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Sequential Mapping of Game Patterns in Men and Women
Professional Padel Players
Rafael Conde-Ripoll,
1
Diego Mu ˜noz,
1
Adrián Escudero-Tena,
1
and Javier Courel-Ibá ˜nez
2
1
Sport Sciences Faculty, University of Extremadura, Cáceres, Spain;
2
Sport Sciences Faculty, University of Granada, Granada, Spain
Purpose: This study analyzed the sequences of actions in professional men and women padel players to identify common game
patterns. Methods: The sample comprised 17,557 stroke-by-stroke actions (N =1640 rallies) of the championship World Padel
Tour. Multistep Markov chains were used to calculate the conditional probabilities of occurrence of actions during the rally.
Results: Results revealed that men’s and women’s padel is mainly defined by 36 patterns constituting 55% and 63% of all actions
in the game, respectively, with the 10 most common sequences accounting for 42% to 45% of the game. There were recurrent
technical–tactical actions with specific offensive and defensive functions that were constantly reiterated during the rallies. In
men, the use of smash, volley, bandeja, direct, back wall, back-wall lobs, and direct lobs followed a foreseeable pattern up to 8
lags, whereas women described predictable interactions for volley, bandeja, direct, lobs, and direct lobs up to 5 lags and for smash
and back wall up to 4 lags. Conclusions: The ability of padel players to recall these patterns and enhance their anticipation skills
may potentially improve their performance. These findings contribute to a better knowledge of professional padel game dynamics
while providing coaches and players with useful information to optimize training and decision-making strategies.
Keywords:racket sports, performance analysis, anticipation, gender
The game of padel is spiking worldwide and has become one of
the most practiced racket sports, with 25 million active players and a
strong presence in over 60 countries.
1
Similarly, scientificinforma-
tion in padel has been constantly increasing in recent years, with a
particular interest in examining players’technical and tactical
performance during the competition.
2
Data informing on players’
performance is valued by coaches and athletes to set data-driven
benchmarks, devise suitable training environments, and design
tailored training drills to better prepare players for the competition.
3,4
Sports are dynamic environments wherein players are con-
stantly interacting and making decisions to minimize errors, deceive
the opponent, and successfully score the point. Accordingly, tech-
nical and tactical performance analysis of sports should provide
insight into the players’dynamics to explain the likely consequences
of a given action and determine potential successful strategies.
5
To this purpose, sequential network analyses are recommended to
identify likely sequences of players’interactions (ie, game patterns)
based on the probability of occurrence.
6
Notwithstanding, available
research on sport technical and tactical performance analysis has
been focused on individual-level performance, and the interaction
between players remains poorly understood.
7
This is particularly the
case of padel, with very few studies describing game patterns in
professional male players
8,9
and no data on women.
The players’ability to recall game patterns, anticipate what
will happen next, and make accurate decisions (ie, “read the game”)
is fundamental to sport talent development and achieving expert
performance.
10–12
Game pattern recognition is a trainable skill. A
traditional approach to improving sport pattern recognition and
anticipation has been through video-based interventions focused on
key perceptual–cognitive skills (eg, identifying postural cues, the
ability to differentiate genuine and deceptive actions). Cognitive
training has been widely examined in racket sports
13,14
given the
structured nature of the game and the existence of repeatable
patterns. However, because game context, physical and psycho-
logical load, and players’interactions matter to make better
judgements,
12,15
emerging approaches in racket sports advocate
for competitive training situations simulating match play condi-
tions to better transfer the decision-making performance in applied
and representative settings.
16,17
Despite the increasing knowledge
of how to develop training programs to enhance anticipation skills,
training interventions in padel remain an area of investigation.
Afirst step toward the development of match play conditions
to improve anticipation is the identification of key game patterns.
Recent studies in racket sports are successfully applying classifi-
cation techniques and machine learning algorithms to unveil
players’performances in a dynamic context (ie, how players
behave during a rally), including random forest, decision tree,
Markov chain, or bipartite networks.
