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Home advantage in professional tennis

Article (PDF Available) inJournal of Sports Sciences 29(1):19-27 · January 2011
DOI: 10.1080/02640414.2010.516762 · Source: PubMed
Ruud H. Koning at University of Groningen
  • 24.97
  • University of Groningen
Abstract
Home advantage is a pervasive phenomenon in sport. It has been established in team sports such as basketball, baseball, American football, and European soccer. Attention to home advantage in individual sports has so far been limited. The aim of this study was to examine home advantage in professional tennis. Match-level data are used to measure home advantage. The test used is based on logit models, and consistent specification is addressed explicitly. Depending on the interpretation of home advantage, restrictions on the specification of the model need to be imposed. We find that although significant home advantage exists for men, the performance of women tennis players appears to be unaffected by home advantage.
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Home Advantage in Professional Tennis!
Ruud H. Koning!
.
May 9, 2009
Abstract
Home advantage is a pervasive phenomena in sport. It has been well-established
in team sports as basketball, baseball, and American football in the US, and soccer
in Europe. Less attention has been given to home advantage in individual sports.
In this paper we examine the existence of home advantage in professional tennis.
We use match-level data to measure home advantage. Our test is based on logit-
models, and consistent specification is addressed explicitly. Depending on the in-
terpretation of home advantage, restrictions on the specification of the model need
to be imposed. We find that significant home advantage exists for men, but such an
advantage cannot be found for women.
Keywords: home advantage, tennis, logit model, consistent specification.
1INTRODUCTION
Home advantage is a well established phenomena in different professional sports; a
recent overview is given in Stefani (2008). Most of the published results concern home
advantage in team sports, although it is well known that the medal count of a coun-
try organizing Olympic Games tends to be higher than expected (Balmer, Nevill, and
Williams, 2001, 2003). Because most of the elements of the Olympic competition are
individual events, this suggests that home advantage exists in individual sports as well.
Only a few studies focus explicitly on home advantage in individual sports. A small
but significant home advantage in speed skating is established in Koning (2005), and
Nevill, Holder, Bardsley, Calvert, and Jones (1997) document little evidence of home
advantage in professional tennis and golf. In this paper, we estimate home advantage
!This paper has benefitted from research assistance by Wim Siekman, Stefanie Bouw, and Tirza de Rond.
!Department of Economics & Econometrics, Faculty of Economics and Business, University of Gronin-
gen, PO Box 800, 9700 AV Groningen, The Netherlands, r.h.koning@rug.nl.
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in professional tennis, using a different methodology from the ones used before. Our
approach uses individual match outcomes as opposed to individual player performance
at a tournament level. The contribution of this paper is three fold. First, we propose a
logit model to analyze outcomes of tennis matches. Home advantage is defined in the
context of this model, and special care is taken to obtain a logically consistent model
specification. Second, we measure not only home advantage in individual matches, but
also as improved access to tournaments for home players. Third, we use a much ex-
panded data set compared to earlier studies. This allows us to assess whether home
advantage has changed over time and/or varies between type of tournament.
The plan of the paper is as follows. In section 2 we present a literature review and
introduce our measures of home advantage. Specification of the model is presented in
section 3. A description of the data and empirical results are given in section 4 and
section 5 concludes.
2LITERATURE REVIEW
Home advantage is a well established phenomena in different professional sports. Re-
cent overviews documenting and interpreting its existence are given in Nevill, Balmer,
and Wolfson (2005) and the articles following in that special issue, and Stefani (2008).
Usually, home advantage is defined as ‘the consistent finding that home teams in sports
competitions win over 50% of the games played under a balanced home and away
schedule’ (Courneya and Carron, 1992). However, in the context of individual sports,
where winners are usually determined in a tournament held in a fixed location, this def-
inition is not applicable. Instead, a definition of home advantage as in Koning (2005)
is more useful: ‘Home advantage is the performance advantage of an athlete, team or
country when they compete at a home ground compared to their performance under
similar conditions at an away ground’.
