Sports Coaching – Page 28 – The Sport Journal

Utilizing the Defenseman’s “Off” Hand: A Discussion of Theory and an Empirical Review

Submitted by: Timothy McCarthy, USA Hockey,
Vikkie McCarthy, Assistant Professor, Austin Peay State University

Abstract

This research explored whether an advantage exists in playing an ice hockey defenseman on his or her “off” hand. The study included a cross-sectional experiment with 10 hockey defensemen who were males aged 14–16 years. Success rates for several defenseman tasks were analyzed to determine if there was a significant difference in performance when the defensemen played on the off hand side rather than the traditional “on” hand, dominant side. The tasks involved were blue line puck containment, defenseman-to-defenseman (D-to-D) passes, one-timer shots in the offensive zone, and breakouts on the strong and weak sides of the ice in the defensive zone. A chi-square analysis was used to look for a significant relationship between the testing variables and success rates. Overall, no significant difference was found between playing off hand and play ing on hand in the defensive zone. However, in the offensive zone, success rates were higher for off-hand play than for on-hand play, in terms of puck containment (72% success for off-hand play) as well as D-D passes and one-timer shots (90% success for off-hand play). A significant difference was found between off-hand one-timer shots (p = .000) and puck containment (p = .001). The main conclusion drawn from this study is that there are advantages to playing defensemen on the off hand.

Utilizing the Defenseman’s “Off” Hand: A Discussion of Theory and an Empirical Review

Stagnant waters eventually cloud and precipitate, vibrant life evaporating, giving way to slow-moving swamps and finally becoming solid earth. In much the same way, the fluid movements and dynamics of hockey must continually change, or die. Anatoli Tarasov, writing in 1969, displayed a vision well beyond that of his contemporaries, when he cautioned that,

If a training period does not offer a creative atmosphere or depth in grasping a particular topic, if it does not stimulate the player to a higher level of technique, and finally, if you can feel that the players are not ready to do battle, if they show no hustle or daring, you should not expect such a team to improve its game.

Tarasov’s teams dominated others through unpredictable deviations from established norms of hockey. Like those teams, in order to remain competitive in the world theater of ice hockey, those who today coach youth ice hockey must be willing to deviate from well-established practices. This paper will explore the advantages and disadvantages to defensemen of “switching sides”; the introduction of techniques unlike those we are used to may develop players’ skills far beyond current boundaries. The operation of defensemen in both defensive and offensive zones will be discussed empirically and subjectively. Efficiency of transitioning between defense and offense during breakouts, along with puck protection, control, and offensive power, will be explored.

The Russian teams coached by Tarasov used what many thought to be strange training techniques, but the training enabled them to dominate world hockey almost as soon as they joined the competitive ranks (Tarasov, 1969). No Russian player ever seemed to maintain any one position. Movement was constantly fluid, from defense to forward and from left to right. Players were equally skilled whether playing on their strong side (forehand) or weak side (backhand). Indeed, many European training techniques challenge hockey norms. From the very beginnings of youth play to the advanced training of adult hockey, the Europeans continually incorporate weak-side training. It is this training that enables European players to move comfortably anywhere on the ice, for their mindset is that they have no weak side: As other players move from right side to left side, the European player can take advantage of that movement, with no loss of firepower.

In North America, norms for positional play in ice hockey are well established. From some of the oldest training manuals to the current ones, young players are taught to “stay in your lane” (Smith, 1996). Why is the left-handed shooter automatically placed on the left side, the right-handed shooter on the right? Perhaps there is a feeling that common sense dictates it. In some circles, positions are actually defined as strong side or weak side based on whether a right-handed shooter is playing on the right or not. By defining sides in this way, players may be placed at a psychological disadvantage before teaching even begins. I believe it is time to redefine what is called strong or weak: to turn the rink around and view it differently. By concentrating training on the so-called weak side, a point is reached when it can no longer be called weak but can instead be called an asset; by eliminating any reference to a weak side, we may become more willing to interchange left- and right-handed players. A careful look at advantages of off hand play for defensemen is one means of beginning to overcome the tendency to follow the norm. Like a southpaw boxer in the ring, off-hand defensemen’s unlooked-for attacks may be the twist that leads to victory.

Actions over the whole of the ice surface need to be taken into account as the defenseman’s use of so-called strong and weak sides is evaluated. As a player moves from side to side through the defensive, neutral, and offensive zones, he transitions between positions of relative advantage and disadvantage. Maintaining puck control, either individually or through coordinated efforts (passing to teammates), is of utmost importance. to maximize these efforts, the most advantageous positions on the ice must be utilized. The defensive zone breakout is arguably the most important transition a defenseman will orchestrate. It may originate from three basic locations: puck in open ice (forward of the goal line), puck in the corner (behind the goal line, located from below the face-off dot to the outer-board radius), or puck behind the net (behind the goal line, between the face-off dots). By attacking these puck positions in the most efficient manner, the defenseman can save time, fractions of seconds that differentiate successful breakouts from turnovers (Lothian & Farrally, 1995).

In the event a defenseman defeats the inside-out fore check or is chased with an outside-in fore check, he or she has the puck on forehand while traveling behind the net, if he or she started on the off hand. In this case, the defenseman is set up to succeed. Either a hard breakout pass can be immediately sent to the winger, or momentum built on the forehand can be maintained as the defenseman heads up ice. On the other hand, the defenseman playing the same side as his or her shot will have to reposition, exposing the puck, in order to make the quick pass. Additionally, he or she will have to clear the net before passing the puck. The defenseman should obtain a better passing angle by being forced to carry the puck wider than the face-off dot, but unfortunately, that advantage may be negated by the additional reaction time afforded to the fore checkers.

Offensive-zone training for defensemen is neglected by many youth coaches. This is evident in a lack of point usage by forwards when attacking in the offensive zone. Additionally, the lack of offensive-zone training is evident in visible weaknesses among defensemen attempting to hold this critical zone, whether manifested in leaving the blue line too early during a breakout or failing to contain the puck. A defenseman needs every bit of confidence that can be mustered in order to overcome such deficiencies, many people believe, and they view it best to have defensemen play on the “on” hand while on the offensive blue line (Parise, 2004). In truth, the greater advantage lies in properly training defensemen to play the off hand in the offensive zone.

While the point is arguable, for the sake of this discussion it will be assumed that the defenseman’s primary role in the offensive zone is containing the play in the zone. Given this role, puck containment, pinching, passing, and shooting will be examined, from both the on-hand and off-hand, or strong and weak, sides. An equally important but perhaps secondary role of the defenseman on the blue line is assisting and scoring. Finally, the offensive blue line is where the defenseman begins many battles with attacking forwards, setting up and securing greatest tactical advantage to protect the middle of the ice. Body positioning on the blue line offers the defenseman an opportunity to gain the slight advantage necessary to prevail.

In order to maintain the offensive zone, a defenseman must be able to contain the puck as it is moved up the boards (Kingman & Dyson, 1997). Control of the situation is demanded, whether the puck is rimmed along the boards, carried out by an opposing player, or shot off the glass. Playing on the side opposite to his or her shot leads the defenseman to realize many benefits, as compared to playing on the strong side. It is probably the rimmed puck that leads some people to believe it best to keep defensemen playing on the strong side. However, when examined closely, the seeming commonsensical advantages of such a traditional method may not hold. The argument for strong side puck containment on a rimmed puck plays to the fact that, in this case, the defenseman’s stick blade will be along the boards for an apparent easy trap and containment of the puck (Constantine, 2004). Among very young players this may be true, but among maturing and developing players the puck is rarely moving slowly up the boards. When the stick blade is at the boards, the player’s body is forced away from the boards. This causes a few problems. First, if the puck is bouncing at all, which is often the case, the opening caused by the player’s body position provides an excellent escape route for the puck if it is mishandled.

Conversely, if the player is playing the opposite side, the best course of action for puck containment is to press the back of the body to the boards. In doing so the player creates a solid barrier from skate blade to hips, while maintaining the stick on the ice in a forehand position. If the defenseman playing the on-hand side attempts this type of containment, he or she will end up on the backhand shot. This may not provide the best option for returning the puck deep into the zone. Should the defenseman press the side of the body against the boards to prevent the backhand situation, more problems arise. First, when pressing with the side of the body, the player’s equipment may prevent a solid seal along the boards. Shin pads in particular may keep the lower leg from making full contact and leave gaps for the puck to exit through. (Pressing with the back of the leg offers softer padding that is more readily formed to the shape of the boards.) Secondly, with the stick on the board side, the player’s position is awkward, the stick jammed close to the body. This may make puck control difficult. In contrast, playing with the off-hand or weak side—even if a defenseman presses with the side of the body rather than the back¬—slight advantage is retained in terms of stick position. Because the stick remains on the forehand, the defenseman is in an excellent position to bang the puck hard off the boards, returning it to the zone. Finally, as skills strengthen, the defenseman may become able to position his or her skate in such a manner as to play the puck directly onto the stick blade, for a quick shot on net.

If the puck is being carried up the boards by an opposing player in an attempt to clear the zone, once again, the defenseman needs any advantage available. If the defenseman maintains a position at the blue line and challenges the opposing player, stick position and body position become critical. If the strong side defenseman chooses to play slightly off the boards to maintain a good forehand stick position, the opposing player may take advantage of the gap presented to flip the puck past (Leetch, 2005). Additionally, the gap may provide a lane the opponent uses or fakes to. In short, it provides options for the opposing player and uncertainty for the defenseman. If the defenseman presses against the boards in order to block the attacker, his or her stick position will be on the backhand if the attacker tries to angle the puck off the boards and out. When viewed from the other side of the ice, however, some of these disadvantages are erased. For example, the defenseman can block out the attacker along the boards and still keep the stick to the middle of the ice, in the forehand position. This stick position may enhance agility, helping the player to maintain a puck angled off the boards and put it back in the zone. In addition, “baiting” the attacker into a hip check may be slightly easier from this side, due to defenseman’s stick position and body position.

