Betting 360 Ep 029: Dan Weston Discusses Finding Value In Tennis Betting

Betting 360 Podcast - Betting From All Angles Dan Weston is the founder of Tennis Ratings, a UK based tennis trading advice website with some of the most valuable tennis resources on the internet. With the Australian Open coming up, the timing was perfect to have Dan on the Betting 360 Podcast to discuss how punters can gain an advantage in tennis betting, how to analyze the performance data, and which players are likely to offer some good value.

Punting Insights You’ll Find

  • How to get a decent edge when betting the Australian Open.
  • What to look for in your tennis form analysis.
  • The major differences between the men’s and women’s matches.
  • Which stats are the most critical ones to review.
  • The major players worth laying (and why!).
Dan has a special offer for Champion Picks members: Simply enter the code ‘Champion’ when purchasing a Tennis Ratings Tips Subscription and you will be upgraded: (1) A 30 day subscription will get you an extra 10 days free (so 40 days in total) or (2) A 3 month subscription will get you an extra month free (so 4 months in total).

Today’s Guest:

 Dan’s Closing Tip:

It’s clear to see from the stats that the favorites are justified (in the Australian Open).”


Episode 29 : Dan Weston Discusses Finding Value in Tennis Betting
 Welcome to Betting 360, your number one source for horse racing and sports betting insights. Coming around the bend is your host David Duffield, with another expert view to give you the winning edge.
David: Hi this is David Duffield and welcome to another episode of the Betting 360 Podcast. My special guest today is Dan Weston and he runs a tennis betting site and he’s going to give us some really good pointers on how to find value in Tennis betting. So with the Australian Open starting shortly there’s some players who he thinks are reasonable value and also some outsiders that are big value. Then there’s a few that he thinks are under the odds as well. A couple in particular that are worth laying so he’ll run through what he thinks is important when you’re doing the form so to speak in tennis and how you might be able to get a decent edge betting on the Australian Open. So let’s have a chat with Dan.
David: Welcome to the show Dan
Dan: Thank you very much David.
David: Look forward to learning a bit more about tennis betting so it’s the time and season where a lot of Australian punters get into it a lot more than the rest of the year. So speaking specifically of the Australian Open, what should punters be aware of? What’s unique to the surface or even Grand Slams compared to the regular tour.
Dan: Okay well the first thing I would really recommend bettors and punters look at is the different facets of the mens and the women’s format in the Australian Open and all Grand Slams actually is quite underestimated area. The five set nature of the match as opposed to the typical three set nature of the ATP Tour and also the Women’s Tour. The effect of five set matches cannot be underestimated. I’ve read an article about it already and the very core record the players tend to have, especially lower rank players of playing a five set match in a previous round. So it’s the accumulated fatigue that’s being incurred by playing over various five set matches and its not a positive thing for a player. That’s something that you need to look at. There’s certain players as well with the poor records as well on third set. Some players off the top of my head I can think of, Jack Sock, Martin Crimson, Julian Benneteau who have got awful third set records. That doesn’t lend themselves to play long four, five set matches either. So they are particularly players that I’ve looked to maybe give a swerve to in a Grand Slam match.
David: So you’re saying that regarding the five sets that there can be a recovery issue but also does form hold a little bit truer in the fact that they are five set matches there’s less upsets?
Dan: Well that is historically true that favorites are much more favored in Grand Slams. The data I’ve got gives about 76 – 77% favorite win percentage in Grand Slams which compares to about 70% in most and 500 of that near ATP. That comes to about 63% into 50’s so you can see that there’s quite a big advantage for favorites in Grand Slams because obviously it’s like a snooker match. Best of nine in a snooker match, it’s a lot more prone to cause an upset whereas when you play the world champions is like best of 25 or whatever. That does favor the better player, he tends to come through in the long run. It also effects players in tennis. They might go two sets down or two sets to one down and then power through the fourth set because of their superior fitness and mental strength as well.
David: So it’s a bit like Twenty20 vs Test cricket. You can have a lot more upsets in T20 compared to the five day version. So you’ve mentioned that the strike rates there and there’s less unpredictability in Grand Slam matches, is that already factored into the market? Is there still some value there or is it so well known that there’s no profitable edge there?
Dan: It is well known that favorites do thrive in Men’s Grand Slams and it is factored into the price to some extent. Favorites in the opening rounds tend to be priced prohibitively shorter so you’re looking at sub 1.20 or less which is obviously 5 to 1 on or shorter. Which is not really great for a casual punter who’s looking to get a better price. There are some opportunities that tend to come from that. Some favorites start a little slower so I might look to lay them pre-match with a late to back position. Or, the difference then comes towards the women’s, because the women’s favorites tend to be quite short as well, but because it’s a three set format, favorites don’t tend to thrive that much. So a lot of times there’s some value in understocked in the women’s because of that.
