I'm going to attempt to lay out a method for this new stat. However, it comes with a significant problem.
So this article might outline a great idea that will never amount to anything. But in spite of this hurdle, I'm going to present my case for SRF. At the very least, teams may want to keep track of this on their own to see how often they are faced with these decisions, what amount of risk they take, the impact and the eventual outcome.
So here goes.
Skip Risk Average (SRA)
Recorded in percentage. Divide the total number of times a skip has an option on his/her final shot to take a risky option (to score 2 or more) rather than a simple draw or hit (for 1) at risk of the opponent stealing 1 or more points by the number of times they chose the risky option.
For example. A skip has an option to draw for 1 or try a double for 2 points, at risk of giving up a steal.
If a skip attempts the shot for 2, it counts as a risk attempt towards their risk factor. A skip with a .50 or 50% SRA would be one who choses to attempt the riskier shot half the time.
Skip Risk Outcome (SRO)
Only counting instances when the skip chose a risky option, what is the percentage of time they are succesful? From our previous example, a skip with a .50 SRA might have an SRO of .75. Meaning that when they attempt a riskier shot, they are successful 3 out of every 4 attempts.
So what is the Skip Risk Factor or SRF?
By using Win Expectancy (WE) charts, we can see what impact the result of a risky decision will have on a teams chances at victory. From our example, let's suggest the team deciding on the double attempt is down 1 and this is the final shot of the 5th end in an 8 end game. That leaves 3 ends remaining following the outcome of this end. If they take 1, their WE = .35. If they take 2 WE = .61 and if there is a steal WE = .19.
The baseline is the draw for 1. If they choose to attempt the draw for 1 then their SRF = 0, even if they miss. If they attempt the double and make, their SRF = .26, the difference between making the double for 2 and taking 1. If they miss, the result becomes an SRF = -.16.
The cumulative SRF becomes the addition of the result of all of these shots. Between SRA, SRO and SRF, we can then see a pattern for the amount of risk a skip takes and how those results are impacting the team's chances of winning.
We could also create an average of SRF (SRFa) by dividing this total by the number of times the skip chooses to be risky.
It seems to me these key decision points during games are critical to the outcome of games and could be interesting to examine.
Another idea could be to incorporate points available and generate an average point scored in these situations (SRP?). From our example, the skip had a shot for 2. If you add up all of these decisions and incorporate what the succesful risky shot result was (2, 3, 4, etc) subtract the results of the misses (steal of 1, 2, etc) and then divide by the total number of key decisions, you would see what the average score return was to their team. A skip that is in the negative might want to look at changing their future decisions, or stepping down a position.
As always, any and all comments welcome. This stat will ultimately depend on the ability to train score keepers to recognize a clear key decision moment in a game and then properly record the likely outcomes (take 2, 3 steal 1, etc). Not all situations are equal and some shots have multiple possibilities. Often a shot at 2 can still result in a score of 1. But it would be interesting to track these situations.
Then again, it would also be great to have SportVu for curling, but we aren't there yet. Maybe one day...