Offensive transitions in football as a decisive way to obtain goals in Top, Marginal and Emerging leagues.

doi:10.21203/rs.3.rs-3771103/v1

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Offensive transitions in football as a decisive way to obtain goals in Top, Marginal and Emerging leagues.

https://doi.org/10.21203/rs.3.rs-3771103/v1

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Offensive transitions in modern football have been identified as a decisive moment in a game, as they offer a high potential for scoring goals and creating defensive imbalances. To investigate the impact of offensive transitions on the performance of different national football leagues, this study analyzed data from 1151 games belonging to nine leagues/countries, which were divided into three categories: Top, Marginal, and Emerging. A total of 3497 goals were scored in these games, and they were classified according to their origin, including non-offensive transitions, set pieces, direct offensive transitions, and positive results of offensive transitions. Results showed that the Top Leagues scored more goals (0.28 goals) and had a higher scoring rate due to ball interference (53%) compared to the Emerging and Marginal Leagues. Furthermore, the Emerging and Marginal Leagues performed better in terms of goals scored from non-offensive transitions and set pieces, suggesting the presence of specific characteristics in these leagues. In conclusion, this study demonstrates the role of offensive transitions on the performance of national football leagues and the superior performance of Top Leagues in utilizing them effectively. These findings highlight the importance for teams to use motion analysis technology and optical sensors to gain valuable insights into offensive transitions, allowing teams to optimize their strategies and capitalize on opportunities. Technology gives modern football a competitive edge to achieve success on the pitch.

Playing patterns

Offensive/Defensive Processes

Counterattacks

Game Moments

Tactical Strategies

Soccer

Optical Sensors

Motion Sensors

Spatial Patterns

Football is a complex, dynamic and interactive game, where the changing of behavior patterns by the teams is hampered by the uncertainty of when the different moments of game will occur, making it difficult to objectify observation and analysis (1). Although teams have complete freedom to start or finish possession of the ball when they deem it convenient (2), they define their standard of tactical behavior based on having or not having possession. Therefore, the teams are always in one of two phases, the offensive or defensive phase, which are established through an offensive and defensive process respectively.

Consensus eludes the commencement and conclusion of these phases in the literature. Some authors refer these begin at the moment of conquest or loss of possession (3, 4) while other authors indicate that begin before the conquest or loss of possession (5). Thus, the defensive process is initiated when a team does not have possession and its principle is to delay or derail the opponent's offensive game (3). Those principles characterize the moments of defensive organization and also attack/defense transition. The offensive process is initiated when a team gains possession of the ball and its principle is to attack the opposing team with the intention of obtaining goals (6). Thus, the moments of offensive organization and also of transition between defense/attack are characterized. Emphasis to the fact that “Set Pieces”, have being consider by several authors as the fifth moment, which due to its specificity, should be considered individually (7, 8).

Transition moments present exciting opportunities and nervous vulnerabilities (9), that the teams should explore to achieve their goals. According to Maneiro and his collaborators (2019), coaches, players and researchers consider offensive transitions as the most important moment in the game, and Vincent & Durny (2017) describe transitions as the most decisive moment in high-level competitive football.

In the literature, there seems to be no clear consensus on when a transition begins and ends, once some authors materialized that the transitions start when a team obtains possession of the ball and ceases when the same team performs a predetermined outcome (3, 10, 11) but others suggest that the transitions starts before the event of losing/regain the possession happen (5). Even the concept remains undefined, it is assumed that in an offensive transition the team that is executing it should take advantage of the opponent being out of position and possibly less prepared to respond to this attack. It is established that in a transition the team that takes possession, the players must try to reach the target as quickly as possible, to capitalize some space that could have resulted from the possession shift (12).

Several studies attribute 18% (13) to 67% (14) of goals are obtained by offensive transitions directly, and 20% (6) to 35.6% (14) of goals are scored by set pieces. Considering that offensive transitions "contribute over 56% of all attempts on goal" (14) p. 447, it seems pertinent to understand the real impact of the offensive transitions in the game.

