#### Followers of the blog will be familiar with my each way betting side hustle on horse racing. This continues to be a great source of side income, but horse racing is not a passion of mine in any way, shape or form. My true sports passions are football and tennis – two sports I also enjoy trading *(rather than betting)* using betting exchanges, such as Betfair.

Almost a year ago to the day *(and pre the creation of my blog)* I wrote an article on the popular blog platform **Medium** – my first attempt at blogging. The article explored the somewhat **mechanical nature of tennis odds pricing** on the exchanges and how this can be exploited when **trading tennis in-play**. It remains one of the most fundamental concepts when trading tennis. I’ve re-posted this article below on its rightful new home and have added a few sections.

I plan on doing some **trading examples and more posts of tennis trading** in the future. In the meantime, I hope you enjoy reading. Links to the original article can be found at the end.

Dan

#### Gaining an understanding of how the prices of tennis players are likely to move during a match is an **essential skill** for any budding tennis trader and will greatly benefit your approach to **disciplined risk management**.

New to tennis trading? Trying to work out how prices move in-play? Well, you are in the right place! Trying to work out where prices might move to during a tennis match can often seem like a finger in the air guesswork!

You might assume that in order to know this, you’ll need a **fancy algorithm** or a swat team of **data scientists** working away in a dark room! Sure, there are countless academic studies on how to model the probability and prices of a tennis match and I’d encourage you to read them if you get the time. But, in the heat of the action is there an easier way to figure this out and benefit our trading decisions?

Wrapped around the **principle of favourable risk:reward** is the idea that we are striving to enter the market where prices offer **limited ****(and known)**** downside** and **maximum potential upside**. It is unlikely a professional trader would enter the market at the start of a tennis match. Instead, we need to see prices deviate from their **starting equilibrium level **to specific points that offer the best **swing trading opportunities** where the downside is defined and limited to just a few ticks.

Thankfully some **simple formulas **and a** bit of common sense** are all you need!

## Key Price Drivers

Before we get into the method, let’s quickly discuss the **two principal events** in a tennis match that **drive prices away from their equilibrium**, namely **breaks of serve and sets won**.

Like in any sport, when one player or team creates an advantage over the other, the **perceived probability** of that player or team ultimately winning the game will **increase ***(leading to a shortening of their odds or price)* and vice versa. While the game state remains **balanced**, the probabilities *(and therefore the prices available)* will largely remain constant.

In tennis trading, everything is **anchored to the players starting prices**. Moments before the match begins, these represent the **consensus view of each players probability and the likelihood of winning the match** based upon everyone’s assessment of those players, the conditions, the court surface etc. There will **always be a favourite** *(even a narrow one)* and an **underdog** in terms of the **prices available**.

Unlike in many other sports, tennis pricing is somewhat predictable as there are only **two possible outcomes**. **Either Play A wins or Player B wins**.

There are no third or fourth players, no draws to content with. This means that as the price of one player moves in one direction *(and as the probability increases or decreases for one player)* the equivalent move occurs for the other player in the other direction. The probabilities of both players winning the match **must equal to 1.0. **

So, while the market will ebb and flow with every point **won or lost**, it will do so in a fairly **narrow range while the match remains equal**. As a player holds their service to take a 1-0 lead, their price will drop slightly. As the opponent subsequently holds their serve, prices will largely revert **back to the starting price** with the match being 1 game apiece.

This pattern will continue until there is an **event that will pull prices away from these equilibrium levels**. The only way to get ahead in a tennis match is to **break your opponent’s serve** *(solidified with a subsequent hold of serve)* and to **win a set.** You can think of these as **scoring a goal** in football to take the lead or, **winning a hole** in a matchplay golf match.

So, **breaks of serve and set wins are the two major events that cause tennis prices to move**. When they move, they **create opportunities for trading**.

Let’s look at each of these in more depth 🙂

## The First Break of Serve

Typically when the **pre-match favourite** breaks their opponents serve, their starting price will **roughly halve in value**. So, in order to establish where a players price will most likely move to we simply take the **starting price and divide it by 2**.

