We find that prices offered by competing bookmakers within the same quote-driven soccer (football) betting market provide arbitrage opportunities. However, the management practices of bookmakers prevent informed bettors exploiting these in practice. We identify two groups of bookmakers, 'position-takers' and 'book-balancers.' Position-takers alter their odds infrequently, while actively restricting informed traders. Book-balancers actively manage inventory by adjusting odds, and place few restrictions on their customers. We identify 545 arbitrage portfolios, and find that around 50% would require a bet on the favourite at the position-taking bookmaker. The management practices of position-takers generally prevent these opportunities being exploited in practice.

We discover mispricing in an apparently transparent market -the European soccer betting market. Efficiency differences between countries are accounted for by variations in league competitiveness. We conclude that barriers to efficiency (e.g., risk evaluation problems) may remain in transparent markets.

Abstract-Depth estimation has long been a fundamental problem both in robotics science and in computer vision. Various methods have been developed and implemented in a large number of applications. Despite the rapid progress in the field the last few years, computation remains a significant issue of the methods employed. In this work, we have implemented two different strategies for inferring depth, both of which are computationally efficient. The first one is inspired by biology, that is optical flow, while the second one is based on a least squares method. In the first strategy, we observe the length variation of the optic flow vectors of a landmark at varying distances and velocities. In the second strategy, we take snapshots of a landmark from different positions and use a least squares approach to estimate the distance between the robot and a landmark. An evaluation of the two different strategies for various depth estimations has been deployed and the results are presented in this paper.

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In this paper a novel biologically inspired method is addressed for the robot homing problem where a robot returns to its home position after having explored an a priori unknown environment. The method exploits the optical flow patterns of the landmarks and based on a training data set a probability is inferred between the current snapshot and the snapshots stored in memory. Optical flow, which is not a property of landmarks like color, shape, and size but a property of the camera motion, is used for navigating a robot back to its home position. In addition, optical flow is the only information provided to the system while parameters like position and velocity of the robot are not known. Our method proves to be effective even when the snapshots of the landmarks have been taken from varying distances and velocities.