Paper : Soccer

New Goalkeeping Ability Index for
Football Goalkeepers: Using a Regression Formula for
Predicting the Probability of Goalkeeping Failure
Yusuke Hirashima*, Kensuke Suzuki**, Masaaki Koido***,Takeshi Asai*** and Masao Nakayama****Shizuoka University

836, Ohya, Suruga-ku, Shizuoka-Shi, Shizuoka-Ken 422-8529 Japanhirashima.yusuke@shizuoka.ac.jp**International Budo University***University of Tsukuba[Received April 14, 2023; Accepted October 24, 2023]

Currently, the most commonly used goalkeeping ability metric for goalkeepers is Save percentage(SV%); however, SV% is not an effective evaluation index of goalkeeping ability because it does
not account for goalkeeping-related difficulty. Recently, a regression equation was constructed to
predict the probability of goalkeeping failure; with this equation, it has been possible to quantify
goalkeeping-related difficulty. The purpose of the present study was to develop a new goalkeeping
ability index for goalkeepers, which would take into account the degree of goalkeeping-relateddifficulty using a logistic regression model for predicting the probability of goalkeeping failure. To
this end, we propose two new evaluation metrics: Saving Indicator (SVI) and Saving Score (SVS).
Using SVI, SVS, and SV%, we evaluated the performance of goalkeepers who competed in the2018 FIFA World Cup in Russia. Comparison of the three evaluation indexes revealed that both
SVI and SVS considered the degree of goalkeeping-related difficulty but SV% did not. In future
research, it will be necessary to evaluate an entire football tournament and season using SVI andSVS and to consider in detail the characteristics of each new goalkeeping evaluation index and how
to specifically apply them.
Keywords: soccer, goalkeeper, objective rating, logistic regression, game performance analysis[Football Science Vol. 20, 62-70, 2023]

1. Introduction
Recently, many attempts have been made in Japanand abroad to quantitatively and objectively evaluate
performances in the game of soccer (Oe et al., 2013).
This is attributed to the fact that the scope of data thatcan be obtained has expanded as the technology has
evolved over time and that the number of people who
handle data and the scope of the use of data have also
been increasing (Kato, 2016). For example, trainers
in the team, who are responsible for improving thefitness and preventing injury, use GPS data for themeasurements to check the intensity of the exercise,
if the intended load was applied, and various otherindices (Koido and Kagino, 2018). Additionally, as issaid, “In the past, when considering the formation of
the next season’s team or the acquisition of players,
we could only make judgments based on actual play62

or impressions seen on video, but now (snip) it ispossible to make judgments based on more concrete
information” (Kato, 2016), there are an increasing
number of situations in which such data are usedby the strengthening department in charge of teamorganization, scouts, and others for evaluating
players.
For the player evaluation, fans and supporters
around the world evaluate players and teams fromtheir own perspectives, using data published by theirrespective leagues, and publish their evaluations on
SNS and other media. In Japan, DataStadium, Inc.,
which collects official J-League data as the J-League
official data supplier, uses its own indices to evaluate
players and teams in its “Football LAB” (https://www.football-lab.jp/). Looking at other parts of theworld, 11 new indices as well as traditional data were
provided for all matches by FIFA’s high performance
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New Goalkeeping Ability Index for Football Goalkeepers

