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Hockey STAR: A Methodology for Assessing the Biomechanical
Performance of Hockey Helmets
BETHANY ROWSON,STEVEN ROWSON, and STEFAN M. DUMA
Department of Biomedical Engineering and Mechanics, Virginia Tech, 313 Kelly Hall, 325 Stanger Street, Blacksburg,
VA 24061, USA
(Received 21 December 2014; accepted 10 February 2015; published online 30 March 2015)
Associate Editor Peter E. McHugh oversaw the review of this article.
Abstract—Optimizing the protective capabilities of helmets is
one of several methods of reducing brain injury risk in sports.
This paper presents the experimental and analytical devel-
opment of a hockey helmet evaluation methodology. The
Summation of Tests for the Analysis of Risk (STAR)
formula combines head impact exposure with brain injury
probability over the broad range of 227 head impacts that a
hockey player is likely to experience during one season. These
impact exposure data are mapped to laboratory testing
parameters using a series of 12 impact conditions comprised
of three energy levels and four head impact locations, which
include centric and non-centric directions of force. Injury risk
is determined using a multivariate injury risk function that
incorporates both linear and rotational head acceleration
measurements. All testing parameters are presented along
with exemplar helmet test data. The Hockey STAR method-
ology provides a scientific framework for manufacturers to
optimize hockey helmet design for injury risk reduction, as
well as providing consumers with a meaningful metric to
assess the relative performance of hockey helmets.
Keywords—Concussion, Linear, Rotational, Acceleration,
Risk, Impact.
INTRODUCTION
Football is often the focal point of concussion re-
search because of its popularity and the high incidence
of concussions associated with it; however, the rate of
concussion is higher in ice hockey.
8,30
Moreover, it is
the most common injury for women’s collegiate ice
hockey, and the second most common for men’s.
1,2
The current helmet safety standards for hockey hel-
mets have changed little over the past 50 years when
they were created to reduce the incidence of serious
head injuries and deaths.
40
The first hockey helmet
standards were instituted by the Swedish Ice Hockey
Association (SIA) in 1962. Shortly thereafter, US and
Canadian organizations developed similar standards.
Today, most hockey helmets bear stickers representing
certification by 3 different organizations: the Hockey
Equipment Certification Council (HECC), the Cana-
dian Standards Association (CSA), and the Interna-
tional Organization for Standardization (ISO)
represented by a CE marking. These standards all have
similar pass/fail criteria that were implemented to re-
duce the risk of catastrophic head injuries.
Recently, concussi on has gained national attention
and become a research priority as the incidence of in-
jury rises and concerns about the long-term effects of
repeated mild injury are brought to light.
10,21,30,37,41,42
Many strategies have been employed in attempts to
decrease the incidence of concussion, such as rule
changes, education programs, legislation, and im-
provements in protective equipment.
5,36
Examples of
rule changes designed to reduce injuries include fair-
play and body-checking rules, which are implemented
in some ice hockey leagues. Studies have shown a re-
duction in the incidence of more serious injuries in-
cluding concussions when these rules are in place.
34,46
Education programs such as the Centers for Disease
Control and Prevention’s ‘‘HEADS UP’’ on concus-
sion initiative and the Hockey Concussion Education
Project (HCEP) were developed to help educate
coaches, players, and their parents on preventing,
identifying, and responding appropriately to concus-
sions.
9,12,14–18,32
Although most states in the US now
have concussion laws in place, it is unclear at this time
how effective they are.
5
These laws usually focus on
Address correspondence to Bethany Rowson, Department of
Biomedical Engineering and Mechanics, Virginia Tech, 313 Kelly
Hall, 325 Stanger Street, Blacksburg, VA 24061, USA. Electronic
mail: bethmv2@vt.edu
Annals of Biomedical Engin eering, Vol. 43, No. 10, October 2015 ( 2015) pp. 2429–2443
DOI: 10.1007/s10439-015-1278-7
0090-6964/15/1000-2429/0 2015 The Author(s). This article is published with open access at Springerlink.com
2429
education, removal from play, and approval required
for return to play.
There is currently no objective information avail-
able to consumers on which hockey helmets provide
better protection against serious, as well as milder,
head injuries like concussions. Prior to the develop-
ment of the Football Summation of Tests for the
Analysis of Risk (STAR) Evaluation System in 2011,
this information was not available for football helmets
either.
48
Since the first set of helmet ratings using this
evaluation system was released, the number of helmets
receiving the highest rating possible of 5 stars has risen
from just one to a total of 12 helmets in 2014.
51
In the
past, there were no conclusive studies on the effec-
tiveness of different helmet types in reducing concus-
sions on the field.
5,36
However, recent research has
demonstrated that the risk of concussion on the field is
lowered with a helmet that better reduces head accel-
erations upon impact.