8,18,19
In particular, Markov
chain models help to identify likely combinations of individual
actions in a sequential order (ie, the probability of occurrence of an
action given a previous one). Interestingly, Markov chains allow
the identification of both simple patterns (ie, action A followed by
action B) and serial patterns (ie, action A followed by B, C, and D).
This information has great applicability to understanding the
dynamic changes of players’performance over time, eventually
obtaining the probability of winning or losing a point once a given
pattern appears in the rally.
19
In practice, data from the competition
are highly valuable for coaches to replicate and improve plausible
contexts and scenarios.
20
In padel, one previous study used Markov
chains to identify simple sequences in elite male players, identify-
ing 40 simple patterns that accounted for 66% of the game actions.
8
However, this study was unable to find serial patterns, probably
because of the limited sample size, which limited our understand-
ing of the actual sequential map of actions during a padel rally,
Conde-Ripoll https://orcid.org/0000-0003-1272-5255
Mu˜noz https://orcid.org/0000-0003-4107-6864
Courel-Ibá ˜nez https://orcid.org/0000-0003-2446-1875
Escudero-Tena (adescuder@alumnos.unex.es) is corresponding author, https://
orcid.org/0000-0002-7196-5606
1
International Journal of Sports Physiology and Performance, (Ahead of Print)
https://doi.org/10.1123/ijspp.2023-0484
© 2024 Human Kinetics, Inc. ORIGINAL INVESTIGATION
First Published Online: Feb. 27, 2024
comprising 10 strokes, on average.
2
Furthermore, because differ-
ences in men’s and women’s playing styles have been documen-
ted,
21
the existence of particular game patterns in men and women
players needs to be confirmed.
According to this rationale, we conducted a stroke-by-stroke
sequential analysis to identify and classify technical–tactical game
patterns of professional men and women padel players in a
sequential map of likely actions during the rally. This information
aimed to provide a data-based knowledge on the most common and
effective game patterns to assist in the development of training
programs in padel trying to replicate and improve plausible con-
texts and scenarios. We expected to find 10 to 40 game patterns
highly repeated during the game that might constitute the basis for
technical–tactical professional padel performance.
8,18
Although
particular differences in men’s and women’s playing styles might
appear,
21
we anticipated finding common patterns at a professional
level in both competitions.
Methods
Sample
Stroke-by-stroke game actions were collected from 12 matches of
the professional men (n =7; 4 quarterfinals, 2 semifinals, and 1
finals) and women (n =5; 2 quarterfinals, 2 semifinals, and 1 finals)
participants in the padel championship World Padel Tour. The
matches took place at the San Pedro Alcántara Sports Palace in
Marbella, Spain. The men players (N =16; age =32 [7.3] y;
height =1.78 [0.05] m; laterality =1 left-handed) and the women
players (N =12; age =29.3 [6.5] y; height =1.68 [0.04] m; later-
ality =1 left-handed) had professional experience competing in
World Padel Tour tournaments, with a mean of 482.5 (100.1)
matches played for men and a mean of 391.8 (65.3) matches played
for women. No injuries were reported from 6 months before the
first match or during the matches under study. All procedures were
conducted according to the ethical standards in sport and exercise
science research
22
and the local ethics committee.
Data Collection and Codification
Game actions were collected through systematic observation using
LINCE PLUS video-analysis software v.2.1.0
23
by 2 sports ana-
lysts specialized in padel. Each analysis collected data from a
training sample (6 games) to test the interrater and intrarater
reliability by Cohen kappa (k) calculation.
24
The reliability of data
collection was high, with a very good strength of agreement
between (interrater reliability: k>.82) and within analysts (intrara-
ter reliability: k>.87). Individual actions were coded as discrete
events considering the stroke type (direct [D], volley [V], bandeja
[Ba], and smash [S]), the use of walls (back wall [Bw], side wall
[Sw], double wall [Dw], or no wall), height (lob [L] or ground
stroke), and effectiveness (continue, winner [Wi], or error [E]).
