2006 – Page 3 – The Sport Journal

Video Recording of Elite Seated Shot Putters During World Class Events

Submitted by: Laurent Frossard

Abstract

The purpose of this paper is to share useful and practical information coming out of the first experience of systematic video recording of seated shot-putters during the Sydney 2000 Paralympic Games. It is anticipated that this paper will provide valuable information to sport scientists facing the challenge of conducting performance analysis of able-bodied or disabled athletes, such as seated shot-putters, during world-class competitive events. More specifically, this paper provides (1) the practical aspects of the cameras’ setup used during this systematic video recording, (2) the number and usability of attempts recorded, taking into consideration the impact of uncontrollable perturbing factors, and (3) recommendations to improve the video recording procedure in such conditions. Two operators recorded each put using two compact, high-speed digital video cameras placed in different locations such as right, left or front of the shot-putter. In this study, 15% of the attempts were not recorded, 72% were recorded and fully available for analysis, 10% were incomplete and 2% were obstructed (as a percentage of expected attempts). This study suggests that the increase of the number and usability of the attempts recorded relies on the number and position of cameras and the operators as well as on other facilitating actions.

Introduction

Video recording is the central element of biomechanical analyses based on kinematic data including the range of motion, the linear and angular momentum of each segment as well as the mechanical energy expended. As for all top athletes, kinematic analysis is particularly important for the elite seated shot-putters because it is one of the rational means available that could be used to improve the understanding of their performance. More specifically, athletes, coaches and sports scientists can exploit this data to improve the shot-put technique and the design of the throwing seat.

Video recording during training

Currently, this understanding is only based on video recording of emerging and elite shot-putters during training (Chow and Mindock, 1999, Chow et al., 2000). On one hand, the recording in this situation presented the advantage of easily accommodating usual experimental requirements including the use of active or passive markers, the positions of cameras, the number of attempts recorded, etc. One the other hand, the data collected during training was partially representative of the technique as performed by these elite athletes while competing in a world-class event. This is mainly because elite athletes do not perform at their best, let alone break a world record, during training sessions particularly during the early stage of the preparation for a major event. For instance, the elite shot-putters participating to the Chow et al.’s, 2000 study performed on average 15?9% less than their personal best.

Video recording during world-class competition

Consequently, a sound understanding of the performance of elite seated shot-putters would require a performance analysis based on video recording during a world-class competition. This should allow sports scientists to produce a systematic and more realistic biomechanical analysis. Here, the term “systematic” refers to the recording of all attempts performed by a target group of athletes, such as a class. During a world-class competition every attempt of each athlete must be recorded in order to capture the best put, which is known only at the end of the event. The data collected in these circumstances is more realistic because it takes into consideration a number of external factors influencing the performance. These factors include the stress and pressure due to the presence of other opponents, mass-media, referees in charge of applying the strict rules as well as the use of official equipment to anchor the throwing seat (use of metal plate on the ground rather than pegs), travelling fatigue, etc.

Benefits of video recording during world-class competition

Furthermore, coaches, athletes and sports scientists, as well as classifiers and referees, could benefit from a kinematic analysis based on video recording of elite seated shot-putters during world-class events as the outcomes of such analysis have the potential to enhance performance and increase the fairness of the event (classification and judging).

Coaches and elite shot-putters use video recording regularly during training. Therefore, conducting similar recording during the actual competition is of value to complement their observations obtained during training. Furthermore, the understanding of the actual performance of World Championship and Paralympic medallists could not only contribute to improve both training methods and seat design but also the curricula of existing courses in training and coaching as well as the talent identification system.

Classifiers divide disabled athletes into classes according to their gender and functional level or “movement potential” depending on the control and strength of various muscle groups. This process involves observation of athletes during the event as well as a physical and functional examination assessing muscle power, range of joint movement, backwards and forwards movement, side to side movement and trunk rotation (Higgs et al., 1990, McCann, 1993, Vanlandewijck and Chappel, 1996, Williamson, 1997, Laveborn, 2000, Tweedy, 2002). A video recording of the athletes during the actual event and subsequent kinematic analysis could provide the classifiers with more accurate and true information about the athletes’ functional level as the actual range of motion of the trunk, for instance, can be quantified. Currently, there is no set video recording procedure during the event aimed at determining this true range of motion of the athletes as well as their true compliance during the classification process. In addition, video recording during the event can show if the athletes’ technique follows the rule, indicating that the shot cannot move behind the line defined by the shoulder and the ear. Therefore, the recordings could be used as a medium to settle protests of athletes against referees’ decisions. This aspect was experienced during the Sydney 2000 Paralympic Games where the tapes used in the following study were required on several occasions by referees needing to review their decision.

Implementation of video recording during world-class competition

Theoretical aspects of successful 2-D or 3-D performance analysis of an athletic technique using video recording in experimental conditions have been previously described in detail (Marzan, 1975, Allard, 1995). Some elements of video recording settings during world-class competition could be found for able-bodied shot-putters (Ariel, 1973, Ariel, 1979). However, these elements were not fully relevant since the seated shot-putters use a different technique from the one used by able-bodied athletes. A few studies included key elements of video recording of seated athletes but only in a training environment (Chow and Mindock, 1999, Chow et al., 2000).

While the general principles presented in these articles might be applied to video recording of seated shot-putters during world-class events, adaptations may be required to compensate for the many extra constraints imposed on such data collection, especially if the recording is required to be systematic. For example, a retake of a performance is impossible and every attempt of each athlete must be recorded in order to capture the best puts, which is known only at the end of the event. The recording cannot interfere in any way with the athletes, the officials, the referees or the TV crews. For instance, no active or passive markers may be placed on the athlete. The camera views can be obstructed at any time by several factors such as broadcast TV crews, referees and equipment. Contacts between the operators of the cameras are limited, as no radio communication may be allowed in the stadium. Furthermore, the number of cameras is limited and their position should not interfere with any of the other on-going sporting events.

The systematic video recording of seated shot-putters during actual world-class competition might be eased by the development of discrete, affordable and user-friendly video recording systems matching the requirement of scientific measurements. Most of these systems use two or more compact, high-speed, digital video cameras allowing at least a bi-planar analysis. The use of compact cameras is important for recording in the conditions of a world-class event since the space available is limited. Furthermore, such cameras are not confused with TV cameras, which could be moved on demand. These compact cameras allow transfer of data recorded in a digital format directly onto the computer and avoids the time consuming digitizing process that can lead to a loss of quality of the data. These cameras could acquire data at a rate ranging up to 100 Hz, which should allowed sport scientists to accurately determine the key events of the put (e.g. end of back thrust, instant of release of the shot). This aspect is particularly important since the accuracy of determining the instant of release of the shot is critical in establishing the initial parameters of the shot’s trajectory including the velocity, angle and height of release (Lichtenburg and Wills, 1978, Linthome, 2001).

Need for practical information

The benefits and key elements of the implementation (general principles and technical means available) of the video recording during world-class events have been provided. Nevertheless, there is yet a lack of studies presenting the technical aspects and the outcomes of such video recording.

However, a systematic video recording of seated shot-putters participating in world-class competition was conducted for the first time during the Sydney 2000 Paralympic Games. The primary aim of this recording was to show the principles underlying elite seated shot-putters’ performance. The secondary aim was to determine the true compliance of the athletes during the classification process. These were achieved using a bi-planar kinematic analysis.

The knowledge and understanding of the perturbing factors listed above were essential in the planning of this video recording. However, the literature review conducted in preparation for this systematic video recording revealed that useful and practical information was only partially available. Furthermore, no studies provided information on how to prevent and accommodate these factors as well as their potential impact on camera setup, and the number and usability of attempts recorded.

In conclusion, the literature review brought to light a need for practical information for successful systematic video recording during world-class competitions.

Purposes

A number of unforeseen difficulties not presented in the literature or listed above were encountered during this initial experience of systematic video recording. Consequently, it was considered desirable to detail these problems so as to recommend protocols to circumvent them at future events.

While the outcomes of the biomechanical analysis as such will be the focus of following articles, the ultimate purpose of this present paper is to share essential and practical information from this experience. This will provide useful benchmark data and guidelines for similar future systematic video recording of the performance of able-bodied and disabled athletes during any world-class event.

The specific purposes of this paper are to provide:

The practical aspects of camera setup for use during the systematic video recording of seated shot-putters suited to kinematic analysis, as well as the location and the field of view of the cameras. This will include essential information about the camera setup for future video recording of seated shot-putters during world-class competition.
The number and the usability of attempts recorded taking into consideration the impact of uncontrollable perturbing factors. This will indicate the amount of usable data that one can expect to collect during world-class competition.
Recommendations to improve the number and the usability of attempts recorded in the conditions of a world-class competition. This section will focus on the camera setup, the number of operators and the reduction of perturbing factors. This will provide a practical guideline for future similar studies.

Method

The video recording as described below aimed to underlie the specific aspects of the elite seated shot-putters performance such as the parameters of the shot’s trajectory and the segmental actions of the athletes (Ariel, 1973, Dessureault, 1978, Lichtenburg and Wills, 1978, Ariel, 1979, Zatsiorsky et al., 1981, McCoy, 1984, Sušanka, 1990, Bartonietz and Borgström, 1995, Tsirakos, 1995, Luhtanen et al., 1997, Chow et al., 2000, Linthome, 2001).

This method could not employ markers on the athletes or any other direct interference with them during the attempt.

Participants

A total of 93 seated shot-putters participating in the Sydney 2000 Paralympic Games were selected from 10 classes (F52 to F58) accordingly to the International Stoke Mandeville Wheelchair Sports Federation (ISMWSF) classification system (Laveborn, 2000). This included 30 women (7 in F52-F54, 5 in F55, 10 in F57, 8 in F58) and 63 men (13 in F52, 9 in F53, 13 in F54, 7 in F55, 10 in F56, 11 in F57). The classes F52 and F54 women were grouped together due to the lack of athletes in each class.

The informed consent for each athlete to participate in this study was obtained through the International Paralympic Committee (IPC).

Camera setup for bi-planar video recording

Ideally, a complete understanding of the action of the body segments and the parameters of the shot’s trajectory requires a three-dimensional kinematic analysis as it provides the actual position and orientation of a given segment in relation to another. In principle, a marker or a given anatomical body landmark must be seen simultaneously by at least two and preferably more cameras in order to be reconstructed and used in subsequent three-dimensional kinematic analysis (Abdel-Aziz and Karara, 1971, Marzan, 1975). Consequently, a three-dimensional kinematic analysis of elite shot-putters will necessitate at least four cameras aligned diagonally with each corner of the plate and preferably a fifth one located above the athlete. While this setup appears trouble-free to implement in an experimental framework, it is not suitable and practical for the real events in the field. It is unrealistic to expect that the field of view of each camera on the floor will not be obstructed during the recording of the attempt since about 30 people work in the throwing area alone. Furthermore, it means that accreditations for up to five camera operators must be obtained from the IPC, whose aim is to reduce the number of people working in the area.

