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


Article Metrics

CrossRef Citations:
0
Total Statistics:

Full-Text HTML Views: 1438
Abstract HTML Views: 652
PDF Downloads: 499
ePub Downloads: 453
Total Views/Downloads: 3042
Unique Statistics:

Full-Text HTML Views: 553
Abstract HTML Views: 238
PDF Downloads: 263
ePub Downloads: 177
Total Views/Downloads: 1231



© 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


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.



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] 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 [2]. 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 [3, 4].

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 [5]. In 1912, Fullerton did the first work in this area of performance analysis for baseball [6]. In football, Reilly and Thomas [7] 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 [8], 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 [9] 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 [5]. Various themes were discussed such as team management, computer-aided performance analysis and decision-making by referees [10]. In the following years, the numbers of research papers concerning football and performance analysis increased steadily [11-15]. Hughes and Bartlett [16] 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 [6, 17].

In the context of this paper, two review studies regarding performance analysis in football are noteworthy. Mackenzie and Cushion [18] 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 [19] 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 [20] introduced network analysis into the research about success factors and Collet [21] revealed new insights into the effect of ball possession using an ordered-logit regression. Liu, Gomez, Lago-Penas and Sampaio [22] 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 [23]. 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.
Keyword 1 OR Keyword 1 AND Keyword 2
soccer football possession
soccer football goal
soccer football pass
soccer football success
soccer football shot
soccer football sprint
soccer football duel
soccer football corner
soccer football win
soccer football lose
soccer football loss
soccer football performance indicator
soccer football match performance
soccer football indicator
soccer football distance
soccer football home advantage

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 [18].

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 [23].

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. [19] and Marcelino, Mesquita, and Sampaio [24]. In the first step, the articles were assigned to [25, 26] comparative, [27] predictive or Home Advantage (HA) analyses [28]. 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.
Variables of interest
Design Win/
Loss
Goal Difference Goals League / Tournament Ranking Points Continuance in League/
Tournament
Row Total
Comparative 7 2 1 9 1 2 22
Predictive 14 5 7 3 3 32
Total*3 21 7 8 12 4 2 54
Home advantage 20 20
* Multiple responses possible.

Of the articles, 30 were predictive analyses, 22 were comparative analyses, and 20 focused on the analysis of home advantage. One of the articles [15] 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 [29-31] the authors tried to find variables that explain differences between winners and losers. Broich et al. [29] 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 [31]. 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. [30] 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 [27, 32, 33]. These studies reported various significant differences between winning, drawing and losing teams. Winners have more entries into the penalty area (p<0.01) [33], more successful attacks (p=0.003) and passes (p=0.015) as well as a higher ball possession rate (p=0.001) [32]. Armatas et al. [27] 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 [34]. 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 [15] used comparative and predictive methods; Mechtel et al. [25] used win/loss and goal difference; Collet [21] used win/loss and points; Carmichael and Thomas [26] used predictive methods and home advantage; Armatas, Yiannakos, Papadopoulou and Skoufas [27] used comparative methods and home advantage; Lago-Penas, Gomez-Ruano, Megias-Navarro and Pollard [28] used predictive methods and home advantage.

Table 3. Comparative articles with regard to wins and losses.
Author(s) Year Sample Data Collection Key Findings
Carron, Bray and Eys 2002 Nine football teams in Canada GEQ questionnaire and secondary data Individual attraction to group-task with significant performance link (p<0.05); group-integration-task not significant; both with high effect size (1.94 und 1.16)
Szwarc 2007 Seven finals European Champions League 1997-2003 Video analysis Efficiency of shots (p<0.05) and goalkeeper efficiency (p<0.01) significant higher in the winners; Losers significant more efficient in general defense (p<0.05) such as interrupt of action, intercepting pass with ball
Armatas, Yiannakos, Papadopoulou and Skoufas 2009 240 matches in first division of Greece 2006-2007 Video analysis 71.4% of the teams that score the first goal win the match
Kapidžić, Mejremić, Bilalić and Bečirović 2010 13 matches European Championship 2008 and 12 matches first division Bosnia and Herzegovina 2008-2009 Secondary data European Championship: winners score more goals, and more shots on goal within penalty area
First division: winners perform more successful passes, shots on goal, goals, throw-in and offensive actions
Janković, Leontijević, Pašić and Jelušić 2011 60 matches World Cup 2010 Secondary data Winning teams perform more successful attacks (ending with a shot) and passes than losing team and in draws; winners have more ball possession and pass accuracy compared to losers
Ruiz-Ruiz, Fradua, Fernandez-Garcia and Zubillaga 2013 64 matches Word Cup 2006 Video analysis Winners perform more entries into penalty area as teams in draws and losing teams
Broich, Mester, Seifriz and Yue 2014 118 matches first division Germany 2013-2014 Secondary data Goal efficiency, shots, passes and ball contacts (in this order) are the most important team parameters for wins