Another common method of breakout that the defenseman must be prepared to counter is the glass-out. If the opponent chooses to shoot the puck off the glass in order to bank it out of the zone, the defenseman must be able to react to the careening puck. In this case, the strong-side player may have an advantage: Because the stick will be on the board side, there may be a natural tendency to play slightly off the boards. This puts the player in a better position to handle a puck ricocheting off the glass. However, because the player is slightly off the boards, the offensive player is not as likely to choose this course of action. On the other hand, if the defenseman is against the boards, he can anticipate and once again bait the offensive player into a glass-out situation. The well-trained defense man can quickly come off the boards in order to knock down the puck. If the puck is knocked down, it will be on the forehand for a player on his or her off hand side. A strong-side player, in contrast, will either have to move to his backhand or shift his whole body an entire stick length for the shot.

During a defenseman’s pinch, many of the advantages noted earlier apply, as do a few others. For instance, when the defenseman pinches down the boards, it becomes possible to body check the offensive player and take away the passing lane if he or she is playing on the weak side (USA Hockey, 2005). The stick position in this situation is superior to an opposite-handed colleague’s stick position. The body check is more likely to be a good, clean check, because the blade of the stick will be away from the opponent and less likely to become tangled up with the opponent. If the offensive player attempts a quick pass to a teammate, the defenseman’s stick is already in the passing lane and positioned to block the pass or retrieve the puck if the body check is successful. If the defenseman is playing on his or her strong side, however, it is more difficult to make a good shoulder check, because, with the stick on the board side, the defenseman must take the opponent head-on in order to prevent any gap along the boards.

Once the offensive zone is gained and under control, the defenseman can focus on offense. In order to become an offensive threat, the defenseman must capitalize on every possible advantage. There are several advantages to working on the weak side in passing. For example, if looking to pass back to the same-side forward, the defenseman playing the off-hand side has some options. First, if the lane is open, the pass can be sent right through the circle to an advancing forward in give-and-go fashion. However, if the defender is taking the passing lane away, this defenseman’s stick is in an excellent position to send a banked pass off the boards and down to the teammate. A defenseman with stick on the board side must send the give-and-go with an angled pass, and it is at a much steeper angle for sending a banked pass. Either of these situations may hinder the success of the pass. In another situation, the defenseman might hope to make a pass across the slot to a forward at the back door of the net. In this case, even though the defenseman playing the off-hand side must give a more steeply angled pass, less ice must be covered with that pass; since the stick is toward the middle of the ice, the pass should reach his or her teammate a fraction of a second sooner than would a pass from the defenseman playing on the strong side. Additionally, the defenseman may need to make a D-to-D pass at the blue line in order to open up shooting lanes (USA Hockey, 2003). If the defensemen are playing on the same side that they shoot, several potential problems may arise. First, as the defensemen face each other for the pass, their sticks are in the zone toward the defenders. This positioning offers the least amount of puck protection and provides better opportunities for poke checking from the defenders. Additionally, even though the puck is deeper in the zone when on the defensemen’s sticks in this circumstance, the potential for losing the zone may be higher if they pass D to D. This is because on the follow-through for the pass, the defenseman making the pass may actually angle the puck toward the blue line. This situation may be exacerbated by the fact that the defensive players may be playing relatively close to the blue line, since their sticks will remain in the zone even when they are standing on the blue line. If the defensemen are put on the sides opposite their shots, these problems diminish. For instance, because their sticks will be toward the blue line, the defensemen will have to play deeper in the zone; their body position affords good puck protection. During a D-to-D pass in this situation, the follow-though from the passing defenseman is in toward the offensive zone. This allows for a greater margin of error on the pass. Finally, during the D-to-D pass while playing on opposite hands, both defensemen are set up for one-timer shots.

The transition to the breakout begins with puck retrieval. Many times, puck retrieval will be initiated by a transition from backward skating to forward skating, as the defenseman turns away from his or her offensive zone and retreats in toward his or her net. In the case of a loose puck in the corner, the defenseman should transition toward the outer boards and travel the shortest distance to the puck (Gendron, 2003). In this situation, there are several advantages to the defender playing the off-hand side.

For example, if a right-handed defenseman is playing on the left side, in the attack on the puck as described above, he or she immediately puts the puck under protection. If the fore checking team’s course of action is an attempt at an inside-out fore check meant to force the puck back up the same side board, the off-handed defender has several advantages. By virtue of stick position, the defenseman will pick the puck up on his or her forehand, body between the puck and the attacker. In contrast, a defensemen playing on the strong side will retrieve the puck on the backhand, exposing it to the fore check. Additionally, because the off-handed defenseman has puck control on the forehand (along with superior puck protection), he or she should be able to accelerate more quickly, improving the opportunity to defeat the fore check (Marino et al., 1987). Even if the initial fore check is successful, the cut back by the defenseman will be tighter, quicker, and easier when on his or her backhand rather than forehand, and the situation once again provides excellent body position for puck protection. Upon recovery from a backhand cut back, the off-handed defenseman maintains the advantage over an opposite-handed colleague, because the stick position of the off-hander naturally lessens the angle of the breakout pass to the winger. Even if the winger is breaking off the boards, such stick position offers an angle that makes receiving the pass easier (Montgomery et al., 2004).

Methods

This one-time, controlled experiment with 10 hockey defensemen who were males aged 14–16 involved observation during an ice rink’s 2-hr open “puck-n-stick” session. Observational data was collected by 3 observers tracking 10 players playing on-handed and off-handed in the defensive and offensive zones. Each player performed 6 iterations of each of several tasks: blue line puck containment, defenseman-to-defenseman passes, one-timer shots in the offensive zone, and breakouts on the strong and weak sides of the ice in the defensive zone. A total of 540 observations were made, 360 in the offensive zone and 180 in the defensive zone. Data were coded as 1=success and 0=failure and were analyzed using SPSS; the mode and rates of success or failure were generated as descriptive statistics. Because the data were categorical and the purpose of the study was to determine the combined effects of the study variables, a non-parametric analysis was pursued (Hayes, 1991). Additionally, a chi-square analysis was used to assess the significance of relationships between variables in the offensive and defensive zones separately.

Results

In the offensive zone, defensive players playing on the off-hand side as opposed to the on-hand side experienced a higher success rate for puck containment, D-to-D pass, and one-timer shots (see Table 1). A significant relationship (p=.000) was found between players playing off-handed and success on one-timer shots. Data analysis also indicated that a significant relationship (p=.001) exists between puck-containment success and players playing off-handed in the offensive zone. No significant difference was found, however, between success rates for on-hand D-to-D passes in the offensive zone and success rates for off-hand D-to-D passes in the offensive zone.

Table 1

Percentage of Offensive-Zone Tasks Accomplished Successfully Using “On” Hand vs. Using “Off” Hand

	With On Hand	Success Rate With Off Hand
Puck Containment	68%	72%
D-to-D Pass	82%	90%
One-Timer Shot	58%	90%

Table 2

Chi-Square Results for Tasks in Offensive Zone

	chi-square	df	Sig.
Puck Containment Using On Hand	8.067*	1	.005
Puck Containment Using Off Hand	11.267*	1	.001
D-to-D Pass Using On Hand	24.067*	1	.000
D-to-D Pass Using Off Hand	38.400*	1	.000
One-Timer Shot Using On Hand	1.667	1	.197
One-Timer Shot Using Off Hand	38.400*	1	.000

*p< .01 ** p

In the defensive zone, there does not appear to be a significant difference between playing with the on hand vs. playing with the off hand, in terms of puck retrieval control and pass success. Although in this experiment players had more success at puck retrieval control when playing the on-handed strong side (78%) than playing the off-handed strong side (67%), there does not appear to be a significant relationship for playing off-handed defensively (p = .248). Differences in success rates are most likely due to spurious environmental factors, in that, during this part of our experiment, the ice became increasingly crowded as players began puck containment drills in the defensive zone (the final set of drills for this portion of the experiment).

Table 3

Percentage of Defensive-Zone Tasks Accomplished Successfully Using “On” Hand vs. Using “Off” Hand

	Success Rate With On Hand	Success Rate With Off Hand
Puck Control Strong Side	78%	67%
Puck Control Weak Side	83%	83%
Pass Success Strong Side	94%	92%
Pass Success Weak Side	92%	92%

Table 4

Chi-Square Results for Tasks in Defensive Zone

	chi-square	df	Sig.
Puck Control Strong Side	22.007	1	.194
Puck Control Weak Side	33.237	1	.248
Pass Success Strong Side	24.067	1	.340
Pass Success Weak Side	29.000	1	.250

*p< .01 ** p

The validity of these results may be somewhat vulnerable to the repeated execution of tasks by the players, in that rates of success increased through the iterations. Because repeating tasks simulates the normal process—with its underpinnings in theory—of practicing tasks to perfect them, it was not deemed necessary to adjust the raw data. These results may not be generalized to levels of hockey beyond the youth level and should be construed specifically in the context of USA hockey development.

Conclusion

There are several practical applications for the findings of this study. First, the finding that no overall difference exists supports a paradigm shift within hockey training. Playing on one’s backhand (i.e., playing off hand) is generally recognized as being more difficult, yet by increasing off-hand training and playing opportunities it can be expected that a change would begin to be seen: the off hand would begin to be the favored play. Coaches should consider playing defensemen off-handed, to gain significant advantage in the offensive zone; the advantage of the off-handed one-timer is already widely acknowledged and exploited in many power plays (USA Hockey, 2003). However, the significant difference with off-handed blue line puck containment was an unanticipated outcome.

The study’s results should strongly urge coaches to play defensemen off-handed, even when a team lacks numerical advantage in terms of players on the ice. The inconclusive data for the defensive zone may, however, engender a certain reluctance to play defensemen on their opposite hands; in such cases, coaches should consider having defensemen switch sides as they move up the ice, in order to maximize the offensive attack. Overall, the data support the idea of changing the training regimes youth hockey participants in the United States pursue, in favor of off-handed defensive play improving not only individual skills but offensive power. An interesting follow-on study would be an analysis of players with predominately off-hand play experience during their careers, or of players trained according to other paradigms (i.e., European players).

References

Constantine, K. (2004). Offensive tactics. Presented at the USA Hockey Advanced Clinic. Gendron, D. (2003). Coaching hockey successfully.Champaign, Ill.: Human Kinetics.

Grillo, R. (2005). The pond. Presented at the USA Hockey National Hockey Coaches Symposium. Hays, W. (1991). Statistics. New York: Harcourt Brace College Publishers.