David: Okay, and what is your approach in general terms? Is it stats based and data driven?
Dan: Absolutely, it’s 100% quantitative stats based. I don’t really take into account any form of opinion because it’s not absolute. I look at stats primarily and solely really as my guide. So what I’ll do to start a match is I’ll price it up based on the basic service and break percentages for each player which then creates the predicted hold. Then I based the protected hold of the two players according to my model which creates a basic starting price. And then I adjust it for match up issues. For the sake of argument, player X might struggle against the left handers or a big server. I’ll take that into account. And head to head record, although I don’t tend to take head to head records quite as heavily as others do, I tend to find head to head records quite overrated by the media. I find it quite lazy journalism by the tv media who tend to trot out quite a lot thinking that one nil, two one or two nil record is absolute and a big advantage. Well it’s not, especially if it’s over two years old or different surfaces. I’m looking at head to head record I might take into account like a three nil that’s within two years all on the same surface, or two out of three on the same surface, or four nil or more dominantly where I might be more happy to take a longer period of time or on different surfaces but on really dominant head to head is what I want head to head rather than a one or two nil.
David: Yeah and you’re talking about small samples as well when they’ve only played a couple of times. So with break and the hold percentages, do you weigh that more heavily in their recent record?
Dan: Yeah, I look at all stats within the last 12 months, a rolling 12 months. I wouldn’t look any further than that unless there’s just not enough sample size. Then I’m gonna be quite reticent to get hugely involved in it from a financial point of view based on the fact that I’ll use old data as well so the stakes might be a lot smaller or I might skip the match as well. Regarding sample sizes, I want a player to at the end of the year play 15 matches on that surface in the last year to get a reasonable guide on how accurate their service, hold and break percentages might be. Obviously whilst 15 matches doesn’t sound like that much, that’s a fair portion over 100 service and return games so it’s becoming a more accurate percentage on that basis.
David: So you mentioned the amount of matches on a particular surface. What is your breakdown or category types for the number of different surfaces there because I’m sure it must vary a lot there throughout the ATP tour.
Dan: Yeah, hard and indoor hard is quite difficult to differentiate because there’s not a huge amount of indoor hard tournaments, especially in the women’s tour. So I will lump them together if I have to but I prefer not to. I won’t look at hard floor, indoor hard or (unknown) with regards to clay because I find it a completely different surface. Obviously there a lot of clay course specialists who aren’t good at hard course and to some extent visa versa as well. Some hard courters who really aren’t good on clay, Andy Roddick was a prime example before he retired. An Australian guy who, Matosovich is pretty similar in that respect, though not nearly at the level that Roddick that ever was, but an example of that nonetheless. So one look at the hard courts and clay that’s a key separate and I’ll treat it as such. Grass is difficult because there’s not a huge sample size on grass. Maybe a month out of the calendar year is grass. Most players I think play one of two warm up events plus Wimbledon so it’s hard to get statistical samples. So I might have to go a bit longer in data size and go back to 2012 as well as 2013 when I’m looking at 2014. It’s not ideal but I try to factor in the improvement of the player as well during that time so I might see how their either service, hold or brake have either improved or declined in that time period. And try and relate it to their grass stats if that makes sense.
David: And which of the stats do you consider important because you mentioned a couple of the fairly well known ones, what else would you factor into the model?
Dan: Like I said their head to head record I try and quantify it, what I did was I went through all the previous data and worked out the return on investment different scenarios, and filled that in. There’s other things that I’ve quantified and built into my model. One thing that I think is hugely underrated by the betting market is traveling and condition of the player. So I kind of touched on it earlier with the five sets in the men’s game in Grand Slams, but one thing people don’t tend to appreciate is the accumulated fatigue that’s in a typical tournament of three sets. So I’m looking at special players that played two or more three set matches which have either gone a long duration such as 2.5 hours plus, preferably even over three. Or over 30 games in duration, so I found that two 30 game plus three set matches over in the same tournament and obviously winning both and playing a subsequent round have a very poor return when backing that player in the next match so I might look to pose that player. And that’s factored into the model as well.
