Escuela de Ciencias del Movimiento Humano y Calidad de Vida
Universidad Nacional, Costa Rica
e-ISSN: 1659-097X
Vol. 23(1), enero-junio, 2026
revistamhsalud@una.ac.cr
Licencia: By NC ND 3.0 Internacional

Doi: https://doi.org/10.15359/mhs.23-1.19947

Data mining and modelling of the determinants of winning in the first-ever Balloon World Cup

Minería de datos y modelización de los factores determinantes del éxito en la primera Copa del Mundo de Globos

Mineração de dados e modelação dos fatores determinantes da vitória na primeira edição da Taça do Mundo de Balões

Daniel Rojas-Valverde1, Darío Mendoza Romero2, Isabel Briceño Suárez3,
Immanuel Cruz-Fuentes4, Cindy Castro Ramírez5 & Carlos D. Gómez-Carmona6

INTRODUCTION

At least once in our lives, we have played with a balloon trying to keep it from touching the floor. This simple child’s game was taken to a higher level by well-known Spanish streamer Ibai Llanos Garatea and former Football Club Barcelona’s footballer Gerard Piqué; who organized the first ever Balloon World Cup (BWC) at the city of Tarragona, Barcelona, Spain. The event was broadcast for free on the Twitch platform and lasted 6 hours, it had an average number of viewers of 518 thousand, with a peak of viewers of 632 thousand and 2.1 millions unique viewers according to a viewership-tracking site (escharts.com, 2021).

Despite being the first time that an event of this type has been organized and broadcast worldwide, the live-streaming video platforms attract a large audience (Watanabe et al., 2021), as has been the case with the BWC. These virtual platforms focusing in the past on e-sports, have recently spread out to other modalities, increasing its audience (Steinkuehler, 2020). This is the case of Twitch.tv channel, which has grown exponentially and attracts more and more viewers, who are interested especially in novel audiovisual content (Matsui et al., 2020), mainly attracting younger generations (Quintas-Froufe & González-Neira, 2022).

Due to the amount of audience that has been interested in the BWC and considering that both streamers and professional athletes such as organizers of the BWC have become a type of media influencers, that could not only inspire new generations to play this game but impact media and sports organizations (Hilvert-Bruce et al., 2018); it is not surprising that this game will grow in the years to come. There is evidence, especially in response to the COVID-19 pandemic (Rojas-Valverde, Campos, et al., 2020), of how sport-related activities that were previously considered only as means of training or as online games evolved into professional, large-scale competitive events (Rojas-Valverde et al., 2021).

That is why it is of particular interest to make a step forward and analyze performance considering technical and tactical behavior from its most basics to understand which factors influence performance, as has been done in previous studies of other disciplines (Abian-Vicen et al., 2013; O’Donoghue, 2008; Rojas-Valverde, Gómez-Carmona, et al., 2020). For this, notational analysis has been a an objectively and popular way of analyzing sports videos (Gillet et al., 2009). In this sense, in individual sports one vs. one, it has been proposed that factors such as serve effectiveness, non-forced points, location of serve scoring first among other factors, can directly influence the match outcome (Gillet et al., 2009; Ibáñez et al., 2018; Jones, 2009).

Knowing these factors and understanding how they influence the achievement of points can be key for future events as BWC. It could even serve as the basis for strategic and tactical planning, as well as physical prescribing, in upcoming competitions. However, unlike established sports where key performance indicators have already been extensively described through notational and multivariate analyses (e.g., volleyball, tennis, and soccer), the performance structure of balloon matches remains largely unknown. Therefore, identifying the underlying relationships among multiple technical and tactical variables requires an exploratory statistical approach that can reduce data complexity and reveal latent performance structures. In this context, exploratory factor analysis represents a suitable method to identify the main components that explain performance in emerging sports disciplines. Consequently, given the novelty of this sport and the lack of scientific evidence regarding balloon matches, this study aimed to identify the technical and tactical indicators that best characterize balloon match performance using exploratory factor analysis and notational analysis, and to analyse how these actions influence match outcomes during the Balloon World Cup.

