Sports Studies and Sports Psychology – Page 48

The Comparison of Maximal Oxygen Consumption Between Seated and Standing Leg Cycle Ergometry: A Practical Analysis

Submitted by: A. Bosak, J. Green, T. Crews & R. Deere

Abstract:

Because previous studies have been equivocal, the current study compared VO₂max between seated and standing cycle ergometry protocols in male (n=14) and female (n=22) volunteers of average cardiovascular fitness. All subjects completed maximal exertion seated (SIT) and standing (STD) cycle ergometry GXT protocols at 60 rev/min (rpm), with resistance increased by 30 Watts/min. SIT required individuals to remain seated for the duration of the test until achieving volitional exhaustion. For STD, subjects performed seated cycling until they felt it was necessary to stand to continue the GXT. Subjects were then required to stand and perform “standing cycling” (resistance increased 30 Watts/min) to volitional exhaustion. VO₂max (ml/kg/min), peak HR (b/min), peak RER, and peak V_E (L/min) were compared between SIT and STD using MANOVA. Results were considered significant at p ≤ 0.05. VO₂max_STD (37.9 ± 8.0) was significantly greater than VO₂max_SIT (36.8 ± 6.6), while HR_STD (190 ± 9.5) was significantly greater than HR_SIT (187 ± 9.6). VO₂max_STD was, on average 2.0% greater than VO₂max_SIT, with a range of -16.9 to +17.4%, while HR_STD was, on average 1.2% greater than HR_SIT, with values ranging from -5.6 to +7.4%. VE_STD (86.0 ± 31.6) was not significantly different than VE_SIT (82.6 ± 26.8), while RER_STD (1.21 ± 0.096) was significantly lower than RER_SIT (1.23 ± 0.065). Results suggest that the utilization of a standing protocol should be considered when cycle ergometry is the selected testing mode. Future research should seek to determine the characteristics of subjects who do/do not benefit from a standing cycle ergometry protocol.

Introduction:

Maximum oxygen consumption (VO₂max) represents the highest rate at which oxygen can be consumed and utilized to produce energy sustaining aerobic activity. VO₂max is regarded as the gold standard for assessing aerobic fitness. It is acknowledged as a substantial backbone for prescribing appropriate exercise and training intensities. Therefore, accurate determination of VO₂max is vital.

Throughout history, VO₂max has been assessed during numerous exercise modes such as treadmill, rowing, and cycle ergometry. Different modes and protocols have been compared to determine which protocol and/or mode permits the highest VO₂max (Beasley, Fernhall, and Plowman, 1989; Coast, Cox, and Welch, 1986; Faria, Dix, and Frazer, 1978; Lavoie, Mahoney, and Marmelic, 1978; McArdle, Katch, and Katch, 2006; Mckay and Banister, 1976; Moffat and Sparling, 1985; Pivarnik, Mountain, Graves, and Pollock, 1988; Ricci and Leger, 1983; and Welbergen and Clijsen, 1990). Compared to seated cycle ergometry, treadmill exercise usually permits a higher VO₂max due to the activation of more muscle mass and less pronounced leg fatigue. One of the more common VO₂max tests implemented in exercise physiology labs is the Bruce treadmill protocol (Beasley et al., 1989; Fernhall and Kohrt, 1990; Kelly et al., 1980; Lavoie et al., 1978; Marsh and Martin, 1993; Moffat and Sparling, 1985; Ryschon and Stray-Gunderson, 1991; Verstappen, Huppertz, and Snoeckx, 1982; and Welbergen and Clijsen, 1990). Despite greater VO₂max values obtained during treadmill exercise, cycle ergometry has many advantages, including preference of subjects to use the cycle ergometer during a VO₂max test, adaptability, safety, ease of calibration, and subjects’ tolerance of non-weight-bearing exercise (Mckay and Banister, 1976; Pivarnik et al., 1988). Therefore, exercise scientists have continued to explore ways to manipulate cycle ergometry protocols to allow subjects to attain the highest possible “cycling” VO₂max values (Faria et al., 1978; Heil, Derrick, and Whittlesey, 1997; Kelly et al., 1980; Lavoie et al., 1978; McKay and Banister, 1976; Moffat and Sparling, 1985; Nakadomo et al., 1987; Tanaka and Maeda, 1984; and Tanaka, Nakadomo, and Moritani, 1987).

Montgomery et al. (1971) concluded, for five male subjects, that VO₂max during standing cycle ergometry (57.35 ml/kg/min) was not significantly different than seated cycle ergometry (49.30 ml/kg/min). Tanaka et al. (1996) also found no significant differences between seated (66.4 ± 1.6 ml/kg/min) and standing (66.4 ± 1.7 ml/kg/min) VO₂max during level cycle ergometry for seven competitive male cyclists. Conversely, in a sub-study, Tanaka et al. (1996) found, for seven male subjects cycling at a 4% incline, a greater VO₂max (2.82%) for standing (56.8 ± 0.9 ml/kg/min) vs. seated (55.2 ± 0.9 ml/kg/min) cycle ergometry. Also, Ryschon and Stray-Gundersen (1991) concluded, with 10 cyclists (eight males and two females), that standing submax VO2 values were 10.8% higher than seated values during 4% incline standing cycling. Kelly et al. (1980) determined, for 12 male university students, that standing (57.91 ± 5.74 ml/kg/min) during a cycle ergometry VO₂max test produced a significantly greater (4.4%) VO₂max compared to the seated position (55.12 ± 6.98 ml/kg/min). Also, Nakadomo et al. (1986) concluded that, in 22 male subjects, VO₂max was 17% higher while standing as compared to the seated position. Support of level standing cycling ergometry eliciting higher VO₂max values continued when Tanaka et al. (1987) showed that 14 well-trained runners, 8 rowers, and 6 males of average fit attained higher VO₂max values when standing as compared to seated cycle ergometry.

Fitness level, as well as the type of athlete and gender, can affect VO₂max values (Basset and Howely, 2000; and Foss and Keteyian, 1998). For example, trained cyclists achieve higher VO₂max values during cycle ergometry compared to sedentary individuals and trained runners (Tanaka et al., 1996). This trained versus untrained comparison supports the notion that athletes who train in a certain mode of exercise can attain a higher VO₂max in that specific mode (Fernhall and Kohrt, 1990; Ricci and Leger, 1983; Tanaka et al., 1996; and Verstappen et al., 1982). Also, males tend to have higher VO₂max values than females due to greater lung capacity and greater amounts of hemoglobin (Foss and Keteyian, 1998). Subjects in previous studies varied in terms of fitness level and preferred mode of exercise, which may have influenced results.

Another important component of cycle ergometry protocols is the revolutions per minute (rpm). As noted earlier, leg fatigue, particularly in the upper thigh, may cause an individual to finish a cycling GXT prematurely (McKay and Banister, 1976). Lower rpm tend to increase leg fatigue (Beasley et al., 1989). Typically, for untrained individuals, 40-60 rpm provide the most economical cadences, yet 80-120 rpm yield the greatest VO₂max and lowest perceived leg fatigue at similar workloads (Beasley et al., 1989; and Marsh and Martin, 1993). Cyclists prefer to cycle at 90 rpm (Marsh and Martin, 1993). However, disparity does exist between the optimal cadences for trained and untrained individuals. Beasley et al. (1989) and Pivarnik et al. (1988) showed there were no differences in VO₂max and peak HR at 50 rpm and 90 rpm with trained male subjects, while Coast, Cox, and Welch (1986) showed the most economic range of rpm for this group was 60-80. Swain et al (1992) determined that VO₂max and HR were actually lower at higher (84) rpm vs lower (41) rpm. Hagan, Weis, and Raven (1992) concluded that, at higher rpm, (90 rpm vs 60 rpm) HR, V_E, and cardiac output will be greater, while cycling economy decreases. In contrast to the results of Hagan et al. (1992), Nickleberry and Berry (1996) determined that recreational cyclists were able to increase their time to exhaustion by 6 minutes, while competitive cyclists continued for 8 minutes longer at 80 versus 50 rpm.

In examining standing cycle ergometry, it may be prudent to recruit a more homogeneous group with respect to fitness and with representatives of both genders being tested. This process may improve validity in comparisons of standing and seated VO₂max values, which can be applied to a larger population. Based on previous results, it is unclear whether standing VO₂max values will be greater than seated VO₂max values. In previous research, all standing cycling protocols varied in terms of when to stand during trials, duration of standing, protocol duration, cadence, fitness levels of subjects, and number of subjects. The differences among procedures and methodology may partially explain the contradictory results. Since equivocal results have occurred regarding standing cycle ergometry, the purpose of this study was to compare VO₂max between standing and seated cycle ergometry protocols in female and male subjects.