Kingman, J. C., & Dyson, R. J. (1997). Player position, match half and score effects on the time and motion characteristics of roller hockey match play. Journal of Human Movement Studies, 1(33), 15–29.

Leetch, B. (2005) Good gap control lets you dictate the play. USA Hockey Magazine, 2(27), 14Lothian, F., & Farrally, M. (1995). A time motion analysis of women’s hockey. Journal of Human Movement Studies, 6(26), 255–265.

Marino, G. W., Hermiston, R. T., & Hoshizaki, T. B. (1987). Power and strength profiles of elite 16–20 years old ice hockey players. International Symposium of Biomechanics in Sport, 314–324.

Montgomery, D. L., Nobes, K., Pearsall, D. J., & Turcotte, R. A. (2004). Task analysis (hitting, shooting, passing, and skating) of professional hockey players. ASTM International, 1446, 288–296.

Parise, Z. (2004). Puck handling and puck protection. USA Hockey Magazine, 9(26), 52

Reilly, T., & Lowe, D. (1994). Ergonomic consequences of executing skills in hockey. London: Taylor & Francis.

Smith, M. A. (1996). The hockey playbook. Richmond Hill, Ontario, Canada: Firefly Books.

Authors Note: Correspondence for this article should be addressed to: Vickie McCarthy, Assitant Professor, Department of Professional Studies, Austin Peay University, Building 604, Bastogne & Air Assault, (931) 221-1407, mccarthy@apsu.edu.

U.S. Sports Academy2015-10-02T23:27:00-05:00April 2nd, 2008|Sports Coaching, Sports Management, Sports Studies and Sports Psychology|Comments Off

Perceived Leadership Behavior and Subordinates’ Job Satisfaction in Midwestern NCAA Division III Athletic Departments

Submitted by: William J. Kuchler, Lynchburg College

Abstract

This study of selected Division III athletic programs at private colleges in the Midwest addressed the association between head coaches’ job satisfaction, assessed using the Minnesota Satisfaction Questionnaire, and perceptions of athletic directors’ leadership behavior, measured with the Leadership Practices Inventory. A statistically significant association was found between coaches’ perceptions of the athletic directors’ leadership and coaches’ satisfaction. No statistically significant association surfaced between the directors’ self-perceptions and coaches’ satisfaction. Additionally, to a significant degree, discrepancy between directors’ perceptions of leadership and coaches’ perceptions of leadership was associated with diminished job satisfaction. Top dissatisfiers were extrinsic factors, which included supervisory behavior. Recommendations include that athletic directors become attuned to how coaches perceive leadership, improving understanding between the groups concerning their discrete expectations for leadership behavior.

Perceived Leadership Behavior and Subordinates’ Job Satisfaction in Midwestern NCAA Division III Athletic Departments

Leadership continues to be a popular topic of analysis and debate. American culture has been obsessed with the development of future leaders as well as the enshrinement of successful leaders. The subculture of sport has long been viewed as a primary environment for the incubation and nurturing of tomorrow’s leaders.

If one supports the view that leadership behaviors can be learned, then the environments in which such learning takes place need to be explored. One suggestion is that, in all societies, successful leaders typically develop largely by first learning to be good followers. One cannot understand the processes of leadership in its many variations without examining the relationships leaders have had with followers (Clark & Clark, 1990). Within American culture, the bulk of sport participation decidedly falls to youth and young adults, while the organization and management of their sport events is handled by adults. For this reason, most examples of leader-follower dyads within sport involve an adult-child relationship that reflects an imbalance of power which diminishes the opportunity to willingly choose to follow. Clark and Clark (1990) commented that the few feeble attempts to incorporate leadership training in secondary-school curricula have been isolated in extracurricular activities. This line of thought can be extrapolated into an argument that sport within the educational system has as one of its purposes the provision of a training ground for the leaders of tomorrow (albeit an inadequate training ground). It could then be hypothesized that leadership training within sport encourages athletic administrators to take for granted the imbalance of power implicit in positional authority, which could lead to a leadership style that is authoritarian in the tradition of the benevolent dictator.

The processes characterizing selection of athletic directors is fundamental to the development of this research problem within sport leadership. Fitzgerald, Sagaria, and Nelson (1994) posited a work history, or an array of occupational experiences, typifying athletic directors. The normative career trajectory derives from sequentially ordered, common positions beginning with a single or fixed portal and culminating in a single top position. The profession of sport management is widely populated by those who have entered athletic administration through the player-coach-manager route. The sport manager is thus regularly assumed to have a “jock” mentality. Reinforcing this assumption as well as the normative career pattern have been such typical practices as selecting a retired coach to become athletic director, regardless of aptitude or training (Williams & Miller, 1983).

Fitzgerald et. al. (1994) concluded that, unlike most other occupations, the athletic director position has as its portal not a first job, but instead a significant, socializing, cocurricular experience, through which leadership and athletic skills alike were cultivated and a glimpse, at least, into collegiate athletic administration was provided. This socializing experience was found to limit leadership experiences, just as the normative progression through positions limits the types and styles of leadership experienced. The socializing experience may well occur in similar environments. That fact, coupled with the dearth of formal preparation in sport management, raises a question about athletic administrators’ understanding of situational leadership. Williams and Miller (1983) supported a thesis that athletic administrators have tended to come from the “university of hard knocks,” starting as coaches and teachers and finding themselves promoted to administration. Such a model returns us to the premise that, within the normative career pattern, the athletic director’s exposure to leadership prior to becoming a director always involved an adult-minor relationship dissimilar to the administrator/coach dyad. Few of today’s athletic administrators, particularly at the Division III level, have degrees in sport administration. On-the-job training and management by trial and error are considered typical preparation for the athletic director (Quarterman, 1992).

There have been almost as many different definitions and descriptions as persons who have attempted to define the elusive concept of leadership. An early description was given by Stogdill (1948), for whom leadership implied activity, movement, getting work done. The leader is a person in a position of responsibility coordinating activities of group members aimed at attaining a common goal. Stogdill also cautioned, however, that a distinction must be made between leader and figure head. While most definitions of leadership involve an influence process, there appear to be few other qualities common among the numerous definitions of leadership that have been proposed (Yukl, 1989).

Leader behavior theory holds that leaders are made, not born; it stands in contrast to leadership trait theory, which argues the opposite. As theorists from the two schools of thought debated the best leadership styles and traits, situational theorists—representing an outgrowth of behavioral theory—came to assert that the one-best-style approach to leadership ignores powerful situational determinants of leader effectiveness. As situations change, different styles of leadership can be effective (Bass, 1990).

Current social, political, and economic pressures require that athletic departments do more with less (Armstrong-Doherty, 1995; Snyder, 1990). Thus athletic departments may benefit from leadership that brings subordinates on board with a leader’s and organization’s vision and motivates them to pursue higher goals (Doherty & Danylchuk, 1996). Contemporary leaders must draw on many qualities to be effective, being at once visionary, willing to take risks, and adaptable to change. A leader must also exemplify the values, goals, and culture of the organization. Furthermore, contemporary leaders must emphasize the delegation of authority and pursuit of innovation. They must empower others, distributing leadership across all levels of the organization. The new leader is one who energizes people to action and transforms organization members into agents of change (Van Seters & Field, 1990).

Such a transformational leader asks followers to (a) transcend self-interest for the good of the group, organization, or society; (b) consider the longer term need for self-development over needs of the moment; and (c) achieve better awareness of what is really important (Bass, 1990). Transformational leadership thus refers to the process of effecting major changes in the attitudes and assumptions of organization members, building commitment to organizational mission, objectives, and strategies. Transformational leadership involves the leader’s influence on subordinates, but the effect of that influence is the empowerment of subordinates to transform the organization. Inherent bureaucratic authority differentiates transformational leadership from influence, and transformational leadership also stands in contrast to transactional leadership, or the motivation of followers through appeal to their self-interest.

Armstrong (1993) summarized the literature presenting the athletic director as the same general sort of leader as the successful coach and cited several qualities broadly held to help athletic directors administer effectively. Much of the early literature concerning leadership and the athletic director (Frost, Lockhart, & Marshall, 1988, Horine, 1985, and Jensen, 1988, as cited in Armstrong, 1993) appeared to focus on leader characteristics. The athletic director, it was emphasized, should have a vision for the department, should be comfortable taking risks, should be a consistent decision maker, and should be ambitious, reliable, fair, high-intensity, enthusiastic, and desirous of leading. One focus of leadership research within sport management has been early approaches to measuring leadership. To a large extent, leaders have been perceived as causal agents determining organizations’ success or failure (Soucie, 1994). Interestingly, Slack (1997) noted that the popular press continues to describe the leadership abilities of coaches and team managers in terms of the traits these individuals exhibit.

Again, sport management is to a great degree the domain of administrators who entered athletic administration through the linear sequence player to coach to manager. A study by Fitzgerald et al. (1994) found 94.5% of their administrator–respondents to have experienced such a career pattern. Cuneen (1992) pointed out how incongruous is the trend toward multi-million-dollar athletic enterprises being directed by individuals with little or no formal preparation in athletic administration. Because so few of today’s athletic administrators have degrees in sport administration, it seems reasonable to conclude that on-the-job training and trial-and-error management constitute the typical preparation of athletic directors (Quarterman, 1992).

In an extensive review of the literature on effective management of sport organizations, Soucie (1994) concluded one apparent consistent finding was that considerate-supportive behavior increases’ subordinates’ satisfaction. The job satisfaction of subordinate employees has long provided an outcome measure in leadership studies, dating back to the leader behavior studies emerging from the University of Michigan and Ohio State University. Employee satisfaction remains one of the most measured and most important and indicators of a leader’s impact (Wallace & Weese, 1995). Moreover, Kushnell and Newton (1986) concluded that leadership style is the significant determinant of subject satisfaction; participants were highly dissatisfied with leadership of an authoritarian style.

