# Forecasting the Winner of a Tennis Match - PowerPoint PPT Presentation

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## Forecasting the Winner of a Tennis Match

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### Forecasting the Winner. of a Tennis Match. Franc Klaassen ... Forecasting in practice: Serena-Venus Williams at Wimbledon 2003 ... Prob{Serena wins match} = 70 ... – PowerPoint PPT presentation

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Title: Forecasting the Winner of a Tennis Match

1
Forecasting the Winnerof a Tennis Match
• Franc Klaassen
• University of Amsterdam (NL)
• Jan R. Magnus
• Tilburg University (NL)
• TST Congress, London
• July 29, 2003

2
Overview
• Forecasting one aspect of a larger tennis
project
• Motivation for forecasting
• How to compute forecasts during a match?
• Forecasting in practice graph of the 2003
• Robustness of the graph
• Conclusion.

3
Tennis project
• Testing hypotheses (six papers)
• 7th game is the most important game in a set
false
• Real champions win the big points true.
• Service strategy (in progress)
• How to choose the strengths of 1st and 2nd
services to maximize the probability of winning a
point?
• Rule changes (one paper)
• How to reduce the service dominance? Presented at
TST-1.
• Forecasting (two papers)
• Forecasting winner while match is in progress
TST-2.

4
Motivation for forecasting
• Forecasting the winner of a tennis match
• Before a match
• Using odds from bookmakers
• Using statistical model, e.g.,
• Boulier and Stekler (1999)
• Clarke and Dyte (2000)
• During a match
• Using statistical model
• ? focus of our paper.

5
Why forecasting during match?
• TV spectators want information on
• Which player leads at this moment?
• Who is most likely to win the match?
• How did the match develop up to now (momentum,
winning mood)?

6
TV spectators get info on
• Score gives info on
• 2 (Likely winner) Partially
• 4-6 for Agassi-Hewitt Hewitt will probably
win,
• 4-6 for Agassi-Henman Agassi will still be
the favorite
• 3 (Development up to now) Partially
• 5-5 can result after 4-4 (match in balance),
• but also after 5-0 (one player is in a winning
mood)
• ? Room for improvement regarding 2 and 3.

7
TV spectators also get info on
• Match/set stats (1st serve in,...) give info on
• 2 (Likely winner) Not much
• 3 (Development up to now) Partially
• Comparison of 2nd set with 1st set statistics
gives some insight,
• but each statistic is too aggregate to give a
clear picture.
• Note summary stats provide detailed info on
specific aspects of each player ? useful, but
beyond scope of our paper.
• ? Still room for improvement regarding 2 and 3
• ? Purpose of current paper.

8
Idea
• Present the probability that a player will win
match update it as match unfolds (real-time
forecasting).
• Example Agassi-Hewitt
• At start of match Agassi wins with prob. 60
• At 4-6 Agassi wins with prob. 30
• At 4-6/0-3 Agassi wins with prob. 20.
• Use graph to visualize the probs. of all points
till now.

9
How to compute the forecasts during a match?
• Suppose match between players A and B.
• Goal ProbA wins match at each point up to now.
• This probability depends on 2 inputs (besides
score)
• ProbA wins match at start of match
• ProbA wins point on serveProbB wins point on
serve.
• Implementation using our computer program
TENNISPROB
• Choose the two inputs before the match and keep
them constant
• Type in the score at each point
• ? TENNISPROB gives ProbA wins match very
quickly.

10
How to choose the two inputs?
• ProbA wins match at start of match
• We provide an estimate based on rankings (e.g.,
80),
• but one can easily improve/overrule that estimate
if one has specific other info (injury problems,
specific ability of surface,...) (e.g., 70)
• ? In the end there is one starting point of the
graph (70).
• ProbA wins point on serveProbB wins point on
serve
• We provide an estimate based on rankings (e.g.,
120 both players win 60 of their points on
service)
• No need for adjustment the graph hardly depends
on our choice
• ? There is an estimate (120).

11
Forecasting in practice Serena-Venus Williams at
Wimbledon 2003
• Before the match starts, we choose inputs
• ProbSerena wins match 70
• ProbSerena wins point on serve
ProbVenus wins point on serve 116.
• Then the match starts and graph builds up
• Note match has not yet been completed
• ? graph does not use info on later points!

12
set 1
13
set 1
14
set 1
set 2
15
set 1
set 2
set 3
16
set 1
set 2
set 3
17
set 1
set 2
set 3
18
Robustness of the graph
• Our choices for the two input probabilities may
be not perfectly correct is that a problem?
• ? Does profile change a lot if one chooses
• Starting probability 60 or 80 instead of 70?
• ProbSerena wins point on serveProbVenus wins
point on serve 110 or 120 instead of 116?

19
set 1
set 2
set 3
20
set 1
set 2
set 3
21
Conclusion
• We have introduced a robust method to
• forecast winner of match as match unfolds
• New existing papers focus on forecasting at
start of match, while we do it also for matches
in progress
• Info on who will win match and on development of
match till now
• Single line makes the information visible at a
glance
• Graph can be generated instantly
• and for any match (not just at Wimbledon)
• ? Graph is useful in addition to score summary
statistics.
• Potential application
• present graph during change of ends ? TV
commentator can discuss match developments so far
(turning points,..)

22
Future research
• So far two input probs. are kept fixed during
match updating may improve graph, but
• Other aspects of tennis project
• Service strategy
• Development of tennis over time
• Has return indeed improved?
• In what respects has the womens game changed?
• Differences between Wimbledon and other
tournaments
• Impact of surfaces grass, clay, hard court
• ? Need more data on grand slam/ATP/WTA
tournaments.

23
Probability S. Williams wins match
1.0
0.8
0.6
0.4
0.2
set 1
set 2
set 3
0.0
0
20
40
60
80
100
120
140
160
180
Point number