I notice that, on the surface, there are some similarities between quantitative sports betting and quantitative finance. Both have the concept of arbitraging, etc.

What are the applications of quantitative finance that lend themselves readily to sports betting?

  • $\begingroup$ I don't think that this is on-topic, so I'm voting to close. See meta discussion here: meta.quant.stackexchange.com/questions/36/… $\endgroup$
    – Shane
    Commented Feb 6, 2011 at 17:21
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    $\begingroup$ I actually think this is an interesting question, assuming we can get a real answer for it. A lot of quant trading is arbitrage in one form or the other. And usually one of the preconditions for stat arb or pure arb is that the transaction costs must be manageable. So any solution must find a way around the bookie's spread, etc. $\endgroup$ Commented Feb 6, 2011 at 18:01
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    $\begingroup$ Binary options would appear to have a very direct connection to sports betting. $\endgroup$
    – user59
    Commented Feb 6, 2011 at 18:12
  • $\begingroup$ I don't think a tag "quantitative" in this forum is really helpful!?! $\endgroup$
    – vonjd
    Commented Feb 7, 2011 at 11:51
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    $\begingroup$ @vonjd, I don't have the privilege to create a new tag; IMO "sports-betting" or "application" would be the appropriate tag. $\endgroup$
    – Graviton
    Commented Feb 7, 2011 at 12:15

4 Answers 4


Well in my opinion a good parallel can be made between sport betting and bookmakers on the one hand and derivative pricing and market makers of those derivative on the other hand. I'll try to explain that if I can.

If you are given a set of bookmakers taking bets for let's say one underlying outcome and that they quote this event as a percentage (as for binary options). Then if they are smart enough they shouldn't care about the statistics of the event itself, what they shall try to do is to balance their quotes with respect the positions of their outstanding books. I mean by this that they should do the P&L scenarios of each outcome of the bets in their books and try to balance things so they can earn a living while taking as little risk as possible. This will make dynamicaly evolve the quotes of the events as the bets come to their books. Of course the clients do care about statistics and so they will think twice before taking bets that rarely occur (or pay too much for it).

If you think at the market making of binary options, the situation is quite the same as the one with the bookie, but at the end what you get is the Risk Neutral Probability. It is set by market participants and is in a way "an opinion". This RN Probability is not always coherent with the statistics of the history of the underlying.

This however can make sense if all the market makers are in some way minimizing the aggregate risk of their returns (I don't explain the word 'risk' here on purpose but here the limits set by risk managers should enter into play).

Of course all this is not properly and mathematically formalised but the appropriatness of the parallel of both situations appears quite striking to me.

Best Regards


There are certain overlaps. One example is the so called favourite-longshot-bias, e.g.,

Another field is arbitrage betting, so called sure bets. The third field that comes to mind is risk management, especially the Kelly formula and this wonderful book (Fortune's Formula by W. Poundstone).


There's a very interesting article about this: http://www.wired.com/magazine/2010/11/ff_midas

Wall Street Firm Uses Algorithms to Make Sports Betting Like Stock Trading

The cornerstone of the operation is a piece of number-crunching software called Midas. It functions like the predictive computer programs that Amaitis dealt with on Wall Street: Midas acquires information, processes it, finds mathematical patterns and correlations, and uses all of that to divine the ever-shifting odds of sporting events. The system is robust enough to handle the play-by-play handicapping that keeps Jimmy E. glued to every pitch of the Tigers-White Sox game. During basketball season, things move so quickly that the bettors at the M have about eight seconds to consider a wager before the odds change.


To be precise, its not the application of quant finance, but rather its the general statistics which is used in myriad fields. For example, you could use monte carlo simulation (part of statistics) to determine the chances of which horse could win the horse race. For that we can form distributions of weather conditions, track conditions, horse's success rates etc. However, as these parameters are not independent so we must know the correlation between them to sample their outcomes.

Having said that, there are other fields where application of quant finance is prevalent. Like in valuation of tangible and real assets using options theory. It is widely used to assess risk of either developing or venturing out a certain product, property etc. Evaluating risk helps us make decisions eventually. So in essence, to do risk and decision analysis for real and tangible properties/products we use options theory.


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