The Potential Impact of Data Mining on Sports

This article discusses the potential impact of data mining on the sports industry. It explores how data mining can be used for performance measurement, scouting, and predictions. The article also delves into the regulations and ethics surrounding data mining in sports.

Running head: THE POTENTIAL IMPACT OF DATA MINING ON SPORTS
The Potential Impact of Data Mining on Sports
Name of the Student
Name of the University
Author’s note

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Table of Contents
Part one – Research.........................................................................................................................2
Part two – Brainstorming.................................................................................................................3
Part three – Regulation and Ethics...................................................................................................4
Part four – Disruption......................................................................................................................5
References........................................................................................................................................7

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Part one – Research
Data mining could be defined as the use of certain tools and techniques that would be
used for extraction or mining of several knowledge areas. With the techniques of data mining,
large amounts of data would be extracted. This could also be described as the technique of
finding the various relationships and patterns within the data. These kind of relationships within
the data would also result in making predictions based on future outcomes (Hutchins 2016). The
importance of the techniques of data mining have been understood by various sectors such as
business applications, medical facilities and also for uncovering the patterns based on predicting
customer purchases. This would be highly useful for understanding the behavioural habits of the
customer and thus react accordingly.
The sports industry also requires a huge form of statistical data analysis, which are
primarily been collected for each of the team, game, player and season. Different kind of
statistical analysis are been collected for each of the aspect. There are different use of statistics
that would be required in each of the aspect of sports that includes calculation of team score,
points of players and many others (Liu 2016). These aspects would result in information
overloading based on deriving of meaning from the provided statistics. In order to solve the
various aspects of sports related calculation, the impact of data mining would be very much
crucial for solving the real-world problems and thus gaining a competitive advantage within the
world market. In the recent times, the sports industry have understood the importance of data
mining within the industry and thus have thought of new ways of including this technique within
their industry. The inclusion of data mining within the industry have been majorly helpful for
evaluating the future prospects and talent searches within the industry (Kaur, Singh and Josan
2015). With the help of data mining technology, the management team within any sports

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organisation, have been able to understand the importance of technology and thus implement
within the sector of sports.
Part two – Brainstorming
The three ways in which the technology based on data mining would be able to impact on
the sports industry are:
 Measuring of Performance – The use of statistics within the industry could leading to
misleading scenarios. Although the impact of data mining would not be meant for
replacing the place of coaches, scouts and general managers, but rather data mining could
be used as a tool for aiding the person in making of decisions. These would help in
undertaking of the several kind of processes within the industry (Mohsen Allameh et al.
2014). Data mining within the sports industry would prove to be extremely helpful for
calculation of hard numbers, which would be otherwise be difficult to calculate by
individuals.
 Scouting – This activity could be defined as the primary art ever since the emergence of
sports industry. There are two kind of primary activities that would be mainly be used
within the sports organisation. These include the role of human resources and advance
scouting (Radicchi and Mozzachiodi 2016). In the traditional times, the advance scouts
within baseball were sent to the location of games for the collection of data, making of
chart pitches and creation of reports that would be concerning the abilities of player and
team.
 Predictions from Data – The use of data mining technology within the sports industry
could be useful for the creation of statistical analysis, discovery of patterns and also
predict the future outcomes based on calculation over recent events. The athletes within

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any event are the biggest investments within a team (Georgiev, Noulas and Mascolo
2014). Thus, the use of data mining technology would be majorly be useful for
calculating the future outcomes of prediction based on the injury concerning a certain
player. This would help in saving millions of money from being invested (Leung and
Joseph 2014). Different researchers have also focused on the fact that data mining within
the industry could also be useful for predicting the physical performance of the player
based on the test data of physical aptitude.
Part three – Regulation and Ethics
One of the technological impact based on the regulations framed by Lawrence Lessig’s is
based on the architecture used in data mining and the impacts made by the prediction from data.
Based on the use of architecture based on Big Data, it could be discussed that the data could be
shared based on a granular level. This would further enhance the experience of professional
sports within various kind of parties involved within the sports industry (Natek and Zwilling
2014). There is an incredible amount of data that would exist within each of the domain of the
sports industry. Artificial Neural Network (ANN) is useful within the sports industry for
predicting the results within the sports.
Each bit of data within the industry would be collected based on the future kind of
outcomes. The data collected within the sports industry is collected from a huge source. There
are several fans within the sports industry that would help in influencing the efforts based on
marketing and making of several decisions within the events based on sporting. The underlying
architecture made within data mining technology should be updated in order to support the major
needs within the industry (Abbasi, Sarker and Chiang 2016). The data collected from every fan
would prove to influence the efforts based on marketing. They would also be able to make

