Raising Awareness of the Severity of Concussions

### Abstract

Concussions have always been a part of physical contact sports, but with athletes becoming bigger and stronger, something has to be done to raise awareness of the severity of concussions and what can happen later down the road if athletes are not given the adequate amount of time to recover. The National Football League has already put regulations on how long a player has to stay out after receiving a concussion and has started fining athletes that deliberately use helmet-to-helmet contact on an opposing player; the National Collegiate Athletic Association has started neurological testing to track a concussed athlete’s progress and have revised the guidelines on not letting athletes return to play the same day and having mandatory check-ups; but high schools have very few regulations to follow. A concussion is the same whether it happens to a pro player or a high school player, so why do the professional players take precedence over high school athletes? Changes need to be made so all athletes are cared for.

**Key Words:** concussions, helmet-to-helmet contact, National Football League, National Collegiate Athletic Association, neurological testing

### Introduction

Owen Thomas, junior lineman for University of Pennsylvania, Andre Waters, former Philadelphia Eagles safety, Chris Henry, the Cincinnati Bengals wide receiver and Chris Beniot, a pro wrestler; these men have been successful athletes, but that all changed after receiving countless blows to the head. They, as well as many others, have been diagnosed with Chronic Traumatic Encephalopathy (CTE), which according to the Center for the Study of Traumatic Encephalopathy is a progressive degenerative disease of the brain found in athletes and others, with a history of repetitive concussions. The brain degeneration is associated with memory loss, confusion, impaired judgment, paranoia, impulse control problems, aggression, depression, and, eventually, progressive dementia (2). After death, these four athletes had tissue from their brain examined, where each had evidence of CTE.

Helmet-to-helmet hits are becoming more aggressive, take for example the hit that Kevin Everett experienced in 2007, or the hit that Josh Cribbs received from James Harrison, and the memorable hit of Eric LeGrand that left him paralyzed from the neck down. Because of this the National Football League (NFL) and the National Collegiate Athletic Association (NCAA) have recently implemented rules to protect players from injuries that occur through these hits, but what about the high school athletes? The University Interscholastic League (UIL), which is the governing body of high school athletics in Texas, has started to take steps in changing the policies and guidelines that are currently being followed, but that isn’t enough.

#### National Football League

The new guidelines for the NFL provide more specificity in making return-to-play decisions. The new statements advise that a player who suffers a concussion should not return to play or practice on the same day if he shows any signs or symptoms of a concussion that are outlined in the return-to-play statement. It continues to say the player shouldn’t return to play until they have had neurological and neuropsychological testing completed and have been cleared by both the team physician and an independent neurological consultant (1). It is also outlines that if an athlete has symptoms of loss of consciousness, confusion, gaps in memory, persistent dizziness, headache, nausea, vomiting or dizziness, or any other persistent signs or symptoms of concussions the athlete should be removed from all activities (1).

#### National Collegiate Athletic Association

According to the NCAA a concussion is a brain injury that may be caused by a blow to the head, face, neck, or elsewhere on the body with an “impulsive” force transmitted to the head (9). An athlete doesn’t have to lose consciousness after a concussion occurs, but there are two things that a coach and athlete need to watch for: a forceful blow to the head or body that results in rapid movement of the head, and any changes in the student-athlete’s behavior, thinking or physical functioning. Some of the signs and symptoms that have been observed by both the coaching staff and student athletes consist of the student-athlete appearing dazed and confused, forgetting plays and being confused about assignments, while they have a headache, feel nauseated, confused, and are sensitive to light and noise (9).

After meeting, the NCAA committee that is responsible for recommending rules and policies made revisions on the previous guidelines found in the NCAA Medicine Handbook that all sports followed on concussion management. These revisions emphasize not letting a student-athlete return play the same day after a long duration of significant symptoms, and if the symptoms continue the athlete should not participate until cleared by a physician (3).

The NCAA wants all coaching staff and student-athletes to have full awareness of the severity of concussions, in doing so they have produced fact sheets for both, which recommend that athletes not hide it and that they tell the athletic trainer or coach so they can receive the proper treatment, and take time to recover. Just like every other injury, a concussion needs time to heal, and repeated concussions can cause permanent brain damage, and even death (9).

For Tarleton State University, located in Stephenville, Texas, neuropsychological testing is being done using ImPACT, which measures athlete’s attention span, working memory, sustained and selective attention time, response variability, non-verbal solving, and reaction time. ImPACT also provides computerized neurocognitive assessment tools and services that are used by coaches, athletic trainers, doctors, and other health professionals to assist them in determining if an athlete is able to return to play after suffering a concussion (6). Athletes start out taking the test to set a base line, they are asked demographic information and health history, what their current symptoms are, then take the neuropsychological test, which measures athlete’s attention span, working memory, sustained and selective attention time, response variability, non-verbal solving, and reaction time with six different modules that are labeled as Word Memory, Design Memory, X’s and O’s, Symbol Matching, Color Matching, and Three Letter Memory, they then get the injury report, and the ImPACT test scores (6). ImPACT is being used by the U.S. Army, professional teams, sports medicine centers, neuropsychology clinics, doctors, colleges, high schools, and club teams all across the United States, as well as Canada and Internationally. Tarleton State University has also required full participation of their athletes by informing them of concussions and having them sign an injury acknowledgement form, stating that they will be an active participant in their own healthcare. Tarleton has also stepped up in making the academic department aware of the severity of a concussion by producing information sheets that state the signs and symptoms, how a person recovers, and what a person with a concussion should and shouldn’t do.

#### High School

According to USA Today only Texas, Oregon, and Washington have enacted laws, all since 2007, to meaningfully tackle the issue. Oregon and Texas require athletes to be removed from play the day of the injury, while Washington gives coaches responsibility for removal (12). But still the UIL leaves it open for an athlete to return to play in the same day, if the athlete hasn’t lost consciousness and concussion symptoms are resolved within 15 minutes; and like its heat guidelines, concussion protocol is merely a set of recommendations and isn’t enforced. According to the Dallas News, fifty-three percent of public schools in Texas and about ninety-three percent of private schools don’t have a full-time certified trainer on staff, and thirty-three percent of public school and eighty-seven percent of private schools don’t have weekly access to a certified trainer (4).

### Conclusion

The awareness of concussions has started to make its way to the top, according to the Fort Worth Star-Telegram the UIL and state education commissioners are currently working on approving that “Texas public high school athletes who get a concussion wouldn’t return to play until the next day, at the earliest, and a licensed healthcare professional would have to approve any return to play (7).”

With the number of athletes in public and private schools in Texas, and all across the United States, why has the issue of concussions not been dealt with before now? For fear of losing playing time there are fewer occurrences reports, but the long-term effects need to be stressed to all student-athletes. Not only athletes, but coaches, athletic trainers and parents need to be informed of the side effects that can happen if a concussion is not reported. Making it mandatory to do testing through concussion-based programs, like ImPACT, could be the first step in raising awareness and helping to give the adequate amount of time to recovery for those athletes who are injured.

### Applications in Sports

Everyone involved in contact sports, including coaches, athletic trainers, athletes, and parents, needs to know the severity of concussions. Many studies have shown what can happen if athletes don’t receive the adequate amount of time to heal after receiving a concussion, but compared to professional athletes there is little that is being done at the high school level to help with these recovery periods. Parents want to make sure their child is being cared for, while coaches have guidelines to follow to make sure their athletes makes a complete recovery, so following the footsteps of professionals and updating concussions guidelines can help in making sure everyone is taking the appropriate steps when a high school athlete has received a concussion.

### Acknowledgements

I would like to thank Dr. Kayla Peak, the Director of the Graduate Program at Tarleton State University, for assisting in the development of this article.

### References

1. (2010). NFL issues stricter guidelines for returning to play following concussion. E-Journal of The Sports Digest. Retrieved from http://www.thesportdigest.com/

2. Center for the Study of Traumatic Encephalopathy, About CTE. (n.d.) What is CTE. Retrieved from http://www.bu.edu/cste/

3. Copeland, Jack. (2009). Safeguard committee acts on concussion-management measures. Retrieved from National Collegiate Athletic Association website: http://www.ncaa.org

4. George, Brandon. (2010, August 1). Hidden dangers: concussions in high school sports. The Dallas Morning News. Retrieved from http://www.dallasnews.com/sharedcontent/dws/spt/stories

5. George, Brandon. (2010, August 2). Texas’ UIL falls behind on concussion policy. The Dallas Morning News. Retrieved from http://www.dallasnews.com/sharedcontent/dws/spt/stories

6. ImPACT-Testing and Computerized Neurocognitive Assessment Tools, About ImPACT. (n.d.) Overview and Features of the ImPACT Test. Retrieved from http://impacttest.com/

7. McCrea, Michael, Hammeke, Thomas, Olsen, Gary, Leo, Peter, & Guskiewicz, Kevin. (2004).Unreported concussions in high school football players. The Clinical Journal of Sports Medicine, (14)1, 13-17. Retrieved from http://journals.lwwlcom/cjsportsmed

8. NCAA, Student-Athlete Experience, Student-Athlete Well-being, Concussions. (n.d.). 23 Sports Specific Poster. Retrieved from http://www.ncaa/org

9. NCAA, Student-Athlete Experience, Student-Athlete Well-being, Concussions. (n.d.). Fact Sheet for Coaches. Retrieved from http://www.ncaa.org

10. NCAA. Student-Athlete Experience, Student-Athlete Well-being, Concussions. (n.d.). Fact Sheet for Student-Athletes. Retrieved from http://www.ncaa.org

11. Schwarz, Alan. (2010, September 13). Suicide reveals signs of a disease seen in the N.F.L. The New York Times. Retrieved from http://nytimes.com

12. Tumulty, Brian. (2010, May 20). Study highlights frequency of concussions in high school athletes. Retrieved from http:// www.usatoday.com

### Corresponding Author
Lindsey Neumann
445 Oak Springs Drive
Seguin, Texas 78155

<lindseyneumann@hotmail.com> 830-305-4312

### Author Bio
Lindsey Neumann is a graduate student studying Kinesiology at Tarleton State University in Stephenville, Texas.

2015-11-06T20:22:56-06:00April 19th, 2011|Contemporary Sports Issues, Sports Coaching, Sports Management, Sports Studies and Sports Psychology|Comments Off on Raising Awareness of the Severity of Concussions

Effect of dynamic versus static stretching in the warm-up on hamstring flexibility

Gayle Silveira, Mark Sayers, Gordon Waddington – Department of Health, Design and Science, University of Canberra

### Abstract

Recent studies have questioned the benefits of static stretching in the sports warm-up. The purpose of our research was to examine the acute effect of static and dynamic stretching in the warm-up, on hamstring flexibility using an intervention study design. Hamstring flexibility was measured using modifications of the Straight Leg Raise test to measure hip flexion range of motion in degrees. The reliability of the test setup was determined in a separate study (n=33), the results of which were also utilised to establish the relationship between static and dynamic SLR tests. There was a significant difference between flexibility measured by the Static-passive and the Dynamic-supine SLR test (p < .05); hence, these were utilised to assess static and dynamic flexibility, respectively, in the intervention study.

Twelve participants were randomly assigned to three interventions of 225 secs. stretch treatment on separate days: No stretching (Treatment 1), Static stretching (Treatment 2) and Dynamic stretching (Treatment 3) in a cross-over study design. When static stretching was included in the warm-up, there were statistically significant differences in pre and post static flexibility (t (11) = 4.19, p < .05). However, there was no significant difference in pre and post dynamic flexibility (t (11) = 0.72, p >.05). Following dynamic stretching there was a statistically significant improvement in both static (t (11) = 2.62, p <. 05) and dynamic (t (11) = 5.69, p < .05) flexibility. Non-parametric tests carried out on the data to corroborate the aforementioned findings.

Static stretching did not improve dynamic hamstring flexibility; however, dynamic stretching improved both dynamic and static flexibility. This has implications for the specificity of stretching in sport.

**Key words:* Range of Motion, hamstring, joint flexibility, Lower extremity, resting tension, stretching

### Abbreviations

ROM
range of motion
SPH
static passive hamstring flexibility test
DSUH
dynamic supine hamstring flexibility test
DSHWB
dynamic standing hamstring flexibility test with knee brace
DSHNB
dynamic standing hamstring flexibility test without knee brace (no brace)
SAID
Specific adaptation to imposed demands

### Introduction

Dynamic stretching consists of simulating movements that are representative of those frequently used in a particular sport (22). Examples of dynamic stretching include the toe walk, heel-walk, hand-toe hamstring stretch, military-walk, sumo groin stretch, and quadriceps kicks (31). In 1996, Alter (2) described a principle put forward by Wallis and Logan in 1964 for strength, endurance and flexibility training, called specific adaptation to imposed demands (SAID). “One should stretch at not less than 75 percent of maximum velocity through the exact plane of motion, through the exact range of motion, and at the precise joint angles used while performing skills in a specific activity” (2). The aforementioned principle lends support to the concept of dynamic flexibility training. There is a lack of studies that examine the effect of dynamic stretching on static as well as dynamic flexibility in the period preceding competition i.e. in the warm-up phase.

