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.