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# Visitors Out! The Absence of Away Team Supporters as a Source of Home Advantage in Football

Federico Fioravanti, Fernando Delbianco, Fernando Tohmé

► To cite this version:

Federico Fioravanti, Fernando Delbianco, Fernando Tohmé. Visitors Out! The Absence of Away Team Supporters as a Source of Home Advantage in Football. Social Science Quarterly, 2025, 106 (4), pp.e70058. 10.1111/ssqu.70058. hal-05444195

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# Visitors Out! The Absence of Away Team Supporters as a Source of Home Advantage in Football

Federico Fioravanti *¹, Fernando Delbianco², and Fernando Tohmé²

¹Université Jean Monnet Saint-Étienne, CNRS, Université Lyon 2, emlyon business school, GATE, 42023, Saint-Étienne, France.

²Departamento de Economía, Universidad Nacional del Sur (UNS)-Instituto de Matemática (INMABB)-CONICET, Bahía Blanca, Argentina

## Abstract

**Objective** This study investigates the distinct impacts of home and away fans on Home Advantage (HA) in football, using a natural experiment in Argentinean football. Since 2013, away fans have been banned due to violence, and during the 2020 COVID-19 lockdown, all fans were excluded.

**Method** We analyze 7,261 top-tier Argentinean matches from 2003–2022, covering periods with full attendance, home-only fans, and no fans. Using linear and logistic regressions, we assess the effects of different crowd conditions on goal differentials and win probabilities, controlling for team and season. Subgroup analyses compare traditionally strong teams to others.

**Results** The absence of away fans had a minor, statistically insignificant effect on HA overall, though Top 5 teams saw a brief increase after the ban. In contrast, the absence of home fans during COVID-19 significantly reduced HA for all teams. Not-Top 5 teams often faced a home disadvantage. HA rebounded sharply for Top 5 teams once home fans returned.

<footnote>*federico.fioravanti@univ-st-etienne.fr (corresponding author)</footnote>

<footnote>The authors are grateful to two anonymous reviewers and the participants of the Reading Online Sports Economics and RedNIE seminars for comments and suggestions that led to an improvement of the paper.</footnote>
**Conclusion** Home fans significantly influence HA, while away fans have negligible impact. Local support is particularly crucial for less dominant teams, with implications for crowd management, match planning, and broadcasting in professional football.

**Keywords:** Home Advantage; Football; COVID-19; Violence

# 1 Introduction

The Home Advantage (HA) effect in sports has been robustly documented since the foundational work of Schwartz and Barsky (1977), with multiple factors contributing to its persistence, for instance, physiological (Neave and Wolfson, 2003), psychological (Agnew and Carron, 1994), economic (Carmichael and Thomas, 2005), and officiating-related influences (Downward and Jones, 2007). While football has received the most attention in this literature (Boyko et al., 2007; Taylor et al., 2008; Belchior, 2020), HA has been analyzed across a broad range of sports and levels of competition, including basketball, rugby, tennis, and athletics (Jones, 2007; Thomas et al., 2008; Nevill et al., 1997; Jamieson, 2010). The COVID-19 pandemic enabled researchers to isolate the role of crowds in shaping HA through a series of natural experiments. Numerous studies found that HA declines significantly when matches are played behind closed doors (Bryson et al., 2021; Bilalić et al., 2021; McCarrick et al., 2021; Leitner et al., 2023), with similar effects observed in rugby, basketball, and American football (Delbianco et al., 2023; Leota et al., 2022; Szabó, 2022). However, results are not uniform across contexts, as some studies report negligible or inconsistent effects (Fazackerley et al., 2022).

A particularly underexplored question is the differential role of home versus away fans in driving HA. This distinction is crucial, as each type of crowd may influence players, referees, and match dynamics through distinct psychological and social mechanisms. Research has shown that regional identity and political decentralization can strengthen local club loyalties and fuel intergroup rivalries, ultimately shaping both fan behavior and competitive outcomes (Lago et al., 2019). In decentralized settings, enhanced regional support and localized investment not only promote competitive balance but also deepen fans' identification with their teams, heightening crowd intensity and engagement. Similarly, recent work on supraordinate fan identities reveals how enduring rivalries (e.g., classic matches) can produce shared psychological frameworks that transcend individual club affiliation, with implications for identity threat, emotional regulation,
and spectator behavior under stress (Navarro-Picado et al., 2024). These dynamics suggest that the presence or absence of rival (i.e., away) fans may activate deeper identity-based mechanisms, like shaping perceived legitimacy, group belonging, and even referee behavior.

A particularly interesting source of insights about how individuals derive their self-concept from their football club affiliation is Social Identity Theory (Turner and Oakes, 1986; Oakes and Turner, 1986). In the case of the home team players, being surrounded by their supporters and playing at home may induce a heightened sense of belonging and collective identity and a feeling of being supported and protected (Neave and Wolfson, 2003). Players may feel a greater responsibility to perform well for their fans (Guerin, 1993). Even without the direct presence of opposing fans, the familiar stadium and the local community support can still trigger a sense of positive social identity for the home team. The absence of an opposing crowd might lessen some aspects of the HA, but not eliminate it if other factors are still present. Conversely, away players might experience a sense of being in a hostile environment, which could lead to increased anxiety, reduced confidence, and a sense of isolation. This can negatively impact their performance (Wunderlich et al., 2021).

Confirming in part these insights, studies focusing on the psychological states of players before home versus away games often report lower anxiety and higher confidence for home teams citing higher testosterone levels before home games (Hyunwoo Kang, 2018). In turn, research on social facilitation suggests that the presence of an audience can enhance performance for well-learned tasks. While the primary effect here is usually linked to crowd noise, the psychological impact of playing in a familiar, supportive environment can still be present (van Meurs et al., 2024). Finally, the literature on territoriality in sports suggests that playing on one's “turf” can elicit a primal, protective response, leading to more aggressive and assertive play from the home team, even without direct fan presence.

