implementation of decision support system for outdoor sports using machine learning techniques

8/3/2019 Implementation of Decision Support System for Outdoor Sports Using Machine Learning Techniques

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Implementation of Decision Support Systemfor Outdoor Sports Using Machine Learning

TechniquesNoreen Akram, Asim Munir, Memoona Khanam and Dr. Malik Sikander Hayat Khiyal

Abstract — This paper presents a Decision Support System for Outdoor Sports. Outdoor Sports are greatly affected by the weather condition

so basically the support provided by the system proposed is the decision whether to carry out the game or not based on the weather and field

conditions. The weather attributes taken into consideration are Outlook, Temperature, Humidity, and Wind. The Field values taken are hard

surface, Grass and Clay. The game considered is Tennis. The system is implemented using Machine Learning technique Decision Tree

Learning, The algorithm selected is ID3 algorithm. The tool used is MATLAB for the frontend and MS Access for the backend. The algorithm

generate decision tree. Rules are formulated from the decision tree. The system makes decision based on these rules. The system is tested

using 25 sample records. The average accuracy of the system comes out to be 84%. This system can be extended for other games.

Index Terms — Decision Support System, Decision tree, ID3 algorithm, Machine Learning, Outdoor Sports

—————————— ——————————

1 INTRODUCTION

ecision support system is an interactive system thatinteracts with the humans to make effective and effi-cient decision. It may be used as a support to identi-

fication and analysis of a problem, providing solutionsand alternative for a particular problem, or making deci-sion on the basis of the data provided to the system. Itaids the decision makers to carry out a decision or we can

say that DSS can replace the decision makers. It dependson what kind of support you need from the DSS. DSS isdesigned according to your needs.While carrying out any game planning has to be done.Planning may include scheduling the game, creating theenvironments, collecting equipments providing travelfacilities to players, selecting venues, providing accom-modations to the players etc. These are things needed tobe managed. For outdoor games weather conditions iscrucial factor that should be considered while planningout for a game. Outdoor sports like cricket, football andtennis are greatly affected by adverse weather conditions.Weather conditions should be suitable for playing e.g. we

cannot play in rain etc. While organizing outdoor games

decisions whether to play or not is made by observing theweather conditions.The DSS which is proposed in this paper is for outdoorsports. It is a DSS which decides whether the game can beplayed or not based on the weather conditions. The deci-sion about carrying out a game depends on the attributesthat are considered. Mainly weather and field attributes

are taken into consideration. Weather attributes taken aretemperature, humidity, outlook and wind. In total 5attributes are considered. Each attribute has its valuesdepending on the tendency up to which it can be ex-panded. E.g. attribute is humidity and values are highand low. The game selected under the category of out-door sport is Tennis. The highlighting point of selection ofthis game is it covers wide variations and is greatly af-fected by the weather. Also it can be played on differenttypes of fields for example clay and grass where as gamesuch as hockey can be played on only grassy field.

2 LITERATURE REVIEW

Many DSS for sports have been built until now. Some are:DSS for scheduling empires in the American baseballleague [24], Study on the decision support system oftechniques and tactics in net sports and the application inBeijing Olympic Games [4], A decision support system todetermine the sport sponsorship response [26], Researchon web-based decision support system for sports compe-titions [19], A Taxonomy of a Decision Support Systemfor Professional sports [5], System for predicting SportingSuccess Conditions [6], A Decision Support System for

Scheduling the Canadian Football League [7]. These DSSare made by analyzing the subject area and finding solu-tions to problems. DSS can be implemented throughnumber of algorithm based on the needs. These algo-

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• Noreen Akram is an under graduate student of Department of

Software Engineering, Fatima Jinnah Women University The Mall,

Rawalpindi, Pakistan.

• Asim Munir is Assistant Professor at Computer Science Depart-

ment at International Islamic University Islamabad. Pakistan.

• Memoona Khannam is Lecturer at the Department of Computer

Sciences, Fatima Jinnah Women University The Mall, Rawalpindi,

Pakistan.

• Dr. Malik Sikandar Hayat Khiyal is Professor and Chairman of Dep

nah Women University The Mall, Rawalpindi, Pakistan.

