Stroke Comparison between Professional Tennis Players and

Amateur Players using Advanced Computer Vision

Lisa Baily

, Nghia Truong

, Jonathan Lai

and Phong Nguyen

The American School in Japan, 1-1-1 Nomizu, Chofu-shi, Tokyo, Japan

Tokyo Techies, Shinbashi 2-16-1, Minato, Tokyo, Japan

Tokyo Coding Club, Nishi Azabu 3-24-16, Minato, Tokyo, Japan

Keywords: Pose Estimation, Pose Tracking, Machine Learning, Computer Vision, Euclidean Distance, Tennis, Analysis.

Abstract: In this paper, we created a method to find how professional and amateur tennis serves differ from each other.

We collected videos from online and from our own recordings and turned those videos into frames. From

those frames, we manually selected ones appropriate for our study and ran those through a pose estimation

system, which turned those frames into simple stick figures of the players including all the x and y coordinates

of the player. By normalizing all data, we were able to calculate the Euclidean distance between two compared

players’ joints and analyze their consistency in their serves. Our results from our t-tests showed that there was

a significant difference between the amateur’s consistency and the pro’s consistency and body parts like both

shoulders showed a significant difference.

1 INTRODUCTION

Tennis is a popular competitive and leisure sport that

is played in a one-on-one or two-on-two format. The

sport is largely composed of various “strokes” to keep

the ball in play, such as the forehand and backhand

strokes during a rally and a serve to start the game. Of

those strokes, the serve plays a critical role, as it has

been shown to be one of the two most important shots

along with the return in determining wins

(O'Donoghue and Brown, 2008). It is also a shot with

high variance, with variability in power, ball speed,

accuracy, ball impact location and angular velocities

(Whiteside, et al. 2014, Martin, et al. 2016,). Given

the serve’s significance and variance, amateur players

often observe professional players who compete at

international tournaments like Wimbledon and the

US Open to emulate the form of those top players and

improve their own serve. However, simply watching

them play is not nearly sufficient if the goal is to

understand the real differences between an amateur

and a professional

Today, computer vision is a rapidly growing

technology within the broader fields of computer

science and artificial intelligence (Arai and Kapoor

2019; Shavit and Ferens 2019). It is both fairly new

and has a wide range of applications. It can take in

images from videos or photos and provide numerical

evaluations. From those outputs, we can analyze data

more specifically and efficiently and derive

compelling results. Applications of computer vision

in the field of sports include but are not limited to

analysis and evaluation of tennis players (Mukai,

Asano, Hara, 2011), highlight detection (Ren, Jose,

2009) and support decision making (Owens, Harris,

Stennett, 2003).

We propose using computer vision to analyze

tennis shots, and potentially provide amateur players

with the level of specificity and data necessary to help

them improve. Although tennis includes many types

of strokes, we chose to focus on one of the most

important: the serve (O'Donoghue and Brown, 2008).

Although the serve does not require much movement,

as the shot is hit in one stationary location, the way

the serves are hit varies between players, thus making

it difficult to improve just by watching professionals'

play. With a computer vision algorithm, recognizing

what is different and how it is different from

professionals to amateurs will become clearer.

We first split the collected videos into frames and

then used an accurate pose estimation system to

simplify the frames into a stick representation of the

player. After normalizing all data into the same size

and making it comparable, we were able to analyze

the similarities and differences between professional

Baily, L., Truong, N., Lai, J. and Nguyen, P.

Stroke Comparison between Professional Tennis Players and Amateur Players using Advanced Computer Vision.

DOI: 10.5220/0010145800440052

In Proceedings of the 8th International Conference on Sport Sciences Research and Technology Support (icSPORTS 2020), pages 44-52

ISBN: 978-989-758-481-7

and amateur players, leading to the conclusion that

not only were the patterns between the professionals

and amateurs different, but that specific body part

positioning showed a significant divergence.

2 RELATED WORK

A survey of what has been already published in this

area revealed a range of existing publications that

agreed on the importance of analyzing the serve in

greater detail along with other strokes, but chose to

focus on different components.