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several kind of decisions based on sporting events. This would also include the decisions based
on scheduling of games and catering to the needs of fans.
Part four – Disruption
One of the primary process within the sports industry that is most likely to get changed in
the future is based on the fast form of predictions about sporting events in the future. With the
impacts of data mining technology within the sports industry, the results could easily be gathered
from a huge number of data sources. This would put a major impact on the communication
process within the sports industry. The data collected from several sources would be gathered
and thus different results would be generated. The results could prove to be extremely useful for
gaining the knowledge about any form of sports related event. With the vast impact made by
data, the various investors, players and support teams would be able to communicate with each
other based on making any kind of changes if necessary. The communication with the
stakeholders, sponsors and various other team members would be the most necessary part in any
kind of event of sporting. The use of data mining within the sports industry would be established
within sports industry could be also used for the prediction of patterns within the data. This
would further help in forecasting of future events and thus make improvisations within the
industry.

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(Fig 1: Before the impact of Data Mining in Sports Industry)
(Source: Created by author)
Based on the entire communication set-up process within the industry, the sports sector is
most likely to get benefited. The collected data from each of the sources of an event would be
gathered. This would thus result to segment the gathered information into various segments. This
would thus result to provide each of the communication sector with the appropriate information
that would be needed by them (Karthikeyan and Ravikumar 2014). Different forms of
biomedical tools that have been created by several associations have made exclusive use of data
mining tools and techniques. The athletes have been always been considered as the biggest
investments within the sports industry. The various teams involved within a sports event would
make use of data mining within the industry in order to predict the outcomes of the future form
of events. This would also help in predicting the nature of injuries within the sports industry and
thus improve the future kind of injuries based on performing research.

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(Fig 1: After the impact of Data Mining in Sports Industry)
(Source: Created by author)
The different collected data would be analysed based on high level of statistical
calculation and techniques based on projection. This would also be helpful for the identification
of the appropriate modes of communication within the sector. Different researchers have also
indicated that data mining could also be used on the test data of physical aptitude for the
prediction of future physical kind of performance. The data mining software could also be used
for the linkage of test data and actual performances that would eb required in a particular fitness
class. With the impact of communication supported by data mining, each of the members within
the sports team would be able to communicate the important information with each other. This
would help each of the individuals to get informed about the latest kind of updates that would be
made within the sector of sports.

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References
Abbasi, A., Sarker, S. and Chiang, R.H., 2016. Big data research in information systems: Toward
an inclusive research agenda. Journal of the Association for Information Systems, 17(2).
Georgiev, P., Noulas, A. and Mascolo, C., 2014. Where businesses thrive: Predicting the impact
of the olympic games on local retailers through location-based services data.
Hutchins, B., 2016. Tales of the digital sublime: Tracing the relationship between big data and
professional sport. Convergence, 22(5), pp.494-509.
Karthikeyan, T. and Ravikumar, N., 2014. A survey on association rule mining. International
Journal of Advanced Research in Computer and Communication Engineering, 3(1), pp.2278-
1021.
Kaur, P., Singh, M. and Josan, G.S., 2015. Classification and prediction based data mining
algorithms to predict slow learners in education sector. Procedia Computer Science, 57, pp.500-
508.
Leung, C.K. and Joseph, K.W., 2014. Sports data mining: predicting results for the college
football games. Procedia Computer Science, 35, pp.710-719.
Liu, D., 2016. Social impact of major sports events perceived by host community. International
Journal of Sports Marketing and Sponsorship, 17(1), pp.78-91.
Mohsen Allameh, S., Khazaei Pool, J., Jaberi, A. and Mazloomi Soveini, F., 2014. Developing a
model for examining the effect of tacit and explicit knowledge sharing on organizational
performance based on EFQM approach. Journal of Science & Technology Policy
Management, 5(3), pp.265-280.

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Natek, S. and Zwilling, M., 2014. Student data mining solution–knowledge management system
related to higher education institutions. Expert systems with applications, 41(14), pp.6400-6407.
Radicchi, E. and Mozzachiodi, M., 2016. Social talent scouting: a new opportunity for the
identification of football players?. Physical Culture and Sport. Studies and Research, 70(1),
pp.28-43.