Yusof’s (1998) study of NCAA Division III institutions showed a statistically significant relationship between athletic directors’ transformational leadership behaviors and the job satisfaction of coaches. Yusof concluded there was a need for more transformational leaders in sport settings, since job satisfaction was positively associated with subordinates’ strong performance, relatively high productivity, low absenteeism, and low turnover. In addition, Lim and Cromartie (2001) suggested that ineffective leadership in organizations is a major cause of diminished productivity. Hater and Bass (1988) concluded that, although transformational and transactional leaders alike can practice a more or less participative method of decision making, transformational leaders appeared compatible with a better educated workforce. There can be little disagreement that a NCAA Division III coaching staff is a highly educated workforce.

Weese (1996) concluded that highly transformational leaders are likelier than other leaders to have strong organizational cultures and culture-building activities. Kouzes and Posner (1987) and Clark and Clark (1990) proposed that leadership behavior can be taught. If they are correct, then athletic directors who are taught transformational leadership skills should generate coaching staffs with relatively stronger performance, commitment, and job satisfaction.

Although coaches and athletic directors share steps on a normative career path, it does not follow that they share identical ideas about leadership in their own departments. Ideal leadership behavior may be viewed quite distinctly by coaches as compared to administrators. Athletic directors may well need to become more attuned to their staffs’ perceptions. Such awareness—an important tool for recognizing the pulse of a satisfied, peak-performing coaching staff—could be gained through formal and informal assessment methods. Again, athletic directors could be advised to be in touch with the perceptions of their coaching staff regarding the assessment of leadership behavior. Results of this study indicated an association between the extent of agreement of perceived leadership behavior and the coaches’ job satisfaction.

The present study data constitute feedback needed by athletic directors at non-scholarship colleges and universities. It is hoped that the findings may encourage them to develop their understanding of leadership behavior through formal training in sport management graduate programs and/or leadership seminars. The study findings should also encourage athletic directors to become more innovative, experimental, and communicative with their coaching staffs. The data suggest it can be useful to administrators to generate feedback from staff members concerning the application of leadership strategies within their organizations.

Method

Athletic directors and selected head coaches at 30 private institutions in four Midwestern NCAA Division III athletic conferences were surveyed using instruments delivered by mail. To select the coaches, I first identified 4 men’s and four women’s sport programs at the institutions, then attempted to contact nearly equal numbers of male and female coaches staffing those programs. The coaches and directors were asked to complete 3 survey instruments: the Leadership Practices Inventory, Minnesota Satisfaction Questionnaire, and a demographic profile created by the author based on a precedent in the literature (Linam, 1999). The Leadership Practices Inventory (LPI) was developed, validated, and employed by leadership experts James M. Kouzes and Barry Z. Posner (1997). The inventory consists of an LPI-Self instrument to be completed by a leader participating in the research, and an LPI-Observer instrument completed by people who directly observe and are influenced by that leader’s behavior. The Minnesota Satisfaction Questionnaire measures employees’ satisfaction with several aspects of their work environment. The questionnaire has the ability to measure intrinsic satisfaction, extrinsic satisfaction, and, most importantly, general satisfaction.

Results

The demographic information gathered for the group of athletic directors appears to support the normative career pattern described in the literature. Of the sample, 85% had been collegiate athletes, and 85% had coached collegiate teams before becoming athletic administrators. Additionally, only 15% of the studied athletic directors reported that they held an academic degree in an administrative discipline. Assuming that those academic programs most likely to formally address the topic of leadership would fall somewhere within administration, a dearth of formal training in leadership can thus be anticipated for the administrators in this group.

The results of this study were in keeping with the literature, in terms of head coaches’ perceptions of leadership providing accurate assessment of supervisory leadership. However, no statistically significant association was found between how athletic directors assessed their own leadership behavior and how satisfied subordinate head coaches were with their jobs. Athletic directors should, therefore, be cautious about ascribing a high level of job satisfaction to coaching staffs, even if the directors make efforts to lead positively and considerately. What athletic directors do by way of serving the cause of perceived good leadership may in the final analysis have no meaningful association with satisfied coaches. On the other hand, the present findings did include a statistically significant association between how head coaches perceived the behavior of their leader, the athletic director, and how satisfied the coaches were with their jobs. For all five leadership behaviors covered by the survey instruments, in fact, this association was found to be significant. According to the results, the greater the discrepancy between an athletic director’s perceptions concerning leadership behavior and the director’s subordinate coaches’ perceptions of that behavior, the less likely the coaches were to report satisfaction with their jobs.

Discussion and Conclusions

The common assumption has been that participation in the coach-player dyad, including leader-follower experiences, prepares coaches to successfully apply leadership behaviors within administrator-coach relationships. The present research deemed this a false assumption: While the normative career pattern player-coach-administrator is normative, it is not sufficient. Studying leadership in NCAA Division III institutions, Armstrong (1993) suggested the possibility that many an athletic director does not know how to be a leader, having been chosen not for leadership ability but for an outstanding coaching record or simply longevity of service. Armstrong’s work is consistent with the idea of a normative career path, since many of the studied athletic directors are former basketball and football coaches whose leadership is behaviorally oriented. If most college athletic directors lack specific sport-management training; if their exposure to models of leadership has occurred strictly in the setting of extracurricular activities (Clark & Clark, 1990); and if that setting involved primarily the adult leader-youth follower relationship, there are at least three reasons to question the degree to which athletic administrators understand the functioning and value of other kinds of leadership, such as transformational leadership.

Recommendations

Many athletic administrators attain their positions by building on experiences as player, first, and then coach. Leadership training in the coaching ranks, or through the coach-player dyad, is thought to cultivate an autocratic style, given the obvious imbalance of power. Results of the present study indicate a positive association between head coaches’ job satisfaction and their perceptions about 5 behaviors associated with transformational leadership. Working from the premise that job satisfaction is a vital component in outstanding job performance and superior organizational effectiveness, athletic directors should have a strong interest in coaches’ perceptions concerning leadership behavior. Assuming leadership behavior can be taught (Kouzes & Posner, 1987; Clark & Clark, 1990), athletic directors who learn to be transformational leaders should foster more job satisfaction, stronger commitment, and better performance on the part of the coaching staff; at the least they should be able to reduce job dissatisfaction. Four specific recommendations arise from the research, as follows:

Division III NCAA athletic directors should attune themselves to the coaching staff’s perceptions of leadership behavior in the department. The staff’s perceptions of leadership say more about their job satisfaction than does the director’s perception of leadership.
Whenever an athletic director’s institution or professional organization conducts leadership training, the director should exploit the opportunity for professional development.
Division III NCAA institutions should begin to include leadership behavior and ability as a criterion for selection of athletic administrators.
Further exploration should seek directional causation within the leadership–job satisfaction relationship: Perhaps being very satisfied (intrinsically and/or extrinsically) with a job influences coaches’ perceptions of leadership, but perhaps, in an opposite direction, the way leadership is perceived influences job satisfaction. Research should settle the matter.

References

Armstrong, S. (1993). A study of transformational leadership of athletic directors and head coaches in selected NCAA Division III colleges and universities in the Midwest. Kent State University. Dissertation Abstracts International, 53(11), 3811A.

Armstrong-Doherty, A. J. (1995). Athletic directors’ perceptions of environmental control over interuniversity athletics. Sociology of Sport Journal, 12, 75–95.

Bass, B. M. (1990). Bass and Stogdill’s handbook of leadership (3rd ed.). New York: Free Press.
Clark, K. E., & Clark, M. B. (1990). What is a leader? What is leadership? In K. E. Clark & M. B. Clark (Eds.), Measures of Leadership (pp. 30–34). Greensboro, NC: Center for Creative Leadership.

Cuneen, J. (1992). Graduate-level professional preparation for athletic directors. Journal of Sport Management, 6, 15–26.

Doherty, A. J., & Danylchuk, K. E. (1996). Transformational and transactional leadership in interuniversity athletics management. Journal of Sport Management, 10, 292–309.

Fitzgerald, M. P., Sagaria, M. A. D., and Nelson, B. (1994). Career patterns of athletic directors: Challenging the conventional wisdom. Journal of Sport Management, 8, 14–26.

Hater, J. J., & Bass, B. M. (1988). Superiors’ evaluations and subordinates’ perceptions of transformational and transactional leadership. Journal of Applied Psychology, 73(4), 695–702.

Kushnell, E., & Newton, R. (1986). Gender, leadership style, and subordinate satisfaction: An experiment. Sex Roles, 14(3–4), 203–209.

Kouzes, J. M., & Posner, B. Z. (1987). The leadership challenge: How to get extraordinary things done in organizations. San Francisco: Jossey-Bass.

Lim, J. Y., & Cromartie, F. (2001). Transformational leadership, organizational culture and organizational effectiveness in sport organizations. The Sport Journal, 2(4), 9

Linam, K. R. (1999). Leadership styles of collegiate athletic directors and head coaches’ satisfaction. Unpublished doctoral dissertation, United States Sports Academy.

Quarterman, J. (1992). Characteristics of athletic directors of historically black colleges and universities. Journal of Sport Management, 6, 52–63.

, T. (1996). From the locker room to the board room: Changing the domain of sport management. Journal of Sport Management, 10, 97–105.

Snyder, C. J. (1990). The effects of leader behavior and organizational climate on intercollegiate coaches’ job satisfaction. Journal of Sport Management, 4, 59–70.

Soucie, D. (1994). Effective managerial leadership in sport organizations. Journal of Sport Management, 8, 1–13.

Stogdill, R. M. (1948). Personal factors associated with leadership: A survey of the literature. The Journal of Psychology, 25, 35–71.

Van Seters, D., & Field, R. (1990). The evolution of leadership theory. Journal of Organizational Change Management, 3, 29–45.

Wallace, M., & Weese, W. J. (1995). Leadership, organizational culture, and job satisfaction in Canadian YMCA organizations. Journal of Sport Management, 9, 182–193.

Williams, J. M., & Miller, D. M. (1983). Intercollegiate athletic administration: Preparation patterns. Research Quarterly, 54(4), 398–406.

Yukl, G. (1989). Managerial leadership: A review of theory and research. Journal of Management, 15(2), 251–289.

Yusof, A. (1998). The relationship between transformational leadership behaviors of athletic directors and coaches’ job satisfaction. Journal of Sport Research, No. 55(4), 170-174.