In Whiteside et al. (2014), the researchers focused

on the tossing component of the serve and how

important the consistency of it is to the resulting serve.

From their research, they were able to recognize that

while professionals were not consistent in the

horizontal placement of the ball, they were

consistently tossing the ball at the same height. This

paper's main topic was about the serve but it differs

from our paper, as they focused mainly on the toss of

the ball, rather than focusing on the whole serving

motion.

Chow et al. (2007) focused on how the activation

of the muscles varied before and after the impact in

the tennis volley, as many players are concerned

about the after effect, potentially leading to severe

injuries on the wrist. They collected data by placing

electrodes on the players’ bodies. This data

collection was conducted with several controls, such

as the tennis string and racket type. From the EMG

data, they were able to conclude that the oversize

tennis balls “do not significantly increase upper

extremity muscle activation compared to regular size

balls during a tennis volley”. While this paper

focused primarily on volleys and not the serve, the

level of detail it went into showing how even

miniscule changes in one’s form can lead to

drastically different physiological impacts in the

long run reinforced how important our research is

when it comes to a stroke that covers a much wider

range of motion than volleys.

This importance is corroborated by Chow et al.

(2009) which looked into how different types of

serves affect the players' conditions. They included 3

types of serves - flat, topspin, and slice, and examined

how those shots activate the middle and lower trunk

muscles. For each subject, their two highest rated

EMG and kinematic data, which are coordinate data

extracted from their raw videos, were used to analyze

the differences. Even though there were no significant

effects for the serve type on muscle activation, they

found that on average, the largest EMG levels were

observed in the “descending windup or acceleration

phases”. While this does identify certain components

of the serve that hold significant weight, our research

hopes to add data and detail to those components in

order to better understand the angles and stroke

lengths that separate the professional player from the

amateur.

Baily and Nguyen (2018) developed a method to

classify different tennis strokes based on an armband

that measures data from its accelerometer, gyroscope,

quaternion, and EMG. The authors use a supervised

learning model, a Support Vector Machine (SVM), to

determine the correct tennis shot based solely data

from the armband.

3 PROPOSED METHOD

In this section we describe our proposed method we

used to analyze differences in player serves. We first

collected sample serve videos from both amateurs and

professionals from the Internet and our own

recordings. We manually looked through each video

and identified the sets of frames that capture the serve

motion. A pose estimation algorithm is used to

reconstruct the poses of each player appearing in

those frames, and the result is put through a pose

tracking system to label each person with an integer

identifier. We then manually labelled the result with

the player name, ID number, and whether they are

left-handed or not. The labelled pose data is then

normalized to account for the difference in body size,

position in image and left-handedness. Finally, we

calculated the Euclidean distance between the same

limbs in all pairs of serve clips collected and made

observations based on statistical analysis. This is

visually represented in Figure 1 below.

Figure 1: Our data pipeline. Black arrows denote manual

steps, and blue arrows denote steps done using computer

programs.

Stroke Comparison between Professional Tennis Players and Amateur Players using Advanced Computer Vision

Essential to standardizing our results was the

algorithm used for pose estimation, which has been

one of the major challenges in computer vision since

its introduction. In pose estimation, an algorithm

attempts to determine the positions and the poses of

the humans in a given digital image and helps to

ensure that the data collected is comparable. In this

case, a human pose is defined as a set of points

describing the important body joints. For our problem,

we used the pose estimation algorithm proposed by

Fang, et al. (2017). The framework, named Alpha

Pose, first detects all human locations in an image.

