Carr & Picron (1998), Static Hedging of Timing Risk, Journal of Derivatives, gives results for options with rebates (and non-zero interest rates and dividends), and Carr, Peter, Katrina Ellis, and Vishal Gupta (1998), "Static Hedging of Exotic Options", Journal of Finance, 53(3), 1165-91 shows the static hedging articles also appear in top journals.
The Carr-articles work with the Black-Scholes model, and end up with quite explicit formulas for "which & how many puts and calls to buy".
Pricing American Options by Monte Carlo Simulation.
(Despite what one might think, there no connection (or is there?
The real reason is of course that betting markets are fun. Ziemba "Efficiency of Sports and Lottery Betting Markets", Chapter 18 in Jarrow et al. This is the exact opposite of how things usually work in financial markets, where low risk means low expected return , while very risky (in some sense) investments must have a high expected rate of return.
(If you don't think so, then this is not the topic for you). There is an obvious explanation: Agents in betting markets are risk-lovers, not risk-averse -- why else would they enter the market at all?
They "simply" make a statistical model to fit/predict outcomes of football matches & compares to (mis)quoted odds. Well, you can probably find a lot on the web (try some Google-searches), but I might be able to get my hands on "some of the really good stuff" through my connections with Betbrain.
The technology boom of the past couple of decades has given the average trader access to a much wider range of financial products than ever before.
See Shin 1991 (the basic idea) Shin 1992 (an extension to more than 2 possible outcomes & competing bookmakers; a lot messier but same qualitative result) and Shin 1993 (empirical evidence).
A "fundamental analysis" approach to finding abnormal returns ("good odds/bets") is given by Dixon and Coles.