Consequently, it is more realistic to attempt a bi-planar analysis under these types of environments. Using two cameras appeared to be a more suitable option because it was less invasive than a three-dimensional analysis and still allowed kinematic analysis in the sagittal and frontal planes which will provide a fair representation of the main rotation between the shoulders and the hips of the athletes. Most of these athletes are seated facing the sector and their putting action occurs essentially in the sagittal plane. As no passive or active markers are allowed on the athlete, automatic tracking could not be used. Thus, the two-camera setup has the advantage of reducing the data that needed to be digitized manually or pointed frame-by-frame. In addition, this method was more cost effective as it required only accreditation for two operators and fewer airfares as well as less time for computing.

The following sections will present the key aspects of the camera setup including the type, the location and the field of view of the cameras.

Type of cameras

Two operators recorded each put using two compact (20 x 20 x 10 cm), high-speed digital video cameras (JVC, Model DVL 9800) set at a sample rate of 100 Hz.

The two cameras were operated simultaneously with one camera on the side and the other in front of the athlete as illustrated in Figure 1, for most of the classes. The camera on the side was placed either on the right or the left of the athlete depending on their throwing hand for the classes F52-F54 and F58 Women. Previous studies placed the second camera behind the thrower (Dessureault, 1978, Chow and Mindock, 1999, Chow et al., 2000). At the Sydney 2000 Paralympic Games, the rear of the plate was a designated area for athletes, assistants and referees and was therefore not accessible. For this reason, the second camera was located in front of the thrower for this study.

A slightly different setup was used for a few classes when the object of research was concerned with a specific aspect of the technique. For example, only one camera was placed in the front for the classes F58 Women, F57 Men and F57 Women since only the rotation in the frontal plane of the athletes in these classes was of interest. Also, the cameras were placed on each side for the class F56 Men in order to accurately determine the position of the upper body segments in the sagittal plane of both sides of the athletes in this class.

A customized calibration frame (2 m length x 1.5 m height x 1 m width) including 43 control points was recorded at the beginning and at the end of each event.

Field of view and position of the cameras

A few pilot studies conducted prior to the Games defined the suitable cameras’ field of view for data analysis including displacements in sagittal and frontal planes of each body segment as well as the determination of the shot’s trajectory. For both cameras, the bottom of the field of view included the full-length (2.29 m) and full-width (1.68 m) of the plate on the ground, used to secure the athlete’s seat. The field of view in the sagittal plane (camera on the side) was enlarged in the direction of the throw to secure the recording of at least the first five frames of the shot’s aerial trajectory (Figure 2). In experimental or training conditions, these fields of view can be obtained by zooming to reduce the perspective error once the cameras were placed at distance from the plate. During this study, the zoom was occasionally used to achieve the appropriate field of view, particularly for the camera in the frontal plane, which had to be placed outside of the sector. The camera on the side was placed relatively closer to the plate in order to reduce the possibilities of intrusion of TV crews, equipment and/or referees in the field of view.

The camera in front of the thrower was placed between 14 to 18 m perpendicular to the width of the plate, while the camera on the side was between 8 to 10 m perpendicular to the length of the plate. The height of both cameras was approximately 1.10 m. The angle between the optical axis of the two cameras and the ground was approximately 900. The positions of the cameras in relation to the plate are presented in Figure 1.

The pixel resolution ranged between 0.95 cm to 1.35 cm for both cameras depending on their positions, which provided sufficient accuracy for further analysis.

Duration of recording

The duration of the recording of each attempt was approximately 7 seconds. The recording started when the referee handed the shot to the athlete, and ended when the shot landed on the ground. Consequently, the recording included the back and forward thrusts of preparation as well as the delivery of the shot. An overall of approximately 100 minutes worth of data was recorded for each camera.

Results and Discussion

Table 1 Breakdown of the success rate for systematic video recording of the shot-put event by class

Figure 2 Example of the field of view for the side camera and an obstruction caused by a referee.

Table 1 reports the number of useful attempts recorded, taking into consideration the effect of uncontrollable perturbing factors. This table details the number and the percentage of attempts recorded, not recorded, incomplete, obstructed and usable for both cameras in each class. The percentages are expressed with regard to the total number of attempts expected in each class. This number of attempts expected was determined by the number of athletes and the number of attempts each athlete was allowed to perform according to competition rules (three attempts in the qualification round for all athletes and three further attempts in the final round for athletes ranked in the first six from the qualification round).

Number of attempts recorded

A total of 717 attempts performed by 93 seated athletes from 10 classes was recorded resulting in an average of 88?13% of the expected attempts recorded per class.

One hundred and thirty two attempts, corresponding to an average of 12?13% of the expected attempts, were not recorded. The number of attempts not recorded is particularly important for the F52 Men and F54 Men classes for both cameras. The qualification round of these classes took place on two separate pits because the number of participants exceeded 12 (13 for F52 Men and 14 for F54 Men). This aimed at reducing the duration of the event for these classes. The operator was unable to record 30 attempts in class F56 Men as the position of a referee completely obstructed the view of the athletes as shown in the example in Figure 2. Overall, 20 attempts corresponding to 2.35% of the attempts expected were not recorded by the operator who was under the impression that the first attempt was a warm up.

Usability of the attempts recorded

Here, the term ‘usability’ refers to the potential use of the recording for a complete tracking of the body landmarks for at least five frames before the beginning of the back thrust and after the release of the put.

There were 614 attempts successfully recorded and fully useable for further analysis, corresponding to an average of 74?23% of the expected attempts. The difference between the number of attempts recorded and those useable was due to the number of attempts either incomplete or obstructed.

An attempt was defined as “incomplete” when it was only partially recorded mainly when the operators triggered the acquisition slightly after the beginning of the put. This occurred while the operators were distracted due to an unforeseen event, or when the athletes put the shot immediately without preparation. This occurred during 16 attempts corresponding to 2% of the attempts expected. The number of attempts incomplete was more important for the camera on the side because the operator was more prone to be distracted by people moving around and in front of the camera.

An attempt was defined as “obstructed” when a part of the athlete’s movement was hidden during the put. The camera in the frontal plane was only obstructed for 14 attempts mainly due to TV crews in the field of view. The camera in the sagittal plane was obstructed almost twice as often. Here, one source of obstruction was equipment such as the posts for the safety net used during the discus event, display panels, tables for referees and boxes. As illustrated in Figure 2, the other source of obstruction was the referees. Some puts were obstructed either at the beginning or at the end by the referee who was required to be perpendicular to the athlete so as to have a proper appreciation of their technique (F52-F54 Women, F54 Men).

However, the incomplete or obstructed attempts might be partially useable for further analysis. For example, the parameters of the shot’s trajectory might be determined for the attempts incomplete or obstructed at the beginning while the analysis of the segmental organisation might be impossible. Therefore, the realistic number of attempts useable for analysis was close to 85% of the expected attempts.

Conclusion

In this study, 15% of the attempts were not recorded, 72% were recorded and fully available for analysis, 10% were incomplete and 2% were obstructed (as a percentage of expected attempts).

While these figures may appear satisfactory in the context of training, they might not be considered acceptable for a competition event, particularly if the recording of the attempts performed by the medallists was incomplete or obstructed. For instance, this could jeopardise a study aiming at analysing the technique of the best athletes.

The measures to improve the capture rate emanating from this first experience of systematic video recording during a world-class event are linked to the number and position of the cameras as well as the number of operators.

Number and position of the cameras

It is shown that two cameras were sufficient to produce a systematic video recording for classes with fewer than 12 athletes. However, two additional cameras would be needed for a systematic video recording of classes exceeding 12 athletes as two or more separate pits might be used simultaneously.

It has been demonstrated that it is practical and efficient to place one camera in front of the thrower. The recordings of this camera were less frequently obstructed than those of the side camera. The side camera can be placed on the right of the thrower on most occasions since only four athletes representing less than 6% of all participants were left-handed.

In addition, one may need to compromise the ideal position and remote distance of the camera from the plate (proper field of view with zoom) as this space is likely to be occupied because it overlaps with other events (e.g. other throwing pits or racing track). Our experience demonstrated that the further the cameras were positioned from the plate, the higher the chances of perturbing factors, such as someone walking in front of the cameras.

Number of operators

Video recording in the conditions of world-class competition will always have unpredictable factors, involving the officials, equipment and broadcast TV crews. This is mainly because the work of the officials and broadcast TV crews traditionally and legitimately prevails over a research project. One way to significantly reduce the number of attempts incomplete or obstructed due to these unpredictable factors is to allow the operator to prevent them.

During the video recording, attempts were made to use the remote control to trigger the recording at a distance. This way the operator could start the recording and potentially prevent interferences of the environment. The operator stood nearby the camera and paid particular attention to the surrounding. However, in our experience this strategy generally failed. It did not reduce the number of obstructions and eventually made the triggering of the recording more difficult.

Our experience showed that a better way to prevent interferences of the environment during the recording is to employ an additional operator per camera, particularly on the side camera. Only one operator per camera conducted the video recording in this study. However, another operator could have prevented officials or other people from walking within the field of view. In addition, this would leave the operator behind the camera in a better position to follow the event and closely concentrate on the recording. Furthermore, the operators could take turns conducting the recording and this way the operator behind the camera might be able to maintain the necessary level of concentration required over the three to four hour duration of the event.

Additional facilitating actions

Our experience revealed that a few other actions might help to improve the video recording, such as providing the participants with an informative document and defining a protected zone.

Officials and broadcast TV crews were formally informed prior to each event. They were both most sensitive and cooperative during the video recording sessions. However, it is likely that providing more formal information in advance about the study will increase their awareness and therefore reduce the number of attempts that were incomplete or obstructed in future event. For example, a one-page flyer could be provided to them, via the IPC, prior to the event, informing them of the recording process and its benefits.

In addition, lines corresponding to the field of view of the camera on the side could be drawn on the ground and presented as a sensitive zone. Such a display might help the participants to be more aware of their position in relation to the camera. However, this required prior approval from the organising committee and officials.

Practical implication for further performance analysis during world-class event

It could be concluded from our experience that the guideline for future bi-planar systematic video recording of seated shot-putters during world-class events should include the following key points:

Assess thoroughly the feasibility of the cameras’ positions prior to opt for a three-dimensional video recording setup,
Use at least two cameras and preferably four cameras for a bi-planar analysis,
Place the cameras reaching the relevant field of view as close as possible to the plate,
Employ two operators per camera. One could be in charge of the recording while the other prevents interferences from the environment,
Provide formal information on the study to officials and broadcast TV crews prior to the event,
Draw a line on the ground corresponding to the field of view of the cameras.

It is hoped that this paper will provide valuable information to the sports scientists facing the challenge of conducting performance analysis based on systematic video recording of able-bodied or disabled athletes, such as seated shot-putters, during world-class competitive events.