Each location is treated as a single-person image and

fed to a Symmetric Spatial Transformer Network

(Jaderberg et al., 2015) to find the region of interest,

continued to a Single Person Pose Estimator (Newell,

et al., 2016) to estimate the pose in local image and

finally through a Spatial De-Transformer Network to

remap the estimated human pose back to the original

coordinate. The estimated poses are then refined

through the use of parametric Pose Non-Max

Suppression (Fang et al., 2018) to obtain the final

human poses. We used the Alpha Pose authors’

official implementation available on GitHub

(Machine Vision and Intelligence Group, 2017),

which outputs human poses in the Microsoft COCO

(Lin et al., 2015) format

One of the common concerns in pose estimation

is that in a 2D image, very often some of the important

body joints are not visible. Alpha Pose addresses this

by representing a joint using 3 numbers: x-coordinate,

y-coordinate and a confidence score. The third

number ranges from 0 to 1, with lower values

assigned to less visible joints. Even when a joint is

completely invisible, unless it lies outside of the

image, the model does a good job predicting its

position and assigning a confidence score. Our videos

were chosen so that the main player is always

completely visible in most of the frames, so missing

data wasn’t a big concern. Also, for the sake of

simplicity, we didn’t use the confidence score in our

analysis.

The pose estimation step is repeated for all frames

we wanted to analyze. Note that this analysis is done

in 2 dimensions and not 3, and because we are

analyzing each frame, we compare sets of static poses

of the players, not their overall motion. Since there

can be multiple people in a frame, we needed to

accurately identify the main player in all frames. We

did this by running the pose estimation results

through a pose tracking system, which analyzed the

connectivity of the poses between consecutive frames

https://cocodataset.org/#format-data

and assigned an identifier to each human, then

manually reviewed the results and recorded the IDs

of the main players as well as whether they’re left-

handed or not. The tracking system used is Pose Flow

(Xiu, et al, 2018), which is available as an open

source project on GitHub (Machine Vision and

Intelligence Group, 2018). In this system, the pose

estimation result is fed to an optimization framework

to build the association of cross-frame poses and form

pose flows, then to a pose flow non-maximum

suppression to robustly reduce redundant pose flows

and re-link temporal disjoint ones. The result of this

step is a database of poses in MS COCO format with

player name, tracking ID, video link and handedness.

3.1 Data Processing

Serve videos of 4 professionals and 3 amateurs were

used to conduct this research. 3 out of the 4

professionals’ data were collected via the internet and

the rest of the videos were collected on our own. In

the data collection, we used videos including 4~13

serves per player and as a control, all of the videos

were captured from the back angle of the player. With

the videos, we turned them all into frames, thus

making the data manipulation easier. All of the videos

were at 30 frames per second. We manually cut the

frames into smaller sections, with only one full stroke

per section. To keep the frame number per cut equal,

we set a constant of 72 frames. This resulted in each

player having 4~13 serve videos, each consisting of

72 frames, and the number for professionals and

amateurs were roughly equivalent, which makes the

comparison more accurate. To further simplify and

make the analysis accurate, we selected 21 frames

from those 72 frames, including the contact point of

the serve and 10 frames before and after. We selected

those specific frames because the time at which a

player takes before and after their contact point of the

ball during a serve is different and only selecting

frames around the contact point reduces variation

between players during analysis.

In Figure 2, the image highlighted in yellow is the

“contact point” frame, which is the point at which the

player makes contact with the ball at the maximum

height. By adding on 10 frames before and after, the

images capture the serve motion around the ball hit of

the serve for a total of 21 frames.

icSPORTS 2020 - 8th International Conference on Sport Sciences Research and Technology Support

Figure 2: One of the professional’s 21 frames, the contact

point frame and 10 frames before and after.

We then ran the Alpha Pose system on all those

frames we manually collected and the output includes

a stick figure of the players with 17 important points

on the player’s body.

In Figure 3 and 4, we display the output of the

Alpha Pose detection so that one can see the lines and

key points drawn on the player’s body, representing

the simple outline of a human body in one frame.

Figure 3: A female professional player before and after

Alpha Pose detection.

Figure 4: A male professional player before and after Alpha

Pose detection.

Even though all of the videos were taken from the

back of the player, the distance between the player

and the camera varied throughout different videos so

normalizing the scales of the players became essential.

It is clear in Figure 5 that because the scaling is not

applied, the poses do not overlap or match well to

each other.