Methodology:

Subjects included 14 males and 22 females. All were apparently-healthy volunteers from 18-28 years of age. Subjects were of average fitness abilities. All subjects were made aware of the risks and requirements of participating in the study and all signed a written informed consent prior to any testing. To ensure the safety of the subjects, individuals were required to complete a physical-activity readiness questionnaire (PAR-Q) and a health status questionnaire prior to data collection.

Subjects were tested on a model 824E Monark Cycle Ergometer. Each subject wore a Hans Rudolph facemask with expired gas being collected and VO₂ being analyzed by a Sensormedics 2900 Metabolic Measurement System. Individuals also wore a Polar Heart Rate Monitor (Model Polar Beat HRM) to determine exercise heart rate. Body-fat percentage was determined using Lange skinfold calipers with a 3-site skinfold method. Weight and height were measured using a detecto balance type scale with an attached measuring rod.

Descriptive data was collected immediately prior to the initial VO₂max test.
After subjects reported to the lab, an explanation of the study was provided and the initial screening procedures were administered. Instructions regarding the exercise trial were also provided to the subjects. Subjects were then assessed for height, body weight, and body-fat percentage using a 3-site skinfold technique (Pollock, Schmidt, and Jackson, 1980).

Subjects underwent two VO₂max tests (SIT and STD) on a cycle ergometer. Because subjects were of average fitness, cadence was set at 60 rpm for the duration of the tests (Beasley et al., 1989; and Marsh and Martin, 1993). Initially, subjects warmed up at a resistance of 30 watts for four minutes at 60 rpm. Every minute thereafter, resistance was increased by 30 watts until the subjects reached volitional exhaustion. SIT required each individual to stay seated until the test was terminated (at volitional exhaustion), while STD required individuals to stand at the point at which they felt they could no longer continue in a seated position. They continued to perform “standing cycling” to volitional exhaustion. All tests were stopped when subjects reached volitional exhaustion or when testers felt it was not safe for the subjects to continue. At the completion of each VO₂max test, subjects were monitored during a low intensity cool-down. SIT and STD lasted approximately 7 to 15 minutes and were completed in a counterbalanced order on two separate days with three to seven days between each session.

Expiratory gas was analyzed using a Sensormedics 2900 Metabolic cart, which was calibrated prior to each test using a three-liter syringe and gases of known concentration. The system provided updates of metabolic data (VO2, VO_E, RER) every 20 seconds. Also, a Polar Heart Rate monitor was used to monitor heart rate response (HR) every 60 seconds. Heart rate, VO₂max, RER, and VO_E were compared between SIT and STD. The highest observed values for metabolic data were considered “max” values for each respective cycle ergometry trial. The criteria for achieving a “true” VO₂max were a) failure of HR to increase with further increases in exercise intensity, b) RER exceeded +1.15, and c) a rating of perceived exertion (RPE) of more than 17 (Balady et al., 2000). In the present study, meeting two out of the three criteria satisfied the requirement for achieving a “true” VO₂max. VO₂max, HR, RER, and VO_E were analyzed using a multivariate repeated measures analysis of variance (MANOVA). Mean time to exhaustion for STD and SIT were compared using a paired t-test. Results were considered significant at p ≤ 0.05.

Results:

Descriptive characteristics of all subjects are displayed in Table 1. Physiological responses to seated and standing cycle ergometry are presented in Table 2. Percent increases of standing cycle ergometry are found in Table 3. The results suggest that VO₂max_STD was significantly greater than VO₂max_SIT with a mean difference of 1.1 ml/kg/min. Also, HR_STD was significantly greater than HR_SIT with a mean difference of 2.4 b/min. For VO_E, VE_STD was not significantly different (p = 0.08) than VE_SIT. However, RER_SIT was significantly greater than RER_STD.

Regarding mean time to exhaustion, subjects cycled 10:15 ± 2:21 minutes during SIT, with individuals cycling between 7-15 minutes. Although the difference only approached significance (p = 0.064), subjects were able to cycle on average 11 seconds longer (10:26 ± 2:06 minutes) during STD, with participants cycling between 7:20, and 15:20. When subjects were in the standing position, the mean duration of standing cycle ergometry time to volitional exhaustion was 50.42 ± 15.57 seconds.

Table 1: Descriptive Characteristics of Subjects (n=36)-Values are means and standard deviations.

	Males (n=14)	Females (n=22)	All Subjects
Age (years)	23.07 ± 2.97	19.73 ± 1.20	21.03 ± 2.63
Height (inches)	70.93 ± 3.17	65.59 ± 2.11	67.67 ± 3.66
Weight (lbs)	190.14 ± 23.36	139.00 ± 15.79	158.89 ± 31.49
Body Fat (%)	10.90 ± 4.45	21.41 ± 4.20	17.33 ± 6.71

Table 2: Physiological Responses during SIT and STD-Values are means and standard deviations. * Significantly different (p ≤ 0.05) (STD versus SIT)

	VO₂max (ml/kg/min)	HR (b/min)	VO_E (L/min)	RER
SIT	36.82 ± 6.63	187.3 ± 9.6	82.64 ± 26.77	1.23 ± 0.065
STD	37.93 ± 8.01*	189.7 ± 9.5*	86.02 ± 31.64	1.21 ± 0.096*

Table 3: Percent Increases for Standing Cycle Ergometry

	Mean Percent Increase	Range of Percent Increase	Standard Deviation
VO₂max	2.0%	-16.9% to +13.7%	+ 6.6%
HR	1.2%	-5.6% to +7.4%	+ 2.9%
VO_E	0.8%	-38.1% to +41.7%	+ 17.5%
RER	-2.3%	-16.4% to +13.6%	+ 6.6%

Discussion:

Finding ways to achieve the highest cycling VO₂max has important implications in exercise prescription, fitness evaluation, and cycling performance and training. Therefore, the results of the current study examined whether standing cycling VO₂max values are significantly greater than seated VO₂max values, which might support the use of a standing cycle ergometer protocol for all cycle ergometry Graded Exercise Tests (GXT) in exercise science and sport-performance laboratories. The use of such a protocol may generate the highest cycle ergometry VO₂max values. In terms of gender, prior research has tested only male subjects. Therefore, it was of practical importance to administer the standing and seated cycle ergometry protocol to female subjects in the current study.

Previous results regarding standing cycle ergometry have been equivocal. Kelly et al. (1980), Nakadomo et al. (1987), and Tanaka et al. (1987) showed significantly greater standing VO₂max, while Montgomery et al. (1971), and Tanaka et al. (1996) showed no significant differences in seated and standing VO₂max. Similar to the results of Kelly et al. (1980), Tanaka et al. (1987), and Nakadomo et al. (1987), as well as Tanaka et al. (1996), the current results suggest that VO₂max_STD and HR_STD are significantly greater than VO₂max_SIT and HR_SIT (Table 2).

The current study showed a significantly greater (2.0%) VO₂max and a significantly greater (1.2%) HR during STD compared to SIT. The greater VO₂max and HR during STD can be explained by a variety of reasons. Based on previous research, it is likely that with greater force production, a larger amount of muscle mass was involved during STD (McLester, Green, and Chouinard, 2004; Nordeen-Strider, 1977). Also, standing during STD may have activated more muscle mass, as the legs supported the individual’s body weight as opposed to being supported by the saddle during SIT (Nakadomo et al., 1987; Ryschon and Stray-Gundersen, 1991; and Tanaka et al., 1987). Also, as noted by Ryschon and Stray-Gundersen (1991), and Tanaka et al. (1987), during standing cycle ergometry, the upper body is involved to a greater degree in torso stabilization and purposeful side-side rocking, compared to seated cycling. Kelly et al. (1980) and Ryschon and Stray-Gundersen (1991) suggested the standing cycle ergometry protocol provides more extensive involvement of the arm and leg muscles, eliciting greater blood flow and higher work output and contributing to a higher peak HR and VO₂max, which may have also contributed to the findings of the current study.

Tanaka et al. (1987) suggested that decreases in subject cycling economy and attenuated leg fatigue might also explain the greater VO₂max_STD and HR_STD. Ryschon and Stray-Gundersen (1991) note that greater cardiorespiratory and metabolic requirements of the standing position decreases the efficiency of the rider, yet provides an increase in the total work output. For leg fatigue, subjects in the current study often verbally reported feelings of intense local discomfort and fatigue in the region of the quadriceps muscle when in the seated position and near or at volitional exhaustion. This leg fatigue and discomfort, coupled with gradual increases in resistance, may have limited the ability of the subject to continue cycling in the seated position (Nakadomo et al., 1987; Tanaka and Maeda, 1984; and Tanaka et al., 1987). However, many subjects verbally reported that at the onset of standing cycling, leg fatigue and local discomfort was comparatively less than during seated cycling, which could have accounted for the extended time to fatigue during STD (Ryschon and Stray-Gundersen, 1991; and Tanaka et al., 1987). Variations in perceived feelings might have been due to the redistribution of the workload over a greater muscle mass and alterations in the muscle recruitment pattern (Ryschon and Stray-Gundersen, 1991).