team at the 2022 FIFA World Cup Qatar (FIFA,2022). This is because until then, objective evaluation
indices calculated from data were mostly used ascontent for fans, supporters, media, etc., and they
were not necessarily used often to make decisions
on team composition or player acquisition, but as
the importance of objective evaluation increased,
the development of new indices that are more usefulin the field has progressed. For example, regarding
the number of shots, O’Donoghue, P (2015) statedthat “not all shots are the same, and some may betaken where there is little chance of scoring and abetter tactical option might have been play a ball toa teammate in a better position to shoot.” The index
developed to resolve this issue is the expected goals(xG). The xG determines the quality of likelihood ofa goal by calculating the likelihood of scoring fromany position on the pitch during a particular phase ofplay (FIFA, 2022).
The same can be said for the save percentage(hereinafter referred to as SV%), which is themain index for evaluating the defensive ability of agoalkeeper (hereinafter referred to as GK). The SV%
is expressed as the ratio of the number of saves to thenumber of shots on target, and it indicates the successrate in saving goals against shots on target (Nishiuchi,2012). However, Hirashima et al. (2014) argued that
the SV% does not “consider the difficulty of saving
goals and is not a valid evaluation index in practice.”
They then used a logistic regression analysis toconstruct a regression equation that predicts theprobability of failures in saving goals (hereinafterreferred to as goal-saving-failure probability
prediction regression equation), allowing them toquantify the difficulty level of goal saving. This has
made it possible to show numerically what percentage
of goals allowed as a team or what number of goals
allowed was the responsibility of the individual GK.
Furthermore, they argued that it is possible to gage
the level of contribution to the goals allowed by a
GK in a single match or season by accumulatingthese data and that this can be utilized as a new GKevaluation index, which is useful for player selection
and acquisition.
The goal-saving-failure probability prediction
regression equation is already being verified in termsof reliability and validity. Hirashima et al. (2018)
found that the inter-rater reliability and external
validity of the failure probability calculated using theregression equation are high and that the equation isFootball Science Vol. 20, 62-70, 2023
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generalizable and useful in predicting goal-savingfailure probability based on the goal-saving abilityof world-class GKs. However, no efforts have yet
been made to develop a new evaluation index using
this regression equation or to actually conduct anevaluation. The purpose of this study, therefore, wasto present a new GK-evaluation index that takes into
account the difficulty of goal saving for each shot byusing the goal-saving-failure probability prediction
regression equation and to compare the evaluation
results of GKs using the new evaluation index andconventional evaluation index SV%.

2. Method
2.1. Subject
The matches covered were all 64 matches of the2018 FIFA World Cup Russia (hereinafter referred toas 2018 WC). Only the first and second halves were
included, excluding extra time and penalty shootouts.
For each match, two GKs appeared from the two
teams; the total number of GKs was 128. There wasa match in which a GK was substituted, but for the
purposes of this study, they were treated as the same
person throughout the match.

2.2. Method of Measurement
The recorded satellite broadcast matches were
replayed to conduct the measurements. In accordancewith the measurement methods of Hirashima et al.
(2018) and Suzuki et al. (2019), video images wereplayed and paused for each shot on target, and theperformance was measured. To minimize errors inmeasuring game performance from video, stadium
miniatures used in the notational analysis (Hughes,
2003), a method that has been widely used in gameanalysis, were applied. With reference to a study by
Grehaigne et al. (1997), 1-m2 grids were drawn on thestadium miniatures so that the measurement accuracy
could be ensured when distributing the location
information of the players.

2.3. Measurement Items
As in the study by Hirashima et al. (2018),
measurement items were the 10 factors incorporated
into the goal-saving-failure probability prediction
regression equation by Hirashima et al. (2014) as63

Hirashima, Y. et al.

the main factors affecting the outcome of the goalsaving; that is, shot duration, existence of defenders(hereinafter referred to as DFs) in front of theshooter, existence of DFs on the side or behind theshooter, shooting body part, shot type, horizontal shot
trajectory, shot height, changes in trajectory causedby other players, shot-position angle, and shot-course
distance were incorporated into the goal-savingfailure probability prediction regression equation as
the main factors affecting the goal-saving result, andthe success or failure of goal saving was measured
for 11 items. These measurement items are described
below, citing Hirashima et al. (2018).
2.3.1. Shot duration
The duration from the time the shooter took
the shot to the time the ball reached the goal wasmeasured. For the shots that were scored without the
GK touching the ball, the time from when the shooter
took the shot until the ball completely crossed theline was measured from the number of frames in thevideo. For the cases where the GK stopped the shot
and where the GK touched the ball but the goal wasscored, the time from when the shooter took the shot
until the GK touched the ball was first measured
from the number of video frames. The position of theball when the shooter took the shot and the positionof the ball when the GK stopped the shot were thenrecorded, and the distance between the two points
was calculated using the Pythagorean theorem. The
average velocity of the ball was then calculated using
the time and distance. Additionally, the distance
between the position of the ball when the shooter
took the shot to the center of the goal was calculatedusing the Pythagorean method. The time taken forthe shot ball to reach the goal when the GK did nottouch the ball was calculated based on the calculated
average velocity of the ball and the distance between
the position of the ball when the shooter took the shot
to the center of the goal. The measurement unit of thetime was 0.01 s.
2.3.2. Existence of DFs in front of the shooter and2.3.3. Existence of DFs on the side or behind theshooter
The defensive situation against the shooter wasmeasured by the status of the defensive team players
within 5 m of the shooter. As for the existence of DFs
in front of the shooter, this is the presence or absenceof DFs within a triangle formed by line segments
64