Author Note

William J. Kuchler, School of Health Sciences and Human Performance, Lynchburg College, kuchler@lynchburg.edu; Lynchburg College, 1501 Lakeside Dr., Lynchburg, VA 24501. (434) 544-8475. Home email: doctorgolf51@hotmail.com

U.S. Sports Academy2013-11-25T22:10:04-06:00April 2nd, 2008|Sports Coaching, Sports Management, Sports Studies and Sports Psychology|Comments Off

An application of means-end theory to analyze the college selection process of female athletes at an NCAA division II university

Submitted by: Peter Finley, Nova Southeastern University,
Jeffrey J. Fountain, Nova Southeastern University

Abstract

While considerable academic attention has been given to the college selection process of student athletes, it has typically relied strictly on survey responses to determine the relative importance of numerous factors. This research applied means-end theory to the problem of understanding college selection among female student athletes at an NCAA Division II university. Through interviews with participants (N=25), the researchers were able to utilize the laddering technique (Reynolds & Gutman, 1988) to identify not only attributes of the university that were salient to the participants as they made their college selection, but also to probe deeper to determine the underlying values that made the factors important. The values cited by participants were security, achievement, belonging, and fun and enjoyment. This study highlights the function of means-end analysis to investigate college selection among student athletes going beyond the superficial identification of important factors. Via means-end interviews, researchers can determine why varied factors are important to individuals.

Review of Literature

College selection is often a difficult process for students in general and is even more complicated for student athletes, particularly those who are recruited by numerous schools (Klenosky, Templin, & Troutman, 2001). To date, considerable academic attention has been paid to assessing the relative importance of factors student athletes consider during their college selection process. The traditionally used method has been to present student athletes with a survey through which various factors were rated. The factors receiving the highest mean scores were then considered to be the most important to the prospects at the time that they made their final college selection. Factors that were commonly cited as important in the college-selection literature in regard to student athletes were concisely detailed in Kankey and Quarterman (2007), and included: (a) opportunity to play (Forseth, 1987; Johnson, 1972; Konnert & Geise, 1987; Slabik, 1995); (b) academic factors (Bukowski, 1995; Cook, 1994; Forseth, 1987, Mathes & Gurney, 1985; Reynaud, 1998; Slabik, 1995); (c) amount of scholarship (Doyle & Gaeth, 1990; Ulferts, 1992); and (d) head coach (Cook, 1994; Mathes & Gurney, 1985; Slabik, 1995).

Recent studies in this area utilized the traditional method for college selection studies. In both studies, Finley (2005), and Kankey and Quarterman (2007), original surveys were constructed and tested for validity and reliability. Surveys were then distributed in packets to coaches with an accompanying cover letter, instructions for administering the survey, and an addressed and stamped return packet. Both studies utilized five-point scales to elicit scores intended to reflect relative importance of numerous factors. Kankey and Quarterman (2007) elected to use a scale ranging from 5 (extremely important) to 1 (unimportant), while the scale used by Finley (2005) was a traditional Likert scale, ranging from 5 (very important) to 1 (very unimportant), with a neutral category.

Karney and Quarterman (2007) surveyed members of NCAA Division I softball teams in Ohio. Participants (N=196) represented 10 of the 11 programs in the state. The descriptive analysis demonstrated that this population considered availability of major or academic program, head coach, career opportunities after graduation, and social atmosphere of the team to be the most important college choice factors, with the mean score for each being above 4 (very important).

Finley (2005) sought to determine the most salient aspects of college selection among NCAA Division III cross country runners (N=427) from around the country. Results indicated that academic reputation, major or degree program, atmosphere of the campus, and the success of the cross country program were the most important. Finley (2005) also determined that the importance of team-related factors was related to the gender and ability of the athletes. Finley split the sample by gender and then subdivided each gender-group into higher and lower ability groups based on the best cross country time each participant had recorded in high school. Several factors proved to be more important to higher ability males than the other groups: The team’s performance in the prior season, the team’s performance over the last several seasons, the performance of individuals on the team last year, and the number of award-winning athletes from the program were all more important to higher-ability males than to lower-ability males or female cross country runners in both the higher and lower ability groups.

While the aforementioned research was important and contributed to the understanding of the college selection of student athletes, it did not address the question of why these factors are important. Klenosky, Templin, and Troutman (2001) introduced a new strategy for assessing college selection criteria with an eye for understanding the underlying values of the student athletes at the time they selected a college. Specifically, the researchers sought to address the “why” question through interviews with 27 NCAA Division I football players at a single university. Their application of means-end theory (Gutman, 1982) demonstrated that college-selection research can move beyond the survey format to answer the more robust question of why particular factors are important to specific participants. The football players described such factors as facilities, the coach, schedule, and academics as important. Players linked these factors to such consequences as getting a good job, personal improvement as a player, playing on television, and feeling comfortable. In turn, these consequences supported the football players’ values of feeling secure, a sense of achievement, a sense of belonging, and having a fun and enjoyable experience. While Klenosky, Templin, and Troutman (2001) successfully introduced Gutman’s means-end theory to the study of college selection by student athletes, they acknowledged that further studies should explore other levels of competition, and female student athletes. This research sought to make that contribution to the college selection literature.

Means-End Theory

Developed by Gutman (1982), means-end theory allows researchers to explore consumer choice beyond the superficial level to understand the emotional underpinnings that drive consumers’ decisions. Through interviews, researchers guide participants through levels of abstraction, moving from the superficial factors that guide their choice, to the consequences that they perceive will arise (consumers seek to maximize positive outcomes) from their choice, and finally to the personal values they are attempting to reinforce. From each attribute of a program or school that an interviewee describes as important, a means-end chain is created to explore the interconnections between the attribute, the anticipated consequences that arise from the attribute, and finally to the personal value being reinforced. The defining aspect of an interview utilizing this theory is to present the participant with the simple question, “Why is that important to you?” After they name a factor or attribute that was important in their college selection, the researcher simply seeks to determine why that factor was important. This generally leads to a connection to a consequence. Asking why the consequence was important leads into further abstraction, to a statement of a value.

According to the theory, individuals base decisions on factors that are likely to lead to desired consequences (Gutman, 1982). The privileging of one consequence over another reflects the value set of the person empowered with the choice, and they will make selections that reinforce what they have deemed valuable (Klenosky, Templin, & Troutman, 2001). While two athletes might cite the location of a school as an important factor on a traditionally used survey format, it would be unclear whether they value location because of proximity to family, the effect of weather on their sport performance, preference for a rural or suburban lifestyle, or for myriad other reasons. Through the application of means-end theory, researchers can make this determination. As applied to college selection, for example, an athlete might rate facilities as an important factor (attribute) in her college selection. Further questioning (via the “why is that important” question) can elicit the response that facilities were import because she believed it would help her play better (consequence). Finally, she might describe that playing better would reinforce her desire for personal achievement (value). See Table 1 for an example of interview responses and the corresponding coding.

Table 1

Example of two interview ladders and the corresponding coding for each

Table 1

Research Goal

The present study sought to apply means-end theory to determine the attributes, consequences, and values that underpinned college selection for female student athletes at an NCAA Division II institution.

Method

Procedure

Semi-structured interviews were conducted with two researchers and individual student athletes. The participants were asked to recall the colleges that they seriously considered as they made their final college selection. Participants were then asked to list factors (attributes) that they relied on as they selected their college over their other finalists. The researchers then utilized the laddering technique as described by Reynolds and Gutman (1988) and later applied to student athletes and college selection by Klensoky, Templin, and Troutman (2001) to create means-end chains, in which each attribute was explored via the question, “Why is that important to you?” This would elicit a response suggesting how this attribute would benefit the participant (consequence). Then the “Why is that important to you?” question would be used to move the participant into deeper reflection, moving from the consequence to a personal value. Participants would create from two to four chains and interviews generally lasted ten to fifteen minutes.

To elicit the most thoughtful and honest answers possible, the researchers utilized the interview methods suggested by Reynolds & Gutman (1988). These included conducting interviews in a non threatening environment (a library area was used, which represented a more neutral site for participants than would a professor’s office or a classroom), making an effort to position the participant as the only expert regarding their college selection, with emphasis being placed on reassuring them that there was no right or wrong answer, and showing interest in responses while refraining from giving cues suggesting judgment. Following each interview, the researchers used interview notes to create means-end chains, which connected each attribute cited by the participants with the corresponding consequences and values stemming from it. Discrepancies were resolved jointly, relying as strictly as possible on key words and phrases used by the participants and recorded in the interview notes.

Participants

The participants in this study were 25 female student athletes at an NCAA Division II university in Florida during the 2005-2006 academic year. Participants represented a variety of sports, including basketball, soccer, softball, golf, tennis, rowing, and cross country.

Results

In total, 77 means-end chains were created, an average of 3.08 per participant. Coding of the means-end chains revealed eight attributes cited as important to the selection of the student athletes’ current college. These attributes led to eight potential consequences, which, in turn, led to four values.