53
Football STAR was developed based on two fun-
damental principles. The first is that the tests per-
formed are weighted based on how frequently a similar
impact would occur on the field during one season of
play.
48
The second is that helmets that decrease ac-
celeration decrease the risk of concussion. There are a
number of concussion risk functions that have been
developed to define probability of concussion as a
function of linear he ad acceleration, angular head ac-
celeration, or both.
19,31,44,48,49,52,58
Debates over the
mechanisms of brain injury and the ability of metrics
that include linear or angular head acceleration to
predict injury risk are long-standing.
27,31
Numerous
studies have attempted to differentiate the effects of
linear and angular head accelerations on brain injury
and determine if one or the other is more likely to
result in concussion.
22,43,55
Current metrics for head
injury safety standards use only linear head accel-
eration, and are based on human cadaver skull fracture
and animal data.
20,24,56
However, more recently it has
been shown that the combination of linear and angular
head acceleration is a good predictor of concussion,
and that helmets reduce both linear and angular ac-
celeration.
28,49,58
Given the fact that all head impacts
have both linear and rotational acceleration compo-
nents, future helmet evaluation should quantify injury
risk using both linear and rotational head kinematics.
The objective of this study is to describe the devel-
opment of a new evaluation system for hockey helmets.
The evaluation system will provide a quantitative
measure of the ability of individual helmets to red uce
the risk of concussio n. Building on the framework of
Football STAR, Hockey STAR will define laborat ory
test conditions weighted to represen t how often hockey
players experience similar impacts.
METHODS
Hockey STAR Equation
The Football STAR equation was developed to
identify differences in the ability of football helmets to
reduce concussion risk.
48
The equation repres ents the
predicted concussion incidence for a football player
over one season. This predictive value is determined
from laboratory tests with a helmeted headform to
simulate he ad impacts at different locations and energy
levels. Each laboratory condition is associated with the
number of times that type of impact would occur over
one season (exposure), and the probability that a
concussion would occur due to the resultant head ac-
celeration during each test (risk). In the Football
STAR equation (Eq. 1), L represents the impact lo-
cation of front, side, top, or back; H represents the
drop height of 60, 48, 36, 24, or 12 in; E represents the
exposure as a function of location and drop height,
and R represents risk of concussion as a function of
linear acceleration (a).
Football STAR ¼
X
4
L¼1
X
5
H¼1
EL; HðÞRaðÞ ð1Þ
A similar equation is presented for Hockey STAR,
with several important modifications (Eq. 2). The risk
function now incorporates both linear and rotational
acceleration since all head impacts result in both, and
the combination has been shown to be predictive of
concussion.
49,58
The exposure component was mod-
ified to reflect data collected from hockey players that
consisted of both linear and rotational acceleration. In
the Hockey STAR equation, L represents the head
impact locations of front, side, top, or back; h repre-
sents different impact energy levels defined by the angle
of the pendulum arm used to impac t the head; E rep-
resents exposure, or the number of times per season a
player is expected to experience an impact similar to a
particular testing condition as a function of location
and impact energy; and R is the risk of concussion as a
function of linear (a) and angular (a) head accel-
eration. The exp osure and risk components of the
equation are described in later sections.
Hockey STAR ¼
X
4
L¼1
X
3
h¼1
EL; hðÞRða; aÞ: ð2Þ
The laboratory testing matrix includes 3 impact
energy levels and 4 impact locations, for a total of 12
testing conditions per helmet. In practice, two helmets
of every model will be purchased. Each of these hel-
mets will be tested in the 12 conditions twice for a total
of 48 tests per helmet model. The two acceleration
values for each helmet’s test conditions will then be
ROWSON et al.2430
averaged for each impact condition prior to using the
risk function to determine probability of concussion.
Concussion risks will then be multiplied by the expo-
sure values for each impact conditi on to determine
incidence values. All incidence values are then aggre-
gated to calculate a Hockey STAR value for each
helmet. The Hockey STAR values for each helmet will
then be averaged to determine a helmet model’s overall
Hockey STAR value.
Hockey Head Impact Exposure
Head impact exposure is defined here as the number
of impacts a player experiences over one season of
play. Data from two different studies were utilized to
determine the median number of impacts per season
over a broader population of males, females, and
youth ice hockey players. Wilcox et al. collected data
from both male and female National Collegiate Ath-
letic Association (NCAA) ice hockey teams over three
seasons from 2009 to 2012 using helmet-mounted ac-
celerometer arrays.
57
Using the same instrumentation,
Mihalik et al. collected data from a population of male
Bantam (13–14 years old) and Midget (15–16 years
old) players over 2 years.
39
These accelerometer arrays
have previously been described in detail, but briefly,
each helmet contains six single-axis linear ac-
celerometers that are oriented tangent ially to the head
and integrated into foam inserts which allow the sen-
sors to maintain contact with the head during im-
pact.