The offensive transition can be claimed based on their outcome (9). They will be named as non-positive if the play leads to the opponent to conquer the next possession of the ball (negative outcome). They can also be positive if the offensive transitions end up in a goal (direct transition), continue the offensive pressure (keeping the possession) and obtain a goal in the subsequent play (positive outcome), meaning after getting a “set piece”.

It is essential to understand the role that offensive transitions have in the normal course of the game, in obtaining goals or in the final result of the games and also to understand if these are more connected or are more relevant for certain leagues than for others. Thus, in the present research, several criteria were used/thought to find similarities between the leagues. Were consider the UEFA/FIFA rankings that classify as leagues, the latitude of the leagues, the usual transfers made by players, the number of international players playing in each of them and the economic potential that each league represents.

This research aims to understand the impact of offensive transitions in obtaining goals in all different groups in the league. The investigation will seek to understand where the goals come from, focusing attention on the importance of goals obtained through direct and indirect offensive transitions (positive result) in the different championships.

The sample of this research was the 1151 games from the season 2020-21, in which there were goals from the first round. Nine national leagues were analyzed and were grouped into three groups of three leagues. The Top Leagues (TL) group made up of the Spanish, Italian and German Leagues ranked as the 2nd, 3rd and 4th of the UEFA ranking (at the date the events were collected) and which are the leagues with the highest turnover and value invested in the transfer markets. The Marginal Leagues (ML), made up of the leagues Portuguese, Netherlands and Russia, which are ranked 6th, 7th and 8th respectively and which have a high number of transfers from these to the other leagues analyzed, and the Emerging Leagues (EL), made up of the Qatar, Saudi Arabia, and UAE leagues that appear as markets with high financial potential that manage to attract some international players but those leagues are played mainly by local players. A total of 3497 of goals were produced and all of them were observed and analyzed. All teams under their league played by the same regulation, equal numbers of games and conditions (home and away) and played against each other.

The instrument used for the analysis was adapted from the system proposed by Turner and Sayers (2017), along with an observation and registration instrument proposed by the authors, which is constituted by systems of categories, which precisely define the target registration criteria. The videos were collected through two video collectors, InStat and WyScout. The observation was carried out with the Windows Media Player, since the software makes it possible to stop the videos frame by frame to characterize the plays in detail. All the analysis were performed by Jamovi – ESCI Software. All the images used are publicly available which means that no informed consent or ethics committee approval was required (15, 16).

All goals (n = 3497) scored in the 1151 games analyzed were characterized as a result of i) non-offensive transition (NT), ii) direct offensive transition (OT), iii) set pieces (SP), or iv) by positive outcome (POS OUT) (all goals that resulted from SP in which his immediately preceding play came from an offensive transition)

The plays were considered from the beginning, which means that the plays were analyzed from the exact moment that a team gains or regains possession of the ball, whether through a tackle, interception or ball rebound. In situations where a team restarts the game quickly and thus creates an offensive transition, the moment in which the players execute the first action was considered, be it a throw-in, a goal kick, a corner kick or when a free kick is taken.

Thus, the goals classified as NT were not described. The goals classified as OT were described in detail from the exact moment the team recovered or gained possession of the ball until the goal was scored. Moves classified as SP were analyzed based on the immediately preceding move. If the immediately previous move that gave rise to the SP was not an offensive transition, the goal and the previous move were not described. However, if the SP were originated by an offensive transition, the move was described and classified by POS OUT. The description of these plays was the same as the description made in the OT, with the end of the play being considered the moment in which the positive result originates, which consequently gives rise to the goal in the immediate play (Fig. 1).

Of the 1151 games, were scored 3497 goals, which are distributed as follows about the league group as well as the goal classification (Table 1). The Top leagues obtained 53% of the goals due the influence of offensive transitions. The number for the Marginal leagues decreases to 43% and in the Emerging leagues to the 42%. The results obtained in each of the leagues can be seen in the table S1.

