Correct Scores - binomial distribution

[The Hare]

The idea here is to see how well the binomial distribution performs as a predictor of the outcome of a football match. If we can estimate the expected number of goals that each side will score, and if the number of goals scored by each side follows the binomial distribution, (that's two big ifs!) then we can estimate the probability of every correct score combination. For example, typically we would expect the home team to score 1.5 goals. Using that figure, the binomial distribution tells us the probability of the home side scoring 0 goals is 0.22, 1 goal = 0.34, 2 goals 0.25 etc. Doing the same for the away side, who typically score 1.0 goals, allows us to caclulate all possible score combinations. I recognise that this must have been tried and no doubt failed by numerous people over the past, and I'd be interested to learn about others experiences. I've done some backtesting and the binomial distribution performs reasonably well, although there are some biases. To get this to work all I need is an estimate of the probability of a home and away goals in each match. I then look for value correct score bets - intitally I will taken anything where the value is +10% (prior to commission). All bets taken from Betfair. Will "dutch" my bets so that if any of my CS selections win, I win 100 units. Starting bank is 1000 units. This is very much a learning experience for me. Its my first go at a strategy, and dont have huge hopes of success, but hope to learn something on the way. Here goes: Stoke City v Coventry City CS Odds Stake 0-2 26 3.8 5 0-3 80 1.25 Inverness CT v Aberdeen CS Odds Stake 0-0 10.5 9.52 0-1 9.6 10.42 1-0 8.6 11.63 2-0 15 6.67

[dr.karl]

Re: Correct Scores - binomial distribution It's been a long time since I've used the binomial distribution but I fail to see how it works in this case. How have you calculated the chance of there being one goal scored by the home team if the team is expected to score 1.5 goals? The way I thought a binomial distribution worked was with combinations of "yes/no" factors occuring, so for example a coin toss, its either head or a tail. If you wanted to know the probabilty of tossing 13456 heads out 20000 throws this is the distribution to use. Could you explain the maths behind it please because it'll bug me for days if I can't figure it out! Oh and gl with the system :ok.