25
The median number of head impacts per player
per season experienced by collegiate athletes was 287
for males and 170 for females.
57
The median number of
impacts per player per season for youth athletes was
223.
39
The median values for each population were
averaged to determine an overall exposure of 227 im-
pacts. This value was used to represent the total
number of impacts for one player over one season. The
exposure value was further defined by impact location
and severity as described below.
Data collected with the helmet-mounted ac-
celerometer arrays was used to map on-ice player im-
pact exposure to lab conditions.
7,57
Data from two
male and two female NCAA ice hockey teams as well
as one male and one female high school team were
included. The data were scaled to reduce measurement
error using a relationshi p determined from correlating
resultant head accelerations calculated from the helmet
instrumentation to a reference measurement in an in-
strumented dummy headform during controlled
laboratory impact tests.
3
The helmet data were then stratified by impact lo-
cation. The locations are defined by the azimuth and
elevation of the impact vector and are generalized into
bins representing the front, right, left, back and top of
the head.
23
The front, right, left, and back consist of
impacts with an elevation less than 65, and are divided
equally into 4 bins that are cen tered on the intersection
of the midsagittal and coronal planes, but offset by
45. The remaining impacts greater than 65 in eleva-
tion are grouped as top impacts. The exposure for each
impact location was weighted by how often they occur
in data collected in the literature.
7,25,39,57
The front,
side (left and right combined), and back were ap-
proximately 30% each, with the remaining 10% of
impacts to the top of the head. These values were used
to weight exposure by impact location.
Hockey Helmet Impact Device
The next step in defining exposure was to transform
on-ice player head acceleration data distributions to
impact conditions in the lab. To do this, a series of
impact tests were performed over a range of input
energies using a custom impact pend ulum to map
laboratory-generated head accelerations to those
measured on-ice directly from hockey players. The
impact pendulum system used for these tests, impact
locations evaluated, and methods for the acceleration
transformation are described in detail below.
A pendulum was chosen due to increased repeata-
bility and reproducibility when compared with other
head impact methods.
45
The pendulum arm is com-
posed of 10.16 9 5.08 cm rectangular aluminum tub-
ing with a 16.3 kg impacting mass at its end. The
length of the pendulum arm from the center of its pivot
point to the center of its impacting mass is 190.5 cm.
The pendulum arm has a total mass of 36.3 kg and a
moment of inertia of 72 kg m
2
. The impacting mass
accounts for 78% of the total moment of inertia. The
nylon impactor face has a diameter of 12.7 cm, which
is flat and rigid in an effort to maximize repeatability
and reproducibility of the tests. Furthermore, a rigid
impacting face was chosen due to rigid surfa ces in
hockey, and to avoid impactor compliancy masking
differences between helmets in comparative testing.
47
The pendulum impactor strikes a medium NOC-
SAE headform, which is mounted on a Hybrid III 50th
percentile neck (Fig. 1). The NOCSAE headform was
used to provide the most realistic fit between helmet
and headform.
11
A custom adaptor plate was used to
mate the NOCSAE headform to the Hybrid III neck
while keeping the relative locations of the occipital
condyle pin and headform center of gravity (CG) as
close as possible to that of the Hybrid III 50th per-
centile male head and neck assembly. Material was
removed from the underside of the headform to opti-
mize the position of the occipital condyle and accom-
modate the neck. The adap tor plate’s mass was equal
to the material removed. Although these dist ances
Hockey STAR Methodology 2431
matched exactly in the anterior-posterior and medial–
lateral directions, the NOCSAE CG was 22 mm su-
perior relative to the Hybrid III CG. The head and
neck assembly are mounted on a sliding mass intended
to simulate the effective the mass of the torso during
impact. This sliding mass is part of a commercially
available linear slide table that is commonly used for
helmet impact testing (Biokinetics, Ottawa, Ontario,
Canada). Contrary to most helmet drop test rigs, this
system allows for linear an d rotational motion to be
generated during impact. To measure the kinematics
resulting from impact, the headform was instrumented
with a 6 degree of freedom sensor package consisting
of 3 accelerometers and 3 angular rate sensors (6DX-
Pro, DTS, Seal Beach, CA).
The front, side, back, an d top of the headform were
chosen to impact in laboratory tests (Fig. 2). In order
to account for a wider array of impact types, two of the
locations wer e centric, or aligned with the CG of the
headform (front and back), and two were non-centric
(side and top). These locations resulted in some im-
pacts with higher rotational components for a given
linear acceleration than others, which was quantified
by the effective radius of rotation at each condition.
Effective radius of rotation was defined as the quotient
of peak linear acceleration and peak rotational accel-
eration. Table 1 specifies the impact locations using
measurement markings provided on the commercially
available linear slide table.