Numerous studies in recent literature examine the effects of static stretching on various performance variables (29, 37). In their 2006 study, Behm et al. (6) found decrements in knee extension, knee flexion, drop-jump contact time and counter movement jump height following an acute bout of static stretching. The analysis of the relationship between static stretching and performance focuses mainly on the variables of strength and power (30). Their study demonstrates that static stretching lowers the maximal strength of the knee flexors and extensors and may even hamper performance of activities involving maximal force output. If increased musculotendinous stiffness enables more efficient transmission of force, stretching just prior to activity might also decrease force output in skills such as jumping to attain maximum height and forceful throwing (12). Even a moderate duration of static stretching could result in quadriceps isometric force and activation decrements (33). Furthermore, it is theorised that this impairment of isometric force production could last for a period of up to 120 minutes.

The purpose of our research was to examine the acute effect of static and dynamic stretching in the warm-up, on hamstring flexibility using an intervention study design. The reliability of the experimental setup was established in a separate study (n=33) that was used to determine the relationship between the tests that measured static and dynamic hamstring flexibility. Analyses of variance and correlation analyses were computed on the collated data. An intervention design was used to determine how an acute bout of static or dynamic stretching affected hamstring flexibility as measured by a modified SLR test. Parametric (t-test) and non-parametric tests (Wilcoxon Matched-Pairs Ranks) were carried out to analyse the raw data.

### Method

#### Participants

Sixteen university students (n = 16) were recruited for the intervention study to examine the effects of dynamic and static stretching on hamstring flexibility. The final sample consisted of 12 students of which five females and seven males served as participants. Two potential participants did not complete all testing sessions and two participants’ data was excluded from the study due to measurement error. The average age of the participants was 24.8 ± 6.8 yrs. (mean ± SD). The average height and weight was 174.5 ± 4.5 cm. and 73.0 ± 15.7 kg. respectively (mean ± SD).

Participants were drawn from a variety of sporting backgrounds which predominantly involved the lower body (42). Most were actively training for a sport. All trained lightly a minimum of three times a week. A condition of entry to the study was that the subjects did not concurrently use any stretch or flexibility training in their regular training program (41). Screening questionnaires were provided to identify subjects with neurological or musculoskeletal abnormalities of the spine and lower limbs. Subjects were examined to determine hip, knee and ankle ROM and a brief examination of the lumbar spine was performed. The final participants were free of any bony or soft tissue injury to the spine and lower limbs. The participants were asked to carry out routine activities and not to exercise strenuously (10). They were also advised not to stretch the hamstrings and avoid initiating or changing any exercise program during the study (35).

All participants provided their written informed consent to participate in the study. Hamstring flexibility was measured in the dominant leg (19), identified by kicking a football towards a wall five times (11). This study received approval from the human ethics committee of the University of Canberra.

#### Materials and Procedure

Reflective markers attached to specific bony prominences utilised for biomechanical analysis (Figure 1). The functional orthopaedic knee brace, Knee Ranger II Universal (dj Orthopaedics, LLC, California, USA) helped to maintain 15º of knee flexion during pre and post-testing. Participants wore the knee brace only during testing and not whilst performing the intervention stretches. The Velcro strapping on the brace eased the removal and fastening process considerably. A warm-up consisting of five minutes of cycling on a stationary cycle ergometer (Exertech, Australia) at 60-70 W (6, 42) was employed. Testing was carried out at around the same time of the day for each participant involved in the intervention study (41). There was no stretching incorporated in the warm-up.

#### Modified SLR test for measuring hamstring flexibility

Previous studies examining stretch and contraction specific changes in ROM utilise the hamstring muscle group most frequently in humans and the SLR test is the most commonly used test (17). The contralateral or non-testing leg was partially flexed at the hip and knee, with a pillow rolled underneath the knee to stabilise the pelvis (11). A Velcro strap fastened around the pelvis and secured beneath the exercise bench to minimise pelvic rotation. In 1982, Bohannon (7) suggested that the pelvis and the contralateral thigh should be maintained in neutral position to decrease contribution to SLR-ROM. During testing, the participant was advised not to lift the upper body off the bench, and the arms were folded across the chest or placed beneath the head. This minimised the contribution from the trunk towards the effort of hip flexion.

The experimental setup included a camcorder placed perpendicular to the plane of motion. The camcorder was mounted on a tripod and placed at a distance of 10 metres from the test area (Figure 1). A PAL digital video camera (Canon MVX3i, Canon Inc., Japan) operating at 50Hz was used to video the participants performing the various flexibility tests. Dartfish ProSuite (Dartfish Connect 4.0, Dartfish Ltd., Fribourg, Switzerland) was used to capture the video data from the camera to a computer for two-dimensional analysis.

#### Measuring Flexibility

After the warm-up period, participants (n=12) undertook static passive (SPH) and dynamic supine hamstring flexibility (DSUH) tests to measure static and dynamic flexibility respectively. The reliability of this experimental setup and correlation between modifications of the SLR test was established in an earlier study involving 33 subjects.

##### Static Passive Hamstring Flexibility test

This test was performed in the supine position on an exercise bench. The functional knee brace was worn for testing. Passive stretching utilises an external agent to assist with the stretch. The participant used a Velcro strap around the ankle to assist with pulling the limb into hip flexion (Figure 1). The dominant leg was flexed to the terminal ROM or until a mild discomfort/tightness was felt in the back of thigh (5). This position was maintained for five seconds following which the limb was slowly lowered to the resting position.

##### Dynamic Supine Hamstring Flexibility test

The test was performed in the supine position on an exercise bench. Dynamic flexibility measures the ability to move a joint quickly through a non-restricted ROM. The participants were instructed to move the dominant limb into hip flexion using maximal effort and as quickly as possible or until a mild discomfort was felt in the back of the thigh. Dartfish analysis of the video frame that captured the terminal phase of movement was used to determine the angle of hip flexion.

Supine stretching is thought to better isolate the hamstrings, allowing for improved relaxation and is generally believed to be safer and more comfortable for people with a history of low back pain (15). Hence, the SPH test was used to measure static hamstring flexibility and the DSUH test was used to measure dynamic flexibility. Reliability testing demonstrated that there is a significant difference between flexibility measured by the SPH and DSUH hamstring flexibility tests (p<.001). There was also a significant difference between DSHWB (with knee brace) and DSHNB (without knee brace) tests (p = .003) and this result supported the use of the knee brace (dj Orthopaedics, LLC, California, USA) to maintain a fixed knee angle during flexibility testing.

An average hip flexion ROM was calculated for both and served as the final measure of hamstring flexibility (4). Post-testing was commenced immediately after the completion of the stretching intervention assigned for the day. In 2002, Klee et al. (26) suggested that participants should be retested as quickly as possible after the intervention stretches because resting tension started to increase after a three minute rest pause.

#### Stretching Program

##### Warm-up only/ No stretching: Treatment 1

No stretches were included in the warm-up, serving as a control. Participants cycled for 75 seconds on a stationary ergometer (Exertech, Australia) at 60-70 W with a 10 seconds rest pause between each of the five 75-second cycle periods. Total duration of cycling was 225 secs.

##### Static stretching: Treatment 2

Participants performed stretches for a total duration of 225 seconds (52). They performed three types of static stretches with a stretch time of 75 seconds for each (Table 1). This time equated to five stretches held for 15 seconds each (9, 29, 30, 34, 47,). A rest pause of ten seconds was allowed between stretches. Each static stretch was performed to the terminal range, defined as the point where the subject felt a mild discomfort or tightness in the back of the thigh (5). The static and dynamic stretching routines were appropriately timed so that the amount of time spent stretching was the same for each group, enabling comparison between the two groups (41).

##### Standing toe-touch

This stretch routine involved bending forward to touch toes whilst making sure that the knees remained fully extended. Participants held the stretched position for 15 seconds until a slight sense of discomfort or tightness felt in the back of the thigh. Ten seconds rest pauses were allowed after each stretch and when switching to a different stretch type.

##### Forward swing static stretch

The heel of the extremity to be stretched was supported on a treatment table to perform this particular stretch (35). The knee remained fully extended and the foot was positioned in relaxed plantar flexion. The pelvis was tilted anteriorly whilst bending forward at the waist avoiding flexion of the spine (15, 35), until the terminal range was reached or discomfort felt in the back of the thigh. This stretch position was held for 15 seconds and repeated five times on the dominant extremity.

##### Passive supine-sling stretch

This stretch was performed in the supine position whilst lying on an exercise treatment bench. A Velcro sling was passed around the ankle to flex the hip and consequently stretch the hamstring group of muscle. The stretch was held for 15 seconds to the terminal range of discomfort or tightness felt in the back of the thigh.

##### Dynamic stretching treatment

Five sets of seven to eight dynamic stretches equalled the amount of time spent (Table 1) on the aforementioned static stretching regimens. The aim was to allot the same amount of stretching time to the static and dynamic stretching interventions enabling comparison among the groups. The 15 seconds hold period for each static stretch equated to around seven to eight dynamic stretches. Five sets of dynamic stretches amounted to 225 seconds of total stretching time. There was a pause of 10 seconds between each set and another 10 seconds when changing over from one type of stretch to another.

Stretches were begun at low velocity and momentum was gradually built up to achieve at least 75% of maximum height and speed while performing the dynamic stretches. The SAID principle of specific adaptation to imposed demands formed the basis of the dynamic stretching routine. Participants stretched at 75% of the maximum velocity through a particular ROM whilst performing a sport-specific movement.

##### Dynamic leg swings

The dominant leg was flexed at the hip in a forward kicking action. The aforementioned SAID principle was applied during performance of all stretches (controlled stretching). Five sets of seven or eight forward leg swings or kicks (9) were carried out to a timed 225 seconds of stretching.

##### Crossed-body leg swings

Dominant leg swung across the midline of the body towards the opposite shoulder. This stretched the biceps femoris which is the lateral muscle of the hamstring group (40).

##### Standing bicycle-kicks

The dominant limb was put through a circumduction-like movement in a rhythmic cyclical manner incorporating the SAID principle (controlled stretching). Total time spent on this stretch was also 225 seconds.

#### Biomechanical analyses

The hip ROM in the dominant leg was used as an indirect measure of hamstring flexibility (44) and served as the only investigated parameter (Fully extended hip = 0°). Dartfish ProSuite (Dartfish Connect 4.0, Dartfish Ltd., Fribourg, Switzerland) is a complete video analysis software package, which includes all necessary functionality to analyse technical performance during and after training. Dartfish motion analysis software was used to quantify the degree of hip flexion. This system enables access to every video frame so that the terminal ROM of hip flexion can be accurately identified. Once the appropriate frame was identified, Dartfish was used to measure hip flexion accurately to the nearest degree. Intra-tester and operator reliability were tested by a repeat analysis of 15 participant performances.

#### Statistical Analysis

The principal dependent variable of interest was the change in hamstring flexibility measured by hip flexion ROM between pre and post-stretch measurements. The paired sample t-test compared the effect of the two treatments on static and dynamic hamstring flexibility. Non- parametric tests conducted on the collected data corroborate the aforementioned findings. Furthermore, Tukey’s Honestly Significant Difference (HSD) test explored the degree of change in static and dynamic flexibility. The data was analysed with the statistical package SPSS for Windows (version 12.1.0; SPSS Inc., Chicago, IL).

### Results & Disscussion

Various modifications of the SLR test were used to measure and compare hamstring flexibility in an earlier study that also tested for reliability (n=33). Static passive hamstring flexibility (SPH), dynamic supine hamstring flexibility (DSUH), dynamic standing hamstring flexibility with knee brace worn (DSHWB), and dynamic standing hamstring flexibility without knee brace (DSHNB). Subjects were tested on two separate occasions one week apart. Each subject had three trials for each tests for the two separate testing times resulting in a total of 30 scores. Test-retest was appropriate as subjects were tested at two points in time a week apart and a Cronbach alpha was used to test for internal consistency and reliability for the three trials of each week’s testing. The tests used in this study evidenced a very high degree of internal consistency for each trial by Occasion 1 and Occasion 2 as well as a high coefficient of reliability or stability as measured by the test-retest procedure (Table 3, Table 4).

Participants were randomly assigned to one of three interventions for each of three testing occasions:

1. No stretching (Treatment 1)
2. Static stretching (Treatment 2)
3. Dynamic stretching (Treatment 3)

A Paired-samples T-test was used to test for differences in static and dynamic flexibility from pre/post-test after each stretch intervention (Table 5).

Intervention Treatment 1, where the subjects did no stretching served as the control. Static and dynamic stretching (Treatment 2, Treatment 3) were the experimental treatments. Following Treatment 1 we expected measures of hamstring flexibility to remain unchanged from pre to post-test. However, our analysis revealed significant differences between pre and post score for static flexibility (t (11) = 2.76, p < .05). There was no significant difference between pre and post hip ROM measured by the dynamic flexibility test (t (11) = 0.315, p >.05). The mean value of difference between pre and post score for static flexibility (mean = 2.13, SD = 2.68) indicates that there is a substantial change.

