Paper : Game Analysis

Construction of Offensive Play Measurement Items and Shot
Prediction Model Applying Machine Learning in Japan
Professional Football League
Hirotaka Jo*,**, Hiroki Matsuoka***, Kozue Ando*** and Takahiko Nishijima****Department of Sports Science, Shizuoka Sangyo University

1572-1 Oowara, Iwata-city, Shizuoka-pref 438-0043 Japan** Doctoral Program in Health & Sport Sciences, University of Tsukuba

1-1-1 Tennodai, Tsukuba, Ibaraki 305-8574 Japan*** Faculty of Health & Sport Sciences, University of Tsukuba

1-1-1 Tennodai, Tsukuba, Ibaraki 305-8574 Japanjotk.lab@gmail.com[Received May 10, 2021; Accepted October 11, 2021]

The demand for sports analytics is increasing because it contributes to the victory of competitive
sports. Although the technology for automatically measuring tracking data has improved, it ismeaningful to systematize analysis using ball touch data because the stadiums that can be used
are limited. Moreover, in recent years, analysis using machine learning has been increasing,and it is necessary to accumulate research. With this background, this study aimed to constructmeasurement items and a model for predicting to shoot in soccer offensive play by applying machinelearning. Using the Delphi method, 6 items were deleted from the old measurement item group in
the previous study, 11 items were newly created, and 45 items were set as the new measurement
item group. A decision tree, random forest, and gradient boosting decision tree were applied to
the new measurement item group, and several shot prediction models were constructed. The
results indicated that the model using 23 items in the gradient boosting decision tree was the best.
Furthermore, a comparison between the old and new measurement item groups revealed that theprediction accuracy of the new measurement item group was higher. In conclusion, 45 measurement
items of soccer offensive play were constructed, and the shot prediction model using them wasconstructed by applying machine learning.
Keywords:football, offensive play, measurement items, machine learning, shot prediction[Football Science Vol.19, 1-21, 2022]

1. Introduction
Sports analytics is the process of managing
data, implementing predictive models, and using
information systems for decision making, which
contributes to competitive advantage in competitive
sports (Alamar and Mehrotra, 2011). In other words,
in competitive sports, measuring, managing, andanalyzing performance during a game can provide
useful insights for practice and training plans, as well
as tactical and strategic planning for the next game,
thereby leading to victory. This concept has been put
into practical use for many years, with one of themost famous examples being Sabermetrics (James,
2003). Sabermetrics is a baseball analysis system
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developed in the 1970s. It is famous for the story of ateam that became a regular contender for the regional
championship by reinforcing its hitters based on
the analysis result that the on-base percentage wasa better indicator of hitting ability than the batting
average. Currently, sports analytics is not limited
to baseball but has become popular in many otherfields. In soccer, Germany won the 2014 World Cup
in Brazil by actively using data analytics (Dmonteand Dmello, 2017), and it was said that “the analytics
system was the 12th player.”
In ball games such as soccer, the data used foranalysis can be divided into two types: ball touch
data and tracking data. Ball touch data is related tothe action of the player with the ball, which cannot be1

Jo, H. et al.

fully measured by an automatic tracking device. It ismeasured by an expert measurer who has undergonea certain amount of training by watching videos(Kato, 2016). Since the data are recorded every time aplayer takes an action, ball touch data are also called
event data. Tracking data can automatically or semiautomatically measure the positions of 22 players on
the pitch if automatic tracking devices are available
in the match. The Japan Professional Football League(J.League) uses the TRACAB system (ChryonHego,
NY, USA), which records position data at 25 frames
per second (Linke et al., 2020).
Since tracking data include the positional
information of players other than the ball carrier, itis thought to greatly expand the scope of analysis ofcollective skills, and research has been conductedon the J.League players (Matsuoka et al., 2020).
However, as automatic tracking devices are
specialized equipment with high installation costs,
they are only available for national teams and at the
professional level. In addition, such devices cannot
be used in national or professional team stadiumsthat have not been designed for the installation of
automatic tracking systems. Therefore, regardless ofthe type of sport, measurement and analysis using
video images (Suzuki et al., 2019; Takahashi and
Haseyama, 2017) are still conducted, and many
applications and analysis programs (Ekin et al., 2003)
have been developed for this purpose. This suggeststhat devising a method to analyze game performanceusing only ball touch data, without relying on tracking
data, is considered significant enough in current
competitive sports.
Data sources, analysts, and analysis methods
are key elements for good sports analytics. Data
sources have developed to the point where they are
now referred to as “big data in sports” (Dmonteand Dmello, 2017), as the volume of information
has grown enormously due to improvements inmeasurement accuracy and automation technology inaddition to lower measurement costs (Tsuchida andYadohisa, 2020). As for analysts, the role of sports
analyst has been recognized in Japan since around2010 (Chiba, 2020). Now, analysts are present not
only in national teams and at the professional level but
also in university sports, as exchanges transcending
the boundaries of categories and organizations havebeen promoting the development of analysts (Sakaori,2016). In the meantime, as a result of the transitionof measured information into big data, conventional2

methods are not thought to be scalable in termsof analysis methods (Wang et al., 2015). In recent
years, machine learning methods have been applied
to sports data analysis (Tamura, 2020). In a study on
the J.League, Matsukoka et al. (2020) applied neuralnetworks to tracking data to develop measures foranalyzing defensive and offensive plays. In addition,
Jo et al. (2014) applied decision tree analysis todevelop a skill rating scale for players and teams.
However, since research on analysis using machinelearning has a short history, it is still necessary to
study, develop, and accumulate systematized analysis
methods at the academic level.
Among the many machine learning algorithms,
neural networks, which belong to deep learning, havebeen noted for their high accuracy. However, neuralnetwork algorithms have the black box problem inthat the prediction process is extremely complex
for human intelligence to understand. Despite many
attempts at improvement (Morinaga, 2015), no clear
solution exists to this problem. By contrast, decision
tree analysis, classified as supervised learning andis represented by CART (Breiman et al., 1984), ischaracterized by the simplicity of interpretation of
results due to the visual verifiability of the prediction
or classification process. Since sports analytics
requires the utilization of analysis results in a seriesof tasks (Dmonte and Dmello, 2017), it is difficult toreflect the results in practice if the prediction process
is a black box in on-field feedback of analysis resultsin soccer. Therefore, decision tree analysis, which hasa relatively clear prediction process, is considered to
be useful for feedback to the sports field.
Decision tree analysis is an algorithm for prediction
and classification, but the success or failure of model
development largely depends on how the objective
and predictor variables are established. The ultimate
goal of a soccer match is to win the match, and forthis purpose, strikers must aim to score, and to scoregoals, they must take shots (Jo et al., 2017). Basedon this, “outcome,” “goal,” and “shot” may be theobjective variables. Kumar (2013) reported theconstruction of a prediction model, with the outcomeof matches as the objective variable. However, theconstruction of a prediction model with goals andshots as the objective variables is still unexplored.
The ability to score goals highly depends on theshooting skill of the attacking player. As it is difficult
to measure shooting skills with ball touch data, it isdifficult to construct a prediction model of scoring
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Offensive Play Measurement Items and Shot Prediction Model in Footballwith ball touch data alone. By contrast, a model thatpredicts whether a player will take a shot may beconstructed by processing the ball touch data from the
beginning to the end of the game and by incorporatingtactical and technical phenomena into the variables.
Based on the above considerations, this study
intended to construct a model to predict whether aplayer would take a shot by listing measurement
items of offensive plays based on soccer ball touch
data and applying a machine learning method similar
to the decision tree analysis. For this purpose, thefollowing hypotheses were considered:Hypothesis 1: T he measurement items of offensive
plays are created based on ball touch data.
Hypothesis 2: A
 machine learning model is constructedto predict whether a player will take ashot based on the measurement items ofoffensive plays by applying a method
similar to the decision tree analysis.

2. Method
2.1. Definition of terms
The principal terms used in this study are defined
as follows:(1) Measurement items
Although “measurement items” in the field of
measurement and evaluation, “variables” in the fieldof statistics and data analysis, and “features” in thefield of machine learning are referred to by different
terms depending on the field, they all have similar
characteristics. In this study, they are considered
to be synonymous and hereinafter referred to as“measurement items.”(2) Action
In ball touch data, actions refer to the individual
actions that players perform during a game, such as
passes, traps, dribbles, and crosses.
(3) Play
A play refers to a series of successive actions
taken by a team in possession of the balluntil thesaid team loses the ball or takes a shot. For instance,
if the sequence of actions from kick-off to trapping,passing, trapping, dribbling, passing, and ball outof play (when the ball leaves the field of play) takes
place, a play comprises the actions from kick-off toball out of play, with the six elements except for thelast ball out of play being actions.
(4) Action data
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A dataset in which each line consists of one action.
(5) Play data
A dataset in which action data is transformed insuch a way that each row consists of one play.

2.2. Research procedure
This study was conducted in accordance with thefollowing procedure:(1) T he new measurement items in this study(hereinafter referred to as the new set ofmeasurement items) were established inaccordance with the measurement items in the
study by Jo et al. (2014) (hereinafter referred toas the old set of measurement items).
(2) The action data were converted to play data basedon the definition of the new set of measurement
items.
(3) D ecision trees, random forests, and gradientboosting decision trees were analyzed using theplay data to validate the best prediction model(hereinafter referred to as the “best model”).
(4) T he play data of the old set of measurement
items were analyzed using the same methods andconditions as the best model, and the prediction
accuracy of the old and new sets of measurement
items were compared.

2.3. Target of analysis
Offensive plays in all 686 J.League Division 1(J1) and Division 2 (J2) matches played in 2011were analyzed. However, as set pieces are sometimes
performed with special attacking patterns, plays thatstarted with corner kicks, penalty kicks, and freekicks (both direct and indirect) were excluded. In
this season, J1 comprised 18 clubs with 34 sections,
J2 comprised 20 clubs with 38 sections, and 1,051
players were registered. The size of the action data
used in this study was 207 columns by 1,312,117
rows, which were measured by trained staff of Data
Stadium Inc. while watching the video footage of thematches. Typical measurement items included thematch information such as the date of the match andthe names of the teams playing the match, in addition
to actions taken by the players and their positions.
Every time a player took an action such as a kick-off,
dribble, pass, trap, header, shot, cross, throw-in, freekick, goal kick, tackle, foul, throw-in, or goalkeeper
catch was recorded.
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Jo, H. et al.

