Journal of Digital Life 2022,2,5
ArticleRanking performance and network structure in world
rugby sevens; 2011-2020 longitudinal data analysis
Koh Sasaki1*, Mitsuyuki Nakayama2, Kensuke Iwabuchi2, Takumi Yamamoto3, Ichiro Watanabe 4,
Hironobu Shimozono5, Jun Murakami5, Takashi Katsuta 6, Ichiro Kono7
1

Research center for health, physical fitness and sports, Nagoya University, 1 Furo, Chikusa-ku, Nagoya-chi, Aichi 4660814 Japan.
National Team & High-Performance Division, Japanese Rugby Football Union, 2-8-35 Kita-aoyama, Minato-ku, Tokyo 1070061
Japan
3
Department of Physical Education, National Defence Academy, 1-10-20 Hashirimizu, Yokosuka-shi, Kanagawa, 2398686 Japan
4
Faculty of Liberal Art, Tokyo City University, 1-28-1 Tamazutsumi, Srts, etagaya-ku, Tokyo 1588557 Japan
5
Faculty of Sports and Health Science, Fukuoka University, 8-9-1 Nanakuma, Jonan-ku, Fukuoka 8140180 Japan
6
High Performance sport center, Japan Sport Council, Nishigaoka, Kita-ku, Tokyo 1150056 Japan
7
Graduate School of Comprehensive Human Sciences, Tsukuba University, 1-1-1 Tennodai, Tsukuba-shi, Ibaraki 3058557 Japan
2

Abstract
The aim of this study was to clarify the attack-defence performances of rugby sevens in 2010-2020 seasons,4,074 matches. Firstly, averaged try scoring rate and try conceding rate of every teams were drawn in twodimensions space. Hypothesis 1 was that the achievement values of the upper teams could be clarified.
Receiver-Operator-Curve analysis was used to understand how attack-defence indicators correctly diagnose
the world rankings. Secondly, the 22m entry attack-defence indicators were drawn in two-dimensional space.
Clustering analysis of ranking teams was executed using try scoring rate, try conceding rate, 22m attackdefence indicators. Hypothesis 2 was that the clustering characteristics of upper teams can be clarified bythese indexes. Thirdly, network analysis was executed to clarify what kind of characteristic in relationshipamong the ranking, team performance, and individual performance. The ROC analysis showed the specificachievement level of the top-ranking teams with high accuracy (hypothesis 1). Cluster analysis extracted 4groups. The gradual attack defence standard values were obtained (hypothesis 2). Network analysis showed
the relationship structure among the ranking, the team performance, and the individual performance withsome interesting results concerned the dependence tendency changing on the ranking stage (hypothesis 3).

Keywords: Rugby sevens, try-scoring rate, ROC analysis, Cluster analysis, Network analysis
1. Introduction
Rugby sevens was developed in the 1880s as an event to save the financial difficulties of the Melrose Club in Scotland.
In 1926, England's major tournament, Middlesex Sevens was founded as a fundraiser for charity, raising £1,600 for
King Edward VII's Hospital. The first international competition took place in 1973 at Murrayfield Stadium as part of
the Scottish Rugby Union's 100th Anniversary event.
The World Rugby Sevens Series was established in 1999, and the World Rugby Women's Sevens Series was held in
2012. In 2016, rugby sevens was launched for the first time at the Summer Olympics. The deciding factor for the
Olympics would be that it is possible to compete on dynamic game aspects in a short time of 14 minutes in total of
the first and second half. As a result, the World Circuit hosts more than 400 games annually around the world.
Rugby sevens analysis has been developed since 2000 and has provided a variety of performance indicators (ball in
play, territory, possession, own restarts won, number of Rucks/mauls, number of offloads etc.) (Hughes & Jones,
2005). Higham et al discussed the relationship of the Scoring points and winning the game (Higham et al., 2014).
They showed that successful international rugby sevens teams tend to maintain ball possession; more frequently avoid
taking the ball into contact; concede fewer turnovers, penalties and free kicks; retain possession in scrums, rucks andmauls. Study about the time control has focused primarily on ball in-play time (Furukawa et al., 2012; Carreras et al.,Received: 8 February 2022, Revised: 7 March 2022, Accepted: 9 March 2022, Published: 17 March 2022
* Correspondence: sasakikoh@htc.nagoya-u.ac.jp
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Ranking performance and network structure in world rugby sevens; 2011-2020 longitudinal data analysis
Koh Sasaki, Mitsuyuki Nakayama, Kensuke Iwabuchi, Takumi Yamamoto, Ichiro Watanabe, Hironobu Shimozono,
Jun Murakami, Takashi Katsuta, Ichiro Kono

