The Open Sports Sciences Journal




ISSN: 1875-399X ― Volume 12, 2019
REVIEW ARTICLE

How to be Successful in Football: A Systematic Review



Hannes Lepschy*, Hagen Wäsche, Alexander Woll
Department of Sports and Sport Sciences, Karlsruhe Institute of Technology, Karlsruhe, Germany

Abstract

Background:

Despite the popularity of football, the analysis of success factors in football remains a challenge. While reviews on performance indicators in football are available, none focuses solely on the identification of success factors and addresses the large and growing body of recent research up until 2016.

Objective:

To find out what determines success in football and to organize the body of literature, a systematic literature review analyzing existing studies with regard to success factors in football was undertaken.

Methods:

The studies included in this review had to deal with performance indicators related to success in football. The studies were published in 2016 or before. The initial search revealed 19,161 articles. Finally, sixty-eight articles were included in this review. The studies were clustered with regard to comparative analyses, predictive analyses and analyses of home advantage.

Results:

In total, 76 different variables were investigated in the reviewed papers. It appeared that the most significant variables are efficiency (number of goals divided by the number of shots), shots on goal, ball possession, pass accuracy/successful passes as well as the quality of opponent and match location. Moreover, new statistical methods were used to reveal interactions among these variables such as discriminant analysis, factor analysis and regression analysis. The studies showed methodological deficits such as clear operational definitions of investigated variables and small sample sizes.

Conclusion:

The review allows a comprehensive identification of critical success factors in football and sheds light on utilized methodological approaches. Future research should consider precise operational definitions of the investigated variables, adequate sample sizes and the involvement of situational variables as well as their interaction.

Keywords: Match analysis, Soccer, Success, Performance, Indicator, Football.


Article Information


Identifiers and Pagination:

Year: 2018
Volume: 11
First Page: 3
Last Page: 23
Publisher Id: TOSSJ-11-3
DOI: 10.2174/1875399X01811010003

Article History:

Received Date: 18/4/2018
Revision Received Date: 16/05/2018
Acceptance Date: 30/05/2018
Electronic publication date: 29/06/2018
Collection year: 2018

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© 2018 Lepschy et al

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


* Address correspondence to this author at the Department of Sports and Sports Science, Karlsruhe Institute of Technology, Engler-Bunte-Ring 15, 76131 Karlsruhe, Germany; Tel: 0018433435477; E-mail: h.lepschy@t-online.de





1. INTRODUCTION

Football or soccer (in this paper the term ‘football’ is used) is the most popular sports in the world. According to the “Big Count” study of FIFA [1 FIFA Big Count 2006: 270 million people active in football. FIFA Communications Divisions; 2007; [cited March 28th, 2016]. Available from: http://www.fifa.com/mm/document/fifafacts/bcoffsurv/bigcount.statspackage_7024.pdf] there are 270 million people involved in the match (players and referees). Moreover, football attracts millions of spectators around the world. For example, the global TV audience that followed the 2015 UEFA Champion’s League final between FC Barcelona and Juventus Turin was estimated to be 180 million people from more than 200 territories [2UEFA.com . Berlin final captures the world's imagination [Internet]. Berlin final captures the world's imagination. UEFA; 2015 [cited March 28th, 2016]. Available from: http://www.uefa.com/uefachampionsleague/news/newsid=2255318.html]. Due to its high popularity, football stands out among sports and games. In contrast

to games such as basketball or handball, football is a low scoring game, and scoring a goal is usually a rare event. For this reason, the final match score does not provide a clear picture of the teams’ technical and physical performances. To understand success factors in football, various other performance indicators next to goals scored have to be considered. Football is also a sport which has elements of chance but nevertheless, this does not mean successful teams are just luckier than others [3Dufour W. Computer-assisted scouting in soccer. In: Reilly T, Clarys JP, Stibbe A, Eds. Science and football II: Proceedings of the Second World Congress of Science and Football, Eindhoven, Netherlands, 22nd-25th May, 1991. 1st ed. London, New York: E & FN Spon 1993; 160–6., 4Reilly T, Williams M. Introduction to science and soccer. In: Reilly T, Williams AM, Eds. Science and Soccer. 2nd ed. Routledge 2003; 1–6.
[http://dx.doi.org/10.4324/9780203417553_chapter_1]
].

To identify the factors which lead to success in football it is necessary to find performance indicators which significantly discriminate winners and losers. However, the identification of critical factors for successful performance poses a major challenge [5Hughes M, Franks I. Notational analysis: A review of the literature. In: Hughes M, Franks IM, Eds. Notational analysis of sport: Systems for better coaching and performance in sport. Psychology Press 2004; 57–102.]. In 1912, Fullerton did the first work in this area of performance analysis for baseball [6Eaves JS. A history of sports notational analysis: A journey into the nineteenth century. Int J Perform Anal Sport 2015; 15(3): 1160-76.
[http://dx.doi.org/10.1080/24748668.2015.11868859]
]. In football, Reilly and Thomas [7Reilly T, Thomas V. A motion analysis of work-rate in different positional roles in professional football match-play. J Hum Mov Stud 1976; 2(2): 87-97.] performed one of the first systematic notational analyses. They used hand notation and audio tapes to analyze in detail the movements of English First Division football players [8Hughes M. Notational analysis. In: Reilly T, Williams AM, Eds. Science and Soccer. 2nd ed. Routledge 2003; 343–61.], and found out, inter alia, that a player is usually in touch with the ball for only two percent of the time. In another early performance analysis, Reep and Benjamin [9Reep C, Benjamin B. Skill and Chance in Association Football. J R Stat Soc [Ser A] 1968; 131(4): 581.
[http://dx.doi.org/10.2307/2343726]
] developed a new approach to study 3,213 matches in England between 1953 and 1968 using frequency distributions. Their analysis revealed that about 80 percent of all goals are scored after three or fewer passes and about 10 shots are needed for one goal.

A milestone for science and football was the first World Congress of Science and Football which was held in Liverpool in 1987 [5Hughes M, Franks I. Notational analysis: A review of the literature. In: Hughes M, Franks IM, Eds. Notational analysis of sport: Systems for better coaching and performance in sport. Psychology Press 2004; 57–102.]. Various themes were discussed such as team management, computer-aided performance analysis and decision-making by referees [10Reilly T, Lees A, Davids K, Murphy WJ. Science and Football (Routledge Revivals): Proceedings of the first World Congress of Science and Football Liverpool, 13-17th April 1987. Taylor & Francis 2011.]. In the following years, the numbers of research papers concerning football and performance analysis increased steadily [11Clarke SR, Norman John M. Home ground advantage of individual clubs in English soccer. Statistician 1995; 44(4): 509-21.
[http://dx.doi.org/10.2307/2348899]
-15Oberstone J. Differentiating the top english premier league football clubs from the rest of the Pack: Identifying the Keys to Success. J Quant Anal Sports 2009; 5(3)
[http://dx.doi.org/10.2202/1559-0410.1183]
]. Hughes and Bartlett [16Hughes MD, Bartlett RM. The use of performance indicators in performance analysis. J Sports Sci 2002; 20(10): 739-54.
[http://dx.doi.org/10.1080/026404102320675602] [PMID: 12363292]
] reviewed and analyzed research on performance indicators in sports and defined a performance indicator as “… a selection, or combination of action variables that aims to define some or all aspects of a performance. Clearly, to be useful, performance indicators should relate to successful performance or outcome” (p. 739). Researchers also monitored match structures, summarized some performance indicators and utilized them (e.g., numbers of shots, passes, dribbles or ball possession) in various subsequent papers which provided more insight into possible success factors in football [6Eaves JS. A history of sports notational analysis: A journey into the nineteenth century. Int J Perform Anal Sport 2015; 15(3): 1160-76.
[http://dx.doi.org/10.1080/24748668.2015.11868859]
, 17Hughes M, Franks I. Analysis of passing sequences, shots and goals in soccer. J Sports Sci 2005; 23(5): 509-14. [https://doi.org/10.1080/02640410410001716779].
[http://dx.doi.org/10.1080/02640410410001716779] [PMID: 16194998]
].

In the context of this paper, two review studies regarding performance analysis in football are noteworthy. Mackenzie and Cushion [18Mackenzie R, Cushion C. Performance analysis in football: A critical review and implications for future research. J Sports Sci 2013; 31(6): 639-76. [https://doi.org/10.1080/02640414.2012.746720].
[http://dx.doi.org/10.1080/02640414.2012.746720] [PMID: 23249092]
] critically reviewed 60 articles (articles published up to 2010) with a focus on methodological approaches and concluded that there is an overemphasis of research on predictive and performance controlling variables (e.g., location, shots). They suggested an alternative approach that focuses on research that investigates athlete and coach learning to enhance our understanding of football performance. However, these factors cannot readily be operationalized as success factors. Sarmento, Marcelino, Anguera, Campanico, Matos and Leitao [19Sarmento H, Marcelino R, Anguera MT, CampaniÇo J, Matos N, LeitÃo JC. Match analysis in football: A systematic review. J Sports Sci 2014; 32(20): 1831-43.
[http://dx.doi.org/10.1080/02640414.2014.898852] [PMID: 24787442]
] systematically reviewed 53 articles (articles published up to 2011) with a focus on major research topics and methodologies. They concluded that most studies used a comparative analysis to analyze differences between players or teams. Unlike Mackenzie and Cushion, they identified a lack of predictive studies. While it was not the main focus of their research, they also identified some success factors for a team such as the number of shots and shots on goal. They concluded that match location, quality of the opposition, match status and match half seem to have a greater importance for success due to a large number of studies that focused on these aspects.

Both aforementioned reviews comprised a wide variety of possible outcomes in the included articles, such as physical conditions or contextual variables. In this study, we focus solely on predictive or comparative studies that considered success as an outcome (win/loss, league ranking, etc.). This allows a clear identification of the critical factors for success. Moreover, this review also considers studies published after 2011, addressing a large and growing body of recent research that has not been covered in previous reviews, and enables an assessment of the current state of the art.1 Not only has the amount of the articles related to performance analysis in football grown substantially since 2011, also various new methodological approaches have been utilized. For example, Grund [20Grund TU. Network structure and team performance: The case of English Premier League soccer teams. Soc Networks 2012; 34(4): 682-90. [https://doi.org/10.1016/j.socnet.2012.08.004].
[http://dx.doi.org/10.1016/j.socnet.2012.08.004]
] introduced network analysis into the research about success factors and Collet [21Collet C. The possession game? A comparative analysis of ball retention and team success in European and international football, 2007-2010. J Sports Sci 2013; 31(2): 123-36. [https://doi.org/10.1080/02640414.2012.727455].
[http://dx.doi.org/10.1080/02640414.2012.727455] [PMID: 23067001]
] revealed new insights into the effect of ball possession using an ordered-logit regression. Liu, Gomez, Lago-Penas and Sampaio [22Liu H, Gomez M-A, Lago-Penas C, Sampaio J. Match statistics related to winning in the group stage of 2014 Brazil FIFA World Cup. J Sports Sci 2015; 33(12, SI): 1205–13
[http://dx.doi.org/10.1080/02640414.2015.1022578]
] used a k-means cluster analysis and a cumulative logistic regression to reveal the factors that differentiate the between winning and losing teams. Overall, the aim of this study is to provide a systematic review of the available literature on performance analysis in elite male football concerning methodologies and results to find out critical factors for success in football and to provide guidance for future research2.

The body of research on this topic has grown significantly in the last years. For example, in the three years between this review and the review of Sarmento et al. [19] the number of predictive studies, which are the most promising studies to deliver new insights to the of success in football, has grown by more than 40 percent (see also tables 6 to 8).

2Actual results of the selected articles are found in the discussion section

2. MATERIALS AND METHODS

The systematic review of performance indicators in elite men’s football was done in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) statement [23Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med 2009; 6(7): e1000097. [https://doi.org/10.1371/journal.pmed.1000097]. [PMID: 19621072].
[http://dx.doi.org/10.1371/journal.pmed.1000097] [PMID: 19621072]
]. The last search was conducted on June 24th, 2017.

To search for relevant publications and ensure the quality of the articles, the following databases were utilized: Web of Science (the modules “Core” and “Medline”), Scopus and PubMed. Articles that were published in 2016 or before and in English were considered. The search strategy comprised search terms that combined one of two primary keywords (soccer OR football) with a second keyword (e.g., success, win, loss) using the Boolean operator and all utilized search terms are presented in Table 1.

Table 1
Search terms.


For inclusion, the articles had to meet the following criteria:

  • The data had to deal with performance analysis in football.
  • The variables of interest were linked to success (win/loss, goals, continuance in league/tournament, league ranking and points won).
  • Adult elite football was investigated.
  • The study was written in English.
  • The study was published in an academic journal.
  • The study design was comparative or predictive or focused on home advantage in football.

It should be noted that we included studies on home advantage in this review as a separate category besides comparative and predictive studies utilizing inferential statistics. Although most of the studies on home advantage used a descriptive approach to reveal the influence of home advantage, we considered these non-inferential studies because home advantage is one of the most investigated variables regarding success factors [18Mackenzie R, Cushion C. Performance analysis in football: A critical review and implications for future research. J Sports Sci 2013; 31(6): 639-76. [https://doi.org/10.1080/02640414.2012.746720].
[http://dx.doi.org/10.1080/02640414.2012.746720] [PMID: 23249092]
].