2.4. Analysis method
2.4.1 Composition of measurement items
Jo et al. (2014) applied the Delphi method(Linstone and Turoff, 1975), a technique forgathering opinions from a group of soccer experts,
to develop 40 items to measure the attacking skills
demonstrated in soccer matches (referred to as the old
set of measurement items in this study). The authors
developed an index to measure the attacking skills of
players and teams (the old set of measurement items
is listed with the new measurement items in Table 1
below). Here, decision tree analysis was used with thebinary data. Whether a player took a shot (hereafterreferred to as the measurement item “shot”) was theobjective variable and a single measurement item
was the predictor variable to identify the bifurcation
value that best determines whether the player would
take a shot. The bifurcation value was treated as aplay achievement criterion for taking a shot, andits degree of contribution for taking a shot was
identified based on the shooting probability after thebifurcation. Subsequently, Jo et al. (2017) developed
an algorithm to optimize offensive plays using thesame measurement items. Based on these previous
studies, the old set of measurement items was judged
to be versatile, and thus, it was used as a referencein this study. However, some measurement items
needed to be newly created or unused. Therefore, in
the initial stage of this study, the measurement items
were reconstructed by applying the Delphi methodin accordance with the method of Jo et al. (2014).
The old measurement items were carefully examined
one by one, and they were not used where there wasa rational reason to discard them. In addition, thefactors that might affect the possibility of taking a
shot were re-examined to create new measurement
items that were not in the old set of measurement
items. The group of experts comprised one university
faculty member specializing in evaluation andmeasurement in health and physical education; onesoccer coach with experience in the professional andthe first division levels; and two active professionalsoccer players. The Delphi method consisted of five90-minute meetings over a two-month period. During
this period, other soccer professionals were asked fortheir opinions as needed when the expert group alone
could not make a decision.

4

2.4.2. Conversion to play data
Ball touch data are difficult to analyze in its raw
form, and even when the amount of data is large,
it is difficult to gather useful insights from them.
Therefore, analysts need to examine complex data
structures and address issues such as dealing withsparsity and scaling a large number of different datameasures (Decroos, 2020). One of the objectives ofthis study was to build a shot prediction model. As
the prediction targets play rather than act, play data inwhich each row consists of one play were prepared.
Since the ball touch data measured by Data Stadium,Inc. were action data, the data were converted intoplay data based on the definition of each item of the
new set of measurement items in accordance with theprocedure of Jo et al. (2014).
2.4.3. Validation of the prediction model
Since players need to take shots at the goal toscore in soccer, constructing a model is crucial topredict whether a player can take a shot. This study
adopted decision tree analysis as a method forconstructing a prediction model. There are methods
called random forests (Breiman, 2001) and gradientboosting decision trees (Friedman, 2001), which areextensions of decision trees. Both these methods
are a type of ensemble learning and are known for
their high prediction accuracy. Random forests
remove variances, and gradient boosting decision
trees method eliminates biases. Few studies haveanalyzed sports performance data using ensemble
learning methods. Thus, the possibility of improving
the prediction accuracy through ensemble learning
remains unknown. Therefore, this study employedrandom forests and gradient boosting decision trees asmethods for building prediction models to determine
the best method.
When constructing a machine learning model, themeasurement items should be carefully examined, as
the performance of the model is largely determinedby the ability to prepare measurement items that
contribute to the prediction results. However, moremeasurement items will require more processing
time, causing the interpretation of the results tobecome more complex. Therefore, the number ofmeasurement items should not be increased blindly.
It is necessary to construct a model that retains only
the items that contribute to the prediction results
and excludes unnecessary measurement items.
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Offensive Play Measurement Items and Shot Prediction Model in Footballby reducing the number of measurement items stepby step using three methods: decision trees, random
forests, and gradient boosting decision trees. Fromamong the multiple models generated in the process,the best model was determined based on the two
perspectives of high prediction accuracy and small
number of measurement items. The procedure is
described below (Figure 1).
The analysis software used in this study was
R (R Core team, 2020), a popular tool in sports
analytics due to its open source development and theavailability of additional features (packages) specificto many fields (Miller, 2015). First, the set of playing
data was divided into training data (n=102,922)
and validation data (n=44,110) in a ratio of 7:3
through random sampling. Subsequently, the training
and validation data were replicated (retaining theoriginals) for the analysis of the three methods.
(1) Decision tree
For the decision tree method, the rpart function of
R’s rpart package (Therneau and Atkinson, 2019) wasused. The objective variable was the measurement

Start
Divide dataset into learningdata(70%) and test data(30%) using
random sampling and save.

1

Decision tree

2

Random forest

LD = copied learning data.
TD = copied test data.

Gradient boosting decision tree

LD = copied learning data.
TD = copied test data.

LD = copied learning data.
TD = copied test data.

md (maximum depth) = 1

md (maximum depth) = 1

Build a “full growth model” with maximum
depth of md using LD.

Extract the highest performance model bytuning with Out-of-Bag error estimate
method (the number of trees set to 100 andthe other parameters are default) using LD.

Predict shot or not and calculate F-measureof full growth model using TD.

Predict shot or not and calculate F-measureof the tuned model using TD, save the value.

Create “pruning model” with one standard
error pruning method from full growthmodel.

no

Number of items > 2
yesPredict shot or not and calculate F-measureof pruning model using TD.
md < 30

Remove the item with the lowest ofimportance parameter in the tuned modelfrom LD and TD.

no

Do tuning by cross validation method (thenumber of sample divisions is 5, maximum
repetition is 2,000, the stop criterion is 100
times, and the other parameters are default).

Build a model with maximum depth of md
with tuned boosting maximum iterations
using LD.
Predict shot or not and calculate F-measureof the model using TD.
md < 10

no

yes

yes

Output all results ofrandom forest.

md = md + 1
Hold a model with the highest F-measurefrom all models, save the value.

Number of items > 22

md = md + 1
Hold a model with the highest F-measurefrom all models, save the value.

Number of items > 2

no

no

yes

yes
Remove the item with the lowest ofimportance parameter in the held model fromLD and TD.

Remove the item with the lowest ofimportance parameter in the held model fromLD and TD.

Output all results ofdecision tree.

Output all results of gradientboosting decision tree.

1

Adopt the best model from thethree decision tree methods,
based on the F-measure and thenumber of items.
End

Figure 1 Flow chart of predictive model construction
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Jo, H. et al.

item “shot” (binary data indicating whether a playerscored a goal or not), and the predictor variables
were all the remaining measurement items. A “fullgrowth model” without pruning and a “pruning
model” with pruning based on one standard deviation(Shimokawa et al., 2013) were constructed. In
both cases, validation data were applied to predictwhether a player would take a shot. A confusion
matrix was developed using the measured valuesof the measurement item “shot” and the predictedvalues of whether a player would take a shot. The
prediction accuracy was evaluated by calculating the
f-measure based on the precision and recall rates after
confirming the accuracy. The full growth and pruning
models were iteratively constructed from a maximum
depth of 1 to 30, and the model with the highest
f-measure was reserved from a total of 60 models tostore its value. To remove unnecessary measurement
items that did not contribute to the prediction results,
the items with the lowest importance (Breiman et al.,1984) in the reserved models were removed from thetraining and validation data, and the same process
was repeated until two items were left.
(2) Random forest
For the random forest method, the tuneRF functionof R’s randomForest package (Liaw and Wiener,
2002) was used. The number of measurement items
in a tree is one of the important hyperparameters in arandom forest, and the tuneRF function uses the outof-bag (OOB) error estimation method to tune theoptimal number of items. In performing the tuneRF
function, the number of trees to be created was set
to 100. Other hyperparameters were set to default to
extract the model with the best performance basedon the f-measure. The measurement items used in theobtained model with the lowest importance (Breiman,
2001) were eliminated, and the analysis was repeateduntil only two items remained.
(3) Gradient boosting decision tree
For the gradient boosting decision tree method, R’s
xgboost package (Chen et al., 2020) was used. We
tuned the maximum number of boosting iterations byrunning the cross validation method with the numberof dataset partitions set to 5, the maximum numberof iterations set to 2,000, the stopping criterion set to
100 times, and other hyperparameters set to default.
Trees with maximum depths from 1 to 10 werecreated using the tuning results, and the model withthe highest f-measure was reserved. As in the previous
two methods, the measurement items with the lowest
6

importance (Friedman, 2001) of the reserved models
were deleted, and the analysis was repeated until only
two items were left.
Eventually, models with predictor variables ranging
from 44 to 2 (129 models in total) were created for thedecision tree, random forest, and gradient boosting
decision tree. From among these, the best model was
determined based on the value of the f-measure andthe number of predictor variables.
The evaluation metrics of machine learning models
generally include accuracy, precision, recall, and thef-measure. A confusion matrix was created based on
the measured values of the measurement item “shot”
and the predicted results of the model. The true
positive (TP), false positive (FP), true negative (TN),and false negative (FN) were as follows:
True positive: The model correctly predicted that theplay “would end in a shot” when theactual value indicated a “shot.”
False positive: T he model incorrectly predicted that theplay “would end in a shot” when theactual value did not indicate a “shot.” he model correctly predicted that theTrue negative: T
play “would not end in a shot” when theactual value did not indicate a “shot.”
False negative: T he model incorrectly predicted that theplay “would not end in a shot” when
the actual value indicated a “shot.”Accuracy is calculated as (TP+TN)/(TP+FP+
TN+FN) and represents the probability of correctlypredicting whether a player takes a shot. In soccer, thenumber of plays that do not result in a shot is muchhigher, and 91.6% of the plays in this study did not
end in a shot. Thus, when the data is heavily biased
toward negative (or positive), accuracy tends to be
inevitably high; therefore, other evaluation metrics
such as precision and recall should also be consulted.
Precision is calculated as TP/(TP+FP) andrepresents the probability that a player actually takes
a shot in the play for which the model predicted theplayer would take a shot. Recall is calculated as TP/(TP+FN) and indicates the probability that the model
correctly predicted that a play would end in a shot.
Both fractions evaluate the predictive accuracy of true
positives; however, precision has the disadvantage
of not being able to judge the accuracy of a negative
prediction at all, even if the model produces anegative prediction that “a player will not take ashot.” Recall has the disadvantage of not being able
to judge the success or failure of predictions for the
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Offensive Play Measurement Items and Shot Prediction Model in Footballmeasured value that “a player did not take a shot.”
Therefore, the f-measure, which is the harmonic meanof precision and recall, was calculated and used as
an evaluation index of the prediction accuracy of themodel.
2.4.4. Comparison with the old set of measurement
items
In this study, since some of the old measurement
items were deleted and new items were added, it wasnecessary to verify which ones were superior. To thisend, a model for the old set of measurement items
was developed using the methods and conditions
used in the best model and compared with the new
set of measurement items. However, two items,“X-coordinate of the last action” and “Primaryarea,” which were discarded from the new set ofmeasurement items, were also removed from the old
set of measurement items, and 37 old measurement
items were analyzed as predictor variables.