Mapping Exposure Data to Laboratory System
A series of tests were performed to map the on-ice
helmet data to laboratory pendulum impacts. For these
tests, the NOCSAE headform was fitted with a size
medium CCM Vector V08 helmet (Reebok-CCM Hockey,
Inc., Montreal, Canada). The V08 model was chosen be-
cause it was one of the helmet types worn by instrumented
players to generate head impact exposure data.
57
The
linear acceleration and angular rate data were collected at
a sampling rate of 20,000 Hz. Linear acceleration data
were filtered to CFC 1000 Hz according to SAE J211,
while angular rate data were filtered to CFC 155. Angular
acceleration was calculated by differentiating the angular
rate data. All data were then transformed to the CG of the
headform. Three V08 helmets were tested, with each im-
pacted from pendulum arm angles of 20,30,40,50,
60,70,80,and90 at each of the four locations defined
above, resulting in 96 impact tests.
After determining the total impact exposure per
player per season and stratifying the on-ice helmet data
by impact location, the data were transformed to
laboratory impact conditions. To do this , the on-ice data
for each location were reduced to include only impacts
with effective radii of rotation in the range of corre-
sponding laboratory impacts. Within these constraints,
the on-ice head acceleration distributions were related to
impact conditions in the lab. Bivariate empirical cu-
mulative distribution functions (CDF) comprised of
peak linear and peak rotational head accelerations were
computed for on-ice data within each impact location’s
constraints. The CDFs were defined by determining the
percentage of impacts less than or equal to each impact’s
peak linear and peak rotational acceleration. Using the
location-specific CDFs, the percentile impact for each
pendulum impact energy was determined by relating
peak linear and peak rotational acceleration average
values generated from each laboratory condition.
Through this process, location-specific impact energy
CDFs were determined for each population (male col-
legiate, female collegiate, male high school, and female
high school). The 4 resulting impact energy CDFs were
then averaged for equal weighting between populations.
Low, medium, and high impact energy conditions
were set prior to computing the weighting used in the
Hockey STAR formula. These conditions were chosen
to be representative of a span of impacts severities that
encompass both sub-concussive and concussive head
impacts, and are defined by pendulum arm angles of
40 (low), 65 (medium), and 90 (high). Weightings to
be used for the Hockey STAR test configurations were
determined by setting bounds on the impact energy
CDFs midway between each test angle. For each
location, the percentage of impacts below 52.5 was
defined as the low energy condition, the percentage of
FIGURE 1. The custom impact pendulum device was used to
strike a NOCSAE headform mounted on a Hybrid III 50th per-
centile neck. The head and neck were mounted on a sliding
mass that simulates the effective mass of the torso during
impact. The slide table has 5 degrees of freedom so that any
location on the helmet could be impacted: translation along
the x axis, translation along the y axis, translation along the z
axis, rotation about the y axis, and rotation about the z axis.
ROWSON et al.2432
impacts between 52.5 and 77.5 was defined as
medium energy condition, and the percentage of im-
pacts greater than 77.5 was defined as the high energy
condition. The weightings for each test configuration
were then computed by multiplying these percentages
by the total number of head impacts that the average
hockey player sustains at each location.
Injury Risk Function
The risk function used in Hockey STAR was up-
dated to incorporate both linear (a) and rotational
head acceleration (a) components (Eq. 3). Develop-
ment of the combined risk function for concussion has
previously been described.
49
Rða; aÞ¼
1
1 þ e
ð10:2þ0:0433aþ0:000873a0:000000920aaÞ
ð3Þ
In short, the risk function was developed using data
collected from high school and collegiate football
players. A multivariate logistic regression analysis was
used to model risk as a function of linear and rota-
tional head acceleration. There is an interaction term
FIGURE 2. Photographs of the front, side, back, and top impact locations used to assess helmet performance. The side and top
impact locations are non-centric, meaning the direction of force is not aligned with the CG of the headform; while the front and
back impact locations are centric.
TABLE 1. Measurement markings and angles of rotation on the linear slide table for each impact location tested.
Y translation (cm) Z translation (cm) Y rotation () Z rotation ()
Front 40.3 8.9 25 0
Side 36.9 3.5 5 80
Top 42.7 13.5 40 90
Back 40.3 4.9 0 180
Hockey STAR Methodology 2433
because linear and rotational acceleration are corre-
lated. This risk function is unique in that it accounts
for the under-reporting of concussion in the underlying
data used to develop the curve.
33,35
The predictive
capability of the risk function was found to be goo d
using NFL head impact reconstructions in addition to
the impacts used to generate the function.
Exemplar Hockey Helmet Tests
Three exemplar helmets are used to demonstrate
Hockey STAR. Each helmet was tested in 12 impact
conditions: 4 locations with 3 impact energies per lo-
cation. Pendulum arm angles of 40,65, and 90 were
tested, which equate to impact velocity of 3, 4.6, and
6.1 m/s. These illustrative tests differ from actual
Hockey STAR tests in that only one helmet per model
was tested, and each test configuration was only tested
once. In practice, each test condition would be tested
twice for each helmet, and acceleration values in each
condition would be averaged before calculating risk.
