What drives intermediation costs? A case of tennis betting market

ABSTRACT

We demonstrate that there is a considerable variation in bookmaker margins across matches, time and bookmakers. Our results imply that using match, tournament and players’ characteristics explains the variations in margins hence, they can be helpful in managing intermediation cost in a market of state-contingent assets: fixed-odds betting markets. We also provide evidence that bookmakers protect themselves by increasing odds on the favourite player, thus attracting more bettors to the favourite player, while deterring bettors from betting on the underdog by reducing the odds. By that process, bookmakers are possibly sacrificing a portion of their margin.

KEYWORDS:

• Intermediation
• margins
• betting market
• sports
• tennis

JEL CLASSIFICATION:

• D40
• D80
• L80

Acknowledgement

We would like to thank Tomáš Výrost and Eduard Baumöhl for their helpful discussion and comments and two anonymous reviewers. All remaining errors are the responsibility of the authors.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ For example, a bettor (backer) expecting a win by player i buys the contingent claim from the person who offers the bet (layer) for one unit. If player i wins, the owner of the claim has the right to sell it to the opposite party for price o;if player i loses the value of the claim is zero. Such a view of the betting contract is similar to a put option. However, one could equally consider the contract to be a call option, e.g. Schnytzer, Lamers, and Makropoulou (2010).

² In our sample it appears to be one of the bookmakers (Expekt).

³ Perhaps the strongest one is that bookmakers strive for balanced books; a portfolio where, regardless of the outcome of an event, the bookmakers have a guaranteed profit. Further, it is assumed, that the amount of money wagered (standardized from 0 to 1) on a particular outcome is equal to the subjective probabilities of bettors about the outcome of the event.

⁴ Within Shin’s model, this can be interpreted as a mean share of insiders.

⁵ Betting exchanges have become popular in recent years (e.g. Smith, Paton, and Williams 2006, 2009; Franck, Verbeek, and Nüesch 2010) and represent a competition to the fixed-odds betting markets.

⁶ By using match and player related variables together with variance of odds as explanatory variables in regressions explaining margins, one has the potential to nest the Shin’s insider trading activity approach more tightly with other pricing approaches. However, as we are using final odds, using variance of odds as explanatory variables raises endogeneity issues. The observed odds and margins are observed at the same time, margins are calculated from odds, and it is unclear whether margins influence odds setting or vice versa.

⁷ We thank the reviewer for raising these issues.

⁸ We thank the anonymous reviewer for pointing out the confounding issue.

⁹ http://www.pinnaclesports.com/en/betting-articles/sport/market-movement-in-betting

¹⁰ Note that, for this coefficient, we report x 100 the value of the estimated coefficient, i.e. 0.031 x 294 (maximum number of days in one season) / 100 = 0.091.

¹¹ Seeding is often based on ATP ranking. In the first round, a lower-ranked player plays a much higher-ranked player. If the favourites always win, the top two favourites will only meet in the final.

¹² We want to show that E_l < E_h, after substituting we have p_lo_{l –} 1 + p_l < p_ho_{h –} 1 + p_h, using the fact that p_l < p_h, it remains to show that p_lo_l < p_ho_h. Substituting odds we have p_l /(p_l + m/2) < p_h/(p_h + m/2), now after some algebra we have p_lp_h + p_lm/2 < p_hp_l + p_hm/2, which leads to p_l < p_h.

¹³ We thank the reviewer for suggesting the last two robustness checks.

¹⁴ All the results from this Section are available upon request from the corresponding author.

Additional information

Funding

This work was supported by the Slovak Grant Agency [grant number 1/0392/15], [grant number 1/0145/14].