The initial search revealed 19,161 articles (Web of Science [Core and Medline]: 9,706; Scopus: 6,038; PubMed: 3,417). After excluding the duplicates 10,833 articles remained. The articles were screened based on an assessment of both the title and the abstract. All articles without a focus on the investigation and analysis of data on the conditions of competition results in elite adult football were excluded. In total, 185 articles were relevant for this review. These articles were read in detail and assessed for relevance and quality. Articles which did not meet the criteria were excluded. After this step, 53 articles remained. Subsequently, the literature references of these 53 articles were screened for more articles meeting the criteria. Fifteen additional articles were identified. Finally, 68 articles were included in the review (Fig. 1).

Then, the articles that met the inclusion criteria were indexed, and each article was summarized. The summaries comprised the study purpose and design, methods of data collection and analysis, and key findings. This enables an overview and comparison of the articles and allows an assessment of the current state of research on performance indicators in football.

Fig. (1)
Flow diagram of this systematic review [23Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med 2009; 6(7): e1000097. [https://doi.org/10.1371/journal.pmed.1000097]. [PMID: 19621072].
[http://dx.doi.org/10.1371/journal.pmed.1000097] [PMID: 19621072]
].


3. RESULTS

The identified articles were published between 1986 and 2016, covering a time span of 31 years. More than half of the articles (exact 61.8%; 42 articles) were published within the last seven years (2010-2016) of the searched time period, indicating that this field of research has recently gained momentum.

To organize the identified analyses, the articles were categorized following a system used by Sarmento et al. [19Sarmento H, Marcelino R, Anguera MT, CampaniÇo J, Matos N, LeitÃo JC. Match analysis in football: A systematic review. J Sports Sci 2014; 32(20): 1831-43.
[http://dx.doi.org/10.1080/02640414.2014.898852] [PMID: 24787442]
] and Marcelino, Mesquita, and Sampaio [24Marcelino R, Sampaio J, Mesquita I. Investigação centrada na análise do jogo: da modelação estática à modelação dinâmica. Rev Port Cienc Desporto 2011; 11(1): 481-99. [Research on the game analysis: from static to dynamic modeling].
[http://dx.doi.org/10.5628/rpcd.11.01.125]
]. In the first step, the articles were assigned to [25Mechtel M, Baker A, Brandle T, Vetter K. Red cards: Not such bad news for penalized guest teams. J Sports Econ 2011; 12(6): 621-46.
[http://dx.doi.org/10.1177/1527002510388478]
, 26Carmichael F, Thomas D. Home-Field effect and team performance: Evidence from english premiership football. J Sports Econ 2005; 6(3): 264-81. [https://doi.org/10.1177/1527002504266154].
[http://dx.doi.org/10.1177/1527002504266154]
] comparative, [27Armatas V, Yiannakos A, Papadopoulou S, Skoufas D. Evaluation of goals scored in top ranking soccer matches: Greek Super League 2006-07. Serb J Sports Sci 2009; 3(1): 39-43.] predictive or Home Advantage (HA) analyses [28Lago-Penas C, Gomez-Ruano M, Megias-Navarro D, Pollard R. Home advantage in football: Examining the effect of scoring first on match outcome in the five major European leagues. Int J Perform Anal Sport 2016; 16(2): 411-21.
[http://dx.doi.org/10.1080/24748668.2016.11868897]
]. In the second step, articles were assigned to one of the three types of analyses from above according to the different operationalization of success (i.e., win/loss, goals, continuance in league/tournament, league ranking and points won) (Table 2).

Table 2
Number of articles in each category.


Of the articles, 30 were predictive analyses, 22 were comparative analyses, and 20 focused on the analysis of home advantage. One of the articles [15Oberstone J. Differentiating the top english premier league football clubs from the rest of the Pack: Identifying the Keys to Success. J Quant Anal Sports 2009; 5(3)
[http://dx.doi.org/10.2202/1559-0410.1183]
] covers both types of analyses (predictive and comparative). In total, 21 articles over all three types of analysis utilized “win/loss” as the success variable. “Goal difference” was used by seven articles, “goals” by eight, “league/tournament ranking” by 12, “points” by four and “continuance in league/tournament” by two.

4. DISCUSSION

In the following section, methods and major results of the identified articles will be presented within the three different categories of type of analysis. Finally, all findings will be summarized and the most frequent and significant variables regarding success factors in football will be discussed.

5. COMPARATIVE ANALYSES

In seven of the 21 comparative analyses, researchers compared wins and losses. In three of the seven papers draws were also included, and in one instance the percentage of wins was considered alongside wins and losses (Table 3). In the three papers that compared only wins and losses [29Broich H, Mester J, Seifriz F, Yue Z. Statistical Analysis for the First Bundesliga in the Current Soccer Season. PAM 2014; 7(2): 1-8. [https://doi.org/10.3968/4886].-31Szwarc A. Efficacy of successful and unsuccessful soccer teams taking part in finals of Champions League. Research Yearbook 2007; 13(2): 221-5.] the authors tried to find variables that explain differences between winners and losers. Broich et al. [29Broich H, Mester J, Seifriz F, Yue Z. Statistical Analysis for the First Bundesliga in the Current Soccer Season. PAM 2014; 7(2): 1-8. [https://doi.org/10.3968/4886].] identified goal efficiency (number of goals divided by the number of shots), shots, passes and ball contacts as the most important team parameter for winning. Efficiency was also analyzed by Szwarc [31Szwarc A. Efficacy of successful and unsuccessful soccer teams taking part in finals of Champions League. Research Yearbook 2007; 13(2): 221-5.]. He showed that players of winning teams are more efficient than their opponents. As a result of the small sample (seven matches) only shots on goal (p<0.05) and shots defended by a goalkeeper (p<0.01) differed significantly between winners and losers. Kapidizic et al. [30Kapidžić A, Mejremić E, Bilalić J, Bečirović E. Differences in some parameters of situation efficiency between winning and defeated teams at two levels of competition. Sport Sci Pract Asp 2010; 7(2): 27-33.] did not analyze efficiency but they also found that the numbers of shots within 16 meters (p<0.05) and accurate passes (p<0.01) are significant indicators for winning teams at the European Championship in 2008. Winners also scored more goals than losing teams in the Championship. Three more papers investigated the differences between wins, losses and draws [27Armatas V, Yiannakos A, Papadopoulou S, Skoufas D. Evaluation of goals scored in top ranking soccer matches: Greek Super League 2006-07. Serb J Sports Sci 2009; 3(1): 39-43., 32Janković A, Leontijević B, Pašić M, Jelušić V. Influence of certain tactical attacking patterns on the result achieved by the team participants of the 2010 FIFA World Cup in South Africa. Physical Culture/Fizicka Kultura 2011; 65(1)., 33Ruiz-Ruiz C, Fradua L, Fernández-García A, Zubillaga A. Analysis of entries into the penalty area as a performance indicator in soccer. Eur J Sport Sci 2013; 13(3): 241-8. [https://doi.org/10.1080/17461391.2011.606834].
[http://dx.doi.org/10.1080/17461391.2011.606834] [PMID: 23679140]
]. These studies reported various significant differences between winning, drawing and losing teams. Winners have more entries into the penalty area (p<0.01) [33Ruiz-Ruiz C, Fradua L, Fernández-García A, Zubillaga A. Analysis of entries into the penalty area as a performance indicator in soccer. Eur J Sport Sci 2013; 13(3): 241-8. [https://doi.org/10.1080/17461391.2011.606834].
[http://dx.doi.org/10.1080/17461391.2011.606834] [PMID: 23679140]
], more successful attacks (p=0.003) and passes (p=0.015) as well as a higher ball possession rate (p=0.001) [32Janković A, Leontijević B, Pašić M, Jelušić V. Influence of certain tactical attacking patterns on the result achieved by the team participants of the 2010 FIFA World Cup in South Africa. Physical Culture/Fizicka Kultura 2011; 65(1).]. Armatas et al. [27Armatas V, Yiannakos A, Papadopoulou S, Skoufas D. Evaluation of goals scored in top ranking soccer matches: Greek Super League 2006-07. Serb J Sports Sci 2009; 3(1): 39-43.] revealed that 71.4 percent of teams that scored the first goal subsequently won the match (p<0.05). In contrast to the other studies, one study focused on the total winning percentage [34Carron AV, Bray SR, Eys MA. Team cohesion and team success in sport. J Sports Sci 2002; 20(2): 119-26. [https://doi.org/10.1080/026404102317200828].
[http://dx.doi.org/10.1080/026404102317200828] [PMID: 11811568]
]. Another difference is the use of group cohesion as the independent variable. The authors showed a statistically significant relationship between individual attraction to the group-task and performance with a very high effect size of 1.94 (p<0.05). The higher the positive feelings of each group member to the group-task, that is, to play football successfully, the higher were the likelihood of winning.

Oberstone [15Oberstone J. Differentiating the top english premier league football clubs from the rest of the Pack: Identifying the Keys to Success. J Quant Anal Sports 2009; 5(3)
[http://dx.doi.org/10.2202/1559-0410.1183]
] used comparative and predictive methods; Mechtel et al. [25Mechtel M, Baker A, Brandle T, Vetter K. Red cards: Not such bad news for penalized guest teams. J Sports Econ 2011; 12(6): 621-46.
[http://dx.doi.org/10.1177/1527002510388478]
] used win/loss and goal difference; Collet [21Collet C. The possession game? A comparative analysis of ball retention and team success in European and international football, 2007-2010. J Sports Sci 2013; 31(2): 123-36. [https://doi.org/10.1080/02640414.2012.727455].
[http://dx.doi.org/10.1080/02640414.2012.727455] [PMID: 23067001]
] used win/loss and points; Carmichael and Thomas [26Carmichael F, Thomas D. Home-Field effect and team performance: Evidence from english premiership football. J Sports Econ 2005; 6(3): 264-81. [https://doi.org/10.1177/1527002504266154].
[http://dx.doi.org/10.1177/1527002504266154]
] used predictive methods and home advantage; Armatas, Yiannakos, Papadopoulou and Skoufas [27Armatas V, Yiannakos A, Papadopoulou S, Skoufas D. Evaluation of goals scored in top ranking soccer matches: Greek Super League 2006-07. Serb J Sports Sci 2009; 3(1): 39-43.] used comparative methods and home advantage; Lago-Penas, Gomez-Ruano, Megias-Navarro and Pollard [28Lago-Penas C, Gomez-Ruano M, Megias-Navarro D, Pollard R. Home advantage in football: Examining the effect of scoring first on match outcome in the five major European leagues. Int J Perform Anal Sport 2016; 16(2): 411-21.
[http://dx.doi.org/10.1080/24748668.2016.11868897]
] used predictive methods and home advantage.

Table 3
Comparative articles with regard to wins and losses.


