Building A Tennis Betting Model

I recently published a post about in-play tennis prices on Betfair Exchange and how, given the mechanical structure of tennis scoring, one could identify ahead of time the likely levels player prices might reach in the event of a pivotal moment (such as breakpoints and sets won). Entering a trade at these stretched prices offers low downside risk entry points to potentially capture swings in prices. This is very valuable data when trading tennis in-play.

But, not everyone wants to sit there trading a tennis match. it requires time and focus to capture and lock in profit opportunities. So, I’ve been looking at ways to implement a different approach for people who just want to make systematic bets on tennis matches and make some extra money in the process. So, is it possible to develop a statistical-based tennis betting model that would allow for making simple picks rather than trading in-play? Let’s find out 🙂

Tennis Model


What I’m about to outline actually has the most unlikely source of inspiration 😉

Some years ago, when I started to get really deep into in-play tennis trading on Betfair Exchange, I would often scour the internet for more knowledge and insights as to how one might model of a tennis match from a math and probability perspective, and how this might relate to in-play prices and odds.

There are countless academic papers and studies on this topic, some of which I list at the end of this article. I warn you though, these are not light reading 🙂

In amongst the academic gems, of course, were the inevitable and countless garbage get rich quick ‘systems’ offered for sale. These are a dime-a-dozen not just in tennis betting, but in any form of sports betting, forex or crypto trading and the like. You can spot them a mile off with their outlandish claims, sales funnel landing pages and ‘one time only discounts’

“I usually charge £5k for this course, but if you order in the next 5 mins, I will give this to you for just £20″…. You know the drill 🙂

One such tennis betting system was marketed so brazenly it actually caught my attention because it appeared to be highly transparent in its methodology, so much so that anyone could quite easily back-test the claims for accuracy.

Like the nerd I am, it became a game for me to see how quickly I could unravel the spin and spot the critical or fatal error in the sales pitch.

I won’t name the offending strategy (amazingly it’s still out there) and, I imagine, some poor suckers continue to part with their cash for access to these top-secret strategies. Amusingly, if you just search for the offending strategy and add the term ‘pdf’ into Google you can find it all there for free… it must be good then 🙂

The basic premise of this get rich quick scheme was something like this….

Favourites win tennis matches most of the time. This is actually true by the way. So, if something is likely to win 65-70% of the time, you’d back it time and time again. Reasonable logic you might think, but the problem is the risk: reward is awful. That being said, the author provided quite detailed history over several years as to how he regularly pulled in a claimed £5-£6k a month from this relatively simple strategy.

So, I went a got hold of some comparable data and replicated the strategy for myself in Excel, rule-for-rule. What I found was almost the total opposite of what was claimed – in fact, it was almost the perfect way to lose money consistently, season after season.

Reverse Engineering

Having proven the marketed strategy as total garbage, I sat on the data I had gathered for a while. I kept asking myself whether there was actually something I could do with this data. Could I reverse engineer something out of nothing?

The advertised system involved backing tennis players via a traditional bookmaker. Nowadays, of course, one can also bet against or ‘lay’ players via the betting exchanges.

You see, betting on favourites in tennis means betting at odds below evens (2.0 or lower in decimal terms) and that means that your potential reward is always going to be less than the risk you assume.

For example, betting £10 on a short-priced favourite like Serena Williams at a price of 1.10 means that you are risking £10 to make £1. Sure, it’s likely she’ll win at that price, but risking £10 to make just £1 is hardly the key to long-term success. You might win 9 of those bets in a row (making £9 profit in the process), but if the 10th bet fails, you’d lose everything you’ve built up. This was essentially the problem with the advertised strategy – the losing bets devastated your bankroll.

So then I thought if backing these favourites was the perfect way to lose money, how would things look if you bet against these favourites instead? In a variation of this strategy, I would bet against pre-match favourites using the exchanges rather than a traditional bookmaker.

This would flip the risk:reward profile on its head. Yes, favourites don’t lose too often, but when they do, you could profit handsomely. A bit like each way betting on horses, you’d only need to win some of the time to more than compensate for losing bets.

Of course, a straight flip of the advertised strategy failed too – it was too simplistic and so, to find an edge, more data and statistics were called for.