2013; Ross et al., 2014). In addition, World Rugby has provided a lot of statistical data concerned with the try
acquisition time in recent years (World Rugby, 2021a; World Rugby, 2021b).
We have focused the defence performance of rugby union playing by 15-men in each side (Sasaki et al., 2013b; Sasaki
et al., 2017a; Sasaki et al., 2021). Especially in close competitions such as international test match or Rugby World
Cup final stage, a defence performance like turn-over contributed to win. Is such result different from rugby sevens,
where the frequency of attacks would increase because there are more one-on-one spaces and opportunities than 15side rugby union? Are there any relationships between defensive performances and attacking performances? We had
also clarified the central players’ role and tactics which contribute to attack and defence as a network system. What
are the tactical characteristics of the rugby sevens?
Network analysis is one method for understanding the cooperative structure among the factors of various organization.
This approach has been widely used in many practical science areas to understanding human behavior and tactics.
This data mining technique has been started in the field of communication network studies (Inoue et al., 2012; Junker& Shreiber, 2008). Some traditional approaches focusing on network relationships were introduced from 1960 to 1970,
including the “small world phenomenon”, “the strength of weak ties”, and “scale free network”. Sociologicalpredictive models are modified in the field of information technology (Newell & Timmons, 2016) and cerebral
functions in biology (Junker & Shreiber, 2008; Sanz-Leon et al., 2015; Suzuki, 2017). Network analysis has also been
applied in socio-psychological and biological approaches in sport sciences (Shalley & Perry-Smith, 2008; Sasaki et al., 2013a;
Pereira et al., 2015; Hambrick, 2019; Putnam, 2001; Sasaki et al., 2017b; Sasaki et al., 2020).
Network analysis would identify the decisive and practical core structures across complex networks. It helps tounderstand the driving force of network dynamism (Yamamoto & Yokoyama, 2011; Ramos et al., 2018; Kojaku &Matsuda, 2018; Nordlund, 2018). The terminated goal of this study is to clarify the temporal and bipartite or multilayered structures of rugby sevens using the longitudinal data.
2. Methods
As of February 2021, data more than 10 years have been accumulated by World Rugby (International Federation).
We used the open data of 4,074 matches in total for the 2011-2020 World Sevens Series (World Rugby, 2021a).
Current study was to elucidate the play characteristics, especially the time structure of rugby sevens international
match in last 10 years longitudinal data.
Firstly, averaged the try scoring rate (the time it took own try score) and the try conceding rate (the time it took oppose
try score) of every ranking teams on longitudinal year of 2011-2020 were drawn in two dimensions space. Hypothesis
1 was that the specific achievement value of the upper teams could be clarified in this space. This relationship structure
would take an exponential change. Linear regression was unrealistic because it leaded to zero cuts. The reality would
be curve fitting with no zero cuts (try scoring in 0 seconds). The ranking reference point by the attack and defence
performance were calculated on this graph space. Receiver-Operator-Curve (ROC) analysis was used to understandhow attack and defence indicators correctly diagnose the world rankings of rugby sevens (cumulative rankings). Thismethod (Akoberg, 2007; Duch et al., 2010; Sasaki et al., 2017a; Sasaki et al., 2021) clarified whether the try scoringrate and try conceding rate diagnose the concrete values of those ranking place or not (1 or 0).
Secondly, the oppose 22-m (about a quarter area of pitch close to opponent's goal line) entry score rate and the own
22-m (about a quarter area of pitch close to own goal line) entry score rate were drawn in the two-dimensional space.
Since these indicators had been introduced from 2015, the data were extracted in 2015-2020. These would mean as
the 22-m attack and defence indicators. The 22-m attack indicator obtained by multiplying “the number of own entries
into oppose 22m area” by “own scoring rate from there (%)”, and 22m defence indicator obtained by multiplying “thenumber of oppose entries into own 22m area” by “oppose scoring rate from there (%)”. These were presented byWorld Rugby (World Rugby, 2021a). However, in this study, placing those in a two-dimensional space was a uniqueapproach. Exponential changes were expected there as well. Clustering analysis of the ranking teams was executedusing try scoring rate and try conceding rate obtained in the first stage analysis and the 22-m attack and defence
indicators obtained in the second stage analysis. Hypothesis 2 was that the clustering characteristics of the upper teams
can be clarified by these indexes.
Thirdly, more detailed longitudinal data for each team were extracted from another Sevens series 2000₋2020 datareported by World Rugby (World Rugby, 2021b). Network analysis could clarify what kind of characteristic in
relationship structures between the cumulative team ranking and the team performance (team-score, team-try-score,