In nine of the articles, the authors compared teams with different positions in the league/tournament ranking (Table 4). Luhtanen, Belinskij, Häyrinen and Vänttinen [35] 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 [15, 36, 37]. This parameter corresponds to Broich et al.’s [29] ‘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 [38], have more successful passes [15, 38, 39], score more goals [36, 37, 40], perform more crosses [15, 40], have more ball possession [37, 39], shoot more often on the goal [37, 39], have more assists [36, 37, 39] and take more shots [15, 37, 39, 40]. The best teams in the league also perform fewer fouls [15] and allow fewer shots and crosses [40]. The worst ranked teams have fewer counter attacks, have less possession with zero to four passes and have less possession longer than 12 seconds [41]. Worse teams also have more very high-intensity running, high-intensity running and total distance covered [39]. Better teams cover more total distance with the ball and very high-intensity running with the ball [39]. Furthermore, the top teams show a faster recovering (recapture is 1.3 to 1.7 seconds faster than mean times) of ball possession [42]. Obviously, top teams score more goals per match [15, 36, 37, 40]. 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.
Author(s) Date Sample Data Collection Key Findings
Luhtanen, Belinskij, Häyrinen and Vänttinen 2001 31 matches European Championship 1996 – 2000 Video analysis Interceptions and success rate interceptions and defensive actions have highest correlation with final ranking (1996). % Successful passes and % successful goals attempts (2000)
Armatas, Yiannakos, Zaggelidis, Skoufas, Papadopoulou and Fragkos 2009 10 seasons second division in Greece Secondary data Top ranked less shot per goal, more goals, more shots in penalty area and more assists
Oberstone 2009 380 matches in first division England 2007-2008 Secondary data Goals per match, number of shots, short passes, total passes, pass completion are higher for better teams; goals conceded per match and fouls are lower for better teams
Rampinini, Impellizzeri, Castagna, Coutts and Wisloff 2009 416 matches in first division Italy 2004-2005 Video analysis Worse teams more total distance, high-intensity running (>14km/h) and very high intensity running (>19km/h); Top teams more total distance with ball and high intensity running with ball, more short passes, tackles, dribbles, shots and shots on goal
Lago-Ballesteros and Lago-Peñas 2010 380 matches in first division Spain 2008-2009 Secondary data Top teams more goals, shots and shots on goal; worse teams need more shots per goal
Janković, Leontijević, Jelušić, Pašić and Mićović 2011 228 matches in first division Serbia 2009-2010 Video analysis Successful attacks (end up with a shot) and pass rate higher for top teams; top teams kick the ball more often form 0-11m to the goal
Tenga and Sigmundstad 2011 997 goals from 1922 matches in first division in Norway 2008-2010 Video analysis Worst teams less goals through counterattack, less possession with 0-4 passes, less possession for 12 seconds or more and less possession started in the midfield
Bekris, Mylonis, Sarakinos, Gissis, Gioldasis and Sotiropoulos 2013 240 matches in first division Greece Secondary data Goals per match, shots, shots in penalty area, crosses and assists are higher for top teams; they conceded less shots, shots in penalty area and crosses
Vogelbein, Nopp and Hoekelmann 2014 306 matches in first division Germany Video analysis Top teams have a faster recovering of ball possession after losing it (defensive reaction time)

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 [43, 44]. Bekris et al. [43] 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. [44] 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 [45] and Delgado-Bordonau, Domenech-Monforte, Guzmán & Mendez-Villanueva [46] 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 [45] 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. [46] 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 [17] 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 [17]. Hoppe, Slomka, Baumgart, Weber & Freiwald [47] 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 [47].

