Journal of Physical Education and Sport ® (JPES), Vol. 24 (issue 8), Art 211, pp. 1902 - 1908, August 2024
online ISSN: 2247 - 806X; p-ISSN: 2247 – 8051; ISSN - L = 2247 - 8051 © JPES

Original Article
Comparative analysis of winning and losing teams in rugby union
OLEH KVASNYTSYA1, VALERIA TYSHCHENKO2, MYKOLA LATYSHEV3, IRYNA KVASNYTSIA4,OLEKSANDR MOZOLIUK5, ANATOLII REBRYNA6, MYKOLA KIRSANOV7, LEONID RYBAK8
1,4,5,6
Department of Theory and Methods of Physical Education and Sport, Khmelnytsky National University,
UKRAINE2
Department of Theory and Methods of Physical Training and Sports, Zaporizhzhia National University,
UKRAINE
3
Department of Physical Education and Pedagogy of Sport, Borys Grinchenko Kyiv Metropolitan University,
UKRAINE
7
Department of Physical Culture and Sports, Kharkiv National Automobile and Highway University, UKRAINE
8Department of Physical Education and Sport, Bohdan Khmelnytskyi National Academy of the State Border
Guard Service of Ukraine, UKRAINE
Published online: August 31, 2024
Accepted for publication : August 15, 2024
DOI:10.7752/jpes.2024.08211
Abstract:
Analyzing performance indicators in rugby is essential for improving training programs and team dynamics. A
key direction is to compare these indicators between winning and losing teams in prestigious rugby tournaments
to identify patterns and strategic insights that can enhance team performance. However, current researchfrequently examines isolated competitions and lacks thorough comparative analysis across different tournaments.
This study aims to analyze and identify the differences between winning and losing men's rugby union teams
across various competitions in 2023. This study analysed performance data from men's rugby competitions in
2023, encompassing matches from The Six Nations Championship, The Rugby Championship, and the Rugby
World Cup. A total of 68 matches were examined, categorizing 47 matches from the Rugby World Cup and 21
from international competitions for comparative analysis. Data collection included 32 performance indicators forboth winning and losing teams, analysed using statistical methods to identify significant differences.
Comparative analysis of team performance indicators between the Rugby World Cup and international
competitions, including The Six Nations Championship and The Rugby Championship, reveals significant
differences. Winning teams in the Rugby World Cup scored significantly higher points on average (41.5 ± 13.2)
compared to international competitions (32.8 ± 7.7), reflecting greater variability in performance at the World
Cup. Conversely, losing teams in the Rugby World Cup averaged fewer points (13.2 ± 8.0) compared tointernational competitions (16.3 ± 6.9). These findings underscore distinct competitive levels across different
rugby tournaments. The analysis revealed that in the World Cup, 18 indicators (56.3%) exhibit statisticallysignificant differences between losing and winning teams, whereas the analysis of performances in international
competitions identified only 4 (12.5%) statistically significant differences between losing and winning teams.
Key Words: rugby, performance indicators, team, analytics, competitions
Introduction
The analysis of player and team performance is foundational in optimizing training process andenhancing competitive success in sports. Over the past few decades, the advent of advanced technologies andanalytical methods has revolutionized performance analysis, particularly in team sports. Studying sports throughthe observation of player and team behavior is essential for the organization, planning, and structuring of the
training process (Hughes & Bartlett, 2002; Korobeynikov et al., 2022). This approach remains relevant
throughout the development of sports. A thorough analysis of the most significant performance indicators of
athletes and teams enables coaches and managers to optimize and more precisely plan the training process (vanRooyen, 2012; Bompa & Buzzichelli, 2015; Latyshev et al., 2020).
In rugby, this shift has led to a more detailed understanding of game dynamics and performanceindicators that differentiate winning teams from losing ones. Performance analysis in rugby has a long history,
primarily focusing on describing the patterns of the game (McKenzie et al., 1989; Eaves & Hughes, 2003;
Rodrigues & Passos, 2013) and performance indicators (Jones et al., 2004; James et al., 2005). Subsequent
research has also developed a focus on identifying differences between winning and losing teams, as well as
determining key indicators that influence game results. Despite significant progress, research in rugby often
remains limited to specific competitions or focuses on narrow aspects of performance without broader contextual
analysis.
1902---------------------------------------------------------------------------------------------------------------------------------Corresponding Author: MYKOLA LATYSHEV, E-mail: nlatyshev.dn@gmail.com

