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Article

The Limited Impact of Running Performance on Football Success in the Turkish Super League

by
Spyridon Plakias
1,
Sotiris Tasoulis
2,
Angelos E. Kyranoudis
3,
Christos Kokkotis
4 and
Serafeim Moustakidis
5,*
1
Department of Physical Education and Sport Science, University of Thessaly, GR-42100 Trikala, Greece
2
Department of Computer Science and Biomedical Informatics, School of Sciences, University of Thessaly, GR-35100 Lamia, Greece
3
Department of Physical Education and Sport Science, Aristotle University of Thessaloniki, GR-57001 Thessaloniki, Greece
4
Department of Physical Education and Sport Science, Democritus University of Thrace, GR-69100 Komotini, Greece
5
AIDEAS OÜ, 10117 Tallinn, Estonia
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(2), 637; https://doi.org/10.3390/app15020637
Submission received: 9 December 2024 / Revised: 6 January 2025 / Accepted: 9 January 2025 / Published: 10 January 2025
(This article belongs to the Special Issue Human Performance in Sports and Training)
Browse Figure
Versions Notes

Abstract

:
Given that performance in football depends on tactical, technical, physical, and mental skills, the purpose of this study was to investigate whether there are differences in running performance between winning and non-winning teams in the Turkish League, taking into account the influence of game location and the comparative quality of the team and its opponents. Utilizing a dataset from the 2021–2022 season provided by InStat Fitness, an optical tracking technology platform certified by FIFA, the analysis included 185 matches after adjusting for matches with red card incidents. The research employed both two-way ANCOVA and binary logistic regression analyses to explore the relationships between running performance (categorized into four intensity zones) and match results, considering factors such as match location and teams’ strength. The results of the two-way ANCOVAs indicate that running performance metrics, specifically the distances covered at different intensities, even in cases where statistically significant differences are observed, have small practical significance (partial eta squared ≤ 0.03 in all cases). Conversely, as shown by the binary logistic regression, home advantage triples the probability of winning (p < 0.001, Exp(B) = 3.119), while the increase in probability caused by team quality (p < 0.001, Exp(B) = 1.085) and the decrease caused by opponent quality (p < 0.001, Exp(B) = 0.911) are also significant. The conclusions highlight that running performance metrics are not decisive predictors of match outcomes in professional football. This suggests the importance of integrating tactical, technical, and psychological factors into team preparation and performance analysis. This study underscores the need for future research to adopt dynamic methods that reflect the game’s fluid nature and to explore these relationships across various leagues and seasons to enhance the generalizability of the findings.