When static stretching was included in the warm-up, there were statistically significant differences in pre and post static flexibility measurements (t (11) = 4.19, p < .05). However, there was no significant difference in pre and post dynamic flexibility measurements (t (11) = 0.72, p >.05). When dynamic stretches were included in the warm-up instead of static stretches, it was expected that there would be changes, at least, in dynamic flexibility of the hamstrings. The analysis shows that there were statistically significant differences in both static (t (11) = 2.62, p <. 05) and dynamic (t (11) = 5.69, p < .05) flexibility. This suggests that participants improved both their static and dynamic hamstring flexibility after dynamic stretching was included in the warm-up.

Non-parametric tests were carried out on the collected data to corroborate the aforementioned findings. Wilcoxon Matched-Pairs Ranks test was performed. The results were similar to those obtained following the Paired samples t-test. Following Treatment 1 (No stretching) there were resultant differences in the static hamstring flexibility (Wilcoxon, Z = -2.41, p < .05). Static stretching only influenced static flexibility (Wilcoxon, Z = -2.67, p < .05) of the hamstrings, while dynamic stretching produced changes in both static (Wilcoxon, Z = -2.39, p < .05) and dynamic flexibility (Wilcoxon, Z = -2.98, p < .05).

Furthermore, the differences in the degree of change in static and dynamic flexibility following dynamic stretching were explored using Tukey’s Honestly Significant Difference (HSD) test. The difference between the degree of improvement in static and dynamic hamstring flexibility following dynamic stretching were not statistically significant (Table 6). To corroborate these findings a Wilcoxon Matched-Pairs Ranks test was performed on pre-post differences of static and dynamic flexibility following dynamic stretching. The analysis failed to identify a significant difference in the changes demonstrated in both static and dynamic flexibility (Wilcoxon, Z = -0.178, p > .05).

The availability of state of the art software and improved video analysis techniques has changed the way flexibility is measured. The methods commonly being used have focussed on the measurement of static flexibility. With the growing trend towards using dynamic stretching and sport-specific drills in the warm-up, there is a need for measuring devices to adapt to these changes. We have provided a simple, reliable setup to measure flexibility. The inadequately defined relationship between flexibility and muscular performance or an athlete’s susceptibility to injury may be attributable to the lack of valid and reliable measures of flexibility (20). The drawback of flexibility assessment tools is the need for testing to be carried out within the confines of a laboratory. Although this study was carried out in a laboratory, the set-up could be used outdoors with the participant performing functional dynamic sporting movements.

Dynamic flexibility has been defined as a measure of the resistance throughout the ROM of a joint or a measure of joint stiffness (3). Dynamic flexibility is important in sport because it measures the ability of an extremity to move through a non-restricted ROM (36). The main findings suggest that static stretching improves static flexibility (p < .05) but may have no impact on dynamic flexibility (p > .05). Increasing ROM achieved through static stretching does not necessarily translate to improvements in dynamic flexibility. In 2004, Behm et al. (6) supported the concept that static stretching improved flexibility and ROM, however, it was believed that the relevance and specificity of the gains remained questionable.

In 1988, Alter (1) argued in support of the specificity of stretching: “ROM is a combination of active and passive ranges of motion and if passive stretching exercises are used to develop flexibility, then one should expect changes largely in passive flexibility” (p.179). Even a moderate duration of static stretching could result in quadriceps isometric force and activation decrements lasting for up to 120 minutes (33). The increase in static flexibility may not have translated into expected improvements in dynamic flexibility because of dampened hamstring activation following an acute bout of static stretching.

Static flexibility improved when no stretches were included in the warm-up as well as when the participants underwent a static stretching routine. Similar results were obtained in a other studies (44, 53). The 2003 study by Zakas et al. (53) indicates that flexibility improves significantly even when stretching is not included in the warm-up, however, any comparisons should be made with caution because of differences in methodology. The stationary cycling group in the study in 1997 by Wiemann and Knut (44) cycled for 15 minutes and demonstrated a significant improvement in hip ROM thereafter. They explain that this occurrence may be due to the decreased resting tension and a reduced stretch resistance following stationary cycling. However, other studies have shown that warming up before stretching does not complement the effectiveness of stretching (14, 45).

Following the inclusion of dynamic stretches in the warm-up, dynamic flexibility as well as static flexibility scores improved from pre-test to post-test. However, Tukey’s HSD test did not reveal significant differences between the degree of improvement of static and dynamic flexibility. Muscles have two types of receptors: the primary or annulospiral endings which measure changes in both muscle length and velocity, and the secondary or flower spray endings that measured changes in muscle length alone (2). Thus, Alter (2) reasons that dynamic stretching may be used to condition primary endings for a desired response, and sport-specific drills could be used in warm-up. Dynamic stretching may have caused activation of the primary annulospiral endings resulting in an increase in both static and dynamic flexibility. The dynamic stretching routine may have had a warming up effect, causing an increase in static flexibility.

There may be a need to consider the appropriate time for static stretching in the daily training schedule. There have been suggestions that static stretching may be useful in the cooling down period after a workout (18, 27, 31-32). Evidence remains in support of static stretching for long-term gains in flexibility (31, 39).

### Conclusion

The intervention study comparing the effects of static and dynamic stretching routines in the warm-up on hamstring flexibility demonstrated that dynamic stretching enhanced static as well as dynamic flexibility. Static stretching on the other hand did not have an impact on dynamic flexibility. This has implications for the use of static stretching in the warm-up for dynamic sport. The role of static stretching for injury prevention in dynamic sport is also being questioned.

### Application in Sport

The simplicity of the experimental set-up is the highlight of this research. Coaches can use our method of video analysis to monitor the effectiveness of stretching routines. A single person can carry out testing with ease and accuracy.

Dynamic stretching is synonymous with functional, sport-specific stretching and this research has demonstrated that dynamic stretching improves both static and dynamic hamstring flexibility. Static stretching has no impact on dynamic flexibility and should not be used in the warm-up; however, static stretches may be useful in the cooling down period of training for long term gains in flexibility.

Although our research has demonstrated the effectiveness of dynamic stretching in the warm-up, it is important to follow the training guidelines set aside in 2001 by Mann and Whedon (31) whilst implementing a stretching routine. Dynamic stretching may be most effective if performed according to the training principles discussed earlier, always making sure the needs and the capacities of the individual athlete receive precedence over general training goals.

### Acknowledgements

I would like to acknowledge my supervisors Dr. Mark Sayers and Dr. Gordon Waddington for their invaluable guidance. Their understanding and patience helped me overcome numerous hurdles en route to the completion of this thesis. I would also like to thank the sports studies staff for their help and advice.

I am thankful to the students of the University of Canberra (Sports Studies) for volunteering to participate in this research project. It was wonderful working with such cheerful and enthusiastic young people. Their willingness to participate and report at similar times for each testing session is much appreciated.

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38. Stark, S. D. (1997). The stark reality of stretching: An informed approach for all activities and every sport (3rd ed.). Richmond, BC, Canada: Stark reality corp.
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40. Tubbs, R. S., Caycedo, F. J., Oakes, W. J., & Salter, E. G. (2006). Original communication: Descriptive anatomy of the insertion of the biceps femoris muscle [Electronic version]. Clinical Anatomy, 19. Retrieved January 13, 2006, from <http://www3.interscience.wiley.com/cgibin/fulltext/112141010/PDFSTART>
41. Wallin, D., Ekblom, B., Grahn, R., & Nordenborg, T. (1985). Improvement of muscle flexibility. A comparison between two techniques. The American Journal of Sports Medicine, 13(4), 263-269.
42. Walshe, A. D., Wilson, G. J., & Murphy, A. J. (1996). The validity and reliability of a test of lower body musculotendinous stiffness. European Journal of Applied Physiology & Occupational Physiology, 73(3-4), 332-339.
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53. Zakas, A., Vergou, A., Grammatikopoulou, M. G., Zakas, N., Sentelidis, T., & Vamvakoudis, S. (2003). The effect of stretching during warming-up on the flexibility of junior handball players. Journal of Sports Medicine & Physical Fitness, 43(2), 145-149.

### Tables

#### Table 1
Time spent on each stretch

Stretch Type Stretch Time (seconds)
Static stretching*
Toe-toucha 75c
Forward swinga 75c
Surpine slinga 75c
Dynamic stretching*
Forward leg swingb 75d
Crossed-body leg swingb 75d
Bicycle kicksb 75d

(*) 10 seconds rest pause after each repetition and 10 seconds before switching over to the next type of stretch.
(a) 5 Stretches
(b) 5 Sets
(c) 15 seconds hold for each static stretch
(d) 7-8 swings/ kicks equivalent to around 15 seconds of stretching time for each set.

#### Table 2
Comparison of Dynamic and Static Hamstring flexibility measures in reliability study

Test 1b Test 2a Test 1
Mean (SD)
Test 2
Mean (SD)
F df P Part Eta2
SPH DSUH 91.90 (18.02) 88.61 (16.97) 18.20 1.000 < .001 .363
SPH DSHNB 91.90 (18.02) 89.96 (15.91) 1.28 1.000 .267 .038
DSUH DSHWB 88.61 (16.97) 91.66 (15.65) 4.46 1.000 .043 .122
DSUH DSHNB 88.61 (16.97) 89.96 (15.91) .835 1.000 .368 .025
DSHWB DSHNB 91.66 (15.65) 89.96 (15.91) 10.44 1.000 .003 .246

Significant at p < .05
(a) All measurements are in degrees
(b) Number of participants performing each test = 33

#### Table 3
Cronbach alpha measure of reliability for each test repetition for two test sessions

Flexibility Test Alpha Occasion
(SEM)*
Alpha Occasion 2
(SEM)*
Static-passive hamstring .9950 (1.28) .9946 (1.32)
Dynamic-supine hamstring .9908 (1.71) .9891 (1.77)
Dynamic-standing hamstring with brace .9915 (1.45) .9917 (1.42)
Dynamic-standing hamstring no brace .9905 (1.51) .9897 (1.61)

(*) SEM – Standard Error of Measurement.

#### Table 4
Test – retest reliability

Flexibility Test Coefficient of Stability / Reliability (SEM)
Static-passive hamstring .992 (1.61)
Dynamic-supine hamstring .993 (1.45)
Dynamic-standing hamstring with brace .989 (1.66)
Dynamic-standing hamstring no brace .983 (2.04)

#### Table 5
Paired samples T test comparing the effect of the intervention treatments on dynamic and static hamstring flexibility

Treatmentb Pairs (Pre-Post Test Scores) Mean (SD) Std. Error Mean 95% Conf. Int. of the Difference ta Sig. (2-tailed)
Lower Upper
No stretch Static flexibility 2.13 (2.68) 0.77 0.43 3.84 2.758* 0.019
Dynamic flexibility 0.23 (2.57) 0.74 -1.40 1.87 0.315 0.759
Static stretching Static flexibility 4.04 (3.34) 0.96 1.92 6.16 4.191* 0.002
Dynamic flexibility 1.35 (6.51) 1.88 -2.78 5.48 0.719 0.487
Dynamic stretching Static flexibility 1.86 (2.46) 0.71 0.30 3.42 2.622* 0.024
Dynamic flexibility 1.75 (1.06) 0.31 1.07 2.43 5.694* 0.000

(*) Significant at p < .05
(a) Degrees of freedom = 11
(b) Number of participants undergoing each treatment = 12

#### Table 6
Tukey’s Honestly Significant Difference (HSD) test exploring differences in the degree of change in static and dynamic flexibility following dynamic stretching

Experimental Group Dependent Variable (I) Intervention (J) Mean Difference (I-J) Std. Error Sig.
Dynamic Stretching Post Static Flexibility No Stretching -0.006 4.14 1.00
Static stretching 1.08 4.14 0.96
Post Dynamic flexibility No stretching -1.24 4.60 0.97
Static stretching -1.13 4.60 0.97

### Corresponding Author
Gayle Silveira, MBBS
Modbury Hospital
Smart Road
Modbury, SA 5092
Australia
<gaylerebello@yahoo.com>
+6 (143) 172-1469

2013-11-25T16:34:15-06:00March 3rd, 2011|Contemporary Sports Issues, Sports Coaching, Sports Exercise Science, Sports Management|Comments Off on Effect of dynamic versus static stretching in the warm-up on hamstring flexibility

A Coach’s Responsibility: Learning How to Prepare Athletes for Peak Performance

### Abstract

The coaching profession is ever-changing and coaches at each level of sport competition need to know more than just the Xs and Os in order to be successful. As the primary individuals tasked with developing athletes and helping them achieve their goals, coaches should acquire a working knowledge of all areas affiliated with performance enhancement. Specifically, the disciplines of sports administration, sports medicine, strength and conditioning, and sports psychology can assist coaches while physically and mentally training their athletes. This article illustrates six primary components of these disciplines: risk management, injury prevention, communication, nutrition, goal setting, and athlete development. It is imperative coaches gain a familiarity with these aforementioned components in order to teach athletes about skill development and prepare them to achieve peak performance.
(more…)

2018-10-22T15:29:44-05:00February 14th, 2011|Sports Coaching, Sports Exercise Science, Sports Management|Comments Off on A Coach’s Responsibility: Learning How to Prepare Athletes for Peak Performance

Is Controlling the Rushing or Passing Game the Key to NFL Victories?