In this paper, we consider tennis so we need to define ‘performance’ more pre-
cisely. Also, we need to be more explicit about ‘similar conditions at an away ground’
since this is a unobserved (counterfactual) situation. We deal with this last issue in the
context of the empirical models we use in the next two sections. As far as performance
is concerned, one can think of two different measures of performance of a tennis player
in a given tournament. The first is the tournament ranking of a player. The winner of a
tournament receives tournament raking 1, the losing finalist tournament ranking 2, the
losing semi-finalist tournament raking .3 C4/=2, and so on. The other measure of per-
formance is winning a particular match. The first measure is related to the sum (during
a tournament) of the second measure. Since we want to test for the existence of home
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advantage, we also need to make ‘similar conditions at an away ground’ operational.
We discuss that issue in the context of the statistical models below.
Home advantage is usually attributed to four factors:
"crowd support;
"familiarity with local circumstances;
"fatigue due to travel time;
"specific rules that favor either the home or away team.
Crowd support may help a home player to perform better, to prevent him or her giv-
ing up sooner, and intimidate the opponent (assuming that that is not a home player).
It is less likely that crowd support influences the decisions made by referees in ten-
nis, as these are usually not from the home country. Moreover, the introduction of the
‘challenge’, where a player can ask for a video replay to see if a ball is in or out,
has decreased reliance on decisions by the referee and line judges. Unfortunately, we
don’t have data on the consistency of the crowd (that is, the ratio of home supporters
to away supporters) so we cannot assess the influence of consistency of the crowd on
home advantage as in Nevill, Newell, and Gale (1996). The argument of crowd sup-
port in the case of tournament style competitions can be broadened to include local
sponsor interests. Individual tennis players are frequently sponsored by sponsors from
their home country. Such sponsors may be present at the tournament, or the player may
be involved in promotional activities during the tournament. In any case, good perfor-
mance of a player on hoe ground makes it easier to renew existing contracts or find new
sponsors. The importance of home sponsor support will be greater for subtop players
than for top players, who are usually sponsored by sponsors operating globally. Famil-
iarity with local circumstances may be relevant if players from, say Spain, have had
the opportunity to practice much more on gravel tennis courts than other players. As
tournament venues are usually not used for practice, only the type of surface may be
a factor in determining home advantage. It is unlikely that home advantage is caused
by travel fatigue for away players. All players travel from tournament to the next tour-
nament, travel time does not vary very much between players. Moreover, during the
tournament the players reside in hotels close to the venue. Finally, rules favor home
players to the extent that tournament organizers have some leeway to allow a player
to enter the tournament for other reasons than his or her world ranking. In particular,
most tournaments offer wild-cards to local players, giving home players home advan-
tage through improved access to the tournament. In section 4 we will establish whether
this is indeed the case.
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Home advantage in tennis has been studied before, mainly in Nevill et al. (1997)
and Holder and Nevill (1997). In these papers, the measure of performance is the tour-
nament ranking of a player and the central question that is answered is whether or
not home players achieve a better than expected tournament ranking given their world
ranking at the time of the tournament. The data used to test for home advantage are the
four Grand Slam tournaments of 1993 (the Australian Open, the French Open, Wim-
bledon, and the US Open). Home advantage is estimated by first estimating a model
where the log of tournament rank is regressed on the log of the world rank of a player.
This baseline model is then extended with a dummy measuring home advantage (al-
lowing for a different intercept for home players), and an interaction term between
home advantage and log world rank (allowing for a different slope for home players).
In both papers, the authors find little evidence of home advantage. Home players per-
form significantly better only in the Wimbledon tournament, there is no evidence of
home advantage in the other three tennis tournaments. Holder and Nevill attribute this
finding to ‘an anomaly of the data collection’ (p. 556): in their study ranks are missing
for away players at Wimbledon with a rank lower than 100, but world rank is available
for all home players. Our data used below do not suffer from this issue. The approach
by Holder and Nevill is based on an implicit comparison between the performance of a
home player with a certain world rank and an away player with that same rank. Since
the strength (world rank) of the opponents does not enter as a covariate, one has to
assume that the tournament draws for both players are identical.
In our approach below, we use match level data, and we relate the outcome of a
match to observed covariates. Other studies have identified relevant covariates that we
can use in our baseline model or as variables to moderate the effect of home advantage.
In a recent paper, del Corral (2009) estimates match uncertainty in tennis. He shows
that Grand Slam tournaments have become less competitive since 2002 when the num-
ber of seeded players was increased from 16 to 32. According to his empirical results,
competitiveness in tennis varies by sex, round, and surface. Also, he shows that upsets
are mainly determined by quality differences between players (as measured by the dif-
ference in their rankings). In his empirical analysis, he does not take home advantage
into consideration.
