Social network and sport betting

A bookmaker was a very important person for fixed-odds betting. His challenge was to make the best estimation for event probability. Is there any impact of the IT, internet and social network  on sport betting? This article is focusing on sport betting, Czech television “emociogram”, and EURO 2012 match Czech Republic – Russia.
Full version is available only in Czech language here - Zdrojak.cz on-line magazine for web developers. ISSN 1803-5620.

A little bit of history

Wager and hazard are with people for thousands of years (for example dice games). Usage of modern probability and statistics is significant younger. 12-years old children are able to calculate probability of minimal 1 head on 2 coins (it’s 75%: head-head, head-tail, tail-head, tail-tail). Famous mathematicians Leibnitz and D’Alembert were not able to find correct answer (Jiri Andel: Mathematics of chance). The history of modern fixed odds dates back to the 19thcentury and the origins of football gambling.

Internet impact on sport betting

IT and internet give advantage to bookmakers and also to customers.  Customers are able to get more and newer information about athletes. They have also bigger, significantly bigger choice from sport events and providers.

Providers are able to calculate online behaviors of gamblers and offer easy on-line betting during live match.

1st view: Bookmaker rules

Bookmaker holds advantage (overround) over their customers, so the bookmaker will make a profit over the long term. Even clever customers and rival providers can’t change it. Every long-term customer is going to be in red numbers. Of course there is a big probability of big numbers of short-term losses. There are the betting systems like martingale or D’Alembert. But probability and mean value plays for bookmaker. Advantage of this view is simplicity.

Let’s compute profit mean value (Ez_i) for bookmaker if result is i.

Ez_i=v_i-p_iik_iv_i

Where v_i is stake on result i, p_i for is probability of result i, and k_i is decimal odd of i result. There is just one parameter - p_i. So only problem of bookmaker is to set up k_ip_i<1 for all possible results i.

Problem of this view is situation when customers have better information than bookmaker. And update of all odds during live match for on-line betting is for human bookmaker a big problem.

The Roulette is an example of fix-odd betting game, where the odds are set up correctly and only lucky short-time customer can win.

2nd view: Sport result is useless

Let’s think differently. Is there a possibility for bookmaker to be in profit after every game? It means that stakes are bigger then wins for every result.

Let’s consider football game with probability 1/3 for every possible result (win, draw, loss) and 2 providers. There are stakes 3 000 000 EUR for this match for each provider. Odds for first provider are 2.7-2.7-2.7 and for second provider 2.9-2.9-2.9. So expected profit for the first provider is 300 000 EUR and for the second one is 100 000 EUR.

But what in case of those stakes distribution?

result	win	draw	loss	Sum of stakes	Expected profit
Stakes provider 1	1 400 000	200 000	1 400 000	3 000 000
Provider 1 profit	-780 000	2 460 000	-780 000		300 000
Stakes provider 2	1 000 000	1 000 000	1 000 000	3 000 000
Provider 2 profit	100 000	100 000	100 000		100 000

First provider is going to be in profit only in case of draw. Second is going to be in profit in every case. Which provider is better?

If provider wants profit after every game, provider needs to ensure:

summa_{i, t}v_{i, t} - max_i(summa_tv_{i, t}k_{i, t})>0

where t is time parameter. We consider change of odds in time.

I.e. provider doesn’t care about result of sport game. Provider cares only about stake distribution.

Social network

So provider should estimate behavior (atmosphere) of customers for good stake distribution estimation. But how do you want to measure behavior of group of customers during the game? Social network gives customers a possibility to “like” something.

Czech television uses social network to “Like” or “Dislike” performance of Czech team during EURO 2012 match. I’ve written new article focused on time series of fixed odds on victory of Russians and outputs of Czech television during EURO 2012 match Russia-Czech Republic.

Here is emociogram (time series graph of “Like” and “Dislike”) from Czech television.

Here is graph of time series for (k_t-1)/(k_t-1-1), where k is odd on Russian victory at time t.

They fit together. It is interesting, isn’t it?

So we are able to detect change during game just from liking! There is also strong dependency between liking and odd changing.

Conclusion

Problem of sport betting is more complicated. We don’t consider rival provides, dependency between odds and customer willingness to bet, different limits etc.

Online betting on live games is a big and modern business with quantum of information for bookmakers and customers. Every change during the match has big impact on supply and demand. Every change has also big impact on emotions of spectators.

So what about combination of liking and betting?