OLEH KVASNYTSYA, VALERIA TYSHCHENKO, MYKOLA LATYSHEV, IRYNA KVASNYTSIA,OLEKSANDR MOZOLIUK, ANATOLII REBRYNA, MYKOLA KIRSANOV, LEONID RYBAK--------------------------------------------------------------------------------------------------------------------------------------Most studies focus on a single type of competition, with the objective of analyzing the statisticaldifferences in rugby games between winning and losing teams. For instance, analyses have been conducted solely
on the Six Nations Championship, without examining the impact on match outcomes, only comparing winners
and losers (Ortega et al., 2009). In their articles, Jones et al. (2004) and James et al. (2005) also described
differences between winning and losing teams. However, unlike the aforementioned studies, they assessed theperformance of only one team and its performance in a single confrontation between two teams. The results fromthese studies demonstrate that teams secure more balls in the line-out within the opponent's territory when they
win compared to when they lose.
While numerous studies have identified key performance indicators, the diversity in methodologies andthe focus on single events or leagues pose challenges for coaches and analysts in generalizing findings. In thepast two to three decades, there has been a remarkable growth in performance analysis research across various
sports, particularly in team sports (Latyshev et al., 2017; Doroshenko et al., 2019, Korobeynikov et al., 2020;
Ranaweera et al. 2022; Tropin et al., 2022; Curby et al., 2023). This has been largely facilitated by theadvancement and accessibility of computer and video technologies, as well as the development of machinelearning (Bunker & Susnjak, 2022; Latyshev et al., 2022). It should be noted that today, modeling and prediction
methods are used ubiquitously and at all stages of athlete and team preparation (Fontana, 2017; Rovniy et al.,2018; Brodani et al., 2023).
In the study conducted by Parmar et al. (2017), extensive research was carried out to determine thesignificance of performance indicators that predict match outcomes and points difference in professional rugby
league. This study assessed 24 relative performance indicators (home value minus away) from all rounds of the2012, 2013, and 2014 European Super League seasons. Various methods were used to identify performanceindicators and key performance indicators. Similar studies were conducted on a total of 396 matches played
during the 2017 to 2019 Super Rugby seasons, which demonstrate more contemporary aspects of competitive
activity (Nicholls et al., 2024). Research has also been carried out comparing indicators depending on the
different stages of the World Cup (Vaz et al., 2019): for winning and losing teams across the group stage matches
and the play-off matches (Bunker & Spencer, 2020).
Additionally, an analysis was conducted on twenty-seven performance indicators selected from 96
matches during the 2020–21 United Rugby Championship (Scott et al., 2023a). Similar research has been carriedout for university-level competitions. This study aimed to determine performance indicators that discriminate
between winning and losing male university-level rugby sevens teams (Moolman et al., 2021). It is also worth
noting that such competitions are held for women's rugby, aiming to identify performance indicators thatmaximize prediction accuracy of match outcomes (Hughes et al., 2017; Scott et al., 2023b).
The identification and analysis of the most significant team performance indicators are closely related tomodeling and forecasting. Several studies have been conducted in this direction in recent years (Colomer et al.,2020; Kvasnytsya et al., 2024). However, as demonstrated in the research, most specialists use different
indicators for specific purposes, which complicates comparisons between results but necessitates the search forthe optimal quantity and combination of indicators to assess teams (Scott et al., 2023). In conclusion, it isnoteworthy that we identified studies comparing team indicators across different leagues (Schoeman et al., 2017;