Table 5. Comparative articles with regard to other operationalization of success.
Author(s) Date Sample Data Collection Key Findings
Hughes and Franks 2005 52 matches World Cup 1990 Secondary data Variable of interest is goal scored; successful teams are better in converting possession into shots on goal; for possession with more than 8 passes there is a significant (p<0.05) better chance for successful teams to create a shooting opportunity; shots necessary for a goal increased with more passes per possession
Clemente 2012 208 matches World Cup 2010 Secondary data Variable of interest is continuance in tournament; teams with more matches score more goals per match, through open play, from within the penalty area, and play more passes
Delgado-Bordonau, Domenech-Monforte, Guzmán and Mendez-Villanueva 2013 56 matches World Cup 2010 Secondary data Variable of interest is continuance in tournament; successful teams score more goals, perform more shots, have better efficiency, conceded less goals per match, conceded less shots; during group stage, teams scoring the first goal had a 66.7% chance to win (81.3% for knockout stage)
Bekris, Gioldasis, Gissis, Komsis and Alipasali 2014 64 matches European Leagues 2013-2014 Video analysis Variable of interest is goal difference; wide range results: winners have better performance in duels (aerial and overall), ball possession, passes, shots, shot accuracy, shots on goal;
comparison of wide range with short range winners: wide range winners perform more passes, shots, and have a higher passing accuracy and more shots on goal
Yue, Broich and Mester 2014 74 matches in first division Germany 2011 Secondary data Variable of interest is goal difference; in matches with a goal difference of 2 and more or with 3 and more the most important factors are efficiency, shots, passes and ball contacts (in this order); correlation of this four factors with number of goals shows the same result
Hoppe, Slomka, Baumgart, Weber and Freiwald 2015 306 matches in first division Germany 2012/13 Secondary data Variable of interest is points accumulated; total distance with ball possession only significant predictor for final points accumulated (p<0.01)

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 [48-51]. Shots on goal was a discriminant factor in all four studies. Crosses, match location and ball possession [48, 49] as well as the quality of the opponent (similar to strength or team ability) [49] were other identified factors. Collet [21] and Harrop and Nevill [52] 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 [52]. Collet [21] 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 [21, 29, 31, 44, 46]. Liu, Gomez, Lago-Penas and Sampaio [22], Liu, Hopkins and Gomez [53] and Mao, Peng, Liu and Gomez [54] 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 [22, 55]. They showed that shots on goal, shot accuracy, tackles and aerial advantage have positive effects on winning [22, 54]. Liu et al. [53] 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 [53].

Table 6. Predictive analyses with regard to wins and losses.
Author(s) Date Sample Data Collection Key Findings
Torgler 2004 63 matches World Cup 2002 Secondary data Higher number of shots on goal higher probability to win than not to win; dismissal has strong negative effect; hosting the tournament is a strong advantage
Bar-Eli, Tenenbaum and Geister 2006 743 matches in first division Germany 1963-2004 Secondary data Chance of winning decreases after a red card dependent on match status and match location
Lago-Penas, Lago-Ballesteros, Dellal and Gomez 2010 380 matches in first division Spain 2008-2009 Secondary data Shots, shots on goal, effectiveness, assists, crosses, conceded crosses, possession and match location discriminate best between win, draw and lose
Lago-Penas, Lago-Ballesteros and Rey 2011 288 matches European Champions’ League group-stage 2007-2010 Secondary data Winners perform more shots, better effectiveness, more passes, higher possession and receive less cards; shots on goal, crosses, possession, match location and quality of opponent discriminate best
Mechtel, Baker, Brandle and Vetter 2011 2962 matches in first division Germany 1999-2009 Secondary data Players dismissal increase chance of winning for opponent; team strength (overall and at home) increase chance of winning
Castellano, Casamichana and Lago 2012 177 matches World Cup 2002-2010 Secondary data Shots, shots on goal, shots received and shots on goal received discriminate best
Gómez, Gómez-Lopez, Lago and Sampaio 2012 1900 matches in first division Spain 2003-2008 Secondary data Field subdivided in 19 zones; 7 variables recorded; factor analysis revealed four factors (First: Turnovers in Zone 5.2 and Crosses in zone 4; Second: Goals in zone 5.1, Shots in zone 5.1, Turnovers in zone 4 and Ball recover in zone 1; Third: Goals in zone 5.2, Shots in zone 5.2 and Ball recover in zone 1; Fourth: Turnovers in zone 5.1), factors highest for winners; draw data closer to lose
Collet 2013 6172 matches from several leagues and tournaments Secondary data More time with ball leads to more points and goals; passes and pass accuracy correlate with points and goals; more points on smaller pass to shots on goal relation; if team strength is controlled negative effect for possession; pass and shot accuracy are better predictors
Harrop and Nevill 2014 46 matches in second division England 2012-2013 Secondary data Less passes (p=0.006), more successful passes (p=0.042), more shots (p=0.027), less dribbles (p=0.018) and the match location (p=0.044) are significant in prediction of success; passes (p=0.000), successful passes (p=0.001), and passes in opposition half (p=0.005) are different between wins, draws and losses
Moura, Martins and Cunha 2014 96 matches in group stage World Cup 2006 Secondary data Cluster analysis to generate two groups of data; 70.3% of the winning team were classified into the same group; shots, shots on goal and possession discriminate best the winning teams
Hanau, Wicker and Soebbing 2015 306 matches in first division Germany 2010-2011 Secondary data Actual winning is influenced by difference in ranking last year and home match
Liu, Gomez, Lago-Penas and Sampaio 2015 48 matches World Cup 2014 Secondary data Shots, Shots on goal, Shots from Counter Attack, Shot from Inside Area, Ball Possession, Short Pass, Average Pass Streak, Aerial Advantage and Tackle clear positive effects on winning,
Shots Blocked, Cross, Dribble and Red Card negative relationship to winning
Liu, Hopkins and Gomez 2016 320 matches in first division Spain 2012/13 Secondary data Shots on target and total shots have positive within team effect to winning;
ball possession: small negative within-team effect but a small positive between-team effect;
Game location showed a small positive within-team effect;
Within-team effects varied depending on the strength of team and opposition.
Mao, Peng, Liu and Gomez 2016 480 matches in first division China 2014-2015 Secondary data Shots on goal (positive), shot accuracy (positive), cross accuracy (trivial), tackle (trivial) and yellow cards (trivial) have effects on winning