A Statistical Approach

So, I began with testing my ideas on the most recent full season’s worth of data. A few hours later and hmmm… things looked interesting! But….. perhaps that year was unique, an outlier in a pool of bigger data.

So I pulled another season’s worth, and another until I had not one or two seasons, but ten full seasons worth of tennis data stretching back to 2009 covering the main tours of both the men’s (ATP) and women’s (WTA) game.

This data-set is comprehensive and includes everything from the tournament name; tournament grade; surface; indoor vs. outdoor; player name; ranking; full score and set-by-set results… and most importantly, the player betting odds across several soft and sharp bookmakers, including Bet365 and Pinnacle.

From there, I began running my pricing theories across multiple seasons worth of data, testing varying scenarios and assumptions in trying to find an angle for betting.

So What Did I Find?

My work here continues to be an active side-project but the results I can share with you thus far are encouraging. Broadly speaking, I’ve observed persistence in specific subsets of data which is remarkably consistent season-to-season and therefore, a good predictor of future potential results.

“If my calculations are correct when this baby hits eighty-eight miles per hour… you’re gonna see some serious shit.” – Doc

Through lots of statistical back-testing, I’ve developed a set of qualifying criteria that I look for in screening tennis matches where the pre-match favourite might lose. These would represent winning bets. The starting point for this criteria is each players starting price, as determined by the main bookmaker, and most importantly, the relationship between these prices that I have observed.

Now, before any naysayers out there start to talk about sample size etc, this sample size is …… huge! I’ve run this over more than 46,000 historic tennis matches on the WTA and ATP Tour, covering full season data from 2009 to 2018 inclusive, plus 2019 year-to-date. I’d argue that’s a meaningful enough sample size to work with.

Let me hit you with some numbers 🙂

The Opportunity Set is Vast

The professional tennis calendar is well established and, season-on-season, the main tours of the ATP and WTA combined have an average of 4,600 3-set matches every year. I’m only focused on this highest tier of tennis and ignore the lower grade tours such as the Challenger and ITF circuits. The liquidity for these events is sub-optimal.

I also only focus on 3-set matches in my analysis as the vast majority of the season is played in the 3-set format with the exception of the four Men’s Grand Slam events (Australian Open, French Open, Wimbledon and the US Open). I’m not ruling out looking at 5-set matches, but they obviously introduce more variables and thus should be treated as a different dataset than 3-set matches. To make solid conclusions from the data set, I’m just focused on 3-set matches for now, given 95% of the season is played in that format.

Of these main tour 3-set matches across the ATP and WTA, around 1,970 per year, on average, meet my ‘qualifying criteria’ for a selection. So that’s a pretty healthy 43% (on average) of all matches in a given season provide the opportunity to bet. This is also spread relatively equally across both the men’s and women’s tour with slightly more in the women’s tour on account that their Grand Slam matches are 3-set affairs and are not excluded as they are for the men.

Daily Opportunities

The tennis season runs pretty much runs for 11 months of the year from January to November, with a small number of matches spilling into early December, or late December (pre-season matches just prior to the main season starting in January each year). But really, the vast majority of activity takes place from January to October – that’s 10 solid months of the year to take advantage of tennis betting.

Regardless of the variation towards the end of the season, if we take it all in, that works out at around an average of 195 qualifying selections per month; 45 per week; or 6-7 per day. This is certainly a very manageable number to implement into a consistent trading plan and would require very little time to implement.

In reality, the number of selections per day will be higher at the start of the given week as more matches are being played as tournaments nearly always begin on a Monday. As the field gets whittled down through the advancement of rounds, the number of qualifying bets will then tail off towards the business end of the week. I think this is also quite a nice feature of tennis betting in terms of accommodating betting around the working week/family time etc. Contrast this to another betting side hustle of mine (each way betting), where there may be anywhere from 15-50 bets available per day, requiring time and focus to capture them all.

This chart shows the number of qualifying bets that were identified, based on the day of the week each season. Note how the number of selections peaks mid-week (Tuesdays), before trailing off towards the end of the week. This is a very common distribution.

Strike Rate

So, now we have an expectation for the likely number of qualifying bets we might expect on a typical day, week, month etc, but what about the overall strike rate of success? How many of the qualifying bets were shown to have been winning bets over time?

Like with most of the data observed, things are remarkably consistent. Over the full ten-year period between 2009 and 2018, the strike rate of successful selections averaged around 30% with very little variation season-to-season.