Schoeman & Schall, 2019). However, these studies did not distinguish between winners and losers, which
defined the aim of our research.
The aim of this study is to analyze and identify the differences between winning and losing men's rugby
union teams across various competitions in 2023.
Material & methods
Participants
Data were collected from the performances of teams in three rugby competitions in 2023 among men:
The Six Nations Championship (www.sixnationsrugby.com) – all 15 matches among 6 teams, The Rugby
Championship (www.super.rugby) – all 6 matches among 4 teams, and the Rugby World Cup(www.rugbyworldcup.com) – 47 out of 48 matches among 20 teams (McGarry, 2009). One group stage match in
the Rugby World Cup ended in a draw, which is a rare occurrence in rugby union and was excluded from theanalysis because team performance indicators cannot be categorized into winners and losers. In total, data from
68 matches were analyzed. All matches were categorized into two groups for comparative analysis: 47 matchesfrom the Rugby World Cup and 21 matches from international competitions (The Six Nations Championship and
The Rugby Championship). It is important to note that all countries participating in the international competitions
also competed in the Rugby World Cup.
Data collection
For each match, data were collected separately for 32 indicators for both the winning and losing teams.
These indicators include: Tries, Conversion Goals, Penalty Goals, Kick Success Rate, Meters Run, Kicks from
Hand, Passes, Runs, Total Scrums, Won Scrums, Total Lineouts, Won Lineouts, Total Tackles, Successful
Tackles, Possession 1H, Possession 2H, Territory 1H, Territory 2H, Clean Breaks, Defenders Beaten, Offloads,
Won Rucks, Total Rucks, Won Mauls, Total Mauls, Turnovers Conceded, Possession, Territory, Red Cards,---------------------------------------------------------------------------------------------------------------------------- 1903
JPES ® www.efsupit.roOLEH KVASNYTSYA, VALERIA TYSHCHENKO, MYKOLA LATYSHEV, IRYNA KVASNYTSIA,OLEKSANDR MOZOLIUK, ANATOLII REBRYNA, MYKOLA KIRSANOV, LEONID RYBAK--------------------------------------------------------------------------------------------------------------------------------------Yellow Cards, Total Free Kicks Conceded, Penalties Conceded. The match outcomes were also separately
analyzed. All data (match statistics) were sourced from the official competition websites and the International
Rugby Federation (www.world.rugby).
Statistical analysis
The mean values and standard deviations (SD) of performance indicators for winning and losing teams
were calculated. The analysis revealed that the distributions of some indicators did not follow a normal
distribution. Therefore, the Mann-Whitney U test was used to determine statistically significant differences
between the indicators at the significance levels of 0.05 and 0.01 (Hopkins et al.,1999). Python programming
language was employed for data preparation, processing, and statistical calculations.
Results
An analysis of the Rugby World Cup and international competitions, including the Six Nations
Championship and Rugby Championship, revealed differences in team performance indicators between winners
and losers. In Rugby World Cup matches, winning teams (41.5 ± 13.2) scored more points compared tointernational competitions (32.8 ± 7.7). It is worth noting the higher standard deviation, indicating significant
variability in results at the Rugby World Cup. Teams that lost in the Rugby World Cup had an average score of
13.2 ± 8.0 points, which is lower than the average score of losing teams in international competitions (16.3 ± 6.9
points). These results underscore differences in the level of competition between teams in the Rugby World Cup
and international competitions. A more detailed analysis of match outcomes is presented in the tables. Table One
presents indicators related to points scored and offensive actions by teams.
Table 1. Team Performance Indicators in Rugby Union across Competitions: Scoring (scores) and Offensive
Performance
Indicators
Tries
Conversion Goals
Penalty Goals
Kick Percent
Success
Meters Run
Kicks from Hand
Passes
Runs