In nine of the articles, the authors compared teams with different positions in the league/tournament ranking (Table 4). Luhtanen, Belinskij, Häyrinen and Vänttinen [35Luhtanen P, Belinskij A, Häyrinen M, Vänttinen T. A comparative tournament analysis between the EURO 1996 and 2000 in soccer. Int J Perform Anal Sport 2001; 1(1): 74-82.
[http://dx.doi.org/10.1080/24748668.2001.11868250]
] investigated the influence of offensive and defensive variables on the final ranking of the European Championships in 1996 and 2000. In 1996, interceptions and the success rate of all defensive actions showed a significant correlation (p<0.05) with the final ranking. In 2000, significant correlations with the ranking were found for success rate in passes (p<0.05) and attempts (p<0.05) on goal. In the other papers, different football leagues were investigated and it was shown that better-ranked teams (top-teams) need less shots for a goal than worse ranked teams [15Oberstone J. Differentiating the top english premier league football clubs from the rest of the Pack: Identifying the Keys to Success. J Quant Anal Sports 2009; 5(3)
[http://dx.doi.org/10.2202/1559-0410.1183]
, 36Armatas V, Yannakos A, Zaggelidis G, Skoufas D, Papadopoulou S, Fragkos N. Differences in offensive actions between top and last teams in greek first soccer division. A retrospective study 1998-2008. JPES 2009; 23(2): 1-5., 37Lago-Ballesteros J, Lago-Peñas C. Performance in team sports: Identifying the keys to success in soccer. J Hum Kinet 2010; 25(-1)
[http://dx.doi.org/10.2478/v10078-010-0035-0]
]. This parameter corresponds to Broich et al.’s [29Broich H, Mester J, Seifriz F, Yue Z. Statistical Analysis for the First Bundesliga in the Current Soccer Season. PAM 2014; 7(2): 1-8. [https://doi.org/10.3968/4886].] ‘goal efficiency’. It was also found that top teams have more successful attacks, complete their offensive attacks more frequently between zero and 11 meters in front of the goal [38Janković A, Leontijević B, Jelušić V, Pašić M, Mićović B. Influence of tactics efficiency on results in serbian soccer super league in season 2009/2010. JPES 2011; 11(1): 32-41.], have more successful passes [15Oberstone J. Differentiating the top english premier league football clubs from the rest of the Pack: Identifying the Keys to Success. J Quant Anal Sports 2009; 5(3)
[http://dx.doi.org/10.2202/1559-0410.1183]
, 38Janković A, Leontijević B, Jelušić V, Pašić M, Mićović B. Influence of tactics efficiency on results in serbian soccer super league in season 2009/2010. JPES 2011; 11(1): 32-41., 39Rampinini E, Impellizzeri FM, Castagna C, Coutts AJ, Wisløff U. Technical performance during soccer matches of the Italian Serie A league: Effect of fatigue and competitive level. J Sci Med Sport 2009; 12(1): 227-33. [https://doi.org/10.1016/j.jsams.2007.10.002].
[http://dx.doi.org/10.1016/j.jsams.2007.10.002] [PMID: 18083631]
], score more goals [36Armatas V, Yannakos A, Zaggelidis G, Skoufas D, Papadopoulou S, Fragkos N. Differences in offensive actions between top and last teams in greek first soccer division. A retrospective study 1998-2008. JPES 2009; 23(2): 1-5., 37Lago-Ballesteros J, Lago-Peñas C. Performance in team sports: Identifying the keys to success in soccer. J Hum Kinet 2010; 25(-1)
[http://dx.doi.org/10.2478/v10078-010-0035-0]
, 40Bekris M, Sarakinos G. Gioldasis & Sotiropoulos. Offense and defense statistical indicators that determine the Greek Superleague teams placement on the table 2011 - 12. JPES 2013; 13(3): 338-47. [https://doi.org/10.7752/jpes.2013.03055].], perform more crosses [15Oberstone J. Differentiating the top english premier league football clubs from the rest of the Pack: Identifying the Keys to Success. J Quant Anal Sports 2009; 5(3)
[http://dx.doi.org/10.2202/1559-0410.1183]
, 40Bekris M, Sarakinos G. Gioldasis & Sotiropoulos. Offense and defense statistical indicators that determine the Greek Superleague teams placement on the table 2011 - 12. JPES 2013; 13(3): 338-47. [https://doi.org/10.7752/jpes.2013.03055].], have more ball possession [37Lago-Ballesteros J, Lago-Peñas C. Performance in team sports: Identifying the keys to success in soccer. J Hum Kinet 2010; 25(-1)
[http://dx.doi.org/10.2478/v10078-010-0035-0]
, 39Rampinini E, Impellizzeri FM, Castagna C, Coutts AJ, Wisløff U. Technical performance during soccer matches of the Italian Serie A league: Effect of fatigue and competitive level. J Sci Med Sport 2009; 12(1): 227-33. [https://doi.org/10.1016/j.jsams.2007.10.002].
[http://dx.doi.org/10.1016/j.jsams.2007.10.002] [PMID: 18083631]
], shoot more often on the goal [37Lago-Ballesteros J, Lago-Peñas C. Performance in team sports: Identifying the keys to success in soccer. J Hum Kinet 2010; 25(-1)
[http://dx.doi.org/10.2478/v10078-010-0035-0]
, 39Rampinini E, Impellizzeri FM, Castagna C, Coutts AJ, Wisløff U. Technical performance during soccer matches of the Italian Serie A league: Effect of fatigue and competitive level. J Sci Med Sport 2009; 12(1): 227-33. [https://doi.org/10.1016/j.jsams.2007.10.002].
[http://dx.doi.org/10.1016/j.jsams.2007.10.002] [PMID: 18083631]
], have more assists [36Armatas V, Yannakos A, Zaggelidis G, Skoufas D, Papadopoulou S, Fragkos N. Differences in offensive actions between top and last teams in greek first soccer division. A retrospective study 1998-2008. JPES 2009; 23(2): 1-5., 37Lago-Ballesteros J, Lago-Peñas C. Performance in team sports: Identifying the keys to success in soccer. J Hum Kinet 2010; 25(-1)
[http://dx.doi.org/10.2478/v10078-010-0035-0]
, 39Rampinini E, Impellizzeri FM, Castagna C, Coutts AJ, Wisløff U. Technical performance during soccer matches of the Italian Serie A league: Effect of fatigue and competitive level. J Sci Med Sport 2009; 12(1): 227-33. [https://doi.org/10.1016/j.jsams.2007.10.002].
[http://dx.doi.org/10.1016/j.jsams.2007.10.002] [PMID: 18083631]
] and take more shots [15Oberstone J. Differentiating the top english premier league football clubs from the rest of the Pack: Identifying the Keys to Success. J Quant Anal Sports 2009; 5(3)
[http://dx.doi.org/10.2202/1559-0410.1183]
, 37Lago-Ballesteros J, Lago-Peñas C. Performance in team sports: Identifying the keys to success in soccer. J Hum Kinet 2010; 25(-1)
[http://dx.doi.org/10.2478/v10078-010-0035-0]
, 39Rampinini E, Impellizzeri FM, Castagna C, Coutts AJ, Wisløff U. Technical performance during soccer matches of the Italian Serie A league: Effect of fatigue and competitive level. J Sci Med Sport 2009; 12(1): 227-33. [https://doi.org/10.1016/j.jsams.2007.10.002].
[http://dx.doi.org/10.1016/j.jsams.2007.10.002] [PMID: 18083631]
, 40Bekris M, Sarakinos G. Gioldasis & Sotiropoulos. Offense and defense statistical indicators that determine the Greek Superleague teams placement on the table 2011 - 12. JPES 2013; 13(3): 338-47. [https://doi.org/10.7752/jpes.2013.03055].]. The best teams in the league also perform fewer fouls [15Oberstone J. Differentiating the top english premier league football clubs from the rest of the Pack: Identifying the Keys to Success. J Quant Anal Sports 2009; 5(3)
[http://dx.doi.org/10.2202/1559-0410.1183]
] and allow fewer shots and crosses [40Bekris M, Sarakinos G. Gioldasis & Sotiropoulos. Offense and defense statistical indicators that determine the Greek Superleague teams placement on the table 2011 - 12. JPES 2013; 13(3): 338-47. [https://doi.org/10.7752/jpes.2013.03055].]. The worst ranked teams have fewer counter attacks, have less possession with zero to four passes and have less possession longer than 12 seconds [41Tenga A, Sigmundstad E. Characteristics of goal-scoring possessions in open play: Comparing the top, in-between and bottom teams from professional soccer league. Int J Perform Anal Sport 2011; 11(3): 545-52.
[http://dx.doi.org/10.1080/24748668.2011.11868572]
]. Worse teams also have more very high-intensity running, high-intensity running and total distance covered [39Rampinini E, Impellizzeri FM, Castagna C, Coutts AJ, Wisløff U. Technical performance during soccer matches of the Italian Serie A league: Effect of fatigue and competitive level. J Sci Med Sport 2009; 12(1): 227-33. [https://doi.org/10.1016/j.jsams.2007.10.002].
[http://dx.doi.org/10.1016/j.jsams.2007.10.002] [PMID: 18083631]
]. Better teams cover more total distance with the ball and very high-intensity running with the ball [39Rampinini E, Impellizzeri FM, Castagna C, Coutts AJ, Wisløff U. Technical performance during soccer matches of the Italian Serie A league: Effect of fatigue and competitive level. J Sci Med Sport 2009; 12(1): 227-33. [https://doi.org/10.1016/j.jsams.2007.10.002].
[http://dx.doi.org/10.1016/j.jsams.2007.10.002] [PMID: 18083631]
]. Furthermore, the top teams show a faster recovering (recapture is 1.3 to 1.7 seconds faster than mean times) of ball possession [42Vogelbein M, Nopp S, Hökelmann A. Defensive transition in soccer - are prompt possession regains a measure of success? A quantitative analysis of German Fußball-Bundesliga 2010/2011. J Sports Sci 2014; 32(11): 1076-83. [https://doi.org/10.1080/02640414.2013.879671].
[http://dx.doi.org/10.1080/02640414.2013.879671] [PMID: 24506111]
]. Obviously, top teams score more goals per match [15Oberstone J. Differentiating the top english premier league football clubs from the rest of the Pack: Identifying the Keys to Success. J Quant Anal Sports 2009; 5(3)
[http://dx.doi.org/10.2202/1559-0410.1183]
, 36Armatas V, Yannakos A, Zaggelidis G, Skoufas D, Papadopoulou S, Fragkos N. Differences in offensive actions between top and last teams in greek first soccer division. A retrospective study 1998-2008. JPES 2009; 23(2): 1-5., 37Lago-Ballesteros J, Lago-Peñas C. Performance in team sports: Identifying the keys to success in soccer. J Hum Kinet 2010; 25(-1)
[http://dx.doi.org/10.2478/v10078-010-0035-0]
, 40Bekris M, Sarakinos G. Gioldasis & Sotiropoulos. Offense and defense statistical indicators that determine the Greek Superleague teams placement on the table 2011 - 12. JPES 2013; 13(3): 338-47. [https://doi.org/10.7752/jpes.2013.03055].]. The cited studies showed that a lot of factors influence success (operationalized as league ranking) in football. Overall, it appears that goal efficiency, passes and shots are the most important factors in this research area.

Table 4
Comparative articles with regard to league / tournament ranking.


Six more studies used a comparative approach to investigate success factors operationalized differently to the articles discussed previously (Table 5). Two papers focused on goal difference [43Bekris, Gioldasis, Gissis, Komsis & Alipasali. Winners and losers in top level soccer. How do they differ? JPES 2014; 14(3): 398-405. [https://doi.org/10.7752/jpes.2014.03061]., 44Yue Z, Broich H, Mester J. Statistical analysis for the soccer matches of the first bundesliga. Int J Sports Sci Coaching 2014; 9(3): 553-60. [https://doi.org/10.1260/1747-9541.9.3.553].
[http://dx.doi.org/10.1260/1747-9541.9.3.553]
]. Bekris et al. [43Bekris, Gioldasis, Gissis, Komsis & Alipasali. Winners and losers in top level soccer. How do they differ? JPES 2014; 14(3): 398-405. [https://doi.org/10.7752/jpes.2014.03061].] compared matches with one-goal differences (short range results) as well as matches with three-goal differences or more (wide range results). Their analysis showed that winners in wide range results have more ball possession, perform more passes, win more duels (overall and aerial), and have more shots, shots on target and a higher shot accuracy. In the short range results these differences were not found. A winner-winner comparison showed that wide range winners perform more passes, have a higher pass accuracy, more short distance shots and shots on-target. Yue et al. [44Yue Z, Broich H, Mester J. Statistical analysis for the soccer matches of the first bundesliga. Int J Sports Sci Coaching 2014; 9(3): 553-60. [https://doi.org/10.1260/1747-9541.9.3.553].
[http://dx.doi.org/10.1260/1747-9541.9.3.553]
] used a similar approach. They analyzed matches with a difference of two or more goals and matches with a difference of three or more goals. Goal efficiency, shots, passes and ball contacts were found to be the most important factors for scoring a goal (in this order). Clemente [45Clemente FM. Study of successful teams on FIFA world cup 2010 through notational analysis. PJSS 2012; 3(3): 90-103.] and Delgado-Bordonau, Domenech-Monforte, Guzmán & Mendez-Villanueva [46Delgado-Bordonau JL, Domenech-Monforte C, Guzmán JF, Mendez-Villanueva A. Offensive and defensive team performance: Relation to successful and unsuccessful participation in the 2010 Soccer World Cup. JHSE 2013; 8(4): 894-904.
[http://dx.doi.org/10.4100/jhse.2013.84.02]
] operationalized success as a continuance in a tournament. They compared teams with a different number of matches respectively teams that got to the semifinal. Both analyzed matches of the World Cup 2010. Clemente [45Clemente FM. Study of successful teams on FIFA world cup 2010 through notational analysis. PJSS 2012; 3(3): 90-103.] revealed that teams with more matches in a tournament (the successful ones) score more goals through open play, have more shots inside the penalty area and perform more passes. Delgado-Bordonau et al. [46Delgado-Bordonau JL, Domenech-Monforte C, Guzmán JF, Mendez-Villanueva A. Offensive and defensive team performance: Relation to successful and unsuccessful participation in the 2010 Soccer World Cup. JHSE 2013; 8(4): 894-904.
[http://dx.doi.org/10.4100/jhse.2013.84.02]
] showed that successful teams perform more shots on-target, have a higher efficiency and concede fewer shots. They also revealed that the first goal in the match leads to a victory for 66.7 percent in the group stage and for 81.3 percent in the knockout stage. Hughes and Franks [17Hughes M, Franks I. Analysis of passing sequences, shots and goals in soccer. J Sports Sci 2005; 23(5): 509-14. [https://doi.org/10.1080/02640410410001716779].
[http://dx.doi.org/10.1080/02640410410001716779] [PMID: 16194998]
] used a new and different approach to analyze football. They normalized the data into “goals/shots per 1000 possessions” to analyze the relative importance of ball possession. The authors used this parameter to compare successful teams (getting to the quarterfinals) and unsuccessful teams (first round losers) in the 1990 World Cup. Accordingly, successful teams show a strong trend to be better in converting possession into shots on goal (no significant difference). For ball possessions with more than eight passes, there is a significantly higher chance for successful teams to create a shooting opportunity (p<0.05). In contrast, the necessary shots for a goal increase with more passes per possession [17Hughes M, Franks I. Analysis of passing sequences, shots and goals in soccer. J Sports Sci 2005; 23(5): 509-14. [https://doi.org/10.1080/02640410410001716779].
[http://dx.doi.org/10.1080/02640410410001716779] [PMID: 16194998]
]. Hoppe, Slomka, Baumgart, Weber & Freiwald [47Hoppe MW, Slomka M, Baumgart C, Weber H, Freiwald J. Match running performance and success across a season in german bundesliga soccer teams. Int J Sports Med 2015; 36(7): 563-6. [https://doi.org/10.1055/s-0034-1398578].
[http://dx.doi.org/10.1055/s-0034-1398578] [PMID: 25760152]
] used the final points accumulated by each team during one season in the German Bundesliga. They analyzed the running performance with and without ball possession of the teams. Only total distance with ball possession was a significant predictor for final points (p<0.01). They concluded that not only running performance is important for success, but rather the relation to technical/tactical skill regarding ball possession [47Hoppe MW, Slomka M, Baumgart C, Weber H, Freiwald J. Match running performance and success across a season in german bundesliga soccer teams. Int J Sports Med 2015; 36(7): 563-6. [https://doi.org/10.1055/s-0034-1398578].
[http://dx.doi.org/10.1055/s-0034-1398578] [PMID: 25760152]
].