1. Introduction

Performance analysis in football is a field of extensive research, giving valuable information to team coaches [1,2]. The ultimate goal of performance analysis is to improve performance that depends on physical, technical, tactical, mental and psychological factors [3,4,5]. Time–motion analysis (TMA) is used to study physical performance. This term has been introduced in performance analysis to determine the kinematic parameters of athletes during specific time intervals. TMA involves the systematic examination of athletes’ movements and activities during a sporting event to understand the physical and physiological demands of the sport, typically assessing metrics such as the total distance covered, time spent in different activities, and frequency of activities [6,7,8]. Early research on TMA was carried out through notational analysis, which represents an economic approach to understand the physical and technical requirements of sports. However, the accuracy of this method varies depending on the experience of the observer and the quality of observation [9]. The solution to these problems was provided by modern technologies (GPS, optical systems, and local systems) contributing to training optimization and competitive performance enhancement [10,11]. These systems, which, in 2015, FIFA named Electronic Performance and Tracking Systems (EPTS) [12,13], can accurately provide a large amount of data concerning running performance [14,15,16].
In exploring the impact of running performance on team success, several studies have underscored its significant contribution. For instance, Radziminski et al. [17], analyzing 622 matches from the Polish Ekstraklasa across three consecutive seasons (2017–2020), highlighted that winning teams covered greater total and sprinting distances. Similarly, Klemp et al. [18] connected higher running performance with a greater probability of scoring the first goal, positing that such performance can lead to tactical and fatigue-based advantages over opponents in German Bundesliga during the seasons 2011/2012 to 2016/2017. Moalla et al. [19] used a sample comprising 52 official matches of a professional soccer team from Stars League over two seasons (2013–2014 and 2014–2015). They also found that winning teams exhibited higher low-intensity running distances. Furthermore, Chmura et al. [20] and Konefał et al. [21] used as a sample matches of the German Bundesliga during three seasons (2014–2017). They both found correlations between high-intensity running and winning outcomes. These studies underscore the potential of superior physical performance as a contributory factor to successful match outcomes.
Conversely, several studies challenge the direct correlation between running performance and soccer success, suggesting a more complex relationship influenced by tactical and technical factors. Clemente et al. [22] analyzed matches from the Spanish first division (La Liga). They found no significant differences in physical performance between more and less successful teams, indicating that technical skills and tactical effectiveness might play more decisive roles. Modric et al. [23] and Modric et al. [24] observed that physical metrics like total distance covered and high-intensity efforts were not decisive for winning in elite UEFA Champions League matches (2020/2021 season), suggesting that other aspects such as tactics could be more critical. Similarly, Morgans et al. [25] reported a minimal impact of running metrics on team success in the Russian Premier League during the 2016–2017 to 2020–2021 seasons. On the other hand, they found that some technical performance variables had a great effect on match outcome.
Furthermore, many studies show the effect of situational variables on the success of teams. Research has shown that the match location plays an important role, with teams increasing the probability of success when playing at home [26,27,28,29]. Team strength has the same effect [28,29], while the results are opposite when the opponent’s strength increases [29,30]. The role of location in running performance is also a significant area of study. Konefał et al. [21] demonstrated that the home advantage notably increased the odds of winning, potentially due to players optimizing their physical output in familiar settings. Modric et al. [23] found that away matches involved greater total distances covered, suggesting adaptations in team strategy based on match location. In addition, Barrera et al. [31] noted that home matches saw players covering greater distances, likely due to the supportive environment boosting player performance.
Given the diverse findings in the literature, with some studies affirming while others denying the substantial impact of running variables on soccer success, a notable research gap remains regarding less-studied leagues, such as the Turkish Super League. These leagues exhibit unique tactical and physical characteristics that could provide insights distinct from those observed in higher-profile competitions. The specific claim has been confirmed by previous research data [1,32], while at the same time, it is commonly accepted that a country’s culture is one of the factors that determine the game model of teams [5]. This rationale underpins the importance of integrating such findings into league-specific training and performance analysis frameworks. In the Turkish Super League, factors influencing teams’ running performance [33] and the relationship between running performance and playing styles [34] have been studied, but the impact of running performance on teams’ success has not been investigated. Addressing this gap, the present study explores the Turkish context, offering a nuanced understanding of how running performance and situational factors shape match outcomes.
Therefore, this study aims to examine whether differences in running performance metrics, categorized by intensity zones, exist between winning and non-winning teams in the Turkish Super League. Specifically, this study addresses the following research questions: (i) How do the distances covered in different intensity zones (low, medium, high-speed, sprint) differ between winning and non-winning teams? (ii) What is the role of the home advantage in the influence of physical performance metrics on match outcomes? (iii) How do team strength and opponent strength impact physical performance and winning probabilities? We hypothesized that (1) winning teams will exhibit superior running performance across all intensity zones, and (2) home advantage and team strength will significantly influence these metrics. The hypotheses of this study are grounded in previous research exploring the relationship between physical performance and success in soccer. Studies such as those by Radziminski et al. [17] and Klemp et al. [18] have demonstrated that superior performance in high-intensity zones is associated with higher winning probabilities. Additionally, the impact of home advantage and team quality on success has been well documented by Pratas et al. [27] and Pérez–Sánchez et al. [29]. This body of evidence supports the assumptions underpinning this research.

2. Methodology

2.1. Study Design

This study is an observational, cross-sectional analysis conducted retrospectively. It utilizes match data from the 2021–2022 season of the Turkish Super League, focusing on the relationship between running performance metrics and match outcomes. The study design accounts for situational factors such as match location, team strength, and opponent strength and applies statistical methods to assess these variables.

2.2. Sample

The sample consisted of matches of the 2021–2022 season of the Turkish first division. This league is conducted in two rounds, with the participation of 20 teams over 38 matchdays (10 matches per matchday). Data from the first 24 matchdays (240 matches) were provided by the company Instatscout. Data were not available for two matches. From the remaining 238 matches, 53 were removed because at least one of the teams had a player sent off with a red card. Therefore, the sample ultimately consisted of 185 matches, i.e., 370 observations (2 observations, 1 for each team, per match).

2.3. Ethics

The current study received ethical approval from the local university’s (the University of Thessaly) ethics committee on 12 October 2022 (code, 1973). Additionally, written permission was granted by the company InStat Ltd. on 8 November 2022, authorizing the use of the data for research and publication purposes.