Rugby World Cup
Winning teams
Mean ± SD
Mean ± SD
5.4 ± 3.8
1.5 ± 1.1
4.4 ± 3.4
1.0 ± 1.0
1.9 ± 1.8
1.2 ± 1.2

International competitions
Losing teams
Dif., % p
Mean ± SD
Mean ± SD
265.2 <0.01 4.5 ± 1.2
1.9 ± 1.0
329.2 <0.01 3.1 ± 1.1
1.4 ± 1.1
55.4
0.13
1.2 ± 0.8
1.2 ± 1.3

81.4 ± 15.6

64.3 ± 35.1

26.6

<0.05 72.5 ± 17.3

540.7 ± 210.6
27.3 ± 8.2
149.8 ± 48.2
118.4 ± 28.6

400.8 ± 139.1
22.6 ± 8.2
131.9 ± 47.6
109.6 ± 30.0

34.9
20.7
13.6
8.0

<0.01
<0.01
<0.05
0.17

470.3 ± 119.2
29.0 ± 7.3
155.8 ± 41.0
124.1 ± 30.3

Dif., % p
137.5
113.3
0.0

<0.01
<0.01
0.66

78.5 ± 25.0

-7.6

0.29

414.8 ± 94.4
24.4 ± 7.3
171.0 ± 31.3
124.0 ± 24.5

13.4
18.5
-8.9
0.1

0.17
0.06
0.21
0.63

A comparative analysis of performance indicators between winning and losing teams revealed morestatistically significant differences among World Cup teams (6 indicators) than in international competitions (2indicators). Two indicators, Tries and Conversion Goals, show the greatest difference between winning andlosing teams (over 100%). It is noteworthy that indicators such as Kick Percent Success and Passes are moreprevalent among World Cup winners, whereas in international competitions, losing teams tend to have higher
average scores in these indicators. Table 2 presents the indicators related to territory possession and attacking
actions in set-piece situations.
Table 2. Team Performance Indicators in Rugby Union across Competitions: Territory Possession and Set Piece
Offense
Indicators
Possession 1H
Possession 2H
Territory 1H
Territory 2H
Clean Breaks
Defenders Beaten
OffloadWon Rucks
Total Rucks
Won Mauls
Total Mauls
Turnovers Conceded
Possession
Territory

Winning teams
Mean ± SD
Mean ± SD
53.5 ± 8.5
46.5 ± 8.5
52.4 ± 9.9
47.6 ± 9.9
52.6 ± 10.5
47.4 ± 10.5
51.0 ± 12.1
49.0 ± 12.1
8.6 ± 5.8
4.6 ± 2.6
26.6 ± 13.0
21.2 ± 8.6
8.2 ± 5.6
5.7 ± 3.4
74.9 ± 20.1
75.7 ± 23.0
77.8 ± 20.1
79.8 ± 23.3
5.3 ± 2.5
2.8 ± 1.7
5.9 ± 2.6
3.4 ± 1.8
13.2 ± 4.1
14.5 ± 3.3
52.9 ± 6.9
47.1 ± 6.9
51.7 ± 7.8
48.3 ± 7.8

Dif., % p
15.2
10.2
10.9
4.3
87.9
25.8
44.2
-1.1
-2.5
89.4
75.3
-8.9
12.3
7.2

<0.01
<0.05
<0.05
0.48
<0.01
<0.05
<0.05
0.86
0.67
<0.01
<0.01
0.18
<0.01
0.06

Losing teams
Mean ± SD
53.3 ± 8.5
48.1 ± 8.0
50.3 ± 12.0
51.1 ± 12.2
7.4 ± 2.3
25.1 ± 8.4
6.7 ± 3.2
88.1 ± 25.4
91.5 ± 26.1
5.4 ± 3.2
5.9 ± 3.3
12.7 ± 4.3
50.5 ± 5.8
50.7 ± 9.7

Mean ± SD
46.7 ± 8.5
52.0 ± 8.0
49.7 ± 12.0
49.0 ± 12.2
4.1 ± 2.5
22.3 ± 8.2
8.0 ± 3.4
90.0 ± 20.0
94.1 ± 20.5
4.2 ± 2.3
5.1 ± 2.2
13.5 ± 2.8
49.5 ± 5.8
49.3 ± 9.7

Dif., % p
14.07
-7.52
1.34
4.28
80.23
12.39
-16.17
-2.01
-2.73
28.09
16.98
-5.65
2.12
2.70

