Sports Analytics: Methods and Techniques in Professional Sports

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A Journal Review On The methods and techniques used in different
professional sports analytics.
CHARLES PERIN, ROMAIN VUILLEMOT, C. D. STOLPER & J. T. STASKO “STATE OF THE ART OF SPORTS DATA VISUALIZATION”,
IN COMPUTER GRAPHICS FORUM 37(3), DATE: JUNE 2018, DOI: 10.1111/CGF.13447.
Keywords— sports analytics, sports, techniques in sport, methods in sports
I. ABSTRACT
His research has been contributed to the state of
the art of sports data visualization where recent
advanced methodologies and technique whatever used
on the field of sports has been analyzed. The author
Charles Perin, Romain Vuillemot, C. D. Stolper & J. T.
Stasko present research on techniques that are used in
a different type of sports. Prime investigation of this
report is a visualization of research on sports data, its
importance, and timeliness. It will allow to explore the
design of new techniques as well as adapting existing
techniques by the sportspersons, practitioners, and
experts. While many of the research paper concludes
different state of art analytics over the different sports.
Analytical study on sports data allows setting strategies
by team experts. For example in the first Olympic
spectator records all older scorecard data of baseball
game in Olympic. Within this research, authors
concluded that a successful analysis over the sports
data could offer better team performance and notably a
positive economic impact on talent identification to find
the talented sports person or even a trainer to make
improvement in player performance. This research also
simplifies that analysis over sports methods and
techniques increase chances to defeat rivals. So the
research for visualization of sports analytical data offers
T a new approach to enhance skills, exploring sense and
also allow the capability to extract meaningful sports
data than traditional approaches of analysis.
The research specifies that an increasing interest of
researchers and practitioners increased in last decades
and they often use box score data, tracking data and
metadata techniques to gather information and
perform analytical research on statistical data of events,
data for actions or trajectories and data of the
participants.
In addition, the research states about methodologies
and scope then discuss box score, tracking, and
metadata. Discussion on these three types of data plays
a significant role in organizing the best reflective sports
visualization.
The scope of this research is to review several
types of sports data describe useful techniques i.e. used
in building the research.
A detailed analysis over collection is being also
concluded in this research where the author
independently analyzes dozen of resources with which
they are familiar and after several attempts and
refinement, they identified best three types of data for
any type of sports. These data are box score, tracking,
and metadata. In section 7, the author also discusses

the latest trend that is useful for analysis of contribution
and discusses the method of evaluation. [1]
This literature research
discussed the different visualization of specific sport
domain whatever makes it unique. To discriminate the
useable techniques that are used in the visualization of
sports data allow having a look in all given three types.
However, these data could not be always perfect or
completely clear.
This report details three types of technology that could
be used in the extraction of information from different
sports and allow vision to understand constant
recording for critical observations. The author says that
researcher continuously tracks the history of each sport
from data to yearly completed competitions including
Olympic and national games and then prepare a stats
over games result including win, lose, score and
performance of each and every team player. This type
of activity is being done by sports official, journalists,
and specific game fans. The other method that could be
used for sport analytical research is tracking of data.
Here the author concluded about tracking and useable
technique and details that this data is so modern than
the traditional one. This tracking of data research
contrast that the box score approach and pick out
volume and variety that is useful in cost reductions.
Performing analytical activities on such type of complex
data are not so easy. So to perform tracking approach a
machine is being utilized with the vision of precisely
gather information about the player and all used
equipment on real-time during the game. Variety of
camera with tracking equipment is being used like
Hawk-Eye, K2 panoramic and etc with a comprehensive
comparison of data. On the basis of research, the
author prepares a motivation for sports analytics and
states that it is actually a detailed dataset that is
formulated using several methodologies and techniques
allow to prepare visual demonstration on the
complexity of data with statistics of tracked data i.e.
provided by sports officials or news agencies. The
author also provides detail discussion over the
metadata technique and enlist as it is a combination of
Box-score and tracking data result. As per this survey,
both of the earlier methods gather information about
specific games or events but the data which is used to
handle the whole game, occupancy of the stadium,
characteristics of players. Badges of team and sponsor
info all come into the meta-data. They make a review
over box score and in their review, they majorly focus
on all these types of data for different types of sports.
The research also concluded time-evolution
championship statistical record including raking where
the author describes different types of a championship
like a soccer, football, ice hockey and basketball or
leagues that has been held every year. The raking table
represents championship including a list of the team
with status for a win or loses and earning goals.
The given ranking table has significant importance in
understanding the relative gaps by a timeline and graph
evolved every year. This time graph didn’t convey about
the score of the gap in between team.

