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7.2 Limitations further research
References

Discussion

In this section the contribution of this study to the academic literature is discussed. The most important limitations of this study are summarized and some interesting directions for future research are discussed.

7.1 Contribution

As seen in the literature review in section 2, there was a lot of research on the favorite longshot bias in different kind of sports. Most of these studies are based on horseracing, football and soccer. Investigations on the favorite longshot bias in tennis are scarce, so a new research on the favorite longshot bias in the tennis sport will contribute to the existing literature. Cain et al. (2003) did some research on tennis, but they used a very small dataset whereby the results were doubtful. Forrest & Mchale (2007) actually did a valuable study on the favorite longshot bias in tennis tournaments. They used a dataset of 17.000 possible bets and found a favorite longshot bias. This study goes a step further by using a dataset of 37.220 possible bets, which is 219% bigger than the dataset of Forrest & Mchale (2007). In addition, the large dataset used in this investigation, will make this a valuable article that contribute to the existing literature.

7.2 Limitations & further research

Looking at the conclusion in section 6, we see that all hypothesis are reject by the data. In the literature review we did not find any articles that looked at the trend over the years of the favorite longshot bias in tennis matches. So there is no possibility compare the results of this study with a similar study. Also looking at the research method we do not find an explanation for the rejection of the hypothesis. The sample we used is not too small or biased. Also there is no question of noisy proxies or failed manipulation checks.
It is interesting to do further research on the trend over the years. In this study we looked at the trend over 5 years (2009-2013), but you probably have to have a bigger sample size which contain more years. For example, if you use data from the last 30 years, you might see a clearer trend than the trend over 2009 to 2013.

References

Ali, M.M. (1977). Probability and utility estimates for racetrack bettor. Journal of Political Economy 85, 803-815.

Andrikogiannopoulou, A., & Papakonstantinou, F. (2011). Market Efficiency and Behavioral Biases in the Sports Betting Market.

Arsch, P., & Malkiel, B.G. (1982). Racetrack Betting and Informed Behavior. Journal of Financial economics, 187-194.

Busche, K. (1994) Efficient market results in an Asian setting, in: D. Hausche, S.Y. Lo andW. T. Ziemba (Eds) Efficiency in Racetrack Betting Markets, pp. 615–616 (London: Academic Press).

Busche, K. and Hall, C.D. (1988) An exception to the risk preference anomaly, Journal of Business, 61, pp. 337–346.

Busche, K. and Walls, W.D. (2000) Decision costs and betting market efficiency, Rationality and Society, 12, pp. 477–492.

Dixon, M. J., & Pope, P. (2004). The value of statistical forecasts in the UK association. international journal of forecasting, 697-711.

Forrest, D., & Mchale, I. (2007). Anyone for Tennis (Betting)? The European Journal of Finance, 751-768.

Forrest, D., Goddard, J. and Simmons, R. (2005) Odds-setters as forecasters: The case of English football, International Journal of Forecasting, 21, pp. 551–564.

Cain, M., Law, D. and Peel, D. (2000) The favourite-longshot bias and market efficiency in UK football betting, Scottish Journal of Political Economy, 47, pp. 25–36.

Cain, M., Law, D. and Peel, D. (2003) The favourite-longshot bias, bookmaker margins and insider trading in a variety of betting markets, Bulletin of Economic Research, 55, pp. 263–273.

Griffith, R. (1949). Odds adjustment by american horse-race betters. The American Journal of Psychology, 290-294.

Maas, V.S. (2011). A concise guide to quantitative data analysis using SPSS for MSc students.

Maas, V.S. (2011). Writing an MSc thesis in Management Accounting.

McGlothlin, W.H. (1956). Stability of Choices among Uncertain Alternatives. The American Journal of Psychology, 604-615.

Paul, R.J., & Weinbach, A.P. (2005). Bettor Misperceptions in the NBA : The Overbetting of Large Favorites and the ''Hot Hand''. Journal of Sports Economics, 390-400.

Shmanske, S. (2005) Odds-setting efficiency in gambling markets: Evidence from the PGA Tour, Journal of Economics and Finance, 29, pp. 391–402.

Vaughan Williams, L. and Paton, D. (1998) Why are some favourite-longshot biases positive and some negative?, Applied Economics, 30, pp. 1505–1510.

Vlastakis, N., Dotsis, G., & Markellos, R.N. (2008). How efficient is the European football beting market? Evidence from arbitrage and trading strategies. Journal of Forecasting, 426–444.

Woodland, L.M., & Woodland, B.M. (1994). Market Efficiency and the Favorite-Longshot bias: The Baseball Betting Market. The Journal of Finance, 269-279.

Woodland, L.M., & Woodland, B.M. (2001). Market Efficiency and Profitable Wagering in the National Hockey League: Can Bettors Score on Longshots? Southern Economic Journal, 983-995.

Woodland, L. and Woodland, B. (2003) The reverse favourite-longshot bias and market efficiency in Major League Baseball: An update, Bulletin of Economic Research, 55, pp. 113–123.