METHODS

Participants

Fifteen matches in which participated 16 athletes representing different countries (6 from America, 7 from Europe, 2 from Africa, and 1 from Asia) were analysed. Participants were selected through a <1min open casting sent to the organizers in a video format and selected by a panel of streamers. Match fixture was established randomly in a public virtual occasion as usual in other e-sports events. Because athletes were selected through an open casting process evaluated by the event organizers, the sample may reflect a degree of selection bias toward participants with greater media exposure, popularity, or perceived entertainment value rather than purely competitive performance criteria. Therefore, the sample should be interpreted as representative of the first official Balloon World Cup participants rather than of the general population of balloon players. However, the inclusion of athletes from multiple continents and the complete analysis of all tournament matches helped reduce potential sampling bias within the competition itself.

Figure 1
Match bracket system during the BWC.

Procedures and instruments

For each match a notational analysis was conducted by two different researchers using the notation analysis sheet in Figure 2. Hand notational system are methods to analyse dynamic and complex situations of competition and training. Both researchers were previously trained in coding protocol and independently analysed all matches. To ensure data reliability, inter-rater agreement was assessed before the final dataset was generated. A random subsample of matches (20%) was independently coded by both observers and agreement between raters was calculated using the intraclass correlation coefficient (ICC). The reliability analysis demonstrated high agreement between observers (ICC > 0.85), indicating strong consistency in the identification and coding of the technical and tactical actions analysed. In cases of disagreement, the observers jointly reviewed the video footage and reached a consensus before final data entry.

Figure 2
Notational analysis sheet.

Each match of the tournament was played in an 8x8m court, simulating a living room with obstacles (e.g., chairs, tables, sofa, cabinets). Among the established rules, the tournament used direct elimination with five rounds in a simple bracket system. One set of three-minute matches defined the outcome. A point was considered when the balloon hits the ground before the opponent touches it. Balloon hits must be upwards and pass over 1 m high from the ground. The balloon should be hit from the middle-bottom. The players were not allowed to hinder the opponent with the balloon. Also, one court referee and four video assistant referees participated.

In the Figure 3 the distribution of obstacles in the court is represented, the coloured areas represent the zones analysed in this investigation, zone A: a square of approximately 2.66 m per side in the centre of the court, zone B: the four squared lateral zones adjacent the zone A, each with the same dimensions of 2.66 m per side, and the zone C: the four squared zones diagonally placed to the zone A, each with the same dimensions of 2.66 m per side. After each match was observed, the data from the completed notational analysis sheet were processed in the main database to determine the value of each study variable.

Figure 3
Distribution of obstacles and zones in the court.

Variables

The variables analysed were the following:

•Total Points (n): The total number of points scored by all teams or players throughout the tournament.

•Points by Zone (n): The number of points scored categorized by the three zones described in Figure 3. This would involve counting how many points were scored in each specific zone.

•Service Point (n): The number of points scored directly from a service or serve.

•Non-Service Point (n): The number of points scored not directly from a service, such as points won during rallies.

•Unforced Error (n): The number of errors made by a team or player that are not directly caused by the opponent’s action, such as hitting the balloon out of bounds without any pressure from the opponent.

•Forced Error (n): The number of errors made by a team or player due to the opponent’s pressure or action, such as hitting the balloon into the net because of a strong opponent attack.

•High Touch (n): The number of touches of the balloon directed upwards during play.

•Side Touch (n): The number of touches of the balloon directed horizontally or to the same height during play.

•Low Touch (n): The number of touches of the balloon directed downwards during play.

•Saves under 1 Meter Height (n): The number of times a player successfully saves the balloon when it’s under 1 meter in height, potentially preventing a point for the opponent.

•Saves over 1 Meter Height (n): The number of times a player successfully saves the balloon when it’s over 1 meter in height, potentially preventing a point for the opponent.

•Same Zone (n): The number of touches directed to the same zone as the play was initially.

•Out of Zone (n): The number of touches directed to a different zone than where the play was initially, potentially indicating a strategy shift or an attempt to change the direction of the play.

Statistical Analysis

The information from the 15 BWC matches was described in 13 variables, summarized, and presented with mean (M) and standard deviation (SD). An analysis of the main components was developed to summarize the number of variables that can explain performance in these sports modalities and to gain a better understanding of the underlying structure of the data.