connecting the ball to both goal posts and within 5 mfrom the shooter. With regard to the existence of DFs
on the side or behind the shooter, this is the presenceor absence of DFs outside the triangle formed by line
segments connecting the ball to both goal posts but
within 5 m from the shooter.
2.3.4. Shooting body partThe body part from where the shot was made wasdetermined. The shooting body parts were classified
into (a) foot and (b) head.
2.3.5. Shot type
The categories of shot type are as follows: (a)grounder: a shot that bounced at least twice after theshooter takes the shot and before the ball crossed
the goal line or hit the goal post or goalkeeper, or
alternatively, a shot that never exceeded knee height;(b) loop: a shot past the GK on parabolic trajectory;and (c) liner: any shot other than the grounder or theloop.
2.3.6. Horizontal shot trajectory
Determination was made as to which lateral
direction the shot was kicked with respect to the line
segment connecting the position from which theshooter took the shot and the position of the GK atthat time. The categories for horizontal shot trajectory
are as follows: (a) near: a shot kicked in a direction
close to the shooter as seen from the GK’s positionand (b) far: a shot kicked far away from the shooter
as seen from the GK’s position.
2.3.7. Shot height
The height of the goal was divided into threeequal ranges and classified in the order of (a) low,(b) medium, and (c) high, beginning with the lowest
range.
2.3.8. Change in trajectory caused by otherplayers
The change in the trajectory of the shot by otherplayers from the time the shooter takes the shot until
the ball reaches the goal or the GK.
2.3.9. Shot-position angle
The angle formed by connecting with straight lines
the position of the ball when shot, the goal post near
the position of the ball when shot, and the point wherea straight line is drawn perpendicular to the goal line
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New Goalkeeping Ability Index for Football Goalkeepers

from the position of the ball when shot and intersects
it was calculated using the Pythagorean theorem. If
the ball was between the lines extended from bothgoal posts when the shot was taken, the shot-position
angle was set to 90°. The distance from the center ofthe goal to the goal post was assumed to be 3.7 m.
The measurement unit of the angle was 1°.
2.3.10. Shot-course distanceThe distance from the center of the goal to thecenter of the ball when the shot ball completely
crossed the goal line was calculated. For shots thatwere scored without the GK touching the ball, theposition of the center of the ball as it completely
crossed the line was calculated using a goal miniatureand the distance from the center of the goal was
calculated using the Pythagorean theorem. A grid of
0.1 m2 was drawn on the goal miniature. For cases
where the GK stopped the shot or touched the shot,
the position that the ball would be expected to passthrough when crossing the goal line if the GK did
not touch the ball was recorded and the distancefrom the center of the goal was calculated using the
Pythagorean theorem. The measurement unit was 0.1
m.
2.3.11. Success or failure of goal saving
For the success or failure of goal saving, success
was awarded when the GK caught or deflected theshot ball to prevent it from going into the goal andfailure was awarded when the goal was scored.