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Ranking performance and network structure in world rugby sevens; 2011-2020 longitudinal data analysis
Koh Sasaki, Mitsuyuki Nakayama, Kensuke Iwabuchi, Takumi Yamamoto, Ichiro Watanabe, Hironobu Shimozono,
Jun Murakami, Takashi Katsuta, Ichiro Kono

individual-try-score, individual-clean-breaks and total individual-runs). Fruchterman–Reingold algorithm is a forcedirected layout algorithm (Fruchterman & Reingold, 1991). The goal was to position the vertices of the graph in a
two- or three-dimensional space so that all edges were of approximately equal length and the number of crossing
edges was minimized (Eades, 1984; Inoue ey al., 2012; Kamada & Kawai, 1989; Gajdos et al., 2016). A force-directed
algorithm achieves this by assigning forces to the set of vertexes and the set of edges. The forces applied to the vertices
pull them closer together or push them further apart.
From the adjacency matrix (ranking teams in rows, performance in columns), the repulsive force (Fr) and attractive
forces (Fa) those affect vertices are calculated. Based on those values, the vertices are moved and drawn within thespecified area.

Fa(d)=d2/k,

Fr(d)=-k2/d,

k=C(d; distance between vertices, k; ideal distance between vertices, area; drawing area, |v|;
number of vertices, c; certain constant)
This procedure was iterated until the system reached an equilibrium state. Hypothesis 3 was that this method canclarify the performance characteristics of the top teams in particular. Since these approaches were secondary data
analyses of the provided materials, there was the limitation to use more detailed performance engagement structure.
3. Results
Table 1 showed the longitudinal shape of rugby sevens in 2011-2020 including 2016 Rio Olympic summer game andthe average value. No significant changes have been seen in any of the indicators, but World Rugby reported that thescoring points had a little decrease from 2011 to 2015, and had recovered to 2020 (World Rugby, 2021a). The time toscoring a try had decreased slightly. The 22-m scoring rate (%) had been increased slightly.
Table 1. The shape of the rugby sevens world series game in 2011-2020 (including 2016 Olympic game)Year（16R*:Rio Olympic games）

2010

2011

2012

2013

2014

2015

2016

16R*

2017

2018

2019

2020

AVG

Matches

368

368

392

392

405

405

450

34

450

450

450

230

366

BALL in PLAY (average per game (%)

50

52

51

51

51

50

49

52

51

52

55

50

51.2

POINTS SCORED (average per game)

37

37

34

33

33

36.4

36.6

32.4

36.4

37.8

39.4

38.4

36.0

TRIES SCORED (average per game)

6

5.9

5.4

5.4

5.2

5.8

5.8

5.2

5.8

6

6.2

6

5.73

TRY SCORING RATE (1 every * secs)

75

74

79

80

83

73

71

84

74

73

73

69

75.9

CONVERSION SUCCESS (%)

62

64

62

63

65

65

64

65

65

63

63

64

63.8

TRIES SCORED - NO RUCK/MAUL (%)

61

55

70

62

67

64

62

64

63

50

51

51

60.0

TRIES SCORED - OWN HALF (%)

43

44

44

44

40

42

43

39

45

46

44

43

43.1

OPPOSITION 22M ENTRIES (avg./game)