2.4. Procedure

The data were gathered using the optical tracking technology supplied by InStat Fitness (https://football.instatscout.com/, accessed on 22 August 2022). InStat’s optical tracking method is licensed by FIFA and has demonstrated high levels of both absolute and relative reliability [23,34]. A comprehensive analysis of its reliability is available on the official FIFA website [35]. For the 2021–2022 Turkish league season, InStat’s tracking system was the official EPTS [34].

2.5. Variables

In the current study, four running variables were used. These variables related to the distances covered by the players of the teams in four different intensity zones. The intensity zones were defined based on the distinction used in previous studies [23,34,36]. However, in the present research, all the distances covered by the players with a speed of up to 4 m/s were unified in a category called low intensity. In the context of team performance analysis, combining the zones reduces potential noise in the data caused by slight variations in measurement or categorization thresholds. The unified “low-intensity” category up to 4 km/h captures all recovery-related activities effectively, providing a more robust and meaningful parameter for statistical analyses. This is why other authors in previous research have also made the same merging of zones [33,37]. Table 1 presents the four running variables with their abbreviations.
Additionally, the binary variables Location (Home/Away) and Result (Win/No win) were used. The “No win” category included both losses and draws. Finally, to create variables representing the quality of the teams and their opponents (Team strength, Opp. strength), the positions of the teams in the league standings were reversed. Thus, the team in first place was assigned a value of 20, second place a value of 19, and so on down to the team in 20th place, which was assigned a value of 1. The strength of each team was determined based on their final position in the league table at the end of the 2021–2022 season, ensuring an accurate representation of their overall performance throughout the competition.

2.6. Statistical Analysis

After initially examining the normality assumption of running variables in each group using the Kolmogorov–Smirnov test, a two-way ANCOVA analysis was conducted. This analysis was performed four times, using each of the four running variables from Table 1 as the dependent variable. The independent variables used were Result and Location, while z-scores of Team strength and Opp. strength serving as covariates. For the variables Team strength and Opp. Strength, z-scores were used in order to meet the requirements for ANCOVA analysis, in which the covariates must be a continuous quantitative variable [38]. The null hypothesis that the error variance of the dependent variable is equal across groups was tested with Levene’s Test.
Subsequently, to determine which variables significantly contribute to team victories, a Binary logistic regression was conducted. In this analysis, the dependent variable was Result, and the independent variables included Location, Team strength, Opp. strength, and the four running variables.
All statistical analyses were performed using the Statistical Package for Social Sciences (SPSS IBM Corporation, Armonk, NY, USA, version 29.0). The significance level was set at p < 0.05. Partial eta squared was used as a measure of effect size. The effect sizes were classified as follows: very small 0–0.01; small 0.011–0.060; moderate 0.061–0.140; and large >0.140 [32,39].

3. Results

3.1. Descriptive Statistics

In Table 2, the descriptive statistics (means, standard deviations and cases) for the four running variables are presented by Location and by Result.

3.2. Two-Way ANCOVA

Table 3 shows the main effects of the variables Location and Result on the four running variables after adjusting for covariates. From the “sig” column, we find that a statistically significant difference exists only between Win/No win in the LOW variable (p = 0.005) and between Home/Away in the SPR variable (p = 0.02). However, from the value of the partial eta squared, it appears that the effect is small (0.021 and 0.015, respectively).
Table 4 and Figure 1 shows all the pairwise comparisons resulting from the interactions Location–Result of the four two-way ANCOVAs. From the ‘sig’ column, it is evident that, after adjusting for covariates, a statistically significant difference exists only (a) between the Win/No win conditions in away games for the LOW variable and (b) between the Home/Away conditions in non-winning games for the SPR variable. However, in both cases, the effect size is small, as indicated by the values of partial eta squared (0.03 and 0.011, respectively).

3.3. Binary Logistic Regression

The omnibus test was highly significant (χ2 = 71.638, df = 7, p < 0.000), suggesting that the model was a significant improvement over the intercept-only model. The overall model fit was evaluated using a −2 Log likelihood of 422.966, Cox & Snell R Square of 0.176, and Nagelkerke R Square of 0.239, indicating a moderate fit. The model’s accuracy in predicting match outcomes was assessed, showing that the overall percentage of correctly classified cases was 72.2%. Specifically, the model correctly predicted 52.8% of wins and 84.5% of non-wins. From Table 5, it is evident that the variables Location, Team strength, Opp. strength, and LOW significantly contribute to predicting the outcome. However, the LOW variable does not change the probability of winning (Exp(B) = 1). On the contrary, the following applies: (a) home games triple the probability of winning (Exp(B) = 3.119) compared to away games, (b) an increase in team strength by one unit leads to an 8.5% increase in the probability of winning, and (c) an increase in Opp. strength by one unit results in an 8.9% decrease in the probability of winning.