Acknowledgments

The authors wish to express their gratitude to Chris Nunn (Australian Institute of Sport), Scott Goodman (Athletics Australia), Dale Hudson (Sydney Academy of Sport), Lyn Phillips (Sydney Academy of Sport), and Chris Cohen (International Paralympic Committee).

The authors wish to thank Dr James Smeathers (Human Movement Studies-QUT) in particular and Professor Mark Pearcy (School of Engineering Systems-QUT) as well as Professor John Evans (School of Engineering Systems-QUT) for their insights and suggestions, which have greatly influenced the content of this manuscript.

This study was partially founded by Athletics Australia and QUT Strategic Links with Industry Scheme.

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U.S. Sports Academy2015-03-27T12:00:36-05:00June 5th, 2006|Contemporary Sports Issues, Sports Studies and Sports Psychology|Comments Off

A New Market Research Approach in Sport-Data Mining

Submitted by: Chen-Yueh Chen & Yi-Hsiu Lin

Introduction

Numerous organizations in the field of business have shown that great success and lucrative outcomes can be accomplished through implementing data mining. For example, Wal-Mart used data mining and found a link between the sales of babies’ diapers and beer. Based on this result, Wal-Mart placed beer close to the babies’ diapers, which resulted in a significant increase in terms of beer sales (Saban, 2001). Another salient example is American Express. American Express built a data mining model to examine millions of data and calculated “purchase scores”—customer’s propensity to make purchases, which not only provided merchants with valuable information, but also reduced American Express’ marketing expenses (Saban, 2001). As a result, research efforts made in data mining are warranted due to numerous successes accomplished while utilizing it.

Although data mining has been widely and successfully used in the domain of business operations, data mining in sport is just in its infancy (Fielitz & Scott, 2003; Lefton, 2003). In other words, the sports industry has generally been a poor and light user of data mining (Jutkins, 1998). It turned out that few papers related to data mining in the area of sport were found in sport journals. However, Lewis (2004) pointed out that data mining will become a critical component of selling and marketing sports teams. Similarly, the concept of data mining will become main stream in sports as an effective complementary marketing tool in the future (Martin, 2005). As a result, data mining warrants sport marketing researchers’ attention and efforts.

The purpose of the present article was to advocate the data mining approach to be utilized in the sport industry in order to effectively achieve sport organizations’ marketing goals. The organization of the current article is as follows: first, definitions and benefits of data mining were discussed; second, successful cases of application of data mining in sport were illustrated; third, proposed techniques of data mining that are appropriate and potentially useful in the sport industry were described followed by discussions and conclusions.

Definitions and Benefits of Data Mining

Data mining is a process of extracting previously unknown, valid, actionable, and ultimately comprehensible information from large databases and then using the information to make crucial business decisions (Cabena et al., 1998). From a different perspective, Kotler (2003) described data mining as “involving the use of sophisticated statistical and mathematical techniques such as cluster analysis, automatic interaction detection, predictive modeling, and neural networking” (p.54). Most of the definitions of data mining fall into these two aforementioned categories. As a result, from the combination of the two definitions, data mining is the process of using sophisticated mathematical or statistical models to extract valuable, valid, and actionable information from a database to accomplish an organization’s goals. (For similar definitions, also see Berry & Linoff, 2004; Hair, Anderson, Tatham, & Black, 1998).

The benefits of executing data mining are as follows: implementing up-selling, increasing season-ticket sales, monitoring season-ticket usage, raising transplanted-fan ticket sales, and executing cross-selling (James, 2004). Additionally, other benefits include (a) retaining current customers, (b) determining customers’ lifetime value, (c) developing relationships with customers, (d) improving delivery of sales promotion, (e) reinforcing consumers purchase decisions, (f) customizing consumer services, (g) facilitating marketing research, (h) profiling the customers, and (i) identifying the best customers for an organization (Aaker, Kumar, & Day, 2000; “Happy Customer,” 2004; Kotler, 2003).

Cases of Executing Data Mining in Sport

Although data mining has not been as widely employed in sport as it has in business, various successful applications in sport still exist. The following observations demonstrate how effective data mining can be for sport organizations and how sport organizations benefit from implementing data mining.

Dick and Sack (2003) conducted a study about effective marketing techniques in the NBA. They contended that a more effective and efficient way to ensure that advertising messages are received by the target markets was to use data mining. Several NBA teams such as the Cleveland Cavaliers, the Seattle SuperSonics, the Portland Trail Blazers, and the Miami Heat have successfully utilized data mining. The Cleveland Cavaliers created a database that includes customers’ names, addresses, telephone numbers, and other detailed information on the products purchased. By analyzing that database, the Cleveland Cavaliers consistently gave a follow-up call to those who bought tickets from Ticketmaster to determine whether they were interested in other games or events (Bonvissuto, 2005). The Seattle SuperSonics also developed a data mining program to raise its revenues and increase its season ticket holders. Additionally, the Portland Trail Blazers analyzed their customer database to help forecast advertising revenues and spot ticket-sale trends (Whiting, 2001). Finally, Miami Heat officials contend that data mining delivers an even more effective targeted audience than traditional advertising or traditional mass-media marketing. By using data mining, the overall Miami Heat season-ticket renewal rate in 2005 was expected to be around eight-five percent (Lombardo, 2005).

Data mining can also be applied by coaches to identify player patterns that box scores do not reveal, which helps win games by extracting relevant information from the database. In a 1997 playoff series, the Orlando Magic discovered Darrell Armstrong’s talent through data mining and inserted him into the starting lineup. The coach increased Armstrong’s responsibility in this series because the data showed that if Armstrong was on the court, the probability of an Orlando Magic win increased. Finally, the Orlando Magic won two consecutive games, and Armstrong personally won the Sixth Man Award in 1999 (Restivo, 1999). In addition, Brian James, assistant coach of the Toronto Raptors, employed a data mining application to know what kinds of plays opponents will use. Utilizing data mining in this way makes it easier for coaches to make decisions about when and how to position their players for maximum effect (Baltazar, 2000). Francett (1997) and Hudgins-Bonafield (1997) stated that data mining applications help analyze a huge amount of data to reveal winning player combinations for coaches. Moreover, the data mining approach to postgame analysis and improvement takes much less time than the traditional approach—forever rewinding the videotape. Namely, data mining makes analysis more efficient.

In summary, not only have the major league teams adopted data mining to increase ticket sale revenues and season-ticket holders’ renewal rates, but also sport coaches have utilized data mining to achieve their goals and objectives. Information extracted from records about players’ performance enables coaches to position and direct their players in a game. Consequently, data mining is a powerful technique with flexible applicability in sport.

Proposed Techniques for Data Mining in Sport

This section briefly presents an overview of the frequently used statistical models or techniques for data mining in terms of marketing, sales, and customer relationship management. The tasks that have been performed in the area of data mining are as follows: classification, estimation, prediction, and profiling (Berry & Linoff, 2004). The overview will include the definitions and the properties of the models along with the circumstance under which a model should be used. These models include (a) Discriminant Analysis, (b) Logistic Regression, (c) Decision Trees, (d) Artificial Neural Networks, (e) Collaborative Filtering, (f) Market Basket Analysis, and (g) Survival Analysis.

Discriminant Analysis

Discriminant analysis is a statistical method using linear functions to distinguish groups based on the independent variables. Discriminant analysis is the appropriate statistical technique when the dependent variable is categorical and the independent variable is continuous (Hair, Jr., Anderson, Tatham, & Black, 1998; Tabachnick & Fidell, 2001). It is an old and extensively used parametric statistical approach in classification. It works by comparing a weighted sum of the input variables to a constant value in the weights, and the constants are determined in such a way that the least square error of misclassification is minimized (Tabachnick & Fidell, 2001). Sport organizations can use it, for example, to classify customers as high-, medium-, or low-value customers in terms of their monetary contribution to the sport organization. This enables a sport organization to allocate marketing resources more effectively.

Logistic Regression

Logistic regression is a widely used technique for classifying subjects into two mutually exclusive exhaustive categories (Ratner, 2003). In logistic regression, the maximum likelihood estimation is employed to estimate the probability of classifying a subject into a group. The logistic regression is often used as a benchmark in the field of data mining when comparing the accuracy of model prediction. Professional sport teams can employ it to investigate the characteristics of the season ticket holders who end up terminating season ticket purchases and predict the probability of terminating season ticket purchases.

Decision Trees

Decision trees are one of the most popular methods in data mining and are frequently used for data exploration (Borisov, Chikalov, Eruhimov, & Tuv, 2005). Decision trees are a data mining technique that can be used to divide or partition a large collection of heterogeneous data into successively smaller sets of homogeneous data by using a sequence of simple decision rules with respect to a selected target variable (Berry & Linoff, 2004). In essence, decision trees are utilized to partition the data by employing independent variables to identify the subgroups that contribute most to the dependent variable (Chakrapani, 2004). This technique can also be used to classify and/or predict in the sport settings.

Artificial Neural Networks

Artificial neural networks (ANNs) are computer-intensive computational techniques that simulate the function of neural activity in a human brain (Chakrapani, 2004). To put it differently, ANNs are the computational tools for data exploration and model development to help identify patterns or structures in the data (Smith & Gupta, 2002). ANNs consist of three layers of processing units: input layer, hidden layer, and output layer. Since the final decision is binary (0 or 1), the value for the output layer is the predicted value of the decision. If the output value is 0.5 or above, then the decision is assumed to be an acceptance, while if it is 0.5 or below, then the decision is a rejection (Kumar & Olmeda, 1999). Compared to the traditional statistical methods, which are usually linear-based, ANNs use the non-linear approach (Cho & Ngai, 2003) and do not depend on a set of specified procedures. ANNs have been widely used in recent years as a classification technique and have been applied to a variety of business fields including bond rating, bankruptcy prediction, and stock market prediction. ANNs are superior over the regression-type models because of their ability to detect non-linear relationships and to adapt to changing input (Chakrapani, 2004). Sport organizations can use it to predict and classify their customers to better allocate marketing resources, i.e., more accurately segmenting the market and targeting custoemrs.

Collaborative Filtering

Collaborative filtering (CF) is a new technique in the area of data mining, assisting people to make choices based on other people’s choices. Similarly, Berry and Linoff (2004) described collaborative filtering as an approach to making and providing personalized recommendations. The collaborative filtering approach starts with evaluating a history of customer product preferences as well as demographics and ends up with determining similarities so that people who may like the same products will be put together (Berry & Linoff, 2004). Namely, this approach employs the reactions or preference of others within the database as well as their similarity to generating recommendations. Professional sport teams can utilize it to make recommendations or promote sporting events/sport merchandise to their customers based on what other customers purchased or consumed.

Market Basket Analysis

Market basket analysis is a data mining technique aiming to understand point-of-sale transaction data (Berry & Linoff, 2004). In other words, market basket analysis deals with such business problems as which products tend to be purchased together as well as which are most appropriate to promotion (Berry & Linoff, 2004). To perform basket analysis, three levels of market basket data are required: customers, orders, and items. Customer data refers to customer information including customer’s IDs, names, addresses and so on. Order data represents a single purchase event by a customer including the total amount of the purchase, payment type and whatever other data is related to and relevant to this transaction. Item data contains the price paid for the purchased item and the number of items (Berry & Linoff, 2004). Sport organizations can acquire benefits, such as deciding which product should have a promotion, the segmentation of customers, and the identification of the relationships among product items by using this technique.