Additionally, the components retained through the sedimentation graphic are shown. A Ward’s method-based cluster analysis was performed to identify similar variables in the data, and Euclidean distance was used to establish the groups and present the results as a dendrogram. All the analyses followed the protocols described previously by (Rojas-Valverde, Pino-Ortega, et al., 2020). Before statistical modelling, inter-observer reliability was verified to ensure consistency of the notational dataset.

Finally, a non-parametric Kruskal-Wallis ANOVA was performed to evaluate the differences between groups identified by cluster analysis. The statistical analysis was performed using IBM SPSS version 28 (California, United States). Statistical significance was set at p<0.05.

RESULTS

Descriptive data are summarized in means, and standard deviations from 14 variables initially evaluated are described in Table 1.

Table 1
Characteristics of variables assessed at the Balloon World Cup.

Variable	Mean	S.D.	r
Points	3.05	1.81	0.99
First Point	0.50	0.50	0.24
Points Zone 1	1.03	1.14	0.97
Points Zone 2	0.66	0.89	0.95
Points Zone 3	1.37	1.22	0.98
Service Point	1.69	1.69	0.90
Non-Service Points	1.45	1.13	0.62
Unforced Errors	1.52	1.46	0.36
Forced Errors	1.50	1.33	0.34
High Touches	0.31	0.71	0.37
Low Touches	23.66	7.17	0.26
Side Touches	9.90	5.86	0.29
Saved	1.68	1.60	0.33
Unsaved	0.08	0.33	0.22

Note. S.D= Standard deviation. R=Multiple coefficient correlation.

In addition, calculation of multiple correlation coefficient is presented before the principal component analysis to determine which variables can relate with more strength and exclude those variables scoring less than 0.3.

Table 2
Variance components and self-values.

Component	Starting self-values			Sum of squared extraction charges
	Total	Variance %	% Stored	Total	Variance %	% Stored
1	3.19	31.93	31.93	3.19	31.93	31.93
2	1.55	15.55	47.48	1.55	15.55	47.48
3	1.44	14.39	61.87	1.44	14.39	61.87
4	1.15	11.49	73.36	1.15	11.49	73.36
5	0.83	8.26	81.62
6	0.78	7.84	89.46
7	0.53	5.34	94.80
8	0.45	4.51	99.31
9	0.06	0.63	99.94
10	0.01	0.06	100.00

Note. Extracting method: principal component analysis.

Coming out of 14 variables, 4 were excluded for having a multiple coefficient correlation lower than r<0,3 (First point, Low touches, Side touches and Unsaved). Among 10 variables left, 4 main concepts are retained with starting self-values higher than 1 that explains 73.36% of variance (Table 2).

The sedimentation graphic (Figure 4) distinguishes among 4 components that explain the determinative characteristics related to winning a match. First component has in common scoring points at distinct points and moments, the second component emphasizes high touches and points made in zone 2, not scoring points in service and avoiding forced errors were the main characteristics of the third component, and finally, the fourth component is determined by saves made by the player (Table 3).

Figure 4
Sedimentation plot.

Table 3
Resume of principal component analysis for variables which determined performance in Balloon World Cup.

Variable	Component
	1	2	3	4
Points	0.955
Service points	0.871
Points zone 1	0.676
Points zone 3	0.574
Forced errors	0.408
Points zone 2		0.59
High touches		0.539
No service point			0.692
Unforced errors			0.575
Saved				0.594

Note. Method of extraction: principal component analysis.

Conglomerates analysis through Ward’s method was applied over 62 cases and 10 variables that conformed the 4 main components. A Euclidean distance of 6 was set to conform 5 clusters, however, number 5 cluster was only conformed by Marruecos team, therefore, it was excluded from comparative analysis done with Kruskal Wallis test, considering the no parametric distribution of variables (Table 4).

Table 4
Comparison between clusters, through Kruskal-Wallis test in the variables evaluated at Balloon World Cup.