2.5. Ethical Considerations
The data for this study were purchased from Data
Stadium Inc., and usage permission was obtained. In
addition, this study was conducted with the approvalof the Research Ethics Committee at the Faculty of
Health and Sport Sciences, University of Tsukuba(Approval No.: 30-28).

All measurement items were quantified on a perplay basis. “Dribble,” “Pass,” “Success of pass,”“Success rate of pass,” “Direct pass,” “Consecutive
direct pass,” “Through-pass,” “Success of throughpass,” “Cross,” “Success of cross,” “Trap,”“Rebound-ball,” “Flick-on,” and “Feed” measure
the number of times the action occurs in a play andits success rate. “Throw-in” indicates binary data
recorded as 1 when the first action of the play is athrow-in. “Total of attack actions” is the total numberof attack actions in one play. “Duration of attack”
represents the time taken from the beginning to theend of the play (in seconds). “Average time of attack
action” indicates the average time taken per action(in seconds). “Number of attackers” is the number ofattackers involved in the play.
“Total distance: TD,” “Total vertical distance:
TVD,” “Total horizontal distance: THD,” “Distance:
D,” “Vertical distance: VD,” “Horizontal distance:
HD,” “Maximum vertical distance: MVD,”“Maximum horizontal distance: MHD,” “Mean ofpass distance: MPD,” and “Standard deviation of passdistance: SDPD” are measurement items related tothe distance traveled by the ball (Figure 2). These arecalculated using the following formula based on thecoordinates where each action has occurred.
TD = ∑ i = 1 din－1

di = (xi+1－xi) 2 + (yi+1－yi) 2

3. Results

TVD = ∑ i = 1 vi
n－1

3.1. Developed measurement items
The Delphi method was applied by a groupof soccer experts. After reviewing the old set ofmeasurement items, five items, namely “X-coordinateof the last action,” “Primary area,” “Mean of moving
direction,” “Triple speed,” and “The number oftactical attackers” were deleted. Eleven new items,
namely “Success rate of pass,” “Success of throughball,” “Success of cross,” “Flick-on,” “Throw-in,”“Feed,” “Mean of pass distance,” “Standard deviationof pass distance,” “Forward propulsion,” “Widepropulsion,” and “Same direction” were created. The
old measurement items “Turn-back of sideward” and“Turn-back” were merged into one measurement item.
Finally, 45 items, including the objective variable“shot,” were developed as the new measurement
items (Table 1).
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・・・Formula ①・・・Formula ②vi = (xi+1－xi) 2
THD = ∑ i = 1 hi
n－1・・・Formula ③

hi = (yi+1－yi) 2
D = (xn－x1) 2 + (yn－y1) 2 ・・・Formula ④

VD = (xn－x1) 2 ・・・Formula ⑤

HD = (yn－y1) 2 ・・・Formula ⑥

MVD = (max{x1,…, xn}－min{x1,…, xn}) 2


・・・Formula ⑦7

Jo, H. et al.

Table 1 Measurement items and definitions between new and old groups
Item Measurement items
no. (in present study)
1 Shot2 Dribble
3 Pass
4 Success of pass
5 Success rate of pass
6 Direct pass
7 Consecutive direct pass
8 Through-ball
9 Success of through-ball
10 Cross
11 Success of cross
12 Trap
13 Rebound-ball
14 Flick-on
15 Throw-in
16 Feed
17 Total of attack action
18 Duration of attack
19 Average time of attack action
20 Number of attackers
21 Total distance

Old measurement items(in previous study)
Whether shot or not in one play.
Shot
The number of dribbles in one play.
DribbleThe number of passes (including fail) in one play.
Pass
The number of successful passes in one play.
Success of pass
The rate of successful passes in one play.
(New)
The number of direct pass (pass action with no trapping) in one play.
Direct pass
The maximum number of consecutive direct pass in one play.
Consecutive direct pass
The number of successful through-ball in one play.
Through-ball
The number of successful through-balls in one play.
(New)
The number of driving a cross ball in one play.
Cross
The number of successful cross-balls in one play.
(New)
The number of trapping (receive the ball and set it down at foot) a ball in one play.
Trap
The number of getting a rebound ball in one play.
Rebound-ball
The number of flick-on (touch the ball lightly to direction shift) in one play.
(New)
Whether the play started with throw-in.
(New)
Whether the play started with feeding a ball from goalkeeper.
(New)
The number of attack actions in one play.
Total of attack action
The time (seconds) from the start to the end of the attack.
Duration of attack
The average time (seconds) of attack action in one play. Formula is " = duration of attack / total of attack action".
Average time of attack action
The number of attackers involved in one play.
Number of attackers
The sum of the distance (meter) between an action and the next action in one play, excluding defensive actions. a + Total distanceb + c in Fig.2.
22 Total vertical distance
The sum of the vertical distance (meter) between an action and the next action in one play, excluding defensive
Total vertical distanceactions. v1 + v2 + v3 in Fig.2.
23 Total horizontal distance
The sum of the horizontal distance (meter) between an action and the next action in one play, excluding defensive Total horizontal distanceactions. h1 + h2 + h3 in Fig.2.
24 DistanceThe distance (meter) between the first action and the last action in one play, excluding defensive actions. D in Fig.2. Distance
25 Vertical distance
The vertical distance (meter) between the first action and the last action in one play, excluding defensive actions. V Vertical distancein Fig.2.
26 Horizontal distance
The horizontal distance (meter) between the first action and the last action in one play, excluding defensive actions. Horizontal distance
V in Fig.2.
27 Maximum vertical distance
The maximum value (meter) in each vertical distances in one play. Maximum vertical distance is v1 in Fig.2, andMaximum vertical distancemeans the depth of attack.
28 Maximum horizontal distance
The maximum value (meter) in each horizontal distances in one play. Maximum horizontal distance is h3 in Fig.2, Maximum horizontal distanceand means the width of attack.
29 Mean of pass distance
The mean value of pass distances (meter) in one play.
(New)
30 Standard deviation of pass distance The standard deviation of pass distances (meter) in one play. Large SD means attack players use short passes and(New)long passes.
31 Area of attack
The sum of triangle areas (sq. meter) created by consecutive three actions in one play, excluding defense action. In Area of attack
Fig.3, area of attack is triangle①②③ + triangle ②③④.
32 Forward propulsion
Set forward (0 degrees) to 1, rightward (90 degrees) to 0, backward (180 degrees) to -1, leftward (270 degrees) to 0, (New)and converted the angles of ball moving into value between -1 and +1 (Fig.4). The forward propulsion is the total ofthe values, and the more attackers pass or dribble forward, the more the total value is larger.
33 Wide propulsion
Set rightward (90 degrees) and leftward (270 degrees) to 1, forward (0 degrees) and backward (180 degrees) to 0, (New)and converted the angles of ball moving into value between -1 and +1 (Fig.4). The wide propulsion is the total of thevalues, and the more attackers pass or dribble widely (leftward or rightward), the more the total value is larger.
34 Proportion of forward
The percentage of forward moving actions in one play excluding defensive actions. The forward moving action is
Proportion of forwarddefined as the angle between 315 degrees and 45 degrees in Fig. 5.
35 Proportion of backward
The percentage of backward moving actions in one play excluding defensive actions. The backward moving action Proportion of backward
is defined as the angle between 135 degrees and 225 degrees in Fig. 5.
36 Proportion of rightward
The percentage of rightward moving actions in one play excluding defensive actions. The rightward moving action Proportion of rightward
is defined as the angle between 45 degrees and 135 degrees in Fig. 5.
37 Proportion of leftward
The percentage of leftward moving actions in one play excluding defensive actions. The leftward moving action is Proportion of leftward
defined as the angle between 225 degrees and 315 degrees in Fig. 5.
38 Trun backThe total of actions that the angle (internal angle) formed by three consecutive actions is under 60 degrees. The line Trun-backtied 1st action and 2nd action sets to 0 degrees, the used angle is formed by 1st, 2nd, and 3rd actions (Fig.6).
39 Change in direction
The total of actions that the angle (internal angle) formed by three consecutive actions is over 60 degrees and under Change in direction
120 degrees. The line tied 1st action and 2nd action sets to 0 degrees, the used angle is formed by 1st, 2nd, and 3rdactions (Fig.6).
40 Same direction
The total of actions that the angle (internal angle) formed by three consecutive actions is over 120 degrees and 180 (New)degrees or less. The line tied 1st action and 2nd action sets to 0 degrees, the used angle is formed by 1st, 2nd, and3rd actions (Fig.6).
41 Twice speed
The number of actions that moved the ball more than twice as fast as the previous action.
Twice speed
42 Penalty area
Whether penetrated into penalty area in one play (Fig. 7).
Penalty area
43 Side of penalty area
Whether penetrated into side of penalty area in one play (Fig. 7).
Side of penalty area
44 30m line
Whether penetrated into 30m area from goal line in one play (Fig. 7).
30m line
45 Vital area
Whether penetrated into vital area in one play (Fig. 7).
Vital area
(Removed)
X-coordinate of the last action(Removed)
Primary area
(Removed)
Mean of moving direction
Integrated into "Turn back"Same with "Turn back"Turn-back of sideward(Removed)
Triple speed(Removed)
The number of tactical attackers"(Removed)" is included in the previous study, but is not used in this study. "(New)" is a newly created measurement item in present study.
Definitions

MHD = (max{y1,…, yn}－min{y1,…, yn}) 2・・・Formula ⑧
MPD = TnD

8・・・Formula ⑨

SDPD =n∑ i=1(di－d̄ ) 2
n・・・Formula ⑩

In the above formula, i denotes the attacking action
number, n denotes the number of attacking actions,and x and y are the coordinates of the location where
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Offensive Play Measurement Items and Shot Prediction Model in FootballVer�calHorizontal

3

2

1

The place where the play started(getting the ball or set-play).

