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For this article, my plan is to detail how to create and use an effective ‘script’ to trade a tennis match. There has been a lot of interest in me creating a trading video, but I’m not convinced live trading a tennis match totally suits a video format, due to there being some wait for action on a regular basis. Therefore I thought it would be much more useful to write an article on creating a script, which would form the basis of what a trading video would cover.
It’s my belief that having a script is highly useful for several reasons:-
1 – It ensures you have all statistical information to hand prior to and during the match.
2 – It ensures that you don’t make impulsive decisions without any logical reason.
When creating a script my first job is to assess the projected holds of both players.
For this article, I’m going to use a hypothetical match between Victoria Azarenka and Li Na on hard court at the Australian Open. It’s quite apt because they were the two finalists at the upcoming Grand Slam, the Australian Open, in 2013. It’s also useful because they are similarly ranked and there isn’t a huge ability difference. For the purpose of simplicity, I’m going to assume that both players are equally fit and there are no match-up issues.
Projected holds for this match up were as follows:-
These projected holds have also taken into account a surface adjustment. In the 2013, the average surface hold of the Australian Open was 61.5%, and when including 2012 data, was also 61.5%. This is below the current WTA hard court average of 63.1% so it can be assumed conditions play a little slow.
Both players have much lower projected holds than the WTA hard court average, so from this data we can draw the conclusion that there will be more breaks than average in this match. Interestingly, in the 2013 final, that was indeed the case – there were 16 breaks in 29 service games (just 44.83% of service games were held).
It’s also worth noting that Li has much better break point stats. She saved 60.4% to Azarenka’s 58.4%, and converted 52.6% to Azarenka’s 50.5% in 2013 (across all surfaces). This is borne out by her superior break point ‘clutch score’ of 7.1, compared to Azarenka’s 1.5. A break point clutch score of over 3.0 is considered pretty strong, and is definitely worth noting and taking into account when compiling a trading script.
This data would give a starting price of around 1.80 on Azarenka, which isn’t too dissimilar to her starting price of 1.71 in both the Australian Open Final, and at the recent WTA Tour Championships in Istanbul.
It’s also reasonable to assume that as Azarenka lost the match in Istanbul, her price would lengthen a little for a match-up in the near future, so the 1.80 on her looks pretty accurate. For the purposes of this article, I’m going to assume that’s the case.
So as it stands, we have the following information:-
· Starting prices are correct.
· Both players are expected to hold serve much less than average, especially Li.
· Li should save break points more than average.
All this information is available for every match in the daily ATP/WTA spreadsheets.
On that basis, we can start to formulate a trading plan…
With both players expected to hold serve much less than average, a good starting point would be to lay both servers for their individual service games when the first set is on serve. At the end of each service game we hedge our position, taking a profit if there is a service break, or a loss if there is a service hold. Essentially this is a short term trade.
In normal circumstances, because her projected hold is very low, it would be worth looking at laying Li’s serve for a higher stake than Azarenka’s, but in this match-up her break point clutch score needs to be considered. It would also make me consider taking a more conservative approach to her service games. One way of doing this would be to take some liability out at a scoreline such as 0-30 or 15-40, and definitely 0-40.
Several approaches can be considered here:-
· You could clear all liability at one of these scorelines, leaving all potential profit on Azarenka and a scratch position on Li, guaranteeing profit (higher profit if Azarenka still breaks).
· You could leave some liability on Li and more potential profit on Azarenka, knowing that if Li does save break points to hold, you could guarantee a pretty much scratch trade.
Because the markets on the exchanges generally base themselves on the tour surface hold average when moving to a service hold or break, finding players – especially those that the market may not necessarily expect – who should struggle to hold serve is a valuable asset.
A further, more detailed approach, would be to use the ‘Rolling Projected Holds’ newly available via the Tier Two Daily Spreadsheets.
My research found that there was a very strong relationship between a player’s previous service game scoreline (e.g. hold to 0) and their next service game scoreline. I don’t want to go too much into the percentages as a) they vary from match to match according to a player’s base projected hold and b) I feel that I need to keep that information for my subscribers, but I will say that projected holds can vary by as much as 10% either way based on previous service game scorelines.
Once there is a break of serve, prices will start to deviate strongly from the starting price. This has a more pronounced effect in ATP matches due to the men holding serve more (hence a break is a rarer commodity) but still has a strong influence in WTA matches. It’s very difficult to give a price guide to how much this will change because time decay (games elapsed) is a strong factor – for example the price will decrease sharper if the break comes to give a player a *5-3 lead as opposed to a *2-1 lead, for example.