Year-to-date in 2019, it’s running at 29% currently – bang on trend with the long-term history.

Now you might think that a 30% strike rate is far too low. However, remember that we are betting against favourites who are typically priced significantly below evens (2.0). This means that our risk:reward is tilted heavily in our favour, offering a greater upside potential return to downside risk.

For example, if I bet against (lay) a player priced at 1.25 on Betfair, in points terms, I’m risking 0.25 points to potentially make 1 full point. So, if we made that exact bet with a £10 maximum liability (the maximum amount we could lose), we’d be risking £10 to make £40 should the bet be a winning bet. We’d have to deduct 5% commission to Betfair on a winning bet (in this case £2), but you can see, in this example, the risk: reward is roughly 1:4 in our favour.

So, a winning bet can potentially offset a handful of losing bets. In other words, we don’t require a particularly high strike rate to be profitable.

For those of you that also do Each Way Betting on the horses, you’ll know that our strike rate of winning horses runs at about 10% over the long-term, and horses that place runs at around 25-30% in the long-term. In much the same way, we can expect to lose 60-65% of our bets but still produce incredible gains.

Net Points: Laying To Win 1 Point

Before, introducing monetary variables into a model, it’s important to first give a sense of return from a pure points perspective. This is because how any one individual chooses to implement a strategy from a risk management and staking perspective will vary from one person to another. So let’s first look at the purest form of return – points risked versus points won.

Here we are betting on our qualifying selections (i.e. laying pre-match favourites) in order to win 1 full point. Let me explain:

I’ll repeat the same principle from the earlier example. If we are laying a favourite at the desired price of 1.25 on Betfair Exchange, in doing so, we are assuming 0.25 points of risk in order to win 1 full point. If the bet goes our way, our net points won will be +1.0. If we lose the bet, our net points will be -0.25. Pretty simple right 😉

You then simply tally up net points won or lost relative to the total points risked and you arrive at the purest form of return on investment (ROI) calculation. With a small sample size, the ROI is somewhat meaningless, but as the sample size grows it becomes a more dependable measure such as having 10 seasons worth of bets.

So, let’s see how many net points were won or lost over 10 full seasons worth of bets that met my qualifying criteria. This bar chart provides the total net points per season and the ROI based on total points won divided by total points risked.

So, we can see that the strategy has yielded healthy net points for ten consecutive seasons with an average of 118 points won from an average of 710 points risked, yielding an average ROI of +16.7%. The next table, adds a bit more detail as to the number of qualifying bets per season; winners versus losers and a repeat of the Net points and ROI figures above.

Taking this a step further, the following chart shows the cumulative net points achieved per season. This helps us look beyond the final, end of year numbers and consider the variance of results throughout a season. As you’d expect, there is some variation each year (the lowest net points achieved was still a very good 85.6 in 2010, while the highest net points achieved was 136.1 in 2015), but it is undeniably a consistent pattern year-on-year, and most importantly a profitable one at that.

Finally, it is worthwhile to examine results month-by-month. The following table groups the net points achieved every month over the ten season period. You’ll observe that losing months, from a net points perspective, are generally few and far between.

Looking just at net points, of course, will not account for an individual’s staking plan or commissions charged by the exchange for winning bets. We’ll look at the potential impacts of both below.

Laying to a Fixed Liability

Okay, so far so good. We can prove, conceptually at least, that from a pure points gained relative to points risked standpoint, the strategy appears to be consistently profitable and that those profits are observable over more than 10 seasons and a universe of 46,000 completed tennis matches. Let’s then run the same analysis with some money factored in.

In this example, I’m showing the results for adopting a flat staking plan season after season. Let’s assume an individual is willing to risk a flat £10 for every selection and chooses not to adjust this up or down as his or her bank changes based on overall profitability. We’ll also now introduce the 5% commission charge on winning bets. This is the current going rate charged by Betfair Exchange. Other exchanges are cheaper and might, therefore, result in less chargeable commission which would increase profits.

So laying to a relatively conservative £10 fixed risk has yielded consistent profits each season, averaging around £2,357 a year with a 12.9% ROI. That’s around £25,000 in net profits over the entire period, just placing fixed £10 bets. Not bad at all. Again, observe the remarkable consistency across the number of qualifying bets, winners versus losers; strike rate; liability risked; profits earned and ROI.

