ISSN: 1543-9518|Vol. 24

Compatibility of Adaptive Responses With Hybrid Simultaneous Resistance and Aerobic Training

Submitted by: Don Melrose Ph.D. CSCS & Ronald G. Knowlton Ph.D. FACSM

ABSTRACT

The purpose of this investigation was to examine the effects of a hybrid, simultaneous, resistance and aerobic training program on aerobic power and muscular strength. Free-weight 1RM elbow flexor strength and cycle ergometer maximal aerobic power (CE VO2 max) were assessed for 15 untrained subjects. All tests were performed prior to and following a six-week training program. Subjects were randomly assigned to three training groups: an aerobic-training group, a strength-training group, and a simultaneous-training group. All training was performed three times per week. Aerobic training consisted of five to six, three-minute bouts of high-intensity exercise performed on a calibrated Monark cycle ergometer. All training intervals occurred at 85 to 100% of the subject’s CE VO2 max. Training intervals were separated by three minutes of rest. Strength training consisted of performing arm-flexion exercise with the subject’s dominant arm using a free-weight dumbbell. The strength training protocol consisted of performing four working sets of exercise per session separated by three minutes of rest. The first two weeks of training consisted of four sets of 10RM, the third week at 8RM, the fourth at 6RM, the fifth at 4RM, and the sixth at 2RM. The simultaneous training group performed both the aerobic and strength training protocols simultaneously. The aerobic and simultaneous groups significantly (p< 0.05) increased aerobic power 33.6 ± 6.1 to 39.1 ± 6.8 and 36.2 ± 3.7 to 42.3 ± 5.4 ml×kg-1×min-1 respectively. There was no significant difference in aerobic power increase between the aerobic and simultaneous training groups. The strength and simultaneous training groups significantly (p < 0.05) increased 1RM strength 11.36 ± 3.2 to 16.81 ± 5.1 kg and 13.81 ± 5.13 to 17.72 ± 6.15 kg respectively. There was no significant strength difference between the strength and simultaneous training groups. In conclusion, simultaneous high-intensity, cycle ergometer, aerobic training and one-arm, free-weight, strength training can be effectively utilized to increase maximal aerobic power and dynamic elbow-flexor strength. This study shows that the concept of simultaneous, high-intensity, aerobic and strength training is viable and that this approach to training may perhaps become a conditioning option for athletes and non-athletes.

INTRODUCTION

Strength and endurance training serve as the cornerstone of both athletic training and basic fitness regimens. A seemingly endless variety of modes, methods, and techniques are routinely utilized to achieve greater performance and fitness. At the forefront of these training methods is concurrent training. Concurrent training generally refers to the performance of both aerobic and anaerobic exercise within a fitness or athletic training program. To that end, strength and endurance training are applied in varying sequences within the same workout, daily, or weekly schedule. Athletes as well as popular and commercial fitness applications capitalize on these basic themes and supply the consumer with unlimited exercise options. Included within this variety are techniques which combine both resistance and aerobic training at the same moment in time, not separately. Such techniques are now very popular and are most commonly utilized in group-exercise settings in which individuals utilize barbells or dumbbells with the upper body and some kind of aerobic movement with the lower body at the same moment in time. For clarity, this type of training will be referred to as simultaneous training.

Currently, available research does not document simultaneous training as defined above. However, numerous studies have investigated the interactions of strength and aerobic training on muscular strength and aerobic power resulting from traditional same day or different day simultaneous training. These investigations often report mixed results (Abernethy & Quigley, 1993; Dudley & Djamil, (1985), Gravelle & Blessing, 2000; Hennessy & Watson, 1994; Hickson, 1980; Hunter, Demment, and Miller, 1987, McCarthy, Pozniak, and Agre, 2002; McCarthy, Agre, Graf, Pozniak, and Vailas, 1995). In all reviewed investigations, experimental training groups that performed concurrent training had no impairment in the magnitude of aerobic power increase as compared to those training groups that performed aerobic training only. The numerous physiological and structural adaptations resulting from aerobic training appear to be unaffected when combined concurrently with strength training. Some studies in which concurrent training was performed showed significantly less increase in muscular strength as compared to those experimental groups that performed strength training only (Dudley & Djamil, 1985; Hennessy & Watson, 1994; Hickson, 1980). Then again there are a number of studies which show little, if any, impairment in the magnitude of strength gain (Abernethy & Quigley, 1993; Hunter et al. 1987, McCarthy et al., 2002; McCarthy et al, 1995, Volpe, Walberg-Rankin, Webb-Rodman, and Sebolt, 1993). Most investigations reporting strength decrement report that strength gain decrement is isolated to the same muscle group that was utilized during the aerobic training portion of the study. Currently there is a lack of consensus among investigators as to the exact cause(s) of strength gain impairment as a result of concurrent training

Regardless of the degree of compatibility concurrent training may afford to increases in muscular strength and aerobic capacity, each of the aforementioned studies utilizes unique training methodologies and experimental designs. These key differences make it difficult to discern the degree of effectiveness and optimal application of concurrent training. Simultaneous training further complicates training and training outcomes due to its hybrid nature. This type of training is physically complicated and requires full body coordination. Since it does not involve a separation of the two modes of training and is relatively difficult to effectively coordinate, the efficacy of this training is unclear in either laboratory or group-exercise settings. The objective of this experiment was to examine the efficacy of synchronizing strength and endurance training and its effect on muscular strength and aerobic power.

METHODS

Subjects

Fifteen subjects, nine women and six men, ranging in age from 18 to 28, were recruited for this study (Table 1). Prior to data collection, subjects had not participated in a regular exercise program for a period of six months. All subjects were required to fill out a medical history questionnaire for the purpose of screening for contraindications to participation. The Southern Illinois University at Carbondale Human Subjects Committee granted approval for this study. Subjects were informed of the risks associated with participation in the study and subsequently signed an informed consent prior to data collection.

Table 1. Subject characteristics (mean ±SD )
Variable Women (n = 9) Men (n = 6)
Age (y) 21.1 ± 2.6 21.2 ± 1.5
Height (cm) 158.5 ± 16.6 180 ± 6.7
Weight (kg) 69.8 ± 7.7 88.0 ± 20.7
Body Fat (%) 26.1 ± 5.2 15.2 ± 5.5

Experimental Design

Subjects were assigned to one of three training groups. Each training group was randomly assigned three women and two men. The first training group was a strength-training group (STG) only, the second was an aerobic-training group (ATG) only, and the third was a simultaneous-training group (SNTG). All subject testing occurred one week pre- and one week post-training. Subjects in all three training groups performed both strength and aerobic testing. All training was conducted three times per week at regular intervals, typically on an alternating daily basis. The duration of the training period was six weeks. All pre-testing took place within one week prior to and following the training period.

1RM Testing

A one repetition maximum (1RM) elbow flexion (bicep curl) test was performed unilaterally using the subject’s dominant arm. A plate loaded dumbbell was utilized for 1RM testing. Subjects were seated with their feet on the floor. Bicep curling was performed with the hand in the supinated position throughout the lift’s range of motion. A 1RM protocol consistent with NSCA guidelines was utilized prior to maximal testing (Baechle & Earle, 2000). A maximal lift was determined when the subject could complete only one repetition in strict form.

Aerobic Power Testing

A calibrated Monark cycle ergometer (Varberg, Sweden) was utilized for all aerobic power testing. Maximal cycle ergometer oxygen consumption (CE VO2max) was measured using a Parvo Medics, True Max 2400 Metabolic Measuring system (Concentius Technology). Subjects wore a Polar heart rate monitor during all testing. A five-minute submaximal warm-up period preceded commencement of the aerobic power testing protocol. A pedaling rate of 60 rpm was maintained throughout the test. An initial work load of 60 Watts (W) was performed for one minute. At the beginning of each minute following the first minute, pedaling intensity was increased by 30 W. Heart rate was annotated at the end of each respective workload. Cycle ergometer VO2max was determined by the occurrence of one of the following; a plateau or decrease in oxygen consumption with a subsequent increase in workload, obtaining age predicted maximum heart rate or volitional fatigue. A brief cool-down period followed test termination.

Strength Training

Strength training was performed unilaterally with the subject’s dominant arm. A plate-loaded dumbbell was used to perform elbow flexion (bicep curl) exercise. As with the 1RM trial, strength training was performed in the seated position. The first and third training sessions of each week were designated “heavy” training days while the second was a “light” training day. Pilot testing revealed that muscular and joint soreness were an issue with three heavy training sessions per week. A brief warm-up period, consisting of two to three sets of 12-15 repetitions at about 50% of the subject’s 1RM, preceded each training session. Four working sets were performed during each training session following the warm-up period. The strength-training protocol was periodized by RM loads over the course of the six-week training program. The first two weeks of training consisted of performing arm flexion exercise at the subject’s 10RM load. The third week was performed at the subject’s 8RM load. The fourth was performed at the 6RM load, the fifth at 4RM, and the sixth at 2RM. Training loads were adjusted as needed throughout training sessions to achieve target repetitions across all sets. Light-day training sessions were performed at approximately 75 to 80% of the heavy training loads. All working sets were separated by three minutes of rest.

Aerobic Training

Aerobic training was performed on a Monark (Varberg, Sweden) cycle ergometer. Cycles were calibrated each week. A heart-rate monitor was worn by each subject during training to monitor exercise intensity during training. Following a brief warm-up period consisting of five to 10 minutes of light, sub-maximal pedaling, aerobic training commenced. Training sessions consisted of five, three-minute exercise intervals separated by three minutes of rest. All training intervals were performed at a pedaling rate of 60 rpm. Exercise bouts were performed at power outputs corresponding to the subject’s 85 to 100% CE VO2 max. Beginning the fourth week of training a sixth training interval at 85 to 100% VO2 max was added. Percentages of the subject’s CE VO2 max were calculated using the Karvonen method (American College of Sports Medicine [ACSM], 2000).

Simultaneous Training

Simultaneous training consisted of both the strength and aerobic training protocols performed at the same time. Upon initiating the aerobic training protocol and achieving the desired pedaling rate of 60 rpm, subjects were handed an appropriately loaded dumbbell. Subjects continued pedaling while curling the dumbbell until the desired repetition number for that set was achieved. Coordination of simultaneous exercise activities was achieved quickly by each subject. Upon completion of the set the dumbbell was removed and the subject completed the aerobic interval.

Statistical Analyses

All statistical analyses were performed using the SIUC mainframe Statistical Analysis Systems (SAS) program. Measures of central tendency and spread of data were represented as means and standard deviations. The experimental protocol employed a repeated measures design. A two by three repeated measures analysis of variance (ANOVA) was performed to analyze within and between group differences. Between- and within-group analyses consisted of the following for each group: 1) pre- and post- training 1RM and 2) pre- and post-training aerobic power measurements. The criterion alpha level was set at p < 0.05. All statistically significant interactions were analyzed to determine if either of the training groups had greater increases in either aerobic power or muscular strength from pre- to post-training than other training groups. Differential effects, a post-hoc technique, were utilized to analyze significant interactions between training groups (Khanna, 1994).