ND

ND

ND

ND

ND

4.5

4.5

4.1

4.4

4.1

4

8

4.8

OPPOSITION 22M SCORING RATE (%)

ND

ND

ND

ND

ND

65

64

62

68

65

75

75

67.7

PASSES (average per game)

70

71

68

66

64

64.1

64

66

66.8

67.6

73.6

70

67.6

RUCKS/MAULS (average per game)

17

19

18

17

17

17.2

16.4

17

15.4

22.6

21.8

22.4

18.4

RUCK/MAUL RETENTION (%)

84

85

84

81

79

80

80

84

84

87

85

86

83.3

KICKS (average per game)

3.7

2.4

2.4

2.6

2.8

2.3

2

2.8

2

1.8

1.8

2

2.4

CONTESTABLE RESTARTS (%)

77

74

70

71

70

72

73

62

75

76

78

81

73.3

CONTESTABLE RESTARTS REGAINED (%)

37

36

33

36

36

34

36

26

33

34

30

33

33.7

RESTART ERROR RATE (1 every * restarts)

18

20

17

15

16

13.3

15

15.3

13.8

13.4

13.1

13

15.2

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Ranking performance and network structure in world rugby sevens; 2011-2020 longitudinal data analysis
Koh Sasaki, Mitsuyuki Nakayama, Kensuke Iwabuchi, Takumi Yamamoto, Ichiro Watanabe, Hironobu Shimozono,
Jun Murakami, Takashi Katsuta, Ichiro Kono
SCRUMS (average per game)

4

4

4

2.8

3.6

3.2

3.2

3.6

2.8

2.8

2.9

3.1

3.3

SCRUMS RETAINED (%)

84

91

94

94

96

96

96

98

97

97

98

99

95.0

LINEOUTS (average per game)

2

2

2

2.4

2.5

2.6

2.4

3

2.4

2.4

2.2

2.6

2.4

LINEOUTS RETAINED (%)

78

74

74

77

79

79

80

79

80

85

86

86

79.8

PENALTIES/FREE KICKS CONCEDED

5.4

5.4

6

6.2

6.8

6.2

6.2

7.4

6.6

6

6.5

7.4

6.3

Figure 1 showed the two-dimensional spaces of the try scoring and conceding time. The lower left side of the figuresummarized the total for 10 years. On the above and lower left side of the figure 1, the vertical axis showed the attack
performance (the lower the direction, the higher the attack efficiency), and the horizontal axis showed the defence
performance (the right direction, the higher the defence efficiency). Those could mean the lower right direction
showed comparatively good performance.

Figure 1. Try scoring rate (TSR, secs: Vertical axis), try conceding rate (TCR, secs; horizontal axis) and theranking line on TCR by ROC analysis in 2011-2020 rugby sevens.
The exponential functions were not large, but it could be seen that the difference in defence ability tended to determine
the upper rankings. For reference, the R-squared values were shown in all figures. It could be considered that thecloser the R-squared values are to 1, the better the fit to the existing data (Patel et al., 2020).
To test the precision of sensitivity and specificity of this try scoring and conceding rate, we analyzed the Area Under
the Curve (AUC) of ROC (Akoberg, 2007; Duch et al., 2010; Sasaki et al., 2017a; Sasaki et al., 2021). The highest
AUC scores were obtained with each cut-off values of rugby sevens world ranking. “Sensitivity” showed the rate toevaluate the positive thing correctly (Sasaki et al., 2017a; Sasaki et al., 2021) and the true positive rate were morethan 50 %. “1-specificity” showed the rate to evaluate falsely the negative thing as positive (Sasaki et al., 2017a;
Sasaki et al., 2021) and the false positive rate (the smaller the better) were less than 7 %. In each ranking value,“Accuracy” was obtained more than 84 %. The AUC values were extremely high at 90% or more, and it could be said
to reflects accurately the ranking of 1st to 8th place.
Try scoring rate (time) of 1st place of ranking was within 50 seconds, 2nd ~ 4 th was within 66 seconds, and 5th ~ 8th
was within 72 seconds in calculated cutoff values by ROC. Try conceding rate of 1st place of ranking was 124 seconds
or more, 2nd ~ 4th was 102 seconds or more, 5th ~ 8th was 82 seconds or more. Attacking and defencing time baselines
for try were shown as the gradual structure in the graph (red dot line in Figure 1).