8,9
Because the service action is restricted by rules, serves were
excluded from the analysis. Detailed definitions are included in
Supplementary Table S1 (available online).
Sequential Analysis
Multistep Markov chains were used to calculate the conditional
probabilities (CProb) of occurrence of a target action given that
another action occurred through the rally.
18
An n-step transition
probability matrix was calculated to determine the likelihood of
occurrence of actions in a sequential order (ie, given x, then y
happens after nlags during the rally, where the lag means the time
from xto yin a sequential order during the rally) considering all
possible paths.
19
Sequences were defined as simple patterns (ie, 2
hits, 1 lag after the given action) and serial patterns (ie, more than 2
hits, n-lags after the given action). Pearson chi-squared test (χ
2
) and
degrees of freedom (df) were used to compare observed and
expected frequencies. Secondary analyses were performed to
obtain the probability of scoring (winners) or losing (errors) a
point once a given pattern appears in the rally. Adjusted standard-
ized residuals (zscores) were calculated for each possible combi-
nation of actions and time to identify game patterns greater than
those that would be obtained by chance, considering values from
1.96 to 2.58 as little, 2.58 to 3.29 as weak, and over 3.29 as strong
associations. The level of significance was set at P<.05 and z
scores >1.96 for all interactions with more than 10 occurrences.
Calculations were made in SDIS-GSEQ (version 5.1).
25
Results
The final sample comprised 10,634 game actions from 992 rallies
in men and 6923 actions from 648 rallies in women (Table 1). Both
men and women similarly used volley and bandeja (42%–44%),
walls (25%), and direct (24%–30%) to continue the point. Men
scored most of the points by smash and volley winners (88%),
whereas women used smash, volley, bandeja, and back wall
winners (72%). Lobs accounted for 24% to 28% of all actions
in both men and women.
Most common simple patterns (1 lag) are displayed in Table 2.
There were 47 simple patterns accounting for 63% of all actions
(men: 40 simple patterns, 61% of the sample; women: 41 simple
patterns, 70% of the sample). Men and women shared 36 simple
patterns constituting 55% and 63% of all actions in the game,
respectively. The top 10 most common patterns accounted for
42% and 45% of total actions in men and women, respectively.
The n-step probability matrix for serial patterns included 676
possible paths in men (Figure 1) and 573 in women (Figure 2). In
men (Figure 1), there were significant patterns from 1 to 8 lags for
smash, volley, bandeja, direct, back wall, back wall lobs, and direct
lobs (df =675, χ
2
from 757 to 14,335, zscores from 2.0 to 43.5,
P<.01) but not after 9 lags (P=.780). Most common serial
patterns for 2 lags were V-DL-Ba (n =396, Cprob =0.05), V-V-
V(n=275, Cprob =0.04), Ba-D-V (n =267, Cprob =0.03), D-V-
DL (n =246, Cprob =0.03), V-BwL-Ba (n =235, Cprob =0.03),
V-D-V (n =229, Cprob =0.03), and V-BwL-V (n =178, Cprob =
0.03). Most common serial patterns for 3 lags were Bw-Ba-D-V
(n =84, Cprob =0.02), Ba-D-V-DL (n =78, Cprob =0.02), and
D-V-Bw-V (n =54, Cprob =0.02).
In women (Figure 2), there were significant serial patterns
from 1 to 4 lags in smash and back wall (df =572, χ
2
from 745 to
965, zscores from 2.0 to 3.1, P<.01) and from 1 to 5 lags in volley,
bandeja, direct, lobs, and direct lobs (df =572, χ
2
from 745 to 8573,
zscores from 2.0 to 36.4, P<.01) but not after 6 lags (P=.577).