Survival Analysis

Survival analysis method, also known as Event History Analysis, Reliability Analysis, Time to Failure and Duration Analysis, is developed mainly to deal with the probability that a certain event will occur but also deals with when it will occur (Harrison & Ansell, 2002). Namely, it deals with the time between events (Drye, Wetherill, & Pinnock, 2001). For example, survival analysis can identify when existing customers will re-attend professional sporting events based on their past game attendance records, which provides valuable information for professional sport teams in terms of promotional decision-making.

Discussions and Conclusions

Both advances in information technology and organizations’ needs have facilitated the upsurge of data mining. Even though a data mining approach has been successfully adopted to accomplish a number of organizations’ marketing goals and objectives in business, it is still in the infancy stage in the domain of sport. However, lack of use of data mining in the sport business does not mean that it is not applicable or important in the sport business. Instead, it is a great opportunity for sport businesses to adapt data mining and benefit from implementing it. With correct and appropriate use of data mining, sports organizations can benefit from the strategies and tactics developed from analyzing customer databases.

The development of models or algorithms in the area of data mining is upsurging to fulfill a variety of problems in practice. Various models have been commonly and successfully employed to solve real world problems. Tasks that are performed vary from model to model. Consequently, no rule of thumb exists that explains which model is the best model in solving a practical problem. In other words, the selection of the model depends heavily on the type of problems, the data structure an organization possesses, and the objective of an organization. Therefore, it is critical to have a thorough examination of organizational goals and data structure before choosing data mining techniques.

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U.S. Sports Academy2015-03-27T11:53:07-05:00June 4th, 2006|Contemporary Sports Issues, Sports Management, Sports Studies and Sports Psychology|Comments Off

Gender, Skill, and Performance in Amateur Golf: An Examination of NCAA Division I Golfers

Submitted by: Scott J. Callan, Ph.D. & Janet M. Thomas, Ph.D.

Abstract

In a previous study, it was found that male amateur golfers must possess a variety of shot-making skills to be successful and that relative to driving ability, putting skills and reaching greens in regulation contribute more to explaining tournament success. This present research extends these findings by expanding the investigation to analyze the performance determinants of both female and male amateur golfers. In so doing, we are able to test for the presence of gender-based differences in skill levels and in the relationship between skills and tournament performance. Using a sample of NCAA Division I male and female golfers who participated in tournament play during 2004-2005, our research offers two interesting observations. First, on average, male and female amateur golfers possess different levels of shot-making skills. Second, these disparate skills influence tournament performance differently across genders. Although the causality of these gender-based disparities cannot be identified with certainty, several plausible explanations are considered.

Introduction

In an earlier research study of amateur golfers, we empirically examine the relationship between a male golfer’s tournament performance and a set of shot-making skills (Callan and Thomas, 2004). This initial investigation was the first of its kind to focus on a sample of NCAA Division I male golfers. Statistically, those findings validate analogous research on the performance of professional golfers. What we discovered is that male amateur golfers, like their professional counterparts, must possess a wide variety of shot-making skills to be successful. Moreover, we found that, relative to driving ability, putting skills and reaching greens in regulation contribute more to explaining the variability in a player’s success. This present study extends that research to study both men and women amateur golfers and, in so doing, allows us to test for the presence of gender-based differences in skill levels or in any skill-to-performance relationship.

That gender-specific skill differences exist in the game of golf is explicitly recognized by the United States Golf Association (USGA), which is the governing body for the rules of golf. For example, in its rating system of golf courses, the USGA specifically defines a bogey golfer and a scratch golfer according to the golfer’s gender, as noted below (United States Golf Association, 2005).

Bogey Golfer:

“A male bogey golfer is a player who has a Course Handicap© of approximately 20 on a course of standard difficulty. He can hit tee shots an average of 200 yards and can reach a 370-yard hole in two shots at sea level.

A female bogey golfer is a player who has a Course Handicap© of approximately 24 on a course of standard difficulty. She can hit tee shots an average of 150 yards and can reach a 280-yard hole in two shots at sea level.”

Scratch Golfer:
“A male scratch golfer is a player who can play to a Course Handicap© of zero on any and all rated golf courses. A male scratch golfer, for rating purposes, can hit tee shots an average of 250 yards and can reach a 470-yard hole in two shots at sea level.

A female scratch golfer is a player who can play to a Course Handicap© of zero on any and all rated golf courses. A female scratch golfer, for rating purposes, can hit tee shots an average of 210 yards and can reach a 400-yard hole in two shots at sea level.”

Following these and other gender-specific distinctions made by the USGA, it is reasonable to expect that on average, male golfers are able to drive the ball longer distances off the tee, and female golfers have shorter approach shots to each green. Similar assertions, some with supporting data, are found in the literature, for example, Shmanske (2000) and Wiseman, Chatterjee, Wiseman, and Chatterjee (1994). Such observations motivate the need to learn how such gender-based skill differences translate into scoring performances under actual tournament conditions.

While no existing research papers examine this skill-to-performance relationship across male and female amateur golfers, there are studies that investigate the existence and degree of gender differences among professional golfers. Generally in such investigations, two questions are examined:

Do the data support the hypothesis that there are statistically different shot-making skills across male and female golfers?
How do shot-making skills influence a golfer’s tournament performance, and is this set of relationships gender specific?

To illustrate, we offer a few salient examples of this research and an overview of the approach used in each case.

Using performance measures for the 1992 season, Wiseman, Chatterjee, Wiseman, and Chatterjee (1994) investigate the influence of gender differences across golfers in the Professional Golfers’ Association (PGA), Senior PGA (SPGA), and Ladies PGA (LPGA). Overall performance is measured in their study by the average score per round of golf, and the shot-making skills considered are driving distance, driving accuracy, hitting greens in regulation, and putting. What these researchers find is that, on average, male PGA golfers drive the ball farther and hit a larger percentage of greens in regulation than female professionals. Driving accuracy was approximately the same across genders. Because the PGA and LPGA do not collect putting data in the same manner, no gender comparisons could be made about putting ability. However, using multiple regression analysis, Wiseman, et al. (1994) discover that the two most influential skills for female golfers are putting and hitting greens in regulations. These two skills explained 88 percent of the variability in an LPGA member’s average score per round. However, male PGA golfers need a more well-rounded game, as indicated by the importance of all shot-making skills in determining their tournament performance.

In a more recent study, Moy and Liaw (1998) examine golfers’ shot-making skills and tournament performance during the 1993 tournament season for the same professional tours used by Wiseman, et al. (1994). For the most part, Moy and Liaw’s findings agree with those of Wiseman, et al. (1994) regarding PGA golfers’ skills at driving the ball and reaching greens in regulation relative to LPGA golfers. However, they add a variable that captures sand saves, measured as the percentage of time a player gets out of a greenside bunker and scores par or better on the hole. They find that, on average, PGA golfers achieve a higher proportion of sand saves relative to their female counterparts in the LPGA. Given that no comparable putting statistic was available across the two tours, Moy and Liaw were unable to test for gender differences with respect to putting skills.

Shmanske (2000) statistically compares the skill-to-performance relationship for a sample of PGA golfers and LPGA golfers for the 1998 tournament season. In addition to the conventional shot-making skills, Shmanske constructed a comparable putting skill measure for each set of tour professionals. Overall, his results on gender differences are consistent with those of previous researchers. Specifically, he finds that male professional golfers drive the ball farther off the tee, have a higher sand save percentage, and demonstrate a higher putting proficiency. For the other key shot-making skills, namely driving accuracy and hitting greens in regulation, Shmanske observes no meaningful difference across genders.

While these studies have contributed to our understanding of gender differences in professional golf, no analogous investigations have been done for amateur golf. Recognizing the importance of this issue, we extend our previous study of amateur golfers (Callan and Thomas, 2004) to an analysis of skills and performance across male and female amateurs. Using the fundamental framework suggested by Wiseman, et al. (1994) and others, we use a two-pronged approach to our investigation. First, we statistically test for gender-specific skill differences at the amateur level. Second, we use regression analysis to assess the influence of a player’s shot-making skills on tournament performance and statistically determine if these skill-to-performance relationships are affected by gender.

Method

Sample

To conduct our investigation, we use a subset of NCAA Division I male and female golfers who participated in at least one tournament during the 2004–2005 season. Data on members of all Division I teams are not available. The colleges and universities represented in this study are identified in Table 1 along with the number of players on each team, the number of tournaments played during the season, and the average length in yards of the typical course on which the teams played. Most of these data are obtained from Golfstat, Inc. (2005), which is accessible on the Internet at www.golfstat.com.

Notice that the data presented in Table 1 suggest some important distinctions across genders. At a fundamental level, we observe that male golf teams, on average, comprise between 8 and 9 players, while female teams are smaller, averaging between 7 and 8 players. We also note that males play in slightly more tournaments than females, averaging 10.8 for males and 9.7 for females. Consistent with the USGA’s rating system, we also observe that the average male golfer plays courses that are almost 1,000 yards longer than those played by females, specifically 7,042 yards for men versus 6,104 for women. As a consequence, one might infer that male golfers place a higher premium on driving distance, while female golfers might focus more on developing their short game skills.

Measures

For each of the universities included in this research, Golfstat, Inc. collects and reports statistics for player skills and tournament performance. In this study, we use data for the 2004–2005 NCAA Division I tournament season for men and women teams from the same group of institutions. Just as we argue in our 2004 study, AVERAGE SCORE per round is a viable measure of tournament performance, since earnings are not relevant at the amateur level. Moreover, Wiseman et al. (1994) assert that correlation results are actually stronger when scoring average, as opposed to earnings, is used. As for the shot-making skills, we use a set of variables that collectively capture each player’s golf game from tee to green. Among these are measures of driving ability, fairways hit, greens in regulation, sand saves, and putting, which follows the approach used in Callan and Thomas (2004). We briefly discuss each measure in turn, starting with those capturing a player’s long game.

To capture each amateur’s ability to drive the golf ball, we use the variable EAGLES, defined as the cumulative number of recorded eagles (i.e., two strokes under par on any hole) a player makes each season. This variable serves as a proxy for driving distance, which is a statistic not reported by Golfstat. In support of this proxy measure, Dorsel and Rotunda (2001) report a positive correlation between a player’s driving distance and the number of eagles made. Related to driving distance is accuracy in driving the ball into the fairway. To measure this skill, we use the variable FAIRWAYS HIT, measured as the percentage of time a player drives the golf ball off the tee and into the fairway. We also define a variable called GREENS IN REGULATION (GIR) as the percentage of time a player reaches a green in the requisite number of strokes, specifically one for a par three, two for a par four, and three for a par five. This follows the work of Belkin, Gansneder, Pickens, Rotella, and Striegel (1994), who assert that GIR captures a player’s iron skill and success in reaching a green within the regulation number of strokes.