Variables	Cluster 1 n=13				Cluster 2 n=20				Cluster 3 n=17				Cluster 4 n=11				Valor p
	Mean	S.D.	Median	I.R.	Mean	S.D.	Median	I.R.	Mean	S.D.	Median	I.R.	Mean	S.D.	Median	I.R.
Points	5.5	0.9	5.0	1.0	3.0	0.9	3.0	2.0	1.3	0.8	1.0	1.0	2.6	1.2	2.0	2.0	<0.001	*†‡§
Points zone 1	2.2	1.3	2.0	2.0	0.8	0.7	1.0	1.0	0.7	0.9	0.0	1.0	0.5	0.7	0.0	1.0	<0.001	*†‡
Points zone 2	1.2	1.1	1.0	2.0	0.4	0.5	0.0	1.0	0.3	0.5	0.0	1.0	1.2	1.3	1.0	2.0	0.033
Points zone 3	2.3	1.4	2.0	2.0	1.8	1.1	2.0	1.5	0.4	0.5	0.0	1.0	1.0	0.8	1.0	2.0	<0.001	†§
Service points	4.2	0.9	4.0	1.0	1.3	0.9	1.0	1.5	0.4	0.6	0.0	1.0	1.2	1.2	1.0	2.0	<0.001	*†‡
No service points	1.3	0.8	1.0	1.0	1.7	0.9	2.0	1.0	1.0	0.9	1.0	0.0	1.5	0.8	1.0	1.0	0.032
Unforced errors	0.8	1.0	1.0	1.0	1.1	1.2	1.0	2.0	1.2	1.0	1.0	0.0	3.6	1.1	3.0	1.0	<0.001	‡¶**
Forced errors	1.9	1.7	2.0	2.0	1.6	0.9	1.5	1.0	1.5	1.7	1.0	1.0	0.7	0.6	1.0	1.0	0.144
High touches	0.1	0.3	0.0	0.0	0.0	0.0	0.0	0.0	0.3	0.5	0.0	1.0	1.2	1.3	1.0	2.0	<0.001	‡¶
Saved	1.3	1.4	1.0	2.0	2.7	1.5	2.5	2.0	1.0	1.1	1.0	1.0	0.8	0.9	1.0	2.0	<0.001	§¶

Note. * Significant post hoc differences clusters 1 and 2; † Significant post hoc differences clusters 1 and 3; ‡ Significant post hoc differences clusters 1 and 4; § Significant post hoc differences clusters 2 and 3; ¶ Significant post hoc differences clusters 2 and 4. ** Significant post hoc differences clusters 3 and 4. S.D.= Standard deviation. I.R.= Interquartile range.

Table 5 shows how the participating countries were distributed across the four main clusters identified in the analysis. This distribution reflects similarities in the technical and tactical profiles observed during the matches. Some countries appeared in multiple clusters, suggesting variability in match performance across different rounds of the tournament. In contrast, countries appearing predominantly in a single cluster reflect more consistent technical and tactical patterns. These results highlight the heterogeneity of performance strategies among competitors during the Balloon World Cup. Figure 5 shows the hierarchical clustering of the analysed cases based on the technical and tactical variables included in the principal component analysis. The dendrogram illustrates the similarity between observations according to Euclidean distance using Ward’s method. Five initial clusters were identified at a Euclidean distance threshold of 6; however, cluster 5 was composed exclusively of one observation (Morocco), and therefore it was excluded from subsequent comparative analyses to avoid bias in the statistical comparisons. The remaining clusters represent groups of matches with similar technical and tactical performance patterns.

Figure 5
Dendrogram produced by cluster analysis. Groups were selected starting with a Euclidian distance of 6.

Table 5 shows the classification of countries in each cluster.

Table 5
Position of every country in each cluster.