Intergroup dynamics may also explain the emergence of HA. The shared identity of the home team and its fans can foster strong group cohesion (Hogg and Abrams, 2007). This can lead to a collective sense of purpose and increased motivation among home players to defend their territory and satisfy their supporters (Mackie et al., 2008). The knowledge that they are playing in an environment traditionally hostile to away teams can create a psychological burden for visiting players. The historical context of HA might lead away teams to subconsciously expect a tougher challenge, even without direct crowd pressure (Nevill and Holder, 1999).
Home teams might adopt more aggressive or offensive tactics, leveraging their perceived
advantage. Away teams, in contrast, might be more conservative, aiming to minimize risk due to
the historical difficulties of playing away. The negative impact of travel and the positive effect of
familiarity with the venue contribute to intergroup dynamics (Pollard, 1986). Home teams are
more familiar with the place and routines, reducing cognitive load and stress, while away teams
face travel fatigue and unfamiliarity, being these factors independent of crowd presence.

Referee bias is a significant contributor to HA (Boyko et al., 2007). Evidence of games played during the pandemic indicates that referee bias decreases significantly in the absence of crowds. This strongly supports the idea that direct crowd pressure is a primary driver of biased decisions (Scoppa, 2021). But even without direct crowd pressure, subconscious biases might still exist (Petersen and and, 2021). Referees might implicitly associate the home team with certain expectations even if there's no overt pressure. This could be due to a familiarity with the playing styles or reputations of home teams they officiate more frequently and the knowledge that a controversial decision against the home team could still lead to negative media scrutiny or online backlash (from home fans). In the absence of opposing fans, there is no counter-pressure to balance out any subtle or subconscious pro-home biases. The referee will not receive any cues or pressure from the away side, potentially allowing any latent pro-home leanings to manifest more easily (Bilalić et al., 2021).

Despite all these theoretical insights, few studies have empirically disentangled the separate
effects of home and away fans, outside the pandemic setting. Argentina's top football division
provides a rare opportunity to investigate the influence of home and away fans by separate.
Argentinean football fans are world-known for their intensity and passion when supporting their
respective teams. They do not consider themselves as mere spectators but as the twelfth player.
There is even a *barra-brava*<fnref n="1" /> from one of the most important teams in Argentina, Boca Juniors,
that call themselves “La 12” (“The 12”). Smoke bombs, firecrackers, confetti, balloons, and the
display of giant flags that cover entire stands, or part of them, before the match starts, are
common scenarios that can be seen in Argentinean football stadiums. But this behavior may
lead, in the case of local tournaments, to clashes between fans, usually ending in violent riots.
A death in a clash in June of 2013 forced the authorities to take radical action to prevent such
incidents from happening again. Since then, the access of away team fans to the matches has

<footnote n="1">¹The Argentinean version of the European ultras or English hooligans</footnote>
been banned.<fnref n="2" /> This ban was originally thought to be held until the end of that tournament, but a later incident between Boca Juniors fans, that ended up with the death of two of them, made the authorities decide to hold the ban indefinitely. After the implementation of this policy, there were many (failed) isolated attempts to return to the “normality”. This ban will not likely be overturned in the foreseeable future, as the number of football-related deaths since the policy was implemented increased in comparison with the number of deaths during the previous 10 years (61 deaths in 2003 – 2013 and 71 deaths in 2013 – 2023).<fnref n="3" /> This longstanding policy, in combination with periods of full fan attendance and the COVID-era empty stadiums, creates a natural experiment encompassing three distinct conditions: (i) matches with both home and away fans, (ii) matches with home fans only, and (iii) matches with no fans at all (‘ghost games’, as in Leitner and Richlan, 2021). Leveraging over two decades of match-level data from Argentina’s Primera División, we estimate how variation in crowd composition affects HA. An advantage of studying a single competition within the same country, during a politically stable period, is that it allows us to rule out potentially confounding structural factors such as gender norms (Santos, 2024), racial bias (Nesseler et al., 2025), or regime-type effects (Lago et al., 2016). Our analysis reveals that the absence of away fans has a modest and temporary positive effect on HA, primarily among dominant clubs during the early years of the ban. In contrast, the absence of home fans produces a large and statistically significant decline in HA, with some teams even experiencing a home disadvantage.

These findings suggest that home crowd support, and not the antagonistic presence of rival fans, is the primary driver of HA, although its magnitude may be moderated by factors such as club dominance, fanbase size, regional identity, and historical rivalries. Our results complement recent research on Egypt’s Premier League (Singleton et al., 2023) and reinforce prior findings linking football outcomes to cultural and institutional variables, including decentralization (Lago et al., 2019), identity salience (Navarro-Picado et al., 2024), and national collectivism (Lago and Lago-Peñas, 2021). By disaggregating the impact of home and away fans, this study contributes new theoretical and empirical insights into the social dynamics of crowd influence in sport. From a policy perspective, our findings suggest strategies for balancing safety and competitive integrity. For instance, encouraging fan engagement among underdog teams or managing dominant-club atmospheres through alternative incentives, such as digital participation or controlled broadcast-

<footnote n="2">²See BBC, or TN for more details in Argentinean media.</footnote>

<footnote n="3">³See TELAM (in Spanish).</footnote>
ing, could help level the playing field in structurally unbalanced leagues.

The paper proceeds as follows. Sections 2 and 3 describe the database used in our study, and how some of the variables are defined. Section 4 describes the model and presents the results, while also addressing some limitations of our study. Finally, Section 5 concludes and gives possible interpretations of the results.

## 2 Data

Our main data set consists of 7261 football matches from all the seasons from 2003 to 2022 of the top Argentinean club's tournament. For commercial reasons, the tournament had different names during the period under examination. Besides these changes, since June 2013, away crowds are no longer allowed into the stadiums. Thus 3389 games were played with only home team fans in the stadium. The COVID-19 pandemic also forced (as in most parts of the world), after a relatively short suspension of the competition, to play 103 games without public. That leaves us with 3769 games played with no restriction on attendance. There is no available information about the number of spectators at each game. There are 44 teams that played at least one tournament during the 2003 – 2022 period. We use as a measure of the magnitude of the HA the difference between the mean of goals scored by the home and the away team in a game.

We mainly employ goal difference as a measure of the HA for several key reasons. Firstly, it provides a more sensitive measure of the magnitude of the advantage. This is because goal difference transcends a simple binary outcome of win/loss/draw. It captures the extent of the HA. For instance, a 3-0 victory indicates a considerably stronger HA than a 1-0 win. By analyzing the average goal difference, one can gain insight into the degree to which playing at home influences the scoreline. Utilizing a mere win/loss measure renders the analysis overly simplistic. A home team might win by a single goal in one match and by five in another, yet both would be recorded identically as a 'win'. Consequently, valuable information regarding the intensity of the advantage is lost. Nevertheless, in order to ensure comparability with the existing literature, which predominantly relies on the proportion of matches won as an indicator of HA, we also include this latter measure as a complementary analysis.