Hockey STAR values for the two helmets of each
model are averaged to determine a helmet model’s
overall Hockey STAR value. For demonstrative pur-
poses, two hockey helmets and one football helmet
were tested under these conditions and Hockey STAR
values calculated.
RESULTS
Mapping Exposure Data to Laboratory System
Bivariate CDFs for linear and rotational accel-
erations experienced by male collegiate hockey players
are shown in Fig. 3 for each impact location. Peak
linear and rotational head acceleration values gener-
ated during the pendulum tests are overlaid on the
CDFs to illustrate how the laboratory tests relate to
the on-ice head impac t distributions. Constant impact
energies varied in percentile by impact location. For
example, releasing the pendulum arm from 40 was
representative of the 88.2 percentile impact to the front
location, 90.4 percentile impact to the side location,
81.4 percentile impact to the back location, and 80.7
percentile impact to the top location. This demon-
strates that higher head accele rations were more
commonly associated with back and top impact loca-
tions in the on-ice helmet data. The tails of these right-
skewed distributions exhibited similar trends. Releas-
ing the pendulum arm from 70 was representative of
the 98.2 percentile impact to the front location, 98.6
percentile impact to the side location, 95.5 percentile
impact to the back location, and 98.9 percentile impact
to the top location.
On-ice head acceleration distributions were trans-
formed to impact energy distributions (represented by
pendulum arm angle) by determining the percentage of
on-ice data that fell below each energy for each impact
location. This process was done for each population
(male and female collegiate, male and female high
school). Resulting impact energy CD Fs were then av-
eraged to determine an overall impact energy CDF
that gave equal weighting to each population (Fig. 4).
The impact energy CDFs were related to generalized
impact energy conditions: a low energy condition (40
pendulum arm angle), a medium energy condition (65
pendulum arm angle), and a high energy condition (90
pendulum arm angle). For all locations, the low energy
condition accounts for greater than 90% of head im-
pacts. The medium energy condition ranged between
3.2 and 6.8% of impacts for each condition. The high
energy condition generally accounted for less than 1%
of impacts for each location, with the exception of the
back location. From this analysis, weightings were
determined for each laboratory impact condition based
on how frequently a player might sustain a similar
impact (Table 2). Summating these laboratory condi-
tion-specific exposure values results in the 227 head
impacts that the average player experiences throughout
a season of hoc key.
Exemplar Hockey Helmet Tests
Three helmets were evaluated with the Hockey
STAR evaluation methods described above: two
hockey helmets and one footbal l helmet. The detailed
results for each helmet are shown in Tables 3, 4, and 5.
Hockey STAR values were 7.098 for hockey helmet A,
12.809 for hockey helmet B, and 1.213 for the football
helmet. Lower STAR values equate to lower risk of
concussion. Given the assumptions that all player s
experience an identical head impact exposure to that
which was modeled and had the same concussion tol-
erance to head impact, these STAR values suggest that
the concussion rate for players in hockey helmet A
would be 44.6% less than that of player s in hockey
helmet B. Comparing the hockey helmets to the foot-
ball helmet, players in the football helmet would ex-
perience concussions rates 82.9% less than players in
hockey helmet A and 90.5% less than players in
hockey helmet B.
DISCUSSION
The purpose of this paper is to introduce a new
evaluation system for hockey helmets that can provide
information to consumers on the relative performance
of different helmets. Hockey STAR is in no way meant
ROWSON et al.2434
to diminish the importance of, or replace, the current
ASTM standards enforced by HECC. Since the in-
troduction of these standards and other rule changes in
the game, the rate of catastrophic head injuries has
greatly decreased.
6
The standards also require impor-
tant specifications regarding the elongation of the chin
strap and appropriate area of coverage of helmets. The
Hockey STAR evaluation system intends to only test
hockey helmets that have already been certified by
HECC. HECC and other helmet certifications are
analogous to the Federal Motor Vehicle Safety Stan-
dards (FMVSS) and regulations which have pass/fail
standards. These standards provide baseline safety re-
quirements that are crucial for protecting drivers. The
New Car Assessment Program (NCA P) developed by
the National Highway Traffic Safety Administration
(NHTSA) augments the existing standards by provid-
ing consumers with a rating system to help guide their
selections.
26,29
Hockey and Football STAR serve the
same purpose as NCAP: to provide additional infor-
mation to consumers after the minimum safety re-
quirements have been met through certification.
Advances from Football STAR
Like Football STAR, Hockey STAR is based on
two fundamental principles: (1) helmets that lower
head acceleration reduce concussion risk and (2) each
test is weighted based on how often players experi-
ence similar impacts. An Institute of Medicine (IOM)
report on sport-related concussion in youth reviewed
Football STAR and characterized it as a theoretically
grounded approach to evaluating helmet protect ion
that is based on sound principles.
