Time to Trade Tennis

Tennis is my favourite television sport, after cycle racing. The sport is also my favourite sport to play now that I am getting on in years and am no longer able to cycle at the level I once did. I have never considered trading tennis before because horse racing was sufficiently challenging but with my book out of the way I have time to consider new projects.

The sport of tennis is relatively simple to model as the sport is a head to head between a pair of players or a pair of partners in doubles. Unlike football there is always a winner in tennis so the draw is not a consideration. With the exception of Davis/Federation Cup games there is no concept of home and away. The days of home nations dominating Grand Slam tournaments are probably over. 

The scoring system in tennis favours the player who wins the most points (an obvious statement to make but stay with me). This can be seen in an interesting simulation using Monte Carlo methods created by Michael Maboussin author of The Success Equation. If you click on the following link then you will see the simulation in action.

Tennis Simulation

With players of equal ability the probability of either player winning the game is 0.50 but you only have to play around with the simulation to see that a player who is only marginally better than another player has by far the greater chance of winning a match. Indeed with a 57.7% chance of winning you guarantee victory with a 1.0 probability of winning the match. I can vouch for that personally. Being rather middle-aged and decrepid I tend not to play singles games as I am guaranteed to lose. I am better suited to the slightly more static role of net poaching in doubles.

In reality not all games go the way a simulation might have you believe. The top seeded players lose games for a variety of reasons. Their abilities might be waning but it takes time for them to drop down through the rankings. A player might treat a tournament as a training exercise and withdraw after a couple of rounds or their mind might be on a more prestigious future tournament and they make too many mistakes and lose a match to a lowly ranked player.

Data for tennis matches is very easy to come by and is usually free. There are also websites where you can match up a pair of players to see how they faired against each other in the past. Once such site is MatchStat and can be found at the following link.

MatchStat

Just enter the names of the two players in a singles match and you will be shown their previous matches against each other. However, it is not just a simple matter of creating a probability from the number of times a player has beaten another. As you will see in MatchStat, games are played on different surfaces with some players preferring one surface to another. The players at the top of the ATP rankings will be mindful of their ranking points and build their season around the bigger tournaments such as the four Grand Slams and the ATP Tour Masters 1000 tournaments. The lucrative ATP World Tour final at the end of the year is also borne in mind too.

Currently, I am working on my own Monte Carlo methods simulator, which will allow the user to model some of the aspects mentioned above and permit "What if?" scenarios to determine how various factors alter the probability of winning a tennis match. With probabilities you can derive odds and use them as a basis for trading with.

See Also

Tennis Citations - Some academic work on tennis trading

The Hidden Mathematics of Sport - Contains useful mathematical facts on tennis and other sports

8 comments:

  1. Hi James! What's the approach to tennis trading? Is the market structure less important?

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    1. Hola Diego,

      There are a few other markets besides the Winner market but they are not very liquid and therefore not tradeable. Therefore its treating the decay of odds like options trading coupled with match reading and in and out for a percentage profit.

      The best way to trade tennis is court-siding and as The Eudaemonic Pie (aka The Newtonian Casino) is a favourite read of mine then the miniaturisation of a court side transmitter is right up my street. I did have an offer from a chap to do that but given his criminal record I had to bite my lip and pass.

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  2. I guess that the volatility must be greater, right? Regarding the automation, is it more complicated than horse racing due the fact that match reading is necessary?

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    1. All my trading is automated and I act as an analyst who can pull the plug if the model misbehaves. That is a standard quant activity in black box trading.

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  3. Hi James,

    I found that a Markov chain in Excel with the chance of winning a service point for each player and then fed in to Gruss worked well enough. You can then scrape scoreboard data which then feeds in to the spreadsheet and you then just need to adjust the service percentages via what you observe or you could automate by decaying in the current serving percentages in to the calculation over the course of the match, regression to mean always a fear with this. I found taking the opening price of Betfair as my starting point and then waiting to see if the prices contradicted the opening prices in-running and asking myself if I agreed with this basis my observations and the in-running statistics. I also calculate my own service percentages for the purpose of straight betting prior to the match start. I look forward to seeing your approach.

    Regards,

    Matthew Trenhaile.

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    1. I was thinking along the lines of serve percentages too.

      At first I will create a Monte Carlo to see the effect and then decide how to model it after that.

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  4. Hi James, I'm a long standing tennis trader and have just brought your book to help me build an automated tennis application, its good to see your doing the same, hopefully we can bounce ideas off each other.

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    1. Thanks for the purchase, I hope it all goes smoothly for you.

      There is a companion webpage mentioned in the book so I'll see you there to bounce those ideas.

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