As for a player’s short game, we employ two skill variables that are commonly used in the literature. The first is SAND SAVES, which measures the percentage of time a player gets out of a greenside bunker and achieves a score of par or better. The second is a player’s ability to putt the ball into the hole once on the green. To capture this shot-making skill, we use the variable PUTTS PER ROUND, which measures the average number of putts a golfer makes per round of golf. This follows Belkin, et al. (1994).

Beyond the effect of shot-making skills, we hypothesize that a golfer’s overall performance is influenced by two other key factors – a player’s experience level and any associated team effects. Recognizing experience as a determinant of a golfer’s performance follows Shmanske (1992) and others. In the professional literature, experience is typically captured by the number of years a player has been a professional player. For this analysis of amateur golfers, we construct two experience variables. One is the variable ROUNDS, which is simply the number of tournament rounds completed by each player during the 2004–2005 season. This variable effectively measures a player’s short-term experience, because it captures the way each additional round played in a season adds to the knowledge a player can call upon in subsequent rounds. The second experience measure controls for longer-term cumulative experience and is modeled through a set of dummy variables that reflect the player’s academic age, specifically FRESHMAN, SOPHOMORE, JUNIOR, or SENIOR. The underlying expectation is that the more advanced is a player’s academic age, the more collegiate golfing experience has been gained and, therefore, the lower the expected average score.

The other theorized non-skill determinant of amateur performance is characterized as team effects. These are expected to arise from various factors, including the expertise and experience of the coach and the relative challenge of the courses played by the team. Coaches can directly affect the success of each player in myriad ways, such as through mentoring, leadership, instruction, and guidance. As a leader, the coach is responsible for setting team strategy and for determining the extent of each player’s tournament participation. As an instructor, the coach guides and motivates the development of each player’s athleticism and skills. Hence, collegiate golfers can achieve varying levels of success in the sport based in part on the expertise and experience of their coach, holding skill levels constant.

Likewise, a player’s amateur performance might be affected by the courses played by their team, because course venues, and hence their relative difficulty, vary across collegiate teams. Therefore, a member of a team that plays on relatively easy courses in a tournament season might enjoy a lower average score for that season, and, of course, the converse is true. To account for such team effects, we construct university-specific dummy variables for each player, whereby each identifies the team to which a player belongs.

Procedures

For this study, two conventional statistical procedures were used to analyze the skill and non-skill determinants of amateur golf performance, controlling for gender. One is the two-sample t-test, which was used to statistically examine the difference between mean values of male and female shot-making skills. The second procedure is the use of a multiple regression model that estimates the influence of skill, experience, and team effects on a player’s tournament score, holding constant all other score determinants. Ordinary least squares (OLS) is used to derive the regression estimates.

Results and Discussion

In Table 2, we present descriptive statistics for the sample of 179 amateur golfers, comprising 94 males and 85 females. At the collegiate level, tournaments generally consist of 3 rounds of golf, and each round comprises 18 holes of play. In our sample, the average NCAA Division I male golfer had an average score per round of approximately 75 strokes during the 2004–2005 season. In comparison, the average female golfer had a higher average score per round of about 79 for the season.

Based on the two experience variables, the average male amateur has more experience than the average female. For short-term experience, we observe that males play slightly more than 24 rounds of golf in the season, while females play fewer rounds, at about 22. As for longer-term experience based on academic age, approximately 61 percent of male team members are juniors and seniors, while the comparable value for females is lower at 47 percent.

Turning our attention to shot-making skills, we observe the following distinctions across genders. The average male golfer hits approximately 64 percent of fairways and reaches greens in the regulation number of strokes 60 percent of the time. Female golfers, on the other hand, hit 70 percent of fairways and reach greens in regulation 50 percent of the time. Over the course of a round, a male golfer makes slightly less than 31 putts, while the female golfer makes slightly more than 32 putts. For sand saves, the data show that the amateur male golfer makes par or better when hitting from a bunker 39 percent of the time, which is notably higher than the amateur female golfer, who has a comparable success rate of 29 percent. Lastly, over the course of the 2004–2005 season, the average male player makes 1.8 eagles, while the average female had 0.34 eagles, suggesting superior driving distance for males.

For each variable in the table, we also find the coefficient of variation for each gender group. As a measure of dispersion, this statistic contributes useful information about performance and skills across genders. Notice that for AVERAGE SCORE, the coefficient of variation is smaller for males than females. The same is true for all shot-making skill variables with the single exception of PUTTS PER ROUND. What these results imply is that there is a greater degree of competition among amateur male golfers than among females, an interpretation that follows Moy and Liaw (1998).

By simple observation, these data suggest that there may be statistically significant differences in skill levels across genders. To formally examine this theory, we use two-sample t-tests across the gender-specific skill variables and present our findings in Table 3. Not surprisingly, there are indeed statistically significant differences across genders (i.e., p < 0.0001) for all shot-making skills. Specifically, NCAA Division I male golfers, on average, possess superior shot-making skills relative to their female counterparts for EAGLES, GIR, PUTTS PER ROUND, and SAND SAVES. These findings generally agree with those found in research studies of professional golfers (Wiseman, et al., 1994; Moy and Liaw, 1998; Shmanske, 2000). The opposite relationship holds for FAIRWAYS HIT, the measure of driving accuracy, for which female collegiate golfers are statistically superior to males, on average.

While certainly of interest, the observation of gender-specific skill differences does not ensure that they translate into comparable changes in tournament performances. Investigation of this important issue requires the use of a multiple regression model. To that end, we specify a model to estimate the relationship between an amateur golfer’s average score and each of the determinants identified previously, specifically the set of five shot-making skills, the two experience measures, and the team dummy variables. To identify whether these determinants affect average score differently across males and females, we explicitly control for gender through the use of an interactive binary variable, FEMALE. This variable equals 1 if the golfer is female and 0 if male. It enters the model by itself as well as multiplicatively with each of the other explanatory variables. That way, each score determinant enters the model directly to represent males and multiplicatively with FEMALE to represent any incremental differences for females. In so doing, the estimation results quantify not only how shot-making skills, experience, and team effects influence average score but also whether those effects vary across genders.

The results of this multiple regression analysis are given in Table 4. Based on the adjusted R2 statistic, the regression model explains approximately 95 percent of the variability in a golfer’s tournament performance. Of particular interest are the gender-specific estimates that communicate the relative importance of each shot-making skill on overall performance, holding constant all other skills, team effects, and player experience. We also can assess the influence of all non-skill factors on a player’s average score independent of skill levels, and again, we can do so by gender. The estimated values for male golfers are listed in the first two columns of the table, and the estimates of any incremental differences for females are given in the second pair of columns.

To determine if the gender-based distinctions are collectively relevant, we conducted several F-tests, the results of which are shown at the bottom of Table 4. Other than the test for academic age variables for which gender differences are only marginally significant, all other F-tests indicate that gender differences exist and are statistically significant. These include tests for the overall model, for shot-making skills, and for team effects. These are important findings, which, to the best of our knowledge, have not yet been identified in the literature. They communicate far more than differences in skill levels across males and females. Rather, these results suggest that improvements in skill levels do not translate equivalently to better performance outcomes for both gender groups.

Next consider the individual results for each of the explanatory variables, starting with the set of shot-making skills. With the exception of FAIRWAYS HIT, each shot-making skill has a statistically significant influence on a player’s tournament performance, and each bears the expected algebraic sign. We also find that for several of these shot-making skills, gender differences exist and are statistically significant. Specifically, male golfers gain more tournament success than females from improving SAND SAVES. Conversely, increasing the GIR proportion statistically improves a female golfer’s tournament performance more than it does for a male. An analogous argument is relevant to reducing PUTTS PER ROUND. There are no apparent gender-based differences for EAGLES. Perhaps this outcome is due to the USGA establishing different tee boxes for males and females, which may correctly adjust for any inherent gender-based differences in driving ability.

As for the experience measures, the results suggest that short-term experience measured through ROUNDS does improve tournament performance and does so with no difference between the genders. For cumulative experience, captured through the academic age variables, FRESHMAN, SOPHOMORE, JUNIOR, and SENIOR, only the results for females are reasonable. Specifically, we find that female sophomores achieve higher average scores relative to seniors (the suppressed academic age variable). This makes sense, suggesting that greater collegiate experience improves performance. For males, the parameter on SOPHOMORE is significant, but its algebraic sign is negative. This outcome may be an artifact of the data sample, such as an unusually talented group of male sophomores in the 2004–2005 tournament season. It might also be related to the fact that in this sample, there are about 50 percent more male seniors than male sophomores, while for females there are 12.5 percent fewer seniors than sophomores.

We further find that team effects exist for certain universities. Golfers from East Tennessee State, on average, have higher average scores for the season than those from Vanderbilt University (the suppressed variable), regardless of gender. The same is true for players at the University of Texas. This implies that Vanderbilt University may have a better coaching staff and/or the Vanderbilt teams may play on less challenging courses. Interestingly, the team effect results also suggest a gender difference for teams at Indiana University and Kent State. In both cases, female teams perform at a lower level (i.e., have higher average scores), than their male counterparts, holding constant all other score determinants, including shot-making skills.

To quantify the effect of these differences, we follow Shmanske (2000) and compare the fitted value of average score for an arbitrarily defined male (e.g., a sophomore at Kent State University), with a predicted value that uses female parameter estimates with mean values of male score determinants. What we find is that the fitted value for average score is 73.88, but the predicted value is 75.38. This helps to underscore how the skill-to-performance relationship for females causes their scores to be higher than males, holding all else constant. Using the same approach for an analogously defined female (i.e., a sophomore at Kent State University) yields a fitted value for average score of 79.51, but a predicted value of 77.35, using mean values of female score determinants with male parameter estimates. Again, the difference indicates that the skill-to-performance relationship for males contributes to their scores being lower than that of females, holding skills, experience, and team effects constant.

That these collective results provide some evidence of gender-specific differences in how various factors affect performance is an interesting set of findings. That is, we now have a better sense of why amateur golf performance varies across gender groups. The commonly discussed observation of different average scores for males versus females seems not to be solely a function of differences in skill levels or years of experience but also a function of how changes in score determinants affect golfer performance.

How might we explain these differences? Although definitive answers are beyond the scope of this research, we offer three possible explanations based on selected analyses and theories that have been explored in the literature. These are based on: (1) differences in the degree of competition; (2) varying opportunities within and across university athletic programs; and (3) dissimilar physiological and psychological factors. We present a brief overview of each, which may encourage further investigation of these and other possible explanations.

