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paparainbow

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About paparainbow

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    Newbie Punter
  • Birthday 11/08/1982

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  1. Let’s say that someone places a bet: 100€ @4.00 now inplay the odd goes down to @2.00 his potential winnings is 400€ and the fair cashout is: (4/2)*100= 200€ this is not the case though since books apply margin on cashout amount. Does anyone have any idea how to derive this? Assume 8% percentage for the cashout, i know for a fact that margin moves along with the odds. The longer the inplay odd the greater the marginand vice versa. Any idea how to calculate margin?
  2. Does anyone have the slightest idea what mathematical concept is behind odds in in-play basketball? I mean is there a distribution or something to use, as someone could use poisson in football (with time, decaying goal expectancies over time etc)? Even though basketball seams to follow a normal distribution, I don't know what to do with negative values (points cannot be negative). if I model point differences (hence allow negative values) I cannot model the total points rather than the margin of victories, handicaps only... any idea?
  3. They will limit you if your stakes are high and your payout high too. Are you positive that younhave a winner strategy? why not split your bets in different bookies? There are others offering corners too.
  4. These are services paid by companies. Individuals don’t bother paying them. Additionally information is in feed mode, there isn’t a ui you can log in and download live stats. what kind of data do you meed exactly? Odds’ timeseries for instance?
  5. can you re-upload the spreadsheet, please? thanks for replying with respect to your formulas, what are those numbers? and what is the formula for TO(A|X)?
  6. Do you use logistic regression or maybe random Forrest? Also what features did u use?
  7. Hi, i have a question that initially I thought it was simple, but that’s not the case i guess. Let’s say i have a model, and i can calculate the probability of all correct scores for a given match ( it really doesn’t matter the model as long as i have a probability density function f(x,y) that returns correct score x-y). How can I calculate asian handicaps 0, 0.25, 0.5, 0.75 etc? 0.5 is easy, i take all matches that x>=y (0-0,1-0,2-0....., 1-1,2-1,3-1,..... ) similarly 1.5: x>=y+1 etc what about the asian handicaps? Any idea?
  8. Hi, i have a question that initially I thought it was simple, but that’s not the case i guess. Let’s say i have a model, and i can calculate the probability of all correct scores for a given match ( it really doesn’t matter the model as long as i have a probability density function f(x,y) that returns correct score x-y). How can I calculate asian handicaps 0, 0.25, 0.5, 0.75 etc? 0.5 is easy, i take all matches that x>=y (0-0,1-0,2-0....., 1-1,2-1,3-1,..... ) similarly 1.5: x>=y+1 etc what about the asian handicaps? Any idea?
  9. considering your initial input, expected home 0.90 expected away 1.35 you have to revisit your mathematical model.
  10. You do understand that the probability of picking a favourite @ 1.70 is pretty much the same right? Plus, larger column length means larger margin, hence lower profit. But it all come to this at the end of the day: Bookmaker prices a game at 1.15 for the fav team. Do you agree with this? Is it long or short? Don't let your betting choices be driven by bookies predictions but your own.
  11. you should somehow convert asian handicap to another market type, where all outcomes (in fair odds) generate probability distribution (sum of prob.outcomes = 1). In asian handicap outcomes are not mutually exclusive, hence do not form prob.distribution (like 3way winner).
  12. U get value to over 1.5 because 1. you don't apply overound the same way bookies do 2. you use poisson distribution, which is know for overdispersion. Actual life variance is greater than poisson predicts, due to its limitation var= mean. I suggest checking negative binomial to fix this, and also improve your ovr model. For example probabilities : 20%-80% fair odds : 1/0.20 = 5.00 - 1/0.80 = 1.25 lets suppose margin 108% your method : 5.00/1.08, 1.25/1.08 -> 4.60 - 1.16 bookis though do not apply margin like that resulting in an odd set of lets say 4.00-1.25 they tend to trim long odds and be more generous to short ones. they have their reasons: risk management and attractive favourite odds. cheers
  13. you should provide as with the link or pdf, otherwise, whats the point.
  14. Re: handball mathematical approach i guess normal distribution fit data batter, but standard deviation can be a problem. Any idea?