System and Method for Predicting Future Player Performance in Sport

1 A method, comprising:

receiving, by a computing system, a request to project a performance of a first player from a current team on a destination team;

based on the request, generating, by the computing system, player-position features corresponding to the first player, wherein the player-position features comprise a rolling average of historical player performance data of the first player while playing a first position;

generating, by the computing system, team features corresponding to the first player, wherein the team features comprise a first rolling average of historical team performance data of the current team and a second rolling average of historical team performance data of the destination team;

generating, by the computing system, rating features for the first player, wherein the rating features comprise a first rolling average of team-league rating features for the current team and a current league corresponding to the current team and second rolling average of team-league rating features for the destination team and a destination league corresponding to the destination team; and

generating, by the computing system via a prediction model, a player box score prediction based on the player-position features, the team features, and the rating features, wherein the player box score prediction comprises a plurality of per game metrics of the first player on the destination team.

2 . The method of claim 1 , further comprising:

training, by the computing system, the prediction model to generate the player box score prediction by:

generating a training data set, the training data set comprising historical player features and historical team features for a plurality of players and a plurality of teams across a plurality of seasons, and

learning, by the prediction model, relationships between the historical player features and the historical team features.

3 . The method of claim 2 , further comprising:

comparing, by the computing system, a predicted set of box score data for each player of the plurality of players to actual box score data; and

based on the comparing, adjusting, by the computing system, one or more parameters of the prediction model.

4 . The method of claim 1 , wherein generating, by the computing system, the team features corresponding to the first player comprises:

accessing raw team data for the destination team;

determining that the destination team has not played at least a threshold amount of minutes in the destination league; and

based on the determining, adjusting the raw team data based on an average performance of teams in the destination league.

5 . The method of claim 1 , wherein generating, by the computing system, the player-position features corresponding to the first player comprises:

accessing raw player data for the first player in the destination league;

determining that the first player has not played at least a threshold amount of minutes in the destination league; and

based on the determining, adjusting the raw player data based other player data on the destination team that play a same position as the first player.

6 . The method of claim 1 , further comprising:

determining, by the computing system, that the first player has played a new game; and

based on the determining, updating, by the computing system, the player-position features, the team features, and the rating features based on metrics associated with the new game.

7 The method of claim 1 , wherein the rating features are team and player rating features.

8 . A non-transitory computer readable medium having a sequence of instructions, which, when executed by a processor, causes a computing system to perform operations comprising:

receiving, by the computing system, a request to project a performance of a first player from a current team on a destination team;

based on the request, generating, by the computing system, player-position features corresponding to the first player, wherein the player-position features comprise a rolling average of historical player performance data of the first player while playing a first position;

generating, by the computing system, team features corresponding to the first player, wherein the team features comprise a first rolling average of historical team performance data of the current team and a second rolling average of historical team performance data of the destination team;

generating, by the computing system, rating features for the first player, wherein the rating features comprise a first rolling average of team-league rating features for the current team and a current league corresponding to the current team and second rolling average of team-league rating features for the destination team and a destination league corresponding to the destination team; and

generating, by the computing system via a prediction model, a player box score prediction based on the player-position features, the team features, and the rating features, wherein the player box score prediction comprises a plurality of per game metrics of the first player on the destination team.

9 . The non-transitory computer readable medium of claim 8 , further comprising:

training, by the computing system, the prediction model to generate the player box score prediction by:

generating a training data set, the training data set comprising historical player features and historical team features for a plurality of players and a plurality of teams across a plurality of seasons, and

learning, by the prediction model, relationships between the historical player features and the historical team features.

10 . The non-transitory computer readable medium of claim 9 , further comprising:

comparing, by the computing system, a predicted set of box score data for each player of the plurality of players to actual box score data; and

based on the comparing, adjusting, by the computing system, one or more parameters of the prediction model.

11 . The non-transitory computer readable medium of claim 8 , wherein generating, by the computing system, the team features corresponding to the first player comprises:

accessing raw team data for the destination team;

determining that the destination team has not played at least a threshold amount of minutes in the destination league; and

based on the determining, adjusting the raw team data based on an average performance of teams in the destination league.

12 . The non-transitory computer readable medium of claim 8 , wherein generating, by the computing system, the player-position features corresponding to the first player comprises:

accessing raw player data for the first player in the destination league;

determining that the first player has not played at least a threshold amount of minutes in the destination league; and

based on the determining, adjusting the raw player data based other player data on the destination team that play a same position as the first player.

13 . The non-transitory computer readable medium of claim 8 , further comprising:

determining, by the computing system, that the first player has played a new game; and

based on the determining, updating, by the computing system, the player-position features, the team features, and the rating features based on metrics associated with the new game.

14 . The non-transitory computer readable medium of claim 8 , wherein the rating features are team and player rating features.

15 . A system comprising:

a processor; and

a memory having programming instructions stored thereon, which, when executed by the processor, causes the system to perform operations comprising:

receiving a request to project a performance of a first player from a current team on a destination team;

based on the request, generating player-position features corresponding to the first player, wherein the player-position features comprise a rolling average of historical player performance data of the first player while playing a first position;

generating team features corresponding to the first player, wherein the team features comprise a first rolling average of historical team performance data of the current team and a second rolling average of historical team performance data of the destination team;

generating rating features for the first player, wherein the rating features comprise a first rolling average of team-league rating features for the current team and a current league corresponding to the current team and second rolling average of team-league rating features for the destination team and a destination league corresponding to the destination team; and

generating, via a prediction model, a player box score prediction based on the player-position features, the team features, and the rating features, wherein the player box score prediction comprises a plurality of per game metrics of the first player on the destination team.

16 . The system of claim 15 , wherein the operations further comprise:

training the prediction model to generate the player box score prediction by:

generating a training data set, the training data set comprising historical player features and historical team features for a plurality of players and a plurality of teams across a plurality of seasons, and

learning, by the prediction model, relationships between the historical player features and the historical team features.

17 . The system of claim 16 , wherein the operations further comprise:

comparing a predicted set of box score data for each player of the plurality of players to actual box score data; and

based on the comparing, adjusting one or more parameters of the prediction model.

18 . The system of claim 15 , wherein generating the team features corresponding to the first player comprises:

accessing raw team data for the destination team;

determining that the destination team has not played at least a threshold amount of minutes in the destination league; and

based on the determining, adjusting the raw team data based on an average performance of teams in the destination league.

19 . The system of claim 15 , wherein generating the player-position features corresponding to the first player comprises:

accessing raw player data for the first player in the destination league;

determining that the first player has not played at least a threshold amount of minutes in the destination league; and

based on the determining, adjusting the raw player data based other player data on the destination team that play a same position as the first player.

20 . The system of claim 15 , wherein the operations further comprise:

determining that the first player has played a new game; and

based on the determining, updating the player-position features, the team features, and the rating features based on metrics associated with the new game.