Teammate and competition statistics have value for two main reasons:

• Adjusting observed outcomes to account for boosts or drags in performance by factors beyond the player’s control, and
• Assessing the coaching staff’s deployment (“usage”) of a player.

The main two of these are based on all shot attempts (Corsi) and the amount of time these players spend on the ice (since, oddly enough, better players are used more.) Both of these are useful for their purpose, but both are better served by having a summary quantity of these that are predictive of future behaviour, since they reflect both a locally accurate version of player ability and the state of the coaching staff’s current information.

To benefit both these factors (and to make our data processing operation smoother), we first predicted the next game’s CF60, CA60 and TOI60 for each player based on a lag of 30 previous games, then found that an exponential decay model was a satisfactory single predictor of this. For example, we use the formula

TOI60(new predicted) = 0.14 * TOI60 (this game observed) + 0.86 * TOI60 (this game predicted)

to update the game prediction for TOI60.

UPDATE, 2-25-15: We needed a longer “memory” for CF60 and CA60 events, so these are each

Cx60(new predicted) = 0.04 * Cx60 (this game observed) + 0.96 * Cx60 (this game predicted)

To calculate the teammate and competition statistics for that game, we then take the average of each player’s teammates (and opponents) weighed by the amount of common ice time, as before.

In addition, we’ll be adding the exponentially weighted statistics to our player, team and goaltender history once we establish the best predictive measure for each.