D

JOURNAL OF COMPUTING, VOLUME 3, ISSUE 9, SEPTEMBER 2011, ISSN 2151-9617

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© 2011 Journal of Computing Press, NY, USA, ISSN 2151-9617



rithms include AI ANN ML and decision algorithms.Each algorithm has its own pros and cons.Previously the implementation of ID3 algorithm takes 14records in total [8] [9]. The attributes are outlook, temper-ature, humidity and wind. The values of decisionattributes are classes yes and no. The value of eachattribute is up to maximum 3 values. The implementationis done in VB net. Where as in the current implementation25 records are taken attributes taken are 5 and up to max-imum 6 values are considered for an attribute. The im-plementation is done in MATLAB. The results are thenused to design a DSS for users which previously implan-tations’ lacked

3 PROPOSED DECISION SUPPORT SYSTEM

Figure 1 illustrates the Block Diagram of the proposeddecision support system.

Fig. 1: Block Diagram of the proposed decision support system.

Figure 2 illustrates the Block Diagram of the proposeddecision support system from user perspective. This dia-gram describes the flow of how user and system interact

with each other.

Fig. 2: Block Diagram of the proposed decision support system from

user perspective

4 ANALYSIS

Tennis is a game that is played between two players (sin-gles) or two players combine to form a team (doubles).This game consists of a ball and a racket. Tennis is playedboth indoor and outdoor. For indoor tennis the courttemperature is kept fixed. The weather conditions can’t

affect the game. For outdoor tennis the weather condi-tions must be suitable to carry out the game. Also thetype of field is equally important Analysis below coversunder what weather and field conditions the tennis gamecan be carried out and a scale is designed for eachattribute based on assumption. In total there are fivenamely Outlook, Temperature, Wind, Humidity andField. The values and scale for each attribute being consi-dered is shown below

4.1 Values and scale for attributes

Temperature:

TABLE 1SCALE FOR TEMPERATURE

Temperature (in

centigrade)

Temperature (in

Fahrenheit)

Decision

(Play Tennis)

Cold <15 <59 No

Cool 15-19 59-67 YesMild 20-24 68-75 YesWarm 25-41 76-105 YesHot 47-58 106-136 No

Ranges for each temperature values are based on someassumption as shown in table 1. Viewing Australian Bu-reau of Meteorology site [10] facilitated in defining theseranges in term of these values (cool mild etc). Secondlythe temperature scale ranges from 0-100 in centigrade and32-212 in Fahrenheit, the criteria to end the scale at 58 C







or 136 F is the hottest temperature observed on earth sofar at El Azizia Libya on September 13, 1922 [11]. Takingthe range beyond this is not a good assumption and is ofno use. Playing in the two extreme conditions (hot andcold) will affect the player’s health and also the tennis ballgets affected by temperature [12]. Cold temperaturemakes the ball dead which is difficult for the player tostrike and the hot temperature bounces the ball very highalso it cause various heat problems and affects health [13].Tennis can be played in cool temperature range as de-fined above only one has to dress up accordingly to avoidcold [14][15]. Tennis can be played in 39 C according tothe paper “Playing Tennis in the Heat: Can Young PlayersHandle It?” written for the American College of SportsMedicine by Michael F. Bergeron [16]. Playing tennis inwarm scale range one has to drink water more and takeprecautionary measures to prevent heat [12]. Mild tem-perature is the perfect conditions to play tennis.

Outlook:The values for outlook taken are: Thunderstorm, Shower,Drizzle, Cloudy, Partly Cloudy, and Sunny

Wind:TABLE 2

SCALE FOR WIND

Wind (in

km/h)

Decision (Play Tennis)

Calm <1 YesGentle Breeze 1-19 YesModerate

Breeze

19-38 Yes

Strong Breeze 39-49 NoGale >=50 No

These assumptions of ranges for each value are designedafter studying the modern scale for wind forecast whichis the modified Beaufort wind scale [17] which is shownin table 2. The decision to play is based on the answerfrom the people to the question what is good tennisweather for them [18]. Mostly are comfortable with therange of gentle breeze. One can play in moderate by con-sidering some strategies while playing. In Strong Breeze

and gale it is difficult to play

Humidity:TABLE 3

SCALE FOR HUMIDITY

Relative Humidity Decision (Play

tennis)