### Abstract

#### Purpose

To evaluate whether controlling the running game or the passing game contributes more to winning in the NFL.

#### Methods

This analysis uses regression analysis to dispel the myth that controlling the rushing game wins NFL games. Final-game rushing and passing statistics are endogenous because teams that are ahead will rush more in order to protect the ball and run the clock down. To address this issue, I use first-half statistics (essentially stripping the endogenous component from the statistics), with the justification that the halftime leader wins 78 percent of the time. The data are the 256 regular season games for 2005. I use logistic models to model the probability of winning a game based on differences in rushing success and passing success in the first half.

#### Results

I find that having a first-half passing-yard advantage increases the probability of winning, but having a first-half rushing-yard advantage has no significant effect.

#### Conclusions and Applications

The results suggest that the common belief that controlling the running game is the key to winning in the NFL may be a misguided belief. Coaches and teams may have greater success by focusing on the passing games, both offensively and defensively.

**Keywords:** Football, NFL, passing, rushing, coaching

### Introduction

A common assessment of the key to winning professional football games is to control the running game. And a very common statistic used to support this claim is that teams are much more likely to win if they have a 100-yard rusher. This is often used in recapping games and when analysts describe the keys to victory. For example, the recap of a 2005 victory for the St. Louis Rams over the Houston Texans indicated: “[Steven] Jackson finished with 25 carries for 110 yards, improving the Rams’ record when having a 100-yard rusher to 38-0 since moving to St. Louis in 1995” (1). This implies that rushing 100 yards was the catalyst for the victory. Likewise, many analysts say that establishing the running game is a key to victory. For example, one analyst argued that a key to winning for the Tampa Bay Buccaneers over the Oakland Raiders in Superbowl XXXVII was to “contain Raiders’ [running back] Charlie Garner,” citing evidence that: “In the past five seasons, the Bucs are 1-12 when opponents have a 100-yard rusher” (11). In a closely tied statistic to rushing dominance, analysts also argue that teams that control the time of possession are more likely to win. These assessments imply that controlling the passing game is of much less importance.

A set of articles on espn.com using data from the NFL’s 2003 and 2004 regular season games supports these contentions by arguing that preventing an opposing runner from gaining 100 yards and winning the time-of-possession battle increased a team’s chances of winning (5, 6). At the same time, these articles imply that the passing game is insignificant, citing as evidence:

1. Teams having a 100-yard rusher win 75 percent of games;
2. Teams winning the time of possession battles win 67 percent of games;
3. Having a 300-yard passer has no advantage, as those teams only win 46 percent of games.

A problem with these simple assessments is that teams that are winning will rush the ball more to run out the clock and reduce the chance of turnovers and will often wait until the clock runs down before starting a play. So, if a team is heading towards victory, they are likely to increase their rushing yards while boosting their time of possession. Likewise, a team that is behind will pass more for potentially higher-gaining plays and to preserve the clock. Thus, in statistical terms, we could say that rushing yards, passing yards, and time of possession are endogenous, or partly a product of the outcome rather than just a contributor to the outcome. This makes it difficult to attribute any advantages in rushing yards or time of possession to the winner as causal impacts. In fact, what happens in the first half or even the first quarter can dictate the outcome of the game, as teams leading after just the first quarter (in 2003 and 2004 games) won 75 percent of the time (5).

This paper presents an empirical test of these issues with econometric analysis. Primarily, this analysis tests whether controlling the rushing or passing game was more likely to contribute to a victory in NFL games in the 2005 season. Rushing and passing advantages represent efficiency both on offense and defense. In addition, the model examines the relative contribution of turnovers, penalties, and sacks allowed to the probability of winning. These models could represent a more accurate picture of the effects of certain factors on winning, as they hold other factors constant.

The twist in this analysis is that the model corrects for endogeneity by using the first-half statistics. This essentially strips a large portion of the endogenous component from these statistics, as teams are not likely to change strategies to “ball preservation” or “speedy catch up” until the second half. Given that 78.5 percent of teams leading at halftime in 2005 games ended up winning the game, having a halftime advantage in many of these statistics should contribute to a higher probability of winning.

Determining the key contributions to winning in the NFL is important as teams, subject to the college draft and salary caps, attempt to obtain the best allocation of talent among various positions. If it does turn out that big passing games are the keys to victory, then investing relatively more on players in passing-related offensive and defensive positions than on players in rushing-related positions may be wiser.

Research on football issues has been very limited in the academic literature. There have been some interesting analyses on optimal 4th-down strategies (8, 9). Some research has attempted to predict the outcome of a game based on betting markets or power scores (4, 10). Other research has examined the success of teams over the course of a season (2, 3, 12). However, to my knowledge, this is the first analysis attempting to predict outcomes of games in a multivariate framework based on in-game statistics.

The most similar prior research examined how certain factors contributed to the number of wins NFL teams had (7). This article examined how first downs, average rushing yards per carry and passing yards per completion, interceptions, fumbles, and other factors affected the number of wins a team had, and then used the results to judge coaching efficiency. The models use full-game statistics so the results are subject to the biases mentioned above.

In this study, I first present a simple breakdown of the descriptive statistics for the first and second half, which clearly demonstrates the likely existence of endogeneity using the full-game statistics, as the eventual winner or the half-time leader clearly changes strategy in run-pass mix in the second half. The stark contrast found between the models using full-game vs. first-half statistics further corroborates how endogeneity affects the models using the full-game statistics. In particular, while the models using full-game statistics show a connection between controlling the rushing game and the probability of winning and no connection for controlling the passing game, the models using first-half statistics show the opposite: that controlling the passing game matters, but controlling the running game does not. Given that the analyses based on the first-half statistics should be free of biases from endogeneity, it appears that controlling the passing game is the key to winning. In addition, both full-game and first-half models show that the time of possession has no effect on the probability of winning, after controlling for other factors.

### Methods

#### Data

The sample includes all 256 regular season games from the 2005 NFL season. Each of the 32 teams has 16 games in the sample. The data come from the “Gamebooks” that are available on the NFL’s website (nfl.com). These data were used with permission granted from the National Football League’s Licensing Office. The advantage of these data is that they provide both final and first-half statistics, while a disadvantage is that the relevant statistics need to be manually extracted from each game report, which is roughly 10 pages long for each game. The descriptive statistics are presented in the Appendix. Table A1 shows the average team-level first-half and full-game statistics for the 512 team-game observations. Table A2 shows the average game-level statistics used in the econometric models for the 256 regular season games, with the key variables being “moderate” and “great” control of the rushing and passing games.

What is useful to show here are the differences that exist between first-half and second-half statistics for the eventual winners versus the losers and for the first-half leaders versus the trailers. These demonstrate how the second-half strategies can be dictated by first-half success, which is the basis for the argument that full-game statistics are endogenous to the outcome. Table 1 shows these results for the 243 games that did not go into overtime, as the second-half statistics cannot be calculated for the 13 games going into overtime because of how the NFL Gamebooks are set up. The first two columns, based on which team wins the game, show that, whereas the winner had an average of a 22-passing-yard advantage in the first half (119 versus 97), it had 26 fewer yards passing than the loser in the second half.

The next set of columns makes the comparison based on which team had the lead at halftime. There were 227 games in which one team led at halftime and the game did not go into overtime. Table 1 shows that there was little difference between the first half and second half in the rushing advantage for the halftime leader. However, that difference for the passing advantage is much greater. The halftime leader had a 34-yard passing advantage (126 vs. 92) in the first half and a 39-yard passing disadvantage (85 vs. 124) in second-half passing yards. Furthermore, the advantage for the leader in terms of fewer sacks allowed increased from 0.43 to 0.76. The differences are even starker in the final two columns for the 141 games that had a team leading by 7 or more at halftime. The 49-yard first-half passing advantage for the leader turned to a 50-yard disadvantage in the second half. And the 0.49 first-half advantage for the leader in fewer sacks allowed turned to a 0.90 second-half advantage. Note that the lack of much difference between the leader and trailer in first-half versus second-half rushing yards does not indicate that strategy does not shift, as the ratio of passing-to-rushing yards does increase for the trailer and decrease for the leader.

These results offer strong statistical evidence that the halftime leader passes less (probably to help protect the ball and run the clock down) and is more careful with the ball (with fewer turnovers). In addition, the results indicate that the halftime trailer passes more. The implication for statistical analysis is that many full-game statistics are likely endogenous to the eventual outcome. This includes rushing yards, passing yards, turnovers, and the number of sacks allowed. Thus, any comparison of full-game or final statistics for the winner versus the loser would be biased indicators of a causal effect.

#### Econometric Models

Given the likely bias that would come from using full-game statistics, the primary model will use first-half statistics, while still basing the outcome on the eventual game winner. As mentioned above, the justification for this is that 78.5 percent of the teams that led at halftime ended up winning the game. In order to provide a comparison so that readers can gauge the level of bias in using full-game statistics, an initial set of models will show the results from models using the full-game statistics.

The econometric model is the following:

![Formula 1](/files/volume-14/5/formula.png “Formula 1”)

where Yi, the dependent variable, is a dichotomous indicator for whether the home team won game i, Ri and Pi represent measures of the rushing and passing advantages of the home team relative to the visiting team, Xi is a vector of three other statistics for the home team relative to the visiting team, including penalty yards, turnovers, and sacks allowed, and Hi and Ai are vectors of 31 indicator variables for which team is the home team and away team in game i, with one team excluded. Thus, all statistical variables are created in terms of the home-team statistic minus the visiting-team’s statistic or, in a few cases, the advantage of the home team over the away team. For example, the variable for rushing-yards advantage would be the number of rushing yards for the home team minus the number of rushing yards for the visiting team. The results would be the same regardless of whether the model predicts the probability of the home team or the visiting team winning.

For both sets of models with final statistics and first-half statistics, three sets of rushing and passing variables are created. The first set has the raw difference in rushing and passing yards, measured as the advantages the home team has over the visiting team. The second set has a variable indicating whether one of the teams had “moderate” control of the rushing or passing yards, with the threshold being 25 yards for the models with first-half statistics and 50 yards for models with full-game statistics. For the models with first-half statistics, this variable is coded as “1” if the home team had at least 25 more rushing (or passing) yards than the visiting team at halftime, “-1” if the visiting team had at least 25 more yards than the home team, and “0” if the absolute difference in yards between the two teams was less than 25. The third set of variables, constructed similarly to the second set, has variables indicating whether one of the teams had “great” control of the rushing or passing game. The thresholds are 50 yards for the models with first-half statistics and 100 yards for models with full-game statistics. Note that these variables taking on the values of (-1, 0, 1) essentially constrains the absolute values of the following two effects to be the same: (a) the effect of home-team control of the rushing/passing game on the probability of the home team winning and (b) the negative of the effect of visiting-team control of the rushing/passing game on the probability of the home team winning. This helps to give greater power to the model.

The models include three other statistical variables: the difference in penalty yards, the difference in turnovers, and the difference in the number of times the team is sacked. Including the number of penalties had a very small effect, so it was excluded so that the full effect of penalty yards could be estimated.

Finally, the model includes team fixed effects for both being the home team and being the visitor. That is, it includes 31 dummy variables for the home team and 31 dummy variables for the away team, excluding one team as the reference category. They account for differences in team-specific factors, such as the quality of coaching and the strength of home-field advantage (e.g., from fan enthusiasm and weather conditions). In addition, the team fixed effects account for differences in the strength and weakness of the passing vs. rushing games for teams and for opponents.

These team fixed effects are included to help avoid unobserved team heterogeneity affecting the results. For example, one of the better teams in 2005 was the Indianapolis Colts, which had a very strong passing game. Thus, without team fixed effects, the general success of the Colts could contribute to a positive correlation between passing yards and winning that could be due to other unobserved factors. By including team fixed effects, the estimates represent within-team variation across games in winning attributable to within-team variation across games in control of the rushing and passing game. The coefficients on these (not reported) generally reflect differences across teams in both home and away winning percentages, after taking into account the other variables included in the model.

Equation (1) is estimated with logit models. The models have a final sample of 212 games because 44 games were dropped by the model due to perfect prediction of the outcome—e.g., 8 observations were dropped for Seattle home games because they won all those games. In estimation, it turned out that that the marginal effects were highly dependent on the home and visiting teams used for the prediction. Some teams that won (or lost) nearly all their home or away games were too close to a predicted probability of winning of one (or zero), so that the marginal effect of the variables would be close to zero for them. To correct for this, the reported marginal effects are calculated as the averages for all team combinations that played in the 2005 season.

The model presented here is fairly simple. One reason for this is that the home- and away-team fixed effects account for a wide set of team-specific factors (some unobservable and some observable), such as the quality of coaching, having artificial turf, and generally favoring either passing or rushing. The other reason why the model is kept simple is that it is designed to estimate the full effects of having advantages in the rushing game and the passing game. As it turns out, this simple model tells an interesting story.

