Abstract: Multi-faceted machine learning model systems and methods are disclosed that integrate historical data, user-specific criteria, and prioritized real-time event updates. The machine learning model systems and methods may be selected and weighed to optimize simulation accuracy based on event type, player status, and user-defined constraints, such as risk tolerance and preference for specific teams, sports or players. The machine learning model is applied to a first historical outcome and a second historical outcome to generate a predictive outcome responsive to a user query, the user query having a first keyword vector and a second keyword vector. The machine learning model is applied to a real-time event and the predictive outcome to generate an updated predictive outcome. A prediction error indicative of a difference between the predictive outcome and the updated predictive outcome can be determined.

Abstract: A collection of data corresponding to a player is assembled at a server from a data provider. A base value of the player based on at least the primary data is added as an attribute to an internal object stored at the server. A modifier is mapped to a second attribute of the internal object. The modifier is based on at least a portion of the secondary data and the tertiary data. A projected value is generated from the base value by applying a modifier and a rule converting the modified second attribute to a projected value. Internal objects are generated according to constraints including having a sum of limiting factors of the internal objects that does not exceed a maximum factor and a sum of projected values a maximum, subject to the first constraint. The server transmits application data including a list representing the internal objects.

Abstract: Systems and methods for automated personalized incentives are provided. A Universal Rewards Program (URP) automatically collects consumer data regarding spending and wagering behaviors, a provides personalized incentives such as cash back, discounts, deals, bonuses, gifts, retention incentives, referral bonuses, and other personalized incentives. The URP tailors the incentive based upon transaction data collected on the consumer. The URP can also monitor and provide an alert when spending and wagering behaviors can result in negative consequences.

Abstract: A collection of data corresponding to a player is assembled at a server from a data provider. A base value of the player based on at least the primary data is added as an attribute to an internal object stored at the server. A modifier is mapped to a second attribute of the internal object. The modifier is based on at least a portion of the secondary data and the tertiary data. A projected value is generated from the base value by applying a modifier and a rule converting the modified second attribute to a projected value. Internal objects are generated according to constraints including having a sum of limiting factors of the internal objects that does not exceed a maximum factor and a sum of projected values a maximum, subject to the first constraint. The server transmits application data including a list representing the internal objects.

Abstract: A collection of data corresponding to a player is assembled at a server from a data provider. A base value of the player based on at least the primary data is added as an attribute to an internal object stored at the server. A modifier is mapped to a second attribute of the internal object. The modifier is based on at least a portion of the secondary data and the tertiary data. A projected value is generated from the base value by applying a modifier and a rule converting the modified second attribute to a projected value. Internal objects are generated according to constraints including having a sum of limiting factors of the internal objects that does not exceed a maximum factor and a sum of projected values a maximum, subject to the first constraint. The server transmits application data including a list representing the internal objects.

Abstract: A collection of data corresponding to a player is assembled at a server from a data provider. A base value of the player based on at least the primary data is added as an attribute to an internal object stored at the server. A modifier is mapped to a second attribute of the internal object. The modifier is based on at least a portion of the secondary data and the tertiary data. A projected value is generated from the base value by applying a modifier and a rule converting the modified second attribute to a projected value. Internal objects are generated according to constraints including having a sum of limiting factors of the internal objects that does not exceed a maximum factor and a sum of projected values a maximum, subject to the first constraint. The server transmits application data including a list representing the internal objects.

Abstract: A collection of data corresponding to a player is assembled at a server from a data provider. A base value of the player based on at least the primary data is added as an attribute to an internal object stored at the server. A modifier is mapped to a second attribute of the internal object. The modifier is based on at least a portion of the secondary data and the tertiary data. A projected value is generated from the base value by applying a modifier and a rule converting the modified second attribute to a projected value. Internal objects are generated according to constraints including having a sum of limiting factors of the internal objects that does not exceed a maximum factor and a sum of projected values a maximum, subject to the first constraint. The server transmits application data including a list representing the internal objects.

Abstract: A collection of data corresponding to a player is assembled at a server from a data provider. A base value of the player based on at least the primary data is added as an attribute to an internal object stored at the server. A modifier is mapped to a second attribute of the internal object. The modifier is based on at least a portion of the secondary data and the tertiary data. A projected value is generated from the base value by applying a modifier and a rule converting the modified second attribute to a projected value. Internal objects are generated according to constraints including having a sum of limiting factors of the internal objects that does not exceed a maximum factor and a sum of projected values a maximum, subject to the first constraint. The server transmits application data including a list representing the internal objects.

Abstract: A collection of data corresponding to a player is assembled at a server from a data provider. A base value of the player based on at least the primary data is added as an attribute to an internal object stored at the server. A modifier is mapped to a second attribute of the internal object. The modifier is based on at least a portion of the secondary data and the tertiary data. A projected value is generated from the base value by applying a modifier and a rule converting the modified second attribute to a projected value. Internal objects are generated according to constraints including having a sum of limiting factors of the internal objects that does not exceed a maximum factor and a sum of projected values a maximum, subject to the first constraint. The server transmits application data including a list representing the internal objects.

Abstract: A collection of data corresponding to a player is assembled at a server from a data provider. A base value of the player based on at least the primary data is added as an attribute to an internal object stored at the server. A modifier is mapped to a second attribute of the internal object. The modifier is based on at least a portion of the secondary data and the tertiary data. A projected value is generated from the base value by applying a modifier and a rule converting the modified second attribute to a projected value. Internal objects are generated according to constraints including having a sum of limiting factors of the internal objects that does not exceed a maximum factor and a sum of projected values a maximum, subject to the first constraint. The server transmits application data including a list representing the internal objects.