Klaassen and Magnus (2001, 2003) model the probability of winning a tennis match
differently. In Klaassen and Magnus (2001) they focus on winning a point in a game,
instead of winning a complete match. In that paper, the probability of winning the next
point by the player who serves depends on time-constant covariates (quality difference
between players, absolute quality of the match) and on time-varying covariates (who
won the previous point, first point of the game, importance of the next point). Klaassen
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and Magnus (2001) that the probability of winning the next point depends positively
on the quality difference between both players, and positively on the absolute quality
of the match. Their focus is on testing whether or not points are independently identi-
cally distributed, so their attention for other covariates is limited. They do report that
they experiment with some other covariates, but home advantage is not one of them.
Klaassen and Magnus (2003) follow up on that paper and consider the probability that
a player wins a match (instead of the next point). This problem is close to the one we
consider in the next section. They estimate the probability that the better player (as
measured by world ranking) wins against the weaker player. Difference of quality is
the main covariate. Again, they do not take home advantage into account.
3MOD EL SP EC IFI CATI ON
In this paper, we take ‘winning a match’ as the measure of performance in tennis.
The main reason to prefer this measure over tournament ranking is that it allows us
to measure home advantage while taking a given draw into account. When estimating
the performance of a home player, it matters if a home player with world rank, say, 50
plays a top ten player in the first round, or a lucky qualifier. Considering the availability
of individual match data, we can estimate the probability of winning a match easily,
and let this probability depend on certain covariates. Obviously, home advantage and
quality of the players are important covariates to be included in a model. However, the
use of match data imposes restriction on the specification that can be estimated. This
issue we address first.
Consider two arbitrary tennis players, Aand B. Since Aand Bare chosen arbitrar-
ily, we do not know which one is the higher ranked player, or which one plays at home.
One can consider these players as being drawn randomly from the pool of participants
in a tournament. The individual characteristics of Aand Bare measured as vectors
xAand xB. This vector xmay include world ranking, height, weight, age, number of
career wins, etc. Also, there are variables that are common to both players, such as the
time of the match, or the surface, and these common characteristics are denoted by the
vector zAB. The probability that Awins against Bis denoted as Pr.A > B/. We model
this probability by a logit model:
Pr.A > B/ D1
1Ce!f .xA;xB;zAB /#!.f .xA;x
B;z
AB//: (1)
The term f .xA;x
B;z
AB/is also known as the index of the logit model. From equa-
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tion (1), the probability that Adoes not win, and therefor Bwins is
Pr.B > A/ D1$Pr.A > B/ De!f.xA;xB;zAB/
1Ce!f .xA;xB;zAB /
D1
1Cef .xA;xB;zAB /D!.$f .xA;x
B;z
AB//:
Alternatively, it follows directly from equation (1) that
Pr.B > A/ D1
1Ce!f .xB;xA;zAB /D!.f .xB;x
A;z
AB//:
Hence, consistent model specification requires that
f .xA;x
B;z
AB/D$f .xB;x
A;z
AB/for all xA;x
B;z
AB:(2)
If we assume that the index is linear
f .xA;x
B;z
AB/Dˇ0
1xACˇ0
2xBCˇ0
3zAB;
with ˇ1,ˇ2, and ˇ3vectors of parameters to be estimated, restriction (2) takes the form
ˇ0
1xACˇ0
2xBCˇ0
3zAB D$ˇ0
1xB$ˇ0
2xA$ˇ0
3zAB
ˇ0
1.xACxB/Cˇ0
2.xBCxA/C0
3zAB D0:
If this is to hold for all xAand xB, clearly ˇ3must be zero. In other words, covariates
common to both players (such as surface, or day of the week) cannot enter the index as
main effects. Also, the first two terms in the sum collapse to
1Cˇ2/0.xACxB/D0:
If this is to hold for all xAand xB, we have ˇ2D$ˇ1and the index must satisfies
f .xA;x
B;z
AB/Dˇ0
1xACˇ0
2xBCˇ0
3zAB Dˇ0
1.xA$xB/: (3)
In other words, only differences between covariates can enter the index. From an intu-
itive point of view, this is straightforward. Suppose the only covariate is a dummy hA
taking the value 1 if the player Aplays at home. Also, suppose home advantage is posi-
tive. If player Balso plays at home (so player Aand Bare of the same country, playing
in their home country), it is not possible that the winning probabilites of both player
Aand player Bincrease due to the home advantage. Note that another implication of
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restriction (3) is that the model cannot have an intercept.