Most common serial patterns for 2 lags were V-DL-Ba (n =243,
Cprob =0.06), Ba-D-V (n =238, Cprob =0.06), D-V-DL (n =187,
Cprob =0.04), DL-Ba-D (n =174, Cprob =0.04), V-BwL-Ba
(n =124, Cprob =0.03), V-D-V (n =121, Cprob =0.03), and Ba-
DL-Ba (n =120, Cprob =0.03). Most common serial patterns for 3
lags were D-Ba-DL-Ba (n =118, Cprob =0.05), DL-Ba-D-V
(n =114, Cprob =0.05), V-DL-Ba-D (n =76, Cprob =0.03),
Bw-Ba-D-V (n =71, Cprob =0.03), and DL-Ba-DL-Ba (n =61,
Cprob =0.03).
2Conde-Ripoll et al
(Ahead of Print)
Figure 3displays the probabilities of winners and errors after
the most common game patterns. Smash winners reached the
highest winning rates (Cprob =0.07–0.18) after DL-S, Ba-BwL,
BwL-S, V-BwL, V-DL, V-V, BwL-V-D, and D-V-DL. Volley
winners reached higher rates (Cprob =0.05) after V-D and V-V.
Overall error rates were 0.04, with volley errors being more likely
(Cprob =0.05) after V-DL. In women, overall winning rates were
0.03. Volley winners reached higher rates (Cprob =0.075) after V-
D. Smash winners reached higher rates (Cprob =0.04) after V-DL.
Volley errors reached higher rates (Cprob =0.04–0.06) after V-V
and Ba-D.
Discussion
This study aimed to provide a comprehensive analysis of game
patterns in professional men and women padel players. Although
game actions have been widely examined in padel, information
about the dynamic relationships of sequences during the rally
remains poorly understood. To fill this gap, we provide information
on how actions are ordered in sequential game patterns during the
rally by providing the probability of occurrence of actions given the
previous or following one. Our findings identify repeatable patterns
shared by professional players that may serve as a benchmark for
training and highlight particular differences between men and
women to be considered by coaches.
Our results (Table 2) confirm earlier studies
8
suggesting that
padel comprises a reduced number of repeated patterns (36 in our
study) accounting for 60% of all game actions, with the top 10 most
common sequences accounting for 42% to 45% of the total.
Likewise, padel playing structure seems to have been steady in
the last decade. Compared with previous studies examining com-
petitions from 2012 and 2014,
9,26
we observed a similar use of
smashes (7%–8% vs 6%), walls (21%–23% vs 24%), and volleys
(23%–28% vs 27%–34%) but a larger use of lobs (24%–28% vs
12%–16%) and bandejas (15%–18% vs 9%). In essence, offensive
padel effectiveness relies on the ability to play near the net (by
volleys after ground strokes and bandejas after lobs) while the
defense tries to send the opponents to the backcourt (by lobs, both
direct, volley, or wall strokes), with caution to not return an easy
ball that may let the opponents score the point (by smash or volley).
Hence, winning the point is highly related to the time spent near the
net
9,27
and, thus, to the ability to recover the net zone from the
backcourt.
28
Our findings highlight the importance of mastering
lobs and bandejas (for gaining or maintaining control of the net
zone), as well as smashes and volley (for winning points), in
modern padel (Table 1). For this reason, padel players must focus
on improving their lobs and bandejas to get and maintain this
position near the net, increasing their chances of winning.
One main contribution of this study is the provision of a
sequential map of actions during the rally in both male and female
samples. We were able to describe probable sequences of actions
after up to 8 lags in men (Figure 1) and 6 lags in women (Figure 2).
These serial patterns constitute a step forward compared with
previous studies describing simple patterns
8
and represent a more
accurate picture of how players behave during a padel rally,
comprising 10 strokes, on average.
2
In addition, this time window
allows players to foresee how their opponents will likely respond to
their actions during the rally and assists coaches in designing
decision-making training tasks to improve the players’ability to
recall and anticipate the patterns.