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Ranking performance and network structure in world rugby sevens; 2011-2020 longitudinal data analysis
Koh Sasaki, Mitsuyuki Nakayama, Kensuke Iwabuchi, Takumi Yamamoto, Ichiro Watanabe, Hironobu Shimozono,
Jun Murakami, Takashi Katsuta, Ichiro Kono

Figure２. ROC curve of 1st,4th and 8th ranking (left;
try conceding rate, right; try scoring rate）
Figure 2 showed ROC curves of the 1st, 4th, and 8th
ranking. We performed all ROC analyses with the 1st~ 8th place, and shown here only of the 1st, 4th and8th place because those could be raised as almostreference values. Every curve showed high values
close to maximum values. The gradual structure of
the 1st, 4th, and 8th could be seen. In each curve, thevalue closest to the upper left vertex was regarded as
the cut-off value that meant the specific reference
point (seconds) described above.
Figure 3 showed the two-dimensional spaces of the 22m attack and defence indicators. The left side of Fig 3
summarized the total for 6 years (2015-2020). The vertical axis showed the attack performance (the lower the direction,
the higher the attack efficiency), and the horizontal axis showed the defence performance (the right direction, thehigher the defence efficiency). Those could mean the lower right direction showed good performance. The exponentialfunctions were not large. Compared to the previous Figure 1, the scattering of Figure 3 might be clear and theinterpretation of clustering might be easy to obtain.

Figure 3. the two-dimensional spaces of the 22m attack and defence indicators.
Figure 4 showed the result of k-means clustering. Four clusters by 10,000 times iterations were calculated. Fourindicators of TSR, TCR, 22A, and 22D were used for clustering. The top 8 ranks were clearly classified in the rankings,
but the under the 9th place, clustering was mixed on the boundary line. It could be interpreted to be clustered almost
for the ranking. In particular, the top three teams showed “the 22m attack performance” and “high defence
performance”.

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Ranking performance and network structure in world rugby sevens; 2011-2020 longitudinal data analysis
Koh Sasaki, Mitsuyuki Nakayama, Kensuke Iwabuchi, Takumi Yamamoto, Ichiro Watanabe, Hironobu Shimozono,
Jun Murakami, Takashi Katsuta, Ichiro Kono

Figure 4. K-means clustering with 4 clusters of sizes 3, 7, 2, 5 in 22M attack-defence indicators of Rugby Sevens
2015-2020 (22A; [oppose 22-m entry times (OP22E; average per game)] * [oppose 22-m scoring rate (OP22S; %)],
22D; [own 22-m entry times (OW22E; average per game)] * [own 22-m conceding rate (OW22C; %)], WR;cumulative world ranking in 2015-2020)

Figure 5. Network structure of Rugby Sevens all time ranking with decisive performance indicators (WR;world ranking, TRP; team ranking points, TP; team score points, TTP; team try scored points, PTP; players’ tryscores points (team total points of top 30 players; same below), PR; players’ run times, PCB; players’ cleanbreak times, the colored cells are the top 3 teams in each performance)
Figure 5 showed the relationship structure among the ranking points, the team performances, and the individual
performance in a network space. It drew by Fruchterman–Reingold algorithm, a force-directed layout algorithm, inwhich the ones which function centrally in the ranking teams were positioned in the centered area. The team /individual performances were positioned at the center area. These were the evidences in the table on the right side in
the Figure 4. However, the individual performances of the 4th and 5th place teams outnumbered the 1st ~ 3rd place
teams (the orange circles on the right side of Figure 5). The strength of connectivity within these networks (the 4th
and 5th to the individual performances) was very large value compared to the strength of the other connectivity. In the
Figure 5, the width of the arrow indicated the degree of coupling to each standardized indicator.
4. Discussion
The Ball in play in rugby sevens has tended to remain high at almost 50% (Furukawa et al., 2012; Carreras et al.,2013; Ross et al., 2014). Therefore, the current study focused on the try acquisition time, which would be directly
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Ranking performance and network structure in world rugby sevens; 2011-2020 longitudinal data analysis
Koh Sasaki, Mitsuyuki Nakayama, Kensuke Iwabuchi, Takumi Yamamoto, Ichiro Watanabe, Hironobu Shimozono,
Jun Murakami, Takashi Katsuta, Ichiro Kono