Table 1: Distribution of the types of goals per leagues

	Games Observed with Goals	Total Goals	Goals by NT	Goals by OT	Goals by SP	Goals by POS OUT	Goals by OT + POS OUT	% Of goals By OT + POS OUT
Emerging Leagues	262	827	266	265	216	80	345	41,72 %
Marginal Leagues	391	1144	386	397	264	97	494	43,18 %
Top Leagues	498	1526	412	643	304	167	810	53,08 %
Totals	1151	3497	1064	1305	784	344	1649	47,15 %

Note: NT – No Transition. OT – Offensive Transition. SP – Set Pieces. POS OUT – Positive Outcome.

OT + POS OUT – Goals obtained by Offensive transition and Positive Outcome.

Table 2 shows the average of goals scored in each of the respective league groups (TL, ML and EL), as well as the standard deviation and the total number of goals scored in each variable. The results of the same variables in each of the leagues can be seen in table S2.

Table 2: Goals Average and intervals confidence by group league according with the goals classification.

Descriptive Statistics
Descriptive Statistics – Goals by Offensive Transitions (OT)				Descriptive Statistics – Goals by Positive Outcome (POS OUT)
	M	s	N		M	s	N
Emerging	1.01	0.97	262	Emerging	0.31	0.53	262
Marginal	1.02	1.06	391	Marginal	0.25	0.48	391
Top	1.29	1.07	498	Top	0.34	0.56	498

Descriptive Statistics – Goals by Set Pieces (SP)				Descriptive Statistics – Goals by No Transitions (NT)
	M	s	N		M	s	N
Emerging	0.82	0.85	262	Emerging	1.02	1.03	262
Marginal	0.68	0.79	391	Marginal	0.99	1.04	391
Top	0.61	0.75	498	Top	0.83	0.90	498

Note: M – Media of these events per game; S - standard deviation; N – Total number of events

Top Leagues have a higher average of goals per OT (1,29), while the other leagues have very similar values. Regarding goals by POS OUT, the Top Leagues also have a higher average than the other leagues (0,34), being seconded by Emerging Leagues (0,31) and Marginal Leagues (0,25) respectively. Regarding the average of goals, either by SP or NT, the Emerging Leagues (SP = 0,82; NT= 1,02) lead followed in this order by Marginal Leagues (SP = 0,68; NT= 0,99) and Top leagues (SP = 0,61; NT= 0,83).

The analysis of table 3 allows estimating that the number of goals obtained by OT in Top Leagues is higher than in Marginal Leagues and Emerging Leagues. Thus, in Top Leagues it is expected that there will be 0.28 more goals (95% CI [0.12;0.44]) per OT than in the Emerging Leagues and 0,28 more goals (95% CI [0.14;0.41]) per OT than in the Marginal Leagues. Analyzing the Emerging Leagues and Marginal Leagues among themselves, the results show that it is not expected that there are differences between them (95% CI [-0.15;0.16]) in the achievement of goals by OT. The results for OT in each of the leagues are described in S3.

Table 3: – Estimate Ind. Groups Contrasts – Goals obtained by OT – Comparison between TL, ML and EL

Estimate Ind. Groups Contrasts – Goals by OT
Contrasts – Top Leagues vs Marginal Leagues
			95 % CI
	M	95 % MoE	Lower	Upper	p
Top	1.29	0.09	1.20	1.38	NaN
Marginal	1.02	0.10	0.91	1.12	NaN
Top vs. Marginal	0.28	0.14	0.14	0.41	< .001

Contrasts – Top Leagues vs Emerging Leagues
			95 % CI
	M	95 % MoE	Lower	Upper	p
Top	1.29	0.09	1.20	1.38	NaN
Emerging	1.01	0.13	0.89	1.14	NaN
Top vs. Emerging	0.28	0.16	0.12	0.44	< .001

Contrasts – Emerging Leagues vs Marginal Leagues
			95 % CI
	M	95 % MoE	Lower	Upper	p
Emerging	1.01	0.13	0.89	1.14	NaN
Marginal	1.01	0.10	0.91	1.12	NaN
Emerging vs. Marginal	-0.00	0.16	-0.15	0.16	0.96

Note: M – Media of these events per game; S - standard deviation; N – Total number of events; 95% CI - confidence intervals (95%); MoE – Margin of Error; p – p value; NaN – Not a number

Figure 2 allow the visualization of the difference in goals expected by OT between the different leagues, as well as the confidence interval that allows estimating the difference in obtaining goals by Offensive Transition in each of the leagues. For the same variable, figure S1 presents the results of each league comparing the results obtained within the same group of leagues.

