
We want to predict the future. We want to tell you the winner before the first serve. We want to use historical data to predict future outcomes…or at least better than we currently do.

If we point score at a higher rate than our opponent, we win right? This coaching thing is easy…

If PS% isn’t the answer, maybe we can use attacking to predict the future.

Step 3.5 – Quick Sidebar on Attack Efficiency
At least attacking can explain the past…

Is passing a 2.2 always a 2.2?

Step 5 – If he’s a good passer, why doesn’t he pass good?
When’s the last time we evaluated the metrics themselves?

Step 6 – If he’s a good hitter, why doesn’t he hit good?
But can he get on base?

Step 7 – Expected Value (eV) in Attacking
Find efficiencies for all attack outcomes, not just the kills, blocks, and errors.

Step 8 – “Input” Expected Value (eV) in Attacking
What we expect from the attacker should differ based on the quality of the situation the attacker is dealt.

Step 9 – Expected Value Added (eV+)
Results minus Expectations. Did you increase or decrease our ability to win the rally?

Step 10 – Expected Value Added (eV+) in different skills
How do we set up input and output situations for other skills?

Step 11 – How to go from VolleyMetrics to Expected Value, step by step.
Not as complicated as you might think. Let me help you.

Step 11.5 – Probability to Win the Rally, Expected Value, and Other Metrics. A Quick Pitstop.
What is Expected Value?! What is Win Probability?! Everybody panic!!

Step 11.6: Using Score Differential to Strengthen Predictions
A somewhat obvious look at how attacking better than your opponent leads to bigger butt kickings…

Step 12: Point Win Probability
Given any point in the rally, who’s in the better position to win?

Step 13: Optimizing Before Prediction
Throwing out VolleyMetrics’ quality codes (#+!-/=) and using XY coordinates to rebuild our formerly rickety foundation

Step 14: Computer Vision Intro
Where other sports already are – and where volleyball will eventually be. It’s not…not cool