related to the win or loss structure, as a time concerned indicator. In the period of 2011-2020, the recovery after thetry score had a little dropped once might be related to the shortened try acquisition time. It was an interesting issue to
see how the defence performance of a team with high attack ability was going to be.
4.1. ROC analysis
As the first place of world ranking reference by ROC analysis, it was indicated that the defence performance putting
up more than 124 seconds of oppose’ try conceding rate (TCR) and the attacking ability scoring a try within 50 seconds
in own try scoring rate (TSR). These values would be positioned as the achievement points in the game which
competes for the top in the world rugby sevens by the longitudinal evidence of the past ten years. In the 2nd to 4thplace of the rankings, the TCR was 102 seconds or more and TSR was within 66 seconds. And in the 5th to 8th places,
the TCR was 82 seconds or more and TSR was within 70 seconds. These may be regarded as the target values of theteam aiming at the higher order of ranking. The range from 1st to 8th in the TSR was 20 seconds (50 ~ 70 seconds),
while the range of TCR was 42 seconds or more (82 ~ 124 seconds), which was twice. This might suggest that the ups
and downs of the latter, defence performance, have a greater effect on the ranking. As the AUC value exceeds 0.5, thedata would be evaluated as highly rationale (Rodríguez-Flores & Papadopoulos, 2020). Those values showed highaccuracy in this study. The AUC values drawn by the ROC analysis were all 0.9 or higher. The values could be a highaccuracy (Duch et al., 2010; Sasaki et al., 2017a; Sasaki et al., 2021). Hypothesis 1 that the two-dimensional space ofthe try scoring and the try conceding rate clarifies the concrete achievement value of the top teams had been verified.
It was assumed that the relational structure between the try scoring and the try conceding rate would change
exponentially. The exponential value of movement in these indicators was very low and close to the linear regression,
but as mentioned in the method, the linear regression could lead to the zero intercept as unreal structure. It would beappropriate to show the value in the Figure 1 as the situation of curve fitting. Some approaches would clarify with thedifferent structures between the top 8 and subsequent groups might deepen those interpretation (Sanz-Leon et al.,2015; Patel et al., 2020).
4.2. Cluster analysis
In the analysis of attack and defence performance within 22-m, the data tended to be relatively scattered, which
increased the possibility of clustering cumulative ranking. As a result, from k₋means clustering, the concrete valuesof the four clusters were derived. As the attack indicator of the first cluster (1 ST ~ 3RD place), the own scoring rate was“very high” (72% or more when the oppose 22m entries 5 times or more). As the defence indicator, the oppose scoringrate was “comparatively low” (60% or less in 4 times own 22m entries). It was confirmed that the attack and defence
indicators were decreasing gradually in each cluster (2nd cluster’s own score 64~74% in entry 4~5 times and oppose
score 58~74 % in entry 3.8-5 times: 3rd cluster’s own score rate 63~71% in entry 3.8 times and oppose score rate
68~88% in entry 4.2~4.9 times: 4th cluster’s own score 53~60 % in entry 3.2~3.3 times and oppose score rate 74~78 %in entry 5.2~5.3 times). The clusters showed the reference point of the actual teams’ attack and defence performance.
Hypothesis 2 has been verified. A more detailed indicator mechanisms should be discussed for 22-m attack anddefence, which would be important for try scoring and conceding (Hugh & Jones, 2005; Higham et al., 2014; Tiemery
et al., 2017).
4.3. Network Analysis
From the above clustering, we analyzed the tactical differences among the ranking teams. The data was extracted fromteam and individual performance statistics (World Rugby, 2021b). Network analysis showed that the top three teams
had high team performances. An interesting result was that the 4th and 5th place teams outperformed the upper teamsin the number of individual runs (P-R; player runs) and clean breaks (P-CB; player clean-gain line-break). The
performances of a specific individual were high. However, the top three teams might have many players who canscore, and it might be said that they have team performance that does not depend on the individuals (Sasaki et al.,2013b; Sasaki et al., 2017a). It might be necessary to improve individual performances (characteristics of 4th and 5thplace) as a stage for lower teams to enter the upper rankings. However, it might be said that many high-performance
individuals were required to reach more higher rankings. Hypothesis 3 that network analysis could reveal theperformance characteristics of top teams in rugby sevens had been verified.
Rugby Sevens would be a fusion of individual skills and team skills. For example, the success of an "off-road" passis a feature of the high-ranking teams in the 2020 World Series (World Rugby, 2021a), However, it would be difficult
to define whether this play is an individual skill or a team skill.
Furthermore, with the increase in international match events, there would be a risk of one-on-one collisions comparedto the fifteen men’s side rugby. The relationship between injury and performance also needs to be consideredlongitudinally (Sasaki et al., 2017b; Hendricks & Brown, 2017; Tucker et al., 2017; Burger et al., 2020; Sasaki et al.,Copyright: © SANKEI DIGITAL Inc.