4. Discussion

In this study of the 2021–2022 Turkish Super League, the findings partially reject the initial hypotheses. Contrary to the first hypothesis, running performance metrics, including distances covered at varying intensity levels, were not significant predictors of match outcomes, as revealed by both the binary logistic regression and two-way ANCOVA analyses. While the LOW variable exhibited a statistically significant difference, its practical significance was negligible, failing to support the notion that superior running performance across intensity zones differentiates winning from non-winning teams. Conversely, the second hypothesis is supported, as situational factors such as match location (home advantage) and team/opponent strength significantly influenced the probability of winning.
Our main finding suggests that running performance does not significantly impact team success, which aligns with broader research indicating the limited direct influence of such metrics on match outcomes. For instance, Modric et al. [24] demonstrated that running performance had little correlation with team achievement in the UEFA Champions League. Similarly, Clemente et al. [22] found no notable differences in the physical performance of teams across different success levels in the Spanish first division, underscoring that tactical and technical factors might play more significant roles. The study by Modric et al. [23] further supports this, indicating the absence of a significant association between players’ running performance and the achievement of their teams.
Conversely, research indicating that running performance can decisively affect match outcomes often focuses on specific contexts or match scenarios where physical exertion aligns closely with tactical objectives. For example, Klemp et al. [18] linked high running performance with the likelihood of scoring the first goal in Bundesliga games, highlighting situations where physical intensity can directly influence game dynamics. Similarly, Konefał et al. [21] demonstrated that specific running metrics significantly impacted winning odds, especially when considering the match location and tactical setups. This perspective is enriched by studies like those of Moalla et al. [19], which tied higher physical activity levels to winning phases within matches, and Radziminski et al. [17], who found a positive correlation between effective playing time and physical metrics like sprinting distances in winning teams. These contrasting findings illustrate the complex interplay between physical performance metrics and soccer success, influenced by league characteristics, team strategies, and game-specific situations, emphasizing that the impact of running metrics is not universally applicable across all contexts of professional soccer [1,5,32].
Another important finding of the present research is that the location of the match as well as the strength of the team and the opponent significantly change the chances of a winning outcome. These findings are consistent with previous research. In particular, the existence of the home advantage is well known in soccer leagues worldwide [40]. This advantage has been confirmed by research for Turkish professional soccer as well [41]. The support of the crowd, familiarity with local conditions, travel fatigue, referee bias, and psychological factors can affect the performance of football players [42]. For these reasons, not only do bookmakers take the venue into account when setting odds, and the media frequently highlight the venue as an important factor in any result, but researchers also suggest that the effect of match location should be considered when studying the performance of teams or players [43].
Furthermore, recent research emphasizes the importance of team strength, including rankings, as a critical factor in predicting soccer match outcomes [44,45]. Many studies show that team ranking has a significant interaction with goal scoring [46,47], which is a major determining factor for the team’s victory [48]. In fact, Zhou et al. [49] found that an increase in the rank difference would increase the number of goal scoring, implying that the quality of the opponent has the opposite effect. This is also confirmed by the research of García-Rubio et al. [50], who found a negative effect of the opponent’s quality on the match outcome.
This study presents some limitations that should be considered when interpreting its findings. Firstly, we used a static method, as the data were collected at the end of the matches. This means that changes occurring during the game’s progress (match status) are not taken into account [48]. In contrast, the dynamic method more comprehensively covers the context of the match [51]. However, the static method is widely used in performance analysis and provides useful information [34,48]. Another limitation is the study’s focus on a single season and league. The unique characteristics and competitive level of the Turkish first division during the 2021–2022 season may not reflect broader trends applicable to other leagues or seasons, limiting the external validity of the conclusions. Additionally, data were only made available by Instatscout for the first 24 matchdays, which were further limited by removing matches where at least one player was sent off with a red card. However, the 185 matches (370 observations) that were ultimately included provide a sufficient sample for drawing useful and practical conclusions.
Future research could focus on how specific game situations, player roles, and tactics interact with physical performance to affect the game outcomes with a dynamic method that can capture the fluid nature of soccer. There is also a need to replicate this kind of study across different leagues and seasons to determine if these findings hold universally or if they are context-specific. Furthermore, future research could examine additional variables in the evaluation of running performance, such as accelerations and decelerations, as well as take into account weather conditions, which have been found to influence the running performance of soccer players [52]. Finally, implementing longitudinal studies that track changes over multiple seasons could also help in understanding how trends in performance metrics evolve with changes in player rosters and competition levels.