The model could be made more complex by including such factors as the run-pass mix, time-of-possession, and return yards off of kick-offs and punts. These other factors are excluded because they could themselves be products of running and passing success in the game. For example, having a higher time-of-possession is an indicator of rushing the ball successfully. And, having a rush-pass mix favoring passing may be an indicator of success in the passing game. Controlling for these variables would cause the model to factor out part of why having rushing or passing advantages helps win games, so that the coefficient estimates on the rushing and passing advantages would represent partial effects rather than the full effects the model aims to estimate. Separate analyses below do test whether time-of-possession matters, after controlling for rushing and passing yards, as well as the other factors that are in our primary set of models.

Another factor excluded from the model for similar reasons is the number of return yards from kick-offs and punts. Return-yard success (or more generally, special-teams success) could be representative of other factors. Indeed, one of the ESPN articles notes that teams returning a punt or kick-off for a TD win only 42 percent of the time (6). One confounding factor is that teams have a greater chance of return success on kick-offs than on punts, but having more kick-off returns is an indication that the other team has scored more often. Given these complexities, we exclude return-yardage indications. Given that we use team fixed effects, this should not be a problem to our analysis, as within-team variation in special-teams success relative to the other team (holding constant special-teams’ opportunities) should be mostly uncorrelated with the within-team variation in rushing and passing success relative to the other team.

### Results and Discussion

#### Is controlling the rushing or passing game more important to winning?

Table 2 presents the results of the econometric models that examine the relationship between full-game statistics and the probability of winning. These results are subject to biases created by the endogeneity described above, so they are meant to be compared to the results of the preferred model, in Table 3, which is based on the relationship between first-half statistics and the probability of winning.

The results in Table 2 are consistent with the widely held belief that controlling the rushing game is the key to winning and that great passing success is not important. The coefficient estimate on the rushing-yard difference is positive and significant at the one-percent level. The corresponding marginal effect, in brackets, indicates that each 10-yard advantage in rushing yards is associated with a 2.3-percentage-points higher probability of winning (p < 0.01). The coefficient estimate on passing-yards advantage is small and insignificant. Considering the indicators for “moderate” control of the rushing and passing game, having a 50-yard advantage in rushing yards is associated with an estimated 17.2-percentage-points higher probability of winning (p < 0.01). The estimate on having a 50-yard advantage in passing yards is again insignificant. Having “great” control of the rushing game (100-yard advantage) is associated with an estimated 31.4-percentage-points higher probability of winning (p < 0.01). Having “great” control of the passing game is still statistically insignificant.

As for other results, each turnover is associated with a decrease in the probability of winning of about 16 percentage points (p < 0.01), while each sack is associated with an 11-percentage-points decrease in the probability of winning (p < 0.01). These seemingly large effects could be indicative of the extra chances that teams take when they are behind late in the game. Penalty yards do not appear to make a difference, after controlling for other factors.

The main point from the models using full-game statistics is that total rushing yards or controlling the rushing game is positively correlated with the probability of winning, while passing yards and controlling the passing game has little correlation with the probability of winning.

The results from models using first-half statistics give the opposite conclusion. The estimates indicate that controlling the passing game is the key to winning, not controlling the rushing game. In contrast to the results in Table 2, those in Table 3, for the coefficient estimates on first-half statistics, arguably represent causal effects because most teams probably do not start the strategy of protecting the ball and running out the clock to end the game while still in the first half.

All three of the coefficient estimates on the passing yard advantage are positive and significant (p < 0.01). The estimates on rushing yard advantage are still positive, but smaller than those for the passing-yard advantage and statistically insignificant. The estimated marginal effects indicate that each 10 yards of passing gained increase the probability of winning by 2.6 percentage points, while having a 25- or 50-yard-passing advantage in the first half increases the probability of winning by about 21 percentage points. Thus, these estimates indicate that controlling the passing game in the first half increases a team’s probability of winning the game by about 12 percentage points, while controlling the rushing game in the first half has no significant effect on the probability of winning.

Among the other factors, first-half penalty yards again do not affect the probability of winning. Each turnover is estimated to reduce the chance of winning by about 10 percentage points (p < 0.01), while each sack allowed reduced the probability of winning by about 5 percentage points (p < 0.10). The estimated marginal effects of turnovers and sacks allowed are smaller for the first-half model than for the full-game model. This could indicate that, like rushing and passing yards, the full-game statistics on the number of turnovers and sacks allowed are endogenous and reflective of the outcome of the game, as the teams that are behind will be susceptible to more turnovers and sacks as they pass more and take more chances to try to catch up.

#### Does time of possession matter?

Another commonly-held belief is that having a greater time-of-possession is a major key to winning, as 67 percent of the teams that won the time-of-possession battle in 2003 and 2004 had won their games(5). This suggests that winning the time-of-possession battle increases a team’s chances of winning by about one-third. However, this statistic is also a product of a team’s success (or endogenous) and thus subject to biases. For example, teams that are ahead will let the clock run down further between plays.

Table 4 presents the coefficient estimates on variables representing time of possession from models similar to column (1) in Tables 2 and 3—i.e., models that use the rushing- and passing-yard advantage. It includes estimates using the full-game and first-half statistics. The first row has the estimates on the actual time-of-possession advantage; the second row has the estimates on indicators for whether the team had a higher time-of-possession, and the last two rows have estimates on indicators for having advantages of 7 minutes (for the full game) and 5 minutes (for the first half), which are roughly the average mean absolute differences. For the full-game statistics, none of the time-of-possession variables is statistically significant. For the models based on first-half statistics, all of the coefficient estimates on time of possession are negative, with the first one being statistically significant (p < 0.10). These results suggest that time-of-possession is not important to winning, holding constant other factors.

### Conclusions

This paper is the first analysis to model a production function for winning an NFL game based on in-game statistics. This carefully constructed framework, which models victories based on home-team over away-team statistics, can be used for other models for winning games in the NFL or in other sports leagues.

The results of this analysis cast doubt on the contention that the key to winning games in the NFL is to control the rushing game. The results do indicate that having a rushing advantage for the full game is positively correlated with the probability of winning and having a passing advantage for the full game is not correlated with winning, holding other factors constant. However, these correlations are likely due to endogeneity, in that full-game rushing and passing yards are partly products of a team’s success during the game. In other words, as demonstrated in this paper, the strategy for second-half rushing-passing mix depends on where a team stands at halftime. This means that we cannot label these correlations as causal influences.

The econometric strategy in this analysis is to identify a causal effect of various factors by using first-half statistics. These first-half statistics should be exogenous because strategies to run the clock down and to take extra precautions of preserving the ball (and to play catch-up by passing the ball so that incompletions stop the clock) arguably do not start until sometime in the second half. Of course, there could be cases in which teams build such a huge lead early in the first half that they start such a strategy at some point in the second quarter. But, typically teams that are ahead would want to build on their momentum in the first half before shifting strategy at some point in the second half.

One other key result is that having a time-of-possession advantage does not matter, after controlling for other factors (e.g., rushing and passing yards). However, the major findings from models using first-half statistics are that, on average, controlling the passing game contributes significantly to the probability of winning and controlling the rushing game has little impact. Having some level of control over the passing game in the first half is estimated to increase a team’s chance of winning by 21 percentage points. It is not that rushing success does not matter, as many would argue that having the threat of a potent running attack is key to a successful passing game. In addition, a strong running game may help with ball preservation for holding a second-half lead. But, in contrast to conventional thought, holding other things constant, it appears that a big passing day is more important to victory than a big running game. It is important to keep in mind here that passing advantage and control incorporates both how strong a team’s passing game is and how strong its pass defense is.

### Applications in Sport

The results in this analysis suggest that NFL coaches may be more successful if they were to place more emphasis on the passing game than on the running game. This result may translate to lower levels of football (e.g., high school and college). In this case, for professional football or something lower, obtaining and developing premier players for passing-related offensive and defensive positions may be more important than obtaining and developing premier players in rushing-related positions.

### Tables

#### Table 1
A comparison of first-half and second-half statistics for the eventual winner versus the loser and the halftime leader versus the trailer.

Based on eventual outcome (N=243) Based on which team leads at halftime (N=227) Based on which team had 7+ point lead at halftime (N=141)
Winner Loser Led at halftime Trailed at halftime Led by 7+ points at halftime Trailed by 7+ points at halftime
1st-half rushing yards 64 50 66 47 70 42
2nd-half rushing yards 71 39 67 42 71 40
1st-half passing yards 120 98 126 92 136 87
2nd-half passing yards 92 118 85 124 76 126
1st-half penalty yards 28 32 27 32 27 32
2nd-half penalty yards 27 29 28 28 28 28
1st-half turnovers yards 0.65 0.99 0.56 1.04 0.55 1.20
2nd-half turnovers yards 0.49 1.38 0.68 1.22 0.62 1.26
1st-half sacks allowed 0.88 1.26 0.85 1.28 0.87 1.36
2nd-half sacks allowed 0.72 1.68 0.81 1.57 0.73 1.63

**NOTE:** These statistics exclude the 13 games that go into overtime because second-half
statistics cannot be determined.

#### Table 2
Logistic regression model for the relationship between full-game statistics and the probability of winning (N=212)

(1) Using rushing and passing yards difference (2) Using “moderate” control of rushing and passing game (3) Using “great” control of rushing and passing game
Rushing yards difference 0.0266***
(0.0078)
[0.0023]
Passing yards difference 0.0064
(0.0065)
[0.0006]
Had 50-yard rushing advantage 2.081***
(0.690)
[0.172]
Had 50-yard passing advantage 0.485
(0.743)
[0.040]
Had 100-yard rushing advantage 3.918***
(1.290)
[0.314]
Had 100-yard passing advantage 1.375
(0.018)
[0.110]
Penalty yards difference -0.023
(0.019)
[-0.002]
-0.025
(0.020)
[-0.002]
-0.020
(0.018)
[-0.002]
Turnover difference -1.790***
(0.440)
[-0.158]
-1.860***
(0.440)
[-0.153]
-2.045***
(0.495)
[-0.164]
# sacks allowed difference -1.268***
(0.375)
[-0.112]
-1.313***
(0.381)
[-0.108]
-1.211***
(0.366)
[-0.097]

**NOTE:** *, **, and *** indicate statistical significance at the five- and one-percent level. The models also include dummy variables for each visiting team and home team. Standard errors are in parentheses and marginal effects are in brackets.

#### Table 3
Logistic regression model for the relationship between first-half statistics and the probability of winning (N=212)

(1) Using rushing and passing yards difference (2) Using “moderate” control of rushing and passing game (3) Using “great” control of rushing and passing game
Rushing yards difference 0.0090
(0.0060)
[0.0013]
Passing yards difference 0.0177***
(0.0049)
[0.0026]
Had 25-yard rushing advantage 0.155
(0.366)
[0.209]
Had 25-yard passing advantage 1.628***
(0.394)
[0.020]
Had 50-yard rushing advantage 0.781
(0.495)
[0.103]
Had 50-yard passing advantage 1.648***
(0.449)
[0.216]
Penalty yards difference -0.010
(0.007)
[-0.001]
-0.010
(0.007)
[-0.001]
-0.007
(0.007)
[-0.001]
Turnover difference -0.724***
(0.251)
[0.105]
-0.841***
(0.254)
[-0.108]
-0.739***
(0.252)
[0.097]
# sacks allowed difference -0.352*
(0.183)
[-0.051]
-0.357*
(0.184)
[-0.046]
-0.416**
(0.183)
[-0.055]

**NOTE:** *, **, and *** indicate statistical significance at the five- and one-percent level. The models also include dummy variables for each visiting team and home team. Standard errors are in parentheses and marginal effects are in brackets.

#### Table 4
Logistic regression model for coefficient estimates on time-of-possession variables

Full-game First-half
Time-of-possession difference 0.052
(0.107)
[0.002]
-0.126*
(0.073)
[0.017]
Had any advantage in time-of-possession 0.288
(0.670)
[0.024]
-0.427
(0.350)
[-0.057]
Had 5+ minute advantage in time-of-possession -0.698
(0.583)
[-0.039]
Had 7+ minute advantage in time-of-possession 0.448
(1.132)
[0.037]

NOTE: *, **, and *** indicate statistical significance at the ten-, five- and one-percent level. The models also include dummy variables for each visiting team and home team. Each coefficient estimate is based on a separate regression. These regressions include, for either full-game and first-half statistics, the same regressors represented in column (1) of Tables 2 and 3. Standard errors are in parentheses and marginal effects are in brackets.

#### Table A.1.
Average team statistics in key categories for 2005 regular season games (N=512)

Full half Final game
Rushing yards 56.8 (30.3) 112.5 (51.1)
Passing yards 108.1 (51.0) 219.9 (73.7)
Penalty yards 30.2 (22.8) 58.2 (26.0)
Number of turnovers 0.81 (0.88) 1.76 (1.45)
Number of sacks allowed 1.08 (1.06) 2.30 (1.73)

NOTE: Standard deviations are in parentheses. The final-game statistics include 13 overtimes (or 26 observations), all of which lasted less than the full 15 minutes allowed. Thus, the differences do not exactly represent second half statistics.