<0.05
0.17
0.68
0.29
<0.01
0.28
0.21
0.49
0.41
0.24
0.44
0.21
0.82
0.55

1904 ---------------------------------------------------------------------------------------------------------------------------JPES ® www.efsupit.roOLEH KVASNYTSYA, VALERIA TYSHCHENKO, MYKOLA LATYSHEV, IRYNA KVASNYTSIA,OLEKSANDR MOZOLIUK, ANATOLII REBRYNA, MYKOLA KIRSANOV, LEONID RYBAK--------------------------------------------------------------------------------------------------------------------------------------Only one indicator showed statistically significant differences (p < 0.01) between winners and losers
across all analyzed competitions – Clean Breaks. It is noteworthy that this is the sole indicator for international
competitions. However, in the analysis of World Cup results, nine indicators were identified with statisticallysignificant differences between losing and winning teams. The greatest percentage differences between losing
and winning teams were found in the following indicators: Clean Breaks, Possession 1H, Won Mauls, Total
Mauls, and Defenders Beaten. There are also indicators that show contradictory results across different
championships (differences in advantage between winning and losing teams): Offload and Possession 2H.
Table 3. Team Performance Indicators in Rugby Union across Competitions: Set Piece Defense and Discipline
Indicators
Total Scrums
Win Scrums
Total Lineouts
Win Lineouts
Total Tackles
Successful
Tackles
Red Cards
Yellow Cards
Total Free
Kicks Conceded
Penalties Conceded