Table 2

Summary of all attributes, consequences, and values identified throughout the interview process

Table 2

Using the coded data, an implication matrix was constructed (Table 3) as a summary of the connections between attributes, consequences, and values. In addition to showing the number of participants that mentioned a concept (under N), the matrix also lists the number of total times the concept was mentioned. Each cell reflects the number of times the concept was mentioned. For example, location linked to the consequence of feel comfort (C1), three times and connected to the consequence of adventure (C3) twelve times. Location also connected to the value fun and enjoyment (V1) fifteen times. The implication matrix was then used to construct a Hierarchical Value Map (HVM).

table 3

Implication Matrix for female student athletes’ college selection

			N	Chains	C1	C2	C3	C4	C5	C6	C7	C8	V1	V2	V3	V4
Attributes
	A1	Location	22	30	3	1	12	1	5	8			15	5	8	2
	A2	Scholarship	16	16		13					3			7	9
	A3	Academics	7	7				7					3	1	3
	A4	Coach	7	7	5						2		3	2	3	2
	A5	Facilities	6	6	1				5				1	5
	A6	Friend on the team	4	4	4								3			1
	A7	School Size	4	4	3			1							2	2
	A8	Open Spot	3	3								3	2	1
Consequences
	C1	Feel Comfort	15	16									8	1	2	5
	C2	Financial Comfort	14	14										4	10
	C3	Adventure	12	12									12
	C4	Get a Good Job	9	9									3	1	5
	C5	Can Improve	8	10										10
	C6	Friend & Family	7	8									2		5	1
	C7	Feel Valued	5	5										4		1
	C8	Playing Time	3	3									2	1
Values
	V1	Fun & Enjoyment	20	27
	V2	Achievement	14	21
	V3	Security	13	22
	V4	Belonging	5	7

As information from the implication matrix was transferred into the HVM, the researchers selected a cutoff level of two. A cutoff level establishes how frequently a connection had to be made to be depicted in the HVM. Thus, only connections made two or more times are illustrated with a line. Eliminating connections made only one time reducing clutter in the HVM. To assist the reader in interpreting the HVM, an illustrative example is presented (Figure 1). The complete HVM follows (Figure 2). Consistent with the literature (Klenosky, Templin, & Troutman, 2001), values are presented at the top of the map to represent their abstract nature in college selection (they appear within triangles and are spelled with all capital letters). Consequences are represented across the middle (within circles and beginning with a capital letter), and attributes appear at the bottom (within rectangles and all lower case letters) to reflect that they were merely the beginning point in each chain and are the most superficial level of information gathered. Further, the lines between attributes, consequences, and values represent the frequency of the connection between these concepts (more frequent associations depicted with thicker lines). The size of each shape also reflects the number of participants mentioning it, with more frequently mentioned concepts dominating more space. Finally, the first number in each shape reflects the number of participants that mentioned the concept, while the number in parenthesis is the number of times the concept was mentioned in total, reflecting that some concepts would be mentioned multiple times by a single participant.

Figure 1. An illustrative example of an HVM section

Figure 2. Hierarchical Value Map for female student athletes’ college selection

Discussion

Analysis of the HVM revealed several noteworthy findings. First, location was a primary attribute for the selection of this university over other universities the athletes considered as they made a final decision. In fact, 39% of all the chains created in this study began with the attribute of location. While it might not be surprising that a university in the state of Florida is selected for its location, this fact underscores the importance of a means-end analysis. While a college selection survey would also reveal that location was important, it would not discover the true reason for the importance of this attribute. The means-end analysis demonstrated that the attribute of location was important for several different reasons. Of the 30 chains beginning with location, 12 went to the consequence of adventure and then continued on to the value of fun and enjoyment. Other participants indicated that location was important because it kept them close to friends and family, which had a strong connection to the value of security. Others expressed that they simply are comfortable here, which largely connected with fun and enjoyment. Finally, some participants (in outdoor sports) noted that the weather in Florida would allow them to improve their sport performance (largely due to an extended season), which supported the value of achievement.

The different values that underpinned the importance of location supported the belief that college selection is a complicated process and that a single attribute of a campus can be important to prospective student athletes for a wide variety of reasons. This fact should be particularly interesting to coaches who spend considerable time and effort in the recruitment process and could misinterpret a prospects’ motivation for selecting a particular university. For example, coaches might feel confident that a student athlete selected a college because of location and may even presume to know that it is related to a consequence, such as improving sport performance, whereas in the mind of the student athlete, lying on the beach might be the true motivator because she is more driven by her value of fun and enjoyment than by the value of achievement.

Second, the attribute of receiving an athletic scholarship was also frequently mentioned. It was important to 16 of the 25 participants (64%). Predominantly it led to the consequence of financial comfort, which, in turn led to the value of security. For a few participants, however, the consequence of financial comfort led to the value of achievement, which reflected their belief that financial comfort was essentially earned through their years of dedication to sport participation. Comments made during the interviews suggested that the participants viewed the scholarship as a literal indication that they had achieved within their sport and that their achievement became measurable and worthwhile through the scholarship offer. Participants reported being offered scholarship packages of widely varying values and thus scholarship became an important attribute in differentiating between schools. The Klenosky, Templin and Troutman (2001) study did not reveal scholarship as an important attribute among the Division I football players because each participant in the sample reported being recruited by over 20 schools and thus scholarship was likely a non-issue in differentiating between schools.

Third, the attributes of the coach and academics were mentioned by surprisingly few participants. These attributes were seldom used by participants to differentiate their school from others at the time they made their final selection. Still, it is interesting to see that these attributes trailed location and scholarship by a wide margin. For the seven participants who mentioned academics, all of them linked it to the consequence of getting a good job, as opposed to more altruistic notions such as gaining knowledge or growing as a person.

Fourth, the consequence of feeling comfort was frequently mentioned and stemmed from a variety of attributes. School size, location, a friend on the team, and the coach were all attributes that seemed to assure the participants that this school would be a good fit for them and provide a place in which they would feel comfort. This information is valuable for coaches who actively recruit prospects. It is possible that a key to securing recruits is in convincing them that the attributes of the college, team, and campus will help the prospect feel comfort.

Fifth, the value of fun and enjoyment underpinned the college selection for many participants (it was mentioned by 20 of the 25 participants (80%), and several participants had multiple chains end with this value). However, the source of fun and enjoyment was extremely varied. At the time the college selection was made, participants believed that playing time, adventure (from location), proximity to friends and family, a comfortable atmosphere, and opportunity to get a good job all led to the possibility of fulfilling the value of fun and enjoyment.

This study contributes to the college selection literature and furthered the work of Klenosky, Templin, and Troutman (2001) to utilize means-end theory to determine the values that student athletes rely on in this process. However, there were limits to the study. Most notably, it only represented student athletes from one university and results do not generalize to female student athletes overall. Different results could occur among student athletes at other schools based on such traits as school size, region of the country, and NCAA division.

Conclusion

College selection is a complicated and difficult process for student athletes, which is often made even more confusing by the recruitment process. While traditionally researchers have sought to understand college selection by drawing from sizable data sets gathered via surveys, that method fails to explore fully the complexity of any given attribute (such as location). By applying means-end theory researchers can probe further and determine the values on which prospects are basing their selection. Further, a general understanding of means-end theory could be important for coaches to improve the process of attracting prospects in an increasingly competitive college sports climate. It also can assist coaches in understanding what is important to the student athletes once they matriculate to campus.

For the participants in this study, security, achievement, belonging, and fun and enjoyment were the guiding values for college selection. Future research should extend the use of means-end analysis to student athletes in other contexts, for example by sport, NCAA division, and region of the country.

References

Bukowski, B. J. (1995). Influences on student college choice for minority and non minority athletes at a Division III institution (Doctoral dissertation, University of Wisconsin, Madison, WI). Dissertation Abstracts International, 56(7), 126.

Cook, T. (1994). Factors female freshmen student-athletes use in deciding between a NJCAA college and a NAIA college. Unpublished master’s thesis, University of Kansas, Lawrence, KS.

Doyle, C. A. & Gaeth, G. J. (1990). Assessing the institutional choice process of student athletes. Research Quarterly for Exercise and Sport, 61(1), 85-92.

Finley, P. S. (2005). An analysis of team Web site content and college choice factors of NCAA Division III cross country runners (Doctoral dissertation, University of Northern Colorado, Greeley, CO). Dissertation Abstracts International, 66(04), 1291.

Forseth, E. (1987). Factors influencing student-athletes’ college choice at evangelical, church-supported NAIA institutions in Ohio (Doctoral dissertation, The Ohio State Univesity, Columbus, OH). Dissertation Abstracts International, 48(01), 172.

Gutman, J. (1982). A means-end chain model based on consumer categorization processes. Journal of Marketing, 46(2), 60-72.

Johnson, E. A. (1972). Football players’ selection of a university. Unpublished master’s thesis, University of Utah, Salt Lake City, UT.

Kankey, K., & Quarterman J. (2007). Factors influencing the university choice of NCAA Division I softball players. The SMART Journal, 3(2), 35-49.

Klenosky, D. B., Templin, T. J. & Troutman, J. A. (2001). Recruiting student athletes: A means-end investigation of school-choice decision making. Journal of Sport Management, 15, 95-106.

Konnert, W., & Geise, R. (1987). College choice factors of male athletics at private NCAA Division III institutions. College and University, 63(1), 33-44.

Mathes, S., & Gurney, G. (1985). Factors in student-athletes’ choice of colleges. Journal of College Student Personnel, 26(4), 327-333.

Reynaud, C. (1998). Factors influencing prospective female volleyball student-athletes’ selection of an NCAA Division I university: Towards a more informed recruitment process (Doctoral dissertation, Florida State University, Tallahassee, FL). Dissertation Abstracts International, 59(02), 445.

Reynolds, T. J., & Gutman, J. (1988). Laddering theory, method, analysis and interpretation. Journal of Advertising Research, 28(1), 11-31.

Slabik, S. L. (1995). Influences on college choice of student-athletes at National Collegiate Athletic Association Division III institutions. Unpublished doctoral dissertation, Temple University, Philadelphia, PA.

Ulferts, L. (1992). Factors influencing recruitment of collegiate basketball players in institutions of higher education in the upper Midwest (Doctoral dissertation, University of North Dakota, Grand Forks, ND). Dissertation Abstracts International, 54(03), 770.

Authors Note:
Correspondence for this article should go to Peter Finley, H. Wayne Huizenga School of Business and Entrepreneurship, 3301 College Avenue, Fort Lauderdale-Davie, Florida 33314, (954) 262-8115, pfinley@huizenga.nova.edu.

U.S. Sports Academy2016-10-20T10:36:52-05:00April 2nd, 2008|Sports Coaching, Sports Exercise Science, Women and Sports|Comments Off

Service Learning in Sport Management: A Community Health Project

Submitted by: Vincent Mumford, Central Michigan University,
Joseph N. Inumgu, Central Michigan University,
James A. Johnson, Central Michigan University,
Scott J. Smith, Central Michigan University

Abstract

Service learning is increasingly popular in schools, colleges, and universities. Service learning is a form of experiential learning and is an ideal pedagogical strategy to teach students about sport management. Students engaged in service learning typically become involved in specific community-based projects that are a part of their class requirements. These projects usually meet a real community need and link classroom content with community projects and reflection. Students can benefit tremendously from an educational experience that combines service learning and sport management. They can reap benefits in the areas of academic learning, civic responsibility, personal and social development, and opportunities for career exploration. A well-planned and well-executed service learning project can expand the student’s sport management experience well beyond events, contests, and classroom lectures. It can bridge the gap between the school and the community by providing a way for students and community organizations to come together for a worthy cause, making learning more meaningful. The purpose of this article is to examine how sport management classes can be designed and implemented as service learning projects that address critical community health challenges. Specifically, this article addresses service learning design that could be applied to any community health problem. The example used here is fund raising for malaria mitigation projects distributing bed nets as a low-cost means of prevention. The article describes the actual service project and discusses ways to encourage students to deepen their civic engagement to meet critical community and global needs.