Appendix

Overview of ATP tournaments

	Official name	Surface	Matches 2009-2013	Total odds

Grand Slam
Australian Open	Australian Open	Hard-court (o)	545	1.090
Roland Garros	Les Internationaux de France de Roland Garros	Gravel	609	1.218
Wimbledon	The Wimbledon Championships	Gras	599	1.198
US Open	US Open	Hard-court (o)	600	1.200

ATP World Tour Finals
Londen	Barclays ATP World Tour Finals	Hard-court (i)	75	150

ATP World Tour Masters 1000
Indian Wells	BNP Paribas Open	Hard-court (o)	454	908
Miami	Sony Open Tennis	Hard-court (o)	452	904
Monte Carlo	Monte-Carlo Rolex Masters	Gravel	261	522
Rome	Internazionali BNL d'Italia	Gravel	253	506
Madrid	Mutua Madrid Open	Gravel	255	510
Montréal/Toronto	Coupe Rogers	Hard-court (o)	157	314
Cincinnati	Western & Southern Open - Cincinnati	Hard-court (o)	257	514
Shanghai	Shanghai Rolex Masters	Hard-court (o)	256	512
Parijs	BNP Paribas Masters	Hard-court (i)	222	444

ATP World Tour 500
Rotterdam	ABN AMRO World Tennis Tournament	Hard-court (i)	141	282
Memphis	U.S. National Indoor Tennis Championships	Hard-court (i)	150	300
Acapulco	Abierto Mexicano Telcel	Gravel	148	296
Dubai	Dubai Duty Free Tennis Championships	Hard-court (o)	144	288
Barcelona	Barcelona Open Banc Sabadell	Gravel	252	504
Hamburg	bet-at-home Open - German Tennis Championships	Gravel	202	404
Washington	Citi Open	Hard-court (o)	209	418
Peking, Beijng	China Open	Hard-court (o)	147	294
Tokio	Rakuten Japan Open Tennis Championships	Hard-court (o)	150	300
Basel	Swiss Indoors Basel	Hard-court (i)	154	308
Valencia	Valencia Open 500	Hard-court (i)	146	292

ATP World Tour 250
Atlanta	BB&T Atlanta Open	Hard-court (o)	102	204
Auckland	Heineken Open	Hard-court (o)	130	260
Bangkok	Thailand Open	Hard-court (i)	132	264
Bastad	SkiStar Swedish Open	Gravel	130	260
Bogota	Claro Open Colombia	Hard-court (o)	118	236
Brisbane	Brisbane International	Hard-court (o)	138	276
Bucharest	BRD Nastase Tiriac Trophy	Gravel	135	270
Buenos Aires	Copa Claro	Gravel	147	294
Casablanca	Grand Prix Hassan II	Gravel	134	268
Chennai	Aircel Chennai Open	Hard-court (o)	143	286
Delray Beach	Delray Beach Open by The Venetian® Las Vegas	Hard-court (o)	143	286
Doha	Qatar ExxonMobil Open	Hard-court (o)	153	306
Dusseldorf	Power Horse Cup	Gravel	126	252
Eastbourne	Aegon International	Gras	133	266
Gstaad	Crédit Agricole Suisse Open Gstaad	Gravel	136	272
Halle	Gerry Weber Open	Gras	141	282
Houston	US Men's Clay Court Championship	Gravel	132	264
Kitzbuhel	bet-at-home Cup Kitzbühel	Gravel	106	212
Kuala Lumpur	Malaysian Open, Kuala Lumpur	Hard-court (i)	128	256
London	Aegon Championships	Gras	256	512
Marseille	Open 13	Hard-court (i)	134	268
Metz	Moselle Open	Hard-court (i)	127	254
Montpellier	Open Sud de France	Hard-court (i)	78	156
Moscow	Kremlin Cup by Bank of Moscow	Hard-court (i)	132	264
Munich	BMW Open	Gravel	139	278
Newport	Hall of Fame Tennis Championships	Gras	146	292
Nice	Open de Nice Côte d’Azur	Gravel	103	206
Oeiras**	Portugal Open	Gravel	129	258
Sao Paulo	Brasil Open 2013	Gravel (i)	51	102
San Jose	SAP Open	Hard-court (i)	143	286
's-Hertogenbosch	Topshelf Open	Gras	140	280
St. Petersburg	St. Petersburg Open	Hard-court (i)	142	284
Stockholm	If Stockholm Open	Hard-court (i)	131	262
Stuttgart	MercedesCup	Gravel	139	278
Sydney	Apia International Sydney	Hard-court (o)	128	256
Umag	Vegeta Croatia Open Umag	Gravel	133	266
Vienna	Erste Bank Open	Hard-court (i)	131	262
Vina del Mar	VTR Open	Gravel	77	154
Winston-Salem	Winston-Salem Open	Hard-court (o)	131	262
Zagreb	PBZ Zagreb Indoors	Hard-court (i)	141	282
		Total	12.076	24.152