As before, let’s consider this from a cumulative perspective, season over season. In similar fashion to the cumulative net points charts, things here are also quite correlated season-by-season with profits ranging from £1,700 to £3,000 per season from fixed £10 stakes.

And finally, the month-by-month picture gives a sense of what a typical month might return using a flat staking plan such as £10 fixed. Ignoring the quieter months of November and December, the average monthly net profit is roughly £250.

Dynamic, Rolling Bank With Fixed % Risk

Lastly, let’s look at how things would be if we adopted a rolling bank strategy. This is probably the most likely scenario people would choose when betting (myself included). In this scenario, we start each season with a fresh bank of £1,000 and risk 1% per bet. The 1% risk is fixed as a percentage of the bank value, and the amount that is actually risked in nominal terms is adjusted at the start of each day as the bank grows or declines based on the result of the prior day’s selections.

So, as the bank increases over time, so too does the nominal risk per bet, but always staying at 1% overall risk. This does introduce more volatility but also offers potentially more meaningful returns, especially if you rolled your profits from one season to the next.

As with the other measures, the strategy has yielded profits each season, but with noticeably more variation in returns. At its worst, a £1,000 starting bank would have grown to £4,222 (+£3,222 in net profits) in a year such as 2010. Meanwhile, in a standout year such as 2017, a £1,000 starting bank would have grown to £15,112 (+£14,112 in net profits). The ROI ranges from +7% to +18%, averaging +11.5% per season.

As before, the table below adds more granularity to these returns. I also show the percentage growth in the £1,000 starting bank each season together with how low the bank value would have dropped from that £1,000 starting point, and a measure of that in terms of percentage drawdown. You can see that, at its worst, a £1,000 starting bank in 2016 would have dropped as low at £711 – the equivalent of a 29% drawdown from the starting balance. Despite this, that year still produced net profits of £3,222 with an ROI of 7.3%, net of all commissions.

This greater variation in returns is more evident when we look at the cumulative profits chart for this staking strategy. All seasons were highly profitable but clearly some were better than others in a relative sense.

Lastly, we can view that net profit on a month-by-month basis. The worst individual month was a -£1,283 back in September 2009, whilst the best month on record was +£5,461 in July 2014. The overall totals by year and month can be seen at the foot and to the far right. It looks like profits consistently pick up the pace in the second half of the year which is an interesting trend, of course, partly explained by the compounding of the bank during the early part of each season.

Some Challenges

So, across a number of different measures, the strategy appears to be very profitable and consistent. But there are some considerations in trying to implement this strategy real-time.

In thinking how this strategy can be applied in a real-world, forward test situation one major factor became very apparent to me early on. So much so, that once I identified this hurdle I actually binned the whole idea, rendering it inapplicable in a real-world scenario.


However, I soon began to test some workaround assumptions. You see, my qualifying selections are anchored to the player starting prices as recorded by Bet365 shortly before a match starts. These prices are, of course, the prices available to back either player to win the match. While I use these (and their implied probabilities) to screen for qualifying bets, in acting upon them I’m looking to lay the favourites on the betting exchanges.

My first iteration of the model simply took the quoted Bet365 starting prices and applied the lay stakes and liabilities to work out the returns. However, in reality, the back prices at a bookmaker are always lower (at least at a given snapshot in time) than the price available on the exchange to lay that same player.

My historical data and live spot checks suggest that, on average, the lay prices on the exchange are typically anywhere from 4-7 ticks higher than the starting back prices as reported by Bet 365 at the start of the match. This might not sound significant, but every tick matters in this scenario.

Below is one such example. Simona Halep is the clear favourite in this upcoming quarterfinals match at Roland Garros and is available to back at a price of 1.16 on Bet365. However, in jumping straight over to Betfair Exchange, we can see that her price to lay was slightly higher at 1.20 – in this case a 4 tick difference.

Simona Halep can be backed at a price of 1.16 on Bet 365
Yet, at that exact point in time, to lay Halep on Betfair Exchange she is priced at 1.20 – 4 ticks higher than her back price on Bet 365 above

If we layed Halep at 1.16 for a £10 liability, we’d stand to win £62.50 on the exchange if she lost the match (£59.38 after commission). If we took the higher lay price on Betfair of 1.2, we’d win £50 on the exchange if Halep lost (£47.50 after commission)

So, in this example, just 4 ticks difference would lower our net profit on that bet by a meaningful 20%. If you can imagine extrapolating that dynamic out across thousands of bets, you can quickly see how that would serve to severely limit overall profitability.

