Abstract:
A method of determining liability for a plurality of multiples bets placed via a computerised betting system, each multiples bet comprising a single transaction on a plurality of outcomes selected from a group of possible transactions, the method comprising selecting a most popular transaction from the group, evaluating all other transactions within the group on the basis that the most popular transaction is a winning transaction to produce a first result, evaluating all other transactions within the group on the basis that the most popular transaction is not a winning transaction to produce a second result and selecting one of the first and second results that indicates the worst case liability.

Description:
FIELD OF THE INVENTION 
       [0001]    The present invention relates to software for providing liability assessment and risk management, in particular in relation to multiples betting. 
       BACKGROUND 
       [0002]    A multiple, or accumulator bet, is a bet on a number of outcomes occurring. A multiple bet wins if, and only if, all the selected outcomes occur. Typically, the separate odds for each of the outcomes are multiplied together to give odds for all outcomes to occur together. For example, a user might bet on Newcastle, Chelsea and Liverpool all to win their respective matches. If the odds for each, expressed as digital odds, were 2.5, 1.8, 2.9, then the user might bet £10 at odds of 13.05 (2.5×1.8×2.9) that all three win. If they all win then the user wins £120.50 and if one or more fail to win the user will lose £10. The attraction for users is that they get larger odds by combining several favoured outcomes, where the odds are short. 
         [0003]    Since it is impossible for a bookmaker to hedge the risk on an individual multiple bet, the bookmaker has to accept some level of risk. For example, a user may place a £15 bet on a combination of 8 outcomes. If all 8 occur, then the bookmaker may, for example, have to pay out £47,000, but if one does not occur the bookmaker will win £15. There is no way to place a single bet, or combination of single bets, that cover the potential payout. 
         [0004]    The inventors have determined that where liability is looked at over a very large number of multiple bets, sufficient information can be provided to place hedging bets that can significantly reduce the risk involved. 
       SUMMARY OF THE INVENTION 
       [0005]    According to the invention, there is provided a method of determining liability for a plurality of multiples bets placed via a computerised betting system, each multiples bet comprising a single transaction on a plurality of outcomes selected from a group of possible transactions, the method comprising:
       (a) selecting a most popular transaction from the group;   (b) evaluating all other transactions within the group on the basis that the most popular transaction is a winning transaction to produce a first result;   (c) evaluating all other transactions within the group on the basis that the most popular transaction is not a winning transaction to produce a second result; and   (d) selecting one of the first and second results that indicates the worst case liability.       
 
         [0010]    The method may further comprise determining if the worst case liability exceeds a predetermined value and, if so, refusing a submitted bet. 
         [0011]    The method may further comprise selecting a next most popular transaction from the group and repeating steps (b) and (c) for the next most popular transaction based on the transaction previously determined to result in the worst case liability, to provide third and fourth results respectively. 
         [0012]    The method may further comprise determining the worst case liability as being the worst case liability among the third and fourth results, and the one of the first and second results that does not comprise the worst case liability. 
         [0013]    The method may further comprise receiving a new bet for which a worst case liability has previously been calculated, and recalculating the worst case liability to take the new bet into account. 
         [0014]    The new bet may comprise a bet placed by a user or a hedging bet placed by an operator of the management system. 
         [0015]    According to the invention, there is further provided a liability management system for determining liability for a plurality of multiples bets placed via a computerised betting system, each multiples bet comprising a single transaction on a plurality of outcomes selected from a group of possible transactions, the system comprising: 
         [0016]    means for selecting a most popular transaction from the group; 
         [0017]    means for evaluating all other transactions within the group on the basis that the most popular transaction is a winning transaction to produce a first result; 
         [0018]    means for evaluating all other transactions within the group on the basis that the most popular transaction is not a winning transaction to produce a second result; and 
         [0019]    means for selecting one of the first and second results that indicates the worst case liability. 
         [0020]    The system may be arranged to select a next most popular transaction from the group and to repeat the steps of evaluating all other transactions, the evaluation being based on the one of the first and second results that provides the worst case liability. 
         [0021]    The system may further comprise means for recalculating the worst case liabilities for a new bet to be placed on an outcome. 
         [0022]    The system may be arranged to compare the worst case liability with a predetermined liability limit, and to reject the bet if the worst case liability exceeds the liability limit. 
         [0023]    According to the invention, there is still further provided a liability management system comprising: 
         [0024]    a plurality of liability management components; 
         [0025]    a liability engine; and 
         [0026]    a plurality of group processors controlled by the liability engine and arranged to calculate a liability for each of a plurality of groups; 
         [0027]    wherein each of the plurality of liability management components is arranged to receive a combination bet on a plurality of outcomes for a given group and to request the liability engine to determine whether the bet can be accepted, the liability engine being arranged to control one of the plurality of processors to determine the worst case liability for the bet. 
         [0028]    The liability engine may be arranged to reject the bet in the event that the determined worst case liability exceeds a predetermined threshold. 
         [0029]    The system may be arranged to provide information relating to the placement of a hedging bet to reduce the worst case liability. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0030]      FIG. 1  is an illustration of a system according to the invention; 
           [0031]      FIG. 2  is a flowchart illustrating the operation of a system according to  FIG. 1 ; 
           [0032]      FIG. 3  is an illustration of a binary decision tree providing worst case liability information in accordance with the invention; 
           [0033]      FIG. 4  is a flow chart illustrating the continuing process of adding bets for the purposes of a liability calculation; 
           [0034]      FIG. 5  is an illustration of a binary decision tree providing worst case liability information in accordance with the invention, with updated worst case information; and 
           [0035]      FIG. 6  is a graph illustrating a simulation of the liability management tool according to the invention. 
       
