Football - citations

The people's game. The most popular sport in the world. Football is also one of the most popular sports to gamble on with a myriad of bet types to choose from.

The in-play market is particularly busy for bettors with prices constantly updating during the ebb and flow of the game.

Because of its popularity, football attracts a lot of academic interest. Here is a citation list for academic papers concerned with football.

NOTE - If any links to papers are broken then just Google the paper's title to find an alternate. Papers without links might be found in Google Scholar, JSTOR or Arxiv.

Avery, Christopher, and Judith Chevalier. "Identifying Investor Sentiment from Price Paths: The Case of Football Betting (Digest Summary)." Journal of Business 72.4 (1999): 493-520.

Baio, G., & Blangiardo, M. (2010). Bayesian hierarchical model for the prediction of football results. Journal of Applied Statistics, 37(2), 253-264.

Bender, Edward A. "Betting on Football Pools."

Choi, D., & Hui, S. (2012). The Role of Surprise: Understanding Over-and Underreactions Using In-Play Soccer Betting Available at SSRN 2011564.

Constantinou, Anthony Costa, and Norman Elliott Fenton. "Profiting from arbitrage and odds biases of the European football gambling market." J. Gambl. Bus. Econ 7 (2013): 41-70.

Crowder, Martin, et al. "Dynamic modelling and prediction of English Football League matches for betting." Journal of the Royal Statistical Society: Series D (The Statistician) 51.2 (2002): 157-168.

Dixon, M. J., & Coles, S. G. (1997). Modelling association football scores and inefficiencies in the football betting market. Journal of the Royal Statistical Society: Series C (Applied Statistics), 46(2), 265-280.

Dixon, Mark J., and Peter F. Pope. "The value of statistical forecasts in the UK association football betting market." International Journal of Forecasting 20.4 (2004): 697-711.

Dixon, Mark, and Michael Robinson. "A birth process model for association football matches." Journal of the Royal Statistical Society: Series D (The Statistician) 47.3 (1998): 523-538.

Donninger, Chrilly. "Towards the Perfect Football Betting Bot: A First Preliminary Report of Zoccer." Available at SSRN 2498910 (2014).

FANG, L., and Z. ZHENG. "Predicting Soccer League Games using Multinomial Logistic Models." Relatório Técnico (2008).

Fitt, A. D. (2009). Markowitz portfolio theory for soccer spread betting. IMA Journal of Management Mathematics, 20(2), 167-184.

Fitt, A. D., C. J. Howls, and M. Kabelka. "Valuation of soccer spread bets." Journal of the Operational Research Society 57.8 (2006): 975-985.

Forrest, David, and Robert Simmons. "Globalisation and efficiency in the fixed-odds soccer betting market." University of Salford, Centre for the Study of Gambling and Commercial Gaming (2001).

Franck, Egon, Erwin Verbeek, and Stephan Nüesch. "Prediction accuracy of different market structures—bookmakers versus a betting exchange." International Journal of Forecasting 26.3 (2010): 448-459.

Greenhough, J., et al. "Football goal distributions and extremal statistics." Physica A: Statistical Mechanics and its Applications 316.1 (2002): 615-624.

Hardiman, Stephen J., Peter Richmond, and Stefan Hutzler. "Long-range correlations in an online betting exchange for a football tournament." New Journal of Physics 12.10 (2010): 105001.

Karlis, D., & Ntzoufras, I. (1999). On Modelling Association Football Data. Technical Report, Department of Statistics, AUEB.

Liu, F., & Zhang, Z. Predicting Soccer League Games using Multinomial Logistic Models.

Maher, M. J. (1982). Modelling association football scores. Statistica Neerlandica, 36(3), 109-118.

Markovich, Sarit. "The Value of Information: The Case of Soccer." Northwestern University Paper (2008).

McHale, I., & Scarf, P. (2007). Modelling soccer matches using bivariate discrete distributions with general dependence structure. Statistica Neerlandica, 61(4), 432-445.

Misirlisoy, Erman, and Patrick Haggard. "Asymmetric predictability and cognitive competition in football penalty shootouts." Current Biology 24.16 (2014): 1918-1922.