Table 5
Comparative articles with regard to other operationalization of success.


6. PREDICTIVE ANALYSES

Fourteen of the predictive analyses focused on differences between wins, draws and losses (two of these papers considered two groups: winners and non-winners) (Table 6). Four of these papers used a discriminant analysis to reveal the most discriminating factors [48Lago-Peñas C, Lago-Ballesteros J, Dellal A, Gómez M. Game-related statistics that discriminated winning, drawing and losing teams from the Spanish soccer league. J Sports Sci Med 2010; 9(2): 288-93.
[PMID: 24149698]
-51Moura FA, Martins LE, Cunha SA. Analysis of football game-related statistics using multivariate techniques. J Sports Sci 2014; 32(20): 1881-7. [https://doi.org/10.1080/02640414.2013.853130].
[http://dx.doi.org/10.1080/02640414.2013.853130] [PMID: 24742152]
]. Shots on goal was a discriminant factor in all four studies. Crosses, match location and ball possession [48Lago-Peñas C, Lago-Ballesteros J, Dellal A, Gómez M. Game-related statistics that discriminated winning, drawing and losing teams from the Spanish soccer league. J Sports Sci Med 2010; 9(2): 288-93.
[PMID: 24149698]
, 49Lago-Penas C, Lago-Ballesteros J, Rey E. Differences in performance indicators between winning and losing teams in the UEFA champions league. J Hum Kinet 2011; 27: 137-48.
[http://dx.doi.org/10.2478/v10078-011-0011-3]
] as well as the quality of the opponent (similar to strength or team ability) [49Lago-Penas C, Lago-Ballesteros J, Rey E. Differences in performance indicators between winning and losing teams in the UEFA champions league. J Hum Kinet 2011; 27: 137-48.
[http://dx.doi.org/10.2478/v10078-011-0011-3]
] were other identified factors. Collet [21Collet C. The possession game? A comparative analysis of ball retention and team success in European and international football, 2007-2010. J Sports Sci 2013; 31(2): 123-36. [https://doi.org/10.1080/02640414.2012.727455].
[http://dx.doi.org/10.1080/02640414.2012.727455] [PMID: 23067001]
] and Harrop and Nevill [52Harrop K, Nevill A. Performance indicators that predict success in an English professional League One soccer team. Int J Perform Anal Sport 2014; 14(3): 907-20.
[http://dx.doi.org/10.1080/24748668.2014.11868767]
] used a regression analysis/model and showed that higher pass accuracy is a good predictor for success. More shots, fewer passes, fewer dribbling and match location are further predictors [52Harrop K, Nevill A. Performance indicators that predict success in an English professional League One soccer team. Int J Perform Anal Sport 2014; 14(3): 907-20.
[http://dx.doi.org/10.1080/24748668.2014.11868767]
]. Collet [21Collet C. The possession game? A comparative analysis of ball retention and team success in European and international football, 2007-2010. J Sports Sci 2013; 31(2): 123-36. [https://doi.org/10.1080/02640414.2012.727455].
[http://dx.doi.org/10.1080/02640414.2012.727455] [PMID: 23067001]
] investigated the influence of possession on success and showed that possession is not as relevant as assumed. If the strength of a team is controlled, the influence of possession on success will range from -5.7% (in German Bundesliga; significant (p<0.05)) to +1.8% (all national teams; not significant). The fact that possession has a potential negative link to success may be worth further examination. Efficiency measures seem to be better predictors for success [21Collet C. The possession game? A comparative analysis of ball retention and team success in European and international football, 2007-2010. J Sports Sci 2013; 31(2): 123-36. [https://doi.org/10.1080/02640414.2012.727455].
[http://dx.doi.org/10.1080/02640414.2012.727455] [PMID: 23067001]
, 29Broich H, Mester J, Seifriz F, Yue Z. Statistical Analysis for the First Bundesliga in the Current Soccer Season. PAM 2014; 7(2): 1-8. [https://doi.org/10.3968/4886]., 31Szwarc A. Efficacy of successful and unsuccessful soccer teams taking part in finals of Champions League. Research Yearbook 2007; 13(2): 221-5., 44Yue Z, Broich H, Mester J. Statistical analysis for the soccer matches of the first bundesliga. Int J Sports Sci Coaching 2014; 9(3): 553-60. [https://doi.org/10.1260/1747-9541.9.3.553].
[http://dx.doi.org/10.1260/1747-9541.9.3.553]
, 46Delgado-Bordonau JL, Domenech-Monforte C, Guzmán JF, Mendez-Villanueva A. Offensive and defensive team performance: Relation to successful and unsuccessful participation in the 2010 Soccer World Cup. JHSE 2013; 8(4): 894-904.
[http://dx.doi.org/10.4100/jhse.2013.84.02]
]. Liu, Gomez, Lago-Penas and Sampaio [22Liu H, Gomez M-A, Lago-Penas C, Sampaio J. Match statistics related to winning in the group stage of 2014 Brazil FIFA World Cup. J Sports Sci 2015; 33(12, SI): 1205–13
[http://dx.doi.org/10.1080/02640414.2015.1022578]
], Liu, Hopkins and Gomez [53Liu H, Hopkins WG, Gómez M-A. Modelling relationships between match events and match outcome in elite football. Eur J Sport Sci 2016; 16(5): 516-25. [https://doi.org/10.1080/17461391.2015.1042527].
[http://dx.doi.org/10.1080/17461391.2015.1042527] [PMID: 26190577]
] and Mao, Peng, Liu and Gomez [54Mao L, Peng Z, Liu H, Gomez M-A. Identifying keys to win in the Chinese professional soccer league. Int J Perform Anal Sport 2016; 16(3): 935-47.
[http://dx.doi.org/10.1080/24748668.2016.11868940]
] used cumulative logistic-regression in a generalized linear model. They also divided the sample into close matches and unbalanced matches (a cluster analysis based on the goal difference was used) with a cluster analysis and cut-off values. In past research it appeared to be more likely in close matches that both teams play at their best [22Liu H, Gomez M-A, Lago-Penas C, Sampaio J. Match statistics related to winning in the group stage of 2014 Brazil FIFA World Cup. J Sports Sci 2015; 33(12, SI): 1205–13
[http://dx.doi.org/10.1080/02640414.2015.1022578]
, 55Vaz L, Rooyen MV, Sampaio J. Rugby game-related statistics that discriminate between winning and losing teams in Irb and super twelve close games. J Sports Sci Med 2010; 9(1): 51-5. [PMID: 24149385].
[PMID: 24149385]
]. They showed that shots on goal, shot accuracy, tackles and aerial advantage have positive effects on winning [22Liu H, Gomez M-A, Lago-Penas C, Sampaio J. Match statistics related to winning in the group stage of 2014 Brazil FIFA World Cup. J Sports Sci 2015; 33(12, SI): 1205–13
[http://dx.doi.org/10.1080/02640414.2015.1022578]
, 54Mao L, Peng Z, Liu H, Gomez M-A. Identifying keys to win in the Chinese professional soccer league. Int J Perform Anal Sport 2016; 16(3): 935-47.
[http://dx.doi.org/10.1080/24748668.2016.11868940]
]. Liu et al. [53Liu H, Hopkins WG, Gómez M-A. Modelling relationships between match events and match outcome in elite football. Eur J Sport Sci 2016; 16(5): 516-25. [https://doi.org/10.1080/17461391.2015.1042527].
[http://dx.doi.org/10.1080/17461391.2015.1042527] [PMID: 26190577]
] also investigated the within-team effects (changes in team values between matches) and between-team effects (differences between average team values over all matches). Shots on target and total shots have positive within-team effects on winning. Game location showed a small positive within-team effect. Ball possession showed a small negative within-team effect but also a small positive between-team effect. Within-team effects varied depending on strength of team and opponent [53Liu H, Hopkins WG, Gómez M-A. Modelling relationships between match events and match outcome in elite football. Eur J Sport Sci 2016; 16(5): 516-25. [https://doi.org/10.1080/17461391.2015.1042527].
[http://dx.doi.org/10.1080/17461391.2015.1042527] [PMID: 26190577]
].

Table 6
Predictive analyses with regard to wins and losses.


Gómez, Gómez-Lopez, Lago and Sampaio [56Gómez MA, Gómez-Lopez M, Lago C, Sampaio J. Effects of game location and final outcome on game-related statistics in each zone of the pitch in professional football. Eur J Sport Sci 2012; 12(5): 393-8. [https://doi.org/10.1080/17461391.2011.566373].
[http://dx.doi.org/10.1080/17461391.2011.566373]
] used a factor analysis with several factors and the zone of the pitch. For the zone of the pitch they divided the field into five zones from goal to goal and into three to five subzones in each of these zones. They identified four factors. All factors are highest for winners. The best discrimination is given for ball recovery in zone two (2.1, 2.2 and 2.3) (penalty zone to center circle) and offensive actions with long passing sequences in zone 5.1 (six-yard box) and 5.2 (within penalty zone). Bar-Eli, Tenenbaum and Geister [57Bar-Eli M, Tenenbaum G, Geister S. Consequences of players’ dismissal in professional soccer: a crisis-related analysis of group-size effects. J Sports Sci 2006; 24(10): 1083-94. [https://doi.org/10.1080/02640410500432599].
[http://dx.doi.org/10.1080/02640410500432599] [PMID: 17115523]
] and Mechtel et al. [25Mechtel M, Baker A, Brandle T, Vetter K. Red cards: Not such bad news for penalized guest teams. J Sports Econ 2011; 12(6): 621-46.
[http://dx.doi.org/10.1177/1527002510388478]
] investigated the impact of a player’s dismissal. Both found out that a sending-off decreases (sanctioned team) respectively increases (opponent) the chance of winning. Mechtel et al. [25Mechtel M, Baker A, Brandle T, Vetter K. Red cards: Not such bad news for penalized guest teams. J Sports Econ 2011; 12(6): 621-46.
[http://dx.doi.org/10.1177/1527002510388478]
] also identified strength (points earned in the last three seasons) and home advantage as success factors. Torgler [58Torgler B. The Economics of the FIFA Football Worldcup. Kyklos 2004; 57(2): 287-300. [https://doi.org/10.1111/j.0023-5962.2004.00255.x].
[http://dx.doi.org/10.1111/j.0023-5962.2004.00255.x]
] applied an economic win function to determine the influences on winning or not winning during the FIFA World Cup 2002. He showed that a higher number of shots on goal leads to a higher probability to win. He also revealed the negative effect of a player’s dismissal. Hosting the tournament was a strong advantage as well. It increases the chance of winning by 45 percentage points [58Torgler B. The Economics of the FIFA Football Worldcup. Kyklos 2004; 57(2): 287-300. [https://doi.org/10.1111/j.0023-5962.2004.00255.x].
[http://dx.doi.org/10.1111/j.0023-5962.2004.00255.x]
]. Hanau, Wicker and Soebbing [59von Hanau T, Wicker P, Soebbing BP. Determinants of expected vs. actual match outcome: An examination of the German Bundesliga. Soccer Soc 2015; 16(1): 63-75.
[http://dx.doi.org/10.1080/14660970.2014.882823]
] investigated the difference between the expected outcome of a football match and the actual outcome. They found out that the actual outcome is determined by the standing in the last season and home advantage.

