians?

Create a network of sports archival repositories
Exchange information, inventories, microfilms
Implement archival programs

Without archives, there is no history. Partial archives create partial history. Let us create a network of repositories containing sports related records, in order to complement each other and know which archives have what records. Students, scholars and historians will then be able to concentrate on a repository, and receive information on where else to continue their search. As the archivist of the historical records of the IOC, which hold more than a century of documents concerning the renovation of the Olympic Games and the implementation of sports activities throughout the world, I suggest we start this network at once by providing information to the author*. All the information about sports/Olympic related records you have come across as a historian, a sports administrator or an archivist should be described in a few words: Fonds, repository name, inclusive dates, state of indexing if known. This data will be compiled and redistributed in a second phase. The International Council on Archives has agreed to support an Initiative Committee for Olympic and Sport Archives Section, officially created in 1996, of which the IOC archivist is part. Its mission is to overcome the important obstacle of information among the world of archives and of sports, and to collect information both from the public and from the private sector. Please let me know your interest as a representative of sports archives; the new section needs people with experience and ideas. It will take part in the project of network. Once the information is compiled and the network established, it would be interesting to exchange information through the usual archival means like published inventories and microfilms, but also through new and quicker means like Internet. Let it be clear that the idea is not to centralize all the records in one place. How could the IOC, just to give you an example, absorb more than 20,000 boxes of archival material related to the Olympic Games of Los Angeles in 1984? Without even evoking legal problems, it is much more interesting to know that they were well-organized at the time by a joint group of people from the Los Angeles Olympic Organizing Committee and from the Special Collections of the University of California Los Angeles Library, where they are deposited now. A detailed inventory was completed two years after the Games, and will soon be put on the Net. This is the kind of information we have to gather to enrich the pool of information about sports related records. Finally, ideally, strong sports institutions should help smaller ones implement an archival program. Every organization, be it of three or two hundred people, produces its own original records: application and lists of membership, results of competition, selection of representatives, insurance, drug testing, accreditation, sponsorship, ephemeral, etc. Staff should be trained and given advice in appraising and keeping meaningful sport related records. The archivist of the Sports Archives of Finland explains what kind of solutions his country has found. Since its creation in 1985, this specialized sports archives has the support of government, sports organizations and the archival administration. Not only does it keep the records of all Finnish central sports federations, local sports clubs and sportsmen, but one of its main goals is to educate sports organization in archival matters. For the moment, help us create a general raising of awareness among both the archival community and the sporting community by combining our knowledge on sports related records@ thank you in advance for providing this author with valuable information. Comprehensive sports history equals a network of sports archives. Notes: 1. Richard Fagan, Acquisition and Appraisal of Sports Archives, in Australian Society for Sports History, Bulletin No. 16, April 1992, pp. 36-47. 2. Kenth Sjoblom, ATaking care of Sports Archives – Whose Responsibility?, in Canadian Journal of History of Sport Vol. XXIV, No.2, December 1993, pp. 91-93. For more information on how to take part in this important project contact Cristina Bianchi at cristina.bianchi@olympic.org.

U.S. Sports Academy2016-10-12T11:45:58-05:00February 13th, 2008|Contemporary Sports Issues, Sports Facilities, Sports History|Comments Off

Factors Associated with Success Among NBA Teams

Submitted by: Anthony J. Onwuegbuzie

Abstract

Data from the 1997-1998 National Basketball Association (NBA) regular season were analyzed to determine factors that best predicted success, as measured by winning percentage. A total of 20 variables were examined. A multiple regression analysis revealed that field goal conversion percentage was the best predictor of success, explaining 61.4% of the variance in winning percentage. The average three-point conversion percentage of the opposing teams explained a further 18.9% of the variance. These two variables combined explained 80.3% of the variance in winning percentage. The finding pertaining to field goal conversion percentage suggest that the attainments of the offense are more important than are the defensive attainments in predicting the success levels of NBA teams. These and other implications are discussed.