    
    
     DETAILED DESCRIPTION 
       [0036]    Referring to  FIG. 1 , a liability management system  1  according to an example of the invention includes a liability engine  2 , which in turn manages a set of multiples group processors  3   a . . . n , each of which handles the bets placed in a given multiples group. Multiple bets are available on a group of events, for example on a competition such as Wimbledon 2007, or on a set of games over a particular period, such as weekend soccer. This gives a discrete set of manageable outcomes. 
         [0037]    For each multiples group, its multiples group processor calculates the worst case result for the group, this being the set of outcomes or settlements for the markets in the group that will cause the greatest payout, or liability, given the bets placed on that group. This, in conjunction with a liability limit, is used to decide whether further bets should be accepted for that group. This is an ongoing process, as the liability for a multiples group changes every time a bet is placed. For any individual bet, the multiples group processor may reject the bet if it would take the multiples group beyond the liability limit. 
         [0038]    The multiples group processor also provides statistical information for a given multiple group. This enables a multiples group account manager to determine the state of the multiples group and whether it requires hedging bets to be placed to reduce the worst case liability for the group. 
         [0039]    Referring again to  FIG. 1 , the liability management system  1  further comprises one or more liability manager components  4   a . . . n , each of which is deployed on a multiples web site node  5   a . . . n , that runs a multiples web application  6   a . . . n , to permit users to place bets. A pair of liability engine modules  2 ,  7  are provided, only one of which is live at any given time. The liability manager components  4   a . . . n  are connected to the live liability engine  2  via a NetScaler module 8, which routes direct calls via a web service to the live engine in the pair, and is responsible for detecting a liability engine failure and swapping over to the failover instance in the pair. The liability engine is also connected to the liability manager components via a database  9 , which permits asynchronous communications between the liability manager components and the liability engines. 
         [0040]    The liability manager components use API calls to tell the multiples group processors  3   a . . . n  about the multiples groups that are set up in a multiples betting system, such as what markets are included in each group. They are also used to inform the multiples group processors about market events, such as settlement and runner (competitor) removal from a competition. The data for the API calls is written to the database  9  by the liability manager component. 
         [0041]    Bets on multiples groups are submitted by users using the multiples web applications and are stored in the database  9  for processing. 
         [0042]    Both the live and standby liability engines periodically poll the database for new messages and read the multiples group set up/market event/bet placement data. 
         [0043]    In general terms, it would be desirable for the multiples group processors to calculate which of the bets that have been taken could win together, calculate the worst combination of results that could occur and report the potential liability. However, there are generally too many combinations to calculate a precise answer. Therefore, the software is arranged to calculate a pessimistic estimate, which is worse than the actual worst case, and report that. 
         [0044]    When a bet is placed, a check is made on what effect the bet has on the overall position of the multiple group. The liability manager component makes a “Can accept liability” call via the web service to the liability engine, to ensure that the multiples group for the bet has enough exposure left to accept the bet. If this call returns “No”, the bet is rejected. 
         [0045]    In order to evaluate the worst case liability, the multiples group processor for each group uses a procedure that is explained below, with reference to  FIG. 2 . For the purposes of this explanation, it is assumed that there are 10 soccer matches in a group designated M 1 -M 10 , each of which has three possible outcomes, namely a home win (designated 1), an away win (designated 2) and a draw (designated X). 
         [0046]    Processing starts with the most popular selection in the group (step s 1 ). For example, an away win in match 2 (M 2 - 2 ) is assumed to be the most popular selection, featuring in the most multiples bet. Every bet placed is evaluated on the assumption that the selected bet is a winner (step s 2 ). In other words, every multiple that has M 2 - 2  selected is evaluated as a winner and every one that has M 2 - 1  or M 2 -X selected is evaluated as a loser. Then the assumption that the selection is not a winner is evaluated (step s 3 ). In other words, it is assumed that M 2 - 2  is a loser and that one of M 2 - 1  or M 2 -X happens. 
         [0047]    The evaluation is for example based on multiplication of odds maintained for the individual matches. In this embodiment, the liability management software is run in conjunction with a betting exchange system, in which odds are available for individual events. The current exchange odds are used as an indicator of the probability of an outcome occurring. The expected return for each multiples bet can be evaluated, by assessing the probability of the bet winning against the return if it wins and the stake if it does not win. Summing the expected value of each multiples bet gives an overall expected value. 
         [0048]    The result of this process is shown in the tree diagram of  FIG. 3 . Example figures for the outcome of the evaluation are provided, showing that in this example it is assumed that M 2 - 2  is the worst case, at −£ 7123 , resulting in the greater liability, compared with −£ 4581  for NOT M 2 - 2  i.e. M 2 - 1  or M 2 -X. 
         [0049]    Whichever is the worst case of the two is pursued (step s 4 ). Then the next most popular selection is selected (step s 5 ) and assumed to be a winner. In this example, we assume it is a home win in match 5(M 5 - 1 ). All multiples that feature both M 2 - 2  and M 5 - 1  are evaluated as winners (step s 6 ), with all those featuring M 2 - 1 , M 2 -X, M 5 - 2 , M 5 -X as losers. This is assumed to come up with a value −£ 6234 . Next, the process evaluates bets assuming M 5 - 1  does not occur (step s 7 ), which is assumed to produce the value −£ 6526 . The worst case is then reassessed (step s 8 ). This can be either:
   NOT M 2 - 2 , or   M 2 - 2  and M 5 - 1 , or   M 2 - 2  and NOT M 5 - 1     
 