As we are working with

decimal oddson the betting exchanges, we simply subtract 1.0 from the starting price, then divide the remaining number by 2 and then add back the 1.0

Let’s try this with an example of a **pre-match favourite with a starting price on the exchange of 1.75**. If they successfully break their opponent we would, therefore, expect their price to shorten to **around 1.38**. If they were **priced at 1.50** they would move to around **1.25**. If they were **priced at 2.0** they would move to **1.5** and so on.

The somewhat **mechanical nature of tennis pricing** also allows this to be reversed to establish where a players price will likely **revert back to** if, having broken their opponent, they then lose their own service game leaving the game essentially all square.

So our player above, having shortened to 1.38 upon breaking their opponent, would revert back to their **original starting price of 1.75** upon losing his serve and conceding the break advantage *(all things being equal)*

A note on the **underdog**. When a **moderate pre-match underdog** achieves a break of serve, I have found that the price move can sometimes **extend a little further than half**. Over time, I have found that a division of **around 2.15** *(rather than 2 for the favourite)* for a break of serve works fairly well when pricing the underdog. This becomes less accurate the more you extend up the price scale. So, for a huge underdog *(e.g. odds of 5.0 or more)*, the move may be nearer a **division of 2.50 or more**.

So, taking a pre-match underdog with a starting price of 2.25, the price would shorten to around 1.58, using the division of 2.15 as an example.

## Winning the First Set

Building on this further, we can also work out where the pre-match favourites price will likely move to if they win the **opening set**. To do this we take the **starting price and divide it by 4 for the favourite**.

Some consideration should be given for the manner** of winning the set**. This division applies most of the time to a routine set win, e.g a 6-4 or 6-3 scoreline with one or two breaks of serve in favour of the favourite. This will be the **most likely route** to winning the set.

If the pre-match favourite wins the set 6-0, we can, of course, expect the **price to extend below this estimate** a little. Likewise if the favourite labours to a first set victory, perhaps dropping serve on the way or edging out a tie-break, the division may be a **little less**. So, some judgement of the match can help here.

Again, using our pre-match favourite player above with a starting price of 1.75, we would expect them to shorten to 1.38 on achieving the break of serve, and then again to 1.19 if they go on to win the opening set in a routine fashion.

Where the underdog is concerned, I find the move tends to go a little further than when the favourite wins the set. I have found that a **division of between 4.25 and 4.50** *(rather than 4 for the favourite)* works fairly well when pricing the underdog. The **same caveats apply** when judging how much of an underdog they are pre-match.

So, taking our prior underdog player starting price of 2.25, we would expect them to shorten to 1.58 on achieving the break of serve, and then shorten again to between 1.28 and 1.29 if the underdog takes a surprise lead and wins the opening set.

## How To Use This Information to Trade Tennis

The most **powerful way to use this to trade tennis** is in defining your **maximum potential loss** when entering a trade as well as giving an expectation of where prices may revert to if things go our way.

The markets **often overreact to either a break of serve or the first set win**, providing entries with defined, and very small further downside risk, while leaving great potential for a swing in the other direction.

For example, let’s say a player is winning the first set and is a break-up but has not yet won the set. You will often find their price has reached *(or sometimes even surpassed) *their expected **set winning price**. This suggests that the market thinks that the **set is done and dusted… a formality.** This is great news as it means we can **oppose that player** on their next service game *(in the hope that the opponent can break back)* knowing that if this does not happen, the price is unlikely to drop more than a few ticks from where we entered.

If the opponent does break back, we can also work out the **distance back to the starting price** – the most likely level price will return too if the match gets back on even terms. So this may be a **4-5 tick maximum downside for a potential 30-40 tick upside**.

Those that follow the sport closely will know that **breaks of serve happen frequently**, especially when the right combination of players meet *(i.e. evenly matched rather than huge favourites)*. Breaks of serve are also far **more common in the women’s game than the men’s** meaning that trading **WTA matches** are particularly good for these types of trades.