Another potential approach could be to analyze the difference in points accumulated by teams at home versus away. While the cumulative point difference over a season can indicate which teams perform better at home, at the individual match level, it merely indicates whether
the home team secured 3 points (win), 1 point (draw), or 0 points (loss). It fails to reflect the superiority in gameplay that manifests in a greater number of goals. Furthermore, the drawback of analyzing data on a seasonal basis is the loss of data granularity and, consequently, variability. This variability is inherent in a match-by-match analysis and ultimately provides us with greater statistical power.

In Table 1 we can see that the HA during these 20 years is approximately 0.32 goals. We will analyze the evolution of the HA taking into account the presence/absence of the public, together with other factors that will be introduced in Section 4. We can see in the table that the variability is high, as we mentioned previously, which makes the analysis of HA through goal difference attractive.

<table><caption>Table 1: Descriptive Statistics</caption><thead><tr><th>Variable</th><th>Obs</th><th>Mean</th><th>Std. Dev.</th><th>Min</th><th>Max</th></tr></thead><tbody><tr><td>Home goals</td><td>7261</td><td>1.335629</td><td>1.150702</td><td>0</td><td>8</td></tr><tr><td>Away goals</td><td>7261</td><td>1.015014</td><td>1.036286</td><td>0</td><td>7</td></tr><tr><td>Home Advantage</td><td>7261</td><td>0.3203857</td><td>1.538001</td><td>-6</td><td>7</td></tr></tbody></table>

## 3 Motivation

Home Advantage is a complex phenomenon, that may have different causes. We are interested in isolating the effect of the attendance of visiting fans to the games using the natural experiment induced by the prohibition of visiting crowds in the matches of the main football tournament in Argentina. We examine the hypothesis that teams that had a higher HA when attendance was free, have a larger HA when visitors were forbidden (see Table 2 and Figure 1).

In particular, in Figure 1 we can see the evolution of the HA during the years, for all the teams (Total column), and relative to the history of home performance of the teams (classified as Top 5 and Not-Top 5). Figure 2 shows the evolution of HA in time, for the three groups of teams under analysis. The 5 teams with the highest HA in the 2003 – 2022 seasons are: *Boca Juniors*, *River Plate*, *Talleres de Córdoba*, *Vélez Sarsfield*, and *Estudiantes de La Plata*. See Table 3 and Figure 3 in the Appendix for the complete ranking of the HA series by team.

We will focus, in particular, on the shaded areas of Figure 1 which correspond to the periods in which new attendance policies were enacted. While in the long run, the teams may get used to
playing with or without a home or away audience, in a narrow time window (one or two years after the new policy was implemented, for example) their performance may be affected by the different composition of the attending public. At first glance, this seems to be the case, especially in the 2018 – 2022 shaded area.

<table><caption>Table 2: Average HA by year and rank (all the teams, Top 5 and Not-Top 5 ones); 2013b: Results after away teams fans are not allowed; 2020b: Lockdown (no public allowed).</caption><thead><tr><th>Year</th><th>Not-Top 5</th><th>Top 5</th><th>All teams</th></tr></thead><tbody><tr><td>2003</td><td>0.21</td><td>0.44</td><td>0.27</td></tr><tr><td>2004</td><td>0.18</td><td>0.84</td><td>0.32</td></tr><tr><td>2005</td><td>0.31</td><td>0.73</td><td>0.39</td></tr><tr><td>2006</td><td>0.20</td><td>0.96</td><td>0.35</td></tr><tr><td>2007</td><td>0.27</td><td>0.72</td><td>0.36</td></tr><tr><td>2008</td><td>0.35</td><td>0.72</td><td>0.42</td></tr><tr><td>2009</td><td>0.35</td><td>0.77</td><td>0.43</td></tr><tr><td>2010</td><td>0.32</td><td>0.87</td><td>0.43</td></tr><tr><td>2011</td><td>0.07</td><td>0.38</td><td>0.13</td></tr><tr><td>2012</td><td>0.33</td><td>0.44</td><td>0.38</td></tr><tr><td>2013</td><td>0.34</td><td>0.25</td><td>0.32</td></tr><tr><td>2013b</td><td>0.35</td><td>0.51</td><td>0.38</td></tr><tr><td>2014</td><td>0.25</td><td>0.82</td><td>0.36</td></tr><tr><td>2015</td><td>0.29</td><td>0.42</td><td>0.31</td></tr><tr><td>2016</td><td>0.27</td><td>0.83</td><td>0.35</td></tr><tr><td>2017</td><td>0.16</td><td>0.59</td><td>0.24</td></tr><tr><td>2018</td><td>0.20</td><td>0.71</td><td>0.29</td></tr><tr><td>2019</td><td>0.16</td><td>0.64</td><td>0.26</td></tr><tr><td>2020</td><td>0.12</td><td>0.66</td><td>0.23</td></tr><tr><td>2020b</td><td>-0.26</td><td>0.22</td><td>-0.17</td></tr><tr><td>2021</td><td>0.21</td><td>1.18</td><td>0.39</td></tr><tr><td>2022</td><td>0.23</td><td>0.53</td><td>0.29</td></tr></tbody></table>
<figure><img src="image_3.png" /><figcaption>Figure 1: Distribution of annual HA by rank (*Total*: all the teams; *T5*: Top 5; and *NT5*: Not-Top 5 ones). Two intervals are highlighted, that will be the focus of a narrower analysis: 2012 – 2014 and 2018 – 2022.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Figure 2: Trends in HA by rank.</figcaption></figure>
# 4 Empirical Evidence

We define the Home Advantage of a team in a game $i$, as

$$HA_i = GoalsH_i - GoalsA_i$$

where $GoalsH_i$ indicates the number of goals scored by the home team, while $GoalsA_i$ is the number of those scored by the away team.

We consider a categorical variable $Public_i$ with three values: Free, Restricted (corresponding to games that can only be attended by home fans), and Closed (matches played under lockdown with no public at all).

