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
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