The above graph which is used by the author to show
the standing tracer including matches wins relatively
show the points of the team in a league over time. [2]
And the above figure has been used to show the 16
match wins by selecting a team to show the detail
performance for each game and the championship.
The research over sports analytics framed specific rules
for which is being used by the sports person, team and
competitions.
There are many techniques which are used to plotting
the analytical research for different sports and one of
them is contextualizing by overplotting. This research
also concluded a survey that is held by New York Time.
The visualization research clearly shows how the author
outlines the comparison of the player.
Many of the visualization from online researches has
been obtained to create a comparison of players and
team by using several lines. The other technique for
outlining sports analytical data is visualizing all statistics
also have been discussed within this research paper
where the author states that some complex term
explanatory tool has been used to make analysis for
scores, goals and earned point in sports. laying out in
context is another technique which is eventually utilized
by the professionals for visualization of score, goals or
points to show the broader events.
The author concluded graph research over the history
of sumo championship in attractive with the permission
of legislative authorities.[3] In any graphical
representation, no sources confirm for absolute values.
Means the sources show relative differences for sports
like skiing, running, racing where time is matter mostly.
Since this research has utilized a survey of several
studies and so this concluded research on DQ14 for

relative times of participants rather than absolute
values in the variance of a different time. Means the
relative time matters but the slight time difference
makes more matters even in milliseconds. [4]
In the end, the author makes a finalize analysis of
different techniques and methods used for different
sports analytics as visualizing by type of tracking data.
The useful techniques that could be used for analytics
of sports data are to raise an event and trajectories by
events preparation of event density map which is
basically a road map provides enough detail of player
characteristics and the ability for present natural
trajectory. Variation of density map, event
representation and etc. [5]
In addition, to reviewing for the
research, it is identified contribution and evaluation of
the methods and techniques whatever used which
provides identification of point to point approaches and
emerging trends. On the other hand, this research also
discusses and review for the dual lens sport analytical
data and contribution. To complete this research author
enlists challenges to raise while visualizing the sports
data and limitation of this research. Here the research
also consists of a column for the list of contributions for
analytical analysis for different sports those are
published in IEEE InfoVis 2017 paper.
II. REFERENCE
[1]C. Wills, "The competitiveness of games in
professional sports leagues", Journal of Sports Analytics,
vol. 3, no. 2, pp. 103-117, 2017. Available:
https://www.academia.edu/37815609/The_Methods_a
nd_Techniques_used_in_Different_Professional_Sports
_Analytics.
[2]R. Borghesi, "A case study in sports law analytics:
The debate on widespread point shaving", Journal of
Sports Analytics, vol. 1, no. 2, pp. 87-89, 2015.
Available: https://www.agilesportsanalytics.com/sports-
analytics-methods-and-processes/.
[3]C. Wills, "The competitiveness of games in
professional sports leagues", Journal of Sports Analytics,
vol. 3, no. 2, pp. 103-117, 2017. Available:
https://www.agilesportsanalytics.com/common-sports-
analytics-methods/.
[4]J. Poduska and K. Deen, "Here is everything you want
to know about Sports Analytics -
Dataconomy", Dataconomy, 2019. [Online]. Available:
https://dataconomy.com/2018/08/here-is-everything-
you-want-to-know-about-sports-analytics/. [Accessed:
27- May- 2019].
[5]P. Atkinson, "Book Review: Using statistics to
understand the environment", Progress in Physical
Geography: Earth and Environment, vol. 25, no. 3, pp.
451-452, 2001. Available:
https://www.crcpress.com/Analytic-Methods-in-Sports-
Using-Mathematics-and-Statistics-to-Understand/
Severini/p/book/9781482237016.
[6]"CHANGING THE GAME: The Rise of Sports
Analytics", Forbes.com, 2019. [Online]. Available:
https://www.forbes.com/sites/leighsteinberg/2015/08/
18/changing-the-game-the-rise-of-sports-analytics/
#3a358ac84c1f. [Accessed: 27- May- 2019].
[7]"CHANGING THE GAME: The Rise of Sports
Analytics", Forbes.com, 2019. [Online]. Available:
https://www.forbes.com/sites/leighsteinberg/2015/08/
18/changing-the-game-the-rise-of-sports-analytics/
#3a358ac84c1f. [Accessed: 27- May- 2019].