RESULTS

Muscular Strength

There was a significant increase in 1RM for the simultaneous training group from pre- to post-training (13.81 ± 5.13 to 17.72 ± 6.15 kg), an increase of 28.29%. There was a significant increase in 1RM for the strength training group from pre- to post-training (11.36 ± 3.20 to 16.81 ± 5.1 kg), an increase of 48.0% (Figure 1.). There was no significant difference in muscular strength increase between the simultaneous and strength training groups. The aerobic training group had no significant increases in muscular strength.

Figure 1. Changes in muscular strength pre-training to post-training.

Figure One

Aerobic Power

The simultaneous training group significantly increased CE VO2max from pre- to post-training (36.2 ± 3.7 to 42.3 ± 5.4 ml · kg -1 · min-1), an increase of 16.75%. The aerobic training group significantly increased CE VO2max from pre- to post-training (33.5 ± 6.1 to 39.1 ± 6.8 ml · kg -1 · min-1), an increase of 16.49% (see Figure 2.). There was no significant difference in the magnitude of increase of the CE VO2max between the aerobic and simultaneous training groups. There was no significant increase in aerobic power for the strength training group.

Figure 2. Changes in aerobic power, pre-training to post-training.

Figure Two

DISCUSSION

In the present study, simultaneous training induced significant increases in both aerobic power and muscular strength. The independent strength and endurance training programs produced significant increases in both muscular strength and aerobic power respectively. Results indicate that hybrid simultaneous training, consisting of strength training and high-intensity aerobic training is capable of inducing significant increases in both muscular strength and aerobic power.

In simultaneous exercise, especially in group settings, the upper body is most benefited by resistance training since the lower body is performing the primary aerobic movement. Therefore, the greatest muscular strengthening occurs in the musculature of the upper body. Kraemer et al. (1995) referred to this effect as compartmentalization in which the upper body muscle groups are essentially unaffected by any negative effects of aerobic training. Group simultaneous exercise typically involves the use of relatively light barbells, dumbbells, or power bands. Training sessions persist up to an hour and include a variety of aerobic and resistance training movements. In the current study, utilizing lighter weights and a variety of movements was not practical. A primary goal of this study was to explore the efficacy of applying the two types of training so that the respective aerobic and resistance training stimuli occurred at the same time as in group settings. Given the results of the current investigation, it is reasonable to presume that group-style simultaneous training is a viable form of training.

Changes in aerobic capacity represent a durable adaptation in concurrent training. Superficially, it appears as if many physiological and structural adaptations that occur as a result of performing aerobic and strength training exercise may be antagonistic to each other. The specific adaptations common to endurance training include increases in capillary density, myoglobin, mitochondria, and oxygen uptake (Holloszy & Coyle, 1984). Aerobic training also has a tendency to decrease myofibrillar protein production in the muscle (Hoppeler, 1986). Strength training, however limits mitochondria, capillary supply, and production of aerobic enzymes (Luthi, Howald, Claassen, Vock, and Hoppeler, 1986; MacDougall, Sale, Moroz, Elder, Sutton, and Howald, 1979). According to Hurley, Seals, and Eshani (1984) while peripheral changes are important in the development of aerobic power, adaptations of the central circulatory mechanisms such as cardiac output and stroke volume are not affected by strength training. With respect to aerobic and strength training independently, this demonstrates that some physiological and structural adaptations to exercise have a more profound effect on the magnitude of the increase or decrease than others. The lack of significant difference in VO2max increases between the endurance and concurrent groups in several studies demonstrate that the development of aerobic capacity is independent of muscular strength increase (Dudley and Djamil, 1985; Hickson, 1980, Hunter et al. 1987; McCarthy et al. 2002; McCarthy et al., 1995; Volpe et al., 1993). The aerobic results of the current study were in agreement with those of the concurrent training studies.

Resistance training in its various forms elicits increases in muscular hypertrophy, increased stores of ATP and PCr, force generation, and anaerobic enzymes (Costill, Coyle, Fink, Lesmes, and Witzmann, 1979; Fleck & Kraemer, 1988; MacDougall, Sale, Elder, and Sutton, 1982; MacDougall et al., 1979). However, the greatest issue surrounding any type of simultaneous training regimen is strength gain inhibition. In some concurrent investigations in which the lower body was involved in strength and aerobic training, the lower body strength gains in the concurrent training groups were inhibited (Dudley & Djamil, 1985; Hennessy & Watson, 1994; Hickson, 1980). In Leveritt and Abernethy’s (1999) investigation, the ability of subjects to perform strength training was reduced following aerobic training. The strength inhibition experienced in the lower body demonstrates the susceptibility of the legs in general to strength gain impairment in response to concurrent training. Studies that performed resistance training with the upper body noted few if any problems with upper body strength increase when the legs were used to perform aerobic training. Kraemer et al. (1995) reported that effects of upper body strength training performed with endurance training seem to be generally compartmentalized to the upper body musculature, and did not significantly affect the force production or endurance capabilities of the lower body musculature. Interestingly, this does not appear to be the same relationship with aerobic and strength training performed by the arms. Abernethy and Quigley’s investigation (1993) noted no strength gain inhibition in a concurrent group that performed arm ergometry and isokinetic arm strength training. It was noted that further research will be needed to understand the different strength adaptation patterns in the quadriceps and triceps brachii respectively. The current study is in agreement with concurrent training study observations that show the upper body strength increases are not compromised by the aerobic activity performed by the lower body. Sale, MacDougall, Jacobs, and Garner (1990) noted; whether impairment, compatibility, or synergistic enhancement occur, the application of training volume, intensity, frequency, mode, training status of subjects decides the final outcome.

CONCLUSIONS

In the current investigation, aerobic and strength gain adaptations resulting from simultaneous training group were not negatively impacted. The adaptations of hybrid simultaneous training are much aligned with observations of traditional simultaneous training. While simultaneously achieved, muscular strength and aerobic power adaptations in the present study were likely not achieved due to the respective adaptations functioning in a complimentary capacity, but perhaps a compatible or even independent capacity. This training technique does pose limitations with respect to equipment, coordination, and number of exercises possible in combination. However, this type of training appears to be effective and may be used as a legitimate, but limited mode of exercise or conditioning. This type of training may also be used for off-season and pre-season conditioning for athletes as well. In conclusion, in untrained adults, simultaneous strength and aerobic training are as effective for increasing muscular strength and aerobic power.

References

1. Abernethy, P.J. & Quigley, B.M. (1993). Concurrent strength and endurance training
of the elbow extensors. Journal of Strength and Conditioning Research, 7, 234-240.

2. American College Of Sports Medicine (ACSM) (2000). ACSM’s Guidelines for
Exercise Testing and Prescription. Philadelphia: Lippincott, Williams, and Wilkins.

3. Baechle, T.R. & Earle, R.W. (2000). Essentials of Strength Training and Conditioning.
Champaign, IL: Human Kinetics.

4. Costill, D., Coyle, E., Fink, W., Lesmes, G. & Witzmann, F. (1979). Adaptations
in skeletal muscle following strength training. Journal of Applied Physiology, 46, 96-99.

5. Dudley, G.A., & Djamil, R. (1985). Incompatibility of endurance and strength training
modes of exercise. Journal of Applied Physiology, 59, 1446-1451.

6. Fleck, S., & Kraemer, W. (1968). Resistance training: physiological responses and
adaptations. Physician and Sportsmedicine, 16, 108-119.

7. Gravelle, B.L. & Blessing, D.L. (2000). Physiological adaptation in women
concurrently training for strength and endurance. Journal of Strength and Conditioning Research, 14, 5-13.

8. Hennessy, L.C., & Watson, A.W. (1994). The interference effects of training for
strength and endurance simultaneously. Journal of Strength Conditioning and Research, 8, 12-19.

9. Hickson, R.C. (1980). Interference of strength development by simultaneously training
for strength and endurance. European Journal of Applied Physiology, 45, 255-269.

10. Holloszy, J. & Coyle, E. (1984). Adaptations of skeletal muscle to endurance
exercise and their metabolic consequences. Journal of Applied Physiology, 56, 831-838.

11. Hoppeler, H. (1986). Exercise-induced ultrastructural changes in skeletal muscle.
International Journal of Sports Medicine, 7, 187-204.

12. Hunter, G., Demment, R., and Miller, D. (1987). Development of strength and
maximum oxygen uptake during simultaneous training for strength and endurance. Journal of Sports Medicine and Physical Fitness. 27, 269-275.

13. Hurley, B.F., Seals, D.R., and Eshani, A.A. (1984). Effects of high intensity strength
training on cardiovascular function. Medicine and Science in Sports and Exercise, 16, 483-488.

14. Khanna, R. (1994). An analysis of the teaching, understanding and interpretation of
interaction effects in a factorial design. Unpublished doctoral dissertation. Southern Illinois University, Carbondale.

15. Kraemer, W.J., Patton, J.F., Gordon, S.E., Harman, E.A., Deschenes, M.R., Reynolds,
K., Newton, R.U., Triplett, N.T., and Dziados, J. (1995). Compatibility of high-
intensity strength and endurance training on hormonal and skeletal muscle adaptations. Journal of Applied Physiology, 78, 979-989.

16. Leveritt, M. & Abernethy, P.J. (1999). Acute effects of high-intensity endurance
exercise on subsequent resistance exercise activity. Journal of Strength and Conditioning Research, 13, 47-51.

17. Luthi, J.M., Howald, H., Claassen, H., Rösler, P, Vock, P. & Hoppeler, H.
(1986). Structural changes in skeletal muscle tissue with heavy resistance exercise. International Journal of Sports Medicine. 7, 123-127.

18. Macdougall, J., Sale, D., Elder, G., & Sutton, J. (1982). Muscle ultrastructural
characteristics of elite powerlifters and bodybuilders. European Journal of Applied Physiology, 48, 117-126.

19. Macdougall, J.D., Sale, D.G., Moroz, J.R., Elder, G.C.B., Sutton, J.R. &
Howald, H. (1979). Mitochondrial volume density in human skeletal muscle following heavy resistance training. Medicine and Science in Sports and Exercise, 11, 164-166..

20. McCarthy, J.P., Pozniak, M.A. & Agre, J.C. (2002). Neuromuscular adaptations to
concurrent strength and endurance training. Medicine and Science in Sports and Exercise, 34, 511-519.

21. McCarthy, J.P., Agre, J.C., Graf, B.K., Pozniak, M.A., & Vailas, A.C. (1995).
Compatibility of adaptive responses with combining strength and endurance
training. Medicine and Science in Sports and Exercise, 27, 429-436.

22. Sale, D.G., Macdougall, J.D., Jacobs, I., & Garner, S. (1990). Interaction
between concurrent strength and endurance training. Journal of Applied
Physiology, 68, 260-270.