We consider a family of models $M_k$, defined as:

$$HA_i = f_k(Public_i, HomeTeam_i, AwayTeam_i, Calendar_i) \quad (1)$$

Each $f_k$ is a linear function, with a particular range of time during which the game was played ($Calendar_i$) and the restrictions enacted. We analyze the results for models $M_i$, described in Tables 4, 5, 8, and 10 (in the Appendix). In Table 4, we can see the results for the largest possible samples in each comparison. We do not consider teams' form variables, such as the dynamics ELO ratings or win percentages of teams, as they could become contaminated by the effects of playing in stadiums under different restrictions.

Another analysis involves a dependent variable $W_i$ that indicates whether the home team won game $i$ or not. That is, $W_i = 1$ if $HA_i > 0$ and $W_i = 0$ otherwise. This allows us to examine the impact of the restrictions on the access to the games on the probability of winning, instead of on the difference in scores. That is, each model $L_k$ is

$$W_i = g_k(Public_i) \quad (2)$$

where each $g_k$ is defined by the number of teams considered and the period in which the games were played (see Tables 6, 11, and 12, in the Appendix).

We split our analysis to consider the two main events highlighted in Figure 1, namely the prohibition of attendance of visitor fans and the COVID-19 lockdown. We start the analysis in chronological order.
Some clarifications of the statistical work carried out are in order. We define the Top 5 as the teams that during the 20 years covered by our sample ranked on average as one of the five highest values of HA. The Not-Top 5, consequently, are the remaining 39 teams. It is worth noticing that these Top 5 teams are not necessarily the “historical” Top 5 (also known as “Big Five”), the most successful teams in Argentinean history, namely River, Boca Juniors, Racing, Independiente, and San Lorenzo. There are many reasons for not using the “Big Five”. First, we found that there is no significant effect by considering this group of teams. A possible reason for these clubs not being dominant on the HA effect is that over the last 20 years, there are other teams that were more successful. Finally, as we want to consider how the HA changed under different conditions on attendance, we wanted to compare the best teams regarding this characteristic, with the rest of the sampled teams.<fnref n="4" />

On the other hand, we consider different temporal subsamples. First the total sample. Then, we will consider 2003 – 2019, comparing the crowds of both teams vs. just that of the local team. Third, we use 2013 – 2022 to contrast only the home public against a closed stadium. Finally, we will focus on the periods close to the policy changes, 2012 – 2014 and 2018 – 2022 on the other.

To assess the regression results, we carried out heteroscedasticity tests controlling for indicator variables that capture the heterogeneity between teams. Only in one of the specifications does the Breusch-Pagan test reject the null hypothesis of homoscedasticity. In more detail, as can be seen in the last row of Table 4, the Breusch-Pagan (BP) test results yield p-values greater than 0.10, except for Model 5, which has a p-value of 0.02. The White correction is then applied to this model to obtain robust estimates of the standard deviations. In this case, a robust White estimate yields similar results in the standard deviations and maintains the significance of the variables (the standard deviations change a few hundredths, to 0.155 and 0.156 for the *Free* and *Restricted* contexts, respectively).

## 4.1 Visitors Out

First, we focus now on the seasons after 2013 and before 2019 in which only visiting fans were banned. That is when teams played only with the local supporting public.

<footnote n="4">⁴We find that local derbies do not have a significant effect on the HA in our sample, which is why we disregard this variable for this study (see Table 10 in the Appendix).</footnote>
The full sample does not exhibit significant effects on the goal difference in favor of the home team, nor on the probability of winning of teams playing locally. This can be seen in both linear and logistic regressions (see Table 4 and Table 6). But if we restrict the analysis to the Top 5, comparing it to the rest of the teams, we can see in Figure 1 and Table 5 that its average increases (but not significatively) both over the second half of 2013 (when the restriction began), and in 2014 (and even more during the second year of the restriction). But from 2015 to 2019 their HA went back to its pre-restriction historical values. Besides not being a highly significant effect, even for the period close to the policy's implementation, a possible explanation is that in the long run, teams get used to playing only with local fans.

When we consider the winning percentages, the results are consistent with the ones in Table 5, but become significant ($p < 0.1$) for the Top 5 teams (see Table 11). These teams, although not consistently scoring more goals, won more games when playing at home. An intuitive reasoning behind this result is that the top teams benefited when playing in a stadium that was probably full with their fans; and won matches that they previously drew or lost by a small margin when attendance was free.

If we run a difference of means test to compare the average HA for the Top 5 teams before and after the no-visitor's policy was implemented, we find that a positive difference in their HA is significant only at 10% with a p-value of 0.052, as can be seen in Table 7 (in the Appendix). This is due to the great dispersion of results in the different games that were played. It is interesting to note that this result, found for the Top 5, does not hold for the Top 2 (*Boca Juniors* and *River Plate*, arguably the most popular teams in Argentina) or for the Top 10 teams. In the former case, *Boca Juniors* and *River Plate* are the teams with the highest HA (approximately one goal of difference over visiting teams) and are used to playing with their stadiums filled with their fans, with little presence of visiting fans. This might imply that their HA is hardly affected by the presence or absence of visiting fans.

In the case of Top 10 teams, the averages of the teams in the 6–10 range are not far from the rest of the Not-Top 5 teams. These results are shown in the Table 7. We can see that the averages are, respectively 0.55 and 0.52 and thus their difference is very small.

It can be seen in Figure 4 (in the Appendix) that the means for Top 5 teams are higher (the box corresponding to the Top 5 is higher), but the dispersion almost overlaps the intervals (the whiskers of the boxes reach similar intervals, with a few outliers).
The results in this section indicate that playing games with or without away fans does not have a major impact on HA. We can thus conclude that the presence of away fans can be neglected as a determinant of HA.

## 4.2 COVID-19

Columns 1 and 3 of Tables 4 and 6 show that the lockdown affected the advantage of home teams, compared to the periods in which attendance was free as well as when it was forbidden just for visiting fans. This result is consistent with the literature cited in Section 1 on the effects of the lockdown in football and other team sports. This result is robust to the presence of several dummies controlling for the tournament calendar (*Calendar*) and fixed effects of teams (Home and Away teams).

When we narrow down to focus on the Top 5 and Not Top 5, in Table 8 we can see that there is a negative but not significant effect on the HA for the Top 5 teams and a highly significant negative effect for the Not-Top 5 teams.