First, based on the calculations of the coefficient of variation discussed previously and presented in Table 2, there seems to be a greater degree of competition among male amateurs. This is not unlike what Moy and Liaw (1998) find in their analysis comparing professional male and female golfers. More competition among males might encourage longer practice sessions and greater concentration, which in turn should yield higher skill levels and correspondingly greater improvements in performance as those skills develop. Somewhat related to this issue is that male amateurs might also be more highly motivated to practice and may compete more aggressively because of greater earnings potential at the professional level than females. This reality is based in part on higher purses offered on the PGA tour than on the LPGA tour. In fact, some studies of professional golf suggest that golfer success depends on effort, which in turn is influenced by the skewed distribution of tournament purses, meaning that performance improves when the stakes are higher (Ehrenberg and Bognanno, 1990; Shmanske, 2000).

Second, there may be disparate expertise in the coaching staffs and/or significant differences in course difficulty for male teams relative to female teams. This might be the case across institutions or it may arise within a university’s athletic department. The source of such differences is important, since the provision of unequal opportunities based on gender is a violation of Title IX of the Educational Amendment Act of 1972, in which Section 106.41 pertains specifically to athletic programs. Part C of that section identifies several factors that are to be considered when assessing the provision of equal opportunities to both sexes. These include the provision of equipment and supplies, the scheduling of games and practice time, the opportunity to receive coaching and academic tutoring, and the provision of practice and competitive facilities (U.S. Department of Education, 1972).

Third, it is often argued in both the common press and professional journals that gender differences in golf skills and performance might be attributable to physiological or psychological distinctions between males and females. The nature and validity of such arguments are being studied and intensely debated in the literature, and hence no definitive conclusion can be offered here. However, we can identify some of the more salient elements of these arguments, and suggest that they may help to explain the gender differences observed in our sample of amateur golfers.

Some researchers focus on psychological factors that may have differing effects on the play of male and female golfers. To illustrate, consider that Hassmen, Raglin, and Lundqvist (2004) identify a significant correlation between the variability of amateur male golfers’ somatic (or physiological) anxiety levels and the variability of their golf scores. However, Krane and Williams (1992) find no such relationship for their sample of amateur female golfers.

Others ascribe gender-based performance differences to physiological attributes. A common assertion in the professional golf literature is that men’s larger physical size and greater strength explains their ability to drive the ball further than females, and this may in turn explain the lower mean golf scores achieved by males. See, for example, Moy and Liaw (1998). However, others argue that such an assertion is incorrect, because driving a golf ball requires more skill than brute strength alone would provide (Shmanske, 2000). Indeed, in a sophisticated study of the biomechanics of golf, Hume, Keogh and Reid (2005), analyze the two main movements in golf – the swing and the putt, and show that golfers must possess strength, flexibility, and timing to achieve the distance and accuracy necessary for success. Hence, observed gender differences in shot-making skills might be linked to dissimilarities in any or all of these attributes. Some evidence of this hypothesis is offered by Myers, Gebhardt, Crump, and Fleishman (1993), who find within their tests of male and female physical abilities that males scored higher than females on tests of strength and stamina, while females scored higher on tests of flexibility. These findings might explain why male golfers generally drive the ball farther than women and why females typically achieve greater driving accuracy, results found in our analysis and others.

Conclusions

In our previous study of NCAA Division I male golfers, we identified the relationship between an amateur golfer’s tournament performance and various shot-making skills (Callan and Thomas, 2004). This present investigation advances this work by extending the analysis to both females and males. In so doing, we are able to examine whether the skill levels of amateur female golfers differ from those identified for males. Taking this one step further, we also are able to estimate the relationship between each shot-making skill and overall performance for males and females and specifically test for any statistically significant gender differences. To our knowledge, this is the first such study to examine gender differences in amateur golf.

Using a sample of NCAA Division I male and female golfers who participated in tournament play during 2004-2005, our empirical estimation and subsequent analysis supports two important conclusions. First, male and female amateur golfers, on average, possess dissimilar levels of various shot-making skills, and these differences indeed are statistically significant. Such dissimilarities are consistent with the literature examining gender distinctions among professional golfers. In this case, we find that on average, NCAA Division I male golfers possess superior shot-making skills relative to females for all shot-making skills except FAIRWAYS HIT, for which the opposite is true. Second, the manner in which shot-making skills influence tournament performance is not independent of gender. For example, male golfers achieve greater performance improvements by improving SAND SAVES, while females gain more from increasing the GIR proportion and from reducing PUTTS PER ROUND.

Both sets of results are of interest because they improve our understanding of the complexities of amateur golf tournament play. Moreover, through statistical testing, they validate anecdotal evidence of differing skill levels and performance outcomes across male and female collegiate teams. In so doing, the findings suggest the need for further research to learn more about these distinctions and, if necessary, to suggest changes in tournament play that recognize, and perhaps correct for, these disparities.

Although the root causes of gender-based differences in NCAA Division I golf cannot be identified, we do offer several plausible explanations based on research findings from the economics, sports medicine, and physiology literatures. First, we offer the possibility that the higher degree of competition among male golfers may incite more practice and more intensity of play, which in turn may translate into superior skills and/or tournament scores. Second, we suggest that skill and performance distinctions may be related to differences in facilities, coaching, and/or varying course venues. Further study is needed to identify these differences and to determine if there are associated implications for Title IX. Lastly, we consider the role of physiological and psychological factors in explaining gender-specific skill levels and performance in sports, an area that has been, and likely will continue to be, studied in earnest.

We believe that our study and its associated findings are of interest in their own right and contribute to the literature examining both professional and amateur golf. However, it is our hope that this work will have broader implications by encouraging new research in amateur golf and other sports aimed at learning more about the skill-to-performance relationship and the influence of gender and other factors on this important connection in amateur and professional sports.

REFERENCES

Belkin, D.S., Gansneder, B., Pickens, M., Rotella, R.J., and Striegel, D. (1994) “Predictability and Stability of Professional Golf Association Tour Statistics.” Perceptual and Motor Skills, 78, 1275-1280.
Callan, Scott J. and Thomas, Janet M. (2004) “Determinants of Success among Amateur Golfers: An Examination of NCAA Division I Male Golfers.” The Sport Journal, 7 (3). Available at http://www.thesportjournal.org/2004Journal/Vol7-No3/CallanThomas.asp.
Dorsel, T. N. and Rotunda, R. J. (2001) “Low Scores, Top 10 Finishes, and Big Money: An Analysis of Professional Golf Association Tour Statistics and How These Relate to Overall Performance.” Perceptual and Motor Skills, 92, 575-585.
Ehrenberg, Ronald G. and Bognanno, Michael L. (1990) “Do Tournaments Have Incentive Effects?” Journal of Political Economy, 96, 1307-1324.
Golfstat, Inc. (2005) “Customized Team Pages-Men and Women,” (accessed August 16, 2005), various teams.
Hassmen, Peter; Raglin, John S.; and Lundqvist, Carolina (2004) “Intra-Individual Variability in State Anxiety and Self-Confidence in Elite Golfers.” Journal of Sport Behavior, 27 (3), 277-290.
Hume, Patria A., Keogh, Justin, and Reid, Duncan. (2005) “The Role of Biomechanics in Maximizing Distance and Accuracy of Golf Shots.” Sports Medicine, 35(5), 429-49.
Krane, Vikki and Williams, Jean. (1992) “Path Analysis Examining Relationships among Cognitive Anxiety, Somatic Anxiety, State Confidence, Performance Expectations, and Golf Performance. “ Journal of Sports Behavior, December, 15(4), 279-96.
Myers, David C.; Gebhardt, Deborah L.; Crump, Carolyn E.; and Fleishman, Edwin A. (1993) “The Dimensions of Human Physical Performance: Factor Analysis of Strength, Stamina, Flexibility, and Body Composition Measures.” Human Performance, 6 (4), 309-44.
Moy, R. L. and Liaw, T. (1998) “Determinants of Professional Golf Tournament Earnings.” The American Economist, 42, 65-70.
Shmanske, Stephen (2000) “Gender, Skill, and Earnings in Professional Golf.” Journal of Sports Economics 1(4), 385-400.
United States Department of Education. (1972). “Title IX of the Educational Amendment Act.”(Available at http://www.ed.gov/policy/rights/reg/ocr/edlite-34cfr106.html)
United States Golf Association (USGA) (2005), “Course Rating™ Primer.” (Available at http://www.usga.org/playing/handicaps/course_ratings/course_rating_primer.html, accessed April 25, 2006.)
Wiseman, F., Chatterjee, S., Wiseman, D., and Chatterjee, N. (1994) “An Analysis of 1992 Performance Statistics for Players on the U.S. PGA, Senior PGA, and LPGA Tours.” In A. J. Cochran and M. R. Farrally (Eds.), Science and Golf: II. Proceedings of the World Scientific Congress of Golf. London: E & FN Spon. 199-204.

TABLE 1 UNIVERSITIES INCLUDED IN THE STUDY
	MEN’S GOLF TEAM	WOMEN’S GOLF TEAM
UNIVERSITY	NUMBER OF GOLFERS	NUMBER OF TOURNAMENTS	AVERAGE YARDS PER TOURNAMENT (STANDARD DEVIATION	NUMBER OF GOLFERS	NUMBER OF TOURNAMENTS	AVERAGE YARDS PER TOURNAMENT (STANDARD DEVIATION)
Coastal Carolina University	10	11	6971 (119)	5	10	5994 (75)
Ea. Tenn. State University	9	10	7029 (125)	7	9	5978 (106)
Fresno State University	8	14	6924 (238)	8	11	6080 (121)
Indiana University	9	11	7035 (129)	8	10	6120 (111)
Kent State University	8	10	7017 (156)	7	11	6111 (123)
University of Kentucky	7	10	7091 (227)	11	11	6162 (377)
University of New Mexico	8	11	7128 (330)	6	8	6090 (177)
University of So. California	9	11	6934 (186)	10	9	6115 (169)
Texas A & M University	10	11	7066 (213)	9	10	6187 (125)
University of Texas	8	10	7218 (224)	8	9	6169 (186)
Vanderbilt University	8	10	7045 (215)	6	9	6139 (171)
Average (std. deviation)	8.5 (0.93)	10.8 (1.17)	7042 (85.34)	7.7 (1.8)	9.7 (1.01)	6104 (66.99)

Source: Golfstat, Inc. (2005) and individual team Web pages.