POS OUT goals (table 4) are higher in Top Leagues than in other leagues. Comparing the different leagues, it is expected that in Top Leagues there are 0.03 more goals (95% CI [-0.05;0.11]) per POS OUT than in Emerging Leagues, and 0.09 goals (95% CI [0.02;0.16]) than in Marginal Leagues. The comparison between Emerging Leagues and Marginal Leagues also allows to state that it is expected that there will be 0.06 more goals (95% CI [-0.03;0.14]) in Emerging Leagues than in Marginal Leagues by POS OUT. The results for POS OUT in each of the leagues are described in S4.

Table 4: – Estimate Ind. Groups Contrasts – Goals obtained by POS OUT – Comparison between TL, ML and EL

Estimate Ind. Groups Contrasts – Goals by POS OUT
Contrasts – Top Leagues vs Marginal Leagues
			95 % CI
	M	95 % MoE	Lower	Upper	p
Top	0.34	0.05	0.29	0.38	NaN
Marginal	0.25	0.05	0.20	0.30	NaN
Top vs. Marginal	0.09	0.07	0.02	0.16	0.014

Contrasts – Top Leagues vs Emerging Leagues
			95 % CI
	M	95 % MoE	Lower	Upper	P
Top	0.34	0.05	0.29	0.38	NaN
Emerging	0.31	0.06	0.24	0.37	NaN
Top vs. Emerging	0.03	0.08	-0.03	0.11	0.455

Contrasts – Emerging Leagues vs Marginal Leagues
			95 % CI
	M	95 % MoE	Lower	Upper	P
Emerging	0.31	0.06	0.24	0.37	NaN
Marginal	0.25	0.05	0.20	0.30	NaN
Emerging vs. Marginal	0.06	0.08	-0.03	0.14	0.173

Note: M – Media of these events per game; S - standard deviation; N – Total number of events; 95% CI - confidence intervals (95%); MoE – Margin of Error; p – p value; NaN – Not a number

Figure 3 show that the number of goals estimated by POS OUT is higher in Top Leagues than in the others. This value is three times higher for Marginal Leagues than Emerging Leagues. The difference between Top Leagues and Emerging Leagues is considerably small and the expected values are close between them. Still in the goals obtained by the POS OUT, it is expected that there will be slightly more goals in the Emerging Leagues than in the Marginal Leagues. The confidence interval shows that both alloys have relatively close expected values. The figure S2 shows the results of the same variable in each league, comparing the results obtained within the same group of leagues.

For goals by SP, it appears that these are more likely to happen in Emerging Leagues (table 2). When comparing the different leagues (table 5), it is expected that in Emerging Leagues there are 0.21 more goals (95% CI [0.33;0.8]) than in Top Leagues and 0.15 more goals (95% CI [0.03;0.27]) than in Marginal Leagues. Between Marginal Leagues and Top Leagues, it is estimated that there will be 0.06 more goals (95% CI [-0.15;0.04]) in Marginal Leagues. In this way it is possible to say that in Top Leagues goals by SP are less likely to happen when compared to other leagues. The results for SP in each of the leagues are described in S5.