23. Volpe, S.L., Walberg-Rankin, J., Webb Rodman, K., & Sebolt, D.R. (1993).
The effect of endurance running on training adaptations in women participating in a weight lifting program. Journal of Strength and Conditioning Research, 7, 101-107.

2015-03-24T09:56:47-05:00June 5th, 2005|Contemporary Sports Issues, Sports Coaching, Sports Exercise Science, Sports Management, Sports Studies and Sports Psychology|Comments Off on Compatibility of Adaptive Responses With Hybrid Simultaneous Resistance and Aerobic Training
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Considerations for Interscholastic Coaches

Submitted by: Vincent Mumford, Ed.D. & Gerald Gergley, MS

Abstract

This study examines coaches’ learning experiences by identifying some of the major obstacles beginning coaches may encounter. It also suggests ways to prevent potential problems by examining the knowledge of more experienced coaches. Head high school football and basketball coaches were surveyed to determine things they would do the same and things they would do differently, if they were starting their careers over again. Based on survey responses, several themes emerged. The emergent themes were in the areas of relationships, professional development, conditioning and training, organization and administration, scheduling, academics, promotion and fundraising, facilities, job choice, and rules and accountability. When asked what they would do differently, the largest numbers of responses were in the areas of relationships (79%), organization and administration (41%), and job choice (28%). When asked what they would do the same, the largest number of responses were in the areas of professional development (72%), relationships (59%), conditioning and training (59%), and rules and accountability (45%). The results of this study are consistent with previous research on coaching and offer implications for those interested in entering the profession of coaching

Loser or Legend: Beginning Considerations for Interscholastic Coaches

Coaching is probably one of the toughest professions in the world. Contrary to the opinion of many, coaching is not a tough profession because of the pressure to win. Sure coaches are fired everyday based on their win-loss records, but most coaches understand the nature of the sport and live for the intense competition. What makes coaching such a difficult profession are the innate complexities of the game and the specialized body of knowledge required to be a good coach (Martens, 2004). What makes coaching a daunting profession is that coaches are expected to possess knowledge across a wide range of domains, including the ability to master the many roles a coach is required to perform that are unrelated to specific practice or game instruction (Lynch, 2001).

It has often been said that hindsight is always twenty-twenty. This is especially true in the profession of coaching, where split-second decisions and inches are what separate loser from legend. Early in his career at Duke University, basketball coach Mike Krzyzewski was considered a loser. So was former football coach Tom Landry, who had a losing record in each of his first six seasons with the Dallas Cowboys. Both of these coaches are now considered legends. At their best, most coaches have win-loss records of .500 or less. However, coaching is about more than wins and losses. At its best, coaching is about teaching life skills through game strategy. The best coaches know this. Still, most coaches never quite master this art and science either.

Given a chance, even the most experienced coaches would do some things differently, if the decisions would result in more victories on or off the field. Since the ability to go back in time is not an option, the ability to reflect on past experiences and then share that coaching wisdom is the next best alternative. According to O’Donnell (1998), coaches learn through experience (trial and error) or by studying other successful coaches. This theory of learning is what makes sport camps and clinics such a popular and lucrative business. Neophyte coaches often seek the knowledge of highly experienced coaches with the hopes that it will translate into the neophyte becoming a better, more knowledgeable and more successful coach.

Florida is one of the most populated and geographically largest states in the union. According to a study published by the National Sporting Goods Association (2002), the state of Florida is one of the leading states when it comes to sports participation. Thus, Florida is an important state to consider when researching and studying coaching.

Research Questions

With the goal of exploring coaches’ learning experiences in interscholastic sports, the purpose of this study was to identify some of the major problems a beginning coach may encounter, and to suggest recommendations to prevent potential problems. Specific research questions which guided the study were:

  1. If you could start your coaching career over from the beginning, what things would you repeat or do exactly the same?
  2. What things would you not repeat if given a chance to begin again as a new coach?

Methodology

Respondents

Respondents for this study were head football and basketball coaches (n=78) of high schools in the Central Florida area. All high schools solicited in this study hold membership in the Florida High School Athletic Association (FHSAA).

Instrumentation

A survey instrument was developed and used in this study to gather demographic data on coaches at high schools in the Central Florida area. A small pilot study using approximately six coaches was conducted to test the validity and reliability of the instrument. Individuals in the pilot study were from two representative high schools within the Orange County School district. Subjects in the pilot study were asked to complete the questionnaire and comment on the thoroughness of the directions provided, ease of completion, and suitability of questions as they pertain to the content. Using the results of the pilot study, the survey instrument was updated to incorporate recommendations. Problems with the instrument were addressed and corrected.

The survey instrument consisted of 10 items containing closed-ended questions and four items containing open-ended questions (see Appendix C for the complete survey). Data was gathered for comparative purposes only. Confidentiality of responses was guaranteed to all respondents. The overall return rate of the survey was 37 percent, which included responses from 29 subjects.

Procedure

During the fall of 2003, head football and basketball coaches (n=78) from high schools in the Central Florida area were mailed a cover letter, consent form, questionnaire, and a stamped self-return envelope. The statistical software package, SPSS 11.0, was used to analyze the descriptive data.

Another method of gathering data was the review of related documents and archival records. Documents used to gather data included individual high school websites, research papers on coaching, and the National Federation of State High School Associations website. This method of data gathering provided complementary information to that obtained in the surveys. In this manner, the researcher could triangulate and cross-check data provided by the survey (Wolcott, 1994).

Results

The major areas of concern and responses, as self-reported by respondents, were in the following 10 categories: (1) relationships, (2) professional development, (3) conditioning & training, (4) organization & administration, (5) scheduling, (6) academics, (7) program promotion & fundraising, (8) facilities, (9) job choice, and (10) rules & accountability.

What Coaches Would Do Differently

Head coaches were asked to identify three things they would do differently if they could start all over again as a new coach. Responses listed below are based on the 10 categories that emerged from the research.

Relationships

23 of the 29 coaches that responded (79%) indicated that, if they had it to do all over again, they would do things differently in the area of relationships. Their responses included ways they would deal differently with assistant coaches, parents, student-athletes, the administration, and their own family.

Professional Development

3 of the 29 coaches (10%) indicated they would do things differently in the area of professional development. Their responses included ways they would enhance their growth by not pigeon holing themselves by positions coached, re-prioritizing their teaching and coaching roles, and working harder to learn the craft of coaching instead of taking it for granted.

Conditioning & Training

5 of the 29 coaches (17%) indicated they would do things differently in the area of conditioning and training. Their responses indicated that they would practice less, work to develop feeder programs, and reverse the way they introduce offensive and defensive strategies.

Organization & Administration

12 of the 29 coaches (41%) indicated they would do things differently in the area of organization and administration. Their responses ranged from issues involving budgets, pre-game meals, delegating responsibilities, getting rid of players, and handling written agreements.

Scheduling

7 of the 29 coaches (24%) indicated they would do things differently in the area of scheduling. Their responses indicated they would: not over-schedule, not schedule back-to-back games, not schedule as many tough opponents, practice more on the weekends, and like to have more control over their schedules.

Facilities

3 of the 29 coaches (10%) indicated they would do things differently in the area of facilities. Their responses indicated they would do more to improve the condition of their facilities.

Job Choice

8 of the 29 coaches (28%) indicated they would do things differently in the area of job choice. Their responses indicated they would: be more careful about the jobs they selected, and not coach as many sports.

Rules & Accountability

4 of the 29 coaches (14%) indicated they would do things differently in the area of rules and accountability. Their responses ranged from being stricter to being more flexible.

What Coaches Would Do the Same

Head coaches were asked to identify three things they would repeat or do exactly the same if they could start all over again as a new coach. Responses listed below are also based on the 10 categories that emerged from the research.

Relationships

17 of the 29 coaches that responded (59%) indicated that, if they had it to do all over again, they would do things the same in the area of relationships. Their responses included ways they would repeat similar behavior with assistant coaches, parents, student-athletes, the administration, school staff, and religious beliefs.

Professional Development

21 of the 29 coaches (72%) indicated they would do things the same in the area of professional development. Their responses included ways they would enhance their personal and professional growth by being life-long learners.

Conditioning & Training

17 of the 29 coaches (59%) indicated they would do things the same in the area of conditioning and training. Their responses indicated that they would: implement strength training programs, set team and individual goals, spend the majority of their time teaching the fundamentals, and work to develop and train young talent.

Academics

6 of the 29 coaches (21%) indicated they would do things the same in the area of academics. Their responses indicated they would: set academic goals, develop academic support programs, assist students with post graduation plans, and continue their own education.

Program Promotion & Fundraising

3 of the 29 coaches (10%) indicated they would do things the same in the area of program promotion and fundraising. Their responses indicated they would work to develop the image of their program.

Job Choice

7 of the 29 coaches (24%) indicated they would do things the same in the area of job choice. Their responses indicated they would: seek out a good mentor, seek out good talent, develop a network, and take any job to get into the profession.

Rules & Accountability

13 of the 29 coaches (45%) indicated they would do things the same in the area of rules and accountability. Their responses ranged from setting to enforcing rules.

Conclusions and Recommendations

This study examines coaches’ learning experiences by identifying some of the major obstacles beginning coaches may encounter. It also suggests ways to prevent potential problems by examining the knowledge of coaches. Specifically, this study looks at best practices in high school coaching and examines what works and what does not work.

Coaching is about more than “Xs” and “Os”. It is about influence and getting things done through other people. Thus, coaching is part art and part science. As such, the profession of coaching requires a specialized body of knowledge more specific to the sport and a more generalized body of knowledge across a wide range and sphere of influence. To be successful, coaches need to be knowledgeable of game strategy. They also need to be knowledgeable of the many roles a coach must undertake. Possessing this knowledge is crucial for a beginning coach.

This study implies that much of this knowledge can be learned from more experienced coaches. It not only identifies some of the major problems a beginning coach may encounter, it also suggests recommendations to prevent potential problems. To help expedite the learning curve of beginning coaches, we offer the following recommendations:

Build and maintain nurturing, supportive relationships. These relationships will include the school administration, assistant coaches, student-athletes, faculty, parents, and the coaches’ family. Work hard to educate everyone about the positive benefits of the athletic program. Communicate with these different groups on a regular basis and keep them informed of what’s going on. Strive to make them your ally. Demonstrate that you are an integral part of the school and a team player. Show them you are as interested in academic performance as you are athletic performance.

Continue the learning process through yearly professional development. Knowledgeable and well-trained coaches are the key to a successful sports program. Attend camps and clinics to keep current on the latest techniques and strategies. Study successful coaches. Find a mentor as early in your career as possible. Join and become an active member of a professional organization

Develop a cutting-edge conditioning and training program. To build a successful program, the coach must focus on developing the athletes to completely maximize their potential. Learn the latest techniques for developing speed, quickness, agility, jumping ability, explosiveness, reaction time, and strength. Set individual goals with each athlete and work with them to achieve their goals. Develop a feeder program that will provide program consistency. Spend the majority of practice time teaching and reinforcing the fundamentals.