5. Conclusions

Our findings provide valuable insights into the role of running performance and situational factors in football success. Firstly, it was determined that running performance, measured through metrics such as distance covered at various intensities, does not significantly impact match outcomes. This aligns with studies in other leagues that suggest that while physical metrics are a factor in sports performance, they do not overwhelmingly determine the success of football teams. This finding is very important in understanding that football success is multifaceted, involving technical–tactical, psychological, and contextual components that go beyond mere physical exertion. Indeed, our research found that contextual variables, such as home advantage or the ranking of the team and the opponent, are significant predictors of the outcome of a match in contrast to running performance.
Practically, the findings suggest that coaches and sports scientists should consider a broader array of factors when preparing teams for competition. This could lead to more comprehensive training regimes that address technical–tactical and psychological readiness in addition to physical conditioning. Specifically, given that running performance metrics such as distances covered at various intensities were not significant predictors of match outcomes, emphasis should be placed on optimizing tactical strategies and technical execution rather than on solely improving physical parameters. For example, training sessions could focus on replicating in-game scenarios that require players to adapt to the specific strengths and weaknesses of the opposition. Furthermore, the significant influence of situational factors such as match location highlights the importance of preparing players mentally and tactically for away games. Coaches should implement strategies that mitigate the psychological challenges of playing away, such as stress management techniques and fostering team cohesion.

Author Contributions

Methodology, C.K.; Formal analysis, S.T. and A.E.K.; Data curation, S.T.; Writing—original draft, S.P.; Writing—review & editing, S.P., C.K. and S.M.; Visualization, A.E.K.; Supervision, S.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The current study received ethical approval from the local university’s (the University of Thessaly) ethics committee on 12 October 2022 (code, 1973).

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy and ethical reasons.