TABLE 2 BASIC DESCRIPTIVE STATISTICS
	MEAN	STANDARD DEVIATION	MINIMUM	MAXIMUM	COEFFICIENT OF VARIATION
VARIABLE	MALE (N=94)	FEMALE (N=85)	MALE	FEMALE	MALE	FEMALE	MALE	FEMALE	MALE	FEMALE
Score	74.97	79.23	2.22	3.53	69.95	73.50	81.33	94.45	0.030	0.045
Eagles	1.80	0.34	2.23	0.61	0.00	0.00	9.00	2.00	1.239	1.794
Fairways Hit	0.64	0.70	0.08	0.09	0.36	0.47	0.86	0.88	0.125	0.129
Greens in Regulation	0.60	0.50	0.08	0.10	0.33	0.16	0.81	0.65	0.133	0.200
Putts per Round	30.83	32.31	1.42	1.29	23.00	29.84	35.33	35.71	0.046	0.040
Sand Saves	0.39	0.29	0.15	0.13	0.00	0.06	1.00	1.00	0.385	0.448
Rounds	24.11	22.31	12.83	8.89	3.00	3.00	43.00	36.00	0.532	0.398
Freshman	0.17	0.29	0.38	0.46	0.00	0.00	1.00	1.00	2.235	1.586
Sophomore	0.22	0.24	0.42	0.43	0.00	0.00	1.00	1.00	1.909	1.792
Junior	0.28	0.26	0.45	0.44	0.00	0.00	1.00	1.00	1.607	1.692
Senior	0.33	0.21	0.47	0.41	0.00	0.00	1.00	1.00	1.424	1.952

NOTE: Basic statistics for each university dummy variable are available from the authors upon request.

TABLE 3 MEAN DIFFERENCES IN SHOT–MAKING SKILLS ACROSS GENDERS
Variable	Mean Difference (Male – Female)	Standard Error*	t-statistic	p-value
Eagles	1.4567	0.2501	5.82	<.0001
Fairways Hit	–0.0540	0.0128	–4.24	<.0001
Greens in Regulation	0.0982	0.0132	7.46	<.0001
Putts per Round	–1.4810	0.2036	–7.27	<.0001
Sand Saves	0.0984	0.0216	4.56	<.0001

*Standard error calculation assumes male and female populations have equal variances.

TABLE 4 REGRESSION MODEL PARAMETER ESTIMATES
DETERMINANTS	PARAMETER ESTIMATE	INTERACTION TERMS (FOR FEMALES)	PARAMETER ESTIMATE
Intercept	69.40 ***	Female Intercept	–15.24 ***

Shot-Making Skill Variables		Shot-Making Skill Variables
Eagles	–0.10 **	(Female)(Eagles)	0.22
Fairways Hit	0.05	(Female)(Fairways Hit)	–0.40
Greens in Regulation (GIR)	–21.86 ***	(Female)(GIR)	–4.08 *
Putts per round	0.64 ***	(Female)(Putts per Round)	0.53 ***
Sand Saves	–1.32 **	(Female)(Sand Saves)	1.68 **

Experience Variables		Academic Age Variables
Rounds	–0.03 ***	(Female)(Rounds)	0.01
Junior	0.05	(female)(Junior)	–0.23
Sophomore	–0.41 *	(Female)(Sophomore)	0.68 *
Freshman	0.08	(Female)(Freshman)	0.02

Team Variables		Team Variables
Coastal Carolina	0.44	(Female)(Coastal Carolina)	0.95
East Tennessee State	1.11 ***	(Female)(East Tennessee)	0.49
Fresno State	0.58	(Female)(Fresno State)	–0.14
Indiana University	–0.21	(Female)(Indiana University)	1.77 **
Kent State	–0.34	(Female)(Kent State)	1.13 *
Univ. of Kentucky	0.56	(Female)(Univ. of Kentucky)	0.35
Univ. of New Mexico	0.32	(Female)(Univ. of New Mexico)	0.07
Univ. of Southern California	0.44	(Female)(Univ. of Southern California)	–0.55
Texas A&M University	0.06	(Female)(Texas A&M University)	1.01
University of Texas	0.99 **	(Female)(University of Texas)	–0.56

F-Statistic	82.75 (p-value< 0.001)

R-Squared	95.87
Adjusted R-Squared	94.71
F-Statistic (no gender differences overall)	2.83 (p-value < 0.001)
F-Statistic (no gender differences with respect to shot-making skills)	2.84 (p-value = 0.012)
F-Statistic (no gender differences with respect to academic age)	2.10 (p-value = 0.103)
F-Statistic (no gender differences with respect to team variables)	2.39 (p-value = 0.012)

* significant at the 0.10 level, assuming a one-tailed test of hypothesis for skills and two-tailed test elsewhere
** significant at the 0.05 level, assuming a one-tailed test of hypothesis for skills and two-tailed test elsewhere
*** significant at the 0.01 level, assuming a one-tailed test of hypothesis for skills and two-tailed test elsewhere

U.S. Sports Academy2015-03-27T11:50:33-05:00June 3rd, 2006|Contemporary Sports Issues, Sports Exercise Science, Sports Studies and Sports Psychology, Women and Sports|Comments Off

Letter to the Editor

Submitted by: Don Anthony

Thinking About Olympism and the USA

Editor’s Note: Don Anthony, a noted Olympic historian from Great Britain and long-time friend of the United States Sports Academy, wrote this article for USSA President Dr. Thomas P. Rosandich. The Sport Journal is publishing this piece as a letter to the editor.

25 years ago Dr. William Renato Jones died. He was a Springfield graduate. Born in Rome and speaking Italian as a first language, he had British nationality, a Swiss mother, a Scottish father, and spent much of his working life in Germany! His first job on leaving Springfield was for the YMCA in Adana, Turkey. Later, for the YMCA, he organized a global congress in Paris which brought him to the attention of Unesco. This UN body appointed him
director of the Unesco Youth Institute in Gauting, near Munich. Concurrently, as a volunteer, he founded and became general secretary, of the world body
for basketball – FIBA. Furthermore, he was the obvious candidate to lead – again as general secretary – Unesco’s consultative body for sport – ICSPE
(the International Council for Sport and Physical Education) in l960. On retirement from his basketball post, he was invited to be honorary life president.
He refused!

“I will accept the title honorary life secretary” he said – and so it was – “anyone in sport knows it is the secretary who is the kingpin.”
Surrounding Jones were other formidable Springfield people such as Frank Hepp from Hungary. There was Ernst Jokl from Kentucky University; and the
President of ICSPEP, Philip Noel-Baker. Philip became unique – an Olympian – silver in the 1500 metres in l920 at Antwerp -and Nobel Peace Prize in
l959 for his work on disarmament. Philip too, had USA roots: he began his academic career in l907 at Haverford – then the major Quaker (Society of Friends) academy in North America. Another polyglot with a Canadian father and a Scottish mother, he spoke well in French, German, Greek and Italian. This last language he learned on the Serbian front in World War I when he worked as a pacifist with the Friends Ambulance Unit. The Italian government awarded him their highest honour for “bravery in battle.” Despite this, no matter how hard I tried, the organizers of the recent Turin Winter Olympic Games ignored my pleas that they should honor his unique qualifications for mention and commemoration! I had the wonderful pleasure of working closely with Jones and Philip for some 20 years from l960 onwards. Every moment with them was “an education and a joy” I once explained to my wife – to excuse my constant absences on duty for global physical education and sport. Strangely enough, the educational energies of the IOC were rather dormant between and 1960.and l980. In this time, Unesco began to lead matters and eventually the World Conference of Ministers of Sport came into being.

I look again at my profession of “physical education” – with its modern roots in the USA. These were summed-up marvellously by John Lucas in his article
in November 2004, for the Journal of the International Society of Olympic Historians (ISOH): “The Great Gathering of Sport Scientists; the l904 St. Louis
Olympic Games Exposition Fair Physical Education Lectures.” I think of that other significant Springfield man Harold Friermood who was “always there” as world P.E. blossomed. Harold even attended the first meeting of the Amateur Volleyball Association of Great Britain, founded in London at the YMCA
headquarters in l955.

As my researches grew in Olympic matters, I discovered Charles Waldstein (Columbia, I think) and then Heidelberg and Cambridge (England) ending up as Slade Professor of Art at Cambridge and concurrently Director of the Fitzwilliam Museum in that city, and Director of the American School of Classical Studies in Athens. A confidant of Coubertin, he arranged meetings with the Royal Princes in Athens in l896. He first met Coubertin during the young Frenchman’s studies of English schools and universities in l886. They met again in l896 when Waldstein pistol-shot for the USA in the Games. Charles (then Walston and a British national) was chairing the Arts Commission of the l924 Paris Olympic Games near the end of his life. His grandson, Oliver, still farming near Cambridge, was persuaded (by me) to donate his grandfather’s archives to the IOC Museum in Lausanne where they still lie. Again I was startled by the Athens American School of Classical Studies attitude in 2004 – when they showed no interest in honouring their previous Director!

In l889, at the famous Boston Conference on Physical Training, Coubertin delivered a paper. On the same platform was the Earl of Meath from England, the man who got PE on to the British education statute books in Parliament. In l89l both were elected members of the Wenlock Olympian Society (WOS) – in Shropshire, England. Brookes, the founder of the WOS was keenly aware of new developments in the USA, and also in Russia, France, Germany and elsewhere. David Young of Florida wrote a major book on this Olympian root in his distinctive l996 book “The modern Olympics – a struggle for survival”.

Coubertin’s major USA friend and colleague was of course William Sloane (Columbia and Princeton). Charles Battle tells me that Atlanta created a sculpting of Sloane for presentation to the IOC. Its whereabouts need research in Lausanne? It was the same with a sculpting of Philip Noel-Baker “Man of Sport- Man of Peace” given in l986 to the IOC as part of the Birmingham (England) bid. This lurked in the cellars at Lausanne until recent years when it was fished out and now graces the entrance of the Sports Court of Arbitration in Lausanne – a fitting place for one who was a master in international law! In the intervening years a copy of the sculpting was purchased by the University of Hiroshima in Japan. Philip attended the atom-bomb memorial meeting in August at Hiroshima whenever he could. At his last attendance, he was given only one minute to speak before the sounding of the peace gong in a square packed with 200,000 people. Pushing away his prepared speech Philip said “Say after me – no more war – no more Hiroshima!” Boom!!! What a man – what a brain – at nearly 90 years of age. At his last major speech, at the IOC Congress in Baden-Baden (l981), he was granted only 3 minutes under any other business. Taking 11 minutes, much to the consternation of the organizers, he got a three minute mass standing ovation for his last words -“If the IOC…can bring sport for all to the whole world – especially the developing world – I will nominate them for the Nobel Peace Prize!” Indeed I have a letter from the Nobel Committee, saying that such a nomination was made prior to his death in l982. I was left his two ice-axes (he was a keen mountaineer). I gave one to the Winter Sports Museum of Sarajevo and the second to the Winter Sports Museum of Pyongchaeng in Korea. I hope the latter might one day help to rebuild the former. Indeed plans are afoot to try to restore the energy of the Inter-University Centre in Dubrovnik which, organized seminars in the l980’s to celebrate Philip’s work for sport, peace, and development – and to look again at the feasibility of the Noel Baker Medals for outstanding examples of sport and international understanding. The first medals were awarded in l984 to the Sarajevo Winter Olympic Games Organizing Committee – and to President of the IOC at that time, Juan Antonio Samaranch.

For a moment, back to Atlanta: Local man George Hirthler, working with the Pierre de Coubertin International Committee (CIPC), managed to fund the erection of a delightful monument to Coubertin in a main square in his city. I wonder if it is still there and whether its purpose is still recognised and celebrated.