Despite some sex differences being identified, there were
common game patterns highly used in both men and women,
accounting for 15% of the game; namely, V-DL-Ba, Ba-D-V, D-V-
DL, V-BwL-Ba, V-D-V, and V-DL-Ba. This information may
serve as a guideline to design padel technical–tactical drills that fit
with the professional competition demands. For instance, offensive
drills should improve players’ability to similarly perform volleys
and bandejas to maintain the net in response to a variety of ground
strokes and lobs. Furthermore, professional players had the ability
to maintain the net during the following 3 or 4 lags. A recent study
observed that there was no net exchange in 50.7% of the women’s
rallies, whereas in 65.9% of the men’s rallies, the servers kept the
net, increasing the chances of success.
27
In practical terms, after a
volley or bandeja, the opponent will likely respond with a lob or a
direct to send the attackers to the backcourt. According to our
findings, both women and men players were likely to concatenate
net strokes (volley or bandeja) for up to the next 3 and 4 lags.
However, some differences can be observed between men and
women. Volleys were more frequent in men, whereas women used
Table 1 Distribution of Game Actions of Professional Padel Players
Men Women
Continue Error Winner Continue Error Winner
n%n%n%n%n%n%
Volley 2744 28% 123 25% 97 25% 1434 23% 105 29% 87 38%
Bandeja 1540 16% 53 11% 19 5% 1211 19% 51 14% 36 16%
Direct 1228 13% 80 16% 12 3% 873 14% 58 16% 14 6%
Direct lob 1065 11% 36 7% —942 15% 30 8% —
Smash 690 7% 51 10% 246 63% 334 5% 33 9% 82 35%
Volley lobs 172 2% 2 <1% —80 1% 2 1% —
Back wall 513 5% 81 17% 14 4% 274 4% 42 12% 10 4%
Back-wall lob 869 9% 25 5% —592 9% 14 4% —
Side wall 260 3% 16 3% —148 2% 3 1% —
Side-wall lob 221 2% 7 1% —135 2% 4 1% —
Double wall 203 2% 13 3% —128 2% 10 3% 3 1%
Double-wall lob 249 3% 1 <1% 4 1% 184 3% 4 1% —
Sequential Mapping of Game Patterns in Padel 3
(Ahead of Print)
Table 2 Common Simple Sequences (1 Lag) in Men and Women Professional Padel Players
Given Then
Men Women
Given Then
Men Women
Given Then
Men Women
n ConP n ConP n ConP n ConP n ConP n ConP
Bandeja Back wall 141 0.09 75 0.06 Smash Back wall 36 0.09 38 0.12 Direct lob Bandeja 656 0.62 587 0.62
Back-wall lob 256 0.17 138 0.12 Direct 48 0.12 Smash 141 0.13 111 0.12
Direct 346 0.23 347 0.29 Double wall 46 0.12 14 0.05 Smash winner 84 0.08 38 0.05
Direct error 24 0.05 Side wall 25 0.06 Back-wall lob Bandeja 482 0.56 367 0.62
Direct lob 176 0.15 Smash winner 30 0.08 Smash 131 0.15 106 0.18
Double wall 111 0.07 83 0.07 Side-wall lob 23 0.06 Smash winner 54 0.06 19 0.05
Double-wall lob 163 0.11 131 0.11 Volley 140 0.43 Side-wall lob Bandeja 128 0.58 81 0.6
Side wall 29 0.05 24 0.05 Volley Back wall 220 0.08 83 0.06 Smash 35 0.16 22 0.16
Side-wall lob 28 0.05 33 0.05 Back-wall lob 426 0.16 188 0.13 Smash winner 19 0.09 12 0.09
Direct Volley 927 0.76 572 0.66 Direct 196 0.14 Double-wall lob Bandeja 162 0.65 106 0.58
Volley error 34 0.05 30 0.05 Direct lob 598 0.22 404 0.28 Smash 26 0.1 32 0.18
Volley winner 37 0.05 30 0.05 Side wall 25 0.05 Volley lob Bandeja 87 0.51 42 0.53
Back wall Volley 376 0.73 169 0.62 Side-wall lob 58 0.05 35 0.05 Back-wall lob 17 0.21
Volley error 14 0.06 15 0.06 Volley error 32 0.05 Smash 34 0.2
Volley winner 10 0.05 11 0.05 Volley lob 142 0.05 60 0.05
Side wall Volley 214 0.82 113 0.77
Double wall Volley 148 0.74 81 0.64
Volley winner 11 0.09
Abbreviation: ConP, conditional probability of occurrence given the initial action. Note: All results are significant (P<.01, zscores >1.96). Data include interactions with more than 10 occurrences.