If we consider the effect of the lockdown on the winning percentage (see Table 12 in the Appendix), we can see that the effect is still significant, but it loses significance for the Not-Top 5 teams. This possibly implies that home teams scored less, but still got the previous results (now obtaining narrow victories or narrow defeats).

When considering the difference of means, the decrease in the HA becomes significant ($p < 0.1$) for the Top 5, while this negative effect is highly significant for Not-Top 5, Not-Top 2 and Not-Top 10 (see Table 9 in the Appendix).

Three results can be deduced from the regressions as well as from the descriptive statistics. The first is that in fact, the vast majority of teams exhibit a “negative” HA. This can be seen in the total averages by year and in the HA rankings. All the teams not in the Top 5 were harmed by not playing with their public. On the other hand, the Top 5 teams kept a certain degree of advantage, but lower than their historical advantage (this can be seen in the black bar of the annual histogram). One possible interpretation of this is that the HA effect for teams that are not in the Top 5 depends only on the presence or absence of local fans. This means that a better knowledge of the field or the fatigue of the visiting team, are not significant factors for the HA effect. The best teams (those in the Top 5), require further analysis to disentangle all the factors
influencing the HA effect, although our results show that the presence of local fans has a major impact.

The second realization arose immediately after the return of the local public, once the COVID-19 lockdown was lifted (remember that visiting fans are still banned in local championships). During that period the Top 5 teams achieved a historical peak of average home goal advantage. This could be interpreted as an extra boost generated by local fans. The rest of the teams recovered well, on average, but did not perform much better than their historical record. This difference may ensue from the fact that most powerful teams have a larger number of fans. These teams transitioned in this period from playing with empty (generally big) stadiums to playing with a full big stadium filled with their fans.

The last piece of evidence is that the effects of the lockdown are significant compared to both the 2003 – 2012 season, as well as the 2013 – 2019 and 2021 – 2022 seasons. In other words, the HA decreases to a considerable extent in comparison to both the case in which fans of both teams attend and the case with only the local public.

## 4.3 Limitations

There are certain limitations to our analysis, primarily stemming from the absence of data or the non-observable nature of some variables. Firstly, as previously mentioned, to the best of our knowledge, there are no official or systematized data regarding spectator attendance for each match. Clearly, such data would serve to contextualize the level of ‘support’ a team receives when playing at home. We have attempted to mitigate this by incorporating team-specific dummy variables, which would capture not only the typical fan base size of a team (assuming a stable level of spectators throughout the season) but also other stadium characteristics (such as field size, proximity to the stands, etc.).

Secondly, with respect to team quality, we have also attempted to account for this by distinguishing between teams that generally perform better at home versus away, again using dummy variables. Evidently, we do not observe any changes in team quality during the season, but we lack the capacity to measure them. This is because one way to gauge quality would be through the cost of the squad; however, this does not vary during the season, and such data are unavailable for the Argentine league during the period under analysis. Another way to observe changes in a team's quality is through their results, but clearly, this type of measure would be endogenous
to the outcome we aim to explain.

Finally, league structure is often used to avoid potential confounding factors in estimations. However, in our case, we analyze only one league, which has not undergone significant format changes during the years we examine (more recent years have seen the introduction of playoff phases, for example), with the exception of the number of teams. We capture this detail of the variation in the number of teams through the fixture date variable, as tournaments with more matchdays will precisely be those where there were more than 20 participants. Nevertheless, beyond this change, it has consistently been a league where the team with the most points emerges as the champion.

# 5 Conclusions

In this work, we studied the effect of crowd attendance, in particular visiting fans, on the Home Advantage (HA) in Argentina's top football division. The ban on visiting fans after 2013, introduced in response to a violent incident, and the COVID-19 lockdowns that mandated empty stadiums, created two natural experiments. These conditions allow us to separately examine the impact of home and away crowd presence on match outcomes. While HA is a well-established phenomenon, isolating its specific causes remains challenging. Our study contributes by identifying the differential effects of crowd composition.

When comparing games with full attendance to those with only home fans, we find no significant differences in overall HA. However, for the top five teams, HA increased modestly in the early years of the visitor ban, possibly due to smaller teams struggling to adapt to intense home crowds without support from their own fans. Notably, this effect fades when focusing only on the top two teams, namely *Boca Juniors* and *River Plate*, which already enjoy a strong HA regardless of crowd composition. Overall, we infer that the presence of away fans does not play a major role in HA for most teams. In contrast, the COVID-19 restrictions had a large and statistically significant negative effect on HA across the board. While top teams retained a positive HA, weaker teams experienced a reversal, with HA becoming negative. This suggests that for smaller clubs, home crowd support may be the only meaningful contributor to HA. We also observe a temporary post-lockdown boost for top teams, likely reflecting the larger and more passionate nature of their home fan bases. Taken together, these findings highlight the asymmetry in how different teams benefit from crowd dynamics.
This points to several promising avenues for future research. One key question is: *Why does the absence of away fans only affect a few teams, and what distinguishes these teams from others?*

Further work could examine whether team-level factors, such as historical performance, stadium capacity, crowd density, or regional rivalries, moderate the relationship between fan presence and HA. Additionally, qualitative research could complement quantitative analyses by exploring the subjective experiences of players, referees, and coaches. For example, interviews could help assess whether certain atmospheres generate more psychological pressure, or whether referees perceive greater social influence when officiating matches with highly vocal home crowds.

Finally, understanding these dynamics could inform policy. Tournament organizers aiming to improve competitive balance might consider targeted incentives to stimulate attendance among underdog teams, or strategic broadcasting arrangements for dominant clubs. These insights could help design safer, more equitable sporting environments while preserving the excitement of live audiences. In summary, the Argentine case offers a rare opportunity to disentangle the effects of home and away crowd presence on HA. Our findings confirm the critical role of home fans and suggest that crowd influence is far from uniform, opening the door to more targeted, mechanism-oriented studies.

**Declaration**

**Conflict of interest:** The authors have no competing interests to declare that are relevant to the content of this article.