Klaassen and Magnus (2001, 2003) do not address this identification issue explic-
itly. In Klaassen and Magnus (2001) a restriction as in (2) does not appear in their linear
probability model because there is no restriction between the probability that player A
wins the next point when he serves, and that player Bwins the next point when he
serves. Both players do not serve simultaneously for the next point. In Klaassen and
Magnus (2003) they estimate a logit model as implied by (3), without discussing the
general nature of the identification problem above. They do impose one restriction
based on the observation that Pr.A > B/ D1
2if both players are equally strong: they
do not include an intercept in the logit model.
In this framework we measure home advantage by estimating the coefficient "in
ˇ0.xA$xB/C".hA$hB/:
If both players are playing at home, hA$hBis zero, and this is also the case if both play-
ers meet away. If Aplays at home and Bdoes not, Ais expected to have a home advan-
tage of "(on the logit scale), if Bplays at home and Adoes not, Ahas a disadvantage
of ". In other words, Bhas an advantage of ". If home advantage exists, we should
find "> 0 and significant.
In this setup it is easy to moderate the effect of home advantage. Home advantage
may vary with common covariates, for example, it may be weaker in Grand Slam tour-
naments. There are two possibilities to include moderating variables. First, one can
condition on values of xA$xBor zAB and estimate the model for each subgroup. In
other words, one estimates a model as (1) for each value of the moderating variable
(for example, tournament is a Grand Slam tournament or tournament is not a Grand
Slam tournament). Second, one can include interactions as covariates in the index, for
example zABhA$zABhB. In this case, it is not necessary to include main effects in the
index since these will have zero coefficient according to (3).
4DATA AND TESTS
We assess participation in tournaments and estimate the model of the previous section
using data on both professional men and women tennis tournaments. We have data
on 22811 matches for men, over the period 2000 to 2008 (partially). The dataset con-
tains information on the outcome of the match, the tournament, the date of play, round
within the tournament, surface of the court, and the final result. Also, the dataset has
information on the world ranking of both players at the beginning of the tournament,
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Table 1: Access to tennis tournaments, men (2000-2008) and women (2007-2008)
home ERHERAtail number
men
Grand Slam 0:10 221:4 178:7 0:18 37
Masters 0:11 140:8 116:1 0:21 90
International 0:18 206:0 85:7 0:80 474
women
Tier I 0:13 152:8 88:3 0:28 19
Tier II 0:11 129:8 63:4 0:49 29
Tier III/IV 0:10 204:8 90:9 0:88 62
home indicators, and (fixed) betting odds before the match. The men dataset has three
tournament types (in decreasing order of importance): Grand Slam, Masters, and In-
ternational tournaments. The dataset for women has the same information, but is much
smaller in size: 2896 matches played in 2007 and 2008. Tournament types that we can
distinguish are Tier I and Grand Slam tournaments, Tier II and Tier III/IV tournaments.
Our empirical strategy to test the existence of home advantage is as follows. First,
we test for home advantage through better access to tournaments for home players.
Then, we focus of home advantage given the set of participants of a tournament. We
start by finding a baseline model that explains the outcome of a match satisfactorily.
Of course, this specification satisfies consistency condition (3) of the previous section.
Then, we extend this baseline specification with a home advantage dummy to assess
whether or not home advantage exists. Finally, we establish whether a home advantage
effect, if any, is moderated by other variables.
First, we consider improved access to tournaments as a type of home advantage. In
tennis, the organization of a tournament has the option to give so called ‘wild cards’
to eligible players (see for example Chapter VII, article 7.12 of the ATP Rule Book).
These wild cards are entries in the main draw of the tournament, and are awarded at
‘the sole discretion of the tournament’. Usually, they are given to players from the home
country, young and talented players, or players who make a comeback after an injury.
Wimbledon winner of the men’s tournament in 2001, Goran Ivaniˇ
sevi´
c, participated in
that tournament on a wild card, while being ranked only 125th on the World Ranking at
that moment. High ranked players are guaranteed entry to a tournament, lower ranked
players may have to play a qualification tournament or depend on a wild card.