2.4. Calculation of the Goal-saving-failure
Probability
The goal-saving-failure probability was calculated
by substituting the in-frame shot situation measured
by the game performance analysis into the goalsaving-failure probability prediction regression
equation. To evaluate players, it is necessary to use
the goal-saving-failure probability rate per shot ascollected for each GK. For this reason, the goalsaving-failure probability prediction regression
equation used in this study is expressed by adding
the shot number, i, for each shot to the equation
constructed by Hirashima et al. (2014), as follows:
Pi=1/(1 + exp(−(−2.245−0.5204x1i−1.215x2i−0.57
0x3i + 0.885x4i + 0.551x5i + 4.072x6i + 1.333x7i + 0.711x
8i + 0.968x9i + 2.839x10i + 0.029x11i + 1.014x12i)))
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Here, x1: shot duration (s); x2: DFs in front of theshooter, 1 for yes and 0 for no; x3: DFs on the side or
behind the shooter, 1 for yes and 0 for no; x4: shooting
body part, 1 for the head and 0 for the foot; x5: shot
type, 1 for the grounder and 0 for the others; x6: shot
type, 1 for the loop and 0 for the others; x7: horizontal
shot trajectory, 1 for far and 0 for near; x8: shot height,
1 for medium and 0 for the others; x9: shot height, 1
for high and 0 for the others; x10: change in trajectory
caused by other players, 1 for yes and 0 for no; x11:
shot-position angle (°), x 12: shot-course distance(m). The calculated values were rounded to the third
decimal place.

2.5. Method of Evaluating Players
Three types of objective evaluation indices wereused to evaluate the goal-saving ability of the GK.
The first was the conventional evaluation index
SV%. This is calculated by dividing the number ofsaved shots, calculated by subtracting the number ofgoals (c) from the number of shots on target (n), by n.
SV% =

n-c
×100
n

The second was the new evaluation index, saving
indicator. First, an expected value of the projected
goals allowed (hereinafter referred to as projected
goals allowed) is calculated by summing up the goalsaving-failure probability (Pi) of each shot calculatedusing the goal-saving-failure probability prediction
regression equation for each GK who receives the
shots. Then, by dividing the actual goals allowed (c)by the projected goals allowed, it is possible to show
the contribution of the GK to the goals allowed. Thisvalue is called saving indicator (abbreviated SVI) andis used as an evaluation index.
SVI (g) =c∑i∈gPi

The third was another new evaluation index, saving
score. The projected goals allowed is calculated
by summing up the goal-saving-failure probability(Pi) of each shot calculated using the goal-savingfailure probability prediction regression equationfor each GK who receives the shots. By subtracting
the projected goals allowed from the actual goals
allowed (c), it is possible to show to what extent
the goalkeeper increased or decreased the numberof goals allowed. This value is called saving score
65

Hirashima, Y. et al.

(abbreviated SVS) and is used as an evaluation index.
SVS (g) = c - ∑i∈gPi

2.6. Statistical Analysis Method
2.6.1. Inter-rater reliability
Taking the studies by Hirashima et al. (2014) andSuzuki et al. (2019) as references, to investigate theinter-rater reliability for the measurement items, κcoefficients for category variables; that is, DFs infront of the shooter, DFs on the side or behind theshooter, shooting body part, shot type, horizontal
shot trajectory, shot height, changes in caused byother players in the trajectory, success or failure ofgoal saving, and intraclass correlation coefficients forcontinuous variables (i.e., shot duration, shot-position
angle, and shot-course distance) were calculated.
A total of 44 shots on target that appeared in five
matches were included in this analysis. The two raters
had played and coached soccer and were engagedin scientific research on soccer. As the measurement

was a repetitive process of pausing and replaying
the video, each raters performed the measurement
individually.
2.6.2. Relationship among three evaluation indicesand each item
For SV%, SVI, SVS, average goal-saving-failure
probability, number of shots, and projected goals
allowed, the Kolmogorov–Smirnov test showedno normality for all items. Therefore, Kendall rankcorrelation coefficients (rk) were calculated to
examine the correlation. The significance level was
set at 5%.

3. Results
3.1. Intra-rater Reliability
As shown in Table 1, κ coefficients ranged from0.77 to 1 for all items, with an average value being as
high as 0.88. Additionally, the intraclass correlation
coefficients ranged from 0.86 to 0.98 for all items,with the average being as high as 0.92.