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Ranking performance and network structure in world rugby sevens; 2011-2020 longitudinal data analysis
Koh Sasaki, Mitsuyuki Nakayama, Kensuke Iwabuchi, Takumi Yamamoto, Ichiro Watanabe, Hironobu Shimozono,
Jun Murakami, Takashi Katsuta, Ichiro Kono2020). Further data mining methods such as network analysis would be required to elucidate what kind of involvement
structures various indicators in rugby sevens.
It should be discussed how the results could be interpreted from the perspective of previous studies and of the working
hypotheses. The findings and their implications should be discussed in the broadest context possible. Future research
directions may also be highlighted. This study clarified one evidence showing the key performance indicators of therugby sevens top teams.
5. Conclusion
Try scoring rate in rugby sevens had down temporarily during last decade but then recovered. This study focused on
the time required for scoring and conceding the try. Some specific achievement values for those at each ranking wereextracted. Try scoring rate of 1st place of ranking was within 50 seconds, 2nd~4 th was within 66 seconds, and 5th~8th
was within 72 seconds in calculated cutoff values by ROC. Try conceding rate of 1st place of ranking was 124 seconds
or more, 2nd~4th was 102 seconds or more, 5th~8 th was 82 seconds or more. Attacking and defencing time baselines fortry were shown as the gradual structure. To test the precision of sensitivity and specificity of this try scoring andconceding rate, Area Under the Curve (AUC) of ROC analysis showed the high AUC scores with above mentioned
cut-off values of rugby sevens world ranking.
The analysis of attack and defence performance within 22-m showed that the attack indicator of the first cluster, theown scoring rate was 72% or more when the oppose 22m entries 5 times or more. As the defence indicator, the oppose
scoring rate 60% or less in 4 times own 22m entries (2nd cluster’s own score 64~74% in entry 4-5 times and oppose
score rate 58~74 % in entry 3.8~5 times: 3rd cluster’s own score rate 63~71% in entry 3.8 times and oppose scorerate 68~88% in entry 4.2~4.9 times: 4 th cluster’s own score 53~60 % in entry 3.2~3.3 times and oppose score rate
74~78 % in entry 5.2~5.3 times). The clusters showed the reference point of the actual teams’ attack and defence
performance. From world ranking clustering and network analysis, we elucidated the structure in which thecharacteristics of team performance and individual performance changed step by step.
Author Contributions
Conceptualization, K.S.and H.S.; methodology, K.S., M.N.,and T.Y.; investigation, K.I., M.N., I.W., J.M., resources,
K.S.,M.N. writing—original draft preparation, K.S.; writing—review and editing, M.N.; visualization, K.S.;
supervision, T.K., I.K.; project administration, K.S.; funding acquisition, K.S; All authors have read and agreed to the
published version of the manuscript.
Funding
This study was partly supported by JSPS KAKENHI grant number 19K11549(2019-2021) to K.S.
Institutional Review Board Statement
All procedures used in this study were approved by the Ethics Committee of the Research Centre for Health, PhysicalFitness and Sports, Nagoya University, Japan. (19-10)
Informed Consent Statement
Informed consent was obtained from all participants involved in the study.
Conflicts of Interest
The authors declare no conflicts of interest associated with this manuscript.
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