The second most frequent kind of predictive analyses are studies that used goal scoring as the indicator of success (Table 7). Pollard and Reep [12Pollard R, Reep C. Measuring the effectiveness of playing strategies at soccer. Statistician 1997; 46(4): 541-50. [https://doi.org/10.2307/2988603].
[http://dx.doi.org/10.1111/1467-9884.00108]
] developed a quantitative variable, called the ‘yield’, defined as the probability of a goal being scored minus the probability of one being conceded. The yield for the penalty area as starting zone of ball possession and open play is 78.3 (per 1000 possessions you can expect 78.3 more goals scored than goals conceded). They also found that open play always has a higher yield than set play [12Pollard R, Reep C. Measuring the effectiveness of playing strategies at soccer. Statistician 1997; 46(4): 541-50. [https://doi.org/10.2307/2988603].
[http://dx.doi.org/10.1111/1467-9884.00108]
]. Carmichael and Thomas [26Carmichael F, Thomas D. Home-Field effect and team performance: Evidence from english premiership football. J Sports Econ 2005; 6(3): 264-81. [https://doi.org/10.1177/1527002504266154].
[http://dx.doi.org/10.1177/1527002504266154]
] established a match-based production function. They found that shots on goal, shots that hit woodwork, tackles, own goals and free kicks are significant predictive factors (p<0.05) for the home teams. Kapidžić, Bećirović and Imamović [60Kapidžić A, Bećirović E, Imamović J. Situational efficiency analysis of the teams that participated in 2008 European football championship. Sport Sci Pract Asp 2009; 38.] also identified shots on goal as a significant predictor for goal scoring (p=0.027). Wright, Atkins, Polman, Jones and Sargeson [61Wright C, Atkins S, Polman R, Jones B, Sargeson L. Factors associated with goals and goal scoring opportunities in professional soccer. Int J Perform Anal Sport 2011; 11(3): 438-49.
[http://dx.doi.org/10.1080/24748668.2011.11868563]
] postulated position of attempt, goal keepers’ position and type of shot as the three predictors for goal scoring. Tenga, Holme, Ronglan and Bahr [62Tenga A, Holme I, Ronglan LT, Bahr R. Effect of playing tactics on goal scoring in Norwegian professional soccer. J Sports Sci 2010; 28(3): 237-44. [https://doi.org/10.1080/02640410903502774].
[http://dx.doi.org/10.1080/02640410903502774] [PMID: 20391095]
] and Tenga, Ronglan and Bahr [63Tenga A, Ronglan LT, Bahr R. Measuring the effectiveness of offensive match-play in professional soccer. Eur J Sport Sci 2010; 10(4): 269-77. [https://doi.org/10.1080/17461390903515170].
[http://dx.doi.org/10.1080/17461390903515170]
] used the same data set with different methods for their analysis. Both papers showed that counter attacks are more effective than elaborated attacks in producing goals. Grund [20Grund TU. Network structure and team performance: The case of English Premier League soccer teams. Soc Networks 2012; 34(4): 682-90. [https://doi.org/10.1016/j.socnet.2012.08.004].
[http://dx.doi.org/10.1016/j.socnet.2012.08.004]
] used a network analysis to identify success factors. He revealed that networks with high intensity and low centralization have a better performance. An increased passing rate lead to a better performance in this study [20Grund TU. Network structure and team performance: The case of English Premier League soccer teams. Soc Networks 2012; 34(4): 682-90. [https://doi.org/10.1016/j.socnet.2012.08.004].
[http://dx.doi.org/10.1016/j.socnet.2012.08.004]
].

Table 7
Predictive analyses with regard to goal scoring.


In the last group of predictive analyses three variables of interest were collected (Table 8). The most frequent variable is goal difference as utilized in five papers [13Carmichael F, Thomas D, Ward R. Team performance: The case of english premiership football. MDE Manage Decis Econ 2000; 21(1): 31-45.
[http://dx.doi.org/10.1002/1099-1468(200001/02)21:1<31::AID-MDE963>3.0.CO;2-Q]
, 25Mechtel M, Baker A, Brandle T, Vetter K. Red cards: Not such bad news for penalized guest teams. J Sports Econ 2011; 12(6): 621-46.
[http://dx.doi.org/10.1177/1527002510388478]
, 64Papahristodoulou C. An analysis of Champions League match statistics. IJASS 2008; 20(1): 67-93., 65Garcia-Rubio J, Angel Gomez M, Lago-Penas C, Ibanez SJ. Effect of match venue, scoring first and quality of opposition on match outcome in the UEFA Champions League. Int J Perform Anal Sport 2015; 15(2): 527-39.
[http://dx.doi.org/10.1080/24748668.2015.11868811]
]. In all articles match location is positively linked to goal difference. Quality of the opponent was also identified as a significant predictor (p<0.05) [25Mechtel M, Baker A, Brandle T, Vetter K. Red cards: Not such bad news for penalized guest teams. J Sports Econ 2011; 12(6): 621-46.
[http://dx.doi.org/10.1177/1527002510388478]
, 64Papahristodoulou C. An analysis of Champions League match statistics. IJASS 2008; 20(1): 67-93., 65Garcia-Rubio J, Angel Gomez M, Lago-Penas C, Ibanez SJ. Effect of match venue, scoring first and quality of opposition on match outcome in the UEFA Champions League. Int J Perform Anal Sport 2015; 15(2): 527-39.
[http://dx.doi.org/10.1080/24748668.2015.11868811]
]. Moreover, Carmichael et al. [13Carmichael F, Thomas D, Ward R. Team performance: The case of english premiership football. MDE Manage Decis Econ 2000; 21(1): 31-45.
[http://dx.doi.org/10.1002/1099-1468(200001/02)21:1<31::AID-MDE963>3.0.CO;2-Q]
] showed that passes, tackles, interceptions, clearances, blocks, interceptions, free kicks and ball caught by goalkeeper are significant predictors for a positive goal difference(p<0.05). A red card was associated with a negative goal difference [13Carmichael F, Thomas D, Ward R. Team performance: The case of english premiership football. MDE Manage Decis Econ 2000; 21(1): 31-45.
[http://dx.doi.org/10.1002/1099-1468(200001/02)21:1<31::AID-MDE963>3.0.CO;2-Q]
, 25Mechtel M, Baker A, Brandle T, Vetter K. Red cards: Not such bad news for penalized guest teams. J Sports Econ 2011; 12(6): 621-46.
[http://dx.doi.org/10.1177/1527002510388478]
, 64Papahristodoulou C. An analysis of Champions League match statistics. IJASS 2008; 20(1): 67-93.]. Garcia-Rubio et al. [65Garcia-Rubio J, Angel Gomez M, Lago-Penas C, Ibanez SJ. Effect of match venue, scoring first and quality of opposition on match outcome in the UEFA Champions League. Int J Perform Anal Sport 2015; 15(2): 527-39.
[http://dx.doi.org/10.1080/24748668.2015.11868811]
] showed that scoring first is the strongest predictor for a positive goal difference. Lago-Penas, Gomez-Ruano, Megias-Navarro and Pollard [28Lago-Penas C, Gomez-Ruano M, Megias-Navarro D, Pollard R. Home advantage in football: Examining the effect of scoring first on match outcome in the five major European leagues. Int J Perform Anal Sport 2016; 16(2): 411-21.
[http://dx.doi.org/10.1080/24748668.2016.11868897]
] used a tree analysis to determine the effects of scoring first on the outcome of a match. They showed that the first scoring team scored 1.88 goals more than their opponent on average. This is influenced by the quality of the teams and the match period in which the first goal was scored [28Lago-Penas C, Gomez-Ruano M, Megias-Navarro D, Pollard R. Home advantage in football: Examining the effect of scoring first on match outcome in the five major European leagues. Int J Perform Anal Sport 2016; 16(2): 411-21.
[http://dx.doi.org/10.1080/24748668.2016.11868897]
]. Oberstone [15Oberstone J. Differentiating the top english premier league football clubs from the rest of the Pack: Identifying the Keys to Success. J Quant Anal Sports 2009; 5(3)
[http://dx.doi.org/10.2202/1559-0410.1183]
], Hall, Szymanski and Zimbalist [66Hall S, Szymanski S, Zimbalist AS. Testing causality between team performance and payroll the cases of major league baseball and english soccer. J Sports Econ 2002; 3(2): 149-68.
[http://dx.doi.org/10.1177/152700250200300204]
], and Kringstad and Olsen [67Kringstad M, Olsen T-E. Can sporting success in Norwegian football be predicted from budgeted revenues? ESMQ 2016; 16(1): 20-37. [https://doi.org/10.1080/16184742.2015.1061032].] investigated relevant factors for the league ranking in a predictive design. Hall et al. [66Hall S, Szymanski S, Zimbalist AS. Testing causality between team performance and payroll the cases of major league baseball and english soccer. J Sports Econ 2002; 3(2): 149-68.
[http://dx.doi.org/10.1177/152700250200300204]
] focused on the relationship between payroll and performance. They found that there is a higher winning probability of 0.614 for 50% more spending in payroll. The top level is more sensitive to spending. Oberstone [15Oberstone J. Differentiating the top english premier league football clubs from the rest of the Pack: Identifying the Keys to Success. J Quant Anal Sports 2009; 5(3)
[http://dx.doi.org/10.2202/1559-0410.1183]
] developed a regression model to predict the league ranking. He revealed six variables which are sufficient for predicting the league ranking (in terms of points earned). These six variables are the percentage of goals to shot (goals divided by shots), the percentage of goals outside penalty area (goals from outside penalty area divided by goals within penalty area), ratio of short to long passes, total crosses, average goals conceded per match and yellow cards. Kringstad and Olsen [67Kringstad M, Olsen T-E. Can sporting success in Norwegian football be predicted from budgeted revenues? ESMQ 2016; 16(1): 20-37. [https://doi.org/10.1080/16184742.2015.1061032].] studied budgeted revenue and success. They showed that budgeted revenues are a significant factor (p<0.05) but only for the bottom-half of the teams and not for the top-half of the teams. The remaining three papers focused on points as the variable of interest. Lago [68Lago-Penas C. Are winners different from losers? Performance and chance in the FIFA World Cup Germany 2006. Int J Perform Anal Sport 2007; 7(2): 36-47.
[http://dx.doi.org/10.1080/24748668.2007.11868395]
] defined performance as shots performed minus shots conceded, and found that this is a predictor for more points. Furthermore, he showed that the higher the FIFA ranking is, the higher the chance to win. Collet [21Collet C. The possession game? A comparative analysis of ball retention and team success in European and international football, 2007-2010. J Sports Sci 2013; 31(2): 123-36. [https://doi.org/10.1080/02640414.2012.727455].
[http://dx.doi.org/10.1080/02640414.2012.727455] [PMID: 23067001]
] focused on ball possession. His result was that more time with the ball leads to more points and goals, but if it is controlled by team strength a negative effect for possession can be observed. Passes and shot accuracy turned out to be better predictors for points. Coates, Frick and Jewell [69Coates D, Frick B, Jewell T. Superstar salaries and soccer success: The impact of designated players in major league soccer. J Sports Econ 2016; 17(7): 716-35. [https://doi.org/10.1177/1527002514547297].
[http://dx.doi.org/10.1177/1527002514547297]
] investigated the relationship between salary structure and success. They revealed that salary inequality has a negative effect on success but the wage bill of a team has a positive relationship with success by a similar amount. This results support the cohesion theory [69Coates D, Frick B, Jewell T. Superstar salaries and soccer success: The impact of designated players in major league soccer. J Sports Econ 2016; 17(7): 716-35. [https://doi.org/10.1177/1527002514547297].
[http://dx.doi.org/10.1177/1527002514547297]
].

Table 8
Predictive analyses with regard to other operationalization of success.