Gómez, Gómez-Lopez, Lago and Sampaio [56] 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 [57] and Mechtel et al. [25] 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. [25] also identified strength (points earned in the last three seasons) and home advantage as success factors. Torgler [58] 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 [58]. Hanau, Wicker and Soebbing [59] 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 [12] 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 [12]. Carmichael and Thomas [26] 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 [60] also identified shots on goal as a significant predictor for goal scoring (p=0.027). Wright, Atkins, Polman, Jones and Sargeson [61] postulated position of attempt, goal keepers’ position and type of shot as the three predictors for goal scoring. Tenga, Holme, Ronglan and Bahr [62] and Tenga, Ronglan and Bahr [63] 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 [20] 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 [20].

Table 7. Predictive analyses with regard to goal scoring.
Author(s) Date Sample Data Collection Key Findings
Pollard and Reep 1997 22 matches World Cup 1986 Video analysis Calculation of “yield” (probability of a goal being scored, minus the probability of one being concede); starting zone of ball possession, open/set play and playing strategy as factors for the yield calculation; open play higher yield as set play; the closer it gets to the opponent goal the higher the yield
Carmichael and Thomas 2005 380 matches in fist division England 1997-1998 Secondary data Attacking play seems more important for home team and defensive play for away teams; shots on goal, tackles, free kicks and cards given are important factors
Kapidžić, Bećirović and Imamović 2009 31 matches European Championship 2008 Secondary data Shots within penalty area are the only significant single predictor (p=0.003),; shots on goal, shots off goal, shots blocked, pass completion, long, middle and short passes and completion explained 36% of the variance
Tenga, Holme, Ronglan and Bahr 2010 163 matches in first division Norway 2004 Video analysis More goals during counter attacks; counter attacks better than elaborate attacks; attacks starting in the last third better as first third; long possession is better than short possession
Tenga, Ronglan and Bahr 2010 163 matches in first division Norway 2004 Video analysis Counter attacks better than elaborate attacks; scoring
opportunities and score box possessions (shooting opportunities) can be used as a proxy for goals scored under certain circumstances
Wright, Atkins, Polman, Jones and Sargeson 2011 167 goals in first division England 2010-2011 Video analysis Three factors are significant predictors of goal success (p<0.05),: position of attempt, goal keepers’ position and type of shoot
Grund 2012 76 matches in first division England 2006-2008; 283,259 passes to create network Secondary data A clear network intensity effect is found. Increases in the passing rate lead to increased team performance. a clear network centralization effect is present; Increases in the centralization of team play lead to decreased performance

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 [13, 25, 64, 65]. 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) [25, 64, 65]. Moreover, Carmichael et al. [13] 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 [13, 25, 64]. Garcia-Rubio et al. [65] showed that scoring first is the strongest predictor for a positive goal difference. Lago-Penas, Gomez-Ruano, Megias-Navarro and Pollard [28] 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 [28]. Oberstone [15], Hall, Szymanski and Zimbalist [66], and Kringstad and Olsen [67] investigated relevant factors for the league ranking in a predictive design. Hall et al. [66] 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 [15] 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 [67] 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 [68] 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 [21] 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 [69] 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 [69].