4

The place where the play ended(losing the ball or making a shot).

1

Ball movement (pass or dribble).

4

Total distance (TD) = d1 + d2 + d3
Total vertical distance (TVD) = v1 + v2 + v3
Total horizontal distance (THD) = h1 + h2 + h3
Distance = D
Vertical distance (VD) = V
Horizontal distance (HD) = H
Maximum vertical distance (MVD) = v1
Maximum horizontal distance (MHD) = h3A�ack direc�on

Figure 2 Definition and the example of measurement items related to distancethe action occurred. However, since a play may
contain defensive actions (tackles and fouls), whenconverting action data into play data, a program wascreated to skip the loop if either or both of the actions
i and i+1 were defensive actions.
“Area of attack: AA” was calculated based on
Heron’s formula using the following formula (Figure3): = ∑ n－2s(s－a)(s－b)(s－c)
AA
i=1

3
2

1

・・・Formula ⑪4s = a + 2b + ca =(xi－xi+1) 2 + (yi－yi+1) 22b =(xi+1－xi+2) 2 + (yi+1－yi+2) 22c =(xi+2－xi) 2 + (yi+2－yi) 2
2

In this formula a, b, and c indicate the distances
between two points at each coordinate of the threeconsecutive actions. The area of the triangle formed
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Area of a�ack (AA)= triangle ①②③ + triangle ②③④

1

The place where the play started(getting the ball or set-play).

4

The place where the play ended(losing the ball or making a shot).
Ball movement (pass or dribble).

Figure 3 Definition and the example of “area of attack”
9Jo, H. et al.

propulsion, w the degree of wide propulsion, and d
the distance between two points.
“Proportion of forward: PF,” “Proportion of
backward: PB,” “Proportion of rightward: PR,” and“Proportion of leftward: PL” were in the old set ofmeasurement items and continued to be used in the
new set. These indices were calculated by counting
the different directions (forward, back, right, and left)
in which each action takes place in a play and by
calculating the ratio of these directions. The angle ofeach direction was defined as 0 degrees in the attack
direction (vertical direction), more than 315 degrees
but less than 45 degrees as forward, more than 45
degrees but less than 135 degrees as rightward,
more than 135 degrees but less than 225 degrees as
backward, and more than 225 degrees but less than
315 degrees as leftward (Figure 5).
“Turn back” is a movement to return the ball tothe direction it came from. “Change in direction”
is a movement to change direction from vertical tohorizontal or horizontal to vertical. “Same direction”
refers to a movement to connect the ball in the same
direction. “Turn back” is defined as the internalangle formed by three consecutive attacking actions
between 0 and 60 degrees. “Change in direction”
is defined as the internal angle formed between

by the three consecutive actions is calculated bysumming the areas of the n-2 triangles in the play.
However, a program was created to exclude actions
from the calculation if any or all of the actions i, i+1,
and i+2 were defensive actions.
“Forward propulsion: FP” and “Wide propulsion:
WP” were calculated by determining and summing
the degree of forward propulsion, an index using
the direction and distance of propulsion in eachaction (Figure 4). The angles were set to 0 degrees
for forward, 90 degrees for rightward, 180 degrees
for backward, and 270 degrees for leftward. The
degree of forward propulsion was defined to be 1
for 0 degrees, -1 for 180 degrees, and 0 for 90 and
270 degrees. The degree of wide propulsion was
defined to be 0 for 0 and 180 degrees and 1 for 90 and
270 degrees. “Forward propulsion: FP” and “Widepropulsion: WP” were calculated using the following
formula:n－1
FP = ∑ i = 1 ( f i d i)

・・・Formula ⑫n－1
WP = ∑ i = 1 ( wi d i)

・・・Formula ⑬

In this formula, f indicates the degree of forward

Degree offorwardpropulsion(f )( 270°)

1( 0°)( 180°)

d1 = 10

f1 = 1w1 = 03( 90°)( 270°)

Degree of wide
propulsion (w)( 0°)( 180°)

5( 90°)

f4 = -1
w4 = 0d4 = 82

Forward propulsion (FP)=1×10+(-0.4)×12

d2 = 12f2 = -0.4
w2 = 0.6

d3 = 15
f3 = 0w3 = 14

di = distance (m)1The place where the play started(getting the ball or set-play).

4

The place where the play ended(losing the ball or making a shot).

+0×15+(-1)×8= - 2.8

Wide propulsion (WP)

=
+
+
+

=0×10

+0.6×12+1×15+0×8= 22.2

Ball movement (pass or dribble).

Figure 4 Definition and the example of “forward propulsion” and “wide propulsion”
10

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Offensive Play Measurement Items and Shot Prediction Model in Football2

Proportion of forward (PF)50.0%（3 passes or dribbles）

1

Proportion of backward (PB)
16.6%（1 passes or dribbles ）3

Proportion of rightward (PR)33.3%（2 passes or dribbles ）

5

4

7

Proportion of leftward (PL)
0%（0 passes or dribbles ）6

1

The place where the play started (getting the ball or set-play).

7

The place where the play ended (losing the ball or making a shot).
Ball movement (pass or dribble).

Figure 5 Definition and the example of items related to the ratio of movement directions(actioni-1)

Turn back(actioni+1)(actioni)

Change indirec�on2
3

Change indirec�onSame
direc�on(actioni+1)(actioni+1)

Turn back1

Same
direc�on4

5

1

The place where the play started(getting the ball or set-play).

5

The place where the play ended(losing the ball or making a shot).
Ball movement (pass or dribble).

Figure 6 Definition and the example of items related to the angle of ball movement

60 and 120 degrees. “Same direction” is definedas the internal angle formed between 120 and 180
degrees (Figure 6). Furthermore, the number ofthese movements in a play was measured. However,a program was designed to exclude movements fromthe calculation if any or all of the actions i, i+1, andi+2 were defensive actions.
Football Science Vol.19, 1-21, 2022
https://www.jssf.net/home.html“Twice speed” is a binary data set where 1 isrecorded when the speed of the ball is at least twice
as fast as the previous action. The speed of the ballis calculated by dividing the distance between two
consecutive actions by the time required. “Penalty
area,” “Side of penalty area,” “30 m line,” and “Vitalarea” are binary data where 1 is recorded if players
11Jo, H. et al.

Primaryarea (in old measurement items)

3m
3m

Side of penalty
areaSide of penalty
area

Penalty area
3m
3m

30m

Vitalarea
30m line

Figure 7 Definition of items related to area penetrationenter the area even once (Figure 7).The penalty areaand vital area are partially overlapped, but this had asmall effect on the analysis results. In addition, since
this definition of areas is provided by Data Stadium
Inc., it is desirable to keep it unchanged as much as
possible (if it is changed, the process of changing
the definition will have to be incorporated into theanalysis flow when others analyze the data).

3.2. Conversion to play data
After converting the action data into play data
based on the definition of the new set of measurement
items, the converted play data (excluding plays
starting with a corner kick, penalty kick, and freekick) comprised 147,032 rows. The basic statisticsof the numerical measurement items are presented inTable 2, and the frequency distribution of the binary
measurement items is indicated in Table 3.

3.3. Validation of the prediction model
The results of the prediction models for thenumber of items from 44 to 2 for the decision tree,
random forest, and gradient boosting decision tree
are presented in Table 4. The range of accuracy
was 0.958 to 0.943 in the decision tree, 0.952 to
0.928 in the random forest, and 0.980 to 0.943 in thegradient boosting decision tree. At the same time, thef-measure ranged from 0.735 to 0.660 in the decision
tree, 0.678 to 0.516 in the random forest, and 0.876 to
0.660 in the gradient boosting decision tree, with the12gradient boosting decision tree tending to be higher
than the other two methods.
In the gradient boosting decision tree, the models
with 44 to 23 items indicated an f-measure of 0.85 or
higher. However, when the number of items reached
22, the f-measure dropped significantly to 0.816.
Therefore, the model with 23 items in the gradientboosting decision tree was selected as the bestmodel. Predictor variables were reduced one by one
based on their low importance, but the decision tree
and gradient boosting decision tree tended to haveeliminated similar items. By contrast, the random
forest indicated a different trend. For instance, “Twice
speed” was removed in the model with 43 items inthe decision tree and the gradient boosting decision
tree, and it was removed in the model with 38 itemsin the random forest.
In the best model, “Vital area” constituted 36.6%of the importance (Figure 8). Next, “Success rate ofpass” constituted 11.3%, “Pass” constituted 9.4%,“Total of attack action” constituted 8.8%, “Trap”
constituted 5.1%, “Penalty area” constituted 4.1%,
and “Horizontal distance” constituted 2.3%. The othermeasurement items constituted less than 2%.

3.4. Comparison with the old set of measurement
items
Since the gradient boosting decision tree indicatedthe highest prediction accuracy, this method wasapplied to the old set of measurement items (Table 5)
to compare with the new set of measurement items.
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Offensive Play Measurement Items and Shot Prediction Model in Football

Table 2 Statistics for measurement items of numerical type in play data
Item
no.