There are no standard measures for home advantage through improved access to
a tournament. We propose to measure improved access by looking at the world ranks
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of weaker participants of a tournament. A tournament with krounds has 2kpartic-
ipants. Hence, the strongest draw possible would have players with world ranking 1
to 2k. In practice, players with a weaker ranking than 2kwill also participate, for ex-
ample because they receive a wild card from the organization of the tournament, or
because they qualify through a qualification tournament that precedes the main tour-
nament. Especially tournaments of lower importance are played simultaneously and
therefor one would expect that more lower ranked players participate in such tourna-
ments. Organizers have to contract enough players to have a complete schedule, and a
player can participate in only one tournament at a given moment in time. Should home
players have improved access to tournaments through this mechanism, we should find
that among the weaker participants , home players are weaker than away players. We
measure this as follows. If the best players participate in a tournament, the weakest
observed rank is 2k. The number of players with a lower rank (i.e., ranked on a higher
position than 2k), and their ranks, indicate the strength of the tournament. For each
tournament in our sample, we calculate the median position of home and away play-
ers ranked lower than 2k. Then, this median is averaged over all tournaments, and the
results are in columns labeled ERHfor home players and ERAfor away players in ta-
ble 1. The numbers should be interpreted as follows. In Grand Slam tournaments for
men, 128 players participate. In an average Grand Slam tournament, the median rank
of home players whose rank is weaker than 128, is 224.5. The same number is 178.2
for away players. Participating home players not in the top 128 of the world ranking
are of lesser quality than participating away players not in the top 128 of the world
ranking. This effect is found for all types of tournaments, both for men and women.
The difference is significant as can be established using a permutation test approach.
For each tournament, we condition on the observed ranks of the weaker players (those
with a world rank weaker than 2k) and the number of home players. The ‘home’ label
is then permuted over the observed ranks and we calculate the average median ranks
for home players and away players, and its difference, for this permutation. We repeat
this procedure a large number (10000) of times to obtain the distribution of the dif-
ference of the average median ranks, under the null hypothesis that the ranks of home
players and away players are distributed similarly (that is, according to the observed
ranks of the weaker players for each tournament). The p-values of the observed differ-
ences obtained from Table 1 is 0 in all six cases. Moreover, notice that the difference is
largest in tournaments of lowest importance, as expected. In the fifth column we give
the fraction of players with a lower rank than 2k. 18% of the players in Grand Slam
tournaments for men are ranked lower than 128. Clearly, this fraction is much higher
for the least important types of tournament in our dataset, both for men and women.
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The last column in Table 1 gives the number of tournaments of each type in our dataset.
From Table 1 we conclude that there is clear evidence that home players have improved
access to tournaments, especially to tournaments of lower importance.
Now that we have established that there is home advantage by improved access to
participation in tournaments, we proceed to measure home advantage within the tour-
nament. We follow the approach discussed in the previous section, so first we specify a
baseline model that depends on individual specific covariates only, possibly interacted
with moderating variables. In our data set, only three individual specific covariates are
available: home advantage, odds of winning as offered by different bookmakers, and
world ranking. We do not want to include odds offered by bookmakers as a covariate,
even though that information will generate a very good baseline model, see McHale and
Forrest (2005) and Koning (2009). The reason is that home advantage will be priced
into such fixed odds already. In fact, if betting odds contain all relevant information on
the result of a tennis match, we could use betting odds as the dependent variable. How-
ever, McHale and Forrest (2007) and Koning (2009) show that betting odds in tennis
are not unbiased predictors of the outcome of a tennis match so we do not use betting
odds as the dependent variable. This leaves us with world ranking as the individual
specific covariate. Variables common to both players are type of tournament, quality of
the match (measured by the sum of the rankings of both players, this variable is catego-
rized in four quartiles), surface, and year. The choice between entering the difference
of world ranks in equation (1) in levels, or in logarithms with base 2 as suggested by
Klaassen and Magnus (2001) is left to the data. As our baseline specification, we use
the model that provides the best fit.