Min, Byungho, Chongyoun Choe, and R. I. McKay. "A Compound Approach for Football Result Prediction." Proc. the 2006 Asia-Pacific Workshop on Intelligent and Evolutionary Systems. 2006.

O’Shaughnessy, Darren. "OPTIMAL EXCHANGE BETTING STRATEGY FOR WIN-DRAW-LOSS MARKETS."

Rowan, Mark. "Evolving strategies for prediction of sporting fixtures." (2007).

 

Rotshtein, Alexander P., Morton Posner, and A. B. Rakityanskaya. "Football predictions based on a fuzzy model with genetic and neural tuning." Cybernetics and Systems Analysis 41.4 (2005): 619-630.

Rotshtein, A., M. Posner, and H. Rakytyanska. "Prediction of the results of football games based on fuzzy model with genetic and neuro tuning." Eastern European journal of enterprise technologies 10 (2003): 15.

Skinner, G.K. & Freeman G.H (2009). Are soccer matches badly designed experiments? Journal of Applied Statistics 36-10, pp. 1087-1095.

Štrumbelj, E., and M. Robnik Šikonja. "Online bookmakers’ odds as forecasts: The case of European soccer leagues." International Journal of Forecasting 26.3 (2010): 482-488.

Tsakonas, A., et al. "Soft computing-based result prediction of football games." The First International Conference on Inductive Modelling (ICIM’2002). Lviv, Ukraine. 2002.

Tunaru, Radu, Ephraim Clark, and Howard Viney. "An option pricing framework for valuation of football players." Review of financial economics 14.3 (2005): 281-295.

Vlastakis, Nikolaos, George Dotsis, and Raphael N. Markellos. "How efficient is the European football betting market? Evidence from arbitrage and trading strategies." Journal of Forecasting 28.5 (2009): 426-444.

Vlastakis, Nikolaos, George Dotsis, and Raphael N. Markellos. "Nonlinear modelling of European football scores using support vector machines." Applied Economics 40.1 (2008): 111-118.

Recommended Reading

I recommend that you read The Numbers Game. The book contains many statistical insights that are not always appreciated by football fans but which could help the aspiring football trader.

Soccernomics is packed full of insights that most football fans don't appreciate.

Review here.

Who's #1?: The Science of Rating and Ranking is an excellent book for those interested in creating sports ratings. The book is mathematical in nature but well-described and easy to understand. Many ranking methods are covered. There is also discussion about ranking offence and defence, point spreadds, Markov chains, Elo rankings and much more besides. 

Sports Arbitrage - citations

Listed here are papers related to the field of sports arbitrage.

Cherkashin, D., Farmer, J.D. & Lloyd, S. (2009) The Reality Game, Journal of Economic Dynamics & Control, 33, pp. 1091-1105.

Drayer, J., Rascher, D. A., & McEvoy, C. D. (2012) An examination of underlying consumer demand and sport pricing using secondary market data. Sport Management Review.

Edelman, D. C., & O'Brian, N. R. (2004) Tote arbitrage and lock opportunities in racetrack betting. The European Journal of Finance, 10-5, pp. 370-378.

Franck, E., Verbeek, E., & Nüesch, S. (2009) Inter-market Arbitrage in Sports Betting.  NCER Working Paper Series.

Gramm, M., McKinney, C. N., & Owens, D. H. (2012) Efficiency and arbitrage across parimutuel wagering pools. Applied Economics, 44-14, pp. 1813-1822.

Hausch, D. B., & Ziemba, W. T. (1990) Locks at the racetrack. Interfaces, 20-3, pp. 41-48.

Luckner, S., & Weinhardt, C. (2008) Arbitrage Opportunities and Market-Making Traders in Prediction Markets. E-Commerce Technology and the Fifth IEEE Conference on Enterprise Computing, pp. 53-59.

Marshall, B. R. (2009) How quickly is temporary market inefficiency removed? The Quarterly Review of Economics and Finance, 49-3, pp. 917-930.

Paton, D., & Williams, L. V. (2005) Forecasting outcomes in spread betting markets: can bettors use ‘quarbs’ to beat the book? Journal of Forecasting, 24-2, pp. 139-154.