*** Update – Simona Halep lost this match – she was beaten easily 🙂 ***

Keep In-Play Orders

So what can be done to get as close too, or even better than Bet365 starting prices in a lay scenario on the exchange?

Thankfully, the mechanical price nature of tennis that I explored in my prior post is such that a players price will comfortably shorten 5-10 ticks upon winning their service game. So, orders to lay can be placed in the exchange at the equivalent Bet 365 starting price (or even a tick or two lower) and these will almost always get hit shortly after the match begins.

Given favourites are expected to win their matches most of the time, it is therefore very likely that they will at least hold their service game against an inferior opponent at some point early in the match. That’s all we need in order for our price to get filled.

The only scenario this is unlikely to happen would be a favourite getting blitzed off the court (perhaps losing 2 or more service games from the start). This is quite unlikely. I’d also add that the criteria I choose, does not include narrow favourites, but moderate to strongly priced favourites which only adds to the likelihood that they will hold their service game at some point early in the match.

To test this, I placed 50 qualifying bets over a period of 2 weeks a tick or two lower than the advertised starting prices on Bet365 and every single one was matched either pre-match or shortly after play started. More testing is clearly needed to ensure this is an observable trend over many hundreds of bets, but I’m quite confident it will be the case.

The downside of not getting filled is, of course, not the end of the world. Yes, we may have missed out on a potential winning bet, but no entry also means no money lost. I’d rather miss the trade than chase an entry point a few ticks higher than the starting price given the headwinds this would result in terms of reduced profits and higher liability.


Another challenge all betting activity faces is variance and the possibility of a sustained downturn in results. Looking across the full data set of qualifying bets over the 10 seasons, you can see the typical ratio of consecutive winning bets versus consecutive losing bets below. As the chart shows, there have been periods where 20 or more consecutive bets lost. That is a mental challenge to overcome, but equally knowing this can happen ahead of time (and that profits were still achieved if you stayed the course) should help manage those emotions.

Just imagine for a moment, laying to a £30 or £40 liability and losing 25 consecutive bets – that would translate into a loss of between £750 to £1,000, most likely in a just a matter of days (given our average number of selections is around 6-7 per day). Not everyone would be able to handle that, even if these stakes still represented just 1% of your bank.


So, there it is (in a nutshell) my phase 1 tennis betting model. The early results are very compelling and there are more refinements that can be explored to identify further persistencies and trends in the data that may eliminate areas of weakness.

For example, it will be useful to consider results in the early part of the season versus the latter part. Think player tiredness; more injuries; players going through the motions etc. The higher ranked players have the motivation to finish strongly and make the lucrative tour finals whilst the lower ranked players seasons are effectively over (having likely played many more matches over a season, qualifying rounds etc)

The data set is also fully consistent across time, so analysis can be done by any number of variables, such as surface, the round of the tournament, player ranks and ranking differentials etc. One must be mindful of not over-fitting the data to find a desirable outcome, which is why these initial results (that take just a handful of high-level variables) are so encouraging.

Next Steps

The next steps are to forward-test the data with real money in a dedicated exchange account, away from other betting/trading activity. While Betfair is clearly the best for liquidity, pricing etc, Smarkets and others have lower commissions (all my results are based on net returns, after the 5% winning commission from Betfair Exchange). A few years ago Matchbook even offered zero per cent commissions for an entire tennis season!

The nature of these bets is to find moderate volume selections that can be set and forget bets each day, requiring little time. It is very quick to identify qualifying matches each morning and then it’s just a case of placing orders into the exchange at our required prices. There is no watching of matches, trading in-play etc.

There will be long periods of losing returns as there is with any betting strategy, but despite those we have observed, the data supports season over season ROI’s of 10-15% typically which is very compelling. Just as is the case with each way betting, if you can handle the variance, it might be something for you.

