Time.

I have been running a bot on tennis for a while now, probably started 4/5 years ago and has developed since. I’ve assessed its performance in ROI and it’s done pretty well, like all automation I develop i try and look at it from a new angle.

Here’s how it performed this week, a WTA match in Japan, i think the semi-final:

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Pretty good, 25 or so successful trades and i achieved what i wanted to achieve with it during the match. Here’s how it began:

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and ended:

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Here’s the second match, B which achieved the same profit:

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It started like this:

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And ended here:

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We have 2 major variables I look at, number of triggers (bet placed) met and time the bot ran for. Match B had less triggers which might tell me I was exposed for a greater amount during the match. It makes sense because the bot ran for 1hr 20 mins less time than the first match.

Exposure and time are the 2 most important pieces of data I look at. The first match took almost twice as long to hit the same profit amount than match B.

It got me thinking about other markets. Take horse racing, you create a back to lay rule, how often do you aim for a target price? and how has this come about over the years? Obviously we have a fixed amount in our minds which we want to achieve.

Back to lay is so popular these days, I see more and more punters looking to take their cut at 80% of the target price, dog eat dog it might move to 85% and so on until the price become back-able.

Relating back to the tennis, i often grade a successful trade based on the time spent in the market. It has to be your number 1 aim to be in and out of the market as soon as possible and to achieve that automation can help.

Look back to some previous blog and I like to talk about multiple trades reducing liability for further trades and it’s something that can be implemented perfectly what with the National Hunt season round the corner.

It is competitive however, as the races are run at a slower pace than the flat there is more time to decide what to do. Of course this means more thinking time for the on-course traders and those with a better knowledge of jumping/running style than you.

It is tough, but I will be keeping an eye on the early money left on the ladders to lay, chances are it won’t move and I can take advantage. I will be logging my trade times this season, based on furlong length along with liquidity in running and volume pre-race to see where I stand at the end.

To conclude, one simple piece of advice I will be following is to keep an eye on liquidity in running, the days of getting ahead of the market above a whole number on the ladders are gone, be flexible and if the price isn’t moving consider the time your money has been at risk for and decide if it’s worth it. Good luck, i might need a slice too!

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Bet analysis part 2

So this is the follow up post to: https://lebotman.wordpress.com/2016/09/19/bet-analysis/ 

A bit more background on this project. It’s a tipping service who the client pays for and the stakes he used justified further analysis. 

Lets get stuck in. I will be frank and put it out there that lots of tipsters with a slim profit margin put up unrealistic prices which a bettor who is limited by bookmakers, cannot profit from. 

Take another look at the graph and the spikes are where a bet is successful. At these points the difference in profit between his and the model was mixed. 

Not shown on he graph were whether the selections were each way or to win. The each way bets produced a difference in favour of the bettor. He was using the exchange which paid less places but offered a higher price. There were no selections which benefitted for he extra place in the data I was given. I suggest long term it might. 

His each way staking was also different to the model, for 1u each way he was staking 2u where as the model staked 1u in total. His staking was biased toward the each way selections which would is not true to the overall staking in the model.

We have a slump around bet 42, this was a missed winning bet.

I highlighted a trend around bets 102-115 which was interesting and could refer to what I mentioned about unrealistic pricing. This was a sequence of winning trades yet the difference decreased. 

So my recommendations were as follows:

1) correct the each way staking and continue to monitor whether the extra bookmaker place (where advised) benefits longer term.

2) be consistent in following selections 

With these addressed and with a bigger data set we could see if the difference is still negative. One strategy I use is to look at the difference % long term and retrieve that difference in asking for a higher price in running. 

In running price analysis is something I would apply to this data if there were over 3 months and then filter based on race type. 

I will leave it there but that is a snippet of what I do. If you are busy punter or tipster and like what you see do get in touch. It is quite time consuming so I don’t do a lot of it. 

Should this particular project continue I will update should it be of interest.

Appreciate this is the unsexy realm of punting, but it’s important – well done if you got this far!!


Bet analysis

I’ve been working a new project last few weeks which doesn’t involve betting. I’ve always been open to help other in the betting world so it made sense to offer some detailed analysis and consultancy. I wanted to provide something relevant to the person I was helping, rather than the general advice handed to novices going in one ear and out the other.

For one client, I took a months work of results (blue) and matched them against a model (green). Here it is – 181 horse racing bets for the calendar month of August.

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You’ll see the individual under-performed against a losing model (for this month). The yellow line is particularly important as that shows the difference between the model and actual results. The fact the system under-performed this month is not relevant to me, the difference in yellow is.

I haven’t fully analysed the reasons for the difference, but thought I’d share the level I think you need to go to, to analyse where bettors are going right, and wrong. Its too easy just to keep on trading without giving enough time to review performance and fine tune methods to become more profitable.

For system bettors amongst you, its great to see how you compare to a system with positive expectancy, the aim would be to mirror it as closely as possible. For more flexible traders, I have encouraged regular notes that accompany trades with financial input coming from limits of risk per trade and sometimes a target profit per trade if the system is rigid enough and suits that punters risk profile.

I’ll put up analysis of the graph above next time, and suggest some pointers going forward to drag that difference from the negative into the positive.