         [0053]    In this example, it is M 2 - 2  and NOT M 5 - 1 . The process is then repeated by selecting and evaluating the next most popular selection. 
         [0054]    It will be apparent from  FIG. 3  that the result is a binary decision tree where the worst case node can be expanded on at any open node. 
         [0055]    As new bets come in, they are added to the relevant tree nodes and all the worst cases are updated. As mentioned above, and referring to  FIG. 4 , the liability engine continues to periodically poll the database for new bets (s 10 ) and will receive all new bets submitted since the last poll time (step s 11 ). These bets may include hedging bets placed in response to previous information from the liability management system. If there is a bet to be added to an existing multiple (step s 12 ), this is dealt with (step s 13 ) before expanding the tree structure with a new multiple (step s 14 ). For example, a bet of £200 is placed at digital odds of 2.5 backing M 2 - 2  and M 5 - 1  to occur. This combination has already been evaluated, as shown in  FIG. 3 , so there is no need to add a new node to the tree structure, but the existing worst case values need to be updated (step s 13 ). 
         [0056]    Referring to  FIG. 5 , the calculated value for M 2 - 2  and M 5 - 1  is recalculated to increase the liability by £300, since that is the additional liability that will result if the bet wins. The liability for each of the other open nodes (NOT M 2 - 2 , and M 2 - 2  and NOT M 5 - 1 ) is reduced by £200, since if the bet does not win, the user forfeits the stake of £200. As a result, the worst case changes from M 2 - 2  and NOT M 5 - 1  to M 2 - 2  and M 5 - 1 . The next most popular selection will therefore be branched from the latter node. 
         [0057]    By systematically working through as many cases as possible, the liability management system provides a worst case that is always worse than the actual worst case, but the estimate is always improving as more selections are evaluated. 
       EXAMPLE 1 
       [0058]    Betting on a sample of 14 games in a football (soccer) competition week was simulated taking 5,000 bets which were different combinations of the games. Reasonable assumptions were made about how the bets would be divided between the possible selections and what stakes and odds would be used. The liability management tool was run against this data to show how the liability developed. 
         [0059]    If all the possible payouts from the 5,000 bets were simply added together, a liability of £12m would result. The estimate is shown in the graph of FIG.  6 —the liability reaches only £0.65m. Another simulation was then run, where 5 hedge bets were placed—one after every 1,000 multiples. By doing this, the liability management tool could further manage the liability, so it never exceeds £0.4m. 
         [0060]    While embodiments of the invention have been described by way of example, it will be appreciated that many modifications and variations are possible that still fall within the scope of the invention.