Table 8. Predictive analyses with regard to other operationalization of success.
Author(s) Date Sample Data Collection Key Findings
Carmichael, Thomas and Ward 2000 380 matches in first division England 1997-1998 Secondary data Variable of interest is goal difference; fixed effects for relative performance of teams; match location, differences in successful passes, passes in penalty area, tackles, clearances, blocks, interceptions, free kicks, red card and ball caught by goalkeeper are significant predictors (p<0.05)
Hall, Szymanski and Zimbalist 2002 39 teams in the first four divisions England 1974-1999 Secondary data Variable of interest is league ranking; 50% more spending in payroll leads to 0,614 higher winning probability; Granger causality from higher payrolls to better performance
cannot be rejected
Lago-Penas 2007 64 matches World Cup 2006 Germany Secondary data Variable of interest is points earned; performance (shots minus shots conceded) is a predictor for more points; the higher the FIFA-Ranking, the higher the chance to win
Papahristodoulou 2008 806 matches European Champions League 2001-2007 Secondary data Variable of interest is goal difference; goals are an effect of shooting; red cars are negative for winning probability; match location important for winning probability
Oberstone 2009 380 matches in first division England 2007-2008 Secondary data Variable of interest is league ranking; % goals to shot, % goals outside penalty area, proportion (ratio) short/long passes, total crosses, average goals conceded per match and yellow cards are sufficient to predict league ranking/point earned
Mechtel, Baker, Brandle, and Vetter 2011 2962 matches in first division Germany 1999-2009 Secondary data Variable of interest is goal difference; players’ dismissal increase chance of winning for opponent; team strength (overall and at home) increase chance of winning
Collet 2013 6172 matches from several leagues and tournaments Secondary data Variable of interest is points earned; higher ball possession leads to more points and goals; passes and pass accuracy correlate with points and goals; more points with lower pass-to-shots-on-goal-ratio (how many passes before a shot); if team strength is controlled there is a negative effect for possession; pass and shot accuracy are better predictors
Garcia-Rubio, Gomez, Lago-Penas and Ibanez 2015 475 matches European Champions League 2009-2013 Secondary data Variable of interest is points earned; Positive influence of match location, scoring first and quality of opposition in match outcome,
scoring first strongest predictor then match location, then quality of opposition,
Structural coefficient significant underlines that teams that score first achieve more shots on goal in both stages of competition (p<0.01)
Coates, Frick and Jewell 2016 138 team year observations in first division USA 2005-2013 Secondary data Variable of interest is points earned; Negative relationship between salary inequality and team success; the
best-fit model suggests that increasing salary inequality and the team wage bill work in opposite directions by similar magnitudes
Kringstad and Olsen 2016 720 matches in first division Norway 2011-2013 Secondary data Variable of interest is league ranking; Budgeted revenues are a significant factor of success for the bottom-half teams but not for the top-half teams (p<0.05); money could be a significant driver of success, but only to a certain extent
Lago-Penas, Gomez-Ruano, Megias-Navarro and Pollard 2016 1826 matches in France, Italy, Spain, England and Germany 2014/15 Secondary data Three independent variables were significant factors on the final outcome: the quality of the opposition (p<0.001), the minute in which the first goal is scored (p<0.01) and the team scoring first (p<0.001); teams that scored first scored 1.88 goals more than the opponent

