E.G. Tennis Match Prediction

1. E.G. Tennis Match Prediction

2. Problem • Wish to predict outcome of championship tennis matches.

3. Q1 What info do we have • Full history of previous matches on different courts at different times by different players. Court info. Player info. Weather etc. • New players: less info. • Surrogate information: Assoc. Tennis Prof. Rankings (ordinal), ATP Points (numeric). • Some player pairs will never have player before.

4. Q2 What factors might affect things • Known • ATP Rank, Score, Age, Court type, • Particular pairing issues (A seems to always lose to B). • More recent history. • Latent • Recent injuries • Latent rank • Current form

5. Q3 How to model • Particular pairings matter • But too many pairings (O(n^2)) • Rank (O(n) ) easier to work with. • Use only known info for now. • One approach • Use rank and other O(n) to predict probability of outcome of any given pairing using discriminative approach. E.g. neural network • Remember symmetry in design! Remember things change with time – check results for different eras. • Then use a prediction for a particular pair as a prior for a Benoulli-Beta distribution for individual pairing. • Use historic data for this particular pairing to refine posterior Beta distribution.

6. Q3 How to model • Particular pairings matter • But too many pairings (O(n^2)) • Rank (O(n) ) easier to work with. • Use only known info for now. • One approach • Use rank and other O(n) to predict probability of outcome of any given pairing using discriminative approach. E.g. neural network • Remember symmetry in design! Remember things change with time – check results for different eras. • Representation matters. Represent things in the most informative way you can, without compromising too much on flexibility. • E.g. rank. Difference in rank (maybe – simple)? Both players ranks (yes probably). Represent numerically (probably not just this). Represent using temperature encoding? • Maybe. But do we want separate labels for rank 200 and rank 201? Probably not. • Maybe use numeric ranks AND temperature encoding on log scale). • Maybe work on refining rank representation before doing anything else.

7. Q4 What problems • No good as different matches at different times • Players change. • Need MLP output for different times. But this gives different Beta distributions. • So instead use output of neural network before going through the sigmoid. • Try to estimate bias for individual pairing. • Little data for individual pairings: need to be Bayesian. • Put Gaussian prior distribution on bias. Use approximate Bayesian methods to update bias distribution (next lectures).

8. Further still • Players have styles: use who beats who to provide player groupings. • See e.g. collaborative filtering. • Experts may have access to info that is hard to encode. Incorporate expert predictions into data. • At all stages: check it answers the questions you want it to.

9. Q5 What next • Get data. Check data. Check outliers. Check consistency: • Do things change over time. Courts change. Rules change. Etc. • Get data into the right format. How to represent ordinal data. • Build network. Add constraints, train, validate. Check assumptions. Is this actually going to work well enough – should see at this stage if it is definitely not. • Refine predictions using individual pairings. • Revalidate. Recheck assumptions. • Anything not quite right? Look carefully. Explain all observations. Know what is going on.

10. Q6 How to deploy • Test in the field. • Test in the field. • Refine. • Test in the field. • Test in the field. • Refine • Test in the field. • Test in the field. • Refine. • Test in the field. • Test in the field. • Refine. • Test in the field. • Test in the field. • Freeze • Test in the field x10. • Deploy (or ditch at earlier stage).