Punting Pointers: Dan Weston

Can punters get an edge on tennis? With the French Open starting this weekend, we look back at our Betting 360 podcast chat with Dan Weston, the founder of Tennis Ratings.

tennis betting
tennis ball on a tennis court

 

Betting in Grand Slams (five sets)

There’s quite a big advantage for favourites 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 favour 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.

It is well known that favorites do thrive in Men’s Grand Slams and it is factored into the price to some extent. Favourites in the opening rounds tend to be priced prohibitively shorter so you’re looking at sub 1.20 or less. 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.

Overall approach

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.

Stat sample

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.

Surface

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 court specialists who aren’t good at hard court 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. 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.

Which stats?

Like I said, I try and quantify their head to head record.  I go 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.

Fatigue

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.

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.

 

In-play betting

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 they’re 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 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.

Click here to listen to Dan’s full interview on the Betting 360 Podcast.