1. Using Microsoft Excel to model a tennis match
Tristan Barnett &
Stephen R Clarke

2. Introduction Markov Chain Model Conditional Probabilities
Microsoft Excel
Match Prediction

3. P(Game | 40-30) = 0.54(G12) + 0.46(F13) = 0.54(1) + 0.46(0.58) = 0.81

4. Simple Model
p = probability of A winning a point
D = p2 + 2p(1-p)D

6. M=2[p2+(1-p)2+2p(1-p)]+M2p(1-p)

8. Two-parameter model
pA = probability of A winning a point if A is serving
pB = probability of B winning a point if B is serving

10. Six-parameter model First Serve % for each player
Winning % on first serve for each player
Winning % on second serve for each player
pa = (First Serve %)(Win % on first serve) + (1-First Serve %)(Win % on second serve)

11. Match Prediction

12. It highlights the importance of a first serve on certain points

13. Updated % 1st Serve
= Original % 1st serve 
+ Actual % 1st serve 