Finding value

Following the posts on the football match prediction system. we have reached the point whereby a chosen model based on certain variables will provide estimates for the probability of a home win, a draw and an away win in any match. What is left, is the small step of translating these probabilities into odds and a deciding whether a bet would be profitable (in the long run) or not.

Assuming that according to a model, the probability of a home win in a given match is estimated at 40%. That would mean that the "fair" odds for this event occurring would be 1 / 40% = 2.50 in decimal form or 6/4 in fractional form. [I'll be using the decimal form from now on as it's easier in algebraic expressions]. If a bookmaker offers odds of 2.20 then according to the model, this betting opportunity does not represent "value" and therefore should be avoided. This does not mean that the match will definitely not end in a home win, however, if an identical match is priced by the model again and again (and again), in the long run, a punter betting on this event will be losing money since the odds offered do not reflect the probability (according to the punter) of that event happening. On the other hand, if a rival bookmaker offers odds of 2.62 for this event, then it should be profitable (in the long run) to bet on a home win since after a number of bets, the punter will be making money because of the value found in these prices.

Essentially, it all comes down to two things: the first is the estimated probability of something happening. The bookmaker and the punter try to price up a match. They both have their subjective probabilities for HDA which are then translated to odds. Time will tell who is pricing the matches correctly (and thus making money off the other).

The second factor to consider is an advantage to the bookmaker. The bookmaker does not need to offer odds which when translated to probabilities sum up to 1. This means that the odds offered are lower than what should have been offered if the bookmaker was fair. This difference is what is called as the overround, and it represents the bookmaker's profit margin. Therefore, a punter will not only need to find value in the bookmaker's odds, but also to overcome his profit margin before actually making money in the long run.

However, the punter can also act for himself. By shopping around, finding the best prices offered by different bookmakers, the punter can almost eliminate the overround, thus start from a level-playing field. He can also use the available booking exchanges to find more competitive prices. These exchanges, in effect, give him the opportunity to become a bookmaker by setting odds for others to bet on, thus creating a market for odds which may be better than the odds offered by online bookmakers.

So there you have it: the system picks will depend on the estimated probabilities of each event (HDA) occurring and the best available prices offered by several bookmakers. Time will tell how accurate our predictions were, and how profitable (or disastrous) the developed model actually is. Fingers crossed!

Olympics football picks

Just a short note to post my picks for the Olympics football tournament. Again it will be a tournament of 16 teams, split into 4 groups. There are two strong favourites in Argentina and Brazil who seem to be taking this competition much more seriously than the rest of the teams taking part. Should these two nations top their respective groups, they will probably meet in the semi-finals. For these reasons, both Argentina and Brazil will be two of my picks to win the gold medal.

My third pick will be the exciting Netherlands team who won the Under-21 European Championship playing very nice football. They have a bit tougher group to top, as compared to Italy, but should they manage to come out of the group first, I can see them making it to the final.

It's much more difficult making predictions for the Olympics football tournament because of the squad selection limitations. A total outlay of 10 units (odds from William Hill) is split into:

• Stake 4.92 units on Argentina at 2.62
• Stake 3.79 units on Brazil at 3.40
• Stake 1.29 units on the Netherlands at 10.00

The summary statistics on the left have been updated. Let the Games begin!

Predicting football matches

The background is out of the way, the tools have been selected, now it's the time to get our hands dirty with the small details. Whichever model we choose, it will need some input in terms of factors which may affect our predictions. These have to be chosen wisely otherwise we may replicate the old motto of "Garbage in, garbage out".

Our approach is based on the general football concept of "class" and "form". It is often suggested that form is temporary while class is permanent so we will pick variables to model these general ideas. To account for the class of each team, we start with the obvious choice of variable of ... the actual teams playing! This translates to the fact that when Man Utd are playing at home against Hull City, it is expected that the probability of a home win is much bigger than the probability of a home win when Man Utd are playing against Arsenal. In fact, past performance is a pretty good place to begin and it turns out that decent (but not necessarily profitable) models can be built using the "class" approach. As for how to rate each team, there are both ad-hoc methods and optimization techniques. This is not a statistics lecture, so I'll leave it for now...

The form of each team is something more transient. A lot of punters who are building football prediction systems use the recent matches to judge on a team's form. This has both advantages in the sense that if a team is going through a good patch, it's more likely to have a good game than not. On the other hand, something that is often overlooked is the standard of the opposition teams against which form is being evaluated, compared to the standard of the opposition team in the next match. Maybe, there is scope for improvement here.

A team manages to score a goal after it has created some chances. Sure, some teams are more lethal and take the few chances that come along, but usually, the more (and better) chances a team creates, the higher the number of goals it will score. Therefore, we decided to use variables which describe the attacking capability of each team and its propensity to create chances. Unfortunately, for now, we don't have a measure to classify chances as good or difficult, although this "problem" provides also room for potential improvement.

Again, the small print will be left out of the blog for now, but if anyone out there is reading this and has any comments or views, please feel free to post them and who knows, maybe a fruitful discussion will facilitate further information on this.