## Some Caution!

Because of the number of variables in a tennis match, it is however **impossible to give an exact price change guide** for a player from starting price to a break up to winning the set. Not least because the betting exchange is a live marketplace with many hundreds of thousands of pounds *(often millions!)* being traded in a single match. If a tidal wave of money goes onto one player that can cause an overreaction and prices may deviate from what we expect **most of the time**.

Also, the very nature of the match itself is an important factor. If a player wins a set **very comfortably** markets can again **overreact and assume the match is effectively over**. Similarly, if a heavy favourite struggles to win the opening set prices will adjust to reflect this. **These overreactions often present great trading opportunities offering great value entries or exits**.

What we are seeking here is a **ballpark price that can help us assess our risk and opportunity**.

You’ll notice when we look at the examples below, I **add a tick or two either side** of the calculated prices to offer a range that I’m looking for.

This principle also only really applies to **3-set matches** rather than 5-set matches. This is no big deal as the vast majority of matches, on both the men’s and women’s tour are 3-set affairs. Only the men’s matches in the four grand slam events *(Australian Open, French Open, Wimbledon and US Open)* are played out over 5-sets.

Okay, enough **theory**…. let’s take a look at a few real-world applications of this approach to see how it works in practice. This week, the **ATP and WTA tours are in Madrid** for one of the highlights of the regular **European clay court season**. Both tournaments are edging towards the latter stages which should result in some close match-ups and excellent trading opportunities. Let’s see how this works in practice!

## Example 1

Let’s begin with an example from the men’s draw with a third-round match-up between **#4 seed Juan Martin Del Potro **and unseeded **Dusan Lajovic**. The screenshot below shows the **starting price** for both players just a few moments prior to the match starting. Lajovic is about to kick things off serving first.

We can see that Del Potro is the **heavy pre-match favourite** with a starting price on the exchange of **1.24** *(which is an implied 81% probability of winning)* and Lajovic is the **underdog** with an exchange price of **5.0** *(which is an implied 20% probability of winning)*.

Using our **formulas **from above we can establish before a single point has been scored, the likely price for either player in the event they achieve a **break of serve** or what their price should be if they **win the first set** *(give or take a few ticks)*.

Continuing with our example, if Del Potro were to break first then I’d expect his price to **shorten to around 1.11–1.13**. Similarly, if Lajovic achieves the break I’d expect his price to **shorten by a slightly higher amount to around 2.85–2.87**.

Let’s see how that match unfolds from here 🙂

## The First Break

After some tightly contested opening games with no real opportunities to break serve, **Del Potro seizes his opportunity** in game 7 and secures **the first break** of the match.

As we predicted, his price shortened to **1.11**. From here, we would then expect an end of set price of between **1.05–1.07 for Del Potro** *(starting price divided by four)*. Del Potro then **solidifies the break of serve with an immediate hold** on his own service game putting him in a very strong position to win the first set. His price shortens a little more to **1.09**, edging towards our predicted **set winning price**.

## The First Set

With Lajovic now serving to stay in the set, Del Porto turns the screw and secures a **second consecutive break of the Lajovic serve** and wins the **first set**. As we predicted ahead of time his **price shortens to 1.06**, right where we expected.

## Price Charts

Looking at the **price charts** for both players over the course of the opening set, it is easy to see *(in hindsight at least)* how prices moved as events unfolded in the match.

Prices obviously **react and oscillate to each point won or lost** as service games are played out and held *(i.e. no breaks achieved)*.

Del Potro can be seen below moving between **1.20 and 1.35** as the match plays out before achieving the consecutive breaks of serve taking him through our predicted price of around 1.11 for the break and towards our eventual end of set price of 1.06.

Equally, notice how large the drift is in the price of Lajovic having lost the first set. His implied probability of winning the match has **shortened to just 6%** by the start of the second set.

Amazingly, **Lajovic ended up winning the match** from this position! So **laying Del Potro** at 1.06 *(or backing Lajovic at 16.5)* offered an amazing trade!