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Appendix

<table><caption>Table 3: Ranking of average HA results</caption><thead><tr><th>Home</th><th>HA average</th><th>Away</th><th>HA average (opponent)</th></tr></thead><tbody><tr><td>Huracán (TA)</td><td>-0.736</td><td>River Plate</td><td>-0.199</td></tr><tr><td>Tiro Federal</td><td>-0.736</td><td>Boca Juniors</td><td>-0.196</td></tr><tr><td>Crucero del Norte</td><td>-0.4666</td><td>Vélez Sarsfield</td><td>-0.012</td></tr><tr><td>Almagro</td><td>-0.444</td><td>Defensa y Justicia</td><td>0.043</td></tr><tr><td>Aldosivi</td><td>-0.397</td><td>San Lorenzo</td><td>0.068</td></tr><tr><td>Nueva Chicago</td><td>-0.301</td><td>Estudiantes</td><td>0.105</td></tr><tr><td>Instituto</td><td>-0.289</td><td>Talleres (C)</td><td>0.161</td></tr><tr><td>Chacarita Juniors</td><td>-0.254</td><td>Independiente</td><td>0.161</td></tr><tr><td>Sarmiento</td><td>-0.222</td><td>Racing Club</td><td>0.178</td></tr><tr><td>Central Córdoba (SdE)</td><td>-0.170</td><td>Lanús</td><td>0.185</td></tr><tr><td>Temperley</td><td>-0.115</td><td>Banfield</td><td>0.185</td></tr><tr><td>Quilmes</td><td>-0.033</td><td>Newells</td><td>0.252</td></tr><tr><td>Atlético de Rafaela</td><td>0.024</td><td>Belgrano</td><td>0.335</td></tr><tr><td>San Martín (T)</td><td>0.031</td><td>Argentinos</td><td>0.352</td></tr><tr><td>Gimnasia y Esgrima (J)</td><td>0.093</td><td>Platense</td><td>0.360</td></tr><tr><td>Unión</td><td>0.117</td><td>Tigre</td><td>0.369</td></tr><tr><td>Olimpo</td><td>0.125</td><td>Godoy Cruz</td><td>0.373</td></tr><tr><td>San Martín (SJ)</td><td>0.132</td><td>Rosario Central</td><td>0.400</td></tr><tr><td>Patronato</td><td>0.133</td><td>Arsenal</td><td>0.440</td></tr><tr><td>Arsenal</td><td>0.187</td><td>Unión</td><td>0.444</td></tr><tr><td>Colón</td><td>0.188</td><td>Colón</td><td>0.448</td></tr><tr><td>Gimnasia y Esgrima (LP)</td><td>0.205</td><td>Central Córdoba (SdE)</td><td>0.474</td></tr><tr><td>All Boys</td><td>0.226</td><td>Gimnasia y Esgrima (LP)</td><td>0.513</td></tr><tr><td>Banfield</td><td>0.228</td><td>Atlético Tucumán</td><td>0.527</td></tr><tr><td>Argentinos</td><td>0.230</td><td>Huracán</td><td>0.560</td></tr><tr><td>Huracán</td><td>0.250</td><td>Atlético de Rafaela</td><td>0.581</td></tr><tr><td>Platense</td><td>0.269</td><td>Sarmiento</td><td>0.593</td></tr><tr><td>Godoy Cruz</td><td>0.275</td><td>San Martín (SJ)</td><td>0.641</td></tr><tr><td>Barracas Central</td><td>0.285</td><td>San Martín (T)</td><td>0.645</td></tr><tr><td>Tigre</td><td>0.287</td><td>Chacarita Juniors</td><td>0.653</td></tr><tr><td>Belgrano</td><td>0.295</td><td>Quilmes</td><td>0.661</td></tr><tr><td>Rosario Central</td><td>0.310</td><td>Gimnasia y Esgrima (J)</td><td>0.684</td></tr><tr><td>Atlético Tucumán</td><td>0.378</td><td>Aldosivi</td><td>0.720</td></tr><tr><td>Independiente</td><td>0.436</td><td>All Boys</td><td>0.723</td></tr><tr><td>San Lorenzo</td><td>0.438</td><td>Patronato</td><td>0.739</td></tr><tr><td>Defensa y Justicia</td><td>0.443</td><td>Nueva Chicago</td><td>0.754</td></tr><tr><td>Lanús</td><td>0.453</td><td>Barracas Central</td><td>0.769</td></tr><tr><td>Racing Club</td><td>0.476</td><td>Olimpo</td><td>0.773</td></tr><tr><td>Newells</td><td>0.487</td><td>Almagro</td><td>0.789</td></tr><tr><td>Estudiantes</td><td>0.540</td><td>Temperley</td><td>0.843</td></tr><tr><td>Vélez Sarsfield</td><td>0.562</td><td>Tiro Federal</td><td>1.000</td></tr><tr><td>Talleres (C)</td><td>0.584</td><td>Instituto</td><td>1.000</td></tr><tr><td>River Plate</td><td>0.737</td><td>Huracán (TA)</td><td>1.631</td></tr><tr><td>Boca Juniors</td><td>0.932</td><td>Crucero del Norte</td><td>1.800</td></tr></tbody></table>
<figure><img src="image_5.png" /><figcaption>Figure 3: Distribution of the HA for the 44 teams on the period 2003 – 2022</figcaption></figure>
<table><thead><tr><th>VARIABLES</th><th>M1<br>HA</th><th>M2<br>HA</th><th>M3<br>HA</th><th>M4<br>HA</th><th>M5<br>HA</th><th>M6<br>HA</th></tr></thead><tbody><tr><td>Free</td><td>0.516***<br>(0.154)</td><td>0.0331<br>(0.0391)</td><td></td><td>0.496***<br>(0.153)</td><td>0.513***<br>(0.156)</td><td>0.474***<br>(0.156)</td></tr><tr><td>Restricted</td><td>0.487***<br>(0.154)</td><td></td><td></td><td>0.489***<br>(0.153)</td><td>0.447***<br>(0.154)</td><td>0.456***<br>(0.157)</td></tr><tr><td>Closed</td><td></td><td></td><td>-0.487***<br>(0.152)</td><td></td><td></td><td></td></tr><tr><td>Home team</td><td></td><td></td><td></td><td>YES</td><td></td><td></td></tr><tr><td>Away team</td><td></td><td></td><td></td><td></td><td>YES</td><td></td></tr><tr><td>Calendar</td><td></td><td></td><td></td><td></td><td></td><td>YES</td></tr><tr><td>Constant</td><td>-0.175<br>(0.151)</td><td>0.308***<br>(0.0300)</td><td>0.312***<br>(0.0255)</td><td>-0.855***<br>(0.212)</td><td>0.298<br>(0.221)</td><td>-0.121<br>(0.166)</td></tr><tr><td>Observations</td><td>7,261</td><td>6,383</td><td>3,661</td><td>7,261</td><td>7,261</td><td>7,261</td></tr><tr><td>R-squared</td><td>0.002</td><td>0.000</td><td>0.003</td><td>0.032</td><td>0.037</td><td>0.005</td></tr><tr><td>BP p-value</td><td>0.987</td><td>0.277</td><td>0.718</td><td>0.668</td><td>0.020</td><td>0.881</td></tr></tbody></table>