Create a smooth-running organization with good administration skills. Beginning coaches must be aware of their wide range of duties. They are responsible for developing policies, scheduling practice and game times, planning budgets, ordering equipment, coordinate facility use, evaluating talent, record keeping and paperwork, arranging travel plans, scouting opponents, and arranging for medical care at events. They must develop a personal philosophy and create a system that will aid them in accomplishing all of their tasks. They must surround themselves with good people and learn how to delegate.

Schedule for success. Most new coaches underestimate the importance of scheduling. Creating a good schedule is extremely important for a coach’s success. Not many coaches get fired for who they played. They get fired for wins and losses. Set realistic goals based on the team’s ability. Contrary to public opinion, coaches should not always try to play the best teams. Sometimes they may need to play a few tune-up games. Every conference has at least four tough games (rivals). Always playing the best teams can quickly put the new coach on the path to becoming a loser. Scheduling is part art and part science. Where possible, work closely with the athletic director to create a favorable schedule.

Place academics first. It is vital that new coaches understand the big picture — the proper role of sports as a part of the total educational program of the school. The athletic program should function as a part of the whole curriculum and strive for the development of a well-rounded individual, capable of taking his or her place in modern society. At no time should the coach place the educational curriculum secondary in emphasis to the athletic program. New coaches should set academic goals, monitor student grades, and conduct an academic support program (i.e., study hall). They should push each student to attend college, regardless of the level. They should demonstrate their commitment to education by continuing their own education.

Increase attendance and revenue through promotions and fundraising. Coaches can get fans to focus on the sport program (i.e., attend more events) by first focus on them. Get their attention and get them involved by creating exciting promotions. Promotions and spirit activities help draw more people to the events. Incorporate fun things that meet the needs of the fans or target audience. Food or cash prizes work well. Conduct contest at half time and during intermissions to eliminate idle time. Make the contest as interactive as possible. Give-aways are a good way to grab attention and boost attendance. Develop a strong booster club to generate revenue and ideas. Have coaches, team members, and booster club members promote and/or participate in activities.

Improve facilities to improve performance. Experienced coaches know that state-of-the-art facilities and equipment can help them take their teams sport performances to the next level. New coaches should be knowledgeable about the latest in facility design and equipment for their sport. They should get involved in the planning of any new athletic facilities or renovations. Give input about weight rooms, showers, locker rooms, equipment rooms, training/therapy rooms, team meeting rooms, multi-purpose rooms, and athletic playing and practice fields and courts. It is especially important for them to attend construction meetings and review drafts and blue prints.

Be proactive in making job choices. New coaches should consider all of the possibilities or alternatives before taking a job. They should not make career decisions hastily, but instead should plan for the future. Look into the future and determine what you want to be doing in 5, 10, 20, and 30 years and set goals. Then prepare for potential opportunities. Several possibilities and alternatives to consider are:

  1. Do you want to be an assistant coach or head coach?
  2. Do you want to coach at the high school level forever or coach at the college level one day?
  3. How long do you want to stay at one location?
  4. Do you have a good network and know the right people?
  5. What type of athletes do you want to coach?
  6. Do you have the support of the administration?
  7. Do you want to teach and coach?

The main point is for a new coach to be aware of all the career coaching possibilities and then to determine priorities.

Don’t have a lot of rules. Most coaches have too many rules. Some coaches don’t like long hair. Some coaches don’t like earrings. Some coaches don’t like tattoos. Duke University coach Mike Krzyzewski (2000) says “Too many rules get in the way of leadership and box you in. I think people sometimes set rules to keep from making decisions.” The most important thing a coach can do early in a season, or when they first take a new job is to establish basic ground rules for what is acceptable and non-acceptable behavior. Don’t have too many rules. Three rules a coach should have are:

  1. be good people,
  2. be on time, and
  3. practice hard and give your best effort

When coaches establish a rule, they must stick to it. On championship level teams, players recognize that the “team” is more important than the “individual”.

References

  1. Krzyzewski, M. (2000). Leading with the heart: Coach K’s successful strategies for basketball, business, and life. New York, NY: Warner Books.
  2. Lynch, J. (2001). Creative coaching. Champaign, IL: Human Kinetics.
  3. Martens, R. (2004). Successful coaching. Champaign, IL: Human Kinetics.
  4. National Sporting Goods Association. (2002). Sports Participation in 2002: State-By-State. Mt. Prospect, IL: Author.
  5. O’Donnell, C. (1998, April). So you want to be a college coach . make sure you are good enough and then become the best coach you can be. Scholastic Coach & Athletic Director, 67 (9), p. 45.
  6. Wolcott, H. (1994). Transforming qualitative data: Description, analysis, and interpretation. Thousand Oaks, CA: Sage.

APPENDIX A

Figure 1. What Coaches Would Do Differently

Figure One

 

APPENDIX B

Figure 2. What Coaches Would Do the Same

Figure Two

 

APPENDIX C

Coaching Survey

1.

Gender

_____ Male

_____ Female

2.

Race

_____ African-American

_____ Asian/Pacific Islander

_____ Arab

_____ Chinese

_____ Hispanic/Latino

_____ Indian

_____ Japanese

_____ Korean

_____ Native-American

_____ White/Non-Hispanic

_____ Other (specify) _________________

3.

Age

_____ 18 – 29 years

_____ 30 – 49 years

_____ 50 and over

4.

Education

_____ Doctorate

_____ Masters

_____ Bachelors

_____ Associates

_____ Some college

_____ High School

5.

Income

_____ $50,000 and over

_____ $40,000 – $49,999

_____ $30,000 – $39,999

_____ $20,000 – $29,999

_____ $10,000 – 19,999

_____ $5,000 – $9,999

_____ $2,500 – $4,999

_____ Under $2,500

6.

School Type

_____ Private

_____ Public

7.

School Community Size

_____ Urban

_____ Suburban

_____ Rural

8.

Years in your current coaching position

_____ Under 5 years

_____ 5 – 9 years

_____ 10 – 19 years

_____ 20 – 29 years

_____ Over 30 years

9.

Years coaching (any level)

_____ Under 5 years

_____ 5 – 9 years

_____ 10 – 19 years

_____ 20 – 29 years

_____ Over 30 years

10.

Occupation

_____ Teach and coach at the same school

_____ Teach and coach at different schools

_____ Work in the private sector and coach

11.

Who is your major coaching influence?

12.

If you could start your coaching career over from the beginning, what three things would you repeat or do exactly the same?

13.

What three things would you not repeat if given a chance to begin again as a new coach?

14.

What are the five biggest challenges coaches face today? Please rank order your answers.

2015-03-24T09:51:58-05:00June 4th, 2005|Contemporary Sports Issues, Sports Coaching, Sports Management, Sports Studies and Sports Psychology|Comments Off on Considerations for Interscholastic Coaches
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Can Academic Progress Help Collegiate Football Teams Win?

Submitted by: Jeffrey W. Lucas & Michael J. Lovaglia

INTRODUCTION

Recently, the National Collegiate Athletic Association (NCAA) released its first Academic Progress Rate (APR) scores for its football and basketball programs. The APR measures how well athletic programs educate student athletes and will be used to sanction programs that do not perform well academically. With these new academic reforms, the NCAA has taken the groundbreaking step of linking athletic success to academic success.

Proposed NCAA sanctions for collegiate athletic programs that fail to adequately educate student-athletes highlight the prevailing view that athletic success comes at the expense of academic progress. Some research, including research sponsored by the NCAA, has found that high-visibility athletic programs do not help to financially support the academic missions of universities (Litan, Orszag and Orszag 2003, Shulman and Bowen 2001). Research also has found no link between money spent on athletic programs and academic quality (Litan, Orszag and Orszag 2003). Yet, some clear links have been identified between athletic and academic success. Athletic success increases student applications to universities (Murphy and Trandel 1994, Zimbalist 1999). Theoretically at least, increased applications lead to more selective admissions and thus better students. Moreover, research by Lovaglia and Lucas (2005) suggested that high-visibility athletic programs increase the prestige of a public university’s academic degrees. The APR may be useful in promoting a positive association between academics and athletics in another way: Might providing better education for collegiate athletes now help athletic programs win?

The purpose of the proposed NCAA sanctions for programs with low APR scores is to motivate collegiate athletic programs to do a better job educating student athletes. In addition, the APR has the potential to motivate coaches in more powerful ways. First, it allows a direct test of the hypothesis that the athletic success of collegiate sports programs is negatively correlated with the academic success of their student athletes. If it can be demonstrated that no strong negative correlation exists between athletic and academic success, then coaches might be less ambivalent about insisting that athletes progress academically. Second, and most importantly, athletic recruits can use the APR to decide among competing athletic programs. While young athletes recruited to high profile athletic programs may be most concerned with pursuing a successful athletic career, they (and their parents) nonetheless realize the value of a college education. When deciding between two equally successful athletic programs, it would be in a student’s interest to pick the one with a higher APR. If student athletes begin to favor programs with higher APR scores, then the best athletes will go to schools that promote the academic progress of their athletes. Coaches would then have a powerful reason to promote the academic progress of their athletes. It would help them recruit better athletes and win. The perceived relationship between athletic and academic success would shift from negative to positive.

Comparing the academic and athletic success of collegiate programs, however, is not a simple calculation. If an accessible indicator existed that gave equal weight to academic and athletic success, then the best student athletes might well gravitate toward those programs that offered not only the best chance of athletic stardom but also the best opportunity for a solid education.

We develop a combined measure of athletic and academic success, the Student-Athlete Performance Rate (SAPR). The SAPR assigns programs a score based equally on athletic and academic success. To demonstrate its use, we compute SAPR scores for football programs in major conferences (ACC, Big East, Big 10, Big 12, PAC-10, and SEC plus Notre Dame).

THE APR

On January 10th, 2005, the NCAA Division I Board of Directors approved measures to link athletic scholarships to academic success. In the words of Robert Hemenway, the Chair of the Board of Directors, “This action today is a critical step in our journey to establishing much stronger and significant academic standards for NCAA student-athletes. The ultimate goal is for our student-athletes to stay on track academically and graduate” (NCAA, 1/10/05).

Seven weeks later, on February 28th, the NCAA released its first APR numbers. The APR is based on the eligibility and retention of student-athletes (Brown 2005). Rates of eligibility and retention are exactly the indicators that recruits to a collegiate program would find important in deciding which program to join. Recruits would want to know whether a program is likely to keep them academically eligible to compete and retain them through to graduation.

Each Division I sports program received an APR score on a 1000 point scale. The NCAA set a score of 925, roughly equivalent to an expected 50% graduation rate, as a minimum acceptable standard. About 21% of all athletic teams have APR’s below the 925 cutoff. Perhaps by 2006, programs with subpar APR’s face losing up to 10% of their athletic scholarship allotments.

The number of high-visibility athletic programs that face potential sanctions is substantial. Although 21% of all athletic teams have APR’s below the 925 standard, the percentage is much higher for football and men’s basketball programs. For example, among the 63 football programs in the power conferences representing the Bowl Championship Series, 30 have APR’s below 925 (NCAA, 2/28/05).