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 [25, 26, 48, 49, 53, 58, 57, 64]. 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 [26] further factors related to success, besides home advantage, were also investigated. The first analysis of home advantage in football was done by Pollard [70]. 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 [11] 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 [25, 49, 64]. Clarke and Norman [11] 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 [11]. Overall, home advantage explains around 60 percent with some variations [71-86] (Table 9). Before the 1980s, the explaining percentage of home advantage was moderately higher [71]. Saavedra Garcia, Aguilar, Fernández Romero and Sa Marques [72] 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. [28] 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.
Author(s) Date Sample Key Findings
Pollard 1986 58,123 matches in England 1888-1984 Little variation between the centuries and divisions; no difference between two- and three-point system; home advantage in percent of obtained point is around 64%; local derbies show significant lower home advantage (p<0.01)
Clarke and Norman 1995 20,306 matches in England 1981-1991 Home advantage in terms of goals per match; team ability included; home advantage 0.528 goals per match in average
Thomas, Reeves, and Davies 2004 7834 matches in England 1985-2003 Slightly lower home advantage in recent years (2%-5% lower); home advantage still stable phenomenon
Carmichael and Thomas 2005 380 matches in England 1997-1998 57% of the points obtained at home; home teams won 48% of the matches
Pollard and Pollard 2005 Over 70,000 matches in England 1888-2003 Home advantage was highest in the early years of each league; home advantage seems stable around 60% of the point obtained at home
Pollard 2006 89813 matches around the world 1997-2003 Home advantage is found in all big leagues in the world; in the Balkan countries and in the Andean region home advantage is much higher; home advantage varies from 48.87 (Andorra) to 78.95 (Bosnia) around the world
Pollard, Silva, and Medeiros 2008 2326 matches in Brazil 2003-2007 Average home advantage 65%, calculated by the points obtained at home; north and south teams have a higher advantage
Seckin and Pollard 2008 3672 matches in Turkey 1994-2006 61.5% average home advantage; calculated by the points obtained at home; local derbies (matches in Istanbul) show lower home advantage
Armatas, Yiannakos, Papadopoulou, and Skoufas 2009 240 matches in Greece 2006-2007 47.3% of the matches are won by home team, 26.3% draws and 26.4% won by away team
Pollard and Gomez 2009 81,185 matches in France, Italy, Spain and Portugal 1928 (or beginning) -2007 About 66% average home advantage of the points obtained at home; recent general decline in home advantage since the 1980s; home advantage in Spain highest with an average of 69%; increased home advantage for teams from islands; lower home advantage in capital cities
Poulter 2009 808 matches in European Champions League 2001-2007 Home teams won 67.7% of the matches; home team is 1.98 times more likely to score in match than the away team; home teams perform more shots, shots on goal and corners; away teams have more fouls committed, offside and cards
Sanchez, Garcia-Calvo, Leo, Pollard, and Gomez 2009 20,992 matches in Spain 1980-2007 About 66% average home advantage calculated by the points obtained at home; slightly significant decrease of home advantage after introduction of the 3-point system (p=002)
Lago-Penas and Lago-Ballesteros 2011 380 matches in Spain 2008-2009 61.95% victories for home and 38.05% victories for guests (draws excluded); 4 groups according to league ranking; inferior teams benefit less from home advantage than superior teams
Armatas and Pollard 2014 2160 matches in Greece 1994-2011 About 65% average home advantage calculated by the points obtained at home; shots, clearances, headed shots, corners and saves have highest effect size for match variables between home and away teams
Goumas 2014a 765 matches in Australia 2005-2012 57.7% average home advantage of the points obtained at home and 56.5% home advantage in terms of goals scored; home advantage increases with increasing time zones crossed by away teams
Goumas 2014b 1384 matches in European Champions League and Europa League 58.8% (CL) and 58.0 (EL) home advantage in terms of goals scored; in terms of competition points gained in the group stage home advantage was
57.8% in the CL and 59.2% in the EL; crowd density is important in influencing referee bias; more yellow cards against away teams
Goumas 2014c 3277 matches in Europe, Asia, South America and Africa 2007-2013 59% (Europe), 60% (Asia), 63% (South America) and 70% (Africa) home advantage in terms of goals scored; absolute distance travelled and time zones crossed associated with poorer match performance
Saavedra García; Gutiérrez Aguilar, Fernández Romero and Sa Marques 2015 22015 matches in Spain 1928-2011 70.8% average home advantage for the period when 2 points were awarded for a victory;
56.7% average home advantage when three points were awarded for a victory
Goumas 2015 1058 matches European Champions League 2003-2013 Home advantage measured on a team level; home advantage did not vary between teams despite 58% for Inter Milan and 73% for Arsenal London; away disadvantage vary between teams significantly (p<0.05); tendency of higher home advantage and lower away disadvantage; home advantage differs significant between countries 70% English teams to 52% Turkish teams (p=0.01)
Lago-Penas, Gomez-Ruano, Megias-Navarro and Pollard 2016 1826 matches in France, Italy, Spain, England and Germany 2014/15 Results showed that home teams scored first in 57.8% of matches and went on the obtain 84.85% of points; Away team scored first, they obtained only 76.25% of subsequent points

Lago-Penas and Lago-Ballesteros [73] 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 [74] found shots, clearances, headed shots, corners and saves to have the highest effect size for match variables between home and away teams. Goumas [75] 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) [75]. 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 [70, 76, 77].

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 [9], analyses with predictive designs, explaining more and more success factors [21, 22, 49], 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 [22, 29, 46], shots on goal [49, 54], possession [39], pass accuracy/successful passes [32, 35], quality of opponent [25, 28, 64], and match location [49, 76, 65].4

It became apparent that performance in football depends on a high number of variables. For example, Oberstone [15] 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. [30] 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. [22] and Mao et al. [54] 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 [22, 54]. 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 [21] 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. [28] 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.
Country of Sample Study Design Total
Comparative Predictive Home Advantage
Australia 1 1
Brazil 1 1
Canada 1 1
England* 1 7 5 13
Germany 4 3 7
Greece* 3 2 5
International* 9 12 7 28
Italy 1 1
Norway 1 3 4
Serbia 1 1
Spain 1 3 3 7
Turkey 1 1
USA 1 1
China 1 1
Total5 22 30 20 72
* Multiple responses.