#### Table A.2.
Average game statistics in key categories for 2005 regular season games (N=256)

Percent of games with one team having indicated advantage in yards Mean absolute value of difference (with standard deviation in parentheses)
First-half Full-game
Moderate Control of rushing and passing game
First-half advantage of 25 rushing yards 54.7%
First-half advantage of 25 passing yards 71.1%
Full-game advantage of 50 rushing yards 53.1%
Full-game advantage of 50 passing yards 62.5%
Great Control of rushing and passing game
First-half advantage of 50 rushing yards 29.3%
First-half advantage of 50 passing yards 49.2%
Full-game advantage of 100 rushing yards 20.3%
Full-game advantage of 100 passing yards 33.6%
Mean (standard deviation) of absolute value of differences
Rushing yards 37.0 (30.3) 65.5 (50.1)
Passing yards 58.8 (46.8) 80.3 (58.5)
Penalty yards 19.8 (24.3) 27.3 (21.4)
Turnovers 0.86 (0.80) 1.59 (1.42)
# sacks allowed 1.20 (1.06) 2.08 (1.70)

### References

Associated Press (2005). Rams Rally to Down Texans in Overtime. <http://www.tsn.ca/nfl/teams/news_story/?ID=144796&hubname=nfl-rams>, accessed August 28, 2006.

Berri, D. (2007). Back to back evaluations on the gridiron. In Statistical Thinking in Sports. Albert, J., and Konig, R.H. eds. CRC Press, Ann Arbor, MI. pp. 235-56.

Berri, D., Schmidt, M., and Brook, S. (2006). The Wages of Wins. Stanford University Press, Stanford, MI.

Boulier, B. L., and Stekler, H.O. (2003). Predicting the outcomes of National Football League games International Journal of Forecasting, 19, 257−70.

Garber, G. (2005a). Turnovers, early deficits lead to losses. <http://sports.espn.go.com/nfl/news/story?id=2241121>, 2005a, accessed December 2, 2005.

Garber, G. (2005b). Penalties hurt but aren’t indicator of failure. <http://sports.espn.go.com/nfl/news/story?id=2241159>, 2005b, accessed December 2, 2005.

Hadley, L., Poitras, M., Ruggiero, J., and Knowles, S. (2000). Performance Evaluation of National Football League Teams. Managerial and Decision Economics, 2000, 21, 63-70.

Kreider, B. (2006). To Punt or Not to Punt. The UMAP Journal, 17, 353-63.

Romer, D. (2006). Do Firms Maximize? Evidence from Professional Football. The Journal of Political Economy, 114, 340-65.

Song, C, Boulier, B. L., and Stekler, H. O. (2007). The comparative accuracy of judgmental and model forecasts of American football games. International Journal of Forecasting, 23, 405–13.

Stroud, R. (2003). Keys to Victory, St. Petersburg Times, January 26, 2003, <http://www.sptimes.com/2003/01/26/Bucs/Keys_to_victory.2.shtml>, accessed August 25, 2006.

Terry, N. (2007). Investing in NFL Prospects: Factors Influencing Team Winning Percentage. International Advances in Economic Research 13, 117.

### Corresponding Author

**Jeremy Arkes**
Associate Professor of Economics
Graduate School of Business and Public Policy
Naval Postgraduate School
555 Dyer Rd.
Monterey, CA 93943
<arkes@nps.edu>
831-656-2646

### Author Biography

Dr. Jeremy Arkes is an Associate Professor of Economics in the Graduate School of Business and Public Policy at the Naval Postgraduate School.

2013-11-25T16:35:43-06:00February 2nd, 2011|Contemporary Sports Issues, Sports Coaching, Sports Management, Sports Studies and Sports Psychology|Comments Off on Is Controlling the Rushing or Passing Game the Key to NFL Victories?

An Examination of Idaho High School Football Coaches’ General Understanding of Concussion

### Abstract

While the underreporting of concussions to high school football players has been previously documented through an investigation of the general understanding of football players, no studies to date have looked at high school football coaches’ general understanding of concussion. This study was conducted in 2006 with a dual purpose of examining the Idaho high school football coaches’ general understanding of concussion and determining whether or not those coaches were consistent with experts’ recommendations in concussion management, including the determination of the appropriate time for return to play. Questionnaires were sent to all Idaho high school head football coaches (n=128) of which 60% (n=77) responded. Data showed the consistency, or lack thereof, of concussion management and return to play, relative to published expert guidelines. Upon analysis it was clear that these coaches’ practices were not consistent with expert recommendations regarding identifying and managing concussion. Many coaches were unfamiliar with the signs and symptoms of concussion, and were especially naïve when it came to identifying instances of mild concussion, including “bell ringers” and “dings”. There was also a lack of awareness about objective tools related to return-to-play decision making. Coaches who had access to athletic trainers managed concussion more consistently. Across all levels, but especially in smaller schools, there was a lack of concussion education afforded to coaches.

**Keywords:** concussion, coaches, high school, football, education

### Introduction

An estimated 300,000 sport-related concussions occur annually in the United States, with high school football players suffering more than 64,000 of those injuries (4, 12, 29). These are the known cases. Thousands more are believed to go unreported (5,16, 29). A concussion is defined as, “any transient neurological dysfunction resulting from a biomechanical force that may of may not result in a loss of consciousness” (8, p. 228). Unlike a cut, a scrape, or a broken leg, concussive injuries are rarely visually obvious. What makes concussive injuries even more complicated is the fact that concussion is a functional injury, not a structural one, meaning it will affect neurocognitive performance but not necessarily show up on MRI or CT scans (5,6,31). This could contribute to the lack of concussion diagnosis or to the belief that concussion does not necessitate conservative treatment if structural damage is not found. In 1990, Dr. M. Goldstein (9) referred to concussion as “a silent epidemic” (p. 327). Unfortunately, nearly two decades later, Goldstein’s warning still sends shockwaves, as young athletes die from sport-induced concussions (1,13,25). Leading experts agree that high school athletes have a significantly greater risk of sustaining a concussion, and that those concussions take longer to heal when compared with concussions sustained by college-aged athletes (6,7). There are many potential reasons for this, but most researchers agree that the younger brain is more vulnerable because it is not fully developed (11,17). Furthermore, many concussions sustained by younger athletes go unreported because youth sport coaches, leaders, parents and even athletes themselves do not fully understand what concussion is or that it has occurred (6,16). Experts agree, even so-called “bell ringers” and “dings” require medical attention and should be considered concussive injuries (17,31). When such momentary states of disorientation or dizziness are ignored, an additional threat is posed in the form of Second Impact Syndrome, or SIS (1,13,22). SIS may occur when an athlete sustains a second concussion before the symptoms of the first have healed (1). Though rare, SIS is characterized by rapid swelling of the brain and may be fatal (2). SIS is most often associated with adolescent athletes, perhaps because of the sensitivity of their developing brains, and because the seriousness of the first concussion is often overlooked (1,5,13,22,28).

While the national spotlight illuminates instances of deaths that occur from sport-related concussion, there still remains the need to educate sport leaders on ways to protect the athletes who compete (21). The Centers for Disease Control and Prevention (3) offer a free toolkit, Heads Up: Concussion in High School Sports that is available to coaches at no charge. In addition, the National Athletic Trainers’ Association (NATA) and its Appropriate Medical Care for Secondary School-Aged Athletes Task Force (AMCSSAA) have made several recommendations (11). Among them are that every high school in the United States develop and implement a comprehensive athletic health care administrative system. Athletic trainers and physicians are critical components of that system (11,16).

Recognizing a lack of athletic trainers in Idaho’s secondary school setting and especially in the rural school environment, a study was conducted in 2006 with the dual purpose of examining the Idaho high school football coaches’ general understanding of concussion, and determining whether or not those coaches were consistent with experts’ recommendations when it came to managing concussion and determining the appropriate time for return to play following concussion. The findings make clearer the need for proper concussion management in high schools, including the need for athletic trainers and continuing education for coaches. Understanding the characteristics of concussion and recognizing the unavailability of athletic trainers, the following research questions guided this investigation:

1. Who was the person most often called upon to identify and manage concussive injury in Idaho’s high school football programs?
2. What is the Idaho high school football coaches’ general understanding of current research on concussion characteristics, evaluation and management?
3. Relative to published expert recommendations, how consistently did Idaho high school football coaches determine when it was safe to return concussed athletes to play?
4. What, if any, continuing education opportunities have been made available to Idaho high school football coaches in the area of concussion management?

### Methods

#### Participants

The participants consisted of 128 Idaho high schools fielding a high school football program. All head football coaches were invited to participate in the study (N=128) via postcards and e-mails, with contact information obtained through the directory of the Idaho High School Activities Association (IHSAA).

#### Instrumentation

This study involved the use of two instruments. The primary instrument was a questionnaire entitled *Profiles and Perceptions of Idaho High School Football Coaches*. This instrument was developed by the researchers to address the research questions, and employed a forced choice response format, supplemented by two open-ended questions. Once drafted, the questionnaire was subjected to expert review with two of the nation’s leading experts on concussion research and six athletic trainers from the Idaho Athletic Trainers’ Association.

The secondary instrument was *The Concussion Management and Return to Play Protocol*. This instrument employed a semi-structure interview protocol and focused on research questions two and three. Like the questionnaire, it was subjected to expert review as described above. The interview protocol was engaged in person with a small, purposive sample of high school football coaches (n=10). The interview questions were phrased to solicit responses that explained the coaches’ behaviors when it came to managing concussion and determining when it was safe to return an athlete to play.

#### Procedures

Institutional review board approval was obtained from Idaho State University before the study began. In mid-September of 2005, all Idaho head high school football coaches were invited to participate via a mailed postcard. The postcard summarized the study purpose and alerted the coaches that a survey packet would arrive the following week. At the same time, Idaho high school principals and athletic directors were informed about the study via an e-mail blast. Administrators were asked to encourage their coaches to participate. The following week the survey packets were mailed. The packets included an introductory letter, a copy of the primary instrument, and a postage-paid, self-addressed return envelope. Coaches were instructed to complete the questionnaire within a two-week time period. The following week, an email reminder was sent to both the coaches and athletic directors. Informed consent was implied upon completion and return of the questionnaire.

Interviews were conducted approximately 6 weeks after the return of the questionnaires. This time frame was chosen because it coincided with the state high school football playoffs and there was good accessibility to a purposive sample of coaches. The interviews were audiotaped and lasted between 10 to 45 minutes. Recorded interviews were transcribed verbatim and interviewees were sent the transcripts with a request to check for response accuracy. Because of convenience, electronic mail transmission was the preferred method for these communications. Coaches were encouraged to make necessary corrections and/or add additional comments. To ensure confidentiality, final verbatim transcripts were coded, and referenced in the study by those codes.

#### Data Analysis

For the primary instrument, data were analyzed using basic descriptive statistics. The data were also stratified according to athletic classification level (i.e., school size). Narrative data from the two open-ended questions, “In the space below, please describe any other signs or symptoms that you would expect to be a sign or symptom of concussion that are not listed above” and “Please use the space provided below to make comments/suggestions that could benefit you as a coach in recognizing the signs and symptoms of head injuries in sports” were reviewed and read noting common themes.

As Yin (33) pointed out, it is necessary to go beyond the simple collection of descriptive data and begin the complex procedure of analyzing behavioral characteristics. Therefore, it was deemed important to also consider the behaviors that guided the coaches’ decision-making processes. When reviewing the interview transcripts, processes of open and axial coding were used to help with pattern analysis (27). Open coding was the first step toward distinguishing “properties” and “dimensions” in the data (27, p. 102). Themes and subthemes emerged that helped to explain the coaches’ patterns of behavior. Special attention was directed to repeated words and phrases, and to the chronological behaviors of the coaches. We first identified these themes and subthemes and later their presence in the data was confirmed by a data analysis focus group consisting of athletic trainers from the Idaho Athletic Trainers’ Association. Focus group members were instructed to separate narrative data into their own major themes and subthemes. The focus group’s thematic analyses were then compared to the thematic analysis derived by the researchers. Finally, through discussion between the researchers and focus group members, the agreed-upon thematic constructs were narrowed and confirmed (see Table 1).

### Results

Study findings are reported first regarding respondent/interviewee demographics, then by questionnaire areas of inquiry. Specifically these areas of inquiry include: person(s) responsible for concussion identification and management, coaches’ understanding of concussion identification and management, return to play decision-making, coaches’ continuing education relative to concussion identification and management, and findings reviewed relative to school size.

#### Demographics

Of the 128 coaches invited to participate in the study, 77 responded, resulting in a 60.1% response rate. The responses represented all five Idaho high school athletic classification levels. All participating coaches confirmed they were the head varsity football coach at their school. Descriptive data related to participant demographics appear in Table 2. Of the responding coaches, 93.3% (n=70) stated they had taken a basic or advanced first aid course through the American Red Cross (ARC) or the American Heart Association (AHA), and 94.7% (n=71) stated they had taken a CPR course through one of the same organizations. Nearly 88% of the coaches (n=65) also mentioned they had received formal training in sports injury prevention at some time in their past. While 89% (n=66) of coaches could identify formalized educational training in sport-specific issues (such as tackling), only 42% (n=31) stated they had also received formal training in football equipment fitting (see Table 2).