Item name2 Dribble
3 Pass
4 Success of pass
5 Success rate of pass
6 Direct pass
7 Consecutive direct pass
8 Through-ball
9 Success of through-ball
10 Cross
11 Success of cross
12 Trap
13 Rebound-ball
14 Flick-on
16 Feed
17 Total of attack action
18 Duration of attack
19 Average time of attack action
20 Number of attackers
21 Total distance
22 Total vertical distance
23 Total horizontal distance
24 Distance
25 Vertical distance
26 Horizontal distance
27 Maximum vertical distance
28 Maximum horizontal distance
29 Mean of pass distance
30 Standard deviation of pass distance
31 Area of attack
32 Forward propulsion
33 Wide propulsion
34 Proportion of forward
35 Proportion of backward
36 Proportion of rightward
37 Proportion of leftward
38 Trun back
39 Change in direction
40 Same direction
41 Twice speed

Data type
Integer
Integer
Integer
Decimals
Integer
Integer
Integer
Integer
Integer
Integer
Integer
Integer
Integer
Integer
Integer
Integer
Decimals
Integer
Decimals
Decimals
Decimals
Decimals
Decimals
Decimals
Decimals
Decimals
Decimals
Decimals
Decimals
Decimals
Decimals
Decimals
Decimals
Decimals
Decimals
Integer
Integer
Integer
IntegerStandard
Max3rd
1st
MedianDeviation imum
quartilequartile
0.10
0.30
3
0
0
0
3.47
3.30
44
4
2
1
2.68
3.16
41
4
2
1
62.07
35.84
100.00100.00
66.70
50.00
0.55
1.01
16
1
0
0
0.44
0.72
7
1
0
0
0.09
0.30
3
0
0
0
0.05
0.22
3
0
0
0
0.11
0.33
4
0
0
0
0.02
0.16
3
0
0
0
2.11
2.56
32
3
1
0
0.19
0.43
6
0
0
0
0.03
0.17
2
0
0
0
0.09
0.28
1
0
0
0
6.06
5.81
78
8
4
2
10.43
10.79
128
14
7
3
1.66
1.00
19.00
2.00
1.56
1.00
3.24
1.93
11
4
3
2
55.14
64.95
826.03
77.01
30.94
12.13
34.65
38.25
499.83
50.67
21.67
7.33
34.90
46.28
604.33
47.00
16.83
5.00
27.72
22.01
114.70
42.29
22.14
10.17
16.28
23.74
102.17
29.50
11.33
0.00
14.37
14.75
68.50
21.00
9.34
3.17
31.56
28.31
104.67
52.67
25.83
4.33
29.37
24.83
69.33
52.00
26.83
3.33
13.02
10.01
92.17
18.15
13.04
6.52
4.34
5.84
58.87
7.51
1.06
0.00
1211.25 1992.04 28700.50 1626.00
262.00
0.00
12.77
18.93
118.71
22.53
8.14
0.00
27.68
37.69
499.61
36.90
12.90
3.51
34.21
100.00
50.00
33.33
0.00
37.23
13.93
22.93
100.00
21.43
0.00
0.00
20.12
26.62
100.00
33.33
6.25
0.00
19.66
26.65
100.00
33.33
0.00
0.00
0.84
1.44
21
1
0
0
1.57
2.43
36
2
1
0
1.33
2.18
30
2
0
0
0.09
0.32
4
0
0
0
Mean

Minimum

Freq.

0
0
0
0.00
0
0
0
0
0
0
0
0
0
0
0
0
0.00
0
0.00
0.00
0.00
0.00
-93.50
0.00
0.00
0.00
0.00
0.00
0.00
-74.36
0.00
0.00
0.00
0.00
0.00
0
0
0
0

147,032
147,032
147,032
147,032
147,032
147,032
147,032
147,032
147,032
147,032
147,032
147,032
147,032
147,032
147,032
147,032
147,032
147,032
147,032
147,032
147,032
147,032
147,032
147,032
147,032
147,032
147,032
147,032
147,032
147,032
147,032
147,032
147,032
147,032
147,032
147,032
147,032
147,032
147,032

Table 3 Frequency distribution for measurement items of binary type in play data
No.

Item name1 Shot15 Throw-in
42 Penalty area
43 Side of penalty area
44 30m line
45 Vital area

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Frequency
Data type
Yes
Binary12,346
Binary37,127Binary11,042Binary15,498Binary35,766
Binary26,887

No
134,686
109,905
135,990
131,534
111,266
120,145

Ratio(%)
Total
Yes
147,0328.40
147,032
25.25
147,032
7.51
147,032
10.54
147,032
24.33
147,032
18.29

No
91.60
74.75
92.49
89.46
75.67
81.71

Total
100
100
100
100
100
100

13

Jo, H. et al.

Table 4 Prediction models in decision tree, random forest, and gradient boosting decision tree methods
Decision tree
NumOne Item to be removed
ber of Max
S.E.
items depth prun- Item nameing

Random forest
importance

Item to be removed
Numer ofFitems in a
Accu- Prec- RemeaItem nametreeracy ision callsure.954 .702 .743 .722
6
Flick-on

Gradient boosting decision tree

Evaluation indices

Evaluation indices
importance
44.9

Max
FAccu- Prec- Remea- depthracy ision callsure.952 .594 .780 .675
3

Item to be removed
number ofboosting
repetion Item nameEvaluation indices
importanceAccu- Precracy ision.850