Another factor that may have contributed to greater VO₂max during STD is the increase in joint angles when the individual comes out of the saddle and performs standing cycling. When standing, the hip, knee, and ankle joint excursions increase, which provides a greater range of motion within the respective joints (Nordeen-Snyder, 1977). Although not measured in the current study, it is possible that increases in the hip, knee, and ankle joint angles allowed for a more advantageous muscular force production and subsequent extended time to fatigue (Heil, Derrick, and Whittlesey, 1997; Nordeen-Snyder, 1977; and Shennum and deVries, 1976).

Millet et al. (2002), Tanaka et al. (1996), and Ryschon and Stray-Gundersen (1991) showed greater standing cycle ergometry HR. Although those differences occurred during a 4% incline protocol, significantly greater HR (1.2%) occurred during the current study, which utilized a level protocol. The extended time to fatigue allowed by standing may have attributed to a higher HR because earlier termination of the test due to leg fatigue and discomfort may have interfered with attainment of a true max HR.

Although only approaching significance (p = 0.08), an 0.83% greater VO_E occurred during STD compared to SIT. The increases in VO_E can be attributed to some of the reasons that likely contributed to a greater VO₂max during standing cycle ergometry. Generally when VO_E increases, so too does VO₂ (Foss and Keteyian, 1998).

As previously mentioned, when an individual leaves the seated cycle ergomerty position to stand, a greater involvement of upper and lower body muscle mass occurs. The activation of more muscle mass may allow for greater work output (Reiser, et al., 2002), which increases oxygen requirements of the muscles. In turn, ventilation increases. Cardiac output is also increased when participating in the standing position, which contributes to higher VO₂max and VO_E (Kelly et al., 1980). Also, because lower leg fatigue may be altered in the standing position, VO_E increases, and subjects are able to extend time to exhaustion.

For RER, SIT showed a significantly greater (2.3%) RER as compared to STD. Although SIT produced significantly greater RER compared to STD, the difference was of little practical significance. All RER values in both STD and SIT surpassed the criteria indicative of a “true” VO₂max (+1.15).

The current study showed that VO₂max_STD and HR_STD were significantly greater compared to SIT. However, despite the significant differences, it is important to note that discrepancies between the present study and previous studies (Montgomery et al., 1971 and Tanaka et al., 1996) could be a result of the protocol differences, variations in fitness levels, and low subject numbers. Many subjects benefited from the STD protocol as 20 of 36 (55.6%) individuals had greater VO₂max (up to 13.6%) and 25 of 36 (69.4%) subjects had greater peak HR (up to 7.4%). While means were significantly different, it should be noted that inter-individual variability was high. Some subjects had a much lower VO₂max during STD. Differentiating between those who respond positively and those who respond negatively to a standing protocol is difficult and was beyond the scope of the current study.

Conclusions:

The results of the current study support previous findings, showing a greater VO₂max during standing versus seated cycle ergometry (Kelly et al., 1980; Nakadomo et al., 1987; and Tanaka et al., 1987). Results of the current study also show significantly greater HR_STD. The current results support the use of a test protocol that allows an individual to stand during a cycle ergometry GXT. Therefore, since a higher VO₂max value was elicited using the standing protocol in the current study, a standing protocol should be considered for implementation when individuals are assessed for cardiorespiratory responses to maximal work using cycle ergometry. Future research should seek to determine characteristics of subjects who do/do not benefit from a standing versus seated protocol.

References:

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U.S. Sports Academy2017-08-07T11:47:23-05:00March 14th, 2008|Contemporary Sports Issues, Sports Coaching, Sports Exercise Science, Sports Studies and Sports Psychology|Comments Off

College Sport Management Student Perceptions Regarding Special Olympics Curriculum and Service Learning

Submitted by: Reginald F. Overton & Brenda M. Malinauskas

Abstract:

This pre-test/post-test study evaluated college sport management student (N = 21) perceptions of Special Olympics North America curriculum/field experience. Pre-event and post-event values indicate that students had positive perceptions. Significant individual effects were found for General Orientation, Facilities and Safety, and Event Management. The strongest correlate relationships were for General Orientation with Volunteerism (52% predictive), Event Management (50%), and Athletes (53%), and Volunteerism with Event Management (54%) and Athletes (62%). Overall, results indicate that service learning can be implemented successfully into a sport management curriculum, field experience is an effective practical experience, and feedback from students should be used to improve teaching.

Introduction:

The service-learning/practical-experience student is an emerging professional who must guide the course of his/her own career. This is the opportunity for a student to apply professional knowledge and expertise in the field under the direction and supervision of a qualified practitioner. Such a student should receive varied experiences ranging from leadership to program development. The variety and intensity of experiences should allow the student to assess knowledge and skills in relation to career goals. The student should be challenged in a manner in which both strengths and weaknesses are evident. Such experiences can only be assured through careful planning by the student and by the agency supervisor (Overton, 2005).

The Special Olympics North America University (SONA) has developed a curriculum which consists of Special Olympics courses that can be incorporated into university curricula. These include General Orientation and Event Management courses within the Special Olympics Coach Education System and Games Management Training program. Through the Special Olympics University Curriculum, universities play a renewed role in training coaches and sport managers. The SONA curriculum is endorsed by the American Alliance of Health, Physical Education, Recreation, and Dance (AAHPERD) and the National Association for Sport and Physical Education (NASPE). Currently, nine universities have adopted this unique curriculum concept.

While service-learning implementation has increased in higher education curricula throughout the United States, the concept has been around for quite a while (Dewey, 1938). A sport management curriculum could benefit from incorporating service-learning based Special Olympic programs into university curricula. Event management, budgeting, concessions, and personnel management are examples of areas of experience that an organization such as the Special Olympics could provide a student volunteer. Equally important is that this cooperative program would provide sport management students with insight into how a service-oriented agency such as the Special Olympics is managed (Daughtrey, Gillentine, & Hunt, 2002). Many professors utilize academic-based service learning in their classes to provide students with practical experience. In fact, service learning has increased in popularity in higher education due to the many perceived benefits of the method.

Perceptions of the sport management students participating in the SONA curriculum are paramount in the success of the program. Feedback can assist in improving the overall structure of the service-learning experience. Thus, the research questions under investigation seek to first determine college sport management student perceptions regarding sport management Special Olympics training, and Special Olympics field experience. Second, they identify individual effects (individual differences) and determine whether perceptions differ in pre- versus post-event (situation effects) with regard to selected areas of sport management, including general orientation of the SONA training, volunteerism, adequacy of facilities and athlete safety, event management, and event contribution to competition and the well being of athletes, at a specific field-experience event. Third, they identify correlate relationships between perceptions of the selected areas of sport management.

Methods:

Design

The study utilizes a pre-test/post-test design. Participants were recruited from two facilities management courses, one for undergraduate students, the other for graduate students, during the 2006 spring term at a historically-black college and university in south central Virginia. Sport management majors are the only students eligible to take these courses. The pre-test was administered before students had Special Olympics North America University curriculum (SONA) training. The post-test was administered to those who had completed the pre-test, all modules of the 3-module SONA curriculum, and the sport management field-experience requirement (Module 3) at a specific Special Olympics regional track meet. Pre- and post-test surveys were administered to all participants by a sport management professor.

SONA curriculum description and administration

The Special Olympics of North America, in conjunction with selected higher education institutions, collaborate with sport management faculty to implement the SONA curriculum within selected sport management courses. Currently, there are nine universities throughout the United States that participate in this program. Module 1 of the SONA curriculum is a Special Olympics orientation, Module 2 is application of sport management principles to Special Olympics events, and Module 3 is a Special Olympics field experience. The sport management professors coordinated their teaching efforts and standardized their delivery of Module 2 to ensure that the content delivery was the same between the classes participating in the study. Completing the SONA curriculum and a sport management field experience at a Special Olympics event were course requirements of respective undergraduate and graduate courses.