Methodologically, the review showed that in recent years new ways of statistical analyses were introduced. Lago et al. [48] were the first authors who used a discriminant analysis to identify differences between winners and losers. Moura et al. [51] 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. [22] 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 [22, 55]. The concept of close and unbalanced matches also improved the analysis of success factors in football [22, 29]. 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 [55, 87-91]. This concept was first introduced in a discrimination study about rugby in 2010 [55] and is widely used since then [22, 29, 55, 87-91].

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. [25] and Collet [21] 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. [22] and Mao et al. [54] 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 [22, 54]. This approach allows a more realistic and intuitive interpretation of effects [92]. 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. [30] who analyzed 25 matches, rely on small sample sizes. Of the reviewed papers, the sample sizes varied from seven matches [31] to 89,813 matches [76]. 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 [93] 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 [94] 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 [94, 95]. 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 [21, 22, 54]. Second, a focus on counter attacks can be very effective and can be utilized as a successful tactical strategy, especially for underdogs [41]. Ball recovery in the zone between a team’s own penalty area and center circle [56] and a quick ball recovery [42] 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 [62].

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. [57] 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 [96] 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 [21, 22, 25].

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 [18]; Sarmento et al. [19]). 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 [18] 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. [29]). 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 [97], 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 [20, 98, 99]. Second, psychological factors could be taken into account for future research (e.g., reversal theory, see Apter [100]). The investigation of psychological factors is, in fact, more difficult than the analysis of statistical data. The operationalization of cohesion found in this review [34] 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.