Low <= 50% YesHigh >50% No

The assumption for humidity is shown in table 3. JimBrown mentioned in his book Tennis Step to success that

suitable temperature to play tennis is 69 F and Humidity50% [1]. High humidity has more water vapors. Wateraffects the tennis balls [20]. It increases the weight of theball which lowers the ball height [21]. It is tough to push

the ball in high humidity so tennis can be played in lowhumidity

Field:Tennis can be played on four types of field namely, Car-peted, grass, clay and hard surface [22] 1st type is mainlyused for indoor tennis; last three are greatly affected byweather. Keeping in mind these points I have made as-sumption that values for fields are grass clay and hardsurface. Grass is mostly affected by weather even thecloudy, humid and drizzle makes the court slippery. Si-milarly clay field can also not accommodate drizzle butthe hard surface can be used in such weather conditions.

4.2 Collection of Sample Records

After analyzing the values of attributes and the scale, var-

ious weather conditions for randomly selected date andlocation are analyzed. Collected samples covers theattributes values in great variation. A balanced data set isconsidered. The samples are collected with original val-ues from the weather forecasting sites [23]. These valuesare then mapped on the above designed scales.The records used for the implementation of algorithm areshown in table 4 (at the end)

5 IMPLEMENTATION

The technique used is of machine learning, decision treelearning. The tool used is MATLAB. The algorithm cho-sen is ID3 algorithm. The ID3 algorithm is implementedusing SQL statements Database records and the MATLABmathematical functions. ID3 stands for Inductive Dicho-tomiser 3. It is a classification algorithm. It is an algorithmthat calculates entropy and gain of attributes-values. Itgenerates the decision tree of attributes under considera-tion. The tree is then used for future records. On the basesof tree we generate the rules. There are classes (yes or no).The attributes belong to class. Leaf node of decision tree isa class. Non leaf node is a decision attribute. The branchesof tree have the values of attributes.

Entropy: It is the measurement of impurity of an arbitrarycollection of samples. It is calculated by the formula

cEntropy(S) = ∑ P i Log2 Pi (I)

i=1Gain: Gain measures how well a given example orattribute separates the training examples according totheir target classification [3][25]

mG(S,A) = E(S) - ∑ fs (Ai) E (SAi ) (II)

i=1

The algorithm can be explained as: First the entropy ofthe decision attribute (class) is calculated. Then the gainfor all the attributes is calculated. The highest node be-comes the decision node. The branches get the value of







highest gain attribute. The data is again classified accord-ing to this attribute. If the branch value has havingunique class value the leaf node gets the class value oth-erwise the above process is repeated. [2]. ID3 algorithmtakes fixed number of values. It is non incremental. In-cremental is the can add a new sample in it and modify it.The results are shown in command window of MATLAB

5.1 Decision Tree

The decision tree generated is shown is fig. 3

Fig. 3 Decision tree

5.2 Decision Rules

Rule 1: If(humidity==High) then No Game

Rule2:If(humidity==Low&&outlook==Thunderstorm|| Shower) then No Game

Rule3:If(humidity==Low&& outlook==partlycloudy|| sunny) then Yes Game

Rule4:If(humidity==Low&&outlook==Drizzle&& field==Clay||Grass) then No Game

Rule5:If(humidity==Low&&outlook==Drizzle&& field==Hard Surface) then Yes Game

Rule6:If(humidity==Low&&outlook==Cloudy&&field==Hard Surface||Clay) then Yes Game

Rule7:If(humidity==Low&&outlook==Cloudy&& field==Grass) then No Game

5.3 User Interface

User interface is designed for user to interact the system.The user selects the values of attributes and calls for to

display the decision. The system process the attributesbased on the rules. The decision is displayed through amassage box

6 EXPERIMENTAL RESULTS

The system is tested using a selected set of 25 arbitrary

data samples as shown in table 5 (at end). The testedsamples results are match with are analysis or the as-sumptions assumed to check the accuracy of system. Theaccuracy of system is shown through graph in fig. 4.