Beta Testers

If any willing volunteers want to join me in a 3-month experiment putting this into practice please let me know and we’ll set something up. It will be important that you accept these selections in good faith, only put minimal money towards them and stick with the method, even when variance runs against you. Like each way betting, this is not something one can dip in or out of. You’d also have to record your bets diligently to allow for proper analysis.

You’ll observe that I’ve chosen not to reveal my underlying methodology. I feel that is completely my right, given I have spent a great number of hours testing data and hypotheses in reaching this point. I consider this my intellectual property. Of course, one might argue its worthless until proven in a forward test and I’m inclined to agree. But that said, I don’t feel I should just give it away either. Who knows, perhaps it’s something I could monetize down the line and finally offer a tennis betting product that is built on solid statistical fact rather than fantasy get-rich-quick aspirations.

I will document the journey periodically from here by way of progress reports. It will be interesting to see what comes of it. 🙂

I’d be keen to hear any thoughts you might have about what I’m doing here.

Until the next time 🙂


Further Readings:


This Post Has 27 Comments

  1. Twatter

    Fantastic read! This all sounds very promising, and what’s better in my opinion is that it’s a more hands off approach – actually sounds even easier to set up and enter bets than the EW/NL methods. You mention that it’s a case of entering lay/bets on exchange only at desired prices for the qualified matches? I’m assuming that you’re only placing one bet per match that needs to be matched on the exchange? If not and the method involves placing more than one bet at the exchange on a match, is there ever a situation where one bet may get matched and another doesn’t?
    I would obviously be interested in a beta trial, although it depends on the amount of time per day it takes to find the qualifying matches, and volumes of bets to place. If you interested, drop me an email and you can explain your requirements more and see if we can help each other out.

    1. Pursue FIRE

      Hi – Its quite hands-off compared to EWB and also has added benefits in that you can’t ever get gubbed/restricted laying on the exchanges and you only have to lay the bets in 1 place (rather than juggling multiple bookmakers). Selections are a handful per day and you can set the orders in the morning and walk away. Correct, it’s just one order per match so it either gets filled or cancelled. We dont have to be concerned with having partial bets match or not match. We will see how things progress with live testing which started today with a small group. If that goes well I’ll widen it out to some more folk. Thanks!

  2. Andy

    Good stuff Dan. Have you tested your strategy on out of sample data i.e. the first half of 2019? I would be very interested to learn if results were still consistent over this period. Also have you tried tested with other staking plans?

    I would be interested to take part in the trial you are suggesting. Would be good to bet on something other than horses for a change 😉

    1. Pursue FIRE

      Hi Andy – yes I have data through to a week or so ago for 2019. Things are pretty much following the same trend. Bearing in mind it’s not even halfway through the season yet, 2019 shows positive net points of 30 (and ROI of 7.4% for the season), but historically things pick up over the summer months. I tried to demonstrate a selection of the most common staking plans one might use. Of course, there are others (Kelly Criterion etc) but I’ve not tested those as there are not plans I’d personally follow. In terms of beta testers, I have a private group of 5 people now and we are live betting based on the daily selections for the remainder of the month. I’ll write some posts as to how that progresses. Cheers

  3. Craig

    Hi Dan

    I missed the boat on this one but how’s this methodology working with your test group?

    Any update?


    1. Pursue FIRE

      Hi Craig – the test group was invaluable as it pitched into a particularly challenging run of general results. Not uncommon in any model over time of course (and my 10-season analysis had plenty of such runs) but it did prove several things to be consistent with the back-test. Overall, we made a slight loss over a 3-week period and it was actually quite challenging to research matches every day and get the picks out to the group by 9 am – again all part of the testing. It is one thing doing this for my merry self, but if I want to monetize this methodology down the line, I may need some automation or assistance to reach the masses optimally. Following the test group, I then undertook another test, which sought to dramatically cut down the time needed for me to complete my research daily. I blogged about some set and forget strategies that I tested during Wimbledon fortnight (check the blog for how that went). Planning to launch something dedicated to tennis-related betting/trading later in the year as a result. Cheers

      1. Craig

        Sounds like the test provided really valuable feedback, Dan.

        And yes, what you mention about automation/assistance would certainly be a positive, i’d imagine.

        Your Wimbledon blogs were very interesting, albeit they incorporate a quasi-“in-play” methodology, whilst this test you were conducting was purely pre-match. Here in Australia, in-play betting online for sports is banned (although in-play is allowed through telephone betting, which isn’t practical for obvious reasons), hence my interest in this pre-match methodology you were testing.