So first, we estimate two models:
Pr.A > B/ D1
1Cexp.$ˇ.log WRA$log WRB// ;(4)
and
Pr.A > B/ D1
1Cexp.$ˇ.WRA$WRB// :(5)
To choose between these specifications we draw calibration plots in Figure 1, where
the actual outcome of a match is regressed on the predicted probabilities obtained
from models (4) and (5). If the estimated probabilities from these models are unbi-
ased estimators of the actual outcome, the calibration curve would be coinciding with
the 45-degree line. Clearly, the model with difference of the logarithm of world ranks
(model (4), top panels in Figure 1) provides a much better fit than the model with dif-
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fitted probability
outcome
0.2
0.4
0.6
0.8
0.2 0.4 0.6 0.8
men
absolute
women
absolute
men
log
0.2 0.4 0.6 0.8
0.2
0.4
0.6
0.8
women
log
Figure 1: Calibration of baseline model, for men and women, world ranking difference
in logs and levels.
ference of absolute levels of world ranks (model (5), lower panels in Figure 1). For
this reason, we use the difference of log world ranks only, and denote this variable as
log WR #log WRA$log WRB.
In a second step, we test whether the effect of world rank in our baseline spec-
ification needs to be moderated by interacting it with absolute quality of the match
(measured by the sum of world ranks of both players), round, and type of tournament.
We test significance of these interactions at a 99% confidence level, because of the size
of the data set. For men, it turns out that the type of tournament is significant. The es-
timation results of the preferred specifications are given in Table 2. The first four lines
in that table give the estimate of ˇas in model (4) for each of the tournament types
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Table 2: Home advantage and baseline specifications for men and women
O
ˇs.e.( O
ˇ)O
ˇs.e.( O
ˇ)
men
.log WR/%GrandSlam $0:453 0:016 $0:456 0:017
.log WR/%Masters $0:301 0:014 $0:304 0:014
.log WR/%International $0:388 0:010 $0:392 0:010
.log WR/%ATP $0:457 0:061 $0:447 0:066
HA 0:143 0:029
women
log WR $0:510 0:018 $0:517 0:020
HA 0:122 0:083
indicated. The estimated coefficient is negative, as expected: if player Ahas a better
ranking than player B, he is more likely to win. The relation between world rank and
the probability of winning is slightly stronger for women, as is seen from the more
negative coefficient in the first line of the second part of Table 2. In the case of women,
none of the potentially moderating variables has a significant effect.
It is now easy to test for the presence of home advantage in individual tennis
matches, by extending the model with a home advantage dummy. The estimates of
the coefficient are given in the rows labelled HA in Table 2. The effect is positive and
significant at any reasonable level of significance for men, and positive but insignificant
for women. From now on, we focus on home advantage for men only. Similar to our
search for a baseline specification, we interact home advantage with variables that are
common to both players to asses whether other variables moderate the home advantage
effect. Home advantage does not vary by type of tournament. We do find that home
advantage varies significantly with the absolute quality of the match, as measured by
the sum of the world rankings of both players. We recode the quality variable into four
quartiles, so that the first quartile corresponds to matches of high quality (the sum of
both rankings is low), and the fourth quartile corresponds to matches of lowest qual-
ity. The estimation results are given in Table 3 and it appears that home advantage is
stronger in games of high quality, and decreases to zero for games of lowest quality.
Home advantage may tip the balance in a match between two very good players, and
is not important in deciding the outcome in a match between two weak players. In
fact, home advantage does not seem to exist in a match between two weak players.
That outcome is determined by relative world ranking and the type of tournament. We
also examine whether home advantage varies with other common characteristics of the
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Table 3: Home advantage interacted with quality of match (men)
O
ˇs.e.( O
ˇ)
.log WR/%GrandSlam $0:455 0:017
.log WR/%Masters $0:305 0:014
.log WR/%International $0:387 0:010
.log WR/%ATP $0:441 0:066
HA %Q10:341 0:067
HA %Q20:261 0:063
HA %Q30:109 0:057
HA %Q4$0:006 0:048
match, as the type of surface, year, country, round in the tournament, or being the fa-
vorite. Not one of these variables turns out to be significant, so we conclude that our
final specification in Table 3 is quite robust.
To interpret the relative magnitude of home advantage compared to the effect of
differences in world ranking, we use an equivalence scale. Consider two players Aand
Bwho play a match away. Suppose we were to substitute player Aby another player,
A", who plays at home. Because player A"plays at home, he can be a lower ranked
player than player A, and still have the same winning probability against player B.
This, of course, is due to home advantage. Let ıHA be the effect of home advantage.