Scanlon, P. (2011) Home Bias and Prediction Markets: Evidence from the Racetrack. Trinity Economics Papers.

Edward O. Thorp - citations

Edward O. Thorp, author of Beat the Dealer, Beat the Market, builder of a roulette beating computer and hedge fund manager. What more is there to look up to?

Thorp is a mathematics professor who developed card counting in the early 1960s, took it to Las Vegas and won. He then applied his theories to warrant markets and beat that too.

In 1961 Thorpco-designed the world's first wearable computer (paper cited below) with the father of Information Theory, Claude Shannon (whose colleague John L Kelly created the Kelly Criterion for optimal investment growth). The computer was used to determine in which octant the ball would land on a roulette wheel thus dramatically turning the edge away from the house and to the player.

Since then Thorp has used his deep knowledge of statistics and probability to search for pricing anomalies in financial markets and, through Kelly criterion, maximise return on investment for a hedge fund he manages.

Bicksler, J.L. & Thorp, E.O. (1973) The Capital Growth Model: An Empirical Investigation. Journal of Financial and Quantitative Analysis, 8-2, pp. 273-287.

Maclean, L.C., Thorp, E.O. & Ziemba, W.T. (2010) Long-Term Capital Growth: Good and Bad Properties of the Kelly Criterion. Quantitative Finance, 10-7, pp. 681-687.

MacLean, L.C., Thorp, E.O., Zhao, Y. & Ziemba, W.T. (2011) How Does the Fortunes Formula Kelly Capital Growth Model Perform? The Journal of Portfolio Management, 37-4, pp. 96-111.

Thorp, E.O. (1962) Beat the Dealer: a winning strategy for twenty-one. New York: Vintage Books.

Thorp, E.O. (1969) Optimal Gambling Systems for Favorable Games. Review of the International Statistical Institute, 37-3, pp. 273-293.

Thorp, E.O. (1975) Portfolio Choice and the Kelly Criterion. Stochastic Optmization Models in Finance, New York: Academic Press, pp. 599-619.

Thorp, Edward O. "The invention of the first wearable computer." Wearable Computers, 1998. Digest of Papers. Second International Symposium on. IEEE, 1998.

Thorp, E.O. (1997) The Kelly Criterion in Blackjack, Sports Betting and the Stock Market. 10th International Conference on Gambling and Risk Taking.

William T. Ziemba - citations

Dr William T. Ziemba should be well-known to students of horse race betting. Author of Beat the Racetrack, Efficiency Of Racetrack Betting Markets and Handbook of Sports and Lottery Markets (Handbooks in Finance), William Ziemba is a prolific writer.

Dr Ziemba specialises in market efficiency be it at the racetrack or on Wall Street. Along with Donald B. Hausch he wrote the book on exploiting market inefficiencies in the Tote betting market using Kelly Criterion. Ziemba has also edited collections of papers by other well known practitioners in the field of sports betting.

Here is a (not exhausted) list of market efficiency and capital growth (Kelly Criterion) citations for Dr Z.

Bain, R.S., Hausch, D.B., & Ziemba, W.T. (2006) An Application of Expert Information to Win Betting on the Kentucky Derby 1981-2005. The European Journal of Finance, 12-4, p. 283-301.

Canfield, B.R., Fauman, B.C & Ziemba, W.T. (1987) Efficiency Market Adjustment of Odds Prices to Reflect Track Biases. Management Science, 33-11, pp. 1428-1439.

Hakansson, N.H. & Ziemba, W.T. (1995) Capital Growth Theory. Handbooks in Operations Research & Management Science, 9, pp.65-86.

Hausch, D. B., Lo, V.S.Y. & Ziemba, W.T. (1994) Efficiency of Racetrack Betting Markets, San Diego, London: Academic Press.

Hausch, D.B. & Ziemba, W.T. (1985) Transactions Costs, Extent of Inefficiencies, Entries and Multiple Wagers in a Racetrack Betting Model. Management Science, 31-4, pp. 381-394.