Table 5: – Estimate Ind. Groups Contrasts – Goals obtained by SP – Comparison between TL, ML and EL

Estimate Ind. Groups Contrasts – Goals by SP
Contrasts – Top Leagues vs Marginal Leagues
			95 % CI
	M	95 % MoE	Lower	Upper	p
Top	0.61	0.07	0.54	0.68	NaN
Marginal	0.68	0.08	0.60	0.75	NaN
Top vs. Marginal	-0.06	0.10	-0.15	0.04	0.225

Contrasts – Top Leagues vs Emerging Leagues
			95 % CI
	M	95 % MoE	Lower	Upper	p
Top	0.61	0.07	0.54	0.68	NaN
Emerging	0.82	0.10	0.73	0.92	NaN
Top vs. Emerging	-0.21	0.12	-0.33	-0.08	< .001

Contrasts – Emerging Leagues vs Marginal Leagues
			95 % CI
	M	95 % MoE	Lower	Upper	p
Emerging	0.82	0.10	0.73	0.92	NaN
Marginal	0.68	0.08	0.60	0.75	NaN
Emerging vs. Marginal	0.15	0.12	0.03	0.27	0.018

Note: M – Media of these events per game; S - standard deviation; N – Total number of events; 95% CI - confidence intervals (95%); MoE – Margin of Error; p – p value; NaN – Not a number

In figure 4 it is possible to observe that the goals by SP have a greater preponderance in the Emerging Leagues, and the difference is much more accentuated in relation to the Top Leagues and smaller for the Marginal Leagues. Between Marginal Leagues and Top Leagues, the difference is very low, so the estimated number of goals will be approximately the same, with a small probability of more happening in Marginal Leagues. The figure S3 shows the results of the same variable in each league, comparing the results obtained within the same group of leagues.

From table 2 the goals by NT occur more often in Emerging Leagues, followed by Marginal Leagues and finally in Top Leagues. The table 6 presents the respective comparisons, so it is possible to expect 0.19 more goals (95% CI [-0.34; -0.04]) per NT in Emerging Leagues than in Top Leagues. This value has decreased considerably compared to Marginal Leagues, so it is expected 0.03 more goals (95% CI [-0.13;0.18]) per NT in Emerging Leagues. Comparing the Marginal Leagues with the Top Leagues, it is expected that there will be 0.18 more goals (95% CI [-0.29; -0.03]) in Marginal Leagues. All these results can be seen in Figure 5, which shows the lower preponderance of goals scored by NT in the Top Leagues compared to the Marginal and Emerging Leagues. The results for NT in each of the leagues are described in S6 and the figure S4 shows the results of the same variable in each league, comparing the results obtained within the same group of leagues.

Table 6: – Estimate Ind. Groups Contrasts – Goals obtained by NT – Comparison between TL, ML and EL

Estimate Ind. Groups Contrasts – Goals by NT
Contrasts – Top Leagues vs Marginal Leagues
			95 % CI
	M	95 % MoE	Lower	Upper	p
Top	0.83	0.09	0.74	0.91	NaN
Marginal	0.99	0.10	0.89	1.08	NaN
Top vs. Marginal	-0.16	0.13	-0.29	-0.03	0.016

Contrasts – Top Leagues vs Emerging Leagues
			95 % CI
	M	95 % MoE	Lower	Upper	p
Top	0.83	0.09	0.74	0.91	NaN
Emerging	1.02	0.12	0.90	1.13	NaN
Top vs. Emerging	-0.19	0.15	-0.34	-0.04	0.012

Contrasts – Emerging Leagues vs Marginal Leagues
			95 % CI
	M	95 % MoE	Lower	Upper	p
Emerging	1.02	0.12	0.90	1.13	NaN
Marginal	0.99	0.10	0.89	1.09	NaN
Emerging vs. Marginal	0.03	0.15	-0.13	0.18	0.720

Note: M – Media of these events per game; S - standard deviation; N – Total number of events; 95% CI - confidence intervals (95%); MoE – Margin of Error; p – p value; NaN – Not a numberFigure 5 - Estimate Ind. Groups Contrasts – NT expected Goals and CI results