Fmeasure.905 .876

Recall

44

16

Not

Flick-on

1.6

1454

Flick-on

0.000

.980

43

16

Not

Twice speed

5.0

.954

.702

.743

.722

6

Proportion of
backward

49.1

.952

.596

.782

.676

4

805

Twice speed

0.001

.980

.849

.903

.875

42

16

Not

Feed

21.5

.954

.702

.743

.722

6

Feed

74.0

.952

.597

.777

.675

4

399

Consecutive direct
pass

0.001

.979

.843

.907

.874

41

16

Not

Trun back

22.3

.954

.703

.743

.722

6

Maximumhorizontal distance

84.1

.952

.595

.778

.674

4

1340

Feed

0.001

.980

.850

.901

.875

40

16

Not

Proportion of
backward

57.4

.954

.702

.744

.722

6

Total distance

79.4

.951

.595

.775

.673

3

836

Success of throughball

0.001

.979

.848

.902

.874

39

16

Not

Same direction

59.4

.954

.701

.743

.722

6

Proportion of
forward

91.7

.951

.596

.775

.674

3

1029

Through-ball

0.002

.980

.848

.905

.875

38

16

Not

Proportion ofrightward

70.0

.954

.703

.741

.721

6

Twice speed

102.5

.951

.597

.773

.674

3

1345

Side of penalty area

0.002

.979

.848

.902

.874

37

16

Not

Change in direction

75.4

.954

.704

.742

.722

6

Total verticaldistance

107.6

.951

.598

.772

.674

3

1223

Trun back

0.002

.979

.847

.903

.874

36

14

Not

Proportion of
leftward

69.4

.954

.702

.743

.722

6

Wide propulsion

107.6

.952

.603

.775

.678

4

1002

Direct pass

0.003

.979

.851

.900

.874

35

14

Not

Through-ball

85.2

.954

.701

.744

.722

5

Direct pass

103.1

.952

.590

.782

.673

4

821

Same direction

0.003

.979

.844

.900

.871

34

14

Not

Throw-in

88.4

.954

.702

.742

.721

5

Success of pass

108.9

.951

.590

.780

.672

4

774

30m line

0.003

.979

.845

.897

.870

33

14

Not

Side of penalty area

107.8

.954

.705

.741

.723

5

Total of attack
action

129.6

.952

.591

.782

.673

5

514

Change in direction

0.004

.978

.835

.892

.863

32

14

Not

Proportion of
forward

105.7

.954

.700

.742

.721

5

Success of throughball

128.4

.951

.592

.774

.671

3

1539

Proportion ofrightward

0.004

.979

.845

.899

.871

31

14

Not

Consecutive direct
pass

131.3

.954

.698

.740

.719

5

S.D. of passdistance

142.6

.951

.593

.772

.671

3

883

Proportion of
leftward

0.003

.979

.844

.902

.872

30

14

Not

Direct pass

134.2

.954

.699

.740

.719

5

Total horizontal
distance

146.5

.951

.594

.769

.670

4

1037

Throw-in

0.005

.978

.846

.892

.868

29

14

Not

Rebound-ball

161.5

.954

.699

.741

.719

5

Duration of attack

154.8

.950

.593

.766

.669

3

1282

Cross

0.004

.978

.834

.895

.863

28

16

Not

Maximumhorizontal distance

193.1

.953

.695

.733

.713

5

Proportion ofrightward

161.7

.950

.594

.761

.667

4

941

Success of pass

0.005

.977

.833

.886

.859

27

21

Not

Dribble

214.7

.953

.697

.731

.714

5

Maximum verticaldistance

164.7

.950

.595

.765

.669

4

1147

Proportion of
backward

0.006

.977

.835

.888

.861

26

13

Not

S.D. of passdistance

203.3

.954

.677

.750

.711

5

Proportion of
leftward

162.0

.950

.596

.762

.669

3

632

Proportion of
forward

0.003

.977

.829

.895

.861

25

13

Not

Horizontal distance

210.9

.954

.682

.750

.714

5

Trap

164.6

.950

.597

.760

.669

3

898

Total horizontal
distance

0.006

.978

.832

.894

.862

24

14

Not

Cross

248.6

.955

.680

.757

.716

4

Consecutive direct
pass

136.2

.949

.572

.762

.654

4

884

Success of cross

0.008

.977

.833

.891

.861

23

14

Not

Maximum verticaldistance

262.4

.954

.675

.749

.710

4

Average time of
attack action

133.1

.950

.582

.766

.661

4

616

Dribble

0.010

.977

.827

.890

.857

22

14

Not

Forward propulsion

271.0

.953

.678

.745

.710

4

Trun back

127.2

.949

.580

.755

.656

5

267

Total verticaldistance

0.011

.970

.786

.847

.816

21

3

Not

Total verticaldistance

1.7

.945

.669

.672

.671

4

Same direction

130.4

.948

.561

.759

.645

4

356

Rebound-ball

0.010

.970

.788

.850

.818

20

18

Not

Area of attack

417.5

.951

.670

.731

.699

4

Forward propulsion

131.8

.947

.552

.760

.639

4

291

S.D. of passdistance

0.010

.970

.781

.853

.815

19

18

Not

Distance

429.3

.954

.682

.747

.713

4

Horizontal distance

125.4

.947

.552

.760

.639

5

831

Number of
attackers

0.014

.970

.787

.844

.815

18

14

Not

Wide propulsion

349.4

.954

.686

.749

.716

4

Side of penalty area

117.8

.948

.551

.761

.640

4

524

Maximum verticaldistance

0.016

.969

.780

.843

.810

17

14

Not

Mean of passdistance

384.9

.955

.687

.757

.720

4

Dribble

111.0

.947

.549

.760

.637

8

41

Wide propulsion

0.012

.968

.765

.843

.802

16

28

Run

Total horizontal
distance

283.9

.958

.683

.794

.735

4

Change in direction

103.7

.947

.542

.760

.633

6

131

Maximumhorizontal distance

0.017

.969

.779

.846

.812

15

20

Run

Average time of
attack action

325.7

.958

.683

.794

.734

6

Vertical distance

125.8

.947

.554

.747

.636

8

43

Mean of passdistance

0.020

.968

.762

.844

.801

14

18

Not

Success of throughball

540.6

.955

.686

.755

.719

6

Throw-in

131.7

.946

.548

.744

.631

8

44

Distance

0.021

.968

.766

.841

.801

13

18

Not

Number of
attackers

694.8

.956

.696

.758

.725

6

Number of
attackers

123.0

.946

.541

.751

.629

8

61

Average time of
attack action

0.024

.968

.768

.839

.802

12

25

Not

Trap

720.0

.956

.689

.762

.724

6

Area of attack

113.9

.946

.529

.752

.621

7

91

Forward propulsion

0.026

.968

.768

.836

.801

11

6

Not

Total of attack
action

168.0

.949

.696

.700

.698

3

Through-ball

73.4

.944

.508

.745

.604

7

87

Area of attack

0.032

.968

.768

.835

.801

10

6

Not

Success of pass

173.9

.949

.700

.696

.698

6

Distance

128.8

.944

.532

.735

.617

7

55

Vertical distance

0.034

.968

.772

.833

.801

9

6

Not

Total distance

199.7

.949

.700

.696

.698

3

Rebound-ball

76.3

.944

.551

.716

.623

8

45

Horizontal distance

0.042

.967

.771

.829

.799

8

6

Not

Duration of attack

388.5

.949

.698

.697

.697

4

Mean of passdistance

111.7

.944

.568

.710

.631

10

40

Penalty area

0.050

.966

.765

.818

.790

7

3

Not

Vertical distance

337.7

.945

.669

.672

.671

4

Pass

127.5

.943

.559

.708

.625

10

32

Duration of attack

0.068

.964

.754

.811

.781

6

3

Not

Pass

604.1

.945

.669

.672

.671

4

Cross

142.3

.940

.420

.760

.541

9

32

Total distance

0.070

.964

.744

.818

.779

5

5

Not

Success of cross

896.5

.944

.685

.661

.673

4

30m line

58.4

.939

.422

.751

.540

10

33

Trap

0.096

.965

.757

.816

.785

4

3

Not

30m line

1358.9

.943

.682

.655

.668

2

Success of cross

486.4

.939

.384

.788

.516

6

14

Pass

0.083

.949

.674

.709

.691

3

6

Not

Penalty area

1871.3

.943

.698

.650

.673

2

Success rate of pass

1222.2

.939

.392

.767

.519

10

21

Total of attack
action

0.077

.945

.682

.674

.678

2

3

Not

Success rate of pass

1877.0

.943

.661

.659

.660

1

Penalty area

2270.5

.928

.475

.595

.528

2

1

Success rate of pass

0.293

.943

.661

.659

.660

The range of accuracy for the old set of measurement
measurement items in the model with 23 items. These
items was 0.958 to 0.897, and the f-measure ranged results indicate that the prediction accuracy is higher
from 0.799 to 0.528. By the number of items, the in the new set of measurement items.
f-measure was 0.795 for the old set of measurement
items, 0.874 for the new set of measurement itemsin the model with 37 items, and 0.785 for the old setof measurement items and 0.857 for the new set of14

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Offensive Play Measurement Items and Shot Prediction Model in Football

4. Discussion
4.1. Measurement items
This study was based on the measurement items of
Jo et al. (2014), from which six items were deleted(including the integrated items) and 11 new items
were created. This section first discusses the reasons
why the six items were removed. The “X-coordinateof the last action” indicates where the last action of
the play ended. If it ends in the front, this indicates
how far into the opponent's territory the player has
advanced; thus, it is considered to be a measurement
item that helps, to a great extent, to determine
whether a player takes a shot. However, this study
was intended to predict whether the play would end
in a shot. Therefore, It is inappropriate as a predictor
variable because the result shows whether the player
was able to take a shot once the play is over.
The “Primary area” is the area extending from the
5.5-meter line of the goal area to 3 meters in frontof and behind the penalty area. It is a measurement
item that indicates the entry into the area closest to
the opponent’s goal post among the old measurement

Vital area
Success rate of pass
Pass
Total of attack action
Trap
Penalty area
Horizontal distance
Average time of attack action
Vertical distanceArea of attack
Duration of attack
Forward propulsion
Distance
Total distance
Maximum vertical distance
Mean of pass distance
Maximum horizontal distance
Number of attackers
Rebound-ballWide propulsion
Standard deviation of pass distance
Total vertical distance
Dribble

0.051
0.041
0.023
0.019
0.019
0.018
0.018
0.016
0.015
0.015
0.013
0.013
0.012
0.012
0.011
0.011
0.011
0.010
0.010
0

items. When a player enters the primary area, theplayer is highly likely to take a shot. In fact, theshooting rate for the plays in which the player entered
the primary area was 79%. In other words, the model
can predict whether a player will take a shot 79% of
the time based merely on the item “Primary area.”This approach reduces the need to apply machinelearning methods. In addition, since only 7.51% of
the plays entered the primary area, it would be ideal
to develop a model with high prediction accuracy
without this item; thus, it was removed from the new
set of measurement items. Since this also applies
to the old set of measurement items, this item was
not used in the comparison of prediction accuracy
between the old and new sets of measurement items.
The “Mean of moving direction” is the averageof the moving directions of each action, but it was
judged inappropriate as it would be close to zero
when the opponent was propelled from side to side (itinvolves a calculation process of summing positive
and negative values). Instead, new measurement
items “Wide propulsion,” “Turn back,” “Change indirection,” and “Same direction” were added.
As “Triple speed” and “Twice speed” refer to the

0.113
0.094
0.088

0.1
0.2
0.3
Importance (Gain parameter in result of xgboost)

0.366

0.4

Figure 8 Importance of the best model (23 measurement items in the gradient boosting decision tree)
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15

Jo, H. et al.

Table 5 The shot prediction models in gradient boosting decision tree using old measurement items group
Number of
items

Max
depth

37
36
35
34
33
32
31
30
29
28
27
26
25
24
23
22
21
20
19
18
17
16
15
14
13
12
11
10
9
8
7
6
5
4
3

4
3
2
4
4
4
3
3
4
3
3
5
3
3
4
6
4
4
4
4
7
6
8
8
10
8
10
8
8
10
9
9
7
2
7

number of Item to be removed
boosting
repetition Item name
411
595
676
337
359
330
481
445
331
688
406
318
415
596
559
137
354
346
315
382
142
181
28
30
32
32
30
22
26
29
19
46
18
1
1

Triple speed
Trun-backConsecutive direct passTurn-back of sideward
Side of penalty area
Twice speed
Change in direction
DribbleNumber of attackers
Through-ballMean of moving direction
30m line
Total horizontal distance
Proportion of backward
Trap
Total vertical distance
Rebound-ball
Proportion of rightward
Cross
The number of tactical attackers
Proportion of forward
Proportion of leftward
Area of attack
Vertical distance
Duration of attack
Maximum horizontal distance
Success of pass
Maximum vertical distance
Total distance
Horizontal distance
Distance
Direct pass
Pass
Penalty area
Total of attack action

same action, they were deleted from the perspectiveof linear dependence. Although Jo et al. (2014)
mentioned technology and tactics in their analysis,“The number of tactical attackers” was also removed
as this study did not use the concept of technology
and tactics.
The reason why some new items were createdis discussed next. There are three types of passes:
normal pass, through-pass, and cross. Although theold set of measurement items included “Success ofpass,” “Success of through-pass” and “Success ofcross” were not included. “Pass” includes passes thatwere not received by a teammate. “Success of pass”
counts only passes received by a teammate. As both
qualitatively differ, “Through-pass” and “Cross” were16

Evaluation indices
ImportanceAccuracy

Precision

RecallF-measure

0.002
0.001
0.000
0.002
0.002
0.002
0.002
0.002
0.003
0.003
0.005
0.008
0.006
0.008
0.010
0.013
0.012
0.011
0.012
0.014
0.023
0.023
0.016
0.019
0.028
0.023
0.029
0.020
0.029
0.070
0.059
0.099
0.103
0.239
0.058

.957
.958
.958
.958
.958
.957
.957
.958
.956
.958
.957
.956
.956
.957
.955
.955
.956
.955
.955
.954
.952
.953
.950
.950
.951
.951
.950
.948
.949
.948
.946
.947
.927
.907
.897

.754
.751
.755
.757
.751
.756
.755
.757
.747
.762
.750
.747
.744
.756
.751
.740
.749
.740
.743
.738
.721
.728
.707
.708
.717
.713
.714
.687
.691
.700
.680
.705
.583
.538
.526

.840
.846
.845
.846
.847
.838
.837
.842
.835
.837
.837
.831
.838
.840
.824
.831
.831
.831
.833
.828
.818
.821
.817
.817
.813
.816
.809
.812
.812
.806
.797
.790
.700
.582
.530

