SYSTEMS AND METHODS FOR A TRANSFORMER NEURAL NETWORK FOR PREDICTIONS IN RUGBY SPORTING EVENTS
A method of generating a set of predictions associated with a rugby game using an axial transformer neural network, the method including: receiving an input tuple, including a set of tensors representing game context, team strength, player strength, live team features, live player features, game events, and a super feature; inputting the input tuple into an axial transformer neural network by inputting each tensor from the set of tensors within a corresponding initial embedding layer; concatenating the initial embedding layers to form a single tensor; applying self-attention to the single tensor; mapping output embeddings from the axial transformer layers to target layers, each of the output embeddings being of a dimension of a target metric; and generating a set of target metric predictions for each of a set of players, one or more teams, and a match, based on the output embeddings from the target layers.
1 . A method of generating a set of predictions associated with a rugby game using an axial transformer neural network, the method comprising:
receiving an input tuple, including a set of tensors representing game context, team strength, player strength, live team features, live player features, game events, and a super feature;
inputting the input tuple into an axial transformer neural network by inputting each tensor from the set of tensors within a corresponding initial embedding layer;
concatenating the initial embedding layers to form a single tensor;
applying self-attention to the single tensor through axial transformer layers of the axial transformer neural network;
mapping output embeddings from the axial transformer layers to target layers, each of the output embeddings being of a dimension of a target metric; and
generating a set of target metric predictions for each of a set of players, one or more teams, and a match, based on the output embeddings from the target layers.
2 . The method of claim 1 , wherein the rugby game is a union game, the super features includes an embedding to define elements of plays including line-outs, scrums, kicking, break-down, and ruck-and-mauls.
3 . The method of claim 1 , wherein the rugby games is a rugby league game, the super features includes an embedding to define how a team attacks and moves a ball during the rugby game and include an embedding for a predicted time of quick play the balls for each player in the rugby game.
4 . The method of claim 1 , wherein the axial transformer neural network is configured to accept inputs with different modalities.
5 . The method of claim 1 , wherein the super feature is determined based on broadcast data.
6 . The method of claim 1 , wherein the applying self-attention includes applying an autoregressive attention mask to a row in each layer of the single tensor.
7 . The method of claim 1 , wherein the target layers map the output embedding of final transformer layers to a required feature dimension of each target metric.
8 . A system for generating a set of predictions associated with a rugby game using an axial transformer neural network, the system comprising:
a memory configured to store processor-readable instructions; and
a processor operatively connected to the memory, and configured to execute the instructions to perform operations comprising:
receiving an input tuple, including a set of tensors representing game context, team strength, player strength, live team features, live player features, game events, and a super feature;
inputting the input tuple into an axial transformer neural network by inputting each tensor from the set of tensors within a corresponding initial embedding layer;
concatenating the initial embedding layers to form a single tensor;
applying self-attention to the single tensor through axial transformer layers of the axial transformer neural network;
mapping output embeddings from the axial transformer layers to target layers, each of the output embeddings being of a dimension of a target metric; and
generating a set of target metric predictions for each of a set of players, one or more teams, and a match, based on the output embeddings from the target layers.
9 . The system of claim 8 , wherein the rugby game is a union game, the super features includes an embedding to define elements of plays including line-outs, scrums, kicking, break-down, and ruck-and-mauls.
10 . The system of claim 8 , wherein the rugby games is a rugby league game, the super features includes an embedding to define how a team attacks and moves a ball during the rugby game and include an embedding for a predicted time of quick play the balls for each player in the rugby game.
11 . The system of claim 8 , wherein the axial transformer neural network is configured to accept inputs with different modalities.
12 . The system of claim 8 , wherein the super feature is determined based on broadcast data.
13 . The system of claim 8 , wherein the applying self-attention includes applying an autoregressive attention mask to a row in each layer of the single tensor.
14 . The system of claim 8 , wherein the target layers map the output embedding of final transformer layers to a required feature dimension of each target metric.
15 . A non-transitory computer readable medium configured to store processor-readable instructions, wherein when executed by a processor, the instructions perform operations comprising:
receiving an input tuple, including a set of tensors representing game context, team strength, player strength, live team features, live player features, game events, and a super feature for a rugby game;
inputting the input tuple into an axial transformer neural network by inputting each tensor from the set of tensors within a corresponding initial embedding layer;
concatenating the initial embedding layers to form a single tensor;
applying self-attention to the single tensor through axial transformer layers of the axial transformer neural network;
mapping output embeddings from the axial transformer layers to target layers, each of the output embeddings being of a dimension of a target metric; and
generating a set of target metric predictions for each of a set of players, one or more teams, and a match, based on the output embeddings from the target layers.
16 . The non-transitory computer readable medium of claim 15 , wherein the rugby game is a union game, the super features includes an embedding to define elements of plays including line-outs, scrums, kicking, break-down, and ruck-and-mauls.
17 . The non-transitory computer readable medium of claim 15 , wherein the rugby games is a rugby league game, the super features includes an embedding to define how a team attacks and moves a ball during the rugby game and include an embedding for a predicted time of quick play the balls for each player in the rugby game.
18 . The non-transitory computer readable medium of claim 15 , wherein the axial transformer neural network is configured to accept inputs with different modalities.
19 . The non-transitory computer readable medium of claim 15 , wherein the super feature is determined based on broadcast data.
20 . The non-transitory computer readable medium of claim 15 , wherein the applying self-attention includes applying an autoregressive attention mask to a row in each layer of the single tensor.