Winning teams
Mean ± SD
7.8 ± 3.0
6.3 ± 2.5
14.2 ± 4.5
12.5 ± 4.1
150.7 ± 47.0

Mean ± SD
6.7 ± 2.6
5.1 ± 2.3
12.6 ± 3.5
10.0 ± 3.5
154.7 ± 41.7

17.2
24.8
12.8
25.0
-2.6

0.07
<0.05
0.06
<0.01
0.55

Losing teams
Mean ± SD
6.5 ± 2.1
5.0 ± 1.9
13.5 ± 4.3
12.1 ± 4.2
167.5 ± 35.2

129.6 ± 41.0

128.1 ± 34.6

1.1

0.94

145.2 ± 34.6

0.1 ± 0.2
0.5 ± 0.7

0.1 ± 0.3
0.6 ± 0.7

-66.7
-20.0

0.14
0.28

0.7 ± 0.9

0.7 ± 0.812.9

8.7 ± 2.6

11.3 ± 3.3-23.3

Dif., % p

Mean ± SD
6.3 ± 2.9
4.8 ± 2.0
13.7 ± 2.9
12.0 ± 3.0
175.0 ± 47.4

Dif., % p
3.8
4.9
-1.1
1.2
-4.3

0.75
0.70
0.74
0.92
0.92

149.9 ± 43.9

-3.1

0.88

0.1 ± 0.2
0.1 ± 0.4

0.1 ± 0.3
0.8 ± 0.8

-50.0
-81.3

0.14
0.28

0.71

1.0 ± 1.0

0.5 ± 0.5

90.9

0.71

<0.01

9.8 ± 3.5

11.1 ± 2.7

-12.0

<0.01

Performance indicators in set pieces are higher among winning teams in the World Cup compared tolosing teams, although such significant percentage advantages were not identified among winning and losing
teams in international competitions. The only indicator showing statistically significant differences (p < 0.01)
between winners and losers across all competitions is Penalties Conceded. Discipline indicators are lower amonglosing teams compared to winning teams across all competitions.
Overall, the analysis revealed that in the World Cup, 18 indicators (56.3%) exhibit statisticallysignificant differences between losing and winning teams, whereas the analysis of performances in international
competitions identified only 4 (12.5%) statistically significant differences between losing and winning teams.
Absolute values of indicators vary slightly across different competitions (but not significantly), indicating aconsistent overall trend in rugby union. Among the differences noted, international competitions involve more
Rucks and Tackles, whereas the World Cup slightly favors more Scrums and Meters Run.
Discussion
The analysis of performance indicators in rugby teams and players spans a significant historical period.
Studies dating back more than two decades (McKenzie et al., 1989; Hughes & Bartlett, 2002) have alreadyinvestigated to identify the most significant indicators distinguishing winning teams from losing ones. In recent
years, this field has gained even more popularity, coinciding with the increasing use of machine learning methods
for modeling and prediction (Watson et al., 2021; Kvasnytsya et al., 2024). However, the main question remains
the practical application of these findings, providing information for coaches and managers that could optimize
training processes.
Overall, our research complements previously established facts while presenting several distinct
features. Indicators such as Tries and Conversion Goals remain paramount, determining match outcomes asaffirmed by numerous prior studies (Jones et al., 2004; Ortega et al., 2009; Hughes et al., 2017; Watson et al.,2017). Our study identified statistically significant differences between Clean Breaks indicators of winning andlosing teams across both levels of competition, as well as for Penalties Conceded, confirming findings from
specific studies (Schoeman et al., 2017; Watson et al., 2017; Scott et al., 2023). Additionally, Lineout ishighlighted as a significant indicator in many studies (Jones et al., 2004; Hughes et al., 2017), emphasizing its
importance specifically in the World Cup in our research.
The significance of indicators shows varying trends depending on specific aspects: competition level,
time period, research objectives, specific indicator values, and others (Colomer et al., 2020). Some researchers
have highlighted the importance of indicators such as Offload and Mauls (Rodrigues & Passos, 2013), which is
supported by our findings for the World Cup; however, the significance of the Mauls indicator was not confirmedin the study for Super Rugby Competitions (Nicholls et al., 2024).
The importance of indicators such as Possession and Territory is supported by certain studies, but theiruse and interpretation vary (Fontana et al., 2017; Colomer et al., 2020). Our research has established statisticalsignificance for these indicators at both competition levels for only one indicator (Possession 1H). In terms of---------------------------------------------------------------------------------------------------------------------------- 1905
JPES ® www.efsupit.roOLEH KVASNYTSYA, VALERIA TYSHCHENKO, MYKOLA LATYSHEV, IRYNA KVASNYTSIA,OLEKSANDR MOZOLIUK, ANATOLII REBRYNA, MYKOLA KIRSANOV, LEONID RYBAK--------------------------------------------------------------------------------------------------------------------------------------match outcome prediction during the game, this indicator should be noted (differences between winning andlosing teams exceed 10.0%). Meanwhile, the differences in Territory 1H indicators are not as significant.
While several studies (Ortega et al. 2009; Vaz et al., 2019) emphasize the importance of indicators suchas Tackles, we did not find significant differences between winning and losing teams across all competitions. At
this stage, the question remains about the diversity of performance indicators, noted by some authors (Colomer etal., 2020). Specialists apply different indicators depending on the research objectives, complicating comparisonsand correlations. Many indicators can vary depending on their use: total count, number won or lost, percentage of
total, etc. Sometimes, specific indicators are used for modeling or forecasting. All of these factors contribute to
the diversity of indicators and the nuances of analysis. Some specialists have explored ways to optimize thenumber of indicators for analysis (Jones et al., 2004; Parmar et al., 2017; Watson et al., 2017; Scott et al., 2023),
but the issue remains relevant today.
Conclusions
The conducted analysis overall confirms previous studies, with the most significant indicators remaining
unchanged. Coaches and managers should continue to focus on these indicators. However, it has been shown thatteams in The Six Nations Championship and The Rugby Championship are closer in level to each othercompared to teams in the World Championship. It is worth noting that the advantages identified between losing
and winning teams in specific competitions may not fully correspond to results obtained in other competitions,and sometimes contradictory data may even be obtained. Therefore, it is essential to align with the results ofperformance indicator analysis specific to each competition to optimize the training process.
Future research will focus on analyzing the dynamics of team performance indicators over a long period.
This approach will enable us to observe general trends and develop models for training activities, which will be
instrumental in preparing teams for future competitions.
Declaration of Conflicting Interests. The authors declared no potential conflicts of interest
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JPES ® www.efsupit.roOLEH KVASNYTSYA, VALERIA TYSHCHENKO, MYKOLA LATYSHEV, IRYNA KVASNYTSIA,OLEKSANDR MOZOLIUK, ANATOLII REBRYNA, MYKOLA KIRSANOV, LEONID RYBAK--------------------------------------------------------------------------------------------------------------------------------------Scott, G. A., Edwards, O., Bezodis, N. E., Waldron, M., Roberts, E., Pyne, D. B., ... & Kilduff, L. P. (2023).
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