Country / Cluster	1	2	3	4	5
Germany	1	2	2
Andorra	2		1
Algeria				1
Argentina	2		1
Armenia				1
Bolivia	1	2
Brasil	1	1	2
Bulgaria		1
Chile		1
China		1
Colombia			1
Cuba		1		1
Spain	1	1	1	1
France	1
Georgia		1	1
Great Britain				1
Guinea			1	1
Netherlands		2
Italy		1		1
Marruecos	1			1	1
México			1
Mongolia			2
Paraguay				1
Peru	2	2	1
Portugal	1			1
Russia			1
Senegal		1		1
Sweden		1
Ukraine		1
Uruguay			1
USA		1
Venezuela			1

DISCUSSION

This study aimed to identify, via notational analysis, which technical and tactical indicators were the most representative of balloon matches using exploratory factor analysis, and to analyse how technical and tactical actions influence match outcome during the BWC. The study results suggested that data obtained during the BWC could be clustered into four principal components, each containing one to five variables (11-31% of variance explained). There were differences by clusters in total points, points in zone 1 and 3, service points, unforced errors, high touches, and saved plays.

The study outcomes evidenced that the main components that determine the match score correspond to component 1 (points, service points, points zone 1, points zone 3, forced errors), component 2 (points zone 2, high touches), component 3 (no service points, unforced errors), and component 4 (saved). These components explained 73.36% of the probability of winning a match. Principal component analysis usually follows a specific guideline when exploring key performance indicators in sports to guarantee data and outcomes quality (coefficient correlations r <0.3, eigenvalues of each component >1) based on primary sedimentation plotting. In this sense, this study provides evidence of an accepted percentage of variance explained based on previous published criteria (Rojas-Valverde, Pino-Ortega, et al., 2020).

Talking about clusters, significant differences between them were observed, table 5 shows the position of every country and its respective cluster and highlights that the significant variables found correspond to the clusters which has the countries that had the best positions on the competition, these are Peru, Germany, Argentina, Brazil, Bolivia, Spain, France, Marruecos and Portugal, showing concordance with the statistical analysis and justifying this study results.

Considering the notational analysis, total points had a great influence on match results. This tendency have been found in other sports as volleyball (Peña & Casals, 2016; Yu et al., 2018), and soccer (Cuasapud & Hurtado, 2018; Lago-Peñas et al., 2010). This evidence highlighted the role of total points scored in kills as in volleyball, and the match time period in which these goals are scored.

Service points are fundamental factors for sports performance in sports that involve hitting an object, like tennis and volleyball (Gillet et al., 2009; Silva et al., 2014). This variable makes a difference when the player has the necessary skills to send a service particularly hard to receive. The same tendency is present in this study; service points have shown significant differences between clusters 1-2 and clusters 1-3 (p<0.001).

Points got on zones 1 and 3 have shown statistical significance, also showing differences among clusters 1-2, 1-3, and 1-4 (p<0.001). Zone 2 points have not resulted statistically significant, this is explained due to the design of the court, zone 1 is characterized by the obstacles present and zone 3 correspond to the corners of the court, zone 2 is a relatively free playing zone and this can explain the present results, other research has shown the same outcomes in some disciplines like tennis, where 92% of services are sent to the vertex of opponent´s service zone, being these the hardest zones of the court to send back the ball. (Gillet et al., 2009). In volleyball, those zones determined as “excellent zones” are zones 2, 3 and 4, because this rectangular area is closer to the net and it has revealed more efficacy to score points, this could be explained with the very little time defenders have to react to block or receive balls sent from an attacker´s kill (Castro et al., 2011). According to these findings, the efficacy of scoring points related to the court zone is mainly determined by the court design itself and its characteristics, the same principle applies to balloon´s game.

Forced and unforced errors were also found as important variables in matches scores. Some studies have shown the importance of these aspects, demonstrating that the more errors the opponent makes, and the fewer their own errors, the more possibilities to win the match are present, this applies to paddle and tennis, respectively (Courel-Ibáñez et al., 2017; O’Donoghue, 2002). In other study, Mellado Arbelo et al. (2019) found in an analysis of 20 professional paddle players that these variables only explained 2.1% (forced errors) and 5.5% (unforced errors) of points scored; however, this is due to the very high influence of the intercepted shots, which explained 87.6% of the points efficiency on this case.

Saved actions are very exclusive of balloon´s game, in spite of being a very singular technical action, it´s similar to blocks and some actions like receptions done when playing volleyball, on which is demonstrated that every successful block increment 0.6 times the probability to win a match (Peña et al., 2013). On this study, saved actions showed statistically significant differences (p<0.001) among clusters 2-3 and 2-4, so this variable also has its importance when players are preparing themselves to compete. The distribution of countries across clusters (Table 5) suggests that successful performance patterns were not strictly associated with geographical origin but rather with specific technical and tactical behaviours identified in the principal components.