.795
.796
.798
.799
.796
.795
.794
.797
.788
.798
.791
.787
.788
.796
.785
.783
.788
.783
.785
.781
.766
.771
.758
.759
.762
.761
.759
.745
.747
.749
.734
.745
.636
.559
.528

unified and a measurement item for the number ofsuccesses of each was added.
The number of plays with no through passes was
132,487 (91.4% of the total), and the number of playswith no crosses was 132,487 (90.1% of the total).
When these numbers are zero, the denominator iszero; therefore, the success rate cannot be calculated
and is a missing value in the data set. The items“Success rate of through-pass” and “Success rateof cross” were not created as it would be difficult tohandle items with a missing number exceeding 90%.
However, as there were 7,711 plays (5.2% of thetotal) with no normal passes, the item “Success rateof pass” was added.
“Flick-on” is the action of lightly touching theFootball Science Vol.19, 1-21, 2022
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Offensive Play Measurement Items and Shot Prediction Model in Footballball to change its pass course. Flick-on was added
as a new item because it can surprise the opposing
defender if successful. “Feed” is the pass after the
goalkeeper has caught the ball. In modern soccer,
there is often a strategy called “build-up” in which
the goalkeeper and the defender build up the attack.
“Feed” was added as a new item to distinguishit. “Throw-in” was another new entry added to
distinguish between attacks that begin with a throwin and attacks after the ball has been taken from the
opposing defender.
“Mean of pass distance” is the average distance ofpasses in a play. A larger value indicates that morelong passes were used, and a smaller value indicatesthat more short passes were used. “Standard deviationof pass distance” is the standard deviation of the passdistance in a play. A large value indicates that both
long and short passes were used.
“Forward propulsion” indicates the amount offorce that carries the ball forward. “Wide propulsion”
indicates the amount of force that propels the ballfrom side to side. As it is an index that combines
the angle at which the ball moves forward and the
distance, it has characteristics different from themeasurement items that only measure distance orangle.
The old set of measurement items contained items
to measure the forward-backward turn and the leftright turn, but in the new set of measurement items,
these two were combined into “Turn back.” “Turnback” refers to the movement of returning the ballto the direction it came from, such as returning a
received ball to the same player or sending a loweredball back to the front.
“Change in direction” is a measurement item
included in the old set of measurement items. It refers
to a movement that converts a horizontal pass into avertical pass, and vice versa. If, in addition to “Turnback” and “Change in direction,” there was an item
to measure the movement of a horizontal pass into ahorizontal direction and a vertical pass into a verticaldirection, it would be possible to measure all theangles the ball travels. Therefore, “Same direction”
was created.

4.2. Conversion to play data
Before analyzing large-scale data, it is necessary
to analyze the data structure and preprocess it todeal with sparsity and scaling of different data scales
Football Science Vol.19, 1-21, 2022
https://www.jssf.net/home.html(unification of scales) (Decroos, 2020). In this study,
the action data were converted into play data tocreate the data structure necessary for developing theshot prediction model. As the decision tree method
can be used for both quantitative and qualitative
variables, scaling was determined to be unnecessary
at this point. The “Penalty area” and the “Vital area,”
which are closest to the goal, were considered tocontribute greatly to the number of shots. However,as the attacks leading up to them are also important,although differences in the size of the contribution
would be apparent, sparsity was also considered to
not be a problem.
After converting 1,312,117 lines of action data,
147,032 lines of play data were generated, with anaverage of 8.9 actions per play. “Dribble” indicateda lower average of 0.1 times per play (once in 10
plays), but this may be due to the measurement
criteria. When the ball was carried for a certain lengthof time, the action was measured as a dribble. It was
not measured as a dribble when it was moved slightly
with a fine touch.
Among others, the average number of times of“Through-ball,” “Cross,” “Feed,” and “Flick-on”
was low. The mean value of “Total of attack actions,”
which was 6.06, greatly differed from the mean valueof “Number of attackers,” which was 3.24. This isbecause the same players took multiple actions, mostof which were a combination of trapping and passing
as well as receiving and delivering the ball.
The mean values of the measurement items
concerning the distance traveled were 34.65 m for“Total vertical distance” and 34.90 m for “Total
horizontal distance,” showing very little difference.
The differences between “Vertical distance,” which
was 16.28 m, and “Horizontal distance,” which was
14.37 m, and between “Maximum vertical distance,”
which was 31.56 m, and “Maximum horizontal
distance,” which was 29.37 m, were also small.
However, the mean values of “Forward propulsion”
and “Wide propulsion” were 12.77 and 27.68,
respectively, with the former being more than twice aslarge as the latter. The sum of the distances between
the actions tended to be larger in the vertical direction
because the distances were calculated as positive
values, even for backward passes. At the same time,“Forward propulsion” is designed to take a negativevalue for backward passes, which reflects the
movement away from the goal post and thus correctlyreflects the vertical movement in actual soccer play.
17

Jo, H. et al.

4.3. Validation of the prediction model
Many analytical methods have been designed forprediction. Not only traditional statistical methods
but also machine learning methods have attracted
attention in recent years. Machine learning methods
can be broadly classified into “supervised learning,”“unsupervised learning,” and “reinforcement
learning.” Since the data in this study contain items
that measure whether a shot was taken (i.e., answers
exist), supervised learning was appropriate. However,since the study is not at the stage of constructing asystem that automatically improves learning accuracy,
reinforcement learning cannot be used yet. Typicalmethods for supervised learning include neuralnetworks, support vector machines, and decision
trees; however, it is desirable that the prediction
process be as clear as possible as the prediction modelleads to the clarification of the movements necessary
for taking a shot. Given this point, decision tree
analysis is a useful method because it can visualize
the prediction process. In addition, decision trees are
considered to be the best method when prediction
accuracy is important for 100 items or less (Microsoft,2020). For these reasons, decision tree analysis was
adopted as the method for model construction in thisstudy. It was extended to ensemble learning by testing
the random forest and gradient boosting decision tree
methods.
For the verification of prediction accuracy, threemethods were compared: decision tree, random forest,and gradient boosting decision tree. Accuracy wasabove 0.9 for all methods, which may be due to thefact that 91.6% of the total number of plays did not
end in a shot. If the expected value of the prediction
results derived by the model is 50% for both “ending
in a shot” and “not ending in a shot,” the probabilityof accuracy for “not ending in a shot,” i.e., theexpected value of true negative, is 0.5 × 0.916 =
0.458. This indicates that even if the prediction model
has low performance, 45.8% of the predictions will
theoretically be accurate. By contrast, the probabilityof accuracy for “ending in a shot,” i.e., the expectedvalue of true positive, is 0.5 × 0.084 = 0.042 (4.2%).
This means that the prediction accuracy will not
improve unless the performance of the prediction
model is high. Thus, since the data were biased
toward negatives (not ending in a shot), it was better
to use the f-measure, which is the harmonic meanof precision and recall, to verify the accuracy of the
18

prediction model.
Among the three methods, the gradient boosting
decision tree had the best f-measure. The random
forest had the lowest accuracy. The random forest
method, like the gradient boosting decision tree,
is an ensemble learning method, but its accuracy
was lower than that of its base method, the decision
tree. This may be due to the mechanism of random
forests. In random forests, some measurement items
are randomly selected to construct multiple trees. Ifhighly important measurement items are selected,
the prediction accuracy increases; however, if thetrees are constructed with only low importancemeasurement items, the prediction accuracy willremain low. Therefore, when the average value
is finally calculated, the prediction accuracy will
have decreased. For this reason, random forests are
not suitable for data sets where the strength of therelationship between each measurement item andthe objective variable is biased (i.e., sparse), and thisseems to have been the case with the soccer ball touch
data in this study.
The highest f-measure in the gradient boosting
decision tree was 0.876 with 44 items; however, themodel with 23 items, or 21 items less than the modelwith 44 items, also showed a high f-measure of
0.857. A greater number of measurement items does
not necessarily produce a better result. It is desirable
not to use unnecessary measurement items from theperspective of computational load and labor of themeasurer. These reasons led to the conclusion that themodel with 23 items in the gradient boosting decision
tree was optimal within the scope of this study. The
next model with 22 items had dribbles removed,
and the f-value dropped significantly to 0.816. Thissuggests that dribbling is a relatively influential itemin the prediction of shots because movements suchas cutting into the penalty area and running up theside to use a cross are observed in actual games. At
the same time, among the new items, “Flick-on” and“Feed” had low importance and were listed among
the items to be deleted at an early stage, indicatingthat their contribution to shot prediction was small.
The importance of “Vital area” in the bestmodel constituted 36.6% of the total. The fact thatthe players had a 40% chance of shooting whenentering the vital area may have contributed to its
high importance. Although the importance of theremaining items was not very high, the precision of
the best model was 82.7%, suggesting that items other
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Offensive Play Measurement Items and Shot Prediction Model in Football