38
However, the
FIGURE 3. Peak linear and rotational head acceleration values generated during the pendulum tests are overlaid on the bivariate
CDFs for each impact location. These plots relate laboratory impact energies to on-ice head impact data and were used to define
head impact distributions as a function of impact energy. Where a given impact energy (pendulum arm angle) fell within the
distributions varied by impact location. While these plots only illustrate this for male collegiate hockey, this was done for each of
the 4 hockey player populations in which on-ice data were previously collected.
Hockey STAR Methodology 2435
report also noted that adding rotational acceleration
to the methodology would increase its wide-spread
application. Considering this recommendation,
Hockey STAR was developed to evaluate helmets
using both linear and rotational head acceleration.
This addition contributed to the unique head impact
exposure analysis in Hockey STAR. The exposure
distributions used to weight each impact configura-
tion included both linear and rotational head accel-
eration from collegiate hockey players.
57
The total
number of impacts over one season was also an av-
erage of impacts experienced by youth boy’s and
FIGURE 4. Impact energy CDFs for each impact location resulting from the transformation of on-ice data to laboratory impact
conditions. The gray lines represent impact energy CDFs for each population and the black line is the equal-weight average of the
four populations. The dashed red lines show the bounds used to determine the percentage of impacts at each location associated
with the low, medium, and high energy impact conditions. This analysis was used to define the exposure weightings for each
impact configuration in the Hockey STAR formula.
TABLE 2. Mapping of on-ice head impact exposure to generalized laboratory test conditions.
40 65 90 Total
Front 62.9 4.6 0.6 68.1
Side 65.6 2.2 0.3 68.1
Top 21.5 1.1 0.1 22.7
Back 61.4 4.5 2.2 68.1
Total 211.4 12.4 3.2 227
Each impact configuration was related to a number of impacts that the average player experiences during a season of play. These numbers
represent the exposure weightings for each test condition in the Hockey STAR formula.
ROWSON et al.2436
TABLE 3. Hockey STAR evaluation of hockey helmet A helmet with resultant peak linear (a) and angular (a) acceleration, cor-
responding risk of injury, and season exposure for each condition to calculate incidence.
Impact location Angle () Peak a (g) Peak a (rad/s
2
) Risk of injury (%) Exposure per season Incidence per season
Front 40 64 2154 0.34 62.9 0.213
Front 65 108 3591 5.94 4.6 0.273
Front 90 168 6680 86.57 0.6 0.519
Side 40 71 4220 2.39 65.6 1.568
Side 65 124 7149 64.74 2.2 1.424
Side 90 176 9370 98.34 0.3 0.295
Top 40 37 2590 0.16 21.5 0.035
Top 65 103 6061 26.23 1.1 0.289
Top 90 263 12,666 99.99 0.1 0.100
Back 40 41 2020 0.12 61.4 0.072
Back 65 111 4345 11.43 4.5 0.514
Back 90 169 6076 81.60 2.2 1.795
STAR 7.098
The resulting Hockey STAR value is 7.098.
TABLE 4. Hockey STAR evaluation of hockey helmet B with resultant peak linear (a) and angular (a) acceleration, corresponding
risk of injury, and season exposure for each condition to calculate incidence.
Impact location Angle () Peak a (g) Peak a (rad/s
2
) Risk of injury (%) Exposure per season Incidence per season
Front 40 64 2570 0.48 62.9 0.299
Front 65 87 3819 3.21 4.6 0.148
Front 90 164 6333 81.58 0.6 0.489
Side 40 74 5037 5.04 65.6 3.305
Side 65 115 8254 75.17 2.2 1.654
Side 90 155 10,189 98.12 0.3 0.294
Top 40 66 3869 1.47 21.5 0.315
Top 65 124 7001 61.60 1.1 0.678
Top 90 163 9548 97.72 0.1 0.098
Back 40 56 3448 0.71 61.4 0.435
Back 65 135 6647 65.27 4.5 2.937
Back 90 178 9073 98.07 2.2 2.158
STAR 12.809
The resulting Hockey STAR value is 12.809.
TABLE 5. Hockey STAR evaluation of a football helmet with resultant peak linear (a) and angular (a) acceleration, corresponding
risk of injury, and season exposure for each condition to calculate incidence.
Impact Location Angle () Peak a (g) Peak a (rad/s
2
) Risk of injury (%) Exposure per season Incidence per season
Front 40 37 1787 0.08 62.9 0.052
Front 65 76 2679 0.84 4.6 0.039
Front 90 115 3646 8.21 0.6 0.049
Side 40 35 2210 0.11 65.6 0.072
Side 65 64 3940 1.47 2.2 0.032
Side 90 122 7120 61.95 0.3 0.186
Top 40 32 1965 0.08 21.5 0.017
Top 65 67 3554 1.20 1.1 0.013
Top 90 100 4622 9.28 0.1 0.009
Back 40 44 2177 0.16 61.4 0.096
Back 65 78 3886 2.37 4.5 0.107
Back 90 109 5644 24.60 2.2 0.541
STAR 1.213
The resulting Hockey STAR value is 1.213.