The next step at this point would be to assess whether there is any viability in laying the player a break up in the set. At this point, unless the player who is a break up was a fairly strong underdog to win the match before it started, the player a break up will be trading odds-on (1.xx price). Laying players at 1.xx means that we generate more potential profit than our potential liability, so it’s generally preferred to laying players at prices odds against, although I don’t mind laying players between 2.00 and 3.00 (occasionally up to 3.50) for individual service games.
The way I feel is best to assess whether the player a break up can be laid is to see the break lead defence and break recovery stats available via both the Ultimate In-Play Spreadsheets and also via the new Tier Two Daily Spreadsheets. By this I mean the set went back on serve from those points (it does not take into account what happened after the set went back on serve).
In this match-up, the following break-back stats apply:-
· Azarenka gave up a break lead 38.46% in 2013, and recovered a break deficit 62.50%.
· Li gave up a break lead 42.86% in 2013, and recovered a break deficit 66.67%.
From this we can see that there isn’t much between the two players in this area. Li gave up a break lead slightly more, but compensated that by recovering more break deficits.
If Azarenka was a break up, my approach would be to assess the combined score for the scenario (Azarenka break lead loss % + Li break deficit recovery %), which would be 105.13.
If Li was a break up, my approach would be to assess the combined score for the scenario (Li break lead loss % + Azarenka break deficit recovery %), which would be 105.36.
These stats are almost exactly the same, and come marginally above the required 105 combined score to lay a player a break up as detailed in the WTA Break Back Percentages article, so it’s viable to lay either player a break up in this match, as a medium term trade. By this I mean that I will look at keeping my position until either the player a break up gets broken (I can then hedge for profit) or the end of the set is reached (I can then hedge for a loss).
All these stats are available in the new Tier Two Daily Spreadsheets.
So far in the article we’ve looked at how you can use statistics to gain a very workable edge in the tennis trading markets, using basic projected holds when the match is on serve, and break lead/deficit stats when a player is a break up in the set.
My next job is to illustrate how the Ultimate In-Play Spreadsheet can be used to alter projected holds from early and late service game stats, as well as taking positions at the end of sets based on player tendencies.
At this point it’s probably worth clarifying the scenarios covered by early and late games. An ‘early service game’ is either of the first two service games of a set for each player. A ‘late service game’ is any service game from the point that at least one player has reached four games in the set.
It goes without saying that early and late service game stats are of great use – knowing which players start slowly or quickly, or thrive or wilt under the pressure of the end of sets, is hugely valuable in the trading markets.
With trading the player a break up already covered, the main way of using these early and late service game stats is to use adjustments to the projected hold figures when the set is on serve.
In the hypothetical match-up between Azarenka and Li, we have the following figures for the two players:-
12 month average service hold – 67.3%
12 month early service game hold – 67.5%
12 month late service game hold – 67.1%
12 month average service hold – 71.5%
12 month early service game hold – 70.9%
12 month late service game hold – 70.4%
From those stats we can see that Azarenka’s stats barely deviate from the start to finish of sets. Li’s stats have a little more deviation, showing that she holds serve 1.1% less in late service games than average. Subscribers of the Ultimate In-Play Spreadsheet will know that this percentage isn’t a huge drop-off, with many players having far bigger issues at the business end of sets – with some players holding over 10% less than their average in this scenario!
Due to this slight deviation, the adjusted projected hold stats for this match-up barely alter based on the early/late service game set data.
Victoria Azarenka’s projected hold would move from 56.6% to 56.8% in early games of sets, and 56.4% in late games of sets.
Li Na’s projected hold would move from 51.6% to 51.0% in early games of sets, and 50.5% in late games of sets.
I don’t keep stats for the middle part of sets, but based on these stats I probably wouldn’t be far wrong in assuming that Victoria Azarenka holds 67.3% on average in middle parts of sets, and Li Na is pretty strong in middle parts of sets, holding on average about 73.2%, which would be worked out by: 71.5% + (71.5-70.9) + (71.5-70.4).
This deviation is definitely not enough to want to alter our trading plan in any way for this match-up, but there are many cases where it would. A very basic example would be a player that has a projected hold around average for the match (hence it is not viable to lay their serve on that basis) but is one of the players previously mentioned that holds over 10% less than their average at the end of sets. In that case it would make laying their serve extremely viable at the end of sets, when it previously wasn’t at other points in the set.
The final area I want to assess in this article is how to use the ‘set percentages’ data in the Ultimate In-Play Spreadsheet to look at how we can take a longer term position in a match at the end of a set.