Survey development and administration

The study survey was developed by one of the sport management professors who taught Module 2 and who had directed Module 3. The survey was reviewed for content validity by the Special Olympics administrator who taught Module 1. Section 1 of the survey assessed demographic characteristics, Section 2 was composed of six subscales that assessed perceptions regarding General Orientation (e.g., student preparedness, sport management knowledge, and organization of training staff), Volunteerism (e.g., “making a difference” as a volunteer, whether volunteer efforts would be appreciated), Facilities and Safety (adequacy of facilities and athlete safety), Event Management (e.g., job descriptions, operating plans, games meeting schedule), and Athletes (e.g., fair competition for athletes, whether event participation contributes to health and wellness of athletes). The content of the subscales was adapted from the SONA curriculum and from the mission statement of the Special Olympics of North America Curriculum Guide, 2005). Each subscale score was a mean score of five questions that were individually scored using a 5-point Likert scale (1 = strongly disagree, 3 = neutral, 5 = strongly agree). The university-affiliated institutional review board approved the project. Signed informed consent was obtained from all participants. The survey was pilot tested by four graduate sport management students. Based on their responses, modifications to the survey were not necessary.

Statistical analyses

Analyses were performed using JMP IN® software, version 5.0 (Sall, Creighton, & Lehman, 2005). The descriptive analysis included means, standard deviations, and frequency distributions. Item reliability was evaluated using Cronbach’s α. Subsequent data analysis involved Pearson x² and one-way analysis of variance with a comparison for each pair using Student’s t. Multivariate pairwise correlations were used to evaluate relationships between subscales. A significant level of .05 was used for statistical analysis.

Results:

The total sample recruited included 45 students, which represented all students from the undergraduate and graduate students enrolled in one of two facility management courses. Overall, 21 students completed the study, which was a 47% participation rate. The average (M ± SD) age of the participants was 22.3 ± 3.2 years, 71% of participants (n = 15) were male, all were African American, and academic classification was fairly evenly distributed between undergraduate (58%, n = 12) and graduate students.

Cronbach’s α was used to evaluate item reliability for each subscale score in Section 2. The subscales internal consistency ranged from .73 to .90, indicating an acceptable correlation of ranked values among subscale parameters.

Regarding research question 1, participant perceptions of sport management Special Olympics training and a Special Olympics field experience, are reported in Table 1. Pre- and post-event values indicate that students had positive perceptions for all sport management subscales evaluated; mean scores ranged from 4.0 to 4.6 for all subscale scores. A score of 4 indicated participants “agreed” with statements, whereas a score of 5 indicated participants “strongly agreed.”

Table 1: College Sport Management Student Perceptions Regarding Special Olympics Training and a Special Olympics Field ExperienceNote: M ± SD. N = 21. * Each subscale was a mean score based on responses to five questions that were individually scored using a 5-point Likert scale (1 = strongly disagree, 3 = neutral, 5 = strongly agree).

Sport Management Subscale*	Pre-event score	Post-event score
General Orientation	4.2 ± 0.6	4.4 ± 0.5
Volunteerism	4.2 ± 0.7	4.4 ± 0.5
Facilities and Safety	4.4 ± 0.6	4.4 ± 0.5
Event Management	4.0 ± 0.7	4.3 ± 0.7
Athletes	4.4 ± 0.6	4.6 ± 0.3

In reference to question 2, individual and situation effects of student perceptions are reported in Table 2. Significant individual effects indicate that there were significant differences between perceptions of different participants, whereas significant situation effects indicate that perceptions of participants, as a group, differed from pre- to post-test. Significant individual effects were found for General Orientation, Facilities and Safety, and Event Management. The researchers identified no significant situation effects for any of the subscales.

Table 2: Individual and Situational Effects for College Sport Management Student Perceptions Regarding Special Olympics Training and a Special Olympics Field ExperienceNote: N = 21. Each subscale was a mean score based on responses to five questions that were individually scored using a 5-point Likert scale (1 = strongly disagree, 3 = neutral, 5 = strongly agree).

Sport Management Subscale*	df	MS	F	p
General Orientation
Individual effect	20	0.48	3.40	< .01
Situation effect	1	0.50	3.60	.07
Error	20	0.14
Volunteerism
Individual effect	20	0.55	1.93	.07
Situation effect	1	0.21	0.76	.40
Error	20	0.28
Facilities and Safety
Individual effect	20	0.41	2.80	.01
Situation effect	1	< 0.01	0.01	.94
Error	20	2.94
Event Management
Individual effect	20	0.69	2.5	.03
Situation effect	1	0.86	3.0	.10
Error	20	0.28
Athletes
Individual effect	20	0.26	1.55	.17
Situation effect	1	0.42	2.50	.13
Error	20	0.17

Concerning question 3, Special Olympics training and field experience perceptions correlate relationships; all subscales assessed had statistically significant direct correlate relationships with all other subscales (see Table 3). The strongest correlate relationships (r_S² x 10) were for General Orientation with Volunteerism (52% predictive of each other), Event Management (50%), and Athletes (53%), and for Volunteerism with Event Management (54%) and Athletes (62%).

Table 3: Correlate Relationships Regarding Perceptions of Special Olympics Training and a Special Olympics Field Experience Among College Sport Management StudentsNote: N = 21. Multivariate pairwise correlations were used to evaluate relationships between sport management subscales.

Sport Management	Volunteerism	Facilities and Safety	Event Management	Athletes
General Orientation	r_S = .72 p = < .01	r_S = .46 p = .04	r_S = .70 p = < .01	r_S = .73 p = < .01
Volunteerism		r_S = .55 p = < .01	r_S = .74 p = < .01	r_S = .79 p = < .01
Facilities and Safety			r_S = .56 p = .02	r_S = .52 p = < .01
Event Management				r_S = .59 p = < .01

Discussion:

The primary purpose of a service-learning project is to provide a work-study-learning program to further the professional development of students. Agencies electing to accept students in a service-learning environment have an obligation to maintain reputations for professional service. Thus, the general orientation to a service-learning project is critical. Results from this study indicate that the general-orientation portion of a service-learning project sets the tone in preparing the student for volunteer work. It is evident that this module in the curriculum is important in creating a solid base and understanding of the overall service-learning experience. Furthermore, the general orientation portion of the curriculum had positive correlate relationships with several of the subscale areas included in the Special Olympics curriculum modules. Particularly, the Event Management, Athlete, and Volunteerism subscales had direct correlate relationships with General Orientation. Similarly, Volunteerism had positive correlate relationships with the Athlete and Event Management subscales. Thus, these results suggest that the sport management student will recognize that volunteer, philanthropic organizational events are viable career options in the sport industry. In addition, this study indicates that future sport management professionals will be aware of the importance of event-management planning and the significant role volunteerism plays in the development of a successful service-learning event.

The present study allowed the students the opportunity to participate in a sport management event and to evaluate sport management concepts, including event management, volunteerism, athlete mentoring, and facility planning. From an academician’s viewpoint, the significant individual effects indicate areas a faculty member may concentrate in so as to improve the overall effectiveness of the service-learning project. Because students have varied perceptions, an instructor may use open-ended discussions to educate students regarding different sport management areas that are paramount to the success of a service-learning project.

Service learning has been a popular pedagogical tool in academic programs for years. Recently, the concept has gained popularity in other forms, such as class projects and internships (Petkus, 2000). The implications of this study are that, because students have positive perceptions regarding service-learning projects, the projects can be implemented successfully into a sport management curriculum. In fact, specialized internship/field experiences with organizations such as the Special Olympics can work efficiently within an already existing sport management program. When implementing service-learning components into existing sport management curriculums, it is important to receive feedback from the students. This will assist the instructor in evaluating the effectiveness of the curriculum and subsequent service-learning experiences.

References:

Daughtrey, C., Gillentine, A., & Hunt, B. (2002). Student collaboration in community sporting activities. American Alliance for Health, Physical Education, Recreation and Dance, 15, 33-36.

Dewey, J. (1938) Experience and Education, New York: Collier Books.

Overton, R. (2005). Sport Management Internship Manual. Ettrick, Virginia: Virginia State University.

Petkus, E. (2000). A theoretical and practical framework for service-learning in marketing: Kolb’s experiential learning cycle. Journal of Marketing Education, 22(1), 64-70.

Sall, J., Creighton L., & Lehman A. (2005). JMP IN® Start Statistics. Southbank, Australia: Thomson Brooks/Cole.

Special Olympics Special Olympics of North America Curriculum Guide (2005). Washington, D.C.

U.S. Sports Academy2013-11-26T14:29:53-06:00March 14th, 2008|Contemporary Sports Issues, Sports Management, Sports Studies and Sports Psychology|Comments Off

The Effects of Promotions on Attendance in Professional Baseball

Submitted by: Robert Aaron Browning, M.S. & Louisa S. DeBolt, Ph.D.