There’s some players who also don’t perform well after playing three set matches just based on their fitness level as well so when I’m at those sorts of players I’ll factor that into the model as well. And also, there’s another underestimated area is travel. You’ve got a situation next week where players will be traveling to Chennai which is in the current calendar this week to another tournament in Australia now. I remember Benoit Paire last year, he got to the semi-finals of Chennai, flew over and made an abysmal effort in the next round. In the first round of the tournament in Australia, in the subsequent week because of the travel duration and time zone adjustments that are needed to be adjusted to in a short space of time.
So typically, semi-final of the Chennai event might be on a Saturday, and they might be required to play a first round match of the tournament in Australia on Monday. So effectively he’s got some 48 hours to fly from India to Australia and adjust a number of hours time difference. So what I look at is players who played the week previously in tournament who were say 6 hours plus and went though 4 hours plus time zone differences, and they play a first round match real quick the first week. I’ve found that has a very negative effect on the match for that next week. That’s the sort of thing that I’ll build into the model as wells o I know the return investment involved in that scenario and that’s quantified into my model as well.
David: I was going to say, what you said there makes perfect sense but how do you quantify that and the thresholds. Are the guys used to traveling or the women as well but how do you know what the red zone is as far as traveling fatigue.
Dan: What I do is I draw the line at quarter final match. So anyone who plays a quarter final or later, so quarter final is played on a Friday, then they have to play a match of the month on Tuesday, their the ones I’ll include in that. So what I did was I went through the past data, which is pretty much what I do with every single scenario and I work out the return investment over a decent sample, and from that basis, I can then adjust my basic model based on the given negative or positive return investment that was generated from that analysis.
David: That makes sense, I know you do a lot of in-play trading, that’s not as well suited unfortunately as my list is Australian and we have a pretty archaic law that says we need to phone up, but just maybe briefly touch on some of the opportunities in play.
Dan: Well I personally really like in-play betting and trading. I feel that it gives more opportunities than pre-match betting for several reasons. First of all, there’s a lack of in-play data, because it’s quite hard to come across and quite hard to process, very time consuming. By in-play data I mean things like the excepted number of times the player might get broken when their leading by a break. So they lose their break advantage which creates breaks in the trading opportunity. And on the flip side how often they might recover a break deficit where they are a break down. Believe it or not, there are quite a few players who do excel in various scenarios that you wouldn’t quite expect them to so their quite underestimated by the market.
There’s other various input data you can look at. For example, how often a player might get broken in the first two service games of the set, and how often they might get broken in the latter games in the set which is already crucial assets to in-play trading. Having that data is so invaluable for me, and having to create it myself means there’s not much availability on the market so I’ve kinda got a really nice edge in that area, whereas all the pre-match data is a lot more free in the market vs. websites that have got great pre-match data. It’s a lot easier to price up a match pre-match based on the amount of data available compared to in-play. Secondly, in-play the market can be quite irrational, there’s quite a lot of irrational decision making because of market data and impulsive decisions they have to make on the break.
They make decisions very quickly, examples of that is when a player has a medical timeout, and the market overreacts fearing overtime and starts backing the fit player so to speak. While the player is in medical time out, but if that player recovers from medical timeout the price can often really fluctuate back up. I do find the irrationality of laying the injured player really heavily tends to give advantages when that player comes back on court, they can recover from the injury then that can give some good edges to me. I remember a match last year, I think it was Bartoli, I can’t remember where it was but it was in the clay season, and Bartoli turned her ankle, if I remember correctly, the floor went down to that 1.05 low which practically speaking is about 20 to 1 on. And Bartoli came back on court and ended up winning the match, and even after several points, her price had just gone up so much so the opportunities are there.
That was all due to the irrationalities and fear that Bartoli was going to retire. And kind of related to those two points I said about the lack of data to an irrational decision making people have in play is harder to prepare. So what I do in a match, prior to a match, is to create a trading script so I’ve got all the data in front of me before the match starts, and I can be adapt to the different situations with quantitative data as it happens instead of making subjective and irrational decisions. So I find that that gives me a bigger edge over the market than pre-match where people can take their time to assess different situations pre-match.
