Dynamically Predicting Shot Type Using A Personalized Deep Neural Network
A computing system retrieves ball-by-ball data for a plurality of sporting events. The computing system generates a trained neural network based on ball-by-ball data supplemented with ball-by-ball data with ball-by-ball match context features and personalized embeddings based on a batsman and a bowler for each delivery. The computing system receives a target batsman and a target bowler for a pitch to be delivered in a target event. The computing system identifies target ball-by-ball data for a window of pitches preceding the to be delivered pitch. The computing system retrieves historical ball-by-ball data for each of the target batsman and the target bowler. The computing system generates personalized embeddings for both the target batsman and the target bowler based on the historical ball-by-ball data. The computing system predicts a shot type for the pitch to be delivered based on the target ball-by-ball data and the personalized embeddings.
Claim CLM-00001. 1. A method for predicting a shot type, comprising:
retrieving, by a computing system, ball-by-ball data for a plurality of sporting events; generating, by the computing system, a trained neural network, by:
generating a plurality of training data sets based on the ball-by-ball data by supplementing the ball-by-ball data with ball-by-ball match context features;generating, from the ball-by-ball data, personalized embeddings based on a batsman and a bowler for each delivery; andlearning, by a neural network associated with the computing system, to predict a shot type based on the ball-by-ball data and the personalized embeddings;
receiving, by the computing system, a target batsman and a target bowler for a pitch to be delivered in a target event; identifying, by the computing system, target ball-by-ball data for a window of pitches preceding the pitch to be delivered; retrieving, by the computing system, historical ball-by-ball data for each of the target batsman and the target bowler; generating, by the computing system, personalized embeddings for both the target batsman and the target bowler based on the historical ball-by-ball data; and predicting, by the computing system using the trained neural network, a shot type for the pitch to be delivered based on the target ball-by-ball data and the personalized embeddings.
Claim CLM-00008. 8. A system for predicting a shot type, comprising:
a processor; and a memory having programming instructions stored thereon, which, when executed by the processor, causes the system to perform one or more operations, comprising: retrieving ball-by-ball data for a plurality of sporting events; generating a trained neural network, by:
generating a plurality of training data sets based on the ball-by-ball data by supplementing ball-by-ball data with ball-by-ball match context features;generating, from the ball-by-ball data, personalized embeddings based on a batsman and a bowler for each delivery; andlearning, by a neural network, to predict a shot type based on the ball-by-ball data and the personalized embeddings;
receiving a target batsman and a target bowler for a pitch to be delivered in a target event; identifying target ball-by-ball data for a window of pitches preceding the pitch to be delivered; retrieving historical ball-by-ball data for each of the target batsman and the target bowler; generating personalized embeddings for both the target batsman and the target bowler based on the historical ball-by-ball data; and predicting a shot type for pitch the to be delivered based on the target ball-by-ball data and the personalized embeddings.
Claim CLM-00015. 15. A non-transitory computer readable medium including one or more sequences of instructions that, when executed by one or more processors, causes a computing system to perform operations comprising:
retrieving, by the computing system, ball-by-ball data for a plurality of sporting events; generating, by the computing system, a trained neural network, by:
generating a plurality of training data sets based on the ball-by-ball data by supplementing ball-by-ball data with ball-by-ball match context features;generating, from the ball-by-ball data, personalized embeddings based on a batsman and a bowler for each delivery; andlearning, by a neural network, to predict a shot type based on the ball-by-ball data and the personalized embeddings;
receiving, by the computing system, a target batsman and a target bowler for a to be delivered pitch in a target event; identifying, by the computing system, target ball-by-ball data for a window of pitches preceding the to be delivered pitch; retrieving, by the computing system, historical ball-by-ball data for each of the target batsman and the target bowler; generating, by the computing system, personalized embeddings for both the target batsman and the target bowler based on the historical ball-by-ball data; and predicting, by the computing system, a shot type for the to be delivered pitch based on the target ball-by-ball data and the personalized embeddings.