Abstract:

Professional baseball organizations use many types of promotions to increase attendance. The purpose of the study was to determine whether or not different types of promotions effected attendance in professional baseball. Promotions were categorized into price, non-price, and a combination of price and non-price. Attendance and promotion data were collected from four professional baseball organizations located in the Ohio River Area. The results indicated significant increases in attendance in two of the four teams when any promotion was used. Two teams also revealed attendance increases when non-price promotions were present, as well as when combination of price and non-price promotions were employed. Finally, this study supports previous research, which has found higher attendance at games with promotions than games without promotions and when non-price promotions are used rather than price promotions.

(more…)

U.S. Sports Academy2016-10-19T10:51:50-05:00March 14th, 2008|Contemporary Sports Issues, Sports Facilities, Sports Management, Sports Studies and Sports Psychology|Comments Off

More than Just the Ryder Cup: An Examination of Relevant Natural Characteristics in Professional Golf

Submitted by: Mark K. Pyles, Ph.D.

Abstract:

The researcher examined the two major professional golf associations, the Professional Golfer’s Association (PGA) and the Ladies Professional Golfer’s Association (LPGA), to determine physical characteristics relevant for success. The researcher found that those players born outside of the U.S. consistently earn more money and have lower average scores in the most recent professional season. These results are consistent across both tours. The researcher attempted to uncover individual statistical categories that influence this finding. He found that players born outside of the U.S. have significantly superior putting averages, while there appears to be no significant difference in other categories, such as driving and hitting greens in regulation. The superior performance of players born outside of the U.S. remains after controlling for these statistical areas.

Introduction:

Many studies have examined the skills necessary to succeed at the game of golf. Among the first were Davidson and Templin (1986), who found hitting greens in regulation and putting to be the most important determinants of success. These results have been consistent in numerous studies, including Jones (1990), Shmanske (1992), Belkin, Gansneder, Pickens, Rotella, and Striegel (1994), Nero (2001), Dorsel and Rotunda (2001), and Engelhardt (2002).

This paper took a different approach. The researcher examined the impact of professional golfers’ physical characteristics on performance, measured by the money earned and the scoring average for the 2006 season. The researcher examined both the Professional Golfers Association (PGA) and the Ladies Professional Golfers Association (LPGA) tours. There has been considerable evidence that talent and skill level in professional golf have been increasing rapidly over the past decade (e.g. Chatterjee, Wiseman, and Perez, 2001). This is likely attributable to the rapid improvement in equipment, but just as much, if not more, is attributable to increased participant abilities. The researcher examined whether natural characteristics such as size influence professional success in both tours.

Further, the researcher examined the effect of the individual’s origin, specifically segmenting those born in the U.S. and those that were not. Following the two most recent Ryder Cups, which the European teams won handily, there has been much discussion on the “advantages” they must have. Those advantages appear to be more than statistical, as the American team fielded the top three players in the world during the most recent Cup and were still unable to threaten the lesser-known European team. It has been suggested this is potentially due to the team camaraderie the international team enjoys and the American team lacks. While this is a question the researcher cannot answer, he attempted to determine whether this “international effect” is evident in settings other than these group competitions.

This work makes numerous contributions to the literature related to this subject. First, to his knowledge, the researcher is among the first to directly examine the individual physical characteristics of professional golfers. The researcher took an opposite approach than most studies do. Characteristics such as putting and driving are explanatory variables, which the researcher used in an attempt to explain the variation found in relation to the physical characteristics.

Second, the researcher contributed to the literature (e.g. Wiseman, Chatterjee, Wiseman, and Chatterjee, 2004) that examines gender differences in professional golf. While the researcher primarily examined both tours separately, he found the results to be consistent for both tours, suggesting an overall effect, rather than a gender-specific anomaly. In addition, the researcher examined the 2006 professional season, which has just recently concluded. Given the increased quality of equipment, as well as the improved performance of the participants, it is important to examine the most recent data.

The researcher found relations to be consistent with those documented in past literature. In addition, the most interesting finding dealt with the nationality variable. The researcher found those individuals born outside the U.S. scored lower and earned more money than those born within, a result consistent across both professional tours. Further, by examining the primary performance-predicting variables, the researcher found players born outside of the U.S. have lower putting averages, while the other performance-predicting variables are statistically equal. Therefore, it appears that putting explains some of the variation between U.S. born and non-U.S. born players. However, the researcher also examined end-performance controlling for the predictive statistics (including putting average) and found the significant relation remains. Therefore, there appears to be undefined influence. The researcher briefly discussed possible reasons for this, for example, prior experience on international tours.

Literature Review:

Davidson and Templin (1986) were among the first to examine the characteristics that are important in golf success. Examining 1983 PGA data, they concluded that relative to driving, skills of finesse, such as putting and hitting greens in regulation (GIR) were more important statistical areas in relation to performance, as measured by money earned and scoring average. The results suggest that golfers who possess proficiency in many shot-making areas have a higher probability of success than those players with proficiency in a few.

Numerous studies have examined the same topic and have concluded the same, that putting and GIR are the most important determinants of success. Among these are Jones (1990), Shmanske (1992), Belkin, Gansneder, Pickens, Rotella, and Striegel (1994), Wiseman, Chatterjee, (1994), Engelhardt (1995,1997), Moy and Liaw (1998), and more recently Nero (2001), Dorsel and Rotunda (2001), and Engelhardt (2002). This finding is not exclusive to professional golf, as Callen and Thomas (2004) found that amateur golfers must possess a wide array of shot-making skills to be successful, particularly putting and hitting GIR.

Several studies have also examined the incremental significance of certain statistical areas for male golfers in comparison to female golfers. For example, Wiseman, Chatterjee, Wiseman, Chatterjee (1994) examined the PGA, LPGA, and SPGA tours and found that males drive the ball farther and hit more GIR. Consistent with the above results, they also found the most important characteristics for LPGA golfers are putting and greens in regulation. Moy and Liaw (1998) found the same, adding that PGA participants perform better in sand saves relative to the LPGA tour. Shmankse (2000) noted that the PGA tour yielded a superior putting average relative to the LPGA.

Further, Nero (2001) estimated golfers earnings based upon driving distance, driving accuracy, putting average, and sand saves. He concluded that professional golfers would benefit by improved putting more than increased driving distance. Callen and Thomas (2006) extended their previous study by examining male and female NCAA amateur golfers. They reached two primary conclusions: (1) males and females possess different levels of shot-making skills, and (2) these disparate skills influence tournament performance differently across genders. These disparate skills are consistent with those found in professional golf.

Moy and Liaw (1998) asserted that men’s larger physical size and superior strength explained the advantage enjoyed by professional male golfers over their female counterparts, as they can drive the ball farther. However, others have argued that successfully driving the ball requires more than just strength. For example, Hume, Keogh, and Reid (2005) analyzed both driving and putting and found that strength is certainly important in both areas, but flexibility and timing are also critical for success. The related hypothesis is that gender-related differences are therefore related to one or more of those physiological areas. Myers, Gebhardt, Crump, and Fleishman (1993) found statistical support for this; male golfers score higher in strength and stamina, while females have superior flexibility.

Data and Methods:

All data in this study are available online. The researcher obtained PGA and LPGA statistics from the official websites, www.pgatour.com and www.lpgatour.com, respectively. For each tour, the researcher obtained end-performance measures and performance-predicting measures. The researcher’s primary measures of end performance were total money earned during the year (Money) and scoring average (Scoring). Money is defined as the sum total earnings due to end tournament placement throughout the entire season.¹ Scoring is defined as the average score (i.e. number of strokes) obtained through each round (i.e. 18 holes).

Also, the researcher examined four performance-predicting statistics. The first, driving distance (DrivingDist), measured the total length of each participant’s average drive. During each round, two holes were selected to be measured, with special care taken to ensure the holes face in opposite directions to counteract the effects of wind. Drives were measured at the point they come to rest. The researcher also examined driving accuracy (DrivingAcc), which is the percentage of time a player hit the fairway with his/her drive, the first stroke taken on par 4 and par 5 holes. These two variables are included to examine the overall “power game” of each player. Past studies have typically found these variables to be less important than the finesse areas of the game. However, there have been studies (e.g. Engelhardt, 1995) that suggest a reversal in recent seasons, as the game of golf has become a more distance-demanding sport. The researcher attempted to see if this was indeed the case.