Standard errors in parentheses. Robust standard errors for Model (5)

*** p<0.01, ** p<0.05, * p<0.1

<caption>Table 4: OLS regressions: *M1*: all teams both free and restricted entry against lockdown; *M2*: free entry against restricted entry (2003 – 2019); *M3*: lockdown against restricted access (2013 – 2022); *M4*: all teams both free and restricted entry against lockdown plus home team dummy; *M5*: all teams both free and restricted entry against lockdown plus away team dummy; *M6*: all teams both free and restricted entry against lockdown plus tournament date dummy. BP denotes the results of the Breusch-Pagan heteroskedasticity test, displaying the p-value for the rejection of the null hypothesis of constant variance. The BP p-value for Model (5) corresponds to the OLS model without the later error correction.</caption>
<table><thead><tr><th>VARIABLES</th><th>M7<br>HA</th><th>M8<br>HA</th><th>M9<br>HA</th><th>M10<br>HA</th><th>M11<br>HA</th><th>M12<br>HA</th></tr></thead><tbody><tr><td>Free</td><td>0.0331<br>(0.0391)</td><td>0.0271<br>(0.0909)</td><td>-0.0654<br>(0.0898)</td><td>-0.329<br>(0.202)</td><td>-0.366<br>(0.233)</td><td>-0.112<br>(0.268)</td></tr><tr><td>Home Team</td><td></td><td></td><td></td><td></td><td>YES</td><td>YES</td></tr><tr><td>Away Team</td><td></td><td></td><td></td><td></td><td>YES</td><td>YES</td></tr><tr><td>Calendar</td><td></td><td></td><td></td><td></td><td>YES</td><td>YES</td></tr><tr><td>Constant</td><td>0.308***<br>(0.0300)</td><td>0.659***<br>(0.0719)</td><td>0.367***<br>(0.0627)</td><td>0.709***<br>(0.137)</td><td>0.917<br>(0.789)</td><td>1.097***<br>(0.368)</td></tr><tr><td>Observations</td><td>6,383</td><td>1,208</td><td>1,130</td><td>217</td><td>217</td><td>1130</td></tr><tr><td>R-squared</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.012</td><td>0.170</td><td>0.093</td></tr></tbody></table>

Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1

<table><caption>Table 5: M7: all teams 2003 – 2019; M8: Top 5 teams, 2003 – 2019; M9: all teams, 2012 – 2014; M10: Top 5 teams, 2012 – 2014; M11: Top 5 teams, 2012 – 2014 with dummies; M12: all teams, 2012 – 2014 with dummies</caption><thead><tr><th>VARIABLES</th><th>L1<br>W</th><th>L2<br>W</th><th>L3<br>W</th><th>L4<br>W</th><th>L5<br>W</th><th>L6<br>W</th></tr></thead><tbody><tr><td>Free</td><td>0.455**<br>(0.212)</td><td>-0.0273<br>(0.0513)</td><td></td><td>0.461**<br>(0.216)</td><td>0.455**<br>(0.216)</td><td>0.462**<br>(0.216)</td></tr><tr><td>Restricted</td><td>0.475**<br>(0.212)</td><td></td><td></td><td>0.440**<br>(0.215)</td><td>0.505**<br>(0.217)</td><td>0.484**<br>(0.215)</td></tr><tr><td>Closed</td><td></td><td></td><td>-0.469**<br>(0.212)</td><td></td><td></td><td></td></tr><tr><td>Home Team</td><td></td><td></td><td></td><td>YES</td><td></td><td></td></tr><tr><td>Away Team</td><td></td><td></td><td></td><td></td><td>YES</td><td></td></tr><tr><td>Calendar</td><td></td><td></td><td></td><td></td><td></td><td>YES</td></tr><tr><td>Constant</td><td>-0.708***<br>(0.210)</td><td>-0.225***<br>(0.0394)</td><td>-0.239***<br>(0.0338)</td><td>-0.309***<br>(0.291)</td><td>-1.217<br>(0.301)</td><td>-0.717***<br>(0.228)</td></tr><tr><td>Observations</td><td>7,261</td><td>6,383</td><td>3,661</td><td>7,261</td><td>7,261</td><td>7,261</td></tr></tbody></table>

Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1

<caption>Table 6: Logit regressions following OLS regressions controls of Table 4</caption>
<table><caption>Table 7: Difference of means test for the period 2012 – 2014</caption><thead><tr><th>Sample</th><th>Top5</th><th>Top2</th><th>Top10</th></tr></thead><tbody><tr><td>Mean Free</td><td>0.38</td><td>0.68</td><td>0.55</td></tr><tr><td>Mean Restricted</td><td>0.71</td><td>0.79</td><td>0.52</td></tr><tr><td>Pr(T &lt; t)</td><td>0.948</td><td>0.6388</td><td>0.4184</td></tr><tr><td>Pr(T &gt; t)</td><td>0.052</td><td>0.3612</td><td>0.5816</td></tr></tbody></table>

