Backing on the server in Tennis and trading out later
Description
The underlying assumption of this strategy is that the player serving the ball has better chances to win that game, and therefore his/her price will likely go down at the end of the game, creating an opportunity for a green-up. Whether it came out true or false, you’ll find out from my report below.
The triggers start by backing on a player when he/she is serving in a game. You can choose whether you want to only back on the player with the lowest price (the current favourite) or any serving player.
Then the triggers wait for an opportunity to green up, i.e. close the bet for an equal profit on both players. If that opportunity presents itself during the game (with the minimum number of price ticks in profit), the triggers close the bet there and then.
If not, the triggers wait till the end of the game, and then trade out for equal profit or loss, depending on the price of that player.
If the trade-out generated loss, the next back bet on the server is multiplied by the predefined number. After a green-up, the size of bet returns to its initial value.
You can set the maximum number of losing sequences in a row, e.g. the triggers will stop making any further bets in a market if you tried to green up twice, both times without success.
Triggers
Download the trigger setup file.
Profile name: back-on-server-tennis
How to run this trigger package:
1. Download and run the above installation file.
2. It contains three files: the trigger file, the Market Locator search template and the program settings. These files will be copied to corresponding folders on your computer (where other triggers and templates are already stored).
3. Run MarketFeeder Pro and choose the right settings profile from the drop-down list:
Here are the constants you can adjust:
|
bet_size |
Initial bet size (% of bank) |
|
min_price |
Minimum price of the bet |
|
max_price |
Maximum price of the bet |
|
min_vlm |
Minimum volume of money in the market |
|
max_gap |
Maximum gap (in ticks) between the back and lay prices |
|
min_fav_gap |
Minimum difference between the prices of the first and second favs |
|
min_rank |
Minimum selection rank (1 - favourite, 2 - underdog) |
|
max_rank |
Maximum selection rank (1 - favourite, 2 - underdog) |
|
multiplier |
The number to multiply the next bet after a loss (0 -- add up the loss from the previous bets) |
|
max_set |
Max. number of set to place bets |
|
stop_profit |
Profit % after which the triggers stop betting in the given market |
|
max_lss_count |
Max. number of consecutive losing bets in a market |
|
greenup_tcks |
The min. number of green-up ticks for greening up within the same game |
There is a previous version of these triggers, the one that only greened up on a server, without equalizing the loss if the prices went against you. Instead it just doubled the bet and hoped for the better the next game. You can download this version here:
Download original triggers for backing on the server
I have tested the original version too, but honestly, it was a direct path to a disaster. When I’m saying that, I really mean like losing 20% or more of your bank within a day, consistently.
So I had to come up with at least theoretically better idea to test it for the next 7 consecutive days.
Triggers In Action
Day 1, February 12, 2019
I started testing the triggers with the following settings:
| bet_size | 1 |
| min_price | 1.1 |
| max_price | 3 |
| min_vlm | 1000 |
| max_gap | 10 |
| min_fav_gap | 10 |
| min_rank | 1 |
| max_rank | 1 |
| multiplier | 1.5 |
| max_set | 2 |
| stop_profit | 5 |
| max_lss_count | 2 |
| greenup_tcks | 2 |
The market needs to have some liquidity (hence the minimum volume of 1000) in order for the prices to be adequate and leaving you space for manoeuvre.
As you can see from the settings, I focused only on the favourite (and did not change that settings throughout the testing period). The reason for this was my previous experience with this strategy, which resulted in a subjective opinion that underdogs have greater gaps between their back and lay price and therefore worse outlook specifically for a green-up. You can experiment with the min_rank and max_rank constants to disprove me if you want.
Total P/L: -47.98
ROI: -1.48%
Wins: 15, losses: 18.
Download Statement for 12/02/2019
Day 2, February 13, 2019
Pretty gloomy results, aren’t they? I tried to improve them by increasing the multiplier, i.e. by multiplying the bet following a loss by 2.5.
|
multiplier |
2.5 |
Total P/L: -82.53
ROI: -1.0%
Wins: 30, losses: 32.
Download Statement for 13/02/2019
Day 3, February 14, 2019
Yeah, that helped (not)!
To analyse what went wrong, I watched a couple of matches and observed the behaviour of my triggers. I noticed that the bets were being placed at what seemed the wrong moment: the prices often would improve soon after the bet had been matched. I decided to modify my triggers so that they placed the initial bet after the players have already scored 30 points in total, and to trade out in the next game after one of the players scored their first 15 points. With this delay, I was hoping to catch better prices.
Total P/L: -18.33
ROI: -0.43%
Wins: 24, losses: 10.
Download Statement for 14/02/2019
Day 4, February 15, 2019
My previous day’s efforts did not yield any profit, although my loss was reduced significantly. But I did not see where else I could make any change: the triggers were working correctly, the bets (lots of them) were being placed on time, and I did not notice any other patterns that would help me refine the strategy.
From then on, I did not touch the triggers and just watched.
Total P/L: -23.33
ROI: -0.68%
Wins: 35, losses: 10.
Download Statement for 15/02/2019
Day 5, February 16, 2019
There was no correlation between the timing of the bets (at the start or at the end of the match) and whether they ended up in profit or loss. You can see from the previous day’s statement that on two occasions (Arruabarrena v Mrdeza, S Kwon v T Ito), the losing bets were made at the very beginning; on three occasions (Kerber v Mertens, Aragone v Duckworth, Jakupovic v Paquet) - in the middle, and once (Laaksonen v Soeda) – at the very end.
Total P/L: 7.37
ROI: 0.33%
Wins: 39, losses: 6.
Download Statement for 16/02/2019
Day 6, February 17, 2019
I managed to get my P/L above zero the previous day, but I’d put that down to pure luck, as, like I said, I did not change anything.
Total P/L: 11.72
ROI: 0.45%
Wins: 47, losses: 7.
Download Statement for 17/02/2019
Day 7, February 18, 2019
This was the last day of my testing, and I was already convinced that I would not give it another shot. Interestingly, on my last day I had surprisingly few markets where the triggers applied the loss recovery, and still the results were far from impressive.
Total P/L: 1.7
ROI: 0.07%
Wins: 51, losses: 12.
Download Statement for 18/02/2019
My bank balance and statistics:
So to sum it up in a few words: not worth it. Although the graph plateaued at the end and even resembled an upward trend, I don’t believe it will produce any noticeable profit. Contrary to popular belief, servers often mistake and lose points, just like non-servers. You can probably take the service ownership out of the equation and build your green-up strategy on some other prerequisites.
Well, at least you are warned now, so I know this testing session has not been a waste of time.
Besides, you can evaluate the dynamic of prices in a typical tennis match, which might give you some other ideas as to when and how to bet.
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Download the trigger installation file above and start testing this strategy right now! Are you not using MarketFeeder Pro yet? Try now!
How and where I test the triggers?
I use our BetVPS service to pre-set the triggers and Market Locator and leave it to run on its own until I check on the results at the end of the day.I occasionally use Time Machine to get a proof of concept or test any tweaks that I want to make to my triggers, on historical markets similar to the ones in which I bet when testing a particular strategy.
I use Test Mode only.
You can generate your own graph and statistics like the ones in these Triggers in Action reports. Read how to do this.
If you would like a unique guest-post for your blog covering one of such strategies, please email me a request.