The objective of this research was to investigate the relevance that the goals obtained in the offensive transitions have in current football, being innovative the way in which the offensive transitions are interpreted as well as the observed championships. The present research is innovative as it classifies how goals were scored in different national leagues. This fact is more relevant since the data obtained correspond to the same season in all nine national leagues considered. These data allow comparing national leagues between different countries and continents, which had never been done before. The results obtained in each of the championships as well as the comparisons between them can be found in the supplementary information. Also, this research is innovative in the way offensive transitions are analyzed and reported. All goals that resulted directly from an offensive transition (OT) were classified as goals by offensive transition. Also, all goals obtained by set pieces that had in their genesis an offensive transition in the move immediately prior to obtaining the set pieces, which are classified as goals by offensive transition by positive outcome (POS OUT). It is intended to enhance and highlight the disruptive play that had the greatest influence on achieving the goal. Thus, if Team A is in possession (offensive process) and Team B is in defensive process, and the latter regains possession and goes for an offensive transition, if the move ends due to an opposing foul and this in turn gives rise to a goal, this will be counted as goal by offensive transition by positive outcome. This approach attributed preponderance to the moment of the game that allowed the successful outcome.

With regard to transitions, the vast majority of the research carried out relate to major national team tournaments. Some of those that consider goals by transitions are the following: the European Championship in Portugal in 2004, 20.3% of the goals were scored through transitions and 35.6% through set pieces (10) while in the 2006 World Cup, 20.3% were achieved through counter-attacks and 32.6% through set-pieces (17), and in the 2010 Football World Cup the research carried out showed that 18.8% of those were due to transitions 20.0% for set pieces (6). This research, being related to nine national leagues, demonstrates the importance of direct and indirect offensive transitions in obtaining goals in the set of all analyzed national leagues (47.15%). The impact of Offensive Transitions is even more significant in the Top Leagues (53%), and the value of the German Bundesliga League is 57.11% (S1), which is close to the values obtained by the study based on 167 goals from the English Premier League season noted that “in-game transitions accounted for 63% of all goals scored” (14) p. 438.

Goals by offensive transition (OT) have a greater impact on TL when compared to LM and EL, and goals by offensive transition are also more likely to occur in TL when compared to ML and EL. Emphasis on the data obtained in the national league of Netherlands in the ML group and the National league of Qatar in the EL group that present a number of goals obtained by offensive transition superior to the Spanish league of the TL group (Table S2). The data obtained reinforce the impact that goals by offensive transitions have in current football, since in all the league groups represented, goals by transition present a value greater than one goal per game on average. Offensive transitions are actions that emerge from a certain disorder, and these are the result of the change in function that occurs by changing possession of the ball (5), therefore it is imperative that the championships that present better and more capable players respond more effectively to the tactical disruption that Offensive Transitions cause. Hence TL perform better than ML and EL. The lack of difference between ML and EL are certainly related to several factors. It will be interesting to develop studies that can find effective reasons for these results.

Regarding the goals obtained by POS OUT, the values obtained demonstrate that the offensive transitions are decisive moments in obtaining goals directly but also in the subsequent move to the offensive transition that culminated in a set piece. The TL are more competent in scoring goals by POS OUT than ML and EL, although it should be noted that the difference is considerably lower for EL than for ML. It is also interesting to see that the Portuguese league in the ML group and the Qatari league in the EL group have similar values to the Spanish League that belongs to the TL. It should also be noted that the Qatar league has average goals scored per POS OUT per game that are higher than the Spanish and German leagues and identical to the Italian leagues, all of them in TL (table S2). According to Turner and Sayers (2017), transition moments present exciting opportunities and nervous vulnerabilities, which lead the team in offensive transition to take advantage of offensive transitions and, on the contrary, lead the team in defensive transition to make mistakes and concede set pieces. that enable the achievement of the objective. It will be interesting in the future, within the "POS OUT" to understand which are the ones that generate more goals, the areas in which the offensive transitions begin so that the teams can adopt different and/or certain differentiated tactical behaviors. Technology will play a decisive role, namely through motion sensors and spatial recognition in order to identify the areas at the beginning and end of the transition.

For goals from the Set Pieces (SP) variable, these are substantially more likely to occur in EL than in TL and ML. The fact that the same does not happen in the previous criterion (POS OUT) indicates that the Set Pieces that did not come from offensive transitions, but instead, appear in contexts where the teams are balanced. The differences found between the national league groups, although considerable, gain greater prominence when considering the national league of the UAE with the German Bundesliga (Table S2). Considering that the EL are all located in the Middle East and since they are sometimes played under extreme hot temperatures, it will be interesting to investigate in future research to understand if the temperatures (temperature and humidity sensors) influence the pace of the game (motion sensors) and, consequently, the number and the location of Set Pieces originated in situations of continued attack.