Hockey STAR Methodology 2437
collegiate men’s and women’s hock ey, since the same
helmet models are used for all ages and genders with
variations only in helmet size.
39,57
This is one of two
key differences between Football STAR and Hockey
STAR.
The second key difference is that Hockey STAR ac-
counts for a higher underreporting rate of concussion
than Football STAR. The bivariate risk function was
developed with the assumption that only 10% of con-
cussions sustained by players are diagnosed by physi-
cians.
33,49
In contrast, the Football STAR risk function
assumes that 50% of concussions sustained by players
are diagnosed by physicians.
35,48
Recent studies have
suggested that the underreporting rate may be much
greater than 50%, and have even suggested that struc-
tural changes occur as a result of cumulative head impact
exposure in the absence of diagnosed concussion.
4,13,54
Because the risk function utilized by Hockey STAR
assumes that 90% of concussions go unreported, the
Hockey STAR values are not anticipated to be predictive
of the number of diagnosed concussions sustained by
hockey players, but rather the total number of injuries
sustained, diagnosed and undiagnosed.
Biofidelity of Impact Model
The biofidelity of the impact model used for Hockey
STAR was ensured through appropriate headform
selection and comparison of acceleration traces with
other data collected from hockey players. The NOC-
SAE headform was chosen because of its superior
helmet fit at the base of the skull, and around the jaw,
cheeks, and chin compared to that of the Hybrid III
headform.
11
A helmet that does not fit properly can
shift on the head during tests, and if the contact area of
the helmet padding with the headform varie s from
what is realistic, the effective stiffness of the padding
will vary, potentially resulting in a mischaracterization
of a helmet’s energy management capabilities.
The headform responses generated from pendulum
impacts in the lab were compared to on-ice data by
generating co rridors from both on-ice player data and
ice rink testing with a Hybrid III head (Figs. 5 , 6). The
lab impacts fell within the response corridors generated
from both datasets with the exception of the top im-
pacts in the lab compared with the top impacts from
ice rink testing. There are two reasons for this differ-
ence. The first is that the top impac ts for the ice rink
testing were pure axial loading to the top of the
headform, while the Hockey STAR top location is
non-centric and meant to generate rotational accel-
eration. The second reason is that the ice condition was
not tested for the top location on the ice rink, so only
boards and glass responses are averaged. These
impacts are longer in duration and not representative
of the full spectrum of impacts seen by ice hockey
players. Overall, this analysis provides further evidence
that the laborat ory testing is representative of head
impacts in hockey.
Implementing Hockey STAR
Given that there are 32 helmets currently on the
market, a total of 1536 tests are required to evaluate all
hockey helmets using the proposed protocol. While
this methodology proposes a reasonable number of
tests to evaluate helmets, there are practical limitations
to the number of tests that can be run. For this reason,
there are other variables that have been considered and
researched. For example, helmet temperature is not
varied in this protocol. We performed a study inves-
tigating the temperature inside football helmets during
games.
50
When a player wears a helmet, the tem-
perature of the padding will approach that of the head.
For this reason, and that fact that testing multiple
temperatures could double or triple the number of
tests, helmet temperature is not varied in the Hockey
STAR protocol. Additionally, Hockey STAR does not
evaluate helmets with a facemask on. There are a
number of facemask configurations that can be used
on a helmet. These include full cage facemasks and
clear visors. Testing in the lab demonstrated that the
facemask does not significantly affect either linear or
rotational head acceleration, with differences less than
2%. This suggests that hockey helmet performance is
not influenced by the presence of a facemask, and that
testing with and without facemasks is not necessary. In
short, there are a near-infinite number ways to test a
helmet, but there are practical limitations to the
number of tests used to evaluate products.
Star Rating Thresholds
The hockey star methodology wi ll ultimately be used
to apply star ratin gs to hockey helmets, which allows
consumers to easily compare overall helmet perfor-
mance between models. While this is already being
done with football helmets, the STAR value thresholds
used to determine the star ratings of football helmets
cannot simply be applied to hockey helmet evaluations
due to a number of key differences in the Hockey STAR
and Football STAR formulas. The impac t exposure
weightings are specific to each sport, the test conditions
differ, and a more conservative risk function is used in
the Hockey STAR methodology. Current football
helmet ratings were re-analyzed using a similarly con-
servative risk function for linear head acceleration.