Figure 5: Comparison of two players (one left-handed

which is the white stick figure and the other right-handed

with the red stick figure) initially without any scaling or

shifting.

We created separate scales for the x axis and the y

axis. To find the right scales for the x coordinates, we

looked through all of the poses’ x coordinates of the

left and right shoulder and found the distance between

them. We repeated this process for the y coordinates,

the left and right hip, and we selected the greatest

values of both the x and y to create the scale. These

scaling factors were then normalized to a set width

and height. After finding the scaling factors we

applied it to all frames and finally shifted the poses,

in order for them to overlap with each other. With the

scaling and shifting, the poses now are comparable,

as shown in Figure 6.

Figure 6: The same players from Figure 5 but scaled and

shifted.

To further improve the comparison, we also

flipped left-handed players so that their data can be

analyzed as well with the right-handed players, which

is displayed in Figure 7 below.

Figure 7: Final output with scaling, shifting, and flipping

(for left-handed players only).

Stroke Comparison between Professional Tennis Players and Amateur Players using Advanced Computer Vision

3.2 Comparison

As shown in Figure 8, we first collected the data, then

manually selected the important frames and put those

images through pose estimation.

Figure 8: Flow diagram of the comparison process.

Then with the normalization completed, we

analyzed the data by taking the Euclidean distance

between each of the 17 points on the two players for

all of the frames. We calculated the Euclidean

distance between the same joints of two players by

using the equation























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. Each

player has 17 key points detected from the pose

estimation and for each of the key points, the same

point on the other player’s pose estimation was

compared, using the equation above. We repeated this

process for all 17 points and summed up the distances

for us to compare.

To further analyze the differences between

players, we used t-tests to compare the distributions

of the data sets. The t-test data are specifically for the

player’s differences with themselves at their contact

point. Because we were aware that the variances

between each of the players were different, we used a

Welch’s t-test, which can be used on datasets with

varying standard deviations or heteroscedasticity.

Also, we used this type of test because the number of

samples were different for each player.

4 EXPERIMENTAL SETTING

To start off, we gathered videos from several angles

of one player hitting overhead serves. Those videos

were 10 to 30 minutes of a player practicing the serve.

The first couple of serves, around 4 to 5, were ignored

as they showed significant differences with the

following serves and were likely warm-ups, so we

collected 5 to 10 strokes of each player after their

warm-ups. To get a wider variety of players, we

collected data from the Internet where there are plenty

of professional players’ practice videos. In total we

gathered 4 professionals, 3 amateurs, and within those

players, only one player was left-handed. Similar to

the data collection method for the first player, we

ignored the first couple of serves and took the next 5

to 10 serves, making sure that we collected their real

serving style. The point of this research is to compare

pros to pros, amateurs to amateurs, and amateurs to

pros to see whether the consistency amongst those

data sets are significantly different.

5 RESULTS

In this section, we will discuss the results collected

from our data. We first looked at 2 boxplots, side by

side, of the sums of the Euclidean distances between

limbs for amateurs and pros.

The results in Figure 9 clearly show that the

distribution for the amateurs was more spread out

when compared to the pros implying a greater

variance in the data. The median, as well as the

interquartile range of the data, for amateurs are

greater than that for the pros. Knowing that there are

clear distinctions between the distributions of the pros

and amateurs, we looked more closely to where

exactly those differences arise by creating histograms

specific for each player.

Figure 9: Boxplot of the distributions of the sums of the

Euclidean distances between limbs for the amateur and pro

category.

In Figure 10, Figure 11 and Figure 12, the x axis

represents a normalized Euclidean distance between

each of the players, and the y axis represents the

frequency of those distances occurring. Figure 10

compares Amateurs to other Amateurs, Figure 11

compares Professionals to Professionals while Figure

12 compares Professionals to Amateurs. There is a

clear distinction between the distributions of

professionals and amateurs. The professionals’

histograms are more tightly distributed and mostly

skewed to the right, meaning the differences between

their serves were not very large. However, the

histograms of the amateur players have larger ranges

icSPORTS 2020 - 8th International Conference on Sport Sciences Research and Technology Support

and their distributions are not as skewed compared to

the professionals. This shows how amateur players

were not consistently making similar movements,

thus shifting the distribution towards larger values. In

Figure 12, it shows a histogram with pros and

amateurs being compared to each other. Compared to

Figure 10 and 11, there are no distinct features that

stand out when comparing pros to amateurs.