## Example 2

In this second example, we’ll see a **different set of circumstances** but how the projections for price movements can still hold true!

Turning to the women’s draw this time with a quarter-final match-up between **#1 seed Simona Halep** and **#6 seed Karolina Pliskova**. As before, the screenshot below shows the **starting prices** for both players a few moments prior to the match with Halep about to kick things off serving first.

Halep is the defending champion having won this event for the past two seasons!

We can see that with her being the **top seed**, Halep starts as the favourite with a price of **1.44** *(which suggests a 69% implied probability of winning)* and Pliskova is, therefore, the **perceived underdog** with a price of **3.20** *(a 31% implied probability of winning)*.

As in our previous example, if Halep were to break first then I’d expect her price to **roughly halve and shorten to around 1.21–1.23**. Similarly, if Pliskova breaks first I’d expect her price to **shorten a little more than halve to around 2.01–2.03**.

## An Early Break!

Back to the match and in no time at all **Halep breaks Pliskova on her very first service game **to take an early 2–0 lead in the match. Is the writing on the wall for Pliskova?

Halep’s price has therefore **shortened to 1.21** — exactly as we expected using our simple formula. Meanwhile, Pliskova’s price has **drifted out to 5.5**.

Continuing our logic, we would expect Halep’s price to **drop to a price of 1.10–1.12** if she goes on to win the set from this leading position. While it looks unlikely at this stage, I’d expect an end of set price for Pliskova of **1.49–1.52**.

## The Break Back!

However Halep’s day is **not plain sailing** and perhaps to be expected when two highly ranked players meet, there are **twists and turns**.

In a short order of time, Pliskova **breaks back and then solidifies this with a service hold** to take the match back to all square 2–2. Notice how the **mechanical nature of price action** ensures that the prices for both players r**evert to within a few ticks of their starting prices** – back to **equilibrium**.

## Under Pressure!

Things are getting worse for the top seed. Having failed to capitalise on her early break Halep is **then broken by Pliskova a second time** and threatens to pull off a third break with Halep **facing set point on her own service game**. Also, note how Pliskova is now the perceived favourite with a price of **1.59** *(implied probability to win now 63%)* versus 2.62 for Halep *(implied probability to win of 38%)*

## Set Secured!

Halep muddles through an eventual hold of serve but can’t prevent Pliskova from serving out the set for a **6–4 first set victory!**

As predicted our formula *(adjusted slightly for the underdog remember)* predicted a set winning price of **1.49–1.52 for Pliskova**. Here we can see that her price came to just under this at 1.46 upon winning the set. The fact that she was now beating the top seed may have led to a **slight overreaction in price**.

## A Look at Price Action

As before, check out how the set unfolded on the price charts for both players. We can see the **big reaction to Pliskova getting broken early on** where her price drifted out to **5.5–6.5** before **shortening aggressively** as she secured the break back and then eventually the set.

With Halep, we can see how she **shortened to 1.21** on that early break before her price reverted back to the **starting price of 1.45** when she lost the advantage, before then blowing out to **over 3.0** when she lost the set.

Note that more than

£1.3mwas now been trading on this match by the end of the first set! Volume in tennis trading is huge

## Natural Exits

As mentioned, there are lots of other possible factors that impact tennis prices when trading in-play so getting it right 100% of the time is not realistic or the goal. But, broadly speaking this remains a **very useful way to mark out the key levels in-play** and establish **downside risk** when entering trades.

For example, let’s say that, after securing the first break of serve, we decided to **lay** *(bet against)* Del Potro winning the first set in our previous example **for a £25 liability**. If you recall, his price after the break was 1.11 *(or 1.12 to bet against him winning)* as shown below:

We could have layed Del Potro at this point knowing that the **very worst case scenario** was a **6 tick loss to around 1.06** at the end of the set. This is very useful because it allows us to know our **maximum loss** as soon as we take the trade on. Had we taken this trade *(and were prepared to stay in during the second set)* it would have worked out very well as **Del Potro eventually lost the match in three sets**. There would have been ample opportunities to hedge out for equal profits on all possible outcomes during the match.

