At some point several years ago while I still had time to spend on this wonderful stuff I dug deep into the play-by-play data the Sim provides. The aim was to create essentially a visual simulation of the games, sort of similar to this:
Where all events would be logged and located on the ice. The locations would be based on actual NHL data providing tendencies for areas where for example Ovechkin shoots from. Was a pretty cool project that I sadly never got around to finish.
Another example would be Patric Hornqvist who does most of his work in front of the net. Compiling all data of his shooting, hitting, scoring and shot blocking into a huge database would be the base upon which the application would build. Whenever Hornqvist registered a shot on goal in the Sim, the application would randomly place the shot somewhere on the ice, within the areas where Hornqvist most frequently shoots the puck in real life. The idea being to bring the simulation to life in a more vivid way than just reading the score from the boxscore.
It also would provide data for advanced stats like Corsi and so on, since all the information needed to track these stats are available in the play-by-play data. You just need to process that information to extract what you need to provide accurate advanced stats of pretty much every kind. Wish I had time to finish that project lol.
Anyway, back on topic. What that project working on the play-by-play data did was highlight how goals are scored. Many of them are scored from sustained pressure and off of rebounds. Now, this may certainly be down to a combination of factors such as defensemen in my ratings being too bad at clearing the front of the crease or goalies being too good at stopping the initial shot but too bad at giving up rebounds, although I did calculate rebound percentages for all goalies both in the NHL and in the Sim and they were quite similar. Rebound percentage in the NHL was one of the main contributing stats for creating the Rebound control rating for goalies, naturally.
As for the relationship between SK, PH, PA and SC it has many layers to it. Essentially the add up to a number you could refer to as a skill-stat (maximum skill-value would be 396).
We can have a conversation about this using two players (bear in mind it's been years since I actually played around with the Sim, so my theories may be slightly off lol):
Player A: 70 PA, 90 PH, 90 SC and 30 SK has 280 skill-points.
Player B: 70 PA, 70 PH, 70 SC and 70 SK has 280 skill-points.
While we can all recognize that Player A is a much more skilled player, they have the same amount of skill-points. Given an equal amount of ice-time, they are therefore likely to produce about the same amount of points.
While high SK makes the player more prone to just hold on to the puck instead of advancing play through creativity, my impression of the simulation is that he will still factor into the play and be fairly effective given enough ice-time due to a high total value of skill-points.
Essentially I believe points are awarded based on a lottery of sorts. The players on the scoring team are awarded points randomly, and the higher the skill-points in relation to his linemates, the greater the chance of that player getting a point.
When creating ratings, one thing to look at is the distribution of goals between forwards and defensemen. If memory serves this typically is around 16-17% in the NHL, so that is a target to aim for when creating ratings. This is done easily in all engines available in the STHS, with the 2.1 being no more difficult than the others. And to do this you do not need to reduce players down to 40-50 SC. If you are doing that, you are doing everything wrong.
Typically in the ratings I used to make I would have the top goal scoring forwards at 99 SC and then the rest would follow on a sliding scale down to around 70-72 for the lowest scoring forwards (think John Scott or Justin Johnson, remember his NHL debut?). This essentially means all regular NHL forwards are rated somewhere in the 80-99 range. For regular defensemen the range typically sits from about 70-78, so a very narrow range. That narrowness is key for the 2.1 engine. If the distance between star players and the plebs is too great, the top players will score huge amounts of points while the rest wont get a sniff.
PH is a very useful tool in creating separation between top players and lower level guys, and SK also has a big room for tweaking things, such as for example getting Ovechkin to score more goals than his peers. Every year when I went through rigorous testing (hundreds of hours is indeed a very accurate measurement) I would always have teams like Washington frustrate me since they would be terrible due to Ovechkin not scoring enough goals when being rated using only the formulas. I would have to go in and manually boost his SK and PH in order to get him to produce at the level he is supposed to. Going through and tweaking individual players like this on every team is time consuming but ensures a very good set of ratings.
This highlights the power of SK and the nuances of it. While the framework of it being an inverse value of Creativity certainly works and is a very good model for how the 2.1 engine functions, you still have to be very methodical and detailed when creating the ratings in order for them to be as good as they can be. When I was testing my ratings I used NHL rosters and accurate lines (including special teams) to run hundreds of season with tweaks of individual players between each session. Changing SC on one player from 85 to 86 can have a huge impact on team performance, so it is a slow and lovely process which I often miss.
Savard is absolutely right in the connection between SC and PA. These can have a massive impact on performance when adjusted. PH and SK are much more forgiving and can be very useful to tweak individual performance.
Sorry for the rant