Figure 10: Histograms of the distributions of the sums of

Euclidean distances between limbs comparing Amateurs to

Amateurs.

Figure 11: Histograms of the distributions of the sums of

Euclidean distances between limbs comparing Pro to Pro.

Figure 12: Histograms of the distributions of the sums of

Euclidean distances between limbs comparing Pro to

Amateur.

Because the histograms only provide qualitative

data, we then used Welch's unequal variance t-test, a

type of statistical analysis to determine whether there

is a significant difference between the means of two

groups. This test showed a similar result when testing

for significant differences between professional and

amateur players.

We conducted a Welch’s t-test between the

professionals’ sums of distances and the amateurs’

sums of distances and the resulting p-value was

0.0036. From this, we were able to conclude that there

is, in fact, a significant difference between the means

of the two groups, the professionals’ sum and the

amateurs’ sum.

To further analyze where exactly those

differences were, we conducted several t-tests, shown

in Table 1, each for the key points on the player’s

body, and found that, while neither of the right wrist

nor left hip were significant, there were significant

differences in the rest of the body points analyzed (all

p-values less than 5%). The p-values for the shoulder

comparisons were most significant. With this, it is

evident that one of the most consistent differences

between amateurs and professionals is in the

shoulders.

In Figures 13, 14, 15, where we plot the

distribution of differences in left shoulder locations

across different player types, it is clear that the

differences between the distributions for the

professional and amateur players are significant. For

instance, Figure 14 shows that Professionals

compared to other different Professionals have a

significantly right skewed distribution while the

Amateurs compared to other different Amateurs

(Figure 13) or Amateurs compared to Professionals

(Figure 15) have a significantly less right skewed

distribution and in some cases are almost

symmetrically distributed.

Figure 13: Histograms of the distributions of the sums of

Euclidean distances between the left shoulder comparing

Amateurs to Amateurs (only left shoulder).

Stroke Comparison between Professional Tennis Players and Amateur Players using Advanced Computer Vision

Figure 14: Histograms of the distributions of the sums of

Euclidean distances between the left shoulder comparing

Pro to Pro (only left shoulder).

Figure 15: Histograms of the distributions of the sums of

Euclidean distances between the left shoulder comparing

Amateur to Pro (only left shoulder).

We conducted another test to see if there are clear

distinctions between the distributions of differences

of professional player serves compared to other

professional players and the differences of amateur

player serves compared to other amateur players. In

other words, we are comparing the difference in the

pro distribution versus the amateur distribution.

From this we were able to conclude that those two

groups are, in fact, significantly different from each

other, with respect to intra-group differences, with a

p-value of 2.735  10



. In contrast, there was no

significant difference in amateur distribution to the

distribution of pro vs amateur differences.

Although we only focused on some of the p-value

results, the numbers in Table 1 shows all of our results

and although some values are not significant, others

show a significant value, like the pro-to-pro to pro-to-

amateur.

Table 1: All of the collected p-value results for different

types of distributions.

Compared Distributions P-values

Pro Sum to Amateur Sum

0.0036

Pro Right-Sum to Amateur Right-

Sum

0.0532

Pro Left-Sum to Amateur Left-Sum

0.00463

Pro Upper-Sum to Amateur Upper-

Sum

0.021998

Pro Left-Elbow to Amateur Left-

Elbow

0.02279

Pro Right-Elbow to Amateur Right-

Elbow

0.003554

Pro Right-Shoulder to Amateur

Right-Shoulder

3.729  10



Pro Left-Shoulder to Amateur Left-

Shoulder

1.21  10



Pro Right-Wrist to Amateur Right-

Wrist

0.9789

Pro Left-Wrist to Amateur Left-Wrist

0.0346

Pro Right-Hip to Amateur Right-Hip

2.324  10



Pro Left-Hip to Amateur Left-Hip

0.0857

Pro-to-Pro to Amateur-to-Amateur

2.735

10



Pro-to-Pro to Pro-to-Amateur

3.083

10



Amateur-to-Amateur to Pro-to-

Amateur

0.3798

6 DISCUSSION

In this section, we will discuss some possible

explanations and implications of our results and will

evaluate the strengths and weaknesses of our research.