THE APR AND ATHLETIC RECRUITS

Aside from its use as a punitive tool, the APR can provide student-athletes recruited to universities a tool to use when deciding among various programs. Talented young athletes recruited by major collegiate sports programs must weigh a dizzying array of information before deciding on a school. Sometimes that information can be contradictory. To make an informed decision, a recruit should be able to answer at least two questions. First, which program will provide the best athletic experience, including the most visibility and the best opportunity for a professional career? Second, which program will provide the best education and opportunities if a pro career doesn’t materialize?

The APR gives student-athletes a way to measure the academic success of athletic programs. From the standpoint of recruits, however, the APR neglects the athletic half of the equation to focus exclusively on the academic side. The most successful sports programs in athletics may not be the ones that do a good job of educating their student athletes. Similarly, the programs that provide the best educational opportunities for student athletes may not provide the best athletic opportunities. There is no clear way to judge how well a program both educates its players and gives them a chance for success in athletics.

We propose an indicator that combines academic and athletic success. The Student-Athlete Performance Rate (SAPR) described below gives equal weight to the athletic and academic success of sports programs.

COMPUTING THE SAPR

We constructed a method for computing SAPR scores and applied it to Division I-A football programs. The SAPR is calculated on a 2000 point scale, half reflecting athletic success and half academic success. 1000 possible points of each program’s SAPR score is its Academic Progress Rate (APR). The other 1000 points of the SAPR is determined by a program’s Athletic Success Rate (ASR). Table 1 displays the factors used to calculate the ASR and their weightings.

Table 1: Factors in ASR (and weightings)

All-time winning % (.10)

Conference championships in last 5 years (.10)

Attendance average (2003) (.15)

Bowl games in last 5 years (.15)

National rankings in last 5 years (.15)

Players in the National Football League (.15)

Wins in the last 5 years (.20)

A number of factors reflect the current status of a football program, including conference championships in the last 5 years, bowl games in the last 5 years, national rankings in the last 5 years, and wins in the last 5 years. All-time winning percentage is included to reflect the tradition of a program. Attendance and professional players from a program are included because we believe they are factors that reflect the potential visibility and chance for professional success of athletes associated with a collegiate program. Similarly, the weightings reflect the factors that we believe recruits would consider most seriously. For example, an important athletic factor for new recruits would be how much a program wins.

For each of the seven factors in the ASR, we gave each program a score reflecting its percentage of the highest possible value for that factor. For example, the University of Michigan had the highest attendance average at about 111,000 fans per game and received a 1.0 for the attendance factor. A program with an average attendance of 55,500 fans per game would receive a score of .5 for the attendance factor. In the same way, a program that has participated in 3 bowl games in the past 5 years receives a score of .6 for the bowl game factor.

We multiplied each school’s score for each factor by its weighting. We then added the weighted factor scores. The factor weightings add to 1.0 and thus adding each school’s weighted scores for each factor produced a total score with a maximum possible value of 1.0. We then multiplied these values by 1000 to put ASR scores on the same scale as the APR.

Our initial ASR calculations produced a range of scores among football programs in power conferences between 148 and 856. We then standardized the scores to produce a range comparable to that of the APR. We then added ASR and APR scores to produce for each program an SAPR score with a maximum possible value of 2000. Table 2 displays SAPR scores for football programs in conferences represented in the Bowl Championship Series.

Table 2: SAPR scores for football programs in conferences represented in the Bowl Championship Series-ACC, Big East, Big 10, Big 12, PAC-10, and SEC (as well as Notre Dame)

School SAPR School SAPR
1) Michigan 1920 33) Iowa State 1822
2) Miami 1917 t34) Ohio State 1820
3) Florida State 1911 t34) Rutgers 1820
4) Auburn 1903 t34) Washington St. 1820
5) Oklahoma 1897 t37) Arkansas 1818
6) Georgia 1894 t37) Illinois 1818
7) Florida 1891 t39) South Carolina 1817
8) Boston College 1890 t39) Wake Forest 1817
9) Texas 1882 t41) Duke 1816
10) LSU 1880 t41) Northwestern 1816
11) Virginia Tech 1879 t41) Texas Tech 1816
12) Iowa 1876 44) Minnesota 1812
13) Virginia 1870 45) Cal 1808
14) Mississippi 1867 46) Purdue 1806
15) Stanford 1865 t47) Oregon State 1800
16) Maryland 1864 t47) Washington 1800
17) Nebraska 1863 49) Baylor 1798
18) USC 1860 50) Vanderbilt 1792
19) Notre Dame 1854 t51) Kentucky 1790
20) Tennessee 1853 t51) Michigan St. 1790
21) Clemson 1848 53) Oklahoma St. 1789
22) Georgia Tech 1847 54) Indiana 1788
23) North Carolina 1846 t55) Oregon 1787
24) West Virginia 1845 t55) Texas A&M 1787
25) Pittsburgh 1845 57) Alabama 1785
26) Colorado 1841 58) Arizona St. 1784
27) Kansas State 1838 59) Mississippi St. 1768
28) Syracuse 1833 60) Missouri 1767
29) N. Carolina St. 1828 61) UCLA 1765
t30) Penn State 1826 62) Kansas 1749
t30) Wisconsin 1826 63) Arizona 1722
32) Connecticut 1824 64) Temple 1697

ANALYSIS

Comparing the APR and ASR components of the SAPR allow a test of the hypothesis that athletic success is negatively correlated with academic success of major collegiate football programs. If athletic success is antithetical to academic success, then we would expect a strong negative correlation between scores on our ASR scale and on the APR scale. Instead, we found only a slight (Pearson’s r = -..122, two-tailed p = .335) and non-significant negative correlation between the ASR and the APR. Statistically, major collegiate football programs whose athletes make good academic progress are just as successful as those programs whose athletes make little progress.

DISCUSSION

The SAPR has a number of potential uses. One is to give student-athlete recruits a measure of combined athletic and academic success to consider when choosing among various collegiate programs. Some football programs that have been very successful on the football field-Michigan, Miami, and Florida State, for example-also have very high SAPR scores. Others fare less well. Recruits considering alternative programs can use the SAPR as a tool when making their decisions. If use of the SAPR for this purpose becomes widespread, then we can expect the correlation between the athletic and academic success of collegiate programs to shift from neutral to positive. If coaches are able to use high SAPR scores to recruit better athletes, then their success in promoting the academic progress of their student athletes will lead to greater athletic success as well.

Another potential use of the SAPR is to determine the likelihood of programs changing coaches. 10 of the schools with the lowest 15 rankings in our SAPR scores for football programs from major conferences have changed coaches since the end of the 2002 football season. Only 3 of the top 15 programs did so. Some of the changes at both ends of the spectrum reflected coaches being fired, and some reflected coaches moving on to new positions. In a logistic regression analysis with any coaching change as the dependent variable, the coefficient for SAPR approaches significance (B = -.012, SE = .006, two-tailed p = .056) in the direction of schools higher in SAPR scores being less likely to change coaches. More research and a larger sample are necessary to determine the relationship between SAPR scores and coaching changes.

A question for future research is whether the coach or the institutional climate is the primary determining factor in a program’s SAPR score. We can gather more data to test this prediction. We will compute SAPR scores for men’s and women’s basketball programs (which will entail using some different factors in the ASR formula) in power conferences. We will then compare SAPR scores for football and basketball programs at the same institution. If scores for football and basketball are highly positively correlated, then the institution is likely the more important factor. If the correlation is weak or negative, then the coach is probably the driving force.

REFERENCES

  1. Brown, G. T. (2005). “APR 101.” NCAA News Online, February 14.
  2. Litan, R. E., J. M. Orszag and P. R. Orszag (2003). The Empirical effects of collegiate athletics: An interim report. National Collegiate Athletic Association.
  3. Lovaglia, M. J. and J. W. Lucas (2005). “High visibility athletic programs and the prestige of public universities.” The Sport Journal 8(2):1-5.
  4. Murphy, R. G. and G. T. Trandel (1994). “The relation between a university’s football record and the size of its applicant pool.” Economics of Education Review, 13, 383-387.
  5. NCAA. 1/10/2005. “NCAA Division I Board of Directors sets cutlines for academic reform standards.” NCAA Press release.
  6. NCAA. 2/28/05. “Academic Progress Rate data for NCAA schools.” http://www2.ncaa.org/academics_and_athletes/education_and_research/academic_reform/school_apr_data.html
  7. Shulman, J. L. and W. G. Bowen (2001). The Game of Life: College Sports and Educational Values. Princeton, NJ: Princeton University Press.
  8. Zimbalist, A. (1999). Unpaid Professionals: Commercialism and Conflict in Big-Time College Sports. Princeton, NJ: Princeton University Press.
2015-03-24T09:48:32-05:00June 3rd, 2005|Contemporary Sports Issues, Sports Coaching, Sports Facilities, Sports Management, Sports Studies and Sports Psychology|Comments Off on Can Academic Progress Help Collegiate Football Teams Win?
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The Analysis of the Opinions of Supporters of a Football Team in the Turkish Super League; Before and After the Same Game

Submitted by: Dr. Ertan Kilcigil, PhD.

ABSTRACT

This study has been conducted in Turkey by asking a 15-question-lichert type of survey in order to obtain the before and after opinions of 45 Besiktas Gymnastics Sports Club’s football fans from Ankara who went to Besiktas Gymnastics Sports Club’s ( BJK) UEFA second semi-final match versus S.S. Lazio Club that took place in Istanbul on the 20th of March 2003 and returned from the match together on the same bus. Besiktas lost this game. The score was 2-0.

The survey questions the effects of players’, referee’s, spectators’, coach’s individual success and errors, the players’ being unable to play because of injury or penalty and weather conditions on the final score of the game. The survey was prepared by consulting experts’ opinions.

At the end of the research, the below results on the differences of opinion before and after the game were obtained in order of importance:

Before the game, it was thought that the game was to be won by Besiktas (most likely 82%, least likely 82%). The players are to blame for losing the game (most likely 60%, likely 40%).

The coach is unsuccessful, he couldn’t direct the game well and he couldn’t interfere at the right time (most likely 58%, very unlikely 51%). The host team did not have any advantages or could not use this advantage (most likely 56%).

The negative weather conditions did not affect the team’s failure or in other words there were no negative weather conditions during the game (not likely 53%). The players had individual failures (least likely 51%). The goal and problem we aimed to achieve at the end of this research have been achieved. Except for the sub-problem that is the player’s being unable to play because of injury or penalty affecting the game, all the other sub problem’s statistics have been defined as important. Supporters think that their team will definitely win before the game without accepting any excuses but after the defeat, they list all the causes of defeat one by one. Before the game, these causes are not even thought as a probability.