In the variable of goals obtained by "No-Transition" (NT) it is also verified that the EL have supremacy in obtaining this type of goals in relation to the TL which clearly indicates that different types of attack are favored other than offensive transition. These values assume that the goals come from situations where numerical equality occurs more frequently and that the attack is more organized and, therefore, less susceptible to imbalances. This fact could explain why TL have fewer goals in this variable, since they will be more capable of neutralizing this type of collective patterns. Attention should also be paid to the ML where the values found in the different championships that make up the ML, since the results are different from each other. Thus, the national league of the Netherlands, presents averages per game much higher than the EL and in opposition to the Portuguese League, which presents averages lower than the TL (Table S2).

The analysis of all the variables shows that the TL, being the best ranked leagues, present higher rates in the variables of goals by direct offensive transition (OT) and indirect. The TL empirically present more balanced teams and that is why they use the moments when the opponents are unbalanced, either positionally or numerical, to reach successful situations. These results are in line with research carried out on the German Bundesliga 2010 − 211, which states that the best teams recover the ball faster after losing ball possession (18). This fact leads to a defensive imbalance, whether numerical or positional, which provides a more accurate offensive transition. This defensive imbalance also justifies the fact that there are more fouls in limit positions (eg penalties, direct free-kicks), which allow a goal to be scored in the immediately following play. The continuous attacks on TL, where the teams are more often balanced and the defenses have supremacy, provide a smaller number of goals as well as a smaller number of set pieces in dangerous areas, which means that they present lower values when compared to the ML and EL.

The EL have better results in the NT and SP variables, which reveals that there is space for the offensive transitions to be better used, but also the defenses concede more goals from set pieces of continuous attacks. The EL presents better results in the NT and SP variables, which reveals that there is room for the offensive transitions to be better used, but also the defenses suffer more goals in set pieces of continuous attacks. This is in line with that contextual variables, namely the quality of the opposition, affect all styles of play, except transitions (19). It will be interesting in future studies to know the positioning of the goals by SP and POS OUT to understand if there is a difference between them. Still regarding the EL, the fact that they stand out in goals per NT indicates that there is a greater imbalance of values between defense and attack when compared to the other championships. The goals obtained by SP and NT are also more predictable in EL and ML, which shows that these teams take less advantage of offensive transitions to obtain goals.

The investigation focused on how the goals were scored, highlighting the importance that direct and indirect offensive transitions (positive outcome) have in different championships. The results of this paper show that goals by offensive transition and positive outcomes are more predictable and more likely to happen more regularly in Top Leagues, which demonstrate that offensive transitions are a key factor in obtaining goals in higher ranking leagues.

This research introduces innovative insights into the classification of goals in national football leagues, shedding light on the pivotal role of offensive transitions in goal acquisition. Top Leagues exhibit strategic prowess in exploiting these transitions, underlining their superiority. The unique classification approach provides nuanced insights into goal-scoring dynamics, emphasizing the disruptive impact of offensive transitions. Importantly, technology, such as motion analysis and optical sensors, emerges as a critical tool for optimizing strategies and gaining a competitive edge in modern football. Future research should delve into observed distinctions and practical reasons behind them, contributing to a deeper comprehension of offensive transitions in the sport.

TL - Top Leagues.

ML - Marginal Leagues.

EL - Emerging Leagues.

OT – Offensive Transition.

NT – No transition.

SP – Set Pieces.

POS OUT – Positive Outcome.

OT + POS OUT – Goals obtained by Offensive transition and Positive Outcome.

M – Media of these events per game.

S - standard deviation.

N – Total number of events.

95% CI - confidence intervals (95%).

MoE – Margin of Error.

p – p value.

NaN – Not a number.

Ethics approval and consent to participate.

The following study does not reported experiments on humans and/or the use of human tissue samples/ use of the human data/ private, clinical, hospital data/ data which is not publicly available.