For those that are new to trading, the end of the set is a key point in the match – after the first set, a player has won 50% of the sets they need to win the match, and their opponent needs to win both to win the match (unless it’s a 5-set men’s Grand Slam match).
This situation means that the starting price on a player will significantly shorten if they win the first set, somewhat depending on how dominant they are in the process of winning that set. There is a more detailed explanation of this in the TennisRatings Trading Handbook. The WTA tends to have a slightly bigger drop in a player’s starting price when they win the set, compared to the ATP, but the market is correct in this as WTA players have a marginally higher win percentage when they win the first set than ATP players. This may come as a surprise to some readers, who may assume that the WTA is very unpredictable – and it often is. However, much of this unpredictability takes the form of in-set movements as opposed to anything else.
Because a player’s price will significantly shorten when they win the first set, a potentially viable position could be to lay that player at the end of the first set.
A basic way of assessing that viability is to look at whether the player a set up has a lower win percentage in the second set than their average, and whether the player a set down has a higher win percentage in the second set than their average. There are far more advanced ways of doing it, such as using triggers based on first set stats (which is something I have spent a lot of time researching), but for this article I want to concentrate on the set percentages.
These percentages can hugely fluctuate from player to player. My recent article on Petra Kvitova showed that the Czech world number six won 68.6% of first sets, 52.2% of second sets and 65.7% of third sets in 2013 (average set win percentage of 61.5%). These stats illustrated why so many of her matches went to three sets (especially ones where she won the first set) and she’d clearly be a player who it would be a good idea to look to lay when she won the first set.
In this match-up between Azarenka and Li, the stats were as follows:-
Won 75.6% of sets in 2013.
Won 71.2% of first sets in 2013.
Won 80.8% of second sets in 2013.
Won 73.3% of third sets in 2013.
Won 72.1% of sets in 2013.
Won 75.9% of first sets in 2013.
Won 70.7% of second sets in 2013.
Won 61.5% of third sets in 2013.
From these stats we can see that Azarenka was a slightly slow starter in matches in 2013, with a superior second and third set win percentage than her first set win percentage. Her second set win percentage was especially impressive, with it being 5.2% above her average set win percentage, and 9.6% above her first set win percentage.
Li had the opposite problem, starting fast – winning 75.9% of first sets. This was 3.8% above her average set win percentage and significantly eclipsed her other set win percentages.
With Azarenka winning more second sets than other sets, and Li’s second set win percentage being lower than her overall mean, it’s clear that laying Azarenka when she wins the first set is not at all viable, based on those stats.
However, should Li take the first set, laying her would be much more viable, based on the above stats. Azarenka has an excellent second set record, and Li’s drops from her strong first set win percentage. With Li starting the hypothetical match-up at around 2.25, her price after the first set would probably be 1.3x or 1.4x, so if Azarenka did take the second set, we’d have created a very nice position.
If that did happen, the prices wouldn’t be hugely dissimilar to the starting prices. The market tends to make the player that wins the second set (to equalise the match at one set all) a little shorter than their starting price, which is something I don’t necessarily always agree with, as my research shows that momentum isn’t a huge factor in this regard (the TennisRatings Trading Handbook has more information and stats on this).
Roughly speaking, if we had laid Li at 1.3x or 1.4x at the end of the first set, we could then hedge our position by backing her at around 2.3x or 2.4x generally at the end of the second set. This would be a huge tick gain.
There are other ways of approaching the trade. For example, those who like adopting an approach with a little less risk may want to hedge or remove some or all liability should Azarenka lead by a break in the second set (this would be an especially good idea in this match-up based on the fact that Azarenka’s combined score when a break up was over 105).
As mentioned above, at the end of the second set, prices are generally not hugely dissimilar to starting prices. Unless a player is a very short price, opening a new position here isn’t an approach I like, and again I’d need stats to justify that move.
Whilst Li’s stats aren’t especially strong in deciding sets, I’d want her deciding set win percentage to be below 50% to look to oppose her at the start of a deciding set. It’s worth mentioning at this point that there are plenty of players that do have atrocious deciding set records compared to their records in other sets or declining set win percentages as the match progresses (Benneteau and Llodra are several in the men’s game, Date-Krumm and Pironkova are examples in the women’s game).
However, I’ll be aware that Azarenka has a better record than Li in deciding sets and might ease off laying Azarenka’s serve, or when she is a break up, in a deciding set on that basis. Opposing Li’s serve, or laying her when she is a break up, on a slightly heavier basis in a deciding set could also be considered.
Hopefully by now you have a good idea of how to create a tennis trading script based on statistics, and also a better idea of how the stats in the Ultimate In-Play Spreadsheet can help you in your trading.
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