Introduction

The game of basketball was invented in December 1891 by Dr. James A. Naismith while an instructor in the physical training department of the International Young Men’s Christian Association (YMCA) Training School in Springfield, Massachussets (Fox, 1974). Naismith’s goal was to answer the challenge of Dr. Luther H. Gulick, his department head, who wanted an indoor game to be invented that (1) would attract young men during the winter, when baseball and football were out of season, and (2) would replace gymnastics and calisthenics, which provoked little interest (Fox, 1974). Naismith, known as “the father of basketball,” incorporated features of soccer, U.S. football, rugby football, field hockey, and other outdoor sports in developing the game of basketball.

By 1946, professional basketball had acquired a large and faithful following among U.S. sports fans, who wanted to watch their former collegians in action. During this period, there was the American Basketball League (ABL) on the East Coast and the National Basketball League (NBL) in the Midwest. In June, 1946, the Basketball Association of America was formed, which effectively replaced the ABL and competed directly with the NBL (Fox, 1974). The BAA and the NBL merged in 1950 as the National Basketball Association (NBA), comprising 17 teams. The NBA was reduced to 10 teams in 1951, as 7 NBL teams with marginal franchises dropped out (Fox, 1974). However, in the 1970s, the NBA expanded to 22 teams. Presently, the NBA contains 29 teams, with 15 teams in the Eastern Conference (with 7 teams representing the Atlantic division and 8 teams representing the Central division) and 14 teams in the Western Conference (with 7 teams representing the Midwest division and 7 teams representing the Pacific division). Basketball is now one of the most popular sports in the United States. Indeed, in the 1997-1998 season (the last time a full 82-game season was played), a total of 8,877,309 people attended an NBA game (The Sports Network, 1998), with an average attendance of 17,135 people per game (USATODAY, 1999).

Currently, at the end of the regular season, that is, when each team has played 82 matches, the top eight teams in each conference qualify for the playoffs. These eight teams then participate in a knockout tournament with the eventual winners of this stage within each conference advancing to the NBA finals. Because the teams which advance to the playoffs are those that have the highest winning percentages in their respective divisions during the regular season, knowledge of factors which predict success during this period would be of educational value for NBA coaches and analysts. Indeed, the former group could use this information to target coaching interventions.

Basketball is abound with empirical facts. Surprisingly, however, only descriptive statistics (e.g., averages, totals, percentages) tend to be utilized. Conversely, few inferential statistical analyses are undertaken on NBA data. Yet, such analyses provide consumers with information regarding the relationships among variables. As such, inferential statistics can yield very detailed and important information to consumers of professional basketball. Moreover, inferential statistics can be used to determine factors that predict the performance levels of teams.

To date, only a few studies have investigated correlates of basketball-related performance. Of those that have, the majority have involved an examination of psychological antecedents of basketball performance. For example, Whitehead, Butz, Vaughn, and Kozar (1996) found that increased stress (assumed to be present in games as opposed to practices) among members of an NCAA Division I men’s varsity team was associated with longer pre-shot preparations and a greater incidence of overthrown shots.

Newby and Simpson (1994) reported (1) a statistically significant negative relationship between minutes played by a sample of men and women college basketball players and mood, (2) a statistically significant negative relationship between the number of assists and depression, (3) a statistically significant negative relationship between the number of turnovers committed and mood, and (4) a statistically significant positive relationship between the number of turnovers committed and degree of tension. The researchers concluded that success in basketball is negatively related to psychopathology.

Both Pargman, Bender, and Deshaires (1975) and Browne (1995) found no relationship between free-throw and field goal shooting and field independency/field dependency. Additionally, Shick (1971) found no relationship between hand-eye dominance and depth perception and free-throw shooting ability in college women. Hall and Erffmeyer (1983) examined the effect of imagery combined with modeling on free-throw shooting performance among female college basketball students. These researchers noted that players who shot free throws under the conditions of videotaped modeling combined with relaxation and imagery were significantly more accurate than were those who shot in the relaxation and imagery condition only.

