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| − | In today’s digitalised world, technology had greatly transformed gambling behaviour. Customers no longer have to go to the physical outlet to check the odds and place their bets, they are able to do so in their comfort of their house.
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| − | Singapore Pools allows their customers to register an online account with them and then customers are able to place their bet via phone call. With this, customers are able to place their bets without being constrained by the opening hours of Singapore Pools’ physical outlet. Hence, Singapore Pools has to allocate resources, such as phone-betting officer, to assist their customers to place their bets.
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| − | The current process of resources allocation for phone betting is based on the rough estimation provided by the Sports Division. As a result, resources are often not optimised. Moreover, due to time difference, the more well-known leagues such as English Premium League, are often broadcasted in the middle of the night. This makes it difficult to allocate additional resources at the last moment to handle the demand.
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| − | With these challenges, it can prove costly to Singapore Pools as they are unable allocate their resources efficiently to deal with the demand and the affected customers will potentially resort to illegal counterparts to place their bets.
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| − | Hence, Singapore Pools hope to utilize their historical data to derive insights and predict the demand for future matches, so that they are able to better allocate their resources.
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| − | The objective of this project is to build a predictive model for Singapore Pools’ Sports Division staff to predict the demand of soccer matches from different leagues based on the historic data provided by Singapore Pools.
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| − | This project strives to answer the main question:
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| − | * What are the factors which will affect the demand of an individual match?
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| − | ** An example of such factors which can potentially impact the demand of sports event includes the day of the match, date of the match, time of the match, affluence or ranking of the teams involved and the number of matches being played concurrently.
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| − | With this prediction, Singapore Pools is able to achieve the following benefits:
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| − | # Improvements in resources allocation,
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| − | # Better understanding of the factors that affects the demand of the matches,
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| − | # Fraudulent prevention. For example, if there is a sudden spike in the demand of a match, there is a possibility of match-fixing.
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| − | Singapore Pools is also keen to use this project as the foundation of future sports-related (e.g., motorsports) prediction models.
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