0

20

40

60

80

100

120

1 3 5 7 9 11 13 15 17 19 21 23 25

A C C U

R A C Y ( o u t o f 1 0 0 % )

INPUT RETINAL IMAGES

ACCURACY GRAPGH OF OD

LOCALIZATION

Fig. 4: Accuracy Graph

Average accuracy = No. of Accurate decision samples /Total no. of samples

=21/25= 0.84Percentage of Average accuracy = Average accuracy*100

= 0.84*100= 84%

The implemented system shows 84% accurate results ac-cording to the assumption taken in the analysis part. Theresulted ID3 doesn’t take Wind and Temperature intoconsideration which misclassifies the data and the deci-sion displayed is not accurate for some values. This is thedrawback of ID3 Algorithm that it misclassifies the data ifthe data samples used for training contains some noise.The problems are due to data samples taken during train-ing were less. Approximate 5% to 10% of total populationdata must be taken for ID3 to show accurate results. TheAdvantage of ID3 algorithm is only taking the attributesthat are necessary for making decision. Decision may beonly based on one attribute values. As in the case for im-plemented system is attribute humidity value low. Theresult is NO without considering any other attribute.

7 CONCLUSION

This decision support system is implemented using ID3

algorithm. The decision support is provided for a game tobe carried out depending on weather and field condi-tions. It was tested using a data set of 25 sample records.Out of 25 records 4 records were not showing accurate

0

20

40

60

80

100

120

1 3 5 7 9 11 13 15 17 19 21 23 25

A C C U R A C Y ( o u t o f 1 0 0 % )

INPUT RETINAL IMAGES

ACCURACY GRAPGH OF OD

LOCALIZATION







decision based on the assumptions taken in analysis part.This work can be extended for more Games. Presentlyonly Tennis Game is considered. Also the number of datasamples used for training can be increased and the datacan be preprocessed by applying preprocessing tech-niques. This will produce more accurate results.

REFERENCES

[1] Jim Brown, Tennis: Step to success 3rd edition

[2] Tom.Michell, Mc Graw Hill, Machine learning, 1997

[3] Fabrice Guillet, Howard J. Hamilton, Quality measures in data

mining, 2007

[4] Lijuan Yu, Peiliang Ling, Hui Zhang, Study on the Decision

Support System of Techniques and Tactics in Net Sports and the

Application in Beijing Olympic Games, IEEE computer society,

Second WRI Global Congress on Intelligent Systems, Dec 17, 2010

[5] Donald R.Moscato and “Eric D.Moscato, A Taxonomy of a Deci-

sion Support System for Professional sports , issues in information

systems,

volume 5 No.2, 2004,[6] Schisler et al, System for predicting Sporting Success Conditions,

US patent no. US7725420B2, May 25, 2010,

[7] Kent J. Kostuk and Keith A. Willoughby “A Decision Support

System for Scheduling the Canadian Football League” [8] Faraj A. El-Mouadib1, Zakaria S. Zubi2, Ahmed A. Alhouni3

“New Implementation Of Unsupervised ID3 Algorithm (NIU-ID3)

Using Visual Basic.Net” recent advances on data networks, commu-

nications, computers, ISBN: 978-960-474-134-2

[9] Wei Peng, Juhua Chen and Haiping Zhou “An Implementation of

ID3 -- Decision Tree Learning Algorithm”

[10] http://www.bom.gov.au/info/wwords/ 2011

[11] http://ask.yahoo.com/20050712.html2011

[12] http://www.livestrong.com/article/398740-the-temperatures-of-tennis-balls/ 2011

[13] http://www.stms.nl/download/TennisHeat.pdf 2011

[14] http://www.livestrong.com/article/421078-how-to-play-tennis-

in-60-degree-weather/#ixzz1KSXHSsRb 2011

[15] http://tennis.about.com/library/weekly/aa122399.htm 2011

[16]http://www.acsm.org/Content/ContentFolders/Publications/Curr

entComment/2002/heathydr.pdf 2011

[17]http://www.answers.com/topic/beaufortscale#The_modern_scale

://wis.cs.ucla.edu/atlas/doc/atlas- icde02.pdf 2011

[18]http://answers.yahoo.com/question/index?qid=20080722114610A

ANFIzo 2011

[19s]http://spiedigitallibrary.org/proceedings/resource/2/psisdg/7749

/1/77491H_1?isAuthorized=no2011[20] http://www.livestrong.com/article/344381-how-water-affects-

