Here's my reply in the comments to the post at stlouisgametime

1. We do NOT treat all events equally. By assigning each play a different value, the NP20, for the response of our regression, we weight them differently. Thus, a shot in close is worth much much more to a player's value than a faceoff win at neutral ice since the former is much more likely to lead to a goal.

2. We looked at events and saw not much change in the cumulative probability of a goal after 10 seconds and we doubled the time frame to 20 seconds to be conservative. We're looking at different responses from Dellow so we're likely to have different results.

3. The results you've presented here are not the latest. We've updated the model and put out the results from this newer model several times since the results from the paper (which is what you use here). Tyler Kennedy comes out very well (on a per play basis) since he is 2nd in the league in SOG/60 over the time period for these results.

4. We've put out the results for the full model with events from event strength, powerplay and penalty kill. The link is here:

www.statsportsconsulting.com/thor. At that same link are some other details including the fact that we are adjusting for rink effects, score effects, zone starts, and home ice.

5. Running the regressions is not easy. It takes at least 12 hours on a very fast computer. Our design matrix is often 300000 rows by 1200 columns.

6. What makes THoR different is that we adjust for the other players on the ice when the event is occurred. Thus we are trying to estimate the value of a player while accounting for who they play against and who they play with as well as the other contextual factors mentioned in #4 above. We think we've had some success in this regard since THoR has a very high year to year reliability

THoR All Events.

7. Schuckers is spelled with two c's.