Then the world ranking of player A"playing at home that is equivalent to the world
ranking of player Aplaying away follows from
ıHA Cˇ.log WRA!$log WRB/Dˇ.log WRA$log WRB/:
A player with world rank WRA!D2!ıHA WRAwho plays at home has an equal win-
ning probability against player Bas a player with world rank WRAwho does not play
at home. The estimates of Table 3 are translated into the factor 2!ıHAin Table 4. It is
clear that this factor decreases for all tournament types when matches of lesser quality
is considered. The magnitude of the estimates are large. Consider a match in a Grand
Slam tournament., between the world rank number 3 and the world rank number 7, both
playing away. This match is in the first quartile of the quality distribution. According
to the results in Table 4, a home player with world rank 12 (&1:68 %7) has the same
probability of winning against the world rank 3 player as the away player with world
rank 7. Home advantage amounts to five places on the world ranking in this case.
13
(c) 2009 www.rhkoning.com
Table 4: Home advantage expressed as equivalence factor (men)
GrandSlam Masters International ATP
Q11:680 2:169 1:842 1:708
Q21:488 1:810 1:597 1:507
Q31:181 1:282 1:217 1:188
Q41:000 1:000 1:000 1:000
5CONCLUSIONS
In this paper, we have tested for the existence of home advantage in professional ten-
nis, both for men and women. Home advantage was measured along two dimensions:
as improved access to tournaments, and as an increased probability of winning a tennis
match, given the relative strength of both players. Our main findings are as follows.
First, both in men tennis and women tennis we see that home players have signifi-
cant improved access to tournaments, for example through wild cards, qualification
tournaments preceding the main draw, or direct invitation by the organization of the
tournament. This is especially prevalent in tournaments of lesser importance. Second,
using a consistent specification to model outcomes of individual matches, we found
in men tennis a significant and quantitatively important home advantage effect. This
effect is strongest in matches between highly skilled opponents, and absent when we
consider a match between two weak players. No such home advantage effect in indi-
vidual matches is found for women.
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the Summer Olympic Games. Journal of Sports Sciences 21(6), 469–478.
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forthcoming.
Courneya, K.S. and A.V. Carron (1992). The home advantage in sport competitions: A
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15
(c) 2009 www.rhkoning.com
  • ... Tennis is a sport discipline which depends on different fields of study, such as physiology ( Fernández et al., 2006), sport injuries (Van der Hoeven and Kibler, 2006), psychology (Koning, 2011) and sport strategy (Katić et al., 2011). This research focuses on the field of sport strategy, analyzing some common sequences and mechanisms that players − consciously or not − execute to deal with key situations. ...
  • ... Since our data include both home and away games, we also control for the home advantage, which was found to be significant in previous studies (Terry et al., 1998;Garicano et al., 2005;Koning, 2011). Hence, we created a dummy variable that was assigned the value of 1 if a game was played on Team A's home field and the value of 0 if a match was played on Team B's home field. ...
  • ... Other opposing sports such as martial arts have not been left out of the investigation when checking HA (Krumer, 2017). However, the effects of HA in tennis were questioned (Koning, 2011). Continuing with the previous study, the concept of HA was evidenced in qualifying tournaments, but it was especially among highly qualified players when HA showed higher values without distinguishing by gender, the effects disappearing when the quality of both tennis players was low, both in boys and girls. ...
    ... Starting from a quality from 1 negative to more than 8, they were 39% of the records, of these with 409 victories represented 77% of the total. Other researches have shown consistency in the influence of athletes' quality in handball (Oliveira, , water polo ( Gómez et al., 2016), judo (Krumer, 2017), tennis (Koning, 2011), and boxing ( Balmer et al., 2005). The adversary's quality effect on tennis was important when studying HA exclusively with high-quality opponents, but not with inferior opponents (Koning, 2011). ...
    ... Other researches have shown consistency in the influence of athletes' quality in handball (Oliveira, , water polo ( Gómez et al., 2016), judo (Krumer, 2017), tennis (Koning, 2011), and boxing ( Balmer et al., 2005). The adversary's quality effect on tennis was important when studying HA exclusively with high-quality opponents, but not with inferior opponents (Koning, 2011). In his study on handball (Gómez, Lago-Peñas, Viaño & GonzálezGarcía, 2014) showed the influence of quality, which was a consistent predictor of the type of local or visiting boxing victory ( Balmer et al., 2005), depending on the period of analysis. ...
