How to be Successful in Football : A Systematic Review

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.


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 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.1Not 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 research 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 24 th , 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.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.

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).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.

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.

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. 3Oberstone [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.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.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].

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].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.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].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].

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.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].

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 R 2 =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).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.

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.
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.

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

Table 6 . Predictive analyses with regard to wins and losses.
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

Table 7 . Predictive analyses with regard to goal scoring.
Pollard and Reep 1997 22 matches World Cup 1986 Video analysisCalculation 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

Table 9 . Analyses of home advantage.
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 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