To start off, not only have we confirmed the

obvious result that professional body movements

during serves are significantly different to amateurs

in terms of consistency. We have also shown that

professionals are more consistent among each other

icSPORTS 2020 - 8th International Conference on Sport Sciences Research and Technology Support

as a group then amateurs. Our main result however is

our ability to narrow down the differences to each

limb area and do so with only a simple single

recording of the player without the need for special

set ups. Indeed, nearly half of our analyzed player

videos came from publicly available videos.

Among our limb differences, while most limb

areas showed significant differences from pros to

amateurs, the right wrist and left hip were not

significantly different, in fact the right wrist was

significantly similar. Given that we analyzed serves

frames around the ball contact point, this implies most

players, professional and amateurs alike, can manage

to position their racket to an optimal contact point

with the ball, even if the rest of their body and

footwork is dissimilar or suboptimal. Although, the

left hip and leg is where most players are often taught

to keep their weight during a serve, the p-values seem

to indicate there isn’t a significant difference in how

pros and amateurs position this limb even if there

might be some small variations. This may imply that

most players, even amateurs, reach a good level of

consistency with this limb.

Our findings are definitely informative to tennis

players. This gives players points they can focus on

improving and points where they may not need to

spend as much effort, rather than watching

professionals and not knowing where to pay attention.

It allows amateur players to have an objective

understanding in their performance consistency,

compared to other professionals and other amateur

players. This data can be helpful to tennis coaches, as

it gives them a focus point in their lessons. Our data

is applicable to a wide range of players in a wide

range of situations because of our normalization

methods we applied on all stroke data and the

minimal requirements for the analysis videos, limited

to only their shooting angle, without need for special

preparation.

However, the drawbacks are that we had to

manually select the 21 frames (1 contact point frame,

10 frames before and after), which we would ideally

like to automate. Additionally, because we looked

into each video by frames, this means that we only

considered a series of static poses, not a time

evolution and that is one limitation our research has.

The static poses are adequate enough for the research

but it also means that the overall flow of the strokes

are disregarded, meaning we could have been

overlooking important parts regarding the overall

movements of the player’s strokes. Another weak

point of our research is that our analysis was only in

2 dimensions, not 3 dimensions. This is a limitation

as even though the player’s movements are in 3

dimensions, we are only looking at the x and y

coordinates. However, because we are focusing on

analyzing players from only a single camera angle, 3-

dimensional analysis poses significant challenges that

require dedicated testing with a multiple camera setup

to adequately address. Finally, we conducted our

research with only 7 athletes, which included 3

amateur and 4 professional players, and that is

considerably a low number of data points. In our

future work, the research can be further developed by

collecting more data for different players to ensure

more diversity in our collection.

7 CONCLUSIONS

In this paper, we collected videos of both amateur and

professional tennis players, and through the use of

pose estimation and tracking, we were able to

simplify frame images from videos into stick figures.

With the given data, we analyzed the differences

between players’ key points on their body, such as

their shoulders and elbows. This led us to understand

better how the consistency between pros and

amateurs differ and where the biggest differences lie.

For example, in our P-value table, we found

significant differences in both shoulders while the

right wrist showed little difference between

professionals and amateurs. In future works, we look

to further identify differences between professionals

and amateurs looking at differences in limb position

and also body dynamics. Through our t-tests, we

were able to conclude that the distributions of overall

Euclidean distance between limbs as well as specific

limbs such as the left shoulder, right shoulder, and

right hip, for professionals and amateurs were

significantly different.

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