INTRODUCTION

Today supporting a football team is in such a position that it eliminates cultural differences. Intellectual, educated, uneducated, employed, unemployed people are all supporters of their team in the grandstand. What separates these people from each other is not the dosage of fanatics but their response to it. The supporters in the grandstand always want their team to win. The colors in the grandstand have a meaning only when they belong to their team. The supporters can give up everything for the sake of their team. When they have intensive worry or reaction, supporters even commit suicide in Turkey. Although supporters give more than they should for their team, they might receive the least in return. Supporters can change their love in moments of desperation but not their team; they would never go to another team. For the supporter, supporting his team and defending it is as natural a passion as eating or drinking. The game football is not simply a symbol of colors that reflect the social system but it is a social action that unites all the colors. In Turkey, the supporters are all actively involved in this action. The emotional responsibility or reaction towards one’s team sometimes obstructs being objective and thus supporters always want their team to win. Below are the short headlines of the explanation of some of the factors that affect the result of football match in Turkey.

Supporter and Spectator:

“Spectator is the person who watches the game, show, performance or sports competitions in its exact place. According to a study of social psychology, spectators are considered as a group. The approach that defines spectators as “A group made up of individuals that come together in order to meet certain needs” is in accordance with football spectators (Acet, 2001).On the other hand, “A football supporter/fan is a person who is emotionally devoted to a sports event”. As it is understood from these definitions, a football supporter and a football spectator are different concepts. Being a spectator is a superior state that includes football fanaticism; every spectator may not be a football supporter (Kayaoglu, 2000). Most of the spectators are not just spectators. Moreover, just like religious fanatics participating in religious ceremonies, these spectators are real fanatics that can remember previous games very well and plan for future games very well and are extremely devoted to their football team giving it more importance than their colleagues, their friends, their family or important days for them (Sloan, 1979). According to Meri (1999), football supporters are a kind of group that represents the popular Turkish football culture in a micro economic social standpoint and its revival. “There are four elements of football which engrosses millions of people’s attention. These are: the sportsman (footballer), technical staff and director (football coach), spectators and media. Among these, the most honest and sincere is the crowd of spectators. A supporter of a football club is a part of the team whether it wins or loses” (Talimciler, 2003).

Social Identity and Supporter:

The emotional responsibility that comes with supporting a team consciously or unconsciously becomes a part of person’s life. “People find the support they have been looking for at times in religion at times in the team they support. This means by supporting a team, a lot of people change their status from crowd that has unlimited opportunities to a group that has a lot in common” (Imamoglu, 1991). “Football is the most collective among all the social sense of belongings and cultural forms” (Meri, 1999). The widespread of shared fanaticism makes an individual feel stronger. In other words, an individual feels stronger by relying on the protection of a strong and crowded group of people. In Turkey, “people in the society feel themselves under pressure when they can’t fulfill their economic and social needs. By identifying themselves with their team, they try to satisfy their own feelings of pride and confidence when their team succeeds” (nlcan, 1998). Whatever the conditions or circumstances are, the supporter always feels that he can contribute to his team physically and spiritually and that his team needs his support. According to Fin (1994), supporters see themselves as the morale guards of their team even though they don’t participate in the decision making process and they perceive themselves as deflated or diminished. Supporters’ belief that the team belongs to them seems to mislead the financial truth. But the claim that the team belongs to them should not be taken as a financial one, it should be seen as a manifest of the belief that the team is a part of them because of the intensive devotion they feel towards their team.

Supporter and the Referee:

Generally and briefly, a referee is responsible for directing the team. In other words, “a referee is the most designated person of the game; he is the symbol of the rules, limitations and honesty” (Ycel, 1998). As referees draw the line between the rights of one team and the other, it is a difficult job. To finish this job with the least number of errors is only possible with the harmony of experience, knowledge and wisdom. A referee is “out of sight and out of mind as much as he accomplishes to put forward these qualifications properly. A word is enough to describe him and his job. However, he is in the foreground as much as he deviates from the rules (Ycel, 1998). “There is no such thing as defeat for supporters. Therefore, most often referees are not appreciated by either the winning or the losing team” (Kilcigil, 2002).

Supporter and the Footballer:

Some footballers are remembered by some names. Nicknames such as “Brain” and “Professor” describe their styles “in the football field”. Just like everything that addresses to the big masses, footballers’ behaviors in and out of the field may affect some people. For example, a footballer with a high excitement level has the opportunity to direct the society that are there for the same purpose, shares similar feelings and gets their power from their unity. To be in front of the societies naturally brings some responsibilities. The first responsibility of a footballer is to his club, but there is an important point here to consider; “the club’s supporters”. Because they themselves are the team’s spiritual owners and they watch every step of the footballer very carefully. They want a share of the footballer, that is, when they go to a football game, the footballer should play very well and win. At this point, the footballer’s responsibility is conveyed to the supporters; the masses. Today, we can say that what makes football so important is “the supporter”. Therefore, the footballer’s most important duty, according to the supporters, is to make them happy. According to the supporters that say “we created you; we made you who you are”, the footballer should get on well with the supporters and should be able to live up to their expectations. Otherwise, he will be unwanted and the supporters will cheer against him in every game. The cheer “The best in Turkey are the spectators, footballers are impostors” were made up after a game that was expected to be won was lost.

Supporters and Technical Director:

“As he is experienced in football, as there are a lot of people training the team; and as there can be more than one coach or trainers in a team; the person with the most authority is called the “technical director” (Ycel, 1998). Technical Directors, along with their responsibility to train the footballers and the team the best way for the games, also have individual social, cultural duties and responsibilities. With his responsibility towards the supporters of his team or to public opinion of the sports society, his speech before and after the game, his behavior, and his reactions, he should be able to set example and not do these for the sake of winning the game. Reactions that might lead to violence in the football supporters who hide all sorts of their identity and sense of belonging in their fanaticism in Turkey should not be given. The supporter although wanting his expectations to be met firstly by the footballer conveys this expectation indirectly to the technical director. The technical director is responsible to the supporter for all the team members whereas the footballer is only responsible for himself. “Therefore a technical director is often likened to a “commander” or an “orchestra conductor”; he is said to direct the game well or bad; use his baton well or bad” (Ycel, 1998). As it can be understood from above, the technical director of a team is not only the person that directs the game, the team technically but he is also the person that directs the pulse of the supporters’ and lays the groundwork for the positive and negative events with his behavior towards the footballers and the referee.

MATERIAL AND METHODS

This study has been conducted in Turkey by asking a 15-question-lichert type of survey in order to obtain the before and after opinions of 45 Besiktas Sports Club’s football fans from Ankara who went to Besiktas Gymnastics Sports Club’s ( BJK) UEFA second semi-final match versus S.S. Lazio Club that took place in Istanbul on the 20th of March 2003 and returned from the match together on the same bus. It is difficult to ask the same questions after the game that had been answered by the same people before the game in terms of research technique (just like gathering the same group whose upset after the game that was lost 2-0 and asking them to answer the survey questions). This means that although the study group consists of 45 people; there are 90 answer sheets. It is assumed that football players, referees, spectators, coach’s individual success and errors, the players’ being unable to play because of injury or penalty, the team’s being the host team or not and weather conditions are all factors that can affect the final score of the game. The first stage of the study was conducted before the game on the bus from Ankara to Istanbul. The study group was asked to put a code or sign on the survey they have answered so that the same survey could be given to them after the game. After the game, the same people answered the questions at the back of the survey whose first page they had already answered before the game. The data that was obtained after the game has been analyzed according to Z test method by comparing the ratio and percentage distribution. The difference of the views has been evaluated in the range between p < 0.01 and p < 0.05. That there is a difference between the views of football supporters before and after their team’s game, that this difference is an important one and that this research is the first on its subject in Turkey are all factors that contribute to the growing importance of this research.

FINDINGS

Table 1

The data concerning the views of the football supporters before and after the game in terms of the effect of winning this game on the final score of the game

Alternatives Before the game After the game The results of the Z Test in comparing the ratios Interpretation level
n % n % Difference
Not likely 0 0.00 37 82.22 -0.82 Important (p<0.01)
Least likely 2 0.00 5 11.11 -0.11 Important (p<0.01)
Likely 7 15.56 2 4.44 0.11 Not important
Most likely 38 84.44 1 2.22 0.82 Important (p<0.01)

Table 2

The data concerning the views of the football supporters before and after the game in terms of the effect of losing this game on the final score of the game

Alternatives Before the game After the game The results of the Z Test in comparing the ratios Interpretation level
n % n % Difference
Not likely 34 75.56 1 2.22 0.73 Important (p<0.01)
Least likely 9 20.00 0 0.00 0.20 Important (p<0.01)
Likely 2 4.44 7 15.56 -0.11 Not important
Most likely 0 0.00 37 82.22 -0.82 Important (p<0.01)

Table 3

The data concerning the views of football supporters before and after the game in terms of the effects of individual errors of the footballers on the final score of the game

Alternatives Before the game After the game The results of the Z Test in comparing the ratios Interpretation level
n % n % Difference
Not likely 0 0.00 0 0.00 0.00 Not important
Least likely 9 20 4 8.89 0.11 Not important
Likely 33 73.33 11 24.44 0.49 Important (p<0.01)
Most likely 3 6.67 30 66.67 -0.60 Important(p<0.01)

Table 4

The data concerning the views of the football supporters before and after the game in terms of the effects of the tactical success of the technical director(directing the game well, interfering at the right time) on the final score of the game

Alternatives Before the game After the game The results of the Z Test in comparing the ratios Interpretation level
n % n % Difference
Not likely 0 0.00 14 31.11 -0.31 Important (p<0.01)
Least likely 0 0.00 23 51.11 -0.51 Important (p<0.01)
Likely 16 35.56 5 11.11 0.24 Important (p<0.01
Most likely 29 64.44 3 6.67 0.58 Important (p<0.01)

Table 5

The data concerning the views of the football supporters before and after the game in terms of the effect of being the host team on the final score of the game

Alternatives Before the game After the game The results of the Z Test in comparing the ratios Interpretation level
n % n % Difference
Not likely 0 0.00 15 33.33 -0.33 Important (p<0.01)
Least likely 1 2.22 19 42.22 -0.40 Important (p<0.01)
Likely 14 31.11 6 13.33 0.18 Important (p<0.01)
Most likely 30 66.67 5 11.11 0.56 Important (p<0.01)

Table 6

The data concernng the views of the football supporters before and after the game in terms of the effects of negative weather conditions or the field’s having a bad ground on the final score of the game

Alternatives Before the game After the game The results of the Z Test in comparing the ratios Interpretation level
n % n % Difference
Not likely 11 24.4 35 77.78 -0.53 Important (p<0.01)
Least likely 17 37.78 7 15.56 0.22 Important (p<0.01)
Likely 15 33.33 1 2.22 0.31 Important (p<0.01
Most likely 2 4.44 2 4.44 0.00 Not important

Table 7

The data concerning the views of the football supporters before and after the game in terms of the effects of individual success of the footballers on the final score of the game

Alternatives Before the game After the game The results of the Z Test in comparing the ratios Interpretation level
n % n % Difference
Not likely 0 0.00 15 33.33 -0.33 Important (p<0.01)
Least likely 1 2.22 24 53.33 -0.51 Important (p<0.01)
Likely 24 53.33 4 8.89 0.44 Important (p<0.01)
Most likely 20 44.44 2 4.44 0.40 Important(p<0.01)

CONCLUSION AND DISCUSSION

During this study conducted on the March 20, 2003 Besiktas Gymnastics Sports Club’s football team (BJK) lost the football match against S.S Lazio Club’s football team 2-0. The data findings refer to the answers of the views’ of the supporters before and after the game. The evaluation is based on the statistical level of interpretation of the difference in the views before and after the game.