#### Person(s) Responsible for Concussion Identification and Management

To better understand who identifies and manages concussion in Idaho high school football programs, the questionnaire asked the coaches to clarify the person(s) primarily responsible for evaluating sports related head injuries including concussion. Only 35.9% (n=23) acknowledged having an athletic trainer at their disposal regularly for practices and games. Coaches were asked, “When an athlete on your team sustains a head injury or suspected concussion, what is the title of the person who is most often called upon to evaluate the injury?” Understanding that some teams might have medical personnel on hand for game settings but not for practices, coaches were asked to clarify any differences that might exist between practice and game situations. Figure 1 depicts the summary of the coaches’ responses, and reveals the distribution of responsibility when it comes to evaluation of concussion (see Figure 1).

To better understand return to play practices, coaches were also asked, “When an athlete on your team sustains a head injury or suspected concussion, what is the title of the person who is most often called upon to determine when it is safe to return the athlete to play?” Again, responses were specific to practice and game situations. Figure 2 displays these responses, and shows the distribution of responsibility when it comes to determining return to play (see Figure 2).

#### Coaches’ General Understanding of Concussion Identification and Management

Despite the fact that an overwhelming majority of coaches had previously taken first aid or sports injury management courses, most Idaho high school football coaches felt they were unprepared to manage concussion inherent in football. 76.7% (n=56) of participants stated they did not feel they had been adequately trained in this area. Participants were also asked whether or not the risk of concussion in the sport of football concerned them. Overwhelmingly, 94.2% (n=65) of coaches said the risk of concussion in football did concern them.

Coaches acknowledged their job duties extend beyond schematics. 86.3% (n=63) of coaches felt they had a responsibility to be able to recognize the signs and symptoms of concussion and to know how to tell when it is safe to return an athlete to play. However, when participants were asked to identify what they felt those signs and symptoms of concussion were given a list, there seemed to be some confusion. While common signs and symptoms such as headache and disorientation were widely recognized, the majority of coaches did not understand that less-common symptoms, such as difficulty breathing and insomnia, are indicative of concussion, as well. Only 32% (n=24) of participants felt difficulty breathing could be associated with concussion, and 29% (n=22) understood insomnia to be connected to concussion. Other notable signs and symptoms of concussion were also mistaken, including sensitivity to noise (47%, n=35) and sensitivity to light (69%, n=52). Table 3 displays coaches’ responses when asked to identify whether or not a certain sign or symptom could be indicative of concussion. Experts have agreed that all of these signs and symptoms are consistent with concussion (11,17). It was important to note that 97.3% (n=73) of the participants understood that a concussion is not always accompanied by a loss of consciousness. These data may help to dispel the myth that concussion is only associated with a loss of consciousness (see Table 3).

Interview data were grouped according to observations regarding (a) physical signs and symptoms, (b) mental status, and (c) kinesthetic awareness. When asked, “How do you know when a concussion is sustained? Describe the first thing you look for,” nearly all of the coaches said the athlete’s eyes, specifically, “the pupils of the eye” (C7, C10) were the primary focal point. C2’s methods were more unique. Replying that he had been “trained real good” in a “five-minute training”, C2 described his process:

> The only way I’ve been taught is to look at his eyes… to have him shut his eyes and stay real still and if he opens his eyes and his pupils dilate, then he probably doesn’t have a head injury.

Some coaches did not seem to understand the potential seriousness of those concussions that do not result in a loss of consciousness, especially mild (Grade 1) concussions. “Bell ringers” were often not identified as concussions. Participants were asked to respond to a scenario and decide whether or not they felt a player who was “hit hard, feels dazed and confused for just a few minutes (sometimes referred to as ‘getting his bell rung’), but who is able to walk back to the huddle on his own” had suffered a concussion. 57.6% (n=38) felt that the player had sustained a concussion while 42.4% (n=28) felt that the player had not sustained a concussion. Seven participants either did not answer the question or commented that they were unsure. Concussion researchers agree that getting one’s bell rung is characteristic of mild concussion. However, it is often dismissed (11,17). At least one coach acknowledged his uncertainty:

> In my opinion and experience as a player and a coach, every player experiences at least one of the symptoms … at least once a game and practice. Where to draw the line between a real head injury and getting your bell rung is tough. (C15)

#### Return to Play Decision-making

As stated, many coaches acknowledged a duty to determine when it was safe to allow a concussed athlete to return to activity. An additional set of questions in the questionnaire sought to detect whether or not Idaho high school football coaches felt the seriousness of a concussion, formerly referred to as a grade, played a role in allowing an athlete to continue play. When asked if a player who had sustained a Grade 1, or mild, concussion should be immediately removed from a game or practice, 57.3% (n=43) said yes. 34.7% (n=26) said no, and 8.0% (n=6) stated that they did not know. When asked if a player who had sustained a Grade 2, or moderate, concussion should be immediately removed from the game or practice, 88.0% (n=66) said yes, 6.7% (n=5) said no, and 5.3% (n=4) said they did not know. When asked if a player who had sustained a Grade 3, or severe, concussion should be immediately removed from the game or practice, 94.6% (n=70) of coaches said he should, 4.1% (n=3) said he should not, and 1.4% (n=1) said he did not know. Clearly, these coaches were aware that as concussion grade increased, play/participation should be discontinued.

The coaches’ methods for determining return to play were further explored through the interviews. Responses were grouped according to those that typically make referrals to physicians and/or athletic trainers, and those that do not. Coaches who stated they had athletic trainers at their disposal said they are not involved in the decision-making process. When asked, “How do you decide when it is safe to allow an athlete with a concussion to go back into the game?” C3 abruptly responded, “We don’t decide. That’s decided by the team doctor and the trainer.”

Other coaches said they sometimes do not make referrals. C8 said he was hesitant to allow his athletes to be evaluated by physicians. He did not agree that bell ringers were consistent with concussion, nor did he agree that there was an added risk of playing through such an injury. C8 suggested doctors were too quick to diagnose a concussion and remove an athlete from play, thereby making his coaching job more difficult:

> I just think doctors are sometimes being so leery that if there’s any question in their mind then they say the kid’s got a concussion and shouldn’t play. They just don’t want to risk getting sued. There’s got to be a happy medium there.

Influencers were apparent when it came to return to play decision-making. While the majority of coaches said they would always keep the safety of the athlete as the primary focus, and that they would “err on the side of caution” and “sit players out” (C17), several coaches acknowledged the pressure to win or play, or pressure from parents, school administrators, and the athletes themselves, had, at some point, impacted their decisions. C8 said as a coach, his job was “to get the best players on the field” and that sitting players out for something as simple as a bell ringer “can get to the point where we side on the side of over-caution – to the point where it can get a little ridiculous.” C6 said it was “a little hard” to hold one of his better athletes out, “especially when the community recognizes how vital that player is to the team’s success.” C4 suggested he also might follow different rules for different kids. He told me, “When you’re a senior, you know how that works – you’ve been around athletics… you get a senior and he really wants to play.”

Participating coaches were largely unfamiliar with evidence-based concussion assessment tools. These were identified as symptom scale checklists, the Sideline Assessment of Concussion, and computerized neurocognitive assessments, such as ImPACT, HeadMinder and CogState. 56.8% (n=42) of coaches stated they never use concussion assessment tools. Of those who indicated they were familiar with the tools, 25.7% (n=19) said they were familiar with concussion symptom scale checklists, 9.5% (n=7) said they were familiar with the Sideline Assessment of Concussion, or SAC, and 6.8% (n=5) said they knew about computerized neurocognitive testing programs. No coaches were familiar with the Balance Error Scoring System. When asked how frequently they used these evidence-based assessment tools, only 18.9% (n=14) of those coaches who were familiar with one or more of the tools stated that they use them every time a suspected concussion was sustained, and 40% (n=12) said they learned about them from an athletic trainer. Of the eight coaches interviewed, only one described a research-based procedure for determining whether or not an athlete could return to play. This coach was at a 5A school with two athletic trainers. The athletic trainers at this school utilized the ImPACT concussion assessment tool:

> During the week if it’s not a game we hold the player out until they have taken a post concussion test and we evaluate their scores from when they were healthy to after the concussion has happened. Once they score equivalent to where they were prior to a concussion and they feel good and they’re cleared by the trainer or the doctor then they’re able to return. (C9)

#### Continuing Education

Participants were asked whether or not the school they coached at had provided them with training opportunities aimed at concussion and other sports injury management. 60% (n=45) stated that their school had not offered any additional training, while 40% (n=30) stated their school had. The majority stated they would be eager to learn more about the topic. 97.83% (n=72) said they would be more likely to use an evidence-based concussion assessment tool if it were made available to them at no cost. And, when asked whether or not they would be likely to participate in an educational program to teach them how to be more prepared to handle concussion injuries, 98.6% (n=71) said they would be.

#### Data Stratification by School Size
After initial analysis, the data were stratified to see whether or not trends existed relative to school size. As expected, there was a marked difference in the presence of athletic trainers based on school size. At Idaho’s largest (5A) high schools (more than 1280 students), an athletic trainer worked regularly with all football teams. By comparison, only 7% of Idaho’s smallest (1A) schools (less than 159 students) coaches stated that they had an athletic trainer. Table 4 displays these data and shows the presence of athletic trainers at the various athletic classifications (see Table 4).

The availability of athletic trainers at Idaho’s larger schools relieved coaches of the primary responsibility of concussion identification and management. C15 said, “I would rather my trainer do that and I just coach football.” C20 commented, “Having an athletic trainer has been a big relief on me on making decisions on head injuries.” Without athletic trainers, coaches inherited the responsibility. At the 1A level, 70.6% of coaches (n=12) said they were the ones responsible for identifying concussive injuries when they occur at practice. At the 2A level, 46.7% of coaches (n=7) assumed this responsibility, and 73.7% of 3A coaches (n=14) had the responsibility. By comparison, none of the 5A coaches who participated in this study acknowledged having responsibility for concussion identification and management. During game situations, coaches at the smaller schools acknowledged having more medical assistance to rely on. Physicians, nurses and EMTs were often available during games, even at the smaller schools. Because of their presence, just over 35% of 1A coaches (n=6) said they were the ones responsible for identifying concussive injuries in a game setting. Nearly 27% of 2A coaches (n=4) and 33% of 3A coaches (n=3) had this responsibility. All 4A and 5A coaches suggested the responsibility of managing concussion-related injuries was charged to either athletic trainers and/or team physicians during game situations. Table 5 displays these data and the differences between school classification in terms of concussion identification and management (see Table 5).

In Idaho, it was apparent that the smaller the school, the more likely the coach was the one who made return to play decisions. When asked who the primary person responsible for determining the appropriate time for an athlete who had sustained a concussion to return to play during practice situations was, 64.8% of 1A coaches (n=11) said they were. Again, no coaches at 5A schools had this responsibility. In game settings, the trend continued. Just over 47% of 1A coaches (n=8) reported being the person primarily responsible for determining return to play on game day, while no 5A coaches acknowledged this responsibility. Table 6 displays the disparities among the various school classification levels regarding determination of return to play (see Table 6).

When presented with the bell ringer scenario, only coaches from Idaho’s largest schools (5A) were consistently recognizing it as such. Table 7 reveals these data (see Table 7).
While beneficial when it came to managing concussion, the presence of athletic trainers did little to make coaches feel more prepared to handle the duty themselves. Coaches at the 4A and 5A levels who were also more consistent in their identification and management of concussion and who had athletic trainers at their disposal, admitted to being most uncomfortable with their ability in this area. Table 8 displays these findings (see Table 8).

Across all athletic classification levels, most coaches felt a compelling need for additional educational training when it came to managing concussion in their football programs. Not only did 1A schools not have appropriate or adequate medical supervision onsite at practices and games, it was also apparent that the football coaches at Idaho’s smallest high schools were not being provided with educational programs aimed at concussion and other sports injury management when compared to coaches at Idaho’s largest schools. Only 18% of 1A coaches stated that their school had provided them with training opportunities while 63% of 5A coaches were provided with educational outreach. Table 9 shows the data (see Table 9).

### Discussion

Since this study was limited to Idaho high school football coaches, its results may not be generalized to other states, however, findings may provide a snapshot that could provoke further inquiry into coaches’ qualifications and expertise in the area of concussion identification and management. This is consistent with the findings of McCrea et al., (16) who suggested continuing education of coaches is warranted. When it comes to concussion recognition, there is little room for error. A concussion disrupts the brain’s metabolism and the only thing that appears to help it heal is rest (17,30). This study brought to light the compelling need to do more when it comes to training coaches to adequately prepare for and manage concussive injuries. The findings spotlight the need for better concussion education programs for Idaho’s secondary sport coaches, especially those who coach at small schools with limited access to an athletic trainer or other medical personnel support. The findings also highlight the need for replicable studies in other states to determine educational needs of coaches in those areas.