Table 1 Objectivity of measurement items

Measurement items
2) DF in front of the shooter

0. 773) DF on the side or behind the shooter

0. 904) Shooting body part

0. 92

5) Shot type

0. 856) Horizontal shot trajectory

0. 867) Shot height

0. 878) Changes in trajectory caused by other players

0. 8511) Success or failure of goal saving

1. 00

Measurement items

66

κ coefficient

Intraclass correlation coefficient1) Shot duration (seconds)

0. 939) Shot-position angle (degrees)

0. 9810) Shot-course distance (m)0. 86Football Science Vol. 20, 62-70, 2023
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New Goalkeeping Ability Index for Football Goalkeepers

Table 2 Basic characteristics of the subject (n = 128)

M

SD

MIN

MAX

Shots on target

3. 8

2. 2

0. 0

11. 0

Goals allowed

1. 2

1. 1

0. 0

6. 0

Number of saves

2. 6

1. 9

0. 0

9. 0

Average goal-saving-failure probability (%)

29. 3

20. 0

0. 0

95. 3

Projected goals allowed

1. 1

0. 8

0. 0

4. 5

3.2. Basic Characteristics of the Subjects
As the basic characteristics of the targeted GKs,
the number of shots on target, number of goals
allowed, number of saves, average goal-saving-failure
probability, and projected goals allowed per gamewere determined and the corresponding averages
were calculated (see Table 2).
Additionally, the average evaluations of the GKs
based on SV%, SVI, and SVS are shown in Table 3.
Four of the 128 GKs could not be evaluated because
they had no shots on target during the respective
matches and were excluded from the subsequent
sample.
Table 3 Evaluation using three evaluation indices (n = 124)

M

SD

MIN

MAX

SV%

65. 5

29. 7

0. 0

100. 0

SVI

1. 2

1. 3

0. 0

8. 5

SVS

0. 1

0. 8

－2. 9

2. 2

3.3. Relationships among the Three Evaluation
Indices and Each Item
3.3.1. Relationships among evaluations by threeevaluation indicesFigure 1 shows the relationships between theevaluation using SV% and the one using SVI, theevaluation using SV% and the one using SVS,
and evaluation using SVI and the one using SVS.
Moderate negative correlations were found between
SV% evaluation and SVI evaluation and between
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SV% evaluation and SVS evaluation (rk = −0.62・−0.48, p < 0.05). A moderate correlation was also
observed between the SVI evaluation and SVS
evaluation (rk = 0.69, p < 0.05).
3.3.2. Relationships between the Evaluations using
the Three Evaluation Indices and Average Goalsaving-failure probability
Figure 2 shows the relationship between theevaluations using SV%, SVI, or SVS and the averagegoal-saving-failure probability per shot. A moderatecorrelation was observed between the evaluation
using SV% and the average goal-saving-failure
probability per shot (rk = −0.48, p < 0.05).
3.3.3. Correlations between Evaluations using theThree Evaluation Indices and Shots on Target andProjected Goals Allowed
Figure 3 shows the relationship between theevaluations using SV%, SVI, or SVS and shots on
target. No correlation was found between each of thetwo variables (rk = 0.08・−0.06, p > 0.05).
Figure 4 shows the relationship between theevaluations using SV%, SVI, or SVS and the
projected goals allowed. A weak negative correlation
was observed between the evaluation using SV% andthe projected goals allowed (rk = −0.25, p < 0.05).

4. Discussions
Game performance analysis was used for datacollection; however, it is necessary to consider
objectivity in terms of intermeasurer reliability
when measuring game performance throughobservation(Suzuki and Nishijima, 2002). The κ
coefficient for two measurers was ≥0.77 for all items.
67

Hirashima, Y. et al.

rk=-0.62
p<0.05
n=124

8

6
S
V
I

S
V
S

4

3

3

1

1
S
V
S

-1

-1
rk=0.69p<0.05n=124

rk=-0.48
p<0.05
n=124

2

0

-3

-3
0

20

40

60

80

0

100

20

40

60

SV%

80

100

0

2

4

6

8

SVI

SV%

Figure 1 R elationships between evaluations with SV% and SVI, with SV% and SVS, andwith SVI and SVS

80
60

40
20
0
0

20

40

60

80

rk=-0.08
p>0.05
n=124

80
60

40
20
0

100

0

2

4

SV%

6

8

100
Average goal saving failure probability (%)

100
rk=-0.48
p<0.05n=124

Average goal saving failure probability (%)

