Systems and techniques for predictive classification in action sports are described herein. A start point for an action may be identified in a data stream including a plurality of data sets corresponding to the action. The data stream may be collected from a sensor array. Action performance features may be extracted from the data stream subsequent to the start point. The action performance features may be compared in real-time to a set of statistical models. A label may be selected for the action based on the comparison. A likelihood of success may be generated for the action based on the comparison. The label for the action and the likelihood of success may be output for display on a display device.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for predictive action assessment in action sports, the system comprising: one or more processors; a memory including instructions that, when executed by the one or more processors, cause the one or more processors to perform operations to: identify a start point for an action in a data stream including a plurality of data sets corresponding to the action, the data stream collected from a sensor array; extract action performance features from the data stream subsequent to the start point; compare, in real time, the action performance features to a set of statistical models; select a label for the action based on the comparison; generate a likelihood of success for the action based on the comparison; and output the label for the action and the likelihood of success for display on a display device. 2. The system of claim 1, wherein the instructions to extract the action performance features include instructions to extract features from a time period before the start point of the action. 3. The system of claim 1, further comprising instructions that cause the one or more processors to perform operations to: select a map from a set of maps using geolocation data included in the data stream, the map including a plurality of data items corresponding to a location of the action; and wherein the instructions to generate the likelihood of success include instructions to use at least one data item of the plurality of data items corresponding to the location. 4. The system of claim 1, further comprising instructions that cause the one or more processors to perform operations to: collect a set of historical data corresponding to a set of actions for an action sport; extract past performance features for each action of the set of actions; generate a statistical model for each action using the past performance features; and create the set of statistical models using the statistical model for each action. 5. The system of claim 1, further comprising instructions that cause the one or more processors to perform operations to: collect a plurality of historical data sets corresponding to the action, each historical data set of the plurality of historical data sets including scoring data corresponding to the action; extract a set of score indicator features from the plurality of historical data sets; generate a score model for the action using the set of score indicator features; compare the action performance features to the score model; determine an action score based on the comparison; and output, for display on the display device, the action score. 6. The system of claim 5, further comprising instructions that cause the one or more processors to perform operations to: obtain a set of run scores for a run including the action; derive a total run score for the run using the set of run scores and the action score; and output, for display on the display device, the total run score. 7. The system of claim 5, wherein the set of score indicator features includes at least one of a difficulty of the action, a success rate of the action, a statistical measure of the action, and a frequency with which the action is performed. 8. At least one computer readable medium including instructions for predictive action assessment in action sports that, when executed by a machine, cause the machine to perform operations to: identify a start point for an action in a data stream including a plurality of data sets corresponding to the action, the data stream collected from a sensor array; extract action performance features from the data stream subsequent to the start point; compare, in real time, the action performance features to a set of statistical models; select a label for the action based on the comparison; generate a likelihood of success for the action based on the comparison; and output the label for the action and the likelihood of success for display on a display device. 9. The at least one computer readable medium of claim 8, wherein the instructions to extract the action performance features include instructions to extract features from a time period before the start point of the action. 10. The at least one computer readable medium of claim 8, further comprising instructions that cause the one or more processors to perform operations to: select a map from a set of maps using geolocation data included in the data stream, the map including a plurality of data items corresponding to a location of the action; and wherein the instructions to generate the likelihood of success include instructions to use at least one data item of the plurality of data items corresponding to the location. 11. The at least one computer readable medium of claim 8, further comprising instructions that cause the one or more processors to perform operations to: collect a set of historical data corresponding to a set of actions for an action sport; extract past performance features for each action of the set of actions; generate a statistical model for each action using the past performance features; and create the set of statistical models using the statistical model for each action. 12. The at least one computer readable medium of claim 8, further comprising instructions that cause the one or more processors to perform operations to: collect a plurality of historical data sets corresponding to the action, each historical data set of the plurality of historical data sets including scoring data corresponding to the action; extract a set of score indicator features from the plurality of historical data sets; generate a score model for the action using the set of score indicator features; compare the action performance features to the score model; determine an action score based on the comparison; and output, for display on the display device, the action score. 13. The at least one computer readable medium of claim 12, further comprising instructions that cause the one or more processors to perform operations to: obtain a set of run scores for a run including the action; derive a total run score for the run using the set of run scores and the action score; and output, for display on the display device, the total run score. 14. The at least one computer readable medium of claim 12, wherein the set of score indicator features includes at least one of a difficulty of the action, a success rate of the action, a statistical measure of the action, and a frequency with which the action is performed. 15. A method for predictive action assessment in action sports, the method comprising: identifying a start point for an action in a data stream including a plurality of data sets corresponding to the action, the data stream collected from a sensor array; extracting action performance features from the data stream subsequent to the start point; comparing, in real time, the action performance features to a set of statistical models; selecting a label for the action based on the comparison; generating a likelihood of success for the action based on the comparison; and outputting the label for the action and the likelihood of success for display on a display device. 16. The method of claim 15, wherein extracting the action performance features includes extracting features from a time period before the start point of the action. 17. The method of claim 15, further comprising: selecting a map from a set of maps using geolocation data included in the data stream, the map including a plurality of data items corresponding to a location of the action; and wherein generating the likelihood of success includes using at least one data item of the plurality of data items corresponding to the location. 18. The method of claim 15, further comprising: collecting a set of historical data corresponding to a set of actions for an action sport; extracting past performance features for each action of the set of actions; generating a statistical model for each action using the past performance features; and creating the set of statistical models using the statistical model for each action. 19. The method of claim 15, further comprising: collecting a plurality of historical data sets corresponding to the action, each historical data set of the plurality of historical data sets including scoring data corresponding to the action; extracting a set of score indicator features from the plurality of historical data sets; generating a score model for the action using the set of score indicator features; comparing the action performance features to the score model; determining an action score based on the comparison; and outputting, for display on the display device, the action score. 20. The method of claim 19, further comprising: obtaining a set of run scores for a run including the action; deriving a total run score for the run using the set of run scores and the action score; and outputting, for display on the display device, the total run score. 21. The method of claim 19, wherein the set of score indicator features includes at least one of a difficulty of the action, a success rate of the action, a statistical measure of the action, and a frequency with which the action is performed.