REFERENCES

[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
[2] UEFA.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
[3] Dufour 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.
[4] Reilly T, Williams M. Introduction to science and soccer. In: Reilly T, Williams AM, Eds. Science and Soccer. 2nd ed. Routledge 2003; 1–6.
[5] Hughes 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.
[6] Eaves JS. A history of sports notational analysis: A journey into the nineteenth century. Int J Perform Anal Sport 2015; 15(3): 1160-76.
[7] Reilly 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.
[8] Hughes M. Notational analysis. In: Reilly T, Williams AM, Eds. Science and Soccer. 2nd ed. Routledge 2003; 343–61.
[9] Reep C, Benjamin B. Skill and Chance in Association Football. J R Stat Soc [Ser A] 1968; 131(4): 581.
[10] Reilly 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.
[11] Clarke SR, Norman John M. Home ground advantage of individual clubs in English soccer. Statistician 1995; 44(4): 509-21.
[12] Pollard R, Reep C. Measuring the effectiveness of playing strategies at soccer. Statistician 1997; 46(4): 541-50. [https://doi.org/10.2307/2988603].
[13] Carmichael F, Thomas D, Ward R. Team performance: The case of english premiership football. MDE Manage Decis Econ 2000; 21(1): 31-45.
[14] Lago C, Martín R. Determinants of possession of the ball in soccer. J Sports Sci 2007; 25(9): 969-74.
[15] Oberstone 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)
[16] Hughes MD, Bartlett RM. The use of performance indicators in performance analysis. J Sports Sci 2002; 20(10): 739-54.
[17] Hughes 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].
[18] Mackenzie 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].
[19] Sarmento 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.
[20] Grund 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].
[21] Collet 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].
[22] Liu 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
[23] Moher 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].
[24] Marcelino 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].
[25] Mechtel 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.
[26] Carmichael 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].
[27] Armatas 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.
[28] Lago-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.
[29] Broich 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].
[30] Kapidžić 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.
[31] Szwarc A. Efficacy of successful and unsuccessful soccer teams taking part in finals of Champions League. Research Yearbook 2007; 13(2): 221-5.
[32] Janković 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).
[33] Ruiz-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].
[34] Carron 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].
[35] Luhtanen 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.
[36] Armatas 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.
[37] Lago-Ballesteros J, Lago-Peñas C. Performance in team sports: Identifying the keys to success in soccer. J Hum Kinet 2010; 25(-1)
[38] Janković 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.
[39] Rampinini 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].
[40] Bekris 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].
[41] Tenga 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.
[42] Vogelbein 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].
[43] Bekris, 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].
[44] Yue 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].
[45] Clemente FM. Study of successful teams on FIFA world cup 2010 through notational analysis. PJSS 2012; 3(3): 90-103.
[46] Delgado-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.
[47] Hoppe 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].
[48] Lago-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.
[49] Lago-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.
[50] Castellano J, Casamichana D, Lago C. The use of match statistics that discriminate between successful and unsuccessful soccer teams. J Hum Kinet 2012; 31: 139-47.
[51] Moura 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].
[52] Harrop 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.
[53] Liu 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].
[54] Mao 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.
[55] Vaz 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].
[56] Gómez 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].
[57] Bar-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].
[58] Torgler 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].
[59] von 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.
[60] Kapidžić A, Bećirović E, Imamović J. Situational efficiency analysis of the teams that participated in 2008 European football championship. Sport Sci Pract Asp 2009; 38.
[61] Wright 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.
[62] Tenga 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].
[63] Tenga 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].
[64] Papahristodoulou C. An analysis of Champions League match statistics. IJASS 2008; 20(1): 67-93.
[65] Garcia-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.
[66] Hall 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.
[67] Kringstad 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].
[68] Lago-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.
[69] Coates 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].
[70] Pollard R. Home advantage in soccer: A retrospective analysis. J Sports Sci 1986; 4(3): 237-48. [https://doi.org/10.1080/02640418608732122].
[71] Thomas 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.
[72] Saavedra 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.
[73] Lago-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.
[74] Armatas 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].
[75] Goumas 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].
[76] Pollard R. Worldwide regional variations in home advantage in association football. J Sports Sci 2006; 24(3): 231-40. [https://doi.org/10.1080/02640410500141836].
[77] Pollard 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].
[78] Pollard R, Pollard G. Long-term trends in home advantage in professional team sports in North America and England (1876-2003). J Sports Sci 2005; 23(4): 337-50. [https://doi.org/10.1080/02640410400021559].
[79] Pollard R, Silva CD, Medeiros NC. Home advantage in football in Brazil: Differences between teams and the effects of distance traveled. Revista Brasileira de Futebol 2008; 1(1): 3-10. [The Brazilian Journal of Soccer Science].
[80] Seçkin A, Pollard R. Home advantage in Turkish professional soccer. Percept Mot Skills 2008; 107(1): 51-4.
[81] Pollard R, Gómez MA. Home advantage in football in South-West Europe: Long-term trends, regional variation, and team differences. Eur J Sport Sci 2009; 9(6): 341-52. [https://doi.org/10.1080/17461390903009133].
[82] Poulter DR. Home advantage and player nationality in international club football. J Sports Sci 2009; 27(8): 797-805. [https://doi.org/10.1080/02640410902893364].
[83] Sánchez PA, García-Calvo T, Leo FM, Pollard R, Gómez MA. An analysis of home advantage in the top two Spanish professional football leagues. Percept Mot Skills 2009; 108(3): 789-97. [https://doi.org/10.2466/PMS.108.3.789-797].
[84] Goumas C. Home advantage in Australian soccer. J Sci Med Sport 2014; 17(1): 119-23. [https://doi.org/10.1016/j.jsams.2013.02.014].
[85] Goumas C. Home advantage and referee bias in European football. Eur J Sport Sci 2014; 14(1)(Suppl. 1): S243-9. [https://doi.org/10.1080/17461391.2012.686062].
[86] Goumas C. Tyranny of distance: Home advantage and travel in international club football. Int J Perform Anal Sport 2014; 14(1): 1-13.
[87] Gómez 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.
[88] Lupo C, Condello G, Capranica L, Tessitore A. Women’s water polo world championships: Technical and tactical aspects of winning and losing teams in close and unbalanced games. J Strength Cond Res 2014; 28(1): 210-22.
[89] Gomez MA, Gasperi L, Lupo C. Performance analysis of game dynamics during the 4th game quarter of NBA close games. Int J Perform Anal Sport 2016; 16(1): 249-63.
[90] Lupo 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.
[91] Lupo 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.
[92] Hopkins 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.
[93] Bishop D. An applied research model for the sport sciences. Sports Med 2008; 38(3): 253-63.
[94] Nash C, Collins D. Tacit knowledge in expert coaching: Science or art? Quest 2006; 58(4): 465-77.
[95] Sternberg RJ. Wisdom, intelligence, and creativity synthesized. Cambridge University Press 2003.
[96] Borrie 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].
[97] James N. Predicting performance over time using a case study in real tennis. JHSE. 2012;7(2)
[98] Wäsche H, Dickson G, Woll A, Brandes U. Social network analysis in sport research: an emerging paradigm. EJSS 2017; 14(2): 138-65.
[99] Duch 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].
[100] Apter M. Reversal theory and personality: A review. J Res Pers 1984; 18(3): 265-88.