Give Me A Break A Quantitative Analysis of Success Factors in the Association of Tennis Professionals (ATP) www.talksport.c o.uk Nick Korach UP-STAT 2013
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A Quantitative Analysis of Success Factors in the Association of Tennis Professionals (ATP) Nick Korach UP-STAT 2013.
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Slide 1
A Quantitative Analysis of Success Factors in the Association
of Tennis Professionals (ATP) www.talksport.co.uk Nick Korach
UP-STAT 2013
Slide 2
Overview I. Introduction II. Research Objective III. Research
Process A. Data Collection B. Supervised Learning Techniques C.
Unsupervised Learning Techniques IV. Results V. Conclusions VI.
Extensions
Slide 3
Introduction Why Choose Tennis? 1. In the ever-growing field of
Sports Statistics there has been very little research done with
tennis. 2. One of my favorite sports.
Slide 4
Research Objectives To discover what factors are most important
in determining success of male singles players(ATP Singles Points).
To reduce the dimensionality of predictor variables in order to
identify new significant underlying variables.
Slide 5
Data Collection ATP Singles Data Five Years: 2008, 2009, 2010,
2011, 2012 Top 100 Ranked Male Singles Players
www.faconnable.com
Slide 6
Data Collection Cumulated Season Match Stats 1 Response (Y)
Variable 10 Offense/Serving Predictor Variables (X i ) 7
Defense/Return Predictor Variables (X i ) 1 Additional Predictor
Variable (X i ) www.atpworldtour.com
Slide 7
Response (Y) Variable 1. ATP Singles Points Each ATP Tournament
is worth a certain number of ATP Singles Points. Generally 250,
500, 1000, 2000 (GS) Points depend on how far a player advances in
a tournament. The rankings period is the past 52 weeks
Slide 8
Current ATP Rankings RankNameNationalityPointsWeek ChangeTourn.
Played 1Novak DjokovicSRB12,370019 2Andy MurrayGBR8,750119 3Roger
FedererSUI8,67020 4David FerrerESP7,050126 5Rafael NadalESP6,38520
6Tomas BerdychCZE5,145024 7Juan Martin Del PotroARG4,750022
8Jo-Wilfried TsongaFRA3,660026 9Richard GasquetFRA3,230123 10Janko
TipsarevicSRB3,00029
Slide 9
Predictor Variables - Serving Number of Aces Number of Double
Faults 1 st Serve Percentage Win Percentage of 1 st Serve Points
Win Percentage of 2 nd Serve Points Number of Break Points Faced
Percentage of Break Points Saved Service Games Played Win
Percentage of Service Games Win Percentage of Service Points
www.bleacherreport.com
Slide 10
Predictor Variables - Returning Win Percentage of 1 st Serve
Return Points Win Percentage of 2 nd Serve Return Points Number of
Break Point Opportunities Percentage of Break Points Converted
Return Games Played Win Percentage of Return Games Win Percentage
of Return Points www.bleacherreport.com
Slide 11
Predictor Variables - Other Win Percentage of Total Points
www.bleacherreport.com
Slide 12
Data Mining Techniques 1.Supervised Learning Techniques Both
the response variable (Y) and the explanatory variables (X i ) are
used. Multiple Linear Regression 2.Unsupervised Learning Techniques
Only explanatory variables (X i ) are used. Cluster Analysis
Principal Component Analysis
Slide 13
Supervised Learning Regression Analysis: a statistical
technique for finding the relationship between one or more
predictor variables (X i ) and a response (Y). Y = 0 + 1 X 1 + 2 X
2 + + n X n +
Possible Multicollinearity? When two or more predictor
variables are highly correlated with one another. Two best
examples: Win Percentage of Service Points Win Percentage of
Service Games Win Percentage of Return Points Win Percentage of
Return Games
Unsupervised Learning Cluster Analysis: the process of
organizing objects into groups whose elements are similar in some
way. Principal Component Analysis: the process of reducing the
number of predictor variables into components to discover new
underlying variables.
Results Stepwise Regression Results Predictor
Variable20082009201020112012 Number of Aces Number of Double
FaultsXX 1st Serve PercentageXX Win Percentage of 1st Serve
PointsXX Win Percentage of 2nd Serve PointsX Number of Break Points
FacedXXXXX Percentage of Break Points SavedXXXXX Service Games
PlayedXXXX Win Percentage of Service GamesXXX Win Percentage of
Service PointsX Win Percentage of 1st Serve Return Points Win
Percentage of 2nd Serve Return PointsX Number of Break Point
OpportunitiesXXXXX Percentage of Break Points ConvertedXXX Return
Games PlayedXX Win Percentage of Return Games Win Percentage of
Return PointsX Win Percentage of Total Points
Results PCA - Components Predictor Variable20082009201020112012
Number of Aces26111 Number of Double Faults33333 1st Serve
Percentage44444 Win Percentage of 1st Serve Points42444 Win
Percentage of 2nd Serve Points63676 Number of Break Points
Faced33343 Percentage of Break Points Saved56566 Service Games
Played11111 Win Percentage of Service Games22122 Win Percentage of
Service Points22122 Win Percentage of 1st Serve Return Points77577
Win Percentage of 2nd Serve Return Points75727 Number of Break
Point Opportunities11111 Percentage of Break Points Converted57555
Return Games Played11111 Win Percentage of Return Games22222 Win
Percentage of Return Points22222 Win Percentage of Total
Points11111
Slide 28
New Underlying Variables Component 1 Physical Service Games
Played, Return Games Played, No. of Break Point Opportunities, Win
% of Total Points Component 2 Technical Win % of Service Games, Win
% of Service Points, No. of Aces, Win % of Return Games, Win % of
Return Points Component 3 Tactical No. of Double Faults, No. of
Break Points Faced Component 4 Mechanical 1st Serve Percentage, Win
% of 1st Serve Points Component 5 Psychological/Mental % of Break
Points Saved, % of Break Points Converted
Slide 29
Conclusions The factors which are most important in determining
the success of a male tennis players almost all deal with break of
service. Reducing the dimensionality of the data allows us to
identify new underlying variables: Physical, Technical, Tactical,
Mechanical, Mental
Slide 30
Extensions Perform regression analysis on additional response
variables such as win percentage or prize money won. Decompose the
data by studying variables that are not accumulated. Match by
match. nbcsports.msnbc.com
Slide 31
Questions? Special Thanks to Dr. Ernest Fokoue www.inc-anto.net
Probability Formulas and Statistical Analysis in Tennis Journal of
Quantitative Analysis of Sports www.atpworldtour.com
http://statracket.net www.stevegtennis.com