  • ... Since our data include both home and away matches, we also control for the home advantage, which was found to be significant in previous studies (Terry et al., 1998;Sutter & Kocher, 2004;Garicano et al., 2005;Koning, 2011). Hence, we created a dummy variable that was assigned the value of 1 if a match was played on Team A's home field and the value of 0 if a match was played on Team B's home field. ...
  • ... In order to control for players' abilities, based on Klaassen and Magnus (2001), we use the log 2 (rank) of players A (RankA) and B (RankB), where Rank is the most current world ranking of the respective player. We also control for several additional factors that may be important for winning a point on serve, such as height and BMI ( Krumer et al., 2016) as well as one of the players having a home advantage ( Koning, 2011). The variable that indicates having a home advantage by Player A gets the value of one if Player A competes at home and Player B does not. ...
  • ... It is quite a large effect if we take into account that the average time it takes to ski a penalty loop is about 25 seconds, meaning that when competing at home, a biathlete losses on average 2.5 seconds.7 F 8 To put this number into perspective, in the 2014 Sochi Olympic Games, the home biathlete Anton Shipulin was only 0.7 seconds away from a bronze medal after missing one shot. Finally, our findings also shed a new light on a large literature on home advantage, which is a well-documented phenomenon in team ( Dohmen and Sauermann, 2016) and individual sports ( Koning, 2011;Ferreira Julio et al., 2013;Krumer, 2017). This home advantage phenomenon can be attributed to crowd noise (Pettersson Lidbom and Priks, 2010), familiarity with facilities ( Pollard, 2002) as well as referee bias ( Sutter and Kocher, 2004;Garicano, Palacios-Huerta and Prendergast, 2005).8 ...
    ... Moreover, Lakie (2010) suggested that an increased heart rate may even enhance the shooting performance in biathlon by decreasing the pulsatile input to the rifle. Finally, our findings also shed a new light on a large share of literature on home advantage, which is a well-documented phenomenon in team ( Dohmen and Sauermann, 2016) and individual sports ( Koning, 2011;Ferreira Julio et al., 2013;Krumer, 2017). This home advantage phenomenon can be attributed to crowd noise (Pettersson Lidbom and Priks, 2010) and referee bias ( Garicano, Palacios-Huerta and Prendergast, 2005).9 ...
  • ... We also control for the home advantage, which was found to play a significant role in professional tennis ( Koning, 2011). Thus, the variable that indicates that the favorite has a home advantage gets the value of one if the favorite competes at home and zero otherwise. ...
  • ... Magnus (2001), we use the log2(rank) of players A (RankA) and B (RankB), where Rank is the most current world ranking of the respective player. We also control for several additional factors that may be important for winning a point on serve, such as height and BMI (Krumer,Rosenboim and Shapir, 2016) as well as one of the players having a home advantage (Koning, 2011). The variable that indicates having a home advantage by Player A gets the value of one if ...
  • ... Several previous studies have demonstrated the existence of HA in international sports competition. These include World Cup alpine skiing (Bray & Carron, 1993), speed skating ( Koning, 2005), tennis (Koning, 2011) and boxing (Balmer, Nevill, & Lane, 2005). Balmer, Nevill, and Williams (2001) showed that in the Winter Olympic Games HA varied between events, with a significantly greater advantage for disciplines in which officials directly judged outcome, such as figure skating and freestyle skiing. ...
  • ... The advantage of playing at home or Home Advantage (HA) in team sports has been a recurring theme for the last thirty years (Pollard, 1986). Subsequently, studies focusing on HA in speed skating (Koning, 2005), tennis (Koning, 2011) or handball (were undertaken. The existence of HA in the Winter (Balmer, Nevill, & Williams, 2001) and Summer Olympics (Balmer, Nevill, & Williams, 2003) was checked. ...
    ... The advantage of playing at home or Home Advantage (HA) in team sports has been a recurring theme for the last thirty years ( Pollard, 1986). Subsequently, studies focusing on HA in speed skating ( Koning, 2005), tennis ( Koning, 2011) or handball ( were undertaken. The existence of HA in the Winter ( Balmer, Nevill, & Williams, 2001) and Summer Olympics ( Balmer, Nevill, & Williams, 2003) was checked. ...
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