Further thoughts about the USA-Olympic links begin to flood back: John Lucas one asked me to investigate the sermon given in St. Paul’s cathedral by the “Bishop of Pennsylvania” (in Coubertin’s memoirs). It turned out that it was indeed a Bishop – but of Central Pennsylvania – one Ethelbert Talbot. It was this sermon which inspired Coubertin’s own description of the true Olympic ethos “The honor is less in winning than in taking part.” How we need to restate, and restate, and restate, this message – in today’s Olympic world consumed as it is by medal mania. The name John Lucas appears everywhere – even as Honorary Consul for Albania! What a wonderful testament for Olympism in the USA, and the world, his life has been – and still is. I thank him for much inspiration and education. It was much to him that the USOC National Olympic Academy flourished. Indeed in the early l980’s we were so taken with this model that we started the British National Olympic Academy (l982 – a one day affair). This is now institutionalised and we regularly have the problem of over-subscription – a long weekend – 120 people maximum. Two regular USA friends have been, and are, Elizabeth Hanley of Penn State and Robert Merchikoff of Seattle.

My own studies in physical education were nourished by USA texts starting in 1946. I remember Thomas Cureton visiting us at Loughborough in the tests – and- measurement days. When we started the British volleyball association we lacked knowledge of the high-level game. Fortuitously, one Victor Tseirov of the USSR Embassy rang me one day to say that “I am scientific attaché at the Embassy – but mainly because my father pushed me; in my heart I am one percent scientific attaché and 99 percent volleyball enthusiast.” At roughly the same time one John Gay (USA national team player) contacted us from the London Ruislip USAF base to offer us his expertise – and facilities! We now look forward to preparing British volleyball teams for the London 2012 Olympic Games. Such are the fortuitous chances of life – serendipity. Let us glory in it and use it more. In my own field, I have long thought that – with USA leadership – we could enrich the recently restored Great Library of Alexandria. The ancient library linked Pergamon with Alexandria. The revived library could be aided to establish a sports section to record the whole area of African sport – and its possible contribution to health and development in that continent – the one of greatest need. I write this thinking of my ancient namesake giving the Pergamon library to Cleopatra (as a love token!).

Out of all this nostalgia springs a thought. That 2007 will be the centenary of Philip Noel-Baker’s entry into academic life at Haverford – USA. Can we not celebrate this in different ways – e.g. a U.S. seminar to record and examine the USA’s past role in Olympic matters – its current status – and its future potential at one of the USOC’s centers?

On the global level to assist Dubrovnik in its recovery – a session at the IUC could be held. An initial 2006 step could be a small planning meeting at the IUC to also embrace the life and work of William Jones – who’s chosen Crown Prince in FIBA. Boris Stankovic – now of Olympic rank – could play an appropriate role.

I often remind audiences that the Olympic idea came out of moves to strengthen physical education; of the first 12 members of the IOC, six were educationists; Coubertin was the foremost comparative physical educationist of his time. Today in the fight against the dumbing-down of sport these values need urgently to be restored and the major world-power, the USA, has a special responsibility to us all.

U.S. Sports Academy2015-03-27T11:48:22-05:00June 2nd, 2006|Contemporary Sports Issues, Sports Management|Comments Off

Preferred Player Characteristics and Skills of Division I Men’s Basketball Coaches

Submitted by: William F. Stier Jr. & Robert C. Schneider

Abstract

A national survey of selected men’s basketball coaches, at the NCAA Division I level, revealed how essential the respondents felt certain work ethic characteristics were for successful basketball players on their team. The respondents also revealed how important specific skills or talents were for the success of men’s NCAA men’s Division I basketball programs. The survey was completed by means of a 36-item Likert scale questionnaire. This investigation determined to what degree NCAA Division I coaches should seek specific work ethic characteristics and physical skills/talents in their players.

Introduction

College basketball coaches seek athletes with high caliber skills and specific basketball talent as well as a good overall work ethic. Although there is plenty of antidotal information regarding the type of desirable skills and talent desirable in the world of basketball, there has been very little definitive research done in terms of determining exactly what skills, talent and examples of work ethic are highly rated by coaches of men’s basketball at the NCAA Division I level of competition (Stier, 1997).

Successful and effective coaching is a highly complex and multi-dimensional enterprise (Jones, Housner, and Kornspan, 1997). It is very important, according to Owens & Stewart (2003) to be able to understand individual squad members’ physical, emotional, social and cognitive needs if the team is to be successful, that is, win. In a study by Forman (1995), it was determined that college basketball coaches need to make a commitment to each player’s growth and improvement in the sport if the team, as a collective unit, is to emerge victorious in actual competition,

In a study of elite athletes by Mallet & Hanrahan (2004), it was determined that players recognize the need to train hard to be winners. Training hard implies working diligently to improve both individual and team performance in order to produce meaningful results when it counts, in actual competition (Laios and Theodorakis, 2002). This emphasis on both individual and team (collective) training is reinforced by Bursari, (2000).

Elite athletes exhibit significant effort in games as well as in practice and this dedication extends to off-season work habits (Adams, 1996). The ability and willingness to work hard as well as to work harder are important examples of an athlete’s ability that can lead to success on the proverbial playing field, especially if the coach believes in the athlete and is successful in motivating the individual player to work harder (Jowett, 2003).

Literature presented by Stier (1998) included several major factors that distinguish consistent winning teams from teams that consistently lose: (a) better skilled athletes and (b) better conditioned athletes. The importance of adequate strength and conditioning was emphasized by Laios and Theodorakis (2002). Dirks (2000) studied control variables of team performance representing elements of the coach and players’ talent. And, in 1999, Pascarella et al. looked at the topic of physical energy that is required of athletes in actual competition.

Purpose of the Study

The purposes of this study were two-fold. The first purpose was to determine the essentiality of selected work ethic characteristics on behalf of athletes. And, the second was to determine the importance of specific skills or talents of athletes to the success of men’s NCAA men’s Division I basketball programs. In summary, this investigation sought to determine to what degree coaches should seek in their Division I men’s basketball players’ specific work ethic characteristics and physical skills/talents.

Methods

The Questionnaire

A survey instrument was developed from the existing current literature related to work ethic characteristics of players as well as specific athletic skills and talents that might have an effect on the success or failure of NCAA Division I men’s basketball programs. An extensive literature search found basketball related articles in which work ethic characteristics and various athletic skills and talent for athletes engaged in basketball were identified and which served as the foundation for the 36 items included on the Likert scale statements of the questionnaire.

Of the total of 36 Likert scale statements, 15 related to work ethic characteristics while 21 related to athletic skills and talents that might have an impact upon the success or failure of Division I men’s basketball programs. For work ethic characteristics, respondents were instructed to circle the number that corresponded with the degree of essentiality they believed most accurately depicted the impact that selected work ethic characteristics have upon the success [winning games] of their basketball programs and had the following categories of essentiality from which to choose: 5 – Very Essential, 4 – Essential, 3 – Neither Essential nor Unessential, 2 – Unessential, and 1 – Very Unessential. For the second category, specific athletic skills and talents, the coaches had the following Likert scale options which included the following choices of importance from which they were asked to circle the corresponding number: 1 – Very Important, 2 – Important, 3 – Neither Important nor Unimportant, 4 – Unimportant, and 5 – Very Unimportant.

To help address content validity a draft of the survey questionnaire was completed by five Division I head basketball coaches who were determined to be expert coaches for the purpose of gaining feedback regarding the instrument. In order to be deemed an expert coach, the coaches were required to have coached men’s basketball at the Division I level for at least 10 years and won at least 75% of their games during that time. After receiving the feedback from the expert coaches, appropriate suggestions and recommendations were incorporated into the final version of the survey instrument which was then utilized in this national study. The University’s Internal Review Board reviewed the final, revised version of the instrument and gave its approval.

The subjects for this national survey included all 315 men’s NCAA Division I head basketball coaches whose names and addresses were provided by the NCAA national headquarters. Of these, 118 completed and returned usable surveys generating a return rate of 37.5%.

Results

Work Ethic – Training

The category of work ethic contained two general categories, (a) training and (b) effort.

Of the eight characteristics related to training, six pertained directly to players’ training; two pertained to sacrifices made by athletes and the remaining two dealt directly with the athletes’ state of physical conditioning. Training hard was deemed to be the single most essential characteristic for winning, according to the respondents. In fact, 74.6% of the coaches indicated that training hard was very essential to winning while 25.4% classified it as essential.

Strength and conditioning was likewise thought to be very essential by a large percentage of coaches (72.9%), and deemed essential by 25.4%. Individual training was the only other work ethic characteristic thought to be very essential by more than half of the coaches (52.5%), while another sizeable group of coaches (44.1%) also classified this characteristic as essential. Table 1 shows all eight work ethic characteristics and how the respondents classified each in terms of how essential they are to winning basketball games at the Division I level.

Work Ethic – Effort

Of the seven characteristics identified in the survey as being related to effort, three dealt directly with effort, two addressed the conditioning efforts of players, while the remaining two involved how essential were the players’ work habits—in the eyes of the responding coaches. Individual player’s effort, in general, was consistently valued very highly by coaches with five of the seven categories deemed to be very essential by more than sixty percent (64.4%) of the respondents. Only two categories relating to effort were deemed to be very essential by less than half of the coaches, and both related to off-season activities. These were (a) player’s off-season conditioning efforts (45.8%) and (b) player’s off-season work habits. Table 2 illustrates how essential the coaches viewed these seven work ethic characteristics that related to effort.

Athletic Skills and Talents – Performance and Abilities

The section on athletic skills and talents contained two general categories, (a) performance/skills and (b) basketball talent. Of the eight performance skills identified in the investigation, two related directly with performance, two pertained directly to individual and team play while three dealt with abilities of players. The remaining skill is related to the physical energy that a player exudes. Only three performance skills were deemed to be very important by more than half of the coaches responding to the survey: (a) game performance (83.1%), (b) team oriented play (67.8%), and (c) physical energy (66.1%). Table 3 illustrates how the respondents rated each of the eight performance/abilities in terms of their importance or unimportance to winning Division I basketball games.

Athletic Skills and Talents – Basketball Talent

Of the 21 basketball talent categories that the coaches rated in terms of importance, 7 items related to physical talent while the remaining 14 focused on specific basketball skills. Defense, with 57.6% of the coaches, and passing, with 55.5%, were the only talent items that more than half of the respondents rated as very important. Two other talent categories are worth noting in that both (a) overall fundamental base and (b) rebounding were the only two talent categories that all the respondents classified as very important or important. Table 4 shows how the coaches classified all of the categories of basketball talent relative to their importance of unimportance in terms of their impact upon winning.

Conclusions

This national investigation sheds light on how Division I basketball coaches view the essentiality of specific work ethic characteristics and the importance these coaches place on specific skills or talents identified as having impact upon winning in competition. The results have implications for coaches in respect to what qualities, characteristics, skills and talents to look for in terms of potential recruits as well as current team members.

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