Real-time Updates

To ensure that the betting odds remain accurate and up-to-date throughout the event, the OracleContract continuously fetches real-time data from trusted sources and feeds it into the BettingContract. This can be achieved using a combination of event listeners and periodic updates.

The OracleContract can subscribe to relevant APIs and data feeds, such as news outlets, social media platforms, and fact-checking services. Whenever new data becomes available, the contract processes the information and updates the relevant scorecard items and odds in the BettingContract.

Here's a simplified example of how the OracleContract can handle real-time updates:

contract OracleContract {
    BettingContract public bettingContract;
    
    constructor(address _bettingContractAddress) {
        bettingContract = BettingContract(_bettingContractAddress);
    }
    
    function processUpdate(uint256 _itemId, bool _outcome) external {
        // Verify that the update comes from a trusted source
        require(isTrustedSource(msg.sender), "Untrusted source");
        
        // Update the scorecard item in the BettingContract
        bettingContract.updateOutcome(_itemId, _outcome);
        
        // Recalculate the odds for the affected item
        uint256 newOdds = calculateOdds(_itemId);
        bettingContract.updateOdds(_itemId, newOdds);
    }
    
    // ...
}

In this example, the OracleContract maintains a reference to the BettingContract and exposes a processUpdatefunction that can be called by trusted data sources. When an update is received, the contract verifies the authenticity of the source and then calls the updateOutcome and updateOdds functions in the BettingContract to update the scorecard item and recalculate the odds based on the new information.

The calculateOdds function can utilize machine learning libraries, such as TensorFlow.js or natural.js, to perform real-time analysis of the updated data and generate new odds. These libraries can be integrated into the Ethereum environment using tools like TruffleJS [29] or Embark [30], which allow for the deployment of machine learning models alongside smart contracts.