Hausch, D.B. & Ziemba, W.T. (1990) Arbitrage Strategies for Cross-Track Betting on Major Horse Races. The Journal of Business, 63-1, pp.61-78.

Hausch, D.B. & Ziemba, W.T. (1990) Locks at the Racetrack. Interfaces, 20-3, pp. 41-48.

Hausch, D.B. & Ziemba, W.T. (2008) Handbook of Sports and Lottery Markets Amsterdam, Boston: Elsevier/North-Holland.

Hausch, D.B., Ziemba,W.T. & Rubinstein, M. (1981) Efficiency of the Market for Racetrack Betting. Management Science, 27-12, pp. 1435-1452.

Judah, S. & Ziemba, W.T. (1983) Three Person Baccarat. Operations Research Letters, 2-4, pp. 187-192.

Lane, D. Ziemba, W.T. (2004) Jai Alai Arbitrage Strategies. The European Journal of Finance, 10-5, pp. 353-369.

Maclean, L.C., Thorp, E.O. & Ziemba, W.T. (2010) Long-Term Capital Growth: The Good and Bad Properties of the Kelly and Fractional Kelly Capital Growth Criteria. Quantitative Finance, 10-7, pp. 681-687.

MacLean, L.C., Thorp, E.O., Zhao, Y. & Ziemba, W.T. (2011) How Does the Fortunes Formula Kelly Capital Growth Model Perform? The Journal of Portfolio Management, 37-4, pp. 96-111.

MacLean, L.C., Ziemba, W.T. & Blazenko, G. (1992) Growth Versus Security in Dynamic Investment Analysis. Management Science, 38-11, pp. 1562-1585.

Thaler, R.H. & Ziemba, W.T. (1988) Anomalies - Parimutuel Betting Markets: Racetracks and Lotteries The Journal of Economic Perspectives, 2-2, pp. 161-174.

Ziemba, W.T. (2005) The Symmetric Downside-Risk Sharpe Ratio. The Journal of Portfolio Management, 32-1,  pp. 108-122.

Books by William Ziemba
 
     

Going it alone

I don't know why so many people do their thinking out loud on forums, twitter and blogs. Trading is about finding an edge with which to profit from. How are you going to find an edge when you fritter away your valuable knowledge online?

Of course, only losing traders broadcast their knowledge. They want someone to tell them where they are going wrong.

These online system builders are not tipsters trying to influence the market, which I have already discussed in another article. Observing the poor maths these people use, I guess they are beginners who have looked at the market for 5 seconds and found The Mother of All Systems.

In The City of London I learned the value of information. My mother thinks that money is power, it isn't, it's the knowledge that generates money that is real power. Today's £5 note was worth more yesterday and will be worth less tomorrow. Information that generates fivers is more powerful than the fiver itself.

During the 1990s I worked for Reuters in a blue sky research group. In the group we had to keep abreast of all the latest technologies to see if there was any value in it for Reuters. Maybe Reuters would invest in the company that created the technology or licence the technology for a new Reuters data product. I met the creators of Google when it was just a start-up and soon after I ditched Yahoo as my default browser page.

I worked on cryptography, machine translation, artificial intelligence, got depressed through work and personal problems and started crawling into the office at noon. A drunken lunch before staggering home at 3PM.

Before disillusionment with my job set in, I had an idea for machine written news stories. What a trader wants to know is, "Is this information going to give me a buy or sell order?" He doesn't want to know if the story writer fancies himself as the next Ernest Hemmingway or fellow Reuters alumnus Ian Flemming.

I touted my idea to editorial. "No," was the reply. My idea was madness in the eyes of many and a threat to jobs for others. Today, of course, machine written financial news and automated trading on that news is common place.

Then there were the countless times when other employees (with the right contacts) plagiarised my work or managers took all the credit for what I did. That's why I now keep all my cards close to my chest. I had ideas for RFIDs on product labels as well as automated machine translation that have come to pass.

But dwelling on the past, like posting a system on a forum, is for losers. When I was offered voluntary redundancy following the DotCom bubble burst, I jumped at it. I haven't worked for anyone since and never will again. My information is mine alone. I sink or swim by it. I never follow the herd.