The researcher examined each player’s percentage of GIR, defined as having any part of the ball touching the putting surface in two or less strokes than par for each hole. Finally, the researcher examined putting average (PuttAv) for each participant. PuttAv is the average number of putts used only on those greens hit in regulation. Using this measure eliminated biasing the results due to chipping the ball close to the hole and having a relatively short putt. Both of these variables have been found to be significant in relation to performance in almost all studies. Therefore, the researcher attempted to see if this relationship still existed.²

Rather than using the value of each of these performance variables, the researcher chose to use the ranking. In each tour, the participants are ranked based upon each statistical category. In fact, those are the numbers most often quoted when commenting on the various statistics. The researcher chose to use rankings in order to have a consistent relationship between the coefficient signs and each variable. Otherwise, each would have to have its own interpretation. For example, a lower putting average is a positive statistic, while a higher percentage of greens in regulation is a positive. By using the respective rankings, the researcher could consistently say that a higher ranking is a positive signal, regardless of the variable.³

The researcher’ primary contribution was to extend the analysis to control for personal characteristics. Therefore, the researcher also identified several natural physical characteristics. He identified the age (age) of each participant, defined as the number of whole years from the individual’s birth date to the end of the 2006 professional year.^4,5 Also, he defined height (height) and weight (weight) to control for the player’s physical structure. Height is measured in inches; weight is measured in pounds. The researcher did not have data on the LPGA player’s weight; therefore, this variable was defined only for the PGA sample. Finally, the researcher identified each individual’s birth place. Using this, the researcher created USA, which is a dummy variable equal to one if an individual was born in any of the 50 states, zero otherwise. As such, the researcher could examine the difference between performance of USA-born players, both in end performance and in performance-predicting variables. All personal characteristics are available online. After excluding those players for which complete data was unavailable, the final sample consisted of 196 PGA professionals and 166 LPGA professionals.

The researcher initially examined summary statistics of both sub-samples. Consistent with Chaterjee, Wiseman, Chaterjee, and Wiseman (1994), the researcher found the unsurprising result that PGA players drive the ball farther than LPGA players. This is consistent with physiological studies, such as Myers, et al. (1993.) However, the researcher found no significant difference between the two tours in relation to greens hit in regulation. He found the putting average on the PGA tour to be significantly lower than on the LPGA tour, consistent with Schmanske (2000). PGA tour players underperformed LPGA players in driving accuracy, also consistent with Myers et al. (1993.) More important to the study, the researcher found PGA players are older, on average. It appears that there is a higher percentage of American born players on the PGA tour relative to the LPGA tour.

Table 1 – Summary Statistics:

The following table represents summary statistics for the sample, segmented by observations from the Professional Golfers Association (PGA) tour and the Ladies Professional Golfers Association (LPGA) tour. For the PGA tour, Age is defined as number of whole years from the individual’s birth date to November 6, 2006, the day after the end of the Tour Championship. For the LPGA tour, Age is defined as the number of whole years from the individual’s birth date to November 15, 2006 the date following the ADT Championships. Height is the individual’s height in inches. Weight is the individual’s weight in pounds. EventNumb is the number of events each individual participated in during the 2006 year. USA is a dummy variable equal to one if the player was born in any of the 50 United States, zero otherwise. Money is the total amount of prize money awarded to each individual during the 2006 tour year in each respective tour. Scoring is the average 18-round score for each individual. DrivingDist is the average number of yards for each drive. During each round, two holes are selected to be measured, with special care taken to ensure the holes face in opposite directions to counteract the effects of wind. Drives are measured at the point they come to rest. DrivingAcc is the percentage of time the player hits the fairway with their drive. GIR is the percentage of the time the player hits the green in regulation (i.e. when the ball is on the green and the number of strokes taken is two or less than par.) PuttAv is the average number of putts used on those greens hit in regulation.

	PGA	LPGA	t-statistic
N	196	166
Personal Characteristics
Age	35.76	30.80	10.82
Height	71.55	66.28	21.76
Weight	180.85	–	–
EventNumb	25.78	19.93	11.72
USA	.75	.51	4.90

Performance Characteristics
Money	1,188,709.48	252,329.11	10.82
Scoring	71.11	72.89	-16.06
DrivingDist	289.40	250.87	39.47
DrivingAcc	63.41	69.52	-9.66
GIR	65.13	64.58	1.33
PutAv	1.78	1.83	-13.97

Results:

The researcher began by estimating the following regression model via traditional Ordinary Least Squares:

Dep_i = α + β₁EventNumb + β₂Height + β₃Weight + β₄USA + β₅Age + ε_i (1)

where Dep_i is either Money, Scoring, DrivingDist, DrivingAcc, GIR, or PuttAv. Each variable is the rank of the golfer in the respective tour in each statistical category. Therefore, he could interpret each positive coefficient as a negative effect on end performance, as it indicated a higher value for independent variable will result in a higher ranking value for the performance measure. EventNumb is the number of full-field events the participant entered into during the 2006 season on each tour, and it was used to control for variation that is a result of frequency of play. The results are presented in Tables 2 and 3. Table 2 presents the results for the end performance variables, Money and Scoring. By first examining the PGA results, he found that older players make less money. More interesting, American born players score higher and make less money on average than their counterparts. The coefficient indicates that, on average, non-American born players rank 32 and 34 places higher than American born players in money earned and scoring average, respectively.

Table 2 – Multivariate Analyses: Cumulative Performance:

The following table presents results from the following model:

Dep_i = α + β₁EventNumb + β₁Height + β₂Weight + β₃USA + β₅Age + β₆LPGAdum+ ε_i

where the dependent variable is either Money (in columns 1, 3, and 5) or Scoring (in columns 2, 4, and 6). For columns 1 through 6, each dependent variable is the rank of the individual observation in each of the pre-mentioned categories, as defined in Table 1. In columns 7 and 8, the dependent variable is an adjusted rank, segmented by quintiles, where the rank is 1 through 5. LPGAdum is dummy variable equal to one if the individual is on the LPGA tour, zero otherwise. All other variables are as defined in Table 1.

	PGA	LPGA	Total
	(1) Money	(2) Scoring	(5) Money	(6) Scoring	(7) Money	(8) Scoring
	Coef.	t-stat	Coef.	t-stat	Coef.	t-stat	Coef.	t-stat	Coef.	t-stat	Coef.	t-stat
Intercept	64.18	.41	51.15	.36	211.52	2.79	182.16	2.13	5.82	2.59	5.58	2.44
EventNumb	.06	.07	1.59	1.83	-7.38	-12.85	-5.88	-9.06	-.09	-5.74	-.06	-3.79
Height	-1.02	-.43	-1.94	-.91	.25	.22	.26	.20	-.03	-.84	-.03	-.98
Weight	.21	.74	.55	2.16
USA	31.79	2.97	33.96	3.51	19.73	3.59	19.06	3.08	.82	5.30	.92	5.89
Age	1.36	2.02	.61	1.00	-.21	-.56	-.28	-.67	.02	1.72	.01	1.16
LPGAdum					-.34	-1.44	-.21	-.87
N	196	196	166	166	362	362
Adj. R²	.0640	.1107	.5506	.3820	.1566	.1233

The results for the LPGA were consistent with the results for the PGA tour in that non-American born players outperformed their counterparts in both end performance measures. The average increase in ranking was 20 and 19 places for money earned and scoring, respectively. Neither age nor height had any significant relation to end performance.

Although their primary focus was on the two tours separately, the researcher also combined the two tours in a total sample. The difference in the numbers of golfers would create problematic model estimations if the researcher were to simply use the ranking as the dependent variable. He adjusted the rankings by creating quintiles for each performance measure. Therefore, for the total sample, the dependent variable only had 5 values, 1 through 5, where 1 represented those individuals who ranked in the top quintile in that respective category and 5 represented the bottom quintile. In doing this, the researcher assured the rankings were consistent across the two tours. The researcher included LPGAdum, a dummy variable equal to 1 for those individuals on the LPGA tour, zero otherwise. This variable is designed to control for systematic differences between characteristics on the two tours.

The results for the total sample confirmed those found individually in both tours. Specifically, the positive coefficient on USA indicated that across both tours, American born golfers have higher ranking values in both Money and Scoring, which indicates inferior performance. A negative relationship between age and end performance was found in the PGA rankings, but the significance was only marginal, a product of the insignificant relation in the LPGA tour. However, the highly significant and negative relation between USA and end performance was consistent across the two tours and provided an interesting question. It appeared that, of all the physical characteristics the researcher examined, the most important is nationality. This could be a product of many things, some of which the researcher could not examine. For example, it is well known that many players, particularly on the PGA tour, are successful, established players on tours in their native countries prior to participating in the United States. However, the researcher was unaware of any way to fully capture the increased ability attributable to this prior experience.