Table 1: As it was not considered that the team would lose before the game, the alternatives “not likely” and “least likely” (both zero %), these alternatives increased as the game was lost in the end: “not likely” (82.22% and “least likely” (11.11%). The difference between before and after the game for the alternative “not likely” (82%) is at a statistically important level p < 0.01and the alternative “least likely” (difference is 11%) is at a statistically important level p< 0.05.

The probability of winning the game seen as “likely” (15.56%) before the game decreased to 4.44% after the game. But the difference is not important.

Before the game the team was thought as “most likely” to win before the game (84.44%). But as the game was lost, this percentage declined to 2.22%; the difference being 82% is at a statistically important level p< 0.01.

Table 2: As the team was considered “not likely” to lose the game before the match (75.56%), this percentage decreased to 2.22% with the loss of the game. The difference is 73% and is at a statistically important level p<0.01.

As the team was thought “least likely” to lose the game before the match (20%); this alternative was not ticked at all after the game (zero %). The reason is that with the loss of the game, most of the people answered the alternative “most likely”. The difference is 20% and is at a statistically important level p< 0.01.

When we look at the alternative “likely” before and after the game (4.44%, 15.56%); the difference between them (11%) is statistically not important.

The alternative “most likely to lose” is given no chance (0%) as the team was thought to win; with the loss of the game this percentage increased greatly (82.22%). The difference is 82% and is at a statistically important level p<0.01.

Table 3: The study group considered the loss of the game due to individual errors of the footballers as “not likely” and “least likely” (not important). But although the same group that said the individual errors of the footballers were “likely” (73.33%) to affect the game before the game changed their views to “likely” (24.44%) (The difference is 49%); from the statistical point (p< 0.01), it was observed that the footballer’s individual errors were “likely” to affect the loss of the game. The supporters that said that the footballers were not going to make individual errors (6.67%) before the game revealed after the game the footballers made mistakes during the game (66.67%) and that these “most likely” (66.67%) affected the loss of the game (difference 60%) (Important: p< 0.01).>

Table 4: Although before the game the technical director’s success was seen as “likely” and “most likely” ( 35.56% and 64.44%) before the game; no chance was given to the alternatives “not likely” and “least likely” (0%).

After the game, “not likely” and “least likely”( 31.11% and 51.11%) differed than those before the game (0%) creating a 31% and 51% difference and became important p< 0.01.

When we combine this result we obtained from table 7 that technical director’s errors were different looking at the percentages of the alternatives “least likely” “most likely” before and after the game and that this was at a statistically important level, with the result from table 8; the expectation that the technical director was to succeed can be interpreted as certifying his failure in the end.

There has been a decrease in the percentages which reflected that technical director’s success would “likely” affect the final score of the game before the game (35.56) and after the game (11.11%). The difference is 24% and is important p< 0.01.

The percentages of those who expected a “most likely” success from the technical director before the game (64.44%) decreased (6.67%) making the difference between these (58%) important p< 0.01.

Table 5: As being the host team was seen as an advantageous thing before the game: the alternatives “not likely” (zero %) and “least likely” (2.22%); these opinions changed after the game; “not likely” (33.33%) and “least likely” (42.22%) and increased (the difference 33% and 40%). These are at a statistically important level p< 0.01.

Before the game as being the host team was thought to be advantageous the alternative “likely” was 31.11% before the game; this is seen as not advantageous after the game. The difference between the percentages of after and before the game are 18% and are at a statistically important level p< 0.05.

The most important alternative that being the host team “most likely” affects the outcome of the game before the game (66.67%) changed their opinions completely making this alternative have the least percentage (11.11%) after the game. The difference is 56% and is at a statistically important level p< 0.01.

As this is seen as one of the most important reasons of losing the game; it is thought that the advantage of being the host team was not used by the team itself.

Table 6: Although before the game the percentages reflecting that it was “not likely” “least likely” and “likely” that the weather conditions might affect the success of the team were high before the game, the percentage stating it was “least likely” before the game was statistically not important. Although the alternative “not likely” was kept in mind before the game (24.44%), it increased greatly after the game (77.78%). This most important difference for this question made it to be more likely than the other alternatives and it made this statistically important p< 0.01. The negative weather conditions did not affect the team’s failure. During the game, the weather conditions were not unfavorable. The “least likely” probability before the game (37.78%) declined after the game (15.56%). The difference between these (22%) is at a statistically important level p< 0.01.

The answer “likely” which was an important alternative before the game (33.33%) became unimportant after the game (2.22%). The difference is 22% and at a statistically important level p< 0.01. There were no unfavorable or negative weather conditions during the game and the weather conditions during the game did not affect the team’s failure.

The alternative “most likely” before and after the game is more or less the same (4.44%); it is statistically not important as well as it shows that no unfavorable weather condition took place during the game and the weather conditions did not affect the team negatively and did not contribute to the team’s failure.

Table 7: The alternative: “not likely” was not chosen before the game; this proved that the team’s success was expected rather than the individual success of the footballers before the game. But after the game the same alternative increased to (33.33%) and the footballers were considered as unsuccessful (33.33 %) (Important: p< 0.01).

By choosing the “least likely” alternative again team success was expected rather than individual success of the footballers’ (2.22%), but after the game it was said that the game was lost due to the footballer’s individual failures (53.33%). (the difference is 51%; important: p< 0.01).

It was observed that there was a decrease in the alternative “Likely” (53.33%) referring to supporters that expected individual success of the footballers after the game (difference 0.44%; important: p< 0.01). Those who viewed success as certain by choosing “most likely” (44.44%) before the game evaluated the footballers as unsuccessful after the game (difference 40%, important: p< 0.01).

In conclusion, when all the tables are considered in view of their importance (The important percentages of the Z test results in the tables are bold and underlined):

  1. The thought of winning the game is first (least likely 82%; most likely 82%; Tables 1-2).
  2. The players are to blame for losing the game (most likely 60%, likely 49%; Table 3).
  3. Technical director is unsuccessful, he could not direct the game well and he did not interfere well-timed and appropriately (most likely 58%, least likely 51%; Table 4).
  4. Being the host team had no advantages or this advantage has not been used (most likely 56%; Table 5).
  5. The unfavorable weather conditions did not have any effect in the game’s failure or in other words there has not been any unfavorable weather condition during the game (not likely 53%; Table 6).
  6. The footballers have individual errors and failures (least likely 51%; Table 7).

At the end of the research, the aim and problem has been achieved; all the problems except for the sub-problem “the players’ being unable to play due to injury or penalty” proved themselves statistically important. In Turkey, supporters believe that their team will definitely win no matter what happens before the game but start listing reasons for losing the game after the defeat. These reasons are not viewed even as likelihood before the game or considered little as they have unimportant percentages

REFERENCES

1. Acet, M., (2001), “Factors that Steer Football Spectators Towards Fanaticism and Violence”, Marmara University Institute of Medical Sciences Department of Physical Education and Sports PhD. Thesis, pages 16,20-21,23,29-30,36-37,115-117,119-122,126, Istanbul

2. Finn, G., (1994), Football Violence: A Social Psychological Perspective, in “Football Violance and Social Identity”, (ed. R, Gulianotti, N. Bonney, and H., Hepwort), London: Routledge

3. Imamoglu, O., (1991), “Sportsmen’s and Spectators’ Health”, Marmara University Institute of Medical Sciences Department of Physical Education and Sports, PhD Thesis, page 333, Istanbul

4. Kayaoglu, A. G.(2000), “Football Fanaticism, Social Identity and Violence, A study conducted on football supporter”, Ankara University Institute of Social Sciences Department of (Social) Psychology. PhD Thesis, pages 12-15,54-57, Ankara

5. Kilcigil, E., (2002), “Preferring to go to the stadiums instead of watching the matches on television on a soccer team fans in super league” Performance, volume:8, Number 1-2, 10-29

6. Meri, ., ( 1999) “Towards a Conscious Society” Ayyildiz Magazine, page 28

7. Sloan, L.R., (1979), The Function and Impact of Sport for Fans: A Review of Theory and Contemporary Research in H.J., Goldstein, (ed), Sports, Games and Play, (Ed: H., J., Goldstein) Social and Psychological Viewpoints, Hillsdale, Laurence, Erlbaum Associates, New Jersey

8. Talimciler, A., (2003), Football Fanaticism in Turkey and its relation with Media, Baglam Publishing, pages 21,29,33, Istanbul

9. nlcan, ., (1998), “Types of Violence in Turkish Football Spectators”, Marmara University Institute of Medical Sciences Department of Physical Education and Sports, M.A Thesis, pages 14,18, Istanbul

10. Ycel, T., (1998), From the Discourses, Yapi Kredi Publishing, pages 36-37, 43, Istanbul

2015-03-24T09:32:30-05:00June 1st, 2005|Contemporary Sports Issues, Sports Coaching, Sports Management, Sports Studies and Sports Psychology|Comments Off on The Analysis of the Opinions of Supporters of a Football Team in the Turkish Super League; Before and After the Same Game
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A Look at Women’s Participation in Sports in Maryland Two-Year Colleges

ABSTRACT

Much research has been conducted on college athletics.  The populations studied most often are four-year, NCAA member institutions.  In higher education, 40 percent of the institutions in the United States are two-year colleges.  These two-year colleges enroll more than ten million students annually (IPEDS, 2002).  Although 56 percent of the students enrolled in these institutions are women, little research exists that examines the participation in two-year college athletic programs.  The purpose of this study was to examine the degree of participation and opportunity for female students and coaches at two-year colleges within the state of Maryland.  With 18 institutions reporting participation data, results of this study showed that female students participate in far fewer numbers in Maryland than do male students.  Results of this study also showed that relatively few women hold administrative or coaching positions within existing sport programs.

INTRODUCTION

Over the last thirty-two years, female students have seen substantial gains in sports participation opportunities.  These gains came as a result of the federally mandated legislation know as Title IX of the Education Amendments of 1972.  Since the passage of this legislation, opportunities for girls and women to compete in sports have increased dramatically.  According to a longitudinal study by Acosta and Carpenter (1996), participation opportunities for women athletes by the late 1990’s hit an all-time high.  Increased female athletic participation is evident at all levels of sport, including high schools, colleges, and universities (NFSHSA, 2001; NCAA, 2000).

