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November 5, 2019 - 10:28 pm
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I was taught years ago that if a price looks too good to be true, it probably is.

I wonder how others approach games where the price seems crazily in your favour, do you just think of it as extra value or a reg flag that the market knows something you don’t?

As an example this month I’ve had 2 HT lay scores that were both around 3.6. For me that was insane as usually I expect to see at least 4.6-4.8 bare minimum. This was in normal mainstream goal friendly leagues it wasn’t in Brazil, Argentina etc.

Of course both lost and the HT score stuck. I’ve made a note to myself to be careful with that sort of scenario, if in doubt, stay out. Funnily I just spotted my FDD HT lay method needs price to be a minimum of 4.0 so guess there may be something to it about lower prices.

My games maybe were subject to some sort of match fixing, who knows, I watched one half for 20 mins and it was “lackluster” shall we say. …. 

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November 6, 2019 - 9:34 am
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Definitely think there’s something in this. I tend to work within odds ranges now and have basically eliminated anything below 4.0 and in most cases anything below 5.0 to even consider a Ht lay.
You just get a feeling something isn’t right when the odds are “off” don’t you? Interestingly looking at laying Ht scores at odds under 4.0 on FDD does throw up lots of anomalies with the results as games which have gone over 3.5 by Ht (so 4-0, 4-1, 5-0 etc although very rare) are recorded as laying the aoHw or aoAw markets and at odds of 1.01 so losers are not recorded properly and give a false picture anyway. Not scorelines I would tend to include but interesting to note.

Your point does make for an interesting topic though. I am sure there are people out there who really focus on obscure leagues and markets and actually know why a price maybe what it is or indeed take advantage of it. It’s like when you see say Barcelona at half time and it’s below 5.0 to lay the score. Usually you check the team and there’s no Messi, no Suarez etc and even if a game was on my list I’d avoid that as you know why the odds are off. But other lesser known teams having their top scorer or 1st choice defence missing and we wouldn’t be any wiser in most cases. It’s actually fascinating what “drives” the market and where the main money comes from in some cases isn’t it? It is odd, I will check some teams on the odd occasion to see who is playing because I know the team but if I don’t know and it falls within my criteria I will bet it without thinking.

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Lord Dio
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November 6, 2019 - 9:37 am
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The code I use to generate the FTS files tend to use odds ranges.

Arthur

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November 6, 2019 - 11:10 am
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I tend to agree although always get a nagging feeling when the price looks to good to be true that I must be missing something. But I always keep my process the same and take the bet if it is within my parameters. 

Prime example was Swansea at home to Stoke a few weeks ago.  They were over evens at home to the worst performing team in the league.  I still had the bet though and guess what…..Swansea didn’t win.

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November 6, 2019 - 12:00 pm
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This is where mixing fundamental and technical analysis can be dangerous IMO. You can decide not to trade the lower price because it looks too good to be true and you see a star player is missing, but if you are working purely with historical data then how do you know how many times this scenario has occurred in your data set and how profitable it is? You could be cutting off a profitable chunk of that particular system without knowing it.

Ive battled with this in the past and still do with red cards. Easy thing to do is just stay out. But when you’ve got considerable data indicating the filters met are profitable, you have to assume red cards are amongst that data and just removing those games without any card data would be guesswork. 

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November 6, 2019 - 12:10 pm
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danny2 said
This is where mixing fundamental and technical analysis can be dangerous IMO. You can decide not to trade the lower price because it looks too good to be true and you see a star player is missing, but if you are working purely with historical data then how do you know how many times this scenario has occurred in your data set and how profitable it is? You could be cutting off a profitable chunk of that particular system without knowing it.

Ive battled with this in the past and still do with red cards. Easy thing to do is just stay out. But when you’ve got considerable data indicating the filters met are profitable, you have to assume red cards are amongst that data and just removing those games without any card data would be guesswork. 

  

I see where you’re coming from to an extent but I think the scenario/dynamic is much different in my example as it was from the HT position where the game is already live, squads are announced etc. I guess in this scenario I can at least look back through all my previous games that are priced the same, problem being I can count them on 1 hand and they all lost – def dodgy!

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