All the above studies investigated correlates of specific basketball skills (e.g., free-throw shooting), and, with a few exceptions (e.g., Butz et al., 1996), these skills typically were examined under simulated conditions. Such studies, although interesting, have limited utility for basketball coaches, in particular, because they does not provide any information as to why or how a team wins a basketball game. Indeed, the only inquiry found determining factors associated with success among basketball players was that of Steenland and Deddens (1997). These researchers studied the effects of travel and rest on performance, utilizing the results for 8,495 regular season NBA games over eight seasons (1987-1988 through 1994-1995). Findings revealed a statistically significant positive relationship between the amount of the time that elapsed between games and performance level. Specifically, more than 1 day between games was associated with a mean increase of 1.1 points for the home team and 1.6 points for the visitors. Peak performance occurred with 3 days between games. The researchers theorized that the negative effects of little time between games may be due more to insufficient time for physical recovery than to the effects of circadian rhythm (i.e., jet lag). However, although not statistically significant, they also found that visiting teams performed four points better, on average, when they traveled from the west coast to the east coast than when they traveled form east to west.

Surprisingly, no other study has investigated predictors of success among NBA teams. Even more surprising is the fact that no research appears to have examined what factors directly associated with skill level (e.g., field goal conversion percentage) best predict a team’s winning percentage. This was the purpose of the present inquiry. A secondary goal was to determine whether offensive or defensive factors would have more predictive power. It was expected that knowledge of these factors could help coaches to decide where to focus their attention, as well as assist analysts and fans in predicting a team’s performance.

Method
The data comprised all 21 unique team-level variables (when both team averages and totals were presented, only the averages were utilized, since they rendered totals redundant) that were presented on the official NBA website (i.e., http://www.nba.com) for the 1997-1998 regular professional basketball season. (The 1997-1998 NBA season was chosen because it represented the last time a full 82-game season was played.) These variables comprised winning percentage, which was treated as the dependent measure and 20 other variables which were utilized as independent variables. All variables are presented in Table 1. Scores pertaining to each variable for each team were analyzed using the Statistical Package for the Social Sciences (SPSS; SPSS Inc., 1999).

Table 1 Pearson Product-Moment Correlations of Winning Percentage and Selected Variables for the 1997-1998 Regular NBA Season

Variable	Winning Percentage
three-point conversion percentage	.38
field goal conversion percentage	.78*
free-throw conversion percentage	.03
average number of offensive rebounds per game	-.31
average number of defensive rebounds per game	.47
number of total rebounds	.19
average number of assists per game	.61*
average number of steals per game	.08
average number of blocks per game	-.13
number of points scored per game	.57*
field goal conversion percentage of the opposing teams	-.68*
average three-point conversion percentage of the opposing teams	-.50
average free-throw conversion percentage of the opposing teams	.18
average number of offensive rebounds per game of the opposing teams	-.49
average number of defensive rebounds per game of the opposing teams	-.71*
average number of total rebounds of the opposing teams	-.69*
average number of assists per game of the opposing teams	-.70*
average number of steals per game of the opposing teams	-.45
average number of blocks per game of the opposing teams	-.58*
average number of points scored per game of the opposing teams	-.70*
* statistically significant after the Bonferroni adjustment

Results and Discussion
Table 1 presents the correlations between winning percentage and each of the selected variables. It can be seen that, after adjusting for Type I error (i.e., the Bonferroni adjustment), winning percentages increased with field goal conversion percentage, number of assists per game, and number of points scored per game, and decreased with field goal conversion percentage of the opposing teams, average number of defensive rebounds per game of the opposing teams, average number of total rebounds per game of the opposing teams, average number of assists per game of the opposing teams, average number of blocks per game of the opposing teams, and average number of points per game of the opposing teams.

An all possible subsets (APS) multiple regression (Thompson, 1995) was used to identify which combination of independent variables best predicted NBA teams’ success. Again, success was measured by NBA teams’ regular season winning percentages. For this study, the criterion used to determine adequacy of the model was the maximum proportion of variance explained (i.e., R2), which provides an important measure of effect size (Cohen, 1988). Specifically, all variables were included except for those that represented (1) the total number of points scored or the total number of rebounds (use of the number of defensive rebounds and offensive rebounds rendered use of the total number of rebounds redundant). Consequently, a total of 16 independent variables were analyzed.