The findings are discussed relative to: the persons responsible for concussion identification and management—accessibility of athletic trainers, understanding of concussion, return to play decision making and willingness of coaches to refer athletes, and continuing education. Continuing education implications derived from these findings are discussed in detail, specific to evaluation of concussion signs and symptoms, cognitive stability testing, bell ringer recognition and the ongoing need for additional first aid and concussion training.

#### Persons Responsible—Accessibility of Athletic Trainers

Consistently, coaches were charged with the responsibility of initial concussion identification and management. Some coaches also acknowledged having the sole responsibility of deciding when to allow a concussed athlete to return to play. National recommendations point to the need for athletic trainers to do this job (11,16,17). Despite these recommendations, athletic trainers were accessible to coaches at only 36% of Idaho’s high schools. This was below the 2008 national average of 42% (20). The scarcity of athletic trainers in Idaho’s smallest schools was expected. The best-case scenario would be for sport administrators to require onsite athletic trainers at sport practices and games that have significant catastrophic risks such as football. This study indicated concussion was managed more consistently and effectively at schools with athletic trainers. All 5A (large schools) coaches (n=7) who responded to this survey indicated that they had an athletic trainer who worked regularly with their football teams; and all of these coaches correctly identified a scenario involving a bell ringer as concussion and said their standard practice would be to withhold that athlete from play.

#### Understanding of Concussion, Return to Play Decision-making and Willingness of Coaches to Refer Athletes

Coaches should be informed that in cases where concussion is suspected, their primary role is to ensure medical referral for the athlete (11,16). The coaches in this study were inconsistent with regard to making referrals. While most stated they would always refer athletes with a recognized concussion to an athletic trainer or physician, some said they would rather manage the injury themselves. C8 and others seemed to lack an appreciation of the catastrophic risks associated with concussive injuries. In the past, coaches have been held liable for failing to provide adequate assistance to injured athlete. In numerous court cases, including Mogabgah v. Orleans Parish School Board (19), Stineman v. Fontbonne College (26), and Searles v. Trustees of St. Joseph’s College (23), coaches have been held accountable for their failure to recognize the potential severity of a sports-related injury.

#### Continuing Education and the Evaluation of Concussion Signs and Symptoms

Although the majority of the coaches had received basic first aid and CPR training or had identified taking a formal course in sports injury prevention, this training did not imply an understanding of concussion identification and management. Many of the coaches recognized the most common signs and symptoms of concussion, but they failed to recognize many of the more subtle signs and symptoms. While loss of consciousness, headache, disorientation, and memory loss were clearly connected with concussion, more subtle effects, like sensitivity to noise, and insomnia, were not. Concussion is an “individualized, complex injury, and … no particular symptom can provide definitive guidance for every patient and clinical situation” (11, p. 6). Therefore, even though athletes may demonstrate different signs and symptoms, it is important to consider all of the options (11). Even then, symptom scores should not be considered solely reliable. As expected, the coaches in this study relied on subjective measures of concussion assessment. However, responses to such questions like, ‘Do you have a headache’ and ‘Are you dizzy’ are not consistent or reliable indices of concussive injury. This is largely because athletes may be reluctant to report their symptoms for fear of not being allowed to play or because they do not think their injury is serious enough to warrant removal from play (16). A quick clearance and return to play based on subjective responses can increase athlete susceptibility for additional injury, including SIS (1,11,28). Conservative management of even mild instances of concussion is important in athletes under the age of 18, because almost all reported cases of SIS are in young athletes (1,11).

#### Cognitive and Stability Testing

While assessing symptoms is always warranted, baseline cognitive and postural-stability testing should also be considered for athletes playing sports with a high risk of concussion. Use of such functional tests can help to identify deficits caused by concussion and help protect players from potential risks involved with returning to play too quickly (11,17). This study’s findings reflect a lack of such assessment. Evaluation of symptoms should be supplemented with detailed questioning and functional tests, both of the brain and body (10,17). Guskiewicz, Ross and Marshall (10) concluded that simple processes, including concentration, working memory, immediate memory recall, and rapid visual processing have been shown to be mildly affected by concussion. Establishing baseline measurements before the season is recommended for comparison purposes (11,17). No coaches in this study said they conducted functional testing. In fact, none were even aware of the Sideline Assessment of Concussion or the Balance Error Scoring System. Both of these functional tools can be administered at little or no cost. Furthermore, only one coach who participated in the study was aware of neurocognitive testing programs such as ImPACT, another functional concussion assessment. He said he was aware of the test because he had heard about it being used with professional players.

#### Recognition of ‘Bell Ringers’ as Concussion

Study findings revealed coaches’ misconceptions that bell ringers or dings are not concussive injuries, and as such do not necessitate removal from play. The findings also demonstrated coaches’ beliefs that the terms bell ringer and ding carry a connotation that diminishes the potential seriousness of the injury (11,16,17). Nearly half of the coaches indicated they would allow the athlete who had his bell rung to continue physical activity. This lack of initial recognition and diagnoses supports the findings of McCrea, et al., (16), and the likelihood of athletes being allowed to continue to play while being symptomatic. Not only is SIS a factor when returning to play too soon, concussions can accumulate and lead to other long-term impairments. According to King (14), lasting verbal and visuospatial impairments have been directly linked to concussion, and athletes with a history of concussion can suffer for a lifetime from emotional changes including a difficulty to control their own anger. King (14) also contended that athletes with a history of concussion can also suffer permanent decreases in libido, sleep impairments, and can have difficulty adapting to social changes. Severe depression can also linger (12).

#### Need for Additional Training

While most state high school athletic associations require first aid and CPR training, those classes typically fall short of relaying information concerning sports-related concussion. Few states require the medical training of coaches to be supplemented to include concussion management. To date only Texas, Washington, Oregon, and Connecticut have made comprehensive training on the subject a mandate. In Texas, S.B. 82, or “Will’s Bill”, was signed into law and took effect in September of 2007. Washington’s “Zackery Lystedt Law” and Oregon’s “Max’s Law” were both passed in 2009. All three laws require youth and high school sport coaches to be trained in concussion management and cognizant of SIS. Washington’s law goes one step further. It requires a licensed health care provider to oversee each concussive injury and determine the appropriate time for the athlete to return to play (34). McCrea et al., (16) demonstrated the value of concussion education. Their study examined the reasons for the purported underreporting of concussions to high school football players. McCrea et al. concluded that players, like the coaches in this study, were not fully aware of what a concussion was. However, when provided with a definition of concussion and a description of injury signs and symptoms, the players more readily recognized the injury and were more likely to admit to sustaining concussion over the course of a football season.

No coaches in this study recalled a systematic, stepwise approach for returning athletes to play. Experts contend concussed athletes should not be allowed to return to play until all of the following conditions are met: (a) there was no loss of consciousness, (b) the athlete suffers from no amnesia, (c) the athlete is asymptomatic at rest, (d) the athlete is asymptomatic following exertion, and (e) the athlete passes all functional tests (11,17,24). The coaches in this study admitted there were other influences that convinced them to return concussed athletes to play prior to the resolution of symptoms. Some, perhaps refusing to accept responsibility or more concerned with winning, de-emphasized the importance of concussion management. Micheli, Glassman, and Klein (18) suggested coaches might feel the management of injury is not their responsibility. This was clearly the case among the Idaho football coaches in this study. In fact, one coach, C29, reiterated that “trainers are here to make the decisions and deal with the injuries, NOT THE HEAD COACHES [sic].” Because of this, coaches may have felt they needed to be less prepared to identify and manage concussion.

The lack of educational opportunities related to concussion identification and management could be the reason why these coaches are unfamiliar with the topic of concussion management. The lack of educational opportunities was most evident in Idaho’s more rural (smallest school) areas. The overwhelming willingness of coaches in this study to attend professional development workshops could be one solution. Coaches who participated in this study clearly stated they would be much more comfortable managing concussion injury if they were adequately trained to do so. When professional development occurs, it is important that knowledgeable and trained professionals teach them. With new information about concussion being discovered every year, educational workshops would be warranted annually. Such educational efforts can and should be extended beyond administrators and coaches. Parents, and even the athletes themselves, can and would benefit from learning about concussion’s subtle signs and symptoms, and the consequences involved with returning to play too soon. Perhaps then, the outside influencers and pressures coaches noted would diminish.

### Conclusions

This study revealed a lack of understanding among Idaho high school football coaches relative to concussion identification and management. Coaches were especially dismissive of instances consistent with mild concussion, or bell ringers, and their catastrophic potential. Coaches purported to address concussion management with subjective approaches that relied on athletes to self-report their symptoms. They were unaware of functional assessments that objectively measured both the brain and body. Coaches acknowledged that outside pressures contribute to their decisions on when to allow concussed athletes to resume physical activity. Their lack of understanding may be attributed, in part, to the fact that there are few athletic trainers in Idaho’s secondary schools, and there are few or no educational workshops provided to coaches on concussion management.

### Applications in Sport

While this study was limited to Idaho high school football coaches, its findings may be generalized to other coaching populations. All contact sport athletes are susceptible to concussive injury. In the absence of athletic trainers or other health care professionals on the sport sideline, it is imperative that coaches be able to recognize concussive injuries and manage them according to current published guidelines.

### Figures and Tables

#### Figure 1
Identifying Concussion Incidence: Idaho High School Football
![Identifying Concussion Incidence: Idaho High School Football](/files/volume-14/2/figure-1.jpg “Identifying Concussion Incidence: Idaho High School Football”)

#### Figure 2
Determining Return to Play: Idaho High School Football
![Identifying Concussion Incidence: Idaho High School Football](/files/volume-14/2/figure-2.jpg “Identifying Concussion Incidence: Idaho High School Football”)

#### Table 1. Thematic Constructs
Examples of Raw Data Themes and Subsequent Subthemes and Major Themes

Raw Data Theme Subtheme Theme
Glassy eyes.
Dilated pupils.
Physical Signs & Symptoms Recognition
Whether he’s not all together there.
How cognizant they are of where they’re at.
Mental Status
Whether he’s wobbly. Kinesthetic Awareness
It depends on the kid!
Every player experiences at least one of the symptoms.
I look at the severity of the hit.
Mechanism of Injury & other variables
I get him to a trainer.
We have doctors on our sideline.
Referrals Evaluation
I asked them questions, look in the eyes.
We observe him for awhile.
We just keep him out.
We watch them very carefully.
Watch and Wait
We don’t decide. That’s decided by the team doctor and the trainer.
They have to have a doctor’s release.
It’s gotta be a parent.
We let him sit for awhile.
Usually you go about a week and a half.
We sit them out a week.
Time Away
I think we can go too overboard on it.
We can get to the point where we side on the side of over-caution – to the point where it can get a little ridiculous.
It’s No Big Deal
We want to keep our best players in the game.
A kid that wanted to play in the playoffs.
If the parents say it’s okay, then that at least releases the coach of that (responsibility).
He’s a young kid; He’s not a senior.
Pressure to Win (Play) Influencers
I would put the safety above putting him in the game.
It’s too dangerous.
The kid’s health is more important than any game that we play.
Safety Comes First
We need an athletic trainer.
We probably could have more – at least EMT types around for practice.
Resources Needs
I would love the opportunity to learn more.
You have to know what’s happening with your players, especially when concussion is involved.
Education
Helmet issues are going to be real paramount.
The teaching of how to tackle is very important.
Equipment & Instruction

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### Corresponding Author

Caroline E. Faure, EdD
Assistant Professor of Sport Science and PE
Idaho State University
STOP 8105
Pocatello, ID 83209
<faurcaro@isu.edu>
208 282-4085

### Author Biographies

#### Caroline Faure, Ed.D., ATC

Caroline Faure, Ed.D., ATC is an Assistant Professor of Sport Science and Physical Education at Idaho State University, where she teaches undergraduate and graduate courses in sports medicine and sports law. Dr. Faure earned the prestigious Kole-McGuffey Award at Idaho State University for her research on concussion management in secondary schools.

#### Cynthia Lee A. Pemberton, Ed.D

Cynthia Lee A. Pemberton, Ed.D. serves as the Associate Dean of the Graduate School and Professor of Education/Graduate Faculty at Idaho State University. Dr. Pemberton has published and presented locally, regionally, nationally and internationally on Title IX and gender equity in school sport. Her book, More Than a Game: One Woman’s Fight for Gender Equity in Sport, addresses Title IX from both personal and professional perspectives, through a lived experience pursuing gender equity in sport at a small liberal arts college in Oregon. The book received the Phi Kappa Phi Bookshelf Award in October 2002, and has been positively reviewed in a number of publications (Journal of Legal Aspects of Sport, Women in Sport and Physical Activity Journal, Booklist and Choice).

2016-04-01T09:17:20-05:00January 12th, 2011|Contemporary Sports Issues, Sports Coaching, Sports Exercise Science, Sports Management, Sports Studies and Sports Psychology|Comments Off on An Examination of Idaho High School Football Coaches’ General Understanding of Concussion
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