Regardless, if non-American born players are outperforming, it could simply be due to superior performance in individual areas, which the researcher called performance-predicting characteristics. It was not the researcher’ intent to examine where these skills are obtained, but rather to determine whether evidence supported the existence of superior skill in each statistical area. Therefore, the researcher examined these variables in an effort to “explain” the results of Table 2. Those results are presented in Table 3.

Table 3 – Multivariate Results:

The following table presents results from the following model:

Dep_i = α + β₁EventNumb + β₁Height + β₂Weight + β₃USA + β₅Age + β₆LPGAdum+ ε_i

where the dependent variable is either DrivingDist, DrivingAcc, GIR, or PuttAv. For Panels A and B, each dependent variable is the rank of the individual observation in each of the previously-mentioned categories, as defined in Table 1. For Panel C, the dependent variable is an adjusted rank, segmented by quintiles, where the rank is 1 through 5. All other variables are as defined in Tables 1 and 3.

Panel A: PGA	(1)	(2)	(3)	(4)
	DrivingDist	DrivingAcc	GIR	PuttAv
	Coef.	t-stat	Coef.	t-stat	Coef.	t-stat	Coef.	t-stat
Intercept	354.76	2.99	-174.35	-1.23	90.09	.60	149.38	1.01
EventNumb	.82	1.12	.12	.13	.88	.94	.40	.43
Height	-4.46	-2.48	3.95	1.84	-1.10	-.48	-2.39	-1.06
Weight	-.65	-3.04	.39	1.55	.27	1.00	.50	1.89
USA	-5.20	-.64	-5.45	-.56	1.22	.12	19.75	1.95
Age	4.60	8.98	-2.17	-3.55	.47	.72	.11	.17
N	195	195	195	195
Adj. R-Sq.	.3671	.0998	-.0124	.0209

Panel B: LPGA	(1)	(2)	(3)	(4)
	DrivingDist	DrivingAcc	GIR	PuttAv
	Coef.	t-stat	Coef.	t-stat	Coef.	t-stat	Coef.	t-stat
Intercept	436.30	4.15	-472.63	-4.82	179.53	1.83	231.61	2.45
EventNumb	-1.02	-1.28	-2.18	-2.94	-4.54	-6.12	-4.27	-5.98
Height	-5.48	-3.39	9.33	6.19	-.14	-.09	-1.12	-.77
USA	7.67	1.01	-6.56	-.93	4.22	.60	26.68	3.90
Age	.85	1.69	-.52	-1.11	.04	.09	-.22	-.50
N	164	164	164	164
Adj. R-Sq.	.0775	.1956	.1906	.2622

Panel C: Total	(1)	(2)	(3)	(4)
	DrivingDist	DrivingAcc	GIR	PuttAv
	Coef.	t-stat	Coef.	t-stat	Coef.	t-stat	Coef.	t-stat
Intercept	13.24	5.87	-8.90	-3.85	4.81	1.99	6.86	2.92
EventNumb	-.59	-2.52	.69	2.83	-.05	-2.98	-.06	-3.64
Height	-.17	-5.68	.19	6.10	-.02	-.50	-.04	-1.29
USA	.05	.34	-.03	-1.87	.25	1.54	.74	4.59
Age	.06	5.99	-.03	-2.78	.01	1.11	.00	.08
LPGAdum	-.59	-2.52	.69	2.83	-.24	-.95	-.36	-1.46
N	360	360	360	360
Adj. R-Sq.	.1452	.1037	.0225	.0768

Panel A examines the PGA tour, while Panel B examines the LPGA tour. Panel C examines the combined total sample. The researcher found, unsurprisingly, that younger, heavier, and taller players on the PGA tour hit longer drives. The researcher also found that younger players had less accuracy in their drives, as did taller players (although the significance is marginal.) In column 3, he sought to determine whether or not any of the personal characteristics help explain the percentage of greens hit in regulation. However, the researcher found the natural characteristics have no significance to GIR. In the last column of Panel A, the researcher found non-USA born players have lower (better) putting averages that their U.S. counterparts. Since this is a variable consistently found to be greatly important in golfing success (e.g. Davidson and Templing, 1986), this could explain, at least partially, the variation of USA in end performance.

Turning to Panel B, the researcher examined the performance-predicting variables for the LPGA tour. He found that taller, younger players hit longer drives, again consistent with expectations. However, shorter players drive more accurately. Most interesting, he also found a positive relation between U.S. born status and putting average rank, indicating non-American born players putt more efficiently. Again, this could explain some of the variation found in Table 2. In Panel 3, he examined the total sample. As expected, given the results in the first two panels, taller, younger players hit longer drives than their counterparts. However, taller players hit fewer fairways than shorter players. Also, as expected, the total sample results confirmed that non-U.S. born players have superior ranked putting averages than U.S. born players.

It appears some of the variation in performance unexplained by individual statistical categories may be due to physical characteristics. In regards to nationality, it may be that the superior performance is due to superior putting abilities, as he found no relation to other performance-predicting characteristics. However, he needed to examine the influence of natural physical characteristics in congruence with traditional predictors of end performance. In order to do this, the researcher estimated the following OLS model:

Dep_i = α + β₁DrivingDist + β₂DrivingAcc + β₃GIR + β₄PuttAv + β₄EventNumb + β₅LPGAdum + β₆Height + β₇Weight + β₈ USA + (2)β₉age + ε_I

where Dep_i is either Money or Scoring. The results are presented in Table 4. Panel A presents the results for Money, while Panel B presents the results for Scoring. To be consistent with previous studies, the researcher first examined only the performance-predicting variables. Those are presented in columns 1, 3, and 5 of each panel. The results were wholly consistent with those found in the majority of previous studies in that the two most important statistical categories are putting average and percentage of greens hit in regulation. In fact, the researcher found no significance at all in relation to the two driving measures on the PGA tour. However, driving (particularly driving accuracy) appears to be predictive of superior performance on the LPGA tour.

More important to this study, the researcher wanted to see if all of the variation in end performance can be determined by these performance-predicting variables. In other words, does the significance identified in the previous analyses disappear when combined with these more established measures? The researcher examined this in columns 2, 4, and 6 of each panel. The researcher found the results for the PGA tour to be consistent even when controlling for these variables. Specifically, USA maintained significance while GIR and PuttAv also remained highly significant. Therefore, successful players must be proficient at putting and hitting greens in regulation. However, there still seems to be an unexplained contribution from the individual’s nationality that comes from some undefined factor, perhaps prior experience (and success) on a tour in their native country.

The LPGA results are consistent in that the two most important variables for LPGA golfers are also putting and greens hit in regulation. However, there also seems to be a significant effect of driving accuracy on end performance. In both the LPGA sample and the total sample, USA maintains significance.

Table 4 – Multivariate Results with Both Physical and Performance Characteristics:

The following table presents results from the following model:

Dep_i = α + β₁DrivingDist + β₂DrivingAcc + β₃GIR + β₄PuttAv + β₄LPGAdum + β₅EventNumb + β₆Height + β₇Weight + β₈USA + β₉Age + ε_i

where the dependent variable is either Money (in Panel A) or Scoring (in Panel B). For each statistical category, the variable is the rank of the individual observation. In columns 5 and 6, the statistical variables are an adjusted rank, segmented by quintiles where the rank is adjusted to take on a value of 1 through 5. All other variables are as defined in Tables 1 and 3.

Panel A: Money
	PGA	LPGA	Total
	(1)	(2)	(3)	(4)	(5)	(6)
	Coef.	t-stat	Coef.	t-stat	Coef.	t-stat	Coef.	t-stat	Coef.	t-stat	Coef.	t-stat
Intercept	9.33	.61	-32.81	-.26	-14.67	-2.36	67.29	1.59	.17	.69	.52	.30
DrivingDist	-.02	-.26	-.12	-1.20	.07	1.37	.07	1.95	.03	.61	.02	.34
DrivingAcc	-.10	-1.12	-.11	-1.19	.12	2.52	.09	2.30	-.02	-.40	-.02	-.49
GIR	.58	7.81	.59	7.94	.51	10.18	.41	10.48	.50	11.38	.48	11.10
PuttAv	.48	8.11	.45	7.60	.53	13.17	.38	11.42	.43	11.52	.38	10.07
LPGAdum					.01	.05	-.06	-.36
EventNumb		-.52	-.70		-3.56	-10.56		-.04	-3.77
Height		.59	.32		.17	.25		.01	.21
Weight		-.21	-.70
USA		20.96	2.49		8.38	2.91		.41	3.55
Age		1.35	2.15		-.15	-.83		.01	1.25
N	195	195	164	164	361	361
Adj. R2	.4137	.4410	.8028	.8953	.5148	.5495