The multiple regression analysis revealed that the following two variables made a statistically significant contribution (F [2, 26] = 53.12, p < .0001) to the model: field goal conversion percentage and average three-point conversion percentage of the opposing teams. The regression equation was as follows:

winning percentage =
-159.53 + {(7.90) X field goal conversion percentage} – {(4.24) X average three-point conversion percentage of the opposing teams}

The regression equation indicates that every 1 percentage increase in field goal conversion rate is associated with a 7.90% increase in winning percentage. The confidence interval corresponding to this variable suggests that we are 95% certain that every 1 percentage increase in field goal conversion rate is associated with an average increase in winning percentage of between 6.00% and 9.80%. Additionally, every 1 percentage increase in the three-point conversion rate of the opposing teams is associated with a 4.24% decrease in winning percentage (95% confidence interval is 2.49% to 5.99%).

With respect to predictive power of the model, field goal conversion percentage explained 61.4% of the variance in winning percentages, whereas average three-point conversion percentage of the opposing teams explained 18.9%. These two variables combined to explain 80.3% of the total variance in winning percentage (adjusted R2 = 78.8%). In the study of human behavior, this percentage is extremely large, suggesting that an NBA team’s success can be predicted with an excellent degree of accuracy.

Conclusions
The purpose of this study was to determine which variables best predict whether an NBA team’s success rate. The finding that field goal conversion percentage explains more than three times the variance in success than does the average three-point conversion percentage of the opposing teams suggests that the attainments of the offense are more important than are the defensive attainments in predicting whether an NBA team will be successful. Thus, the present finding is in contrast to Onwuegbuzie (1999a), who identified four multiple regression models which adequately predicted the winning percentages of National Football League (NFL) teams for the 1997-1998 regular football season–the most notable being a two-variable model comprising turnover differential (which explained 43.4% of the variance in success) and total number of rushing yards gained by the offense (which explained a further 9.3% of the variance). Based on these models, Onwuegbuzie concluded that, outside the 20-yard zone, the attainments of the defense are more important than are the offensive attainments in predicting whether an NFL team is successful.

The present result pertaining to NBA teams also is in contrast to Onwuegbuzie’s (1999b) replication study of NFL teams for the 1998-1999 football season in which a model was identified containing the following five variables: (1) turnover differential (which explained 54.4% of the variance); (2) total number of rushing yards conceded by the defense (which explained 21.3% of the variance); (3) total number of passing first downs attained by the offense (which explained 9.4% of the variance), (4) percentage of third-down plays that produce a first down (which explained 4.1% of the variance), and (5) total number of penalties conceded by the opponents’ defense resulting in a first down (which explained 4.1% of the variance). Onwuegbuzie concluded that defensive gains are better predictors of success than are offensive gains because the first two variables, which explained more than 75% of the variance, were characteristics of the defense.

The finding that field goal percentage rate explained a very large proportion of the variance in success (i.e., 61.4%) highlights the importance of offensive efficiency not only of the starting players but also of the “bench” players, since the latter group also contribute to the field goal percentage rate. Nevertheless, the fact that three-point conversion percentage also made a contribution to the regression model, albeit a smaller one, suggests the importance of teams forcing the opposition to hurry their three-point shots and to take these shots from non-optimal parts of the basketball court.

Although a significant proportion of the variance in winning percentage was explained by the selected variables, this study also should be replicated using data from other seasons. Furthermore, regression models should be fitted using college basketball data. Information from such analyses should help coaches and analysts alike to obtain objective data which can be used to monitor the performance of NBA teams.

References

Browne, G.S. (1995). Cognitive style and free throw shooting ability of female college athletes. Unpublished master’s thesis, Valdosta State University, Valdosta, Georgia.

Cohen, J. (1988) Statistical power analysis for the behavioral sciences. New York: Wiley.

Fox, L. (1974). Illustrated history of basketball. New York, NY: Grosset & Dunlap.

Hall, E.G., & Erffmeyer, E.S. (1983). The effect of visuo-motor behavior rehearsal with video taped modeling of free-throw shooting accuracy of intercollegiate female basketball players. Journal of Sport Psychology, 5, 343-346.

Newby, R.W., & Simpson, S. (1994). Basketball performance as a function of scores on profile of mood states. Perceptual and Motor Skills, 78, 1142.

