Measuring The Measurers.

Only a few days to go before the eagerly awaited Olympic football kicks off in London and elsewhere, featuring hybrid versions of top international sides such as Spain, Uruguay, Brazil and Great Britain. Sandwiched as it is between the end of the Euros and the beginning of the new Premier League season, the competition may be a slightly hard sell to a footballing public that hasn't exactly been starved of top class action over recent weeks. Beckham's omission won't have helped to attract the casual fan tuning in and out of the Olympic feast, but a rump of under 23 talent combined with a maximum of three over aged players will ensure that every game holds some interest.

The real challenge though lies at the feet of the sports analysis firms who will try to bring order to this infrequent format by predicting the outcome the Olympic football games. Predicting the outcome of Premiership games is usually most difficult at the start of a campaign because of the pre season movement of players and management. Longterm statistical trends are tweaked in one direction or another dependent upon how favourably  each team's squad strengthening has been received and usually a consensus opinion is reached. In the case of the Olympic competition solid information is limited, few of the teams playing the length and breadth of England and Wales will have a large body of results to draw on in formulating their relative chances and the bulk of some teams, such as Great Britain won't have played a competitive game together prior to the tournament kicking off.

It's therefore useful to have a method of evaluating the performance of a group of match selections and the forth coming event, shrouded in a mist of uncertainty is probably the most challenging of world football stages.

All football matches have three possible outcomes when played over 90 minutes, predicting whether a match will end in a "home" win and "away" win or a draw is the lifeblood of football prognostication. Literally hundreds of derived markets have evolved over the last decade or so, ranging from total goals, first scorer to half time result and which team will qualify from a knockout tie, but 1,2,X remains the standard from which football prediction is measured and the baseline from which most of the secondary markets are derived.

Many methods exist to test the measurers, and many are ad hoc and arbitrary. A points scoring system for every correct selection gives no extra credit for correctly predicting an unlikely outcome or for being particularly confident about a particular outcome. Systems based on expected proportions of wins and draws over a series of matches can see poor selections in one direction cancel out equally poor ones in another, leading to the illusion of accuracy.

To cut through the uncertainty we need to judge each model's estimation of a match ending as a home win, draw or away win on the merits of it's original confidence of each selection occurring once we are in possession of the actual outcome. One simple method involves taking the square root of the sum of the squares of the prediction and the actual outcome and the smaller the result, then the better the prediction.

Here's an example from the Euro 2012 Final using the opinions of two Irish bookmakers. Bookmakers estimations of match odds are always quoted with an extra edge of around 9 or 10%, so I've removed this overround to give a reasonable figure for the bookmakers estimation of the true chances of Spain, Italy or a stalemate resulting in 90 minutes.


Paddy Power Get Bested by Boyle Sports in the Final of Euro 2012.

Bookmaking Firm.	PP	BS	Match Outcome	Difference Squared for PP	Difference Squared for BS.
Probability of a Spain Win, 90 mins.	0.448	0.460	1	0.305	0.292
Probability of a Italy win, 90 mins.	0.296	0.298	0	0.088	0.089
Probability of a Draw, 90 mins.	0.256	0.242	0	0.066	0.059
Square Root of the Sum of the Squares.				0.676	0.662

Once Spain's 4-0 win in 90 minutes was confirmed, the likelihood of that result occurring collapses to 1 and the other two possible outcome become zero. BS were slightly more confident in a Spanish win than were PP, hence when the differences between prediction and reality are squared (to eliminate any confusion with minus signs), totalled and the square root taken, BS return a slightly lower figure. In this one match their estimation of what was likely to occur was marginally more accurate than that of their Irish rivals. Visually, BS were closer to the actual result, a Spanish win in regulation, but this method quantifies the difference and can be used over multiple games and multiple markets such as group qualification and pre tournament winners.

The example is from the world of betting, but increasingly predictive models are being developed for a wide range of footballing events and they require a consistent way to gauge their effectiveness. One of the more difficult games anyone has had to evaluate takes place on Friday, when a scratch GB side take on Brazil....in a friendly. Below I've listed the edge free odds of a series of firms and after the game I'll list who showed the best judgement.

How Firms View the Brazil v GB Olympic Warm Up Game.

Bookmaking Firm.	SB	BFr	SJ	888	BFa	WBX
Brazil Win.	0.513	0.532	0.496	0.537	0.541	0.548
Draw.	0.231	0.212	0.235	0.206	0.187	0.202
GB Win.	0.256	0.255	0.269	0.257	0.272	0.250
Square Root of the Sum of the Squares.	0.597	0.573	0.618	0.569	0.565	0.554