Our maximum risk would have been our **£25 liability** and our **maximum reward was £208**. That’s an attractive **risk reward!**

Next time you are on Betfair, I encourage you to watch a few matches alongside the exchange prices and **apply these formulas at the start of the match** and observe how accurately these levels are hit as the match unfolds. Once you are comfortable it will **greatly benefit your tennis trading!**

I’ll follow up on this post with a **simple, low-risk trading strategy** that utilises these expected prices soon 😉

I hope you enjoyed this post. As always, I’d love to hear your thoughts below. Do you currently trade tennis or is this something you might start to consider?

The original can be found **here**

Until next time!

Dan

## This Post Has 7 Comments

## Dr FIRE

13 May 2019Very interesting post, Dan, thanks for sharing. I’ve never tried any form of trading on the exchange (I’ve barely graduated from plain old matched betting!), but I’d be willing to give it a go. I look forward to reading part 2 of your guide!

## Pursue FIRE

13 May 2019Hey Dr Fire – glad you enjoyed it. It can be hard to convey these concepts in written form so I think a video could be called for. This is always a great time of year for tennis trading as the matches are being played mainly in Europe, which is convenient for our time zone and interest always peaks around Wimbledon for those of us in the UK. I’d certainly recommend just watching a match on Betfair (you’ll need funds in your account to watch live pictures) but you can watch the markets even without an account. Just start to observe how prices move in a mechanical fashion. That’s a great first foray. Part 2 coming soon

## Andy

24 May 2019Thanks for the post Dan. This is something I have also looked at recently, and really want to get into more, after reading the book by Dan Weston (www.tennisratings.co.uk). He takes a very stats based view of the sport, and how to trade it. Anyway, looking forward to reading your next post on the subjet!

## Pursue FIRE

24 May 2019Hi Andy, glad you enjoyed the post. I’ve traded tennis on and off for several years and have picked up a few tricks along the way. Funnily enough, I read Dan’s book about five years ago and that helped me move more towards the stats-based approaches. I’ve even met him before at a Betfair Traders event. Even just today, I’ve been working on my tennis model which has 10 seasons of WTA and ATP data behind it. I think I’ve found an edge that is consistent year after year and maybe that will be something I share in some form in the months ahead. That’s more on the picks/selection side of things rather than in-play trading which is my main passion. Sadly, there are not many decent books out there other than Dan’s and while his is good, he seems to focus more on what doesn’t work than what does. I end up reading the academic studies on tennis odds, probabilities, machine learning etc – nerd! Anyway, if you fancy another, more light-hearted read on a related topic, I’m in the middle of ‘Game, Set and Cash: Inside the Secret World of International Tennis Trading’. It’s a few years old now but is a season-long view of trading by a professional court-sider (the ones who relay live points from the court to their betting syndicates hoping to gain an advantage over the exchange delay). It’s a fun read for sure and actually has some useful, unintentional take-aways for trading! Have you done any trading yourself? I’d be keen to hear your stories. Cheers Dan

## Andy

25 May 2019Thanks for the book recommendation, I’ll take a look.

I have no sports trading experience (although I did matched betting in the past, and now EW betting >£10k profit), but as i’m now FI and a recent semi-RE (cut down hours at work to 3 days a week) I’m looking for something fun to do in my days off that also provides an income! Tennis is a sport I enjoy so it seems a good fit for me (I don’t enjoy the horse racing). I’m looking for a way in without losing a whole load of time and money in the process…

## weenie

27 May 2019Only just gotten round to reading this, Dan – very interesting and informative. I’m a big tennis fan and aside from a few MB accumulators in the past, have never tried this, so I may just follow on the exchange to see if I am comfortable with this type of gambling…

## Pursue FIRE

28 May 2019Thanks Weenie – yeah just fire up Betfair Exchange and watch the prices during a match. That’s the best way to follow what I explained above.