7. ANALYSES OF HOME ADVANTAGE

The review of predictive analyses already showed that match location (home advantage) is an important factor in explaining success in football [25Mechtel M, Baker A, Brandle T, Vetter K. Red cards: Not such bad news for penalized guest teams. J Sports Econ 2011; 12(6): 621-46.
[http://dx.doi.org/10.1177/1527002510388478]
, 26Carmichael F, Thomas D. Home-Field effect and team performance: Evidence from english premiership football. J Sports Econ 2005; 6(3): 264-81. [https://doi.org/10.1177/1527002504266154].
[http://dx.doi.org/10.1177/1527002504266154]
, 48Lago-Peñas C, Lago-Ballesteros J, Dellal A, Gómez M. Game-related statistics that discriminated winning, drawing and losing teams from the Spanish soccer league. J Sports Sci Med 2010; 9(2): 288-93.
[PMID: 24149698]
, 49Lago-Penas C, Lago-Ballesteros J, Rey E. Differences in performance indicators between winning and losing teams in the UEFA champions league. J Hum Kinet 2011; 27: 137-48.
[http://dx.doi.org/10.2478/v10078-011-0011-3]
, 53Liu H, Hopkins WG, Gómez M-A. Modelling relationships between match events and match outcome in elite football. Eur J Sport Sci 2016; 16(5): 516-25. [https://doi.org/10.1080/17461391.2015.1042527].
[http://dx.doi.org/10.1080/17461391.2015.1042527] [PMID: 26190577]
, 58Torgler B. The Economics of the FIFA Football Worldcup. Kyklos 2004; 57(2): 287-300. [https://doi.org/10.1111/j.0023-5962.2004.00255.x].
[http://dx.doi.org/10.1111/j.0023-5962.2004.00255.x]
, 57Bar-Eli M, Tenenbaum G, Geister S. Consequences of players’ dismissal in professional soccer: a crisis-related analysis of group-size effects. J Sports Sci 2006; 24(10): 1083-94. [https://doi.org/10.1080/02640410500432599].
[http://dx.doi.org/10.1080/02640410500432599] [PMID: 17115523]
, 64Papahristodoulou C. An analysis of Champions League match statistics. IJASS 2008; 20(1): 67-93.]. Seventeen papers that focused mainly on match locations specifically home advantage were identified in this the review (see Table 9). In one of these papers [26Carmichael F, Thomas D. Home-Field effect and team performance: Evidence from english premiership football. J Sports Econ 2005; 6(3): 264-81. [https://doi.org/10.1177/1527002504266154].
[http://dx.doi.org/10.1177/1527002504266154]
] further factors related to success, besides home advantage, were also investigated. The first analysis of home advantage in football was done by Pollard [70Pollard R. Home advantage in soccer: A retrospective analysis. J Sports Sci 1986; 4(3): 237-48. [https://doi.org/10.1080/02640418608732122].
[http://dx.doi.org/10.1080/02640418608732122] [PMID: 2884328]
]. He investigated different team sports including the first four football divisions in England from 1888 to 1984. There was very little variation between 85 seasons (between 1939 and 1945 there were no official seasons due to World War II). The points won by the home team differed between 62.5 percent and 67.9 percent. Clarke and Norman [11Clarke SR, Norman John M. Home ground advantage of individual clubs in English soccer. Statistician 1995; 44(4): 509-21.
[http://dx.doi.org/10.2307/2348899]
] provided an approach to quantify team ability and home advantage at a team level due to the influence of the quality of opponent (team ability or strength). This approach was also used by other authors to define home advantage for a team [25Mechtel M, Baker A, Brandle T, Vetter K. Red cards: Not such bad news for penalized guest teams. J Sports Econ 2011; 12(6): 621-46.
[http://dx.doi.org/10.1177/1527002510388478]
, 49Lago-Penas C, Lago-Ballesteros J, Rey E. Differences in performance indicators between winning and losing teams in the UEFA champions league. J Hum Kinet 2011; 27: 137-48.
[http://dx.doi.org/10.2478/v10078-011-0011-3]
, 64Papahristodoulou C. An analysis of Champions League match statistics. IJASS 2008; 20(1): 67-93.]. Clarke and Norman [11Clarke SR, Norman John M. Home ground advantage of individual clubs in English soccer. Statistician 1995; 44(4): 509-21.
[http://dx.doi.org/10.2307/2348899]
] stated that it is necessary to consider difference in ability to calculate home advantage. In their research the home advantage relating to goals differed from year to year and between teams. The average home advantage between 1981 and 1990 in England resulted in 0.528 goals per match. Another result is that team ability is more important than home advantage [11Clarke SR, Norman John M. Home ground advantage of individual clubs in English soccer. Statistician 1995; 44(4): 509-21.
[http://dx.doi.org/10.2307/2348899]
]. Overall, home advantage explains around 60 percent with some variations [71Thomas S, Reeves C, Davies S. An analysis of home advantage in the English Football Premiership. Percept Mot Skills 2004; 99(3 Pt 2): 1212-6.
[http://dx.doi.org/10.2466/pms.99.3f.1212-1216] [PMID: 15739847]
-86Goumas C. Tyranny of distance: Home advantage and travel in international club football. Int J Perform Anal Sport 2014; 14(1): 1-13.
[http://dx.doi.org/10.1080/24748668.2014.11868698]
] (Table 9). Before the 1980s, the explaining percentage of home advantage was moderately higher [71Thomas S, Reeves C, Davies S. An analysis of home advantage in the English Football Premiership. Percept Mot Skills 2004; 99(3 Pt 2): 1212-6.
[http://dx.doi.org/10.2466/pms.99.3f.1212-1216] [PMID: 15739847]
]. Saavedra Garcia, Aguilar, Fernández Romero and Sa Marques [72Saavedra Garcia M, Gutierrez Aguilar O, Fernandez Romero JJ, Sa Marques P. Measuring home advantage in spanish football (1928-2011). Rev Int Med Cienc Act Fís Deporte 2015; 15(57): 181-94.
[http://dx.doi.org/10.15366/rimcafd2015.57.010]
] investigated home advantage in the first division in Spain between 1928 and 2011. Home teams won 70.8 percent of the points for the period when 2 points were awarded for a victory and 56.7 percent when three points were awarded for a victory. Lago-Pens et al. [28Lago-Penas C, Gomez-Ruano M, Megias-Navarro D, Pollard R. Home advantage in football: Examining the effect of scoring first on match outcome in the five major European leagues. Int J Perform Anal Sport 2016; 16(2): 411-21.
[http://dx.doi.org/10.1080/24748668.2016.11868897]
] showed a consistent home advantage for all five major leagues in Europe (France, Italy, Spain, England and Germany) for the season 2014/15. Home teams won between 56.47 percent (Italy) and 61.84 (Germany) of the awarded points for a victory.

Table 9
Analyses of home advantage.


Lago-Penas and Lago-Ballesteros [73Lago-Peñas C, Lago-Ballesteros J. Game location and team quality effects on performance profiles in professional soccer. J Sports Sci Med 2011; 10(3): 465-71.
[PMID: 24150619]
] investigated the variables that discriminate best (discriminant value ≥|.30|) between home and away teams. Home teams score more goals, perform more crosses, more passes, have more ball possession and commit more fouls. Away teams show more losses of possession and gather more yellow cards. Armatas and Pollard [74Armatas V, Pollard R. Home advantage in Greek football. Eur J Sport Sci 2014; 14(2): 116-22. [https://doi.org/10.1080/17461391.2012.736537].
[http://dx.doi.org/10.1080/17461391.2012.736537] [PMID: 24533517]
] found shots, clearances, headed shots, corners and saves to have the highest effect size for match variables between home and away teams. Goumas [75Goumas C. Modelling home advantage for individual teams in UEFA Champions League football. J Sport Health Sci 2015. [https://doi.org/10.1016/j.jshs.2015.12.008].] analyzed home advantage on a team level adjusted for team ability (operationalized by UEFA ranking points). Home advantage did not vary between teams despite a home advantage of 73% for Arsenal London and a home advantage of 58% for Inter Milan. Away disadvantage varied between teams ranging from 45% (F.C. Barcelona) to 68% (Olympiacos F.C.). There was also a tendency that teams with a higher home advantage had lower away disadvantage. Home advantage and away disadvantage differed significant between countries 70% English teams to 52% Turkish teams (p=0.01) [75Goumas C. Modelling home advantage for individual teams in UEFA Champions League football. J Sport Health Sci 2015. [https://doi.org/10.1016/j.jshs.2015.12.008].]. The major causes for home advantage discussed are crowd support, travel fatigue, familiarity, territoriality, referee bias, special tactics, rule factors and psychological factors as well as the interaction of these [70Pollard R. Home advantage in soccer: A retrospective analysis. J Sports Sci 1986; 4(3): 237-48. [https://doi.org/10.1080/02640418608732122].
[http://dx.doi.org/10.1080/02640418608732122] [PMID: 2884328]
, 76Pollard R. Worldwide regional variations in home advantage in association football. J Sports Sci 2006; 24(3): 231-40. [https://doi.org/10.1080/02640410500141836].
[http://dx.doi.org/10.1080/02640410500141836] [PMID: 16368633]
, 77Pollard R. Home advantage in football: A current review of an unsolved puzzle. Open Sports Sci J 2008; 1(1): 12-4. [https://doi.org/10.2174/1875399X00801010012].
[http://dx.doi.org/10.2174/1875399X00801010012]
].

8. INTEGRATIVE DISCUSSION

The aim of this study was to review performance analyses in adult male football in order to identify success factors and utilized methods. The review revealed that there is an extensive and growing body of performance analyses literature in football. In contrast to early studies that were often based on descriptive designs [9Reep C, Benjamin B. Skill and Chance in Association Football. J R Stat Soc [Ser A] 1968; 131(4): 581.
[http://dx.doi.org/10.2307/2343726]
], analyses with predictive designs, explaining more and more success factors [21Collet C. The possession game? A comparative analysis of ball retention and team success in European and international football, 2007-2010. J Sports Sci 2013; 31(2): 123-36. [https://doi.org/10.1080/02640414.2012.727455].
[http://dx.doi.org/10.1080/02640414.2012.727455] [PMID: 23067001]
, 22Liu H, Gomez M-A, Lago-Penas C, Sampaio J. Match statistics related to winning in the group stage of 2014 Brazil FIFA World Cup. J Sports Sci 2015; 33(12, SI): 1205–13
[http://dx.doi.org/10.1080/02640414.2015.1022578]
, 49Lago-Penas C, Lago-Ballesteros J, Rey E. Differences in performance indicators between winning and losing teams in the UEFA champions league. J Hum Kinet 2011; 27: 137-48.
[http://dx.doi.org/10.2478/v10078-011-0011-3]
], have gained momentum in recent years. The most frequently studied variables were shots (27 times)/shots on goal (23 times) followed by passes (20 times). Overall 76 different variables were investigated in the reviewed papers. Based on the results in the papers, the most influential variables are efficiency [22Liu H, Gomez M-A, Lago-Penas C, Sampaio J. Match statistics related to winning in the group stage of 2014 Brazil FIFA World Cup. J Sports Sci 2015; 33(12, SI): 1205–13
[http://dx.doi.org/10.1080/02640414.2015.1022578]
, 29Broich H, Mester J, Seifriz F, Yue Z. Statistical Analysis for the First Bundesliga in the Current Soccer Season. PAM 2014; 7(2): 1-8. [https://doi.org/10.3968/4886]., 46Delgado-Bordonau JL, Domenech-Monforte C, Guzmán JF, Mendez-Villanueva A. Offensive and defensive team performance: Relation to successful and unsuccessful participation in the 2010 Soccer World Cup. JHSE 2013; 8(4): 894-904.
[http://dx.doi.org/10.4100/jhse.2013.84.02]
], shots on goal [49Lago-Penas C, Lago-Ballesteros J, Rey E. Differences in performance indicators between winning and losing teams in the UEFA champions league. J Hum Kinet 2011; 27: 137-48.
[http://dx.doi.org/10.2478/v10078-011-0011-3]
, 54Mao L, Peng Z, Liu H, Gomez M-A. Identifying keys to win in the Chinese professional soccer league. Int J Perform Anal Sport 2016; 16(3): 935-47.
[http://dx.doi.org/10.1080/24748668.2016.11868940]
], possession [39Rampinini E, Impellizzeri FM, Castagna C, Coutts AJ, Wisløff U. Technical performance during soccer matches of the Italian Serie A league: Effect of fatigue and competitive level. J Sci Med Sport 2009; 12(1): 227-33. [https://doi.org/10.1016/j.jsams.2007.10.002].
[http://dx.doi.org/10.1016/j.jsams.2007.10.002] [PMID: 18083631]
], pass accuracy/successful passes [32Janković A, Leontijević B, Pašić M, Jelušić V. Influence of certain tactical attacking patterns on the result achieved by the team participants of the 2010 FIFA World Cup in South Africa. Physical Culture/Fizicka Kultura 2011; 65(1)., 35Luhtanen P, Belinskij A, Häyrinen M, Vänttinen T. A comparative tournament analysis between the EURO 1996 and 2000 in soccer. Int J Perform Anal Sport 2001; 1(1): 74-82.
[http://dx.doi.org/10.1080/24748668.2001.11868250]
], quality of opponent [25Mechtel M, Baker A, Brandle T, Vetter K. Red cards: Not such bad news for penalized guest teams. J Sports Econ 2011; 12(6): 621-46.
[http://dx.doi.org/10.1177/1527002510388478]
, 28Lago-Penas C, Gomez-Ruano M, Megias-Navarro D, Pollard R. Home advantage in football: Examining the effect of scoring first on match outcome in the five major European leagues. Int J Perform Anal Sport 2016; 16(2): 411-21.
[http://dx.doi.org/10.1080/24748668.2016.11868897]
, 64Papahristodoulou C. An analysis of Champions League match statistics. IJASS 2008; 20(1): 67-93.], and match location [49Lago-Penas C, Lago-Ballesteros J, Rey E. Differences in performance indicators between winning and losing teams in the UEFA champions league. J Hum Kinet 2011; 27: 137-48.
[http://dx.doi.org/10.2478/v10078-011-0011-3]
, 76Pollard R. Worldwide regional variations in home advantage in association football. J Sports Sci 2006; 24(3): 231-40. [https://doi.org/10.1080/02640410500141836].
[http://dx.doi.org/10.1080/02640410500141836] [PMID: 16368633]
, 65Garcia-Rubio J, Angel Gomez M, Lago-Penas C, Ibanez SJ. Effect of match venue, scoring first and quality of opposition on match outcome in the UEFA Champions League. Int J Perform Anal Sport 2015; 15(2): 527-39.
[http://dx.doi.org/10.1080/24748668.2015.11868811]
].4