Much research (Acosta & Carpenter, 1996; Carpenter, 2003; Fitzgerald, 2003; Kramer & Marinelli, 1993) has been conducted with regards to college athletics, opportunity, and participation.  The populations studied most often are four-year, National Collegiate Athletic Association (NCAA) member institutions.  Within higher education, the two-year (also referred to as community or junior) college is taking on a greater significance.  According to a study by the U.S. Department of Education (2002), 40% of the institutions of higher education in the United States are now two-year colleges.  These two-year colleges enroll more than 10 million students annually.  Many of the athletes at these two-year colleges go on to star in major four-year athletic programs (Douchant, 2002). Although 56% of the students enrolled in these institutions are women, little research exists that examines the two-year college athletic program (Smith, 1997).  Thus, the specific purpose of this study was to examine the degree of participation and opportunity for female students and coaches at two-year colleges within the state of Maryland.

Overview of Title IX

The impetus for the change in opportunity and participation for females can be attributed to the passage of the Education Amendments Act of 1972 and its Title IX provision.  Title IX was enacted to help remedy past discriminatory practices. Title IX of the Educational Amendments Act of 1972 states that: “No person in the United States shall, on the basis of sex be excluded from participation in, or denied the benefits of, or be subjected to discrimination under any educational program or activity receiving federal aid” (Title IX, n.d., para. 1).

The passage of Title IX and the threat of litigation have resulted in the vast improvement in opportunities for girls and women in sport.  With regard to intercollegiate athletics, three primary areas determine if an institution is in compliance: athletic financial assistance, accommodation of interest and abilities, and equity in other specified program areas.

A three-part test for compliance is used in determining whether the required number of participation opportunities is being provided.
An institution must show:

  • that the intercollegiate participation opportunities for its students of each sex are substantially proportionate to its male and female undergraduate enrollments; or
  • a history and continuing practice of program expansion responsive to developing interests and abilities of members of the “underrepresented sex”; or
  • that the interests and abilities of the “underrepresented sex” are fully and effectively accommodated by the existing program (Carpenter, 2003).

Compliance is established when an institution can demonstrate that it has satisfied any one of these three tests.

Title IX requires that, for an institution to be in compliance, the interest and abilities of both sexes must be accommodated.  This includes the institution’s obligation to provide a sufficient number of participation opportunities for male and female athletes.  “Participation opportunities” are defined as the number of slots on teams as determined by the number of athletes on each team.  This definition is important because athletic directors at two-year institutions often define participation by the number of teams offered and not by the number of participants (Mumford, 1998).  According to Title IX policy interpretations and recent judicial decisions, participation in the intercollegiate sports program by women should be substantially proportionate to the number of women enrolled at the given institution.  For example, if 70 percent of the students enrolled at an institution are women, then approximately 70 percent of the students participating in intercollegiate athletics should be women (Lichtman, 1997).

The impact of Title IX policy has been felt a great deal more at the four-year level than at the two-year level of college athletics (Mumford, 1998).  Although many students have benefited from this federal policy, the consequences of this policy have also been unpleasant to many institutions.  Institutions have been subjected to expensive court battles as a result of lawsuits filed by female student-athletes and coaches.  Litigation from lawsuits has risen dramatically.  The costs and consequences of these lawsuits have had a negative impact on institutions.  Institutions found in violation of Title IX have been forced to pay expensive monetary damages, attorney fees, and program support funding.  These awards have been reported as high as $1 million (Fitzgerald, 2003).

Courts have also taken more control of athletic decision making.  They have ordered specific actions, such as hiring coaches and providing practice and other facilities.  In some instances, the litigation of one Title IX claim has generated even more claims (Kramer & Marinelli, 1993).

Research Questions

With the goal of exploring women’s participation in collegiate sports in mind, the purpose of the study was to determine the degree of participation and opportunity at two-year colleges within the state of Maryland for female student athletes and coaches.
Specific research questions which guided the study were:

  • What does the leadership, in terms of the gender of administrators, and coaches, look like at these institutions?
  • At what rates do women and men participate in two-year collegiate athletic programs?  Is their participation in proportion to that of the general student body population or are women underrepresented?
  • Are Maryland two-year colleges in compliance with Title IX?  If so, how?

Methodology

Respondents

Respondents for this study were athletic directors of all two-year colleges with membership in the Maryland Junior College Athletic Conference (MD JUCO).  The MD JUCO is comprised of 18 two-year colleges in the state of Maryland.

Instrumentation

A survey instrument was used in this study to gather demographic data on the leaders (athletic directors and coaches) of two-year colleges in the state of Maryland.  The survey instrument consisted of 33 items containing both closed-ended and open-ended questions.  The survey instrument was also designed to collect institutional programmatic information about coaching and intercollegiate sport opportunities.  Data was gathered for comparative purposes only.  Confidentiality of responses was guaranteed to all respondents.  The overall return rate of the survey was 83 %, which included responses from 15 subjects.

Procedure

Athletic directors (n=18) employed at degree-granting two-year colleges in the state of Maryland (MD JUCO) were mailed a cover letter, consent form, questionnaire, and a stamped self-return envelope.  Three weeks following the initial mailing, a reminder letter, survey, and stamped self-return envelope was sent to all subjects who had not responded (non-respondents).

Another method of gathering data was the review of related documents and archival records.  Documents used to gather data included the MD JUCO website, college catalogs, minutes from MD JUCO meetings, National Junior College Athletic Association (NJCAA) Student Eligibility Forms, the NJCAA 2000-2001 Handbook & Casebook, and the NJCAA website. This method of data gathering provided complementary information to that obtained in the surveys.  In this manner, the researcher could triangulate and cross-check data provided by the survey (Wolcott, 1994).

RESULTS

Administration

The gender of athletic directors in Maryland two-year colleges included 16 men (89%) and two women (11%).  The ethnic background of the athletic directors included 17 Caucasian (94%) and one African-American (6%).

Participation

Respondents were asked to identify the number of teams offered at their institution for men and women.  They were also asked to indicate the total number of student-athletes that participated on those teams.  On average, two-year colleges in Maryland sponsored seven teams per institution (four teams for men and three teams for women).  On average, 96 student-athletes participate across those seven teams (65 male and 31 female). Respondents stated that 134 teams were offered by their institutions.  Of the 134 total teams, 69 teams (51%) were offered for men and 65 teams (49%) were offered for women.  A total of 1,719 student athletes participated on those 134 teams.  Of that number, 1166 participants (68%) were male and 553 participants (32%) were female.

Coaches

Respondents were asked to identify the number of coaches at their institution.  They were also asked to specify whether these coaches were employed on a full or part-time basis.  On average, colleges employed seven coaches per institution. Respondents stated that 117 coaches were employed at Maryland institutions.  Of the 117 total coaches, 22 coaches (19%) were employed full-time at the institutions and 97 coaches (81%) were employed on a part-time basis.

DISCUSSION

This study examined the participation opportunities for female students and coaches in Maryland two-year colleges. The criteria used to measure participation opportunities were based on Title IX guidelines. With regards to Title IX guidelines, the first test (Proportionate Athletic Opportunity) is referred to as a “safe harbor.” The safe harbor test is the measuring stick most often used by institutions to show Title IX compliance (Davis, 2003).

To demonstrate compliance, Maryland two-year institutions must show that the numbers of male and female participants in its intercollegiate sports program are substantially proportionate to its male and female enrollments. If this is the case, no further inquiry needs to be made.

Maryland JUCO institutions do not meet the requirements for compliance based on this first test.  Women comprise 61% of the total enrollment in the Maryland Community College institutions. Men comprise 39% of the total enrollment (see Figure 1 – Appendix A). Women comprise 32% of the total student-athlete population. Men comprise 68% of the total student-athlete population (see Figure 2 – Appendix B). All of the two-year colleges, all 18 institutions, had more male than female participants.

Title IX obligates institutions to provide a sufficient number of participation opportunities for individuals of each sex.  Looking at the number of teams offered gives the appearance of near compliance.  Of the teams offered for students, 49% of the teams (n=65) are for women and 51% of the teams (n=69) are for men.  Looking at the number of participants on each team shows a much different picture. Looking at the number of participants shows that Maryland two-year colleges are not in compliance.  Of the number of participants on the teams, 32% of the participants (n=553) are female and 68% of the participants (n=1166) are male.

One aspect that stands out in this data is that the institutions have relatively small athletic programs.  As a result, they offer very limited opportunities for men or women to participate in sports.  The number of sport offerings was small in comparison to four-year institutions and high schools in the state.

A second important observation from the data is that most of the two-year colleges in Maryland employ their coaches on a part-time basis, as these coaches often hold other full-time jobs outside of the college.  Of the head coaches at two-year colleges in the state, 81% are part-time.  Given the limited resources of many two-year colleges, it is economically advantageous to hire coaches in this manner.  Coaches in two-year colleges are often paid by stipend or released time from teaching or administrative duties.  In some cases, the amount of the stipend is set for a specific coaching position with no relationship to the coach’s background or experience (Bichy, 1997).

The majority of the women’s teams in Maryland two-year colleges are coached by men.  According to the Equity in Athletics Disclosure Act of 1998 (n.d.), women comprise only 23 % of the coaches in the Maryland JUCO. This is significant because the majority of the female student-athletes in the state never get the opportunity to be coached by a woman.  The exclusion of women from the coaching ranks can provide fuel and support for the myth that male coaches are more capable than female coaches (Mumford, 1998).

Concluding Comments

The purpose of this study was to examine the participation of women in sports in Maryland two-year colleges.  Current national participation trends at the high school and college level show that women’s sports participation has increased dramatically and women are participating in sports in record numbers.  However, women remain underrepresented.  In Maryland two-year colleges, that is the case as well.  Female students participate in far fewer numbers in Maryland than do men.  In this area, Maryland’s two-year colleges are not in compliance with Title IX.

More concerns may arise as further examination is made in the areas of administration and coaching.  In these two areas of leadership, the two-year colleges in Maryland have maintained the status quo.  The athletic directors and coaches of these two-year colleges remain mostly Caucasian and mostly male.  Although women have made adequate gains on the playing field, they continue to be left behind in a dramatic fashion, when it comes to coaching or leadership opportunities.  In these areas, Maryland’s two-year colleges are not performing well at all.

References

Acosta, R.  & Carpenter, L.  (1996). Women in intercollegiate sport: A longitudinal study – nineteen year update, 1977-1996.  Unpublished manuscript, Brooklyn College: Brooklyn, NY.

Bichy, T.  (1997). Athletic/gender equity.  Unpublished manuscript, Montgomery College: Rockville, MD.

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APPENDIX A
General Enrollment by Gender in Maryland Two-Year Colleges
Figure 1. Enrollment by Gender
Figure One

APPENDIX B
Total Athletes on Teams by Gender in Maryland Two-Year Colleges
Figure 2. Total Athletes on Teams
Figure 2

2016-10-12T14:44:24-05:00January 10th, 2005|Contemporary Sports Issues, Sports Coaching, Sports Studies and Sports Psychology, Women and Sports|Comments Off on A Look at Women’s Participation in Sports in Maryland Two-Year Colleges
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