David: Yeah, again I’ve seen the script and I think it makes sense to be prepared. Not much that you can see there is unexpected. You’ve mapped out the scenarios and what the likelihood is of following that scenario so I suggest people take a look at the website, there’s scripts, there’s tips themselves, there’s minimum prices spreadsheets, there’s in-play and trading handbooks so there’s plenty to go through there on the site but just to finish up it is the Australian Open, is the highlight of the local calendar. What would stand out, and we’re a couple weeks away, but what would stand out as a reasonable value bet either on the women’s or the men’s side of the stage. A player or two that you might think is undervalued at that moment.
Dan: It’s clear to see from the stats, but the favorites are justified both Novack Djokovic and Serena Williams are the best players on the field and the surface at this current time, so it’s hard to argue against their status. Djokovic looked absolutely superb the end of last season he won his last 24 matches in a row, and he had a real big edge over Nadal, who was the seated he faced in the final. So men’s is hard to look past Djokovic, women’s wise, Serena Williams is so dominant generally, especially with Sharon with a shoulder injury and Azarenka losing her last 6 matches last season, it’s difficult to see her being beaten as well.
So I think you can get just over 3 to 1 on the double, which I wouldn’t discourage at all, and regarding outsiders, as I said previously, favorites do tend to get results in Grand Slams so its hard to see someone coming completely from left field and getting to the semi-finals or the final. I think something like 26 of 28 players from each tour have got the final when they’ve been ranked in the top 10 in the last 10 years. 85-90% of players in the Australian Open final have, in the final they’re ranked in the top 10 and most in the top 5. Left field bets, I like Ernest Gulbis, he’s about 200 and something to 1 trading. His position in the quarter finals or maybe even semi-finals. He’s got some really good services hold and break stats on hard court, which rank him around the top 10. He’s quite a blase player who doesn’t tend to get fazed when he plays top players.
Against Nadal he had a superb match last season, and really doesn’t get overawed against the top players, he beat Andy Murray as well. He’s someone I’d look at with a view to backing out or burrowing a price, and perhaps laying at a latter stage. Regarding big servers, I’m not a big fan of them. Because of the accumulative fatigue they incur by playing the long matches, because they don’t break enough. They are players that I would definitely rule out, from a long term position. But, I say Gulbis for me, mens wise I don’t like to looks of Del Potro because he doesn’t break serve enough and that’s a problem Tsonga has as well. Federer is getting too old but he’s always a threat. Obviously Andy Murray is recovering from surgery himself so it’s touch and go as to how he might get on.
Looking at the women’s, left field positions were Simona Hallep, Hallep had a great season last year, she won six titles. She had really low calibre events though, but she’s got some great hold, break stats, and would definitely put her in the top ten. I can really see hear raising to the top ten this year because she hasn’t got much points to defend until may sort of time. And she’s ranked 11th at the moment so I’d be very surprised if we don’t see her in the top ten quickly. I think she’s around about 50 t0 1 at the moment. It’s difficult seeing many women getting to the business end of the tournament because they lack a lot of mental strength. I know Li Na has got to the final a couple of times in Australia, but her record in finals I generally very poor. Agnes Radwanska doesn’t have the stats to back up any claims to get to the final, I think she’s 33 to 1 at the moment. And she’s only got to one Grand Slam final so she hasn’t really got a great track record in Grand Slams.
There’s not a great deal that stands out in the women’s. I’ll say Hallep’s a solid choice and I’d much rather back someone like Hallep than someone like Petra Kvit0va who’s shorter, who plays 50% of pre-set matches last season which puts her in a lot of danger. Not only from the accumulated fatigue I’ve spoken about several times, but also from being homesick, planner, shootout third set doesn’t put her in the best situation. And she doesn’t break serve as well, she’s only broken serve 37.7% of the time across all surfaces which is pretty much bang-on the WTA average. So for her to be ranked fifth favorite for the tournament, only from a break serve average, that’s not for me. I think she’s at 18-19 to one, that’s not for me either. So for me Hallep is an outsider but Serena Williams definitely looks to be the player to beat.
David: Interesting, I appreciate that Dan and this was your debut on the show today but I think we’ll get you on for each of the Grand Slams if you’re happy to accommodate?
Dan: Absolutely, I’d really like that.
David: Alright, good luck for the tournament yourself and good luck for everyone listening.
Dan: Thank you, take care.
Thanks for tuning in to Betting 360. Get more in depth analysis, tips and that betting edge by heading over to where you’ll find a full transcript of this episode. If you liked the show, share us with a fellow punter or drop by iTunes to leave us your thoughts. Betting 360, punting from all angles.

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