Conflicts of Interest

Author Serafeim Moustakidis was employed by the company AIDEAS OÜ. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Applsci 15 00637 g001
Figure 1. Estimating marginal means if the four running variables: (a) LOW, (b) MED, (c) HS, (d) SPR. Note: Covariates appearing in the model are evaluated at the following values: z-score: Team strength = 0.00, z-score: Opp. strength = 0.00. Error bars: 95% CI.
Figure 1. Estimating marginal means if the four running variables: (a) LOW, (b) MED, (c) HS, (d) SPR. Note: Covariates appearing in the model are evaluated at the following values: z-score: Team strength = 0.00, z-score: Opp. strength = 0.00. Error bars: 95% CI.
Applsci 15 00637 g001
Table 1. Running variables and their abbreviations.
Table 1. Running variables and their abbreviations.
Running VariablesAbbreviations
Distance on speed 0–4 m/s (low intensity)LOW
Distance on speed 4.01–5.5 m/s (medium intensity)MED
Distance on speed 5.51–7 m/s (high-speed running)HS
Distance on speed over 7 m/s (sprint)SPR
Table 2. Descriptive statistics.
Table 2. Descriptive statistics.
Running VariableResultLocationMeanStd. DeviationN
LOWWinHome85,463.692465.6295
Away86,305.243252.1249
No winHome85,319.602908.4790
Away84,826.562732.10136
MEDWinHome18,922.251685.6595
Away19,026.511523.0949
No winHome19,019.761716.3090
Away18,695.011619.35136
HSWinHome8318.17905.9295
Away8129.57826.2349
No winHome8301.38900.5790
Away8114.58950.89136
SPRWinHome1679.93410.6895
Away1580.12366.1549
No winHome1666.01361.1790
Away1561.82396.73136
Table 3. Main effects of the variables Location and Result on the four running variables.
Table 3. Main effects of the variables Location and Result on the four running variables.
Dependent VariableIndependent VariableCategoriesMean Difference (I-J)Std. ErrorSig.95% Confidence Interval for Difference Partial Eta Squared
IJLower BoundUpper Bound
LOWResultWinNo win900.492 *320.4530.005270.3201530.6640.021
LocationHomeAway−187.564304.1850.538−785.744410.6160.001
MEDResultWinNo win11.404181.6290.950−345.769368.578<0.001
LocationHomeAway134.306172.4080.436−204.735473.3470.002
HSResultWinNo win−23.655105.8950.823−231.898184.588<0.001
LocationHomeAway196.198100.5190.052−1.474393.8690.010
SPRResultWinNo win18.75945.6480.681−71.007108.526<0.001
LocationHomeAway101.423 *43.3310.02016.214186.6330.015
* indicate significant differences.
Table 4. Pairwise comparisons resulting from the interactions Location–Result of the four two-way ANCOVAs.
Table 4. Pairwise comparisons resulting from the interactions Location–Result of the four two-way ANCOVAs.
Dependent VariableIndependent VariablesMean Difference (I-J)Std. ErrorSig.95% Confidence Interval for DifferencePartial Eta Squared
Lower BoundUpper Bound
LOWLocationResult
HomeWinNo win227.012411.5560.582−582.3151036.3390.001
AwayWinNo win1573.972469.7330.001650.2402497.7030.030
ResultLocation
WinHomeAway−861.044481.5710.075−1808.05485.9670.009
No winHomeAway485.916371.2050.191−244.0601215.8920.005
MEDLocationResult
HomeWinNo win−183.623233.2650.432−642.339275.0940.002
AwayWinNo win206.431266.2390.439−317.129729.9910.002
ResultLocation
WinHomeAway−60.721272.9490.824−597.475476.033<0.001
No winHomeAway329.333210.3950.118−84.409743.0740.007
HSLocationResult
HomeWinNo win−16.212136.0010.905−283.658251.234<0.001
AwayWinNo win−31.099155.2260.841−336.350274.153<0.001
ResultLocation
WinHomeAway203.641159.1380.201−109.303516.5860.004
No winHomeAway188.754122.6670.125−52.470429.9780.006
SPRLocationResult
HomeWinNo win16.11658.6250.784−99.171131.403<0.001
AwayWinNo win21.40366.9130.749−110.181152.987<0.001
ResultLocation
WinHomeAway98.78068.5990.151−36.120233.6800.006
No winHomeAway104.06752.8770.0500.083208.0510.011
Table 5. Parameter Estimates.
Table 5. Parameter Estimates.
ParameterBStd. Error95% Wald Confidence IntervalHypothesis TestExp(B)95% Wald Confidence Interval for Exp(B)
LowerUpperWald Chi-SquaredfSig.LowerUpper
(Intercept)−10.9113.8635−18.483−3.3387.97510.0051.83×10−59.39×10 −90.035
[Location = Home]1.1380.24170.6641.61122.15710.0003.1191.9425.009
[Location = Away]0 1
Team strength0.0820.02210.0390.12513.72310.0001.0851.0391.133
Opponent strength−0.0930.0215−0.135−0.05118.63810.0000.9110.8740.951
MED0.0000.00010.0000.0000.93410.3341.0001.0001.000
LOW0.0004.88×10−53.88×10−50.0007.58510.0061.0001.0001.000
HS1.71×10−50.00020.0000.0000.00610.9411.0001.0001.000
SPR0.0000.00040.0000.0011.13610.2861.0001.0001.001
Note: Dependent variable: Result, model: (intercept), Location, Team strength, Opponent strength, MED, LOW, HS, SPR. The procedure models Win as the response, treating No win as the reference category.
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Plakias, S.; Tasoulis, S.; Kyranoudis, A.E.; Kokkotis, C.; Moustakidis, S. The Limited Impact of Running Performance on Football Success in the Turkish Super League. Appl. Sci. 2025, 15, 637. https://doi.org/10.3390/app15020637

AMA Style

Plakias S, Tasoulis S, Kyranoudis AE, Kokkotis C, Moustakidis S. The Limited Impact of Running Performance on Football Success in the Turkish Super League. Applied Sciences. 2025; 15(2):637. https://doi.org/10.3390/app15020637

Chicago/Turabian Style

Plakias, Spyridon, Sotiris Tasoulis, Angelos E. Kyranoudis, Christos Kokkotis, and Serafeim Moustakidis. 2025. "The Limited Impact of Running Performance on Football Success in the Turkish Super League" Applied Sciences 15, no. 2: 637. https://doi.org/10.3390/app15020637

APA Style

Plakias, S., Tasoulis, S., Kyranoudis, A. E., Kokkotis, C., & Moustakidis, S. (2025). The Limited Impact of Running Performance on Football Success in the Turkish Super League. Applied Sciences, 15(2), 637. https://doi.org/10.3390/app15020637

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