<figure><img src="image_6.png" /><figcaption>Figure 4: Home Advantage (HA) Box-and-Whiskers plot of the averages of Not-Top 5 (0) versus Top 5 teams (1) for seasons 2012 – 2014.</figcaption></figure>
<table><thead><tr><th>VARIABLES</th><th>M13<br>HA</th><th>M14<br>HA</th><th>M15<br>HA</th><th>M16<br>HA</th><th>M17<br>HA</th></tr></thead><tbody><tr><td>Closed</td><td>-0.502***<br>(0.153)</td><td>-0.476***<br>(0.156)</td><td>-0.525<br>(0.367)</td><td>-0.455***<br>(0.170)</td><td>-0.433***<br>(0.153)</td></tr><tr><td>Home Team</td><td></td><td></td><td></td><td></td><td>YES</td></tr><tr><td>Away Team</td><td></td><td></td><td></td><td></td><td>YES</td></tr><tr><td>Calendar</td><td></td><td></td><td></td><td></td><td>YES</td></tr><tr><td>Constant</td><td>0.328***<br>(0.0182)</td><td>0.301***<br>(0.0395)</td><td>0.747***<br>(0.0894)</td><td>0.196***<br>(0.0435)</td><td>0.00595<br>(0.235)</td></tr><tr><td>Observations</td><td>7,261</td><td>1,605</td><td>303</td><td>1,302</td><td>7,261</td></tr><tr><td>R-squared</td><td>0.001</td><td>0.006</td><td>0.007</td><td>0.005</td><td>0.070</td></tr></tbody></table>

Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1

<table><caption>Table 8: M13: all teams, free and restricted vs closed, 2003 – 2022; M14: all teams, restricted vs closed, 2018 – 2022; M15: Top 5, restricted vs closed, 2018 – 2022; M16: Not-Top 5, restricted vs closed, 2018 – 2022; M17: all teams, free and restricted vs closed, 2003 – 2022 adding dummies.</caption><thead><tr><th>Sample</th><th>Top5</th><th>Top2</th><th>Top10</th></tr></thead><tbody><tr><td>Mean Restricted</td><td>0.74</td><td>1</td><td>0.59</td></tr><tr><td>Mean Closed</td><td>0.22</td><td>0.57</td><td>0</td></tr><tr><td>Pr(T < t)</td><td>0.9235</td><td>0.7477</td><td>0.9906</td></tr><tr><td>Pr(T > t)</td><td>0.075</td><td>0.2523</td><td>0.0094</td></tr></tbody></table><table><thead><tr><th>Sample</th><th>NotTop5</th><th>NotTop2</th><th>NotTop10</th></tr></thead><tbody><tr><td>Mean Restricted</td><td>0.19</td><td>0.24</td><td>0.15</td></tr><tr><td>Mean Closed</td><td>-0.25</td><td>-0.22</td><td>-0.27</td></tr><tr><td>Pr(T < t)</td><td>0.9962</td><td>0.9985</td><td>0.9793</td></tr><tr><td>Pr(T > t)</td><td>0.0038</td><td>0.0015</td><td>0.0207</td></tr></tbody></table>

<caption>Table 9: Difference of means test for the period 2018 – 2022</caption>
<table><caption>Table 10: OLS and logit regressions of the full sample (2003-2022) and all teams, with a dummy for derby matches.</caption><thead><tr><th>VARIABLES</th><th>M18<br>HA</th><th>L18<br>W</th></tr></thead><tbody><tr><td>Free</td><td>0.516***<br>(0.154)</td><td>0.108**<br>(0.0496)</td></tr><tr><td>Restricted</td><td>0.486***<br>(0.154)</td><td>0.113**<br>(0.0496)</td></tr><tr><td>Derby</td><td>0.0241<br>(0.118)</td><td>-0.0323<br>(0.0381)</td></tr><tr><td>Constant</td><td>-0.175<br>(0.151)</td><td>0.330***<br>(0.0489)</td></tr><tr><td>Observations</td><td>7,260</td><td>7,261</td></tr><tr><td>R-squared</td><td>0.002</td><td>0.001</td></tr><tr><td colspan="3">Standard errors in parentheses<br>*** p&lt;0.01, ** p&lt;0.05, * p&lt;0.1</td></tr></tbody></table>

<table><caption>Table 11: Logits regressions of models in Table 5</caption><thead><tr><th>VARIABLES</th><th>L7<br>W</th><th>L8<br>W</th><th>L9<br>W</th><th>L10<br>W</th><th>L11<br>W</th><th>L12<br>W</th></tr></thead><tbody><tr><td>Free</td><td>-0.0273<br>(0.0513)</td><td>-0.0207<br>(0.119)</td><td>-0.150<br>(0.120)</td><td>-0.488*<br>(0.275)</td><td>-0.756**<br>(0.352)</td><td>-0.231<br>(0.144)</td></tr><tr><td>Home Team</td><td></td><td></td><td></td><td></td><td>YES</td><td>YES</td></tr><tr><td>Away Team</td><td></td><td></td><td></td><td></td><td>YES</td><td>YES</td></tr><tr><td>Calendar</td><td></td><td></td><td></td><td></td><td>YES</td><td>YES</td></tr><tr><td>Constant</td><td>-0.225***<br>(0.0394)</td><td>0.186**<br>(0.0943)</td><td>-0.173**<br>(0.0834)</td><td>0.328*<br>(0.187)</td><td>0.584<br>(1.137)</td><td>0.431<br>(0.521)</td></tr><tr><td>Observations</td><td>6,383</td><td>1,208</td><td>1,130</td><td>217</td><td>217</td><td>1130</td></tr><tr><td colspan="7">Standard errors in parentheses<br>*** p&lt;0.01, ** p&lt;0.05, * p&lt;0.1</td></tr></tbody></table>
<table><thead><tr><th>VARIABLES</th><th>L13<br>W</th><th>L14<br>W</th><th>L15<br>W</th><th>L16<br>W</th><th>L17<br>W</th></tr></thead><tbody><tr><td>Closed</td><td>-0.465**<br>(0.211)</td><td>-0.448**<br>(0.216)</td><td>-0.670<br>(0.498)</td><td>-0.391<br>(0.240)</td><td>-0.476**<br>(0.221)</td></tr><tr><td>Home Team</td><td></td><td></td><td></td><td></td><td>YES</td></tr><tr><td>Away Team</td><td></td><td></td><td></td><td></td><td>YES</td></tr><tr><td>Calendar</td><td></td><td></td><td></td><td></td><td>YES</td></tr><tr><td>Constant</td><td>-0.243***<br>(0.0238)</td><td>-0.260***<br>(0.0520)</td><td>0.218*<br>(0.119)</td><td>-0.374***<br>(0.0583)</td><td>-0.445<br>(0.332)</td></tr><tr><td>Observations</td><td>7,261</td><td>1,605</td><td>303</td><td>1,302</td><td>7,261</td></tr></tbody></table>

Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1

<caption>Table 12: Logits regressions of models in Table 8</caption>