(more…)

U.S. Sports Academy2016-10-19T11:05:37-05:00April 2nd, 2008|Contemporary Sports Issues, Sports Coaching, Sports Management|Comments Off

Cross-Country Skiing USSA Points as a Predictor of Future Performance among Junior Skiers

Submitted by: Blair Orr

Abstract:

Junior cross-country skiers’ performances prior to participation in the 2006 Junior Olympics were compared to their results in the 2006 Junior Olympics using USSA points as a measure of performance. Junior class and division (team) were also included as independent variables. Prior performance as determined by USSA points is a poor indicator of performance in the Junior Olympics.

Introduction:

Cross-country skiing times from different races, even those of the same length, are not comparable because the terrain is different for each race. Furthermore, snow conditions may vary, even from hour to hour, on the same course. Merely comparing times of skiers over similar distances is not an accurate comparative assessment of skiers’ abilities. The United States Ski and Snowboard Association (USSA) points list was developed to allow comparison between skiers who may have entered several different races. USSA points are awarded to registered cross-country skiers for participation in sanctioned ski races. A lower value in USSA points indicates that a skier is a better, more competitive skier. USSA points and similar International Ski Federation (FIS) points are used to help select the U.S. national teams, to seed racers in both mass and interval start races, and to monitor the progress of athletes in physiological studies (Bodensteiner & Metzger 2006; Staib, Im, Caldwell, & Rundell 2000).

The USSA formula that allocates points to skiers is based on race performance. It includes a number of variables that capture the relative ability of skiers in the race. Who enters the race and how they place are used in determining the penalty. Each race’s penalty is based upon the current USSA points of top finishers in the race. The type of start or race and a minimum penalty also are used in the calculation of USSA and FIS points assigned to a skier’s race (Bodensteiner & Metzger 2006, International Ski Federation, 2006). Despite the common and, at times, mandatory use of the system, the USSA point system has been criticized by racers and coaches over the years for failure to accurately capture a skier’s ability (Anonymous, 2006; Smith, 2002; Trecker 2005).

Methods:

Given the importance and criticism of USSA points, this study develops a systematic comparison of prior USSA points results of skiers to their USSA points earned in a common competition. One would hypothesize that a skier’s points prior to a competition would predict a skier’s points earned within the competition. Points earned by Junior skiers (ages 14 to 19) in the 2005-2006 season are compared to USSA points in the 2006 Junior Olympics. The use of linear regression allows one to determine if a linear relationship exists between prior performance and performance in the Junior Olympics and whether other, easily obtained variables can improve the ability to predict performance at the Junior Olympics. (Hill, Griffiths, & Judge, 1997; Johnston, 1984)

Before the Junior Olympics, skiers earned USSA points in different races throughout the northern part of the United States. Skiers within any of the ten USSA districts competed against each other, but there was limited competition among skiers from different districts. The top 400 skiers then competed in the Junior Olympics in March, 2006 in Houghton, Michigan. The end of season Junior Olympics allows skiers to be directly compared on the same course and with the same snow conditions, so USSA points assigned in these races can be used in this study free of the bias of course and snow conditions.

A general linear model (equation 1) with USSA points earned in the Junior Olympics as the dependent variable and USSA points prior to the Junior Olympics, junior class (J2, J1, or OJ) division (team) were used as independent variables. The parameters c and ak (where k = 1, 2, and 3) were estimated. Estimated parameters in bold are matrices of parameters associated with a matrix of dummy variables. Equation 1 is the most comprehensive linear model used.

y_i = c + a₁*P_i + a₂* JCLASS_i + a₃*DIV_i + e_i equation 1

Where

y_i = USSA points in the 2006 Junior Olympics for the ith skier,

c = an estimated constant,

P_i = USSA points prior to the Junior Olympics for the ith skier,

a₁ = the estimated parameter associated with Pi,

JCLASS_i = a matrix of junior classes with dummy variables for OJ, J1, and J2 where the value is 1 in the ith skier’s junior class and zero for other classes,

a₂ = a matrix of estimated parameters associated with JCLASSi,

DIV_i = a matrix of regional divisions with dummy variables for Alaska, Great Lakes, Midwest, Intermountain, Rocky Mountain, Mid-Atlantic, New England, Far West, High Plains, and Pacific Northwest where the value is 1 in the ith skier’s division and zero for other divisions,

a₃ = a matrix of estimated parameters associated with DIVi, and

e_i = the residual value for the ith skier.

The model was run using USSA points from all three individual races at the Junior Olympics (yi): freestyle, classic, and sprint. USSA points prior to the Junior Olympics included (Pi) for distance, sprints, and overall points were used in separate regressions. Thus, there are several versions of equation 1 that use different techniques (classic and freestyle) and USSA disciplines (sprint, distance, and overall).

While equation 1 represents the most extensive model tested, other models using a subset of the independent variables were also tested to determine the stability of the model. When sets of independent dummy variables would have resulted in a full rank matrix, one of the variables was not included in the regression. Technical definitions associated with cross-country skiing terms can be found in the USSA’s Nordic Competition Guide (Bodensteiner & Metzger, 2006). Analyses were run using the GLM procedure in SAS 9.1 for Windows.

Data:

Pre-Junior Olympics distance, sprint, and overall USSA points; names; USSA numbers (to confirm this data with results from the Junior Olympics); junior class (J2, J1, or OJ); and year of birth information were obtained from the national list of USSA points, which had been updated just prior to the Junior Olympics. Data were downloaded on March 27, 2006. Junior Olympic classic, freestyle, and sprint USSA points; skier’s division (team); name; and USSA number were obtained from itiming.com via the web in the week following the 2006 Junior Olympics. In all cases, as complete a data set as possible was used in the regression. However, some skiers entered the Junior Olympics without prior USSA points or with only a partial set of information. The most common missing data were USSA sprint points prior to the Junior Olympics. Whenever a valid number was available for a skier, that skier was entered in the data set for a particular regression analysis. In a few cases, skiers did not start or finish a race or were disqualified during the race. The largest data set included information for 271 skiers.

Results:

USSA Points prior to the Junior Olympics – the simplest models.

The first part of the statistical analysis was to determine if USSA points alone could predict USSA points in the Junior Olympics. The model used to test this question was:

y_i = c + a₁*P_i + e_i equation 2

Since skiers have sprint, distance, and overall points prior to the Junior Olympics and compete in sprint, freestyle distance, and classic distance events, there are six logical combinations of dependent and independent variables. Table 1 shows the results of each regression.

Table 1: Results from the regression of USSA points earned at the Junior Olympics (y_i) on USSA points earned prior to the Junior Olympics (P_i). Equation 2

y_i JO Points (Source)	P_i Prior (Source)	estimated c	estimated a	r²
Freestyle	Overall	87.1	0.57	0.59
Freestyle	Distance	82.8	0.59	0.59
Classic	Overall	116.9	0.79	0.36
Classic	Distance	106.7	0.85	0.37
Sprint	Overall	74.4	0.80	0.54
Sprint	Sprint	84.8	0.60	0.49

Note: All estimated parameters were significant at the 0.0001 level.

At best, the USSA points earned prior to the Junior Olympics predict only 59% of the variability in the final USSA points earned at the Junior Olympics. Equation 2 is least effective when used to predict the classic results, explaining only 36% of the variability when the independent variable is Overall USSA points prior to the Junior Olympics. Figure 1 shows the relationship between the Overall USSA points prior to the Junior Olympics and USSA points earned in the Junior Olympics classic race. The top five skiers based upon prior USSA points also ended up with results close to what one would expect. However, after this elite group of skiers, the prior USSA points exhibit poor predictive ability for the remaining skiers. Some skiers with relatively high USSA points skied well and moved up dramatically at the Junior Olympics. The reverse was also true; some skiers skied less competitively than one would have predicted from their prior USSA points. While this is to be expected to some extent (athletes have good and bad days), the large number of skiers who deviated from the expected indicates something other than a few atypical performances by a small number of skiers has occurred. While the correlation between prior USSA points and the freestyle and sprint race results were better than the classic, the same general pattern is evident the results of these two races are plotted. The top skiers were identified by prior USSA points while predictive power diminishes for average and relatively weaker skiers at the Junior Olympics. In fact, even finish order is poorly predicted by prior USSA points.

Figure 1. Relationship between Overall USSA points prior to the Junior Olympics and USSA points earned in the classic race at the 2006 Junior Olympics.

Figure 1 also shows that this data set is heteroscedastic. The heteroscedasticity of the data is discussed in the Appendix.

USSA Points prior to the Junior Olympics – adding independent variables

Given that USSA points earned prior to the Junior Olympics are relatively poor predictors for results at the Junior Olympics, whether or not it is it possible to use other readily available information to improve the estimate of where a skier would finish is of importance. Equation 1, a more robust model, was estimated for the same six data sets used for equation 2. Equation 1 includes the JO class of the ski and the division (team) of the skier. The r2 associated with each equation is shown in Table 2.

Table 2. Comparison of Equation 2, only prior JO points, with Equation 1, prior JO points, Junior class, and division (team).

y_i JO Points (Source)	P_i Prior (Source)	equation 2 r²	equation 1 r²
Freestyle	Overall	0.59	0.69
Freestyle	Distance	0.59	0.68
Classic	Overall	0.36	0.51
Classic	Distance	0.37	0.52
Sprint	Overall	0.54	0.65
Sprint	Sprint	0.49	0.64

Using Junior class and division and team of the skier improved the r2 for all six combinations of Junior Olympics USSA points and points earned prior to the Junior Olympics. Unfortunately, the best r2 is 0.69, indicating that there is still a substantial amount of unexplained variability in the data set. Equation 1 is an improvement, but still does not leave one with the ability to use the model with confidence if the purpose is to use past performance to predict expected performance.