tennis-balls/ 2011

[21]http://www.itftennis.com/technical/research/lab/balls/acclimatisa

tion.asp 2011

[22]http://news.bbc.co.uk/weather/hi/weatherwise/newsid_8483000/

8483698.stm 2011

[23] http://www.wunderground.com/ 2011

[24] http://www.jstor.org/pss/25061160 2011

[25] http://en.wikipedia.org/wiki/ID3_algorithm 27/5/11 19:00

[26] http://www.slideshare.net/mineisik/yl-for-slideshare 2011

Noreen Akram is an undergraduate student at Softwarwe engineer-

ing Department of Fatima Jinnah Women University, The Mall RwpPakistan.

Asim Munir is enrolled in Ph.D. He has done M.S. in ComputerScience and MSc in Computer Science. He is Assistant Professor atComputer Science Department at International Islamic UniversityIslamabad. He is Gold Medalist Microsoft Official Curriculum Train-ing-MCSE

Memmona Khanam has done MS in Computer Sciences and M.Ed.She is Lecturer at Computer Science Department at Fatima JinnahWomen University. She has expertise is in Aritificial Intelligence. Shehas 4 years teaching experience

Dr. M. Sikandar Hayat Khiyal born at Khushab, Pakistan. He isChairman Dept. Computer Sciences and Software Engineering in

Fatima Jinnah Women University Pakistan. He Served in PakistanAtomic Energy Commission for 25 years and involved in differentresearch and development program of the PAEC. He developedsoftware of underground flow and advanced fluid dynamic tech-niques. He was also involved at teaching in Computer Training Cen-tre, PAEC and International Islamic University. His area of interest isNumerical Analysis of Algorithm, Theory of Automata and Theory ofComputation. He has more than hundred research publications pub-lished in National and International Journals and Conference pro-ceedings. He has supervised three PhD and more than one hundredand thirty research projects at graduate and postgraduate level. Heis member of SIAM, ACM, Informing Science Institute, IACSIT. He isassociate editor of IJCTE and Co editor of the journals JATIT andInternational Journal of Reviews in Computing. He is reviewer of the

journals, IJCSIT, JIISIT, IJCEE and CEE of Elsevier.





http://www.bom.gov.au/info/wwords/

http://ask.yahoo.com/20050712.html

http://www.livestrong.com/article/398740-the-temperatures-of-tennis-balls/


http://www.stms.nl/download/TennisHeat.pdf

http://www.livestrong.com/article/421078-how-to-play-tennis-in-60-degree-weather/#ixzz1KSXHSsRb


http://tennis.about.com/library/weekly/aa122399.htm

http://www.acsm.org/Content/ContentFolders/Publications/CurrentComment/2002/heathydr.pdf%202011


http://www.answers.com/topic/beaufortscale#The_modern_scale://wis.cs.ucla.edu/atlas/doc/atlas-icde02.pdf


http://answers.yahoo.com/question/index?qid=20080722114610AANFIzo


http://spiedigitallibrary.org/proceedings/resource/2/psisdg/7749/1/77491H_1?isAuthorized=no


http://www.livestrong.com/article/344381-how-water-affects-tennis-balls/


http://www.itftennis.com/technical/research/lab/balls/acclimatisation.asp


http://news.bbc.co.uk/weather/hi/weatherwise/newsid_8483000/8483698.stm%202011


http://www.wunderground.com/

http://www.jstor.org/pss/25061160%202011

http://en.wikipedia.org/wiki/ID3_algorithm%2027/5/11%2019:00

http://www.slideshare.net/mineisik/yl-for-slideshare

http://www.slideshare.net/mineisik/yl-for-slideshare

http://en.wikipedia.org/wiki/ID3_algorithm%2027/5/11%2019:00

http://www.jstor.org/pss/25061160%202011

http://www.wunderground.com/















http://tennis.about.com/library/weekly/aa122399.htm



http://www.stms.nl/download/TennisHeat.pdf



http://ask.yahoo.com/20050712.html

http://www.bom.gov.au/info/wwords/



TABLE 4

SAMPLES RECORDS FOR IMPLEMENTATION





implementation of decision support system for outdoor sports using machine learning techniques

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