# Scientific sports predictions algorithm of pickforwin.com

The odds proposed in the betting market often reflect the market rather than the true probability of the matches. **Higher probabilities do not coincide with a great opportunities to win!** Similarly to an **investiment** in a financial market, the purpose of our method is to identify the potential mis-pricing of the odds provided by the bookmakers. In the sports betting we have the possibility to decide which are the best events to bet, therefore it is important to evaluate very carefully those more profitable for investing our money. To do that, firstly, it is necessary to compute the probabilities of the single events analyzed. Starting with the pioneering work of Dixon and Coles (1997), many econometric methods have been proposed for predicting football matches as well as for many other sports. Dixon and Coles (1997) empirically demonstrate that the goals scored by a football team is distribuited like a Poisson distribution:

Where x is the number of goals scored and is the intensity to score a goals of a team. In the recent year a lot of works have been deloped in the scientific literature using different approach. Our website follow the mainstream approach to predict the result of a football match. This field of the literature define a model for the number of goals scored and conceded by the two teams based on the Poisson distribution and then aggregate these results to obtain probabilities for different outcomes such as home team win, draw, away team win, total number of goals, and so on (Maher, 1982; Dixon and Coles, 1997; Karlis and Ntzoufras, 2003). To sum up we can define the probabiliies associated to each single result: 0-0, 1-0, 2-0, 2-1 ecc... and than aggregate these value to compute the probability for the different outcomes (for example to define the probability associated to Under 2.5 one has to aggregate the probabilities of 0-0, 0-1, 0-2, 1-0, 2-0, 1-1). In the Home page of our website you can find our probabilities associated to the results 1,X,2,Under 2.5 and Over 2.5. In the column **PICKs** you find our suggestions. These advices are based on the idea that for each match, if the probability of a result (1, X, 2, Under or Over) estimated with our method is higher than the implicit probability provided by the market (the inverse of the odds), this bet if profitable. For example, consider the match Inter - Milan, the probabilities estimated by our model are 40% for the result 1, 30% for the result X and 30% for the result 2. The odds offered in the market are 2.60 for the outcome 1, 3.20 for the outcome X and 2.90 for the outcome 2. The implicit probabilities associated oto the odds are 1/2.60=38%, 1/3.20=31% and 1/2.90=34% for 1,X,2 respectively. In this case, the only profitable bet is the result 1 and **you will find a red 1 in the column PICKs** for this maach, because our probability (40%) is higher than the one propose in the market (38). **This means that you are repaid more than you risk!**

**References**

- Karlis, D. and Ntzoufras, I. (2003). Analysis of sports data by using bivariate Poisson models.
*Statistician*, 52, 381–393.

Summary. Models based on the bivariate Poisson distribution are used for modelling sports data. Independent Poisson distributions are usually adopted to model the number of goals of two competing teams. We replace the independence assumption by considering a bivariate Poisson model and its extensions. The models proposed allow for correlation between the two scores, which is a plausible assumption in sports with two opposing teams competing against each other. The effect of introducing even slight correlation is discussed. Using just a bivariate Poisson distribution can improve model fit and prediction of the number of draws in football games.The model is extended by considering an inflation factor for diagonal terms in the bivariate joint distribution.This inflation improves in precision the estimation of draws and, at the same time, allows for overdispersed, relative to the simple Poisson distribution, marginal distributions. The properties of the models proposed as well as interpretation and estimation procedures are provided. An illustration of the models is presented by using data sets from football and water-polo - Maher, M. J. (1982). Modelling association football scores.
*Statistica Neerlandica*, 36, 109– 118

Summary. Previous authors have rejected the Poisson model for association football scores in favour of the Negative Binomial. This paper, however, investigates the Poisson model further. Parameters representing the teams’ inherent attacking and defensive strengths are incorporated and the most appropriate model is found from a hierarchy of models. Observed and expected frequencies of scores are compared and goodness-of-fit tests show that although there are some small systematic differences, an independent Poisson model gives a reasonably accurate description of football scores. Improvements can be achieved by the use of a bivariate Poisson model with a correlation between scores of 0.2. - Dixon, M.J. and Coles, S.G. (1997). Modelling Association Football Scores and Inefficiencies in the Football Betting Market.
*Applied Statistic*, 46, 265-280.

Summary. A parametric model is developed and fitted to English league and cup football data from 1992 to 1995. The model is motivated by an aim to exploit potential inefficiencies in the association football betting market, and this is examined using bookmakers' odds from 1995 to 1996. The technique is based on a Poisson regression model but is complicated by the data structure and the dynamic nature of teams' performances. Maximum likelihood estimates are shown to be computationally obtainable, and the model is shown to have a positive return when used as the basis of a betting strategy.

Hoping that this explanation is exhaustive for you, we wish you a good browsing!

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