51
The differences in test conditions were also accounted
for by comparing the results of the exemplar football
helmet tested under Ho ckey STAR conditions to the
ROWSON et al.2438
results of the same helmet tested with Football STAR.
Proposed star rating thresholds for Hockey STAR are
based on these equivalent values (Table 6 ).
Exemplar Hockey STAR Results
For the three helmets tested using the Hockey
STAR methodology, the Hockey STAR values were
7.098, 12.809, and 1.213 for helmet A, helmet B, and
the football helmet, respectively. These values are re-
lated to the relative risk of concussion, such that a
player wearing helmet A would be 44.6% less likely to
sustain a concussion than a player wear ing helmet B if
both players had the same head impact exposure over
one season. Similarly, if a player wore the football
helmet and also had the same head impact exposure,
that player would be 82.9% less likely to sustain a
concussion than a player wearing helmet A, and 90.5%
less likely than a player wearing helmet B. Again, it is
important to note that these STAR values are not
representative of the number of diagnosed concussions
players will experience, but rather an overall estimate
of undiagnosed and diagnosed injuries combined.
While these values are tied to concussion risk, ulti-
mately the rating system identifies helmets that best
reduce head acceleration throughout the range of head
impacts that hockey players experience.
Given the proposed thresholds outlined in Table 6,
helmets A and B woul d not be recommended and the
football helmet would receive a 5 star rating. The
disparity in performance between the football and
hockey helmets can be attributed to the differences in
padding and design for energy attenuation.
Specifically, the football helmet has a greater offset,
which allows more compression during impact when
modulating the impact energy. This enables the pad-
ding system to compress on lower severity impacts and
not bottom out on higher severity head impac ts. The
hockey helmets’ padding systems are much thinner,
restricting the ability to reduce head acceleration
throughout the full range of head impacts experienced
by players.
FIGURE 5. Average acceleration traces from the laboratory pendulum tests were compared to corridors developed from on-ice
volunteer data by impact location. The head impact response of the laboratory tests closely matches that which was measured
directly from hockey players, suggesting the impact system generates a biofidelic response.
Hockey STAR Methodology 2439
CONCLUSIONS
This paper presents a novel methodology for com-
paring the performance of different hockey helmets.
The methods are comparable to the existing Football
STAR rating system, however the equation has been
updated to include both linear and rotational accel-
eration. The exposure and testing conditions were also
modified to represent the number and type of head
impacts experienced by hockey players. A new impact
pendulum was designed and built for laboratory test-
ing, and the biofidelity of the syst em was ensured by
comparison with on-ice player data and other testing
methods. Given that Hockey STAR will be used to rate
hockey helmets, exemplar tests of existing helmets were
performed to evaluate and compare of the ability of a
small sample of helmets to reduce risk of concussion.
FIGURE 6. Head impact responses generated in the lab were also compared to dummy head impacts collected at an ice rink. Here,
average acceleration traces from the laboratory pendulum tests were compared to corridors developed from controlled dummy
head impacts to the boards, glass, and ice at an ice rink. The head impact response of the laboratory tests closely matches that
which was measured at the ice rink, which further suggests that impact system generates a biofidelic response.
TABLE 6. Comparison of the proposed Hockey STAR rating thresholds to the current thresholds used in Football STAR and
Hockey STAR thresholds that are equivalent to current Football STAR thresholds using the proposed methodology.
Star rating Current football STAR Equivalent Hockey STAR Proposed Hockey STAR
5 0.300 1.463 1.500
4 0.400 2.069 2.000
3 0.500 2.676 2.500
2 0.700 3.889 4.000
1 1.000 5.708 6.000
To earn a number of stars, a helmet’s STAR value must be below the specified threshold.
ROWSON et al.2440
Similar outcomes to those resulting from Football
STAR are anticipated for Hockey STAR. Consumers
will use the hockey helmet evaluations as a purchasing
tool, which will drive manufacturers to advance
hockey helmet design to reduce concussion risk. This
reduction in concussion risk measured in the lab will
translate to hockey players because the laboratory
evaluations are representative of head impacts experi-
enced by hockey players.
Finally, it is important to note that no helmet can
completely protect a player from all head injuries, and
there are always risks associ ated with playing the
sport. The analysis presented here is based on trends
and probabilities, but an individual’s risk of concus-
sion may vary with a number of factors such as prior
history of head injury or genetic predispositions.
ACKNOWLEDGMENTS
The authors appreciate the support and assistance
from the faculty, students, and staff at the Virginia
Tech – Wake Forest Center for Injury Biomechanics.
We are also grateful for the financial support from the
Virginia Tech Department of Biomedical Engineering
and Mechanics, and the Virginia Tech Institute of
Critical Technologies and Applied Sciences.
OPEN ACCESS
This article is distributed under the terms of the
Creative Commons Attribution License which permits
any use, distribution, and reproduction in any med-
ium, provided the original author(s) and the source are
credited.
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