Panel B: Scoring
	PGA	LPGA	Total
	(1)	(2)	(3)	(4)	(5)	(6)
	Coef.	t-stat	Coef.	t-stat	Coef.	t-stat	Coef.	t-stat	Coef.	t-stat	Coef.	t-stat
Intercept	2.11	.17	-53.35	-.53	-18.29	-4.27	39.85	1.12	-.26	-1.20	.10	.07
DrivingDist	-.05	-.69	.08	-.81	.06	1.71	.06	1.94	.04	1.08	.04	.91
DrivingAcc	-.07	-.94	-.05	-.72	.14	4.39	.14	4.28	.04	.95	.05	1.14
GIR	.61	9.81	.58	9.64	.59	17.18	.56	16.66	.55	14.21	.54	14.12
PuttAv	.49	9.85	.44	9.09	.45	16.51	.39	13.75	.45	13.77	.41	12.44
LPGAdum					-.00	-.00	.06	.38
EventNumb		.97	1.58		-1.27	-4.47		-.01	-.73
Height		-.33	-.22		-.34	-.61		-.01	-.35
Weight		.14	.79
USA		23.91	3.48		.30	3.00		.48	4.70
Age		.47	.92		-.19	-1.22		.00	.63
N	195	195	164	164	361	361
Adj. R2	.5258	.5657	.8995	.9134	.6258	.6466

Conclusions:

The researcher examined natural physical characteristics of professional golfers on the PGA and LPGA tours. He controlled for performance-predicting statistical measures, namely driving distance, driving accuracy, percentage of greens hit in regulation, and putting average. The researcher found the percentage of greens hit in regulation and putting average to be the most important characteristics of end performance (i.e. success) in professional golf. This is consistent with numerous prior studies. Driving, particularly driving accuracy, appears also to be important on the LPGA tour, but not on the PGA tour.

More important to this study, the researcher found a strong relationship between nationality and end performance. Specifically, U.S. born players have inferior end performance relative to their counterparts. One explanation for this is perhaps the superior putting averages enjoyed by the non-U.S. golfers.

The researcher’ results have interesting implications for professional golf. It is obvious that golf is an international game more now than ever before, particularly in the United States, where the professional prizes are higher than any other country. Recent domination in Ryder Cup has led many to comment on the unexplained advantage European golfers seem to enjoy during those events. While this work takes a broader approach by examining all international born golfers (and not just European ones ), it provides a good starting point in investigating whether superior performance is contingent on nationality. The researcher’ primary objective was to identify whether such a relationship exists and not necessarily to describe its origin. Therefore, future research could be designed to examine the cause, for example training methods or coaching practices.

Endnotes:

¹ During the 2006 season, the PGA tour had a total of 48 tournaments, while the LPGA had only 33.

² In unreported results, the researcher examined numerous statistics, such as sand saves. However, the researcher found no significance in relation to those variables. In order to remain consistent with the previous literature, the researcher chose to examine only the variables that have been consistently used in similar studies.
The obvious assumption is that the incremental difference between each ranking category carries the same weight. In other words, the difference between rankings 1 and 2 is the same as the difference between 2 and 3.

³ While this is a restriction, there is no reason to believe it would bias the result as the pertinent question in an individual sport is performance relative to other competitors. In order to be absolutely sure, the researcher conducted all statistical analyses using the actual number rather than the ranking. All results were qualitatively identical. Results are available upon request.

⁴ For the PGA tour, the last event concluded on November 6, 2006 while the last event for the LPGA tour concluded on November 19, 2006. There are events on both tours that are not full-field events, meaning that not all players had the opportunity to participate. While there is no reason to believe this would bias the results, for completeness, the researcher eliminated tournaments in unreported results. The final conclusions are unchanged.

⁵ The researcher also identified a variable labeled experience, defined as the number of years the player has been a professional golfer. However, the variables age and experience were highly correlated (p = .94), therefore the researcher chose to examine only age. However, in unreported results he repeated all analyses replacing age with experience and find the results qualitatively unchanged.

References:

Belkin, D.S., Gansneder, B., Pickens, M., Rotella, R.J., and Striegel, D. Predictability and stability of Professional Golf Association Tour statistics, Perceptual and Motor Skills, 1994, 78, 1275-1280.

Callen, S.J., and Thomas, J.M. Determinants of success among amateur golfers: An examination of NCAA Division I male golfers, The Sport Journal, 2004.

Callen, S.J., and Thomas, J.M. Gender, skill, and performance in amateur golf, The Sports Journal, 2006.

Chatterjee, S., Wiseman, F., and Perez, R. Studying improved performance in golf, Journal of Applied Statistics, 2002, 29, 1219-1227.

Davidson, J.D. and Templin, T.J. Determinants of success in Professional Golf Association Tour statistics, Research Quarterly for Exercise and Sport, 1986, 57, 60-67.

Dorsel, T.N. and Rotunda, R.J. 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, 2001, 92, 575-585.

Engelhardt, G.M. It’s not how you drive, it’s how you arrive: the myth, Perceptual and Motor Skills, 1995, 80, 1135-1138.

Engelhardt, G.M. Differences in shot-making skills among high and low money winners on the PGA Tour, Perceptual and Motor Skills, 1997, 84, 1314.

Engelhardt, G.M. Driving distance and driving accuracy equals total driving: Reply to Dorsel and Rotunda, Perceptual and Motor Skills, 2002, 95, 164-180.

Hume, P.A., Keogh, J., and Reid, D. The role of biomechanics in maximizing distance and accuracy of golf shots, Sports Medicine, 35, 429-449.

Jones, R.E. A correlation analysis of the Professional Golf Assocation (PGA) statistical ranking for 1988, In A.J. Cochran (Ed.) Science and Golf: Proceedings of the First World Scientific Conference of Golf. London: E &FN Spon. 165-167.

Myers, D.C., Gebhardt, D.L., Crump, C.E., and Fleishman, E.A. The dimensions of human physical performance: Factor analysis of strength, stamina, flexibility, and body composition measures, Human Performance, 6, 309-344.

Moy, R.L. and Liaw, T. Determinants of professional golf earnings, The American Economist, 1998, 42, 65-70.

Nero, P. Relative salary efficiency of PGA Tour golfers, The American Economist, 2001, 45, 51-56.

Shmankse, S. Human capital formation in professional sports: Evidence from the PGA Tour, Atlantic Economic Journal, 1992, 20, 66-80.

Shmankse, S. Gender, skill, and earnings in professional golf, Journal of Sports Economics, 2000, 1, 385-400.

Wiseman, F., Chatterjee, S., Wiseman, D., and Chatterjee, N. 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., 1994, 199-204.

U.S. Sports Academy2016-10-12T14:52:28-05:00March 14th, 2008|Contemporary Sports Issues, Sports Studies and Sports Psychology|Comments Off

Book Review: The College Athlete’s Guide to Academic Success: Tips from Peers and Profs

Reviewed by: Jeffrey Chamberlin

The College Athlete’s Guide to Academic Success: Tips from Peers and Profs assists the student-athlete in making a successful academic transition from high school to college. Bob Nathanson and Arthur Kimmel, present a guide that focuses on essential issues for high school seniors and in-coming college freshmen who are trying to be successful in the classroom. The authors’ work is attributed to their direct observations during a combined 56 years of teaching intercollegiate athletes, as well as from input they received from 35 highly-successful, recently-graduated student-athletes from 16 colleges around the country.

The College Athlete’s Guide to Academic Success: Tips from Peers and Profs focuses on the actual transition from high school and athletics to college and athletics by identifying useful resources to make the transition easier, developing strategies to manage time and to schedule classes more wisely, and providing strategies by which to select a college major appropriate for a career. The guide includes direction on maintaining positive relationships with students and faculty, keys to living a healthy lifestyle, and tips for making a smooth transition to life after college and athletics. Each chapter offers helpful hints from peers and professors, provides quotes from recently-graduated student-athletes, and lists questions regarding the many challenges of college.

This is a concise and easy-to-read manual for any college-bound, student-athlete who needs a quick primer on successfully shuffling athletics with academics. However, it is just a guide; it is therefore limited in scope and lacks depth. This is something the reader should be mindful of, considering the expansiveness of the subject matter.

Authors: Bob Nathanson and Arthur Kimmel; Foreword by Myles Brandt, NCCA President.
Published in 2008 by Pearson Education, Inc.
(ISBN: 0-13-237947-3).

U.S. Sports Academy2013-11-26T15:05:35-06:00March 14th, 2008|Contemporary Sports Issues, Sports Management, Sports Studies and Sports Psychology|Comments Off