Future research

These results represent the first specific data analysis of this new sport modality, establishing a starting point for future research on strategy, technique, tactics, physical preparation, and training protocols for emerging activities such as playing with a balloon. This kind of investigation aims to improve the competitive performance and determine factors like players’ precision, individual tactics, and the creation of statistical assessment tools to evaluate effectiveness in balloon competitions. The options presented in this study are widely applicable across different sports and are frequently used by coaches and physical trainers in pursuit of competitive goals.

Limitations

This study presents several limitations that should be acknowledged. First, there is currently limited scientific evidence on balloon competitions, which limits the ability to compare the present findings with previous research on technical, tactical, and performance determinants, although similar analytical approaches have been applied in other sports disciplines (Gómez et al., 2021). Second, this sport is not yet regulated by a national or international governing body, which may limit the standardization of rules and competition structures. Additionally, data from subsequent editions of the Balloon World Cup (e.g., 2023) were unavailable for comparative analysis, limiting the ability to evaluate the consistency of performance patterns over time. Furthermore, there is currently no available information regarding the physiological profiles, performance categories, or specific physical demands associated with this discipline. Finally, a standardized and validated instrument for notational analysis in balloon competitions has not yet been developed, which highlights the need for methodological advancements in future research.

Practical applications

Scoring effectiveness, particularly in the farthest and most obstructed zones of the court, appears to be a key determinant of match success in balloon competitions. The results also suggest that tactical strategies aimed at inducing opponent errors may play a critical role in gaining competitive advantage. In addition, effective defensive behaviours, such as successful save actions that prevent the balloon from touching the ground, may contribute to sustaining rallies and reducing point losses. Conversely, minimizing unforced errors, including technical mistakes or rule infractions, may substantially increase the probability of winning a match. Collectively, these findings highlight the importance of combining offensive efficiency, defensive responsiveness, and error management when designing tactical approaches and training strategies for this emerging sport.

CONCLUSIONS

Points number, service points, points zone 1, points zone 3, forced errors, points zone 2, high touches, no service points, unforced errors, and saved actions explain 73.36% of the variance. These findings address the main research objective of identifying the technical and tactical indicators that characterize performance in balloon matches during the Balloon World Cup. Specifically, the results suggest that scoring efficiency, the spatial distribution of points across court zones, and effective error management are key determinants of match success in this emerging sport. From a practical perspective, players and coaches may benefit from prioritizing strategies that increase scoring opportunities in obstacle-dense areas of the court, reduce unforced errors, and improve defensive saving actions during play. By identifying the principal technical and tactical components that influence match outcomes, this study provides an initial performance framework that can guide training design, tactical preparation, and future performance analysis in balloon competitions. Future research should continue exploring additional variables related to physical demands, player profiles, and contextual match dynamics to further understand performance in this discipline.

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1. Recibido: 17-5-2024 Aceptado: 11-3-2025

   1 Universidad Nacional de Costa Rica, Heredia, Costa Rica. daniel.rojas.valverde@una.cr, https://orcid.org/0000-0002-0717-8827.

2. 2 Universidad Santo Tomás, Bogotá, Colombia. dariomendoza@usta.edu.co, https://orcid.org/0000-0002-8973-1541.

3. 3 Universidad Nacional de Costa Rica, Heredia, Costa Rica. isabel.briceno.suarez@est.una.cr, https://orcid.org/0000-0001-7555-4487.

4. 4 Universidad Nacional de Costa Rica, Heredia, Costa Rica. immanuel.cruz.fuentes@una.cr, https://orcid.org/0000-0003-3335-8079.

5. 5 Universidad Santo Tomás, Bogotá, Colombia. cindycastro@usta.edu.co, https://orcid.org/0000-0001-9442-1532.

6. 6 Universidad de Extremadura, Caceres, España. cdgomezcarmona@unex.es, https://orcid.org/0000-0002-4084-8124.

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