        But whether your tests are pre-match or in-play in future, I find them very interesting and your posts very enjoyable to read … big fan here in Australia :-).

        1. Pursue FIRE

          Hi again Craig – I was not aware that in-play betting was banned in Australia? I thought that Betfair was big over there no? I must point out that all my tennis betting involves doing so on the betting exchanges (rather than with standard bookmakers). Even the approaches laid out above, whilst being pre-match selections, involve taking lay bets (betting against) favourites at certain prices in-play, again with orders placed waiting to trigger. I very rarely find the need to bet with the bookmakers as they a) don’t usually allow you to make lay bets, b) restrict you after a winning run and c) don’t have the volume to match the likes of Betfair and other exchanges. Glad you like the blog – I’ve been umming and ahhing about whether to keep going so comments like these make it all worthwhile 🙂

          1. Craig

            Betfair is still big here in Australia, Dan.

            And in-play is allowed for racing, online, but only via telephone for other sports (which as you can imagine is not ideal). It’s a big issue over here and various reasons behind it which I won’t bore you with but perhaps the situation will change at some point in future.

            Which means sports like tennis are pretty-much restricted to pre-match, and perhaps entering an order at a specific lay price, which you hope to be filled, still pre-match.

            Keep the blog going … you have your own unique personality which makes reading it very enjoyable 🙂

  4. Ravi

    Hey, have you done further testing with this? If so, how has it gone?

    1. Pursue FIRE

      Hey Ravi – yes always testing and implementing. Check out my Wimbledon Project blogs for how some of these ideas were implemented during a Grand Slam week. If time permits, I’m hoping to launch something tennis-related in time for the start of next season.

  5. Tiernan

    Hi Dan,

    Very interesting stuff. I was wondering if you could point me in the direction of where I could get my hands on such data for tennis and/or other sports? I checked out the links in your resources that you appended but it didn’t seem like it was from either of those links.

    1. Pursue FIRE

      Hi Tiernan. For tennis, I source the historic results data via I found this to be the most accurate over long time periods. For player-specific metrics I create a number of these myself based on this historic data, but there are also other services such as that are good for such metrics. Both of these were mentioned in my post above.


      1. Tiernan

        Perfect, thanks for clarifying. Do you source tick-by-tick data from anywhere for your analysis? I’m hoping to be able to do some analysis on the live-betting odds.

        1. Pursue FIRE

          I did look into that but its typically something you’ll have to pay rather a lot for, especially for tick-by-tick level data.

          Betfair do offer historic inplay price data for most sports (I think even the first request may be free) but what you get is a rather crude csv file that is difficult to work with.

          You might want to check out the following

          Beyond that, I did not look much further given Betfair are by far the dominant exchange. I’m not aware of any bookmaker offering to share their historic data, although the tennis source I gave previously does at least capture the starting prices for several bookmakers and the max/min averages (I think) during play.

          For these reasons, I tend to use historic data to build a hypothesis on a potential strategy but then forward test in real-time to monitor and track the actual live prices against my strategy.

          Hope that helps


  6. Kristian


    Very interesting reading.

    Im always keen to try new strategies so if you are looking for volunteers still. Pleaser count me in.

    Best regards


    1. Pursue FIRE

      Thanks for your comment Kristian – I did do a small test group but only for a 3 week period which was not really long enough to determine anything meaningful. Until I can automate things a bit more on my side its a challenge to get selections out to the group real-time. Investigating alternatives as we speak ahead of the new season in Jan.

  7. renier

    This strategy is easily automated through betangel. Backing the underdog at 1-1 in real time with a 30sec delay is very easy to be programmed. This model wont work for ATP.
    I believe on can also look at betting in play when the underdog has reduced by 50% in play as there is also a slight edge to back the Underdog when 1-0. I have created done something similar and ready to go for 2019 , let me know if we can share ideas as I have created an in play odds predictor for 1-0, 0-1 and 1-1 and bac tested the strategy with similar results

  8. Trevor Lewis

    Hey Dan. I’m intrigued and very impressed by your work. I’d love to explore putting this into practice. I’ve made a solid amount betting very conservative parlays in Tennis, I’m very interested in this model you’re developing though. Shoot me an email. Thanks

    1. Pursue FIRE

      Thanks, Trevor – always happy to reach out and discuss with other like-minded folks interested in tennis betting/trading.