Average goal saving failure probability (%)

100

rk=-0.06
p>0.05
n=124

80
60

40
20
0
-3

10

-1

1

3

SVS

SVI

Figure 2 R elationships between evaluations with SV%, SVI, or SVS and average goalsaving-failure probability

rk=0.08p>0.05n=124

Number of shots on target

Number of shots on target10

rk=0.09p>0.05
n=124

10

8
6
4

8
6
4
2

2

12

8
6
4
2

0

0
0

20

40

60

80

rk=-0.02
p>0.05n=124

10
Number of shots on target

12

12

0
0

100

2

4

SV%

6

8

10

-3

-1

1

3

SVS

SVI

Figure 3 R elationships between evaluations withy SV%, SVI, or SVS and shots on target5
rk=-0.25
p<0.05
n=124

4

3
2
1

5

rk=-0.01
p>0.05n=124
Projected goals allowed

4

Projected goals allowed

Projected goals allowed

5

3
2

20

40

60
SV%

80

100

3
2

0

0
0

4

1

1

0

rk=-0.13
P>0.05
n=124

0

2

4
SVI

6

8

10

-3

-1

1

3

SVS

Figure 4 R elationships between evaluations with SV%, SVI, or SVS and projected goals
allowed68

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New Goalkeeping Ability Index for Football Goalkeepers

Moreover, it demonstrated a high value of ≥0.86 for
all items (Table 1) in terms of the intraclass relative
coefficient, which further confirmed that the analysis
items used had a high degree of objectivity.
Validity is a concept concerning if a scale reallymeasures what it is trying to measure (Kamahara etal., 1998), and a criterion-related validity, among
others, is where performance indices for some aspect
are evaluated against reliable standard criteria forthat aspect (O’Donahue, 2015). SV% is a simple—
involves dividing the number of goals saved by the
number of shots on target—and currently the mostimportant evaluation index for the goal-saving ability.
Therefore, the SV% would be a standard criterion
for validating if new indices are indeed assessing thegoal-saving ability. In other words, examining therelationship between the evaluations using SV% andthat using the new evaluation indices, SVI and SVS,
is an indicator to prove the criterion-related validityof the new evaluation indices. The correlations
between the evaluations with SV% and SVI, SV%
and SVS, and SVI and SVS were moderate (Figure1). Konno and Hori (1998) found no clear standard
for correlation coefficients, but they did confirm
the criterion-relevant validity of the scale throughmoderate correlations. It can be argued that, similar
to SV%, SVI and SVS are also considered to be
the indices that evaluate the goal-saving ability ofgoalkeepers.
Furthermore, the relationship between theevaluations using the three evaluation indices andthe average goal-saving-failure probability per shot
(Figure 2) showed a moderate negative correlations
between the evaluations using SV% and the averagegoal-saving-failure probability per shot. Hirashima
et al. (2014) stated that the conventional evaluationindex, SV%, did not consider the difficulty of saving
goals and was not an effective evaluation index inpractice and that there is a need to develop a new
GK-evaluation index by using the goal-saving-failure
probability prediction regression equation. However,the usefulness of the assessment using SV% has notbeen verified in practice to date. The results of thisstudy revealed that the problem with SV% was thatit did not consider the difficulty of the goal saving,
resulting in a higher rating for players who received
easy shots. Moreover, as no significant correlations
were found between the evaluation using SVI or SVS
and the average goal-saving-failure probability, SVI
and SVS are considered to be the evaluation indices
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that consider the difficulty of goal saving.
Additionally, in the relationships between the threeevaluation indices and number of shots taken andbetween these indices and the projected goals allowed,
a weak negative correlation between the evaluations
using SV% and the projected goals allowed. As in thecase of the average goal-saving-failure probability per
shot, having a strong correlation is not good for theevaluation index because the GK cannot contributeto the increase or decrease in the number of shots
taken and the projected number of goals allowed. As
the players with lower average goal-saving-failure
probability as well as lower projected goals allowed
are likely to be rated higher with SV%, it is still not a
useful index for properly evaluating players. SVI and
SVS were not correlated with the number of shots
taken or the projected goals allowed, suggesting thatthey are the evaluation indices not easily affected by
the development of the match.
Thus, SVI and SVS developed in this study are
considered as indices that can be used to evaluate
the goal-saving ability of the GKs based on the goalsaving difficulty and the match situation. As SVI
divides the goals allowed by the projected goals
allowed, it is possible to compare players’ goal-saving
ability across categories, competitions, and number ofmatches with different levels of goal-saving difficulty,
suggesting that it is an effective index for player
selection and scouting. As for SVS, because it iscalculated by subtracting the projected goals allowedfrom the actual goals allowed, it is expected to beaffected by the categories and the number of matches.
However, it can be used to evaluate how many goals
allowed even when the number of goals allowed
was zero, suggesting that it is a useful index when
revealing the most productive GK in a single matchor tournament.
As SV% does not consider the difficulty of
stopping shots, the problem exists that players witha low average shot-stop-failure probability andplayers with low predicted goals were evaluated moreaccurately. However, a moderate correlation with SVI
and SVS was observed, and it can be easily evaluated;
therefore, it is considered to be effective as a simple
evaluation index.
This study was limited to the evaluation of asingle match, but in the future, it will be necessary
to evaluate players over the course of a tournament
or a season to study in detail the characteristics ofeach evaluation index and how they can be utilized.
69

