Abstract: 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.

Abstract: 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.

Abstract: A computing system receives a plurality of game files corresponding to a plurality of games across a plurality of seasons. The computing system generates a prediction model configured to generate a possession value for an event. The computing system receives a target event, in real-time or near real-time, from a tracking system monitoring a target game. The computing system generates target features for the target event based on target event data associated with the target event. The computing system generates, via the prediction model, a target possession value for the target event based on the target event data and the target features. The target possession value represents a likelihood that a team with possession will score within a following x-seconds after the target event.

Abstract: A computing system receives a request to project a performance of a first player from a current team on a destination team. The computing system generates, based on the request, player-position features corresponding to the first player. The computing system generates team features corresponding to the first player. The computing system generates rating features for the first player. The computing system generates, via a prediction model, a player box score prediction based on the player-position features, the team features, and the rating features. The player box score prediction includes a plurality of per game metrics of the first player on the destination team.

Abstract: 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.

Abstract: A system and method for generating a role summary associated with one or more players are disclosed herein. A computing system retrieves event information for a plurality of teams for a plurality of events. The computing system generates a spatial output that describes each player. The computing system identifies a playing style associated with each team. The computing system identifies a subset of paths a player or team takes between two zones. The computing system identifies each player's involvement in a team's process. The computing system generates a score corresponding to a value of a player's involvement in a given play based on the event information. The computing system generates a score associated with each player's passing ability based on the event information. The computing system determines a shot style of each player based on the event information. The computing system identifies a role associated with each player.

Abstract: A ladder loading system includes a base frame and a stowage frame, wherein the stowage frame is rotatable relative to the base frame. The stowage frame and base frame are connected at a least one pivot having a main pivot axis. The ladder loading system includes a linkage mechanism. The linkage mechanism includes an idler link that rotates about an idler axis. The linkage mechanism also includes a coupling means for coupling the idler link to the stowage frame. That is, the rotation of the idler link is linked to the rotation of the stowage frame in a master and slave relationship. Suitably, due to the operation of the ladder loading system, rotation of the stowage frame drives rotation of the idler link about the idler axis. The linkage mechanism further includes a control member connected between the idler link and stowage frame.

Abstract: A ladder loading system includes a base frame and a stowage frame, wherein the stowage frame is rotatable relative to the base frame. The stowage frame and base frame are connected at a least one pivot having a main pivot axis. The ladder loading system includes a linkage mechanism. The linkage mechanism includes an idler link that rotates about an idler axis. The linkage mechanism also includes a coupling means for coupling the idler link to the stowage frame. That is, the rotation of the idler link is linked to the rotation of the stowage frame in a master and slave relationship. Suitably, due to the operation of the ladder loading system, rotation of the stowage frame drives rotation of the idler link about the idler axis. The linkage mechanism further includes a control member connected between the idler link and stowage frame.

Abstract: A ladder clamp including an anchor part and a ladder retaining part, wherein the anchor part includes an anchor able to be arranged to cooperate with a fixing on a vehicle, and the ladder retaining part includes a body and a securing means, the securing means being co-operable with the anchor part to clamp the ladder retaining part to the anchor part in a releasable manner, wherein the securing means includes a locking element that pivots with respect to the body between clamped and unclamped orientations.

Abstract: A ladder clamp including an anchor part and a ladder retaining part, wherein the anchor part includes an anchor able to be arranged to cooperate with a fixing on a vehicle, and the ladder retaining part includes a body and a securing means, the securing means being co-operable with the anchor part to clamp the ladder retaining part to the anchor part in a releasable manner, wherein the securing means includes a locking element that pivots with respect to the body between clamped and unclamped orientations.