Consent for publication

Not applicable.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

All data generated or analysed during this study are included in this published article and its supplementary information files.

The data are not publicly available due to privacy.

Competing interests

The authors declare that they have no competing interests.

Funding

This work is financed by national funds through FCT – Fundação para a Ciência e a Tecnologia, I.P., within the scope of the project “2021.02330.CEECIND” assigned to Rui Marcelino. The authors would like to acknowledge the support of Prince Sultan University for paying the Article Processing Charges (APC) of this publication.

Authors' contributions

Conceptualization, P.E and R.M.; Methodology, P.E and R.M.; Software, P.E and R.M.; Validation, P.E, P.P.-G and R.M.; Formal Analysis, P.E, P.P.-G and R.M.; Investigation, P.E, P.P.-G and R.M.; Resources, P.E and R.M.; Data Curation, P.E; Writing – Original Draft Preparation, P.E and R.M.; Writing – Review & Editing, P.E, P.P.-G and R.M.; Visualization, P.E, P.P.-G and R.M.; Supervision, R.M.; Project Administration, N/A; Funding Acquisition, P.P.-G.

All authors have read and agreed to the published version of the manuscript.

Acknowledgements

The authors express their heartfelt gratitude to the University of Maia for its significant support and contributions to the research. The collaboration protocol with InStat and WyScout, facilitated the access to crucial footage that was pivotal to the investigation. The authors would like to thank Prince Sultan University for their support.

Authors' information (optional)

P.E.: Completed his bachelor’s degree at Instituto Superior da Maia in 2006 and the Master's Degree in "Physical Education in Basic and Secondary Education" at the same institution in 2012. P.E. is affiliated with CIDESD and a member of the Sports Analytics Lab at the University of Maia and is currently pursuing a Ph.D. on the topic "Football, Transition as Principle, Smart Transition as a Future Way To Go" at the University of Maia. P.E. has been a teacher in international schools on various continents for over a decade, specializing in the International Baccalaureate curriculum. Due to his UEFA B license and FIFA11+, P.E. has had experiences in several internationally renowned football academies. P.E. completed a professional internship with the Portuguese national team - A team.

P.P.-G: Physical Education Lecturer, Researcher, and Fitness and Nutrition Consultant at Prince Sultan University in Riyadh, Saudi Arabia, since 2014 to the present. Member of the Sport Sciences and Diagnostics Research Group within the General Studies-Health and Physical Education Department, located in Riyadh 11586, Saudi Arabia. P.P.-G. successfully earned his PhD in Sports Sciences from Presov University (Slovakia) in 2022. The title of his doctoral thesis is "Effects of strength training, endurance training, and concurrent training on recreational endurance athletes’ performance and selected anthropometric parameters." In addition to his doctoral degree, he also holds a Master's Degree in Physical Activity, Health, and Nutrition from Valencia International University (Spain), obtained in 2016, and a Bachelor's Degree in Physical Education from Leon University (Spain) earned in 2002. P.P.-G involvement in sports science research dates back to 2018. P.P.-G has authored approximately 50 published articles, with the majority appearing in journals indexed in Scopus and/or Web of Science.

R.M.: Principal Investigator at the University of Maia, responsible for the academic area of Sports Performance Observation and Analysis, and coordinator of the Sports Analytics Lab. R.M. has completed a master's degree in High-Performance Sports Training (2007) and a Ph.D. in Sports Sciences (2010) at the Faculty of Sports of the University of Porto. R.M. was funded by the Foundation for Science and Technology for his Post-Doctorate at the University of Trás-os-Montes and Alto Douro, titled "Monitoring long-term performance in football during players' development stages (2013-2016). R.M. has authored several scientific articles on sports performance in football and completed the 2022 master’s program "Applied Artificial Intelligence to Sports" at the Sport Data Campus. Currently, R.M also serves as a physiologist at the Portuguese Football Federation, with responsibilities in the men's football national team (under-19).

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No competing interests reported.

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Offensive transitions in football as a decisive way to obtain goals in Top, Marginal and Emerging leagues.

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