Onwuegbuzie, A.J. (1999a). Defense or Offense? Which is the better predictor of success for professional football teams? Perceptual and Motor Skills, 89, 151-159.

Onwuegbuzie, A.J. (1999b, November). Is defense or offense more important for professional football teams? A replication study using data from the 1998-1999 regular football season. Paper presented at the annual meeting of the Midsouth Educational Research Association, Point Clear, AL.

Pargman, D., Bender, P., & Deshaires, P. (1975). Correlation between visual disembedding and basketball shooting by male and female varsity athletes. Perceptual and Motor Skills, 41, 956.

Shick, J. (1971). Relationships between depth perception and hand-eye dominance and free-throw shooting in college women. Perceptual and Motor Skills, 33, 539-542.

SPSS Inc. (1999) SPSS 9.0 for Windows. [Computer software]. Chicago, IL: SPSS Inc.

Steenland, K., & Deddens, J.A. (1997). Effect of travel and rest on performance of professional basketball players. Sleep, 20(5), 366-369.

The Sports Network. (1998). Statistics: 1997-1998 NBA attendance. The Sports Network, 21(21).

Thompson, B. (1995). Stepwise regression and stepwise discriminant analysis need not apply here: A guidelines editorial. Educational and Psychological Measurement, 55, 525-534.

USATODAY. (December 28, 1999). Inside the numbers. Retrieved January 28, 2000 from the World Wide Web: http://www.usatoday.com/sports/basketba/skn/numbers.htm.

Whitehead, R., Butz, J.W., Vaughn, R.E., & Kozar, B. (1996). Stress and performance: An application of Gray’s three-factor arousal theory to basketball free-throw shooting. Journal of Sport Behavior, 19(4), 354-364.

Footnote
1 Due to space constraints, the intercorrelations among all the variables is not presented. However, this can be obtained by contacting the author.

Address correspondence to Anthony Onwuegbuzie, Department of Educational Leadership, College of Education, Valdosta State University, Valdosta, Georgia, 31698 or e-mail (TONWUEGB@VALDOSTA.EDU).

U.S. Sports Academy2013-11-27T16:29:09-06:00February 13th, 2008|Sports Coaching, Sports History, Sports Management, Sports Studies and Sports Psychology|Comments Off

IOC Culture and Olympic Education Forum : Introduction

Submitted by: Juan Antonia Samaranch

Since its origins, the Olympic Movement has always wished to associate the movements of sport with the thought processes linked to cultural activity, be it sculpture, paintings, literature, music or architecture. At the start of the third millennium, this desire clearly remains as relevant as ever, and the Olympic Museum in Lausanne is a living illustration of this.

It is particularly significant that, in the framework of the reforms recently carried out by the International Olympic Committee, its Commission for Olympic Culture and Education has invited a number of internationally renowned specialists to meet to share their thoughts about the development of its cultural policy.

We would like to thank them for their invaluable contribution and wish the numerous participants a pleasant stay in Lausanne, Olympic capital.

Juan Antonia Samaranch President of the International Olympic Committee

U.S. Sports Academy2017-08-07T15:32:47-05:00February 13th, 2008|Sports History, Sports Management|Comments Off

IOC Culture and Olympic Education Forum : Preface

Submitted by: He Zhenliang

It is often said and repeated that Olympism is sport and culture. This is not a simple definition, it is a program that is constantly developing. The cultural dynamism of the IOC and the Olympic Movement is conveyed periodically at Olympic Games opening and closing ceremonies, during all events organized a this magnificent Olympic Museum which hosts our Forum today, and in the actions carried out by the Cultural Commission which has recently merged with another IOC Commission to become the new Commission for Olympic Culture and Education.

Yes, culture is the second dimension of Olympism and the IOC gives and will always give culture the importance it deserves, in accordance with its fundamental principles.

This Forum, the second we have organized, on a theme dear to our founder Pierre de Coubertin, will make us think about the IOC’s cultural policy. Given that the time to do so is limited, we will give priority to considering the future.

I thank the speakers and participants in this Forum for pursuing with us the ongoing consideration of the IOC’s cultural policy.

He Zhenliang
IOC Executive Board Member
Chairman of the Commission for Culture and Olympic Education