4(Ahead of Print)
Figure 1 —Conditional probabilities (x-axis) of game patterns in sequential order during the rally (y-axis) in men’s padel games (N =992 rallies). Sequences start with a “given”action. The
conditional probability indicates the likelihood of occurrence of the following actions during the rally. The lag means the time from the given action to the following actions in sequential order.
Lighter-shaded lags (1, 3, 5, and 7) are the response from the opponents, and darker-shaded lags (2, 4, 6, and 8) are the returned actions from the same pair of players who performed the “given”action.
Data include sequences with more than 10 occurrences.
(Ahead of Print) 5
Figure 2 —Conditional probabilities (x-axis) of game patterns in sequential order during the rally (y-axis) in women’s padel games (N =648 rallies).
Sequences start with a “given”action. The conditional probability indicates the likelihood of occurrence of the following actions during the rally. The lag
means the time from the given action to the following actions in sequential order. Lighter-shaded lags (first, third, fifth, and seventh) are the response from
the opponents, and darker-shaded lags (second, fourth, sixth, and eighth) are the returned actions from the same pair of players who performed the “given”
action. Data include sequences with more than 10 occurrences.
6(Ahead of Print)
more bandejas. On the other side, professional players displayed
the ability to concatenate 3 or 4 defensive actions (lobs, direct, and
wall strokes) without committing errors. Indeed, winners and errors
accounted similarly to score the points (3% and 5% of the actions,
respectively); in other words, 8% of the shots result in ending the
rally, while the remaining shots contribute to sustaining the rally.
Altogether, these findings reinforce the importance of technical
skills and accurate decision making for professional development
in padel.
Another contribution of this study is the identification of the
probability of scoring (winners) or losing (errors) a point once a
given pattern appears in the rally (Figure 3). Whereas the use of
volleys and smashes as final actions is well known,
2
our results
provide a better understanding of preceding game patterns that may
increase the rates of winning. In men, the chances of winning
increased 3 to 4 times (from 4% to 10%–18%) when using a smash
as a response to an opponent’s smash or lob. These high rates can
be explained by the ability of professional players to take advantage
of an opponent’s ineffective offensive (eg, a smash in which the
ball, after bouncing on the ground and rebounding on the wall(s), is
easy for the player to counterattack and score the point) or
defensive actions (eg, a short lob that is susceptible to being
smashed). More specifically, our findings suggest the use of
bandejas and volleys to make it difficult for the opponent to
perform effective lobs, both direct lobs and back wall lobs. Finally,
volleys resulted in higher effectiveness to score the point after a
“volley duel”or an opponent’s direct; however, these sequences
similarly increased the rates of committing an error. Thus, master-
ing the use of volleys after these 2 scenarios could make the
difference between winning and losing the rally. In contrast to men,
women had the highest effectiveness in volleys (from 3% to 8%),
particularly after an opponent’s direct in response to a previous
volley. Likewise, the chances of committing a volley error drasti-
cally increased after a “volley duel.”These unexpected findings
point out the importance of volleys in women’s padel both to get
the opponent in trouble and eventually to score the point.