Because there is little difference between the use of overall points and other prior USSA points as independent variables in equation 1, only results for equation 1 with overall points are reported. Table 3 shows the variables, estimated parameters, and P values for each independent variable for the classic, freestyle, and sprint races at the 2006 Junior Olympics.

Table 3. Estimated parameters and probability level for the parameters, in parentheses, for equation 1. Estimations are for all three individual events at the Junior Olympics using skiers’ overall USSA points, division (team), and junior class as independent variables.

Independent           Estimated Parameter and P Value (Pr > |t|)
Variable                Classic               Freestyle                 Sprint
Constant               135.90                 83.47                   44.38
(<0.001)            (<0.001)                 (0.003)
OVERALL                  0.89                  0.55                     0.77
(<0.001)            (<0.001)              (<0.001)
NE                       -46.43               -17.78                  -22.73
(0.005)              (0.015)                 (0.063)
MA                        -7.61                  4.50                     5.13
(0.731)              (0.647)                 (0.743)
GL                       -28.74               -21.40                   53.06
(0.102)              (0.044)                 (0.012)
MW                         1.15                 -6.50                     0.87
(0.961)              (0.405)                 (0.946)
HP                         50.07                 56.54                   69.19
(0.047)            (<0.001)              (<0.001)
IM                         -5.15                 20.21                   58.61
(0.754)              (0.006)              (<0.001)
RM                        -4.40                 -3.12                   33.66
(0.794)              (0.677)                 (0.004)
FW                       -32.77               -17.09                   51.88
(0.090)              (0.047)              (<0.001)
PN                         -2.75                  0.63                   23.66
(0.887)              (0.942)                 (0.079)
J1                        -16.16                  9.91                   26.69
(0.163)              (0.053)                 (0.002)
J2                        -93.08                  8.23                   13.23
                          (<0.001)              (0.211)                 (0.231)
Notes: Alaska and OJ are omitted to avoid estimation of a full-rank matrix.
NE = New England, MA = Mid-Atlantic, GL = Great Lakes, MW = Midwest,
HP = High Plains, IM = Intermountain, RM = Rocky Mountain, FW = Far West,
PN = Pacific Northwest.

Each of the equations is estimated with Alaska omitted as a team and the OJ class omitted. This prevents full rank estimation of the equation. The Classic estimation shows that New England and Far West skiers ski relatively faster than Alaskan skiers given their predicted times. High Plains skiers are slower than predicted relative to the Alaskan skiers. The estimated parameters for other divisions are not significantly different from zero. In the freestyle race, the estimated parameter for the dummy variable representing skiers from the New England, Great Lakes, and Far West indicated that, given their prior USSA points, members of these teams were relatively faster than the Alaskan skiers as indicated by USSA points earned in the Junior Olympics race. The phrase “relatively faster” is important. In general, Alaskan skiers finished ahead of Great Lakes skiers, although the estimated parameter associated with the Great Lakes is negative. The dummy variables for teams improve the estimation by adjusting for a skier’s team given the other variables used in the estimation, especially the overall USSA points prior to the Junior Olympics. Using Alaska and the Great Lakes as an example, the average Alaskan skier entered the Junior Olympics with a better USSA points ranking and than the average Great Lakes skier. The Alaskan skiers also outperformed the Great Lakes skiers on average at the Junior Olympics. However, in the freestyle competition at the Junior Olympics, the Great Lakes skiers’ improvements from predicted to actual performance was substantially better than that of the Alaskan skiers. Dummy variables capture this distinction.

In the freestyle race, the estimated parameters for the High Plains and Intermountain teams were positive. In the sprint race, the teams from New England again had a significant, negative estimated parameter while the Great Lakes, High Plains, Intermountain, Rocky Mountain, Far West, and Pacific Northwest all had significant, positive estimated parameters. Both the Far West and Great Lakes had significant, negative estimated parameters in the freestyle race but significant, positive estimated parameters in the sprint race. (New England skiers can take heart that they outperformed their expected results and won the Alaskan Cup despite whatever disadvantage may accrue to weaker seeding.)

The estimated parameter for junior class was also significant for one of the classes in each of the equations, indicating that including class in the estimate improves the equation. Junior class can help predict USSA points earned.

Stability of the Models

It would be tempting to state that the use of additional variables improves the equation and would help somebody trying to use prior USSA points in estimating performance or performance gains. However, several factors argue against this.

1. This data set represents only the top junior skiers, ages 14 to 19, over one season.

2. The three versions of equation (1) estimated with classic, freestyle, and sprint results from the Junior Olympics are not similar. Both the constant and parameter associated with the overall points vary considerably with the different estimations, indicating that the model is not stable.

3. The parameters associated with dummy variables representing divisions (teams) and junior classes are not consistent and, in some cases, change dramatically from estimation to estimation. For example, Great Lakes skiers have a positive and significant parameter associated with the dummy variable in the freestyle equation, but they have a negative and significant parameter associated with the dummy variable in the sprint equation.

4. The r2 values associated with all equations estimated are not strong enough to justify the use of the model to predict the future results of skiers.

Given these concerns, it is likely that estimating these equations using data from other years or older skiers would generate substantially different equations. It is unlikely that the model would be stable (that is, the estimated parameters would be similar), if different versions of the model were estimated or different data sets were used.

Conclusions:

This paper provides a clear test of the ability of USSA points to compare the relative ability of skiers. The initial points of skiers earned in their best races prior to the Junior Olympics were used to estimate a linear regression model with points earned in three separate races at the Junior Olympics less than a month after the prior points list was released by the United States Ski and Snowboard Association. The prior points were a poor predictor and the general model showed poor stability from estimation to estimation. While these results were derived from a data set composed of junior skiers, they support the broader anecdotal concerns about USSA points. This study provides a reliable quantitative basis for those concerns with a substantial and consistent data set. Most observers of cross-country ski racing would not be surprised by these results. However, the instability in the data set is striking and is less easily observed through casual observation of ski results. Not only are the predictions relatively poor, those poor predictions vary with the subset of the data and the specific model used to make the prediction. USSA points should be used with caution and with other information for critical decisions in cross-country ski racing. Their value in monitoring skier performance in physiological trials is questionable.

References:

Anonymous. (2006). U.S. Olympic Cross Country Team Announced. Retrieved October 6, 2006 from http://www.fasterskier.com/news2962.html .

Bodensteiner, L., & Metzger, S. (2006). 2006 USSA Nordic Competition Guide. Park City, UT.

Hill, C., Griffiths, W., & Judge, G. (1997). Undergraduate Econometrics. J. Wiley & Sons, New York.

International Ski Federation. (2006). Cross Country Rules and Guidelines of the FIS Points 2006/07. Retrieved October 11, 2006 from http://www.fis-ski.com/data/document/pktrgl0607-neu.pdf

Johnston, J. (1984). Econometric Methods (3rd ed.) McGraw-Hill, New York.

Smith, C. (2002). U.S. Olympic Team Selection. Retreived July 17, 2006 from http://www.xcskiracer.com/rants.shtml

Staib, J.L., Im, J.,Caldwell, Z., & Rundell, K.W. (2000). Cross-country ski racing performance predicted by aerobic and anaerobic double poling power. Journal of Strength and Conditioning 14(3), 282-288.

Trecker, M. (2005). Following the Olympic Trials, Who’s Hot, Who’s Not, and the Strange Anomalies of USSA Scoring. Retrieved July 17, 2006 from http://www.fasterskier.com/opinion2749.html

Appendix – Heteroscedasticity in the Data Set:

This portion of the study on heteroscedasticity is placed in the appendix because most people interested in skiing will not be interested in statistical methods and assumptions. They want to know if current USSA points predict future skiing results. However, from an analytical viewpoint, improper use of statistics can lead to incorrect results and correct procedures lead to improved analysis. One assumption of linear regression is that the variance of the random error term is 2 for all x. If this is not the case, then the estimate remains linear and unbiased but it is no longer the best linear unbiased estimator and standard errors are often incorrect (Johnston, 1984). Confidence intervals and results of statistical tests can be misleading. This appendix covers four topics: heteroscedasticity in equation 2, correcting for heteroscedasticity using data transformations, heteroscedasticity in the complete data set, and a brief conclusion.

Heteroscedasticity in equation 2

Equation 2 is the intuitive equation to test whether prior performance as measured by USSA points can predict future performance.

y_i = c + a₁*P_i + e_i equation 2.

Figure 1 shows a much wider variance in the dependent variables as USSA points increase. White’s test for heteroscedasticity indicates a probability of greater than 99.99% that heteroscedasticity does exist (test statistic= 15.37 with two degrees of freedom).

Correcting for heteroscedasticity using data transformations

Data may be adjusted using transformations to eliminate heteroscedasticity (Hill et al, 1997, Johnston 1984). In the data set used in this study, the variance in the residuals is larger for the larger values of the independent variable. Two logical transformations are to take the logarithm of the independent variable and the square root of the independent variable. Separate regressions were estimated using equation (2) where

(a) P_i = the square root of the competitors USSA points earned prior to the Junior Olympics and

(b) P_i = the natural logarithm of the competitors USSA points earned prior to the Junior Olympics.

In both cases, the r² value improved less than 0.02, and the White’s test indicated that heteroscedasticity remained a problem.

Heteroscedasticity in the complete data set

The complete data set, including division and junior class of the competitor, not only improves the estimation, it is less likely heteroscedasticity exists. White’s test for heteroscedasticity indicates a probability of approximately 80% that heteroscedasticity does exist (test statistic= 49.46 with 42 degrees of freedom). Most researchers would not reject the null hypothesis at this level. This indicates that the additional independent variables have the greatest impact on improving prediction for skiers with the higher (less competitive) prior USSA points.

Conclusion:

The original goal of this study was not only to determine what statistical model would work best for the data, but to determine if USSA points were a good predictor of future performance of athletes. From a practical standpoint, a complex model used in the prediction would indicate that USSA points alone are a poor predictor and a complex model would be difficult to justify and administer. The heteroscedasticity and the development of more complicated, but still unstable, models reinforce the results of the main paper. Prior USSA points are poor predictors of Junior races.

U.S. Sports Academy2016-10-20T10:03:26-05:00March 14th, 2008|Sports Coaching, Sports Management|Comments Off