the shot prediction. The above result indicates that thethan “Vital area” increased the prediction accuracy
new measurement items are better than the old onesby 42.7%. This indicates that items other than “Vitalin the study by Jo et al. (2014).
area” are also important measurement items. By
contrast, the importance of “Penalty area,” which is
closer to the goal post and wider than the vital area,
4.5. Extensibility
was 4.1%. In actual matches, since the penalty area is
The measurement items and prediction model inoften crowded with defenders, shots are often taken
this study have the potential to be applied to further
from outside the penalty area. “Vital area” is more
research and to the field of sport competition. There
important than “Penalty area” in determining whether
have been very few studies on the development ofa player takes a shot.
measurement items for game performance using
In this study, decision tree analysis, which has amachine learning in addition to the Delphi method,
relatively clear prediction process, and its ensemble
and this method is thought to be applicable tolearning methods, random forest and gradientother sports. In addition, this study revealed theboosting, were applied to address the black box
measurement items related to shots in offensive plays
problem. Since the output of a decision tree has ain soccer, which will enable players to construct
single tree structure, the bifurcation of each nodeand evaluate their plays based on the results ofcan be identified directly. By contrast, the gradientmeasurement.
boosting decision tree, in which the best model wasdeveloped, has an algorithm that creates multiple
small trees in series and improves the error of the
5. Research limitations
previous tree in the next tree. Therefore, the same
The limitations of this study by sample, by
measurement items are used many times in different
measurement items, and by the data set used limit thetrees, each time producing different bifurcation values
generalizability of the conclusions. This study utilized
and nodes after bifurcation. Although individualball touch data from all matches and all plays in thetree structures can be interpreted in a similar way
J.League in 2011. Although the amount of information
to decision trees, it is relatively difficult to capturein the ball touch data is sufficient, it does not include
the overall trend. Thus, when analysts interpret the
any tracking data of players not in possession of theresults, they should rely on the importance of theball and thus cannot measure the spatial movement ofgradient boosting decision tree or obtain the average
the group. Therefore, in interpreting the measurementof the bifurcation values of each measurement item
items related to offensive actions in soccer (newacross all tree structures. Further research is required
measurement items) defined in this study, it isin this regard and is a research topic for the future.
important to keep in mind that the information islimited to ball touch data.
4.4. Comparison with the old set of measurement
At the same time, there are also limitations due
items
to the data analysis methods used. Among the manyIn this study, 11 items were newly created andmachine learning methods, three types were applied
5 items of the old set of measurement items werein this study: decision trees, random forests, andremoved. The best model did not include six of thegradient boosting decision trees. However, other
newly measurement items: “Success of throughmethods were not discussed. Although the gradientpass,” “Success of cross,” “Flick-on,” “Throw-in,”boosting decision tree had the highest prediction“Feed,” and “Same direction.” This means that 5
accuracy, it was not compared with other methods.
items were newly added and 5 items were deleted
from the old set of items, resulting in a total of 10
6. Conclusion
items being replaced. The maximum f-measure ofBall touch data in soccer matches are useful; inthe new set of items was 0.876 and that of the oldacademia, it is necessary to accumulate studies on
set was 0.799. The change in the measurement items
analyses applying machine learning. In light of this,improved the f-measure by 0.077. In particular,
this study aimed to construct a machine learning“Success rate of pass” is a measurement item of highmodel capable of predicting whether a player would
importance and is considered to have contributed toFootball Science Vol.19, 1-21, 2022
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19

Jo, H. et al.

take a shot by developing measurement items foroffensive plays based on the soccer ball touch data.
For this purpose, the following hypotheses werediscussed:
Hypothesis 1: The measurement items of offensive
plays are created based on ball touch
data.
Hypothesis 2: A
 machine learning model is constructedthat applies decision tree analysis topredict whether a player will take ashot based on the measurement items
of offensive plays.
Although there are various machine learning
methods, the study was conducted using decision
tree analysis, which has a relatively clear prediction
process, and its ensemble learning methods, random
forests and gradient boosting decision trees. Throughthese methods, the study aimed to address the blackbox problem and identify the model with the highest
prediction accuracy. Six measurement items weredeleted, and 11 items were newly created based on
the 40 items (old set of measurement items) in the
study by Jo et al. (2014). As a result of comparing
the prediction accuracy of the new measurement
items and the old measurement items, the following
conclusions were obtained:(1) Based on the ball touch data, 45 offensive playmeasurement items, including the objective
variable “shot,” were developed.
(2) B y applying gradient boosting decision tree
analysis, a machine learning model was built topredict whether a player would take a shot basedon the 23 offensive play measurement items.

7. Acknowledgements
This research was funded by the Shizuoka Sangyo
University special research support grant, with data
provided by Data Stadium Inc. I would like to express
my gratitude to both institutions.
References

Alamar, B. and Mehrotra, V. (2011). Beyond moneyball: The
rapidly evolving world of sports analytics, Part I. https://
pubsonline.informs.org/do/10.1287/LYTX.2011.05.05/full/.
(accessed 2021-04-17).
Breiman, L., Friedman, J. H., Olsehn, R. A., and Stone, C. J.
(1984). Classification and regression trees. Wadsworth Inc.
Breiman, L. (2001). Random forests. Machine Learning, 45: 5-32.
Chen, T., He, T., Benesty, M., Khotilovich, B., Tang, Y., Cho, H.,
Chen, K., Mitchell, R., Cano, I., Zhou, T., Li, M., Xie, J., Lin,M., Geng, Y., and Li, Y. (2020). xgboost: Extreme gradient
20

boosting. R package version 1.2.0.1. https://CRAN.R-project.
org/package=xgboost. (accessed 2021-04-25).
Chiba, Y. (2020). Fencing and sports analytics: Initiatives for the
Tokyo Olympics. Monthly Statistics, 71: 10-16. (in Japanese).
Decroos, T. (2020). Soccer analytics meets artificial intelligence:
Learning value and style from soccer event stream data.
Dissertation presented in partial fulfillment of the requirements
for the degree of Doctor of Engineering Science in Universityof Liverpool.
Dmonte, R., and Dmello, A. (2017). Big data in sports: Leverage
big data in sports: An insight using SAP HANA. IJERT, 6: 380383.
Ekin, A., Takelp, M., and Mehrotra, R. (2003). Automatic soccer
video analysis and summarization. IEEE transactions on image
processing, 12: 796-807.
Friedman, J. (2001). Greedy function approximation: A gradientboosting machine. The Annals of Statistics, 29(5): 1189-1232.
James, B. (2003). The new Bill James baseball abstract. Free
Press.
Jo, H., Yokoo, T., Ando, K., Nishijima, T., Kumagai, S., Naomoto,
H., Suzuki, K., Yamada, H., Nakano, T., and Saito, K. (2014).
Attack power index of players and teams in J League.
Research on Sports Data Analytics: Theory, Methodology, andApplications, 1:21-26. (in Japanese).
Jo, H., Oosawa, K., Mishio, S., Ando, K., Suzuki, K., andNishijima, T. (2017). Development of optimization algorithm
for attack play in football. Proceedings of the Institute of
Statistical Mathematics, 64: 309-321. (in Japanese).
Kato, K. (2016). Data analysis and team reinforcement in soccer.
IEICE Communications Society Magazine, 10: 29-34. (inJapanese).
Kumar, G. (2013). Machine learning for soccer analytics.
Doi:10.13140/RG.2.1.4628.3761.
Liaw, A., and Wiener, M. (2002). Classification and regression byrandomForest. R News 2(3), pp.18-22.
Linke, D., Link, D., and Lames, M. (2020). Football-specificvalidity of TRACAB’s optical video tracking systems. Plos
ONE, 15: e0230179.
Linstone, A. H., and Turoff, M. (1975). The Delphi method.
Massachusetts: Addison-wesley.
Matsuoka, H., Tahara, Y., Ando, K., and Nishijima, T. (2020).
Development of defence and offence play items for deeplearning model of offence play analysis in soccer game.
Football Science, 17: 69-85.
Microsoft (2020). Machine learning algorithm cheat sheet. https://
docs.microsoft.com/ja-jp/azure/machine-learning/algorithmcheat-sheet. (accessed 2021-04-29).
Miller, W. (2015). Sports analytics and data science: Winning thegame with methods and models. Paul Boger, New Jersey.
Morinaga, S. (2015). Practical application of large-scale
forecasting system based on big data analysis: Simultaneousachievement of "high accuracy", "white box" and "low cost".
Monthly Statistics, 66: 14-19. (in Japanese).
R Core Team (2020). R: A language and environment for
statistical computing. R Foundation for Statistical Computing,
Vienna, Austria. https://www.R-project.org/. (accessed 202105-5).
Sakaori, F. (2016). Sports and statistical science. Monthly
Statistics, 67: 41-44. (in Japanese).
Shimokawa, T., Sugimoto, T., and Goto, M. (2013). Classification
and regression tree (CART) method and the surroundings. In
Kim, M. (eds.), Data science learned in R vol. 9 tree structured

Football Science Vol.19, 1-21, 2022
https://www.jssf.net/home.html

Offensive Play Measurement Items and Shot Prediction Model in Footballanalysis (pp.1-33). Tokyo: Kyoritsu Publishing. (in Japanese).
Suzuki, K., Nagai, S., Ogaki, R., Iwai, K., Furukawa, T.,
Miyakawa, S., and Takemura, M. (2019). Video analysis oftackling situations leading to concussion in collegiate rugby
union. J. Phys. Fitness Sports Med., 8: 79-88.
Takahashi, S., Haseyama, S. (2017). A method of important player
extraction based on link analysis in soccer videos. ITE Trans.
on MTA, 5: 42-48.
Tamura, Y. (2020). Special feature: About sports analytics.
Monthly Statistics, 71: 2-3. (in Japanese).
Therneau, T., and Atkinson, B. (2019). Rpart: Recursive
partitioning and regression trees. R package version 4.1-15.
https://CRAN.R-project.org/package=rpart. (accessed 2021-0422).
Tsuchida, J., and Yadohisa, H. (2020), Recent trends in soccer
data analysis: Tracking data and the surroundings. Monthly
Statistics, 71: 4-9. (in Japanese).
Wang, Q., Zhu, H., Hu, W., Shen, Z., and Yao, Y. (2015).
Discerning tactical patterns for professional soccer teams: Anenhanced topic model with applications. Proceedings of the
21st ACM SIGKDD International Conference on Knowledge
Discovery and Data Mining, 2197-2206.

Name:
Hirotaka Jo
Affiliation:*Department of Sports Science, Shizuoka
Sangyo University**D octoral Program in Health & Sport
Sciences, University of Tsukuba
Address:*1572-1 Oowara, Iwata-city, Shizuoka-pref 438-0043 Japan**1-1-1 Tennodai, Tsukuba, Ibaraki 305-8574 Japan
Brief Biographical History:
2014- Health and physical education teacher, Junior & Senior HighSchool at Komaba, University of Tsukuba.
2019- Associate lecturer, Shizuoka Sangyo University.
Main Works:• Jo, H., Oosawa, K., Mishio, S., Ando, K., Suzuki, K., andNishijima, T. (2017). Development of optimization algorithm
for attack play in football., Proceedings of the Institute of
Statistical Mathematics, 64: 309-321. (in Japanese).
Membership in Learned Societies:• Japanese Society of Science and Football• Japan Society of Physical education, Health and Sport Sciences• J apanese Society of Test and Measurement in Health andPhysical Education

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