This study has some limitations that must be considered when
interpreting the results. The resulting game patterns are highly
dependent on the total number of actions examined. Although this
is the largest study on game patterns in padel to date (n =17,557
actions), future studies examining larger sample sizes are encour-
aged to identify further hidden patterns and confirm our results.
There are missing confounding variables that may alter the current
findings: the meteorological parameters, the scoreboard, the
players’streak (eg, hot hand and momentum), and the particular
pair of players’characteristics (side of play, height, ranking posi-
tion, and technical ability). In addition, the lack of spatiotemporal,
psychological, and time–motion information in our model limits
the full interpretation of the game patterns. The current data,
however, represent the foundation for future studies to conduct
an integrative match analysis involving multifactorial performance
indicators, as has already been done in other sports.
29
It should be
noted that the lack of studies that analyze playing patterns in padel
has been a limitation when discussing the results obtained. Given
the practical implication of sequential technical–tactical patterns to
design training drills, improve game pattern recognition and
anticipation skills, and better prepare players for competition,
future studies addressing missing confounding variables and exam-
ining different samples (eg, formative stages or amateur settings)
are encouraged. Furthermore, experimental studies are now
required to demonstrate the effectiveness of specific game pattern
training interventions in padel players’performance.
Practical Applications
In this study, we provided information on how actions are ordered
in sequential game patterns during the rally by providing the
probability of occurrence of actions given the previous or following
one. Findings identified repeatable patterns shared by professional
players that may serve as a benchmark for training and highlighted
particular differences between men and women to be considered by
coaches. Overall, padel comprised a reduced number of 10 repeated
simple patterns accounting for almost half of the game: D-V, DL-
Ba, V-DL, BwL-Ba, V-BwL, Bw-V, Ba-D, Ba-BwL, V-Bw, and
Sw-V (Table 2). When adding 1 hit to the sequence, men and
women professional padel players should master 6 serial patterns:
V-DL-Ba, Ba-D-V, D-V-DL, V-BwL-Ba, V-D-V, and V-DL-Ba
(Figures 1and 2). To increase the chance of winning a rally, in
addition to definitive smashes, players should master bandejas and
volleys to make it difficult for the opponent to perform effective
lobs, both direct lobs and back wall lobs. In addition, given that most
of the errors come from volleys after directs and volleys (volley duel),
coaches should pay attention to the net game and trained players either
to read the game and identify less risky alternatives to keep the ball on
the court (eg, lob) or to increase the volley performance to increase the
effectiveness after these 2 common scenarios.
Conclusions
Professional padel was classified by 36 simple patterns (sequences of
2 hits) constituting 55% (men) and 63% (women) of all actions in the
game. In particular, the 10 most common simple patterns represented
almost the half of the total game actions. In addition, there were 6
serial sequences (sequences of 3 hits). There were recurrent techni-
cal–tactical actions with specific offensive and defensive functions
that were constantly reiterated during the rallies. In men, the use of
smash, volley, bandeja, direct, back wall, back-wall lobs, and direct
lobs followed a foreseeable pattern up to 8 lags, whereas women
described predictable interactions for volley, bandeja, direct, lobs,
and direct lobs up to 5 lags and for smash and back wall up to 4 lags.
The ability of padel players to recall these patterns and enhance their
anticipation skills may potentially improve their performance.
Acknowledgments
The authors would like to extend their gratitude to Belén García Llanos and
Jose Manuel García Sánchez, padel coaches from ANTEP (Asociacio´n
Nacional de Técnicos de Pádel), who made a great contribution to the
investigation.
References
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International Padel Federation. Published online 2023. Accessed
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2. Martín-Miguel I, Escudero-Tena A, Mu ˜noz D, Sánchez-Alcaraz BJ.
Performance analysis in padel: a systematic review. J Hum Kinet.
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