It became apparent that performance in football depends on a high number of variables. For example, Oberstone [15Oberstone J. Differentiating the top english premier league football clubs from the rest of the Pack: Identifying the Keys to Success. J Quant Anal Sports 2009; 5(3)
[http://dx.doi.org/10.2202/1559-0410.1183]
] investigated 24 different variables. Using a 6-variable regression (percentage of goals to shots, percentage of goals scored outside of box, ratio of short/long passes, total crosses, average goals conceded per match and yellow cards) he predicted the points earned by English football teams in the 2007/2008 season. The fit delivered an R2=0.990 (p<0.0000) indicating strong evidence for his model. Similarly, Kapidžić et al. [30Kapidžić A, Mejremić E, Bilalić J, Bečirović E. Differences in some parameters of situation efficiency between winning and defeated teams at two levels of competition. Sport Sci Pract Asp 2010; 7(2): 27-33.] investigated 21 variables in the first division in Bosnia and Herzegovina 2008/2009 (12 matches) and in the 2008 European Championship (13 matches). While in the first division 13 variables (e.g., shots, passes, and offensive structure) significantly discriminate between winners and losers (p<0.05), in the European Championship only three variables were significant (shots on goal, number of goals scored within penalty area and number of goals scored outside penalty area) (p<0.05). Although both studies considered many variables, it were the obvious variables such as shots and goals that became significant,

explaining only little of the underlying mechanisms of success in football. Liu et al. [22Liu H, Gomez M-A, Lago-Penas C, Sampaio J. Match statistics related to winning in the group stage of 2014 Brazil FIFA World Cup. J Sports Sci 2015; 33(12, SI): 1205–13
[http://dx.doi.org/10.1080/02640414.2015.1022578]
] and Mao et al. [54Mao L, Peng Z, Liu H, Gomez M-A. Identifying keys to win in the Chinese professional soccer league. Int J Perform Anal Sport 2016; 16(3): 935-47.
[http://dx.doi.org/10.1080/24748668.2016.11868940]
] studied very similar variables in two different samples. Shot on target and tackle were the only two discriminating variables in both studies. Other variables had no clear effect or the effect depended on the context [22Liu H, Gomez M-A, Lago-Penas C, Sampaio J. Match statistics related to winning in the group stage of 2014 Brazil FIFA World Cup. J Sports Sci 2015; 33(12, SI): 1205–13
[http://dx.doi.org/10.1080/02640414.2015.1022578]
, 54Mao L, Peng Z, Liu H, Gomez M-A. Identifying keys to win in the Chinese professional soccer league. Int J Perform Anal Sport 2016; 16(3): 935-47.
[http://dx.doi.org/10.1080/24748668.2016.11868940]
]. Based on these results, it seems that not many success factors in football are stable over different contexts and samples. It should be noted, however, that an exclusive focus on statistical data (e.g., shots, possession) will probably be not sufficient to explain these mechanisms. A more sophisticated approach is needed to reveal these mechanisms. This includes more variables and the use of more complex statistical approaches such as ordered logit regressions to determine the influence of these variables. Also, the inclusion of qualitative variables e.g., self-perception and social perception or the evaluation of motivation can help to reveal the nature of performance. A third area of investigation should be more player centric such as questionnaires e.g., about group cohesiveness or personality traits.

Moreover, the review revealed that to date many different types of matches and settings have come into the focus of researchers, providing a more holistic view on success factors in football. Regarding comparative and predictive analyses, 34 articles focused on league matches, 13 on cup matches for national teams and six on cup matches for clubs. Especially studies that integrate different types of matches and settings provide useful insights allowing for generalizable statements. For example, Collet [21Collet C. The possession game? A comparative analysis of ball retention and team success in European and international football, 2007-2010. J Sports Sci 2013; 31(2): 123-36. [https://doi.org/10.1080/02640414.2012.727455].
[http://dx.doi.org/10.1080/02640414.2012.727455] [PMID: 23067001]
] analyzed more than 6,000 matches including league matches from England, Italy, France and Germany, matches from the European Champions League and the Europe League as well as national matches from Europe, America, Africa and Asia. In this way, he found that in the leagues pass accuracy and shot accuracy are more important for success than ball possession, in contrast to the assumptions of many scholars and professionals (for Germany one percent more possession even leads to a winning probability that is reduced by 5.7 percent). Also Lago-Penas et al. [28Lago-Penas C, Gomez-Ruano M, Megias-Navarro D, Pollard R. Home advantage in football: Examining the effect of scoring first on match outcome in the five major European leagues. Int J Perform Anal Sport 2016; 16(2): 411-21.
[http://dx.doi.org/10.1080/24748668.2016.11868897]
] studied over 1,800 matches in the five top leagues across Europe. They could show that scoring first is a crucial part of winning a match. In total, 27 studies chose a design that comprised an international comparison, while among the studies that focused on one nation, England showed to be the most studied country in football (11 articles), followed by Germany (7 articles) and Spain (7 articles) (Table 10).

Table 10
Design and country of the reviewed articles.


Methodologically, the review showed that in recent years new ways of statistical analyses were introduced. Lago et al. [48Lago-Peñas C, Lago-Ballesteros J, Dellal A, Gómez M. Game-related statistics that discriminated winning, drawing and losing teams from the Spanish soccer league. J Sports Sci Med 2010; 9(2): 288-93.
[PMID: 24149698]
] were the first authors who used a discriminant analysis to identify differences between winners and losers. Moura et al. [51Moura FA, Martins LE, Cunha SA. Analysis of football game-related statistics using multivariate techniques. J Sports Sci 2014; 32(20): 1881-7. [https://doi.org/10.1080/02640414.2013.853130].
[http://dx.doi.org/10.1080/02640414.2013.853130] [PMID: 24742152]
] combined this approach with a factor analysis. They investigated 14 variables and performed a factor analysis. Subsequently, a cluster analysis was used to classify the teams into two groups. Finally, they showed that 70.3 percent of the winning teams were classified into the same group (67.8 percent for drawing and losing teams). Shots, shots on goal, playing time with ball possession and percentage of ball possession were the most important variables to discriminate between winning teams and drawing or losing teams in this study. Liu et al. [22Liu H, Gomez M-A, Lago-Penas C, Sampaio J. Match statistics related to winning in the group stage of 2014 Brazil FIFA World Cup. J Sports Sci 2015; 33(12, SI): 1205–13
[http://dx.doi.org/10.1080/02640414.2015.1022578]
] used a cluster analysis to identify only close matches. This approach has the advantage that both teams give probably their best and do not lean

back because the match is already decided [22Liu H, Gomez M-A, Lago-Penas C, Sampaio J. Match statistics related to winning in the group stage of 2014 Brazil FIFA World Cup. J Sports Sci 2015; 33(12, SI): 1205–13
[http://dx.doi.org/10.1080/02640414.2015.1022578]
, 55Vaz L, Rooyen MV, Sampaio J. Rugby game-related statistics that discriminate between winning and losing teams in Irb and super twelve close games. J Sports Sci Med 2010; 9(1): 51-5. [PMID: 24149385].
[PMID: 24149385]
]. The concept of close and unbalanced matches also improved the analysis of success factors in football [22Liu H, Gomez M-A, Lago-Penas C, Sampaio J. Match statistics related to winning in the group stage of 2014 Brazil FIFA World Cup. J Sports Sci 2015; 33(12, SI): 1205–13
[http://dx.doi.org/10.1080/02640414.2015.1022578]
, 29Broich H, Mester J, Seifriz F, Yue Z. Statistical Analysis for the First Bundesliga in the Current Soccer Season. PAM 2014; 7(2): 1-8. [https://doi.org/10.3968/4886].]. Close matches are defined by a small goal difference. In unbalanced matches one team dominates the other team in terms of goal difference very obviously [55Vaz L, Rooyen MV, Sampaio J. Rugby game-related statistics that discriminate between winning and losing teams in Irb and super twelve close games. J Sports Sci Med 2010; 9(1): 51-5. [PMID: 24149385].
[PMID: 24149385]
, 87Gómez MÁ, DelaSerna A, Lupo C, Sampaio J. Effects of situational variables and starting quarter score in the outcome of elite women’s water polo game quarters. Int J Perform Anal Sport 2014; 14(1): 73-83.
[http://dx.doi.org/10.1080/24748668.2014.11868704]
-91Lupo C, Tessitore A. How important is the final outcome to interpret match analysis data: The influence of scoring a goal, and difference between close and balance games in elite soccer: Comment on Lago-Penas and Gomez-Lopez (2014). Percept Mot Skills 2016; 122(1): 280-5.
[http://dx.doi.org/10.1177/0031512515626629] [PMID: 27420321]
]. This concept was first introduced in a discrimination study about rugby in 2010 [55Vaz L, Rooyen MV, Sampaio J. Rugby game-related statistics that discriminate between winning and losing teams in Irb and super twelve close games. J Sports Sci Med 2010; 9(1): 51-5. [PMID: 24149385].
[PMID: 24149385]
] and is widely used since then [22Liu H, Gomez M-A, Lago-Penas C, Sampaio J. Match statistics related to winning in the group stage of 2014 Brazil FIFA World Cup. J Sports Sci 2015; 33(12, SI): 1205–13
[http://dx.doi.org/10.1080/02640414.2015.1022578]
, 29Broich H, Mester J, Seifriz F, Yue Z. Statistical Analysis for the First Bundesliga in the Current Soccer Season. PAM 2014; 7(2): 1-8. [https://doi.org/10.3968/4886]., 55Vaz L, Rooyen MV, Sampaio J. Rugby game-related statistics that discriminate between winning and losing teams in Irb and super twelve close games. J Sports Sci Med 2010; 9(1): 51-5. [PMID: 24149385].
[PMID: 24149385]
, 87Gómez MÁ, DelaSerna A, Lupo C, Sampaio J. Effects of situational variables and starting quarter score in the outcome of elite women’s water polo game quarters. Int J Perform Anal Sport 2014; 14(1): 73-83.
[http://dx.doi.org/10.1080/24748668.2014.11868704]
-91Lupo C, Tessitore A. How important is the final outcome to interpret match analysis data: The influence of scoring a goal, and difference between close and balance games in elite soccer: Comment on Lago-Penas and Gomez-Lopez (2014). Percept Mot Skills 2016; 122(1): 280-5.
[http://dx.doi.org/10.1177/0031512515626629] [PMID: 27420321]
].

However, most researchers (comparative and predictive design) used a form of regression analysis (22 studies). Discriminate analysis (six studies) and ANOVA (five studies) are the second and third most frequently used statistical methods. For example, Mechtel et al. [25Mechtel M, Baker A, Brandle T, Vetter K. Red cards: Not such bad news for penalized guest teams. J Sports Econ 2011; 12(6): 621-46.
[http://dx.doi.org/10.1177/1527002510388478]
] and Collet [21Collet C. The possession game? A comparative analysis of ball retention and team success in European and international football, 2007-2010. J Sports Sci 2013; 31(2): 123-36. [https://doi.org/10.1080/02640414.2012.727455].
[http://dx.doi.org/10.1080/02640414.2012.727455] [PMID: 23067001]
] used an ordered logit regression to identify the influence of a dismissal respective ball possession. An advantage of this method is that it controls for other variables and to investigate a goal-based and result-based approach. Liu et al. [22Liu H, Gomez M-A, Lago-Penas C, Sampaio J. Match statistics related to winning in the group stage of 2014 Brazil FIFA World Cup. J Sports Sci 2015; 33(12, SI): 1205–13
[http://dx.doi.org/10.1080/02640414.2015.1022578]
] and Mao et al. [54Mao L, Peng Z, Liu H, Gomez M-A. Identifying keys to win in the Chinese professional soccer league. Int J Perform Anal Sport 2016; 16(3): 935-47.
[http://dx.doi.org/10.1080/24748668.2016.11868940]
] used a generalized linear model. First they ran a cluster analysis to define cut-off values (see above). Then they applied a cumulative logistic regression to predict winning probabilities. Afterwards they employed non-clinical magnitude-based inferences to evaluate the true effect of the variable [22Liu H, Gomez M-A, Lago-Penas C, Sampaio J. Match statistics related to winning in the group stage of 2014 Brazil FIFA World Cup. J Sports Sci 2015; 33(12, SI): 1205–13
[http://dx.doi.org/10.1080/02640414.2015.1022578]
, 54Mao L, Peng Z, Liu H, Gomez M-A. Identifying keys to win in the Chinese professional soccer league. Int J Perform Anal Sport 2016; 16(3): 935-47.
[http://dx.doi.org/10.1080/24748668.2016.11868940]
]. This approach allows a more realistic and intuitive interpretation of effects [92Hopkins WG, Marshall SW, Batterham AM, Hanin J. Progressive statistics for studies in sports medicine and exercise science. Med Sci Sports Exerc 2009; 41(1): 3-13.
[http://dx.doi.org/10.1249/MSS.0b013e31818cb278] [PMID: 19092709]
]. Since much of current research is still descriptive or comparative, these two approaches are promising with regard to providing new, valuable insights to performance in football.