  9. Trevor Lewis

    Hey Dan. I’m intrigued and very impressed by your work. I’d love to explore putting this into practice. I’ve made a solid amount betting very conservative parlays in Tennis, I’m very interested in this model you’re developing though. Shoot me an email. Thanks

  10. Miguel M

    Hi Dan

    Thanks for sharing some of the principles and statistics of your research and trading. Your approach is very similar to what me and my trading partner have done, although when we started years ago, after winning heavyly on BetFair, we ended up each loosing 100.000$ on a odds- and statistic based tennissystem. Admittedly we made the grave mistake of increasing our bets from 1000$ to 2500$ a match after a six weeks winning steak, only to head directly into a longlasting losing streak and you guessed it…..we stubbornly refused to reduce our betsize. Nevertheless we have always considered those losses “tutorial fees”, or rather an investment that we would get back eventually, but for many years we have both been busy with other things, me becoming an expat in 2013 enjoying the fruits of other online ventures. But now we both have time for sportstrading again, and like you now going through years of tennis odds, analyzing it all, developing ideas and backtesting tennis systems. No doubt there are historical observations to be made, that would have resulted in success, but as you know, those are not guarantees for future winnings, although very good indicators that combined with smart money management will result in success. I am also a writer myself, teaching people how I becase succesful in three out of three online buisness model, so I understand the idea about monetizing on betting systems, but for me at least, it really is about continuing my priviliged lifestyle and the love of trading succesfully and endless endorphin rushes. Although english is not my native language, maybe we could exchange observations and ideas in the future, or maybe help each other out testing new strategies. Best of luck, elimination luck.

    1. Pursue FIRE

      Thanks for your comment Miguel. I certainly have something here and its proving quite successful for me personally. Developing this into something that can be commercially viable is a bigger challenge but something I’m working towards and a goal for next year. Watch this space!

  11. Stuart

    Interesting read Dan. Found your blog whilst searching for tennis data online. Been trading successfully manually on tennis in-play for a while now and looking to automate a few successful strategies. Like someone who posted above I’m using BetAngel and MarketFeeder Pro to build the automation. MarketFeeder Pro is particularly useful as you can back test automation against (purchased) Historical match data using their Time Machine app. It trades out the match as if it was being played live.
    Backing the broken pre-match favourite (or laying the dog) based on known stats has always been a good winner for me. I’m now looking to build those stats into the automation.
    Look forward to seeing how you get on with your analysis. I’d very much think automation would be the way to go, hitting that decent price on service hold would be straightforward to program

  12. Stuart

    Hi Dan. See you didnt approve my previous post. I’ve realised, after the fact, that it may have come across as an advert for the 2 tools I mentioned. Just to stress that I have nothing to do with either app. Was just suggesting possible choices for forward and back testing your approach. They are just the ones I am using personally.
    But, as I mentioned before I’m interested in your research and the modelling. Look forward to seeing your real world results.
    One question I did have. When looking at your data have you come across any way to analyse in bulk, match results and price moves when the pre match favourite is broken first set ? I can do it on an individual basis but looking to do it for all matches going back x years

    1. Pursue FIRE

      Thanks for the comments Stuart – I have just been on a break this past few weeks as I focus on some other things so apologies for not approving the comments sooner. Thanks for the suggestions on back-testing. While I’ve used historical tick data before I was unaware of a tool such as this to back-test as if it were live – useful and will check it out. As you know, we are now in the off-season and so I’ll be doing all sorts of stuff in the background before the new season begins.

      1. Stuart

        Sorry Dan, I thought the mention of the apps might look like I was trying to punt them 🙂
        I’ve only just found MF Pro myself, usually use BetAngel. But am intrigued by the Time Machine function. Meaning to test it out during December, with nothing to trade. You can create your automation triggers and then purchase events at 15p each (fixed BF historical data prices). Once in place you can batch run them in either real time or speeded up to exactly simulate live trading. I can’t think of a better way to test automation in bulk. You can even read from a CSV or spreadsheet. Hence my question re broken serve data. If I can trigger trades based on historical/live stats then my hope is the bot becomes completely hands free.

        Will give me something to do until January 🙂

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