Hirashima, Y. et al.

Moreover, the goal-saving-failure probability
prediction regression equation, which is at the coreof this evaluation, is constructed by Hirashima etal. (2014) using data obtained from the 2010 WC.
However, it is yet to be updated. Furthermore,
improvements can be made, for example, considering
what to do if a shot is made by other than the foot orhead. Therefore, moving forward the goal-savingfailure probability prediction regression equation willneed to be updated and improved.

5. Conclusions
Herein, two new goal-saving-ability evaluation
indices for GKs were presented based on a goalsaving-failure probability prediction regression
equation. The objectives of this study were toactually evaluate GKs by using the new evaluation
indices and a conventional evaluation index, as wellas to compare the evaluation results; the following
conclusions were obtained.
The new indices, SVI and SVS, were found tobe the goal-saving-ability evaluation indices thatconsider the goal-saving difficulty. However, as theconventional evaluation index, save percentage, does
not consider the goal-saving difficulty as predicted,
it became clear that it is a problematic index forproperly evaluating players in the field.

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Name:
Yusuke Hirashima
Affiliation:
Shizuoka University

Acknowledgments

Address:
836 Ohya, Suruga-ku, Shizuoka 422-8529 Japan

References

Brief Biography:
2012-2018 Doctoral Program in Coaching Science, University ofTsukuba
2018-2021 Junior Assistant Professor, University of Tsukuba
2022- Lecturer, Shizuoka University

This work was supported by JSPS KAKENHI
Grant Number JP19K19941.
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Quantification of the degree of difficulty in making a save fora soccer goalkeeper. Japan J. Phys. Educ. Hlth. Sport Sci., 59:805-816. (in Japanese)
Hirashima, Y., Asai, T., Fukayama, K., Nakayama, M. (2018).
Verification of a regression equation to predict the probabilityof a football goalkeeper’s failure to stop shots at goal. Japan J.
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Main Publications:• H irashima, Y., Nakayama, M., Naito, K., Asai, T. (2014).
Quantification of the degree of difficulty in making a save for asoccer goalkeeper. Japan Journal of Physical Education, Healthand Sport Sciences, 59(2), 805-816.
• Hirashima, Y., Asai, T., Fukayama, K., Nakayama, M. (2018).
Verification of a regression equation to predict the probabilityof a football goalkeeper’s failure to stop shots at goal. Japan
Journal of Physical Education, Health and Sport Sciences, 63(1),315-325.
Membership in Learned Societies:• Japanese Society of Science and Football• Japan Society of Physical Education, Health and Sport Sciences• The Japan Society of Coaching Studies• J apanese Society of Test and Measurement in Health andPhysical Education

Football Science Vol. 20, 62-70, 2023
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