Finally, a crucial point that was found is sample size. Many studies, such as Kapidžić et al. [30Kapidžić A, Mejremić E, Bilalić J, Bečirović E. Differences in some parameters of situation efficiency between winning and defeated teams at two levels of competition. Sport Sci Pract Asp 2010; 7(2): 27-33.] who analyzed 25 matches, rely on small sample sizes. Of the reviewed papers, the sample sizes varied from seven matches [31Szwarc A. Efficacy of successful and unsuccessful soccer teams taking part in finals of Champions League. Research Yearbook 2007; 13(2): 221-5.] to 89,813 matches [76Pollard R. Worldwide regional variations in home advantage in association football. J Sports Sci 2006; 24(3): 231-40. [https://doi.org/10.1080/02640410500141836].
[http://dx.doi.org/10.1080/02640410500141836] [PMID: 16368633]
]. In total, only 28 papers analyzed all matches of a whole or several seasons. It appears that many studies lack sample sizes that are adequate to produce generalizable results.

9. PRACTICAL IMPLICATIONS

A critical question is how the results can support football coaches and their staff. Based on the findings of this review, coaches could be advised to instruct their teams to shoot extensively while at the same time considering shot accuracy. However, advice of this kind would not do justice to the complex nature of football and the demands of coaches. Bishop [93Bishop D. An applied research model for the sport sciences. Sports Med 2008; 38(3): 253-63.
[http://dx.doi.org/10.2165/00007256-200838030-00005] [PMID: 18278985]
] emphasized that only results providing performance-enhancing knowledge will be applied in practice. Hence, research has to deliver results that make it more likely to win. This also includes findings with regard to training, match preparation and coaching. Nash and Collins [94Nash C, Collins D. Tacit knowledge in expert coaching: Science or art? Quest 2006; 58(4): 465-77.
[http://dx.doi.org/10.1080/00336297.2006.10491894]
] stated that coaching is a very complex and dynamic process. The actions of coaches are based on knowledge that has been acquired over years of experience and reflection, that is, tacit knowledge [94Nash C, Collins D. Tacit knowledge in expert coaching: Science or art? Quest 2006; 58(4): 465-77.
[http://dx.doi.org/10.1080/00336297.2006.10491894]
, 95Sternberg RJ. Wisdom, intelligence, and creativity synthesized. Cambridge University Press 2003.
[http://dx.doi.org/10.1017/CBO9780511509612]
]. For coaches, the importance of shots for scoring goals is more than obvious. It is also hardly surprising that pass accuracy, the opponent’s quality and home advantage have a positive impact. A benefit for football coaches would be to reveal the partial influence of these variables including their interactions (e.g., by analyzing regression models).

However, there are less obvious findings that provide empirical evidence for beneficial tactical behaviors. First, possession is not as important as might be assumed [21Collet C. The possession game? A comparative analysis of ball retention and team success in European and international football, 2007-2010. J Sports Sci 2013; 31(2): 123-36. [https://doi.org/10.1080/02640414.2012.727455].
[http://dx.doi.org/10.1080/02640414.2012.727455] [PMID: 23067001]
, 22Liu H, Gomez M-A, Lago-Penas C, Sampaio J. Match statistics related to winning in the group stage of 2014 Brazil FIFA World Cup. J Sports Sci 2015; 33(12, SI): 1205–13
[http://dx.doi.org/10.1080/02640414.2015.1022578]
, 54Mao L, Peng Z, Liu H, Gomez M-A. Identifying keys to win in the Chinese professional soccer league. Int J Perform Anal Sport 2016; 16(3): 935-47.
[http://dx.doi.org/10.1080/24748668.2016.11868940]
]. Second, a focus on counter attacks can be very effective and can be utilized as a successful tactical strategy, especially for underdogs [41Tenga A, Sigmundstad E. Characteristics of goal-scoring possessions in open play: Comparing the top, in-between and bottom teams from professional soccer league. Int J Perform Anal Sport 2011; 11(3): 545-52.
[http://dx.doi.org/10.1080/24748668.2011.11868572]
]. Ball recovery in the zone between a team’s own penalty area and center circle [56Gómez MA, Gómez-Lopez M, Lago C, Sampaio J. Effects of game location and final outcome on game-related statistics in each zone of the pitch in professional football. Eur J Sport Sci 2012; 12(5): 393-8. [https://doi.org/10.1080/17461391.2011.566373].
[http://dx.doi.org/10.1080/17461391.2011.566373]
] and a quick ball recovery [42Vogelbein M, Nopp S, Hökelmann A. Defensive transition in soccer - are prompt possession regains a measure of success? A quantitative analysis of German Fußball-Bundesliga 2010/2011. J Sports Sci 2014; 32(11): 1076-83. [https://doi.org/10.1080/02640414.2013.879671].
[http://dx.doi.org/10.1080/02640414.2013.879671] [PMID: 24506111]
] can result in significantly more successful attacks respectively goals (p<0.001). Coaches can build on this evidence to improve tactical concepts. For example, coaches could put more emphasis on the practice of counter attacks, as a tactical element, to overwhelm the opponent’s defense and produce more good scoring opportunities. Also pressing, the attempt to recover the ball as close as possible to the opponent’s penalty area seems to be a promising tactic. It shortens not only the space between the attackers and the goal, it can also cause confusion within the opposing defense. This could lead to more goals since counterattacks are more effective against an imbalanced defense [62Tenga A, Holme I, Ronglan LT, Bahr R. Effect of playing tactics on goal scoring in Norwegian professional soccer. J Sports Sci 2010; 28(3): 237-44. [https://doi.org/10.1080/02640410903502774].
[http://dx.doi.org/10.1080/02640410903502774] [PMID: 20391095]
].

CONCLUSION

The aim of this work was to review research in performance analysis relating to success factors in elite men’s football. In total, 68 articles were identified and clustered based on their study design with regard to comparative, predictive or home advantage analyses. It was found that the most influential variables are efficiency, shots on goal, ball possession, pass accuracy/successful passes, as well as the quality of opponent and match location. New statistical approaches, such as discriminant analysis, factor analysis, regression analysis and magnitude-based inferences reveal interactions between these variables.

Bar-Eli et al. [57Bar-Eli M, Tenenbaum G, Geister S. Consequences of players’ dismissal in professional soccer: a crisis-related analysis of group-size effects. J Sports Sci 2006; 24(10): 1083-94. [https://doi.org/10.1080/02640410500432599].
[http://dx.doi.org/10.1080/02640410500432599] [PMID: 17115523]
] focused also on a psychological factor. However, they focused on the factor that leads to a dismissal and not to a psychological factor that contributes directly to performance

Concerning study design, an increase of predictive studies was found. For future studies, we suggest considering more often one of the ‘Big 3’ leagues (Spain, England and Germany) or all of them to get more representative samples. Furthermore, the consideration of other influences on success such as psychological factors and/or weather conditions would be of interest. Additionally, new methodological ways of analyzing success factors in football could be beneficial. For example, Borrie, Johnson and Magnusson [96Borrie A, Jonsson GK, Magnusson MS. Temporal pattern analysis and its applicability in sport: An explanation and exemplar data. J Sports Sci 2002; 20(10): 845-52. [https://doi.org/10.1080/026404102320675675]. [PMID: 12363299].
[http://dx.doi.org/10.1080/026404102320675675] [PMID: 12363299]
] presented a method to investigate time-based events in sports. Moreover, more advanced statistical methods should be applied to ensure a broader insight into the mechanisms of performance such as regressions and magnitude-based inferences [21Collet C. The possession game? A comparative analysis of ball retention and team success in European and international football, 2007-2010. J Sports Sci 2013; 31(2): 123-36. [https://doi.org/10.1080/02640414.2012.727455].
[http://dx.doi.org/10.1080/02640414.2012.727455] [PMID: 23067001]
, 22Liu H, Gomez M-A, Lago-Penas C, Sampaio J. Match statistics related to winning in the group stage of 2014 Brazil FIFA World Cup. J Sports Sci 2015; 33(12, SI): 1205–13
[http://dx.doi.org/10.1080/02640414.2015.1022578]
, 25Mechtel M, Baker A, Brandle T, Vetter K. Red cards: Not such bad news for penalized guest teams. J Sports Econ 2011; 12(6): 621-46.
[http://dx.doi.org/10.1177/1527002510388478]
].

Most of the studies did not consider the influence of contextual (e.g., home advantage, quality of opponent) and interactional variables (e.g., first goal scored by time of goal scoring). In some studies, the influence of variables is also computed without a clear definition of the investigated variables. This lack of operational definitions poses a problem and, inter alia, does not allow valid comparisons between the studies. In future research, variables should be clearly defined to enable comparable and reproducible results (see also Mackenzie & Cushion [18Mackenzie R, Cushion C. Performance analysis in football: A critical review and implications for future research. J Sports Sci 2013; 31(6): 639-76. [https://doi.org/10.1080/02640414.2012.746720].
[http://dx.doi.org/10.1080/02640414.2012.746720] [PMID: 23249092]
]; Sarmento et al. [19Sarmento H, Marcelino R, Anguera MT, CampaniÇo J, Matos N, LeitÃo JC. Match analysis in football: A systematic review. J Sports Sci 2014; 32(20): 1831-43.
[http://dx.doi.org/10.1080/02640414.2014.898852] [PMID: 24787442]
]). The consideration of interacting variables such as quality of opponent and match location should also be considered in future investigations to provide more insights. Future study designs should also make sure to take the differences between different competitions (e.g. leagues, cup competitions) into account, especially the differences between a league match and a knockout match.

Moreover, we found very different approaches regarding the sample size required for generalization. Sample sizes of considered matches varied between very low numbers and thousands of matches. A small sample size is clearly a limitation in some of the reviewed papers, resulting in no generalizability. Studies investigating league matches should consider at least a sample size of one season. Hence, our review supports the finding of Mackenzie and Cushion [18Mackenzie R, Cushion C. Performance analysis in football: A critical review and implications for future research. J Sports Sci 2013; 31(6): 639-76. [https://doi.org/10.1080/02640414.2012.746720].
[http://dx.doi.org/10.1080/02640414.2012.746720] [PMID: 23249092]
] with regard to small sample sizes that remains a major deficit of performance analyses in football. Additionally, future studies should use effect sizes to interpret the results properly (see also Broich et al. [29Broich H, Mester J, Seifriz F, Yue Z. Statistical Analysis for the First Bundesliga in the Current Soccer Season. PAM 2014; 7(2): 1-8. [https://doi.org/10.3968/4886].]). A last important aspect to consider when designing a study is the context of the analyzed sample. For example, the tactic that is used (e.g., counterattacks vs. elaborate attacks) could vary regarding the opponent.

Based on the idea that performance is a consequence of prior learning, inherent skills, situational factors and influence of the opposition [97James N. Predicting performance over time using a case study in real tennis. JHSE. 2012;7(2)
[http://dx.doi.org/10.4100/jhse.2012.72.08]
], the assumption holds that future performance is to a large extent a consequence of previous performance. Again, this underlines the aforementioned importance of considering the context of a sample as well as the operational definition of the investigated variables. Prior learning and inherent skills are two variables that were not considered in research about success factors in football as defined in this review. Both are exciting new possibilities for future research.

Finally, we would like to point to two methodological approaches that might lead to new insights in analyzing football performance. First, social network analysis provides new methods to analyze different aspects utilizing relational data, (e.g., the passing network of football teams), that have the potential to contribute substantially to a better understanding of success [20Grund TU. Network structure and team performance: The case of English Premier League soccer teams. Soc Networks 2012; 34(4): 682-90. [https://doi.org/10.1016/j.socnet.2012.08.004].
[http://dx.doi.org/10.1016/j.socnet.2012.08.004]
, 98Wäsche H, Dickson G, Woll A, Brandes U. Social network analysis in sport research: an emerging paradigm. EJSS 2017; 14(2): 138-65., 99Duch J, Waitzman JS, Amaral LA. Quantifying the performance of individual players in a team activity. PLoS One 2010; 5(6): e10937. [https://doi.org/10.1371/journal.pone.0010937]. [PMID: 20585387].
[http://dx.doi.org/10.1371/journal.pone.0010937] [PMID: 20585387]
]. Second, psychological factors could be taken into account for future research (e.g., reversal theory, see Apter [100Apter M. Reversal theory and personality: A review. J Res Pers 1984; 18(3): 265-88.]). The investigation of psychological factors is, in fact, more difficult than the analysis of statistical data. The operationalization of cohesion found in this review [34Carron AV, Bray SR, Eys MA. Team cohesion and team success in sport. J Sports Sci 2002; 20(2): 119-26. [https://doi.org/10.1080/026404102317200828].
[http://dx.doi.org/10.1080/026404102317200828] [PMID: 11811568]
] is a good example of the use of psychological concepts.6

As this review has shown, generalizable knowledge about success factors in football can be a helpful resource for coaches to gain a better understanding of the match. While significant progress in the field of performance in football was made in the last years, the review identified various deficits that future research has to address to provide more valuable information about what determines success.

CONSENT FOR PUBLICATION

Not applicable.

CONFLICT OF INTEREST

The authors declare no conflict of interest, financial or otherwise.

ACKNOWLEDGEMENTS

We acknowledge support by Deutsche Forschungsgemeinschaft and Open Access Publishing Fund of Karlsruhe Institute of Technology.

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