1. Beach Volleyball Team Optimization
Student: Matt Oehler
Mentor: Gilbert Fellingham phd
Institution: Brigham Young UNiversity
JSM 2018
Contact:

2. Data Dataset 276 Matches (665 Sets) 71 Teams Over 100,000 touches
Data composition
Every touch or skill executed by each player
Includes: skill type and skill grade
Methodology
From these data we can find success probabilities for each player for each of the following skills.
Serving
Receiving (Passing)
Setting
Hitting
Blocking
Digging
Free balls
Go over the data that I have, and how I went about cleaning it to get the probabilities necessary to run the simulations.
Talk about the specific probabilities that I needed to make the markov chain simulation work.

3. Methods Block Serve Good Pass Ace Good Set Error Bad Pass Bad Kill Hit
Dig
This is where our data will come into play, we will be able to calculate the conditional probabilities for each player/skill combo.

4. Actual vs Simulated Results
Winning Percentage (by set)
Team 18 vs 14
Set 1
Set 2
Set 3 1
21-15
21-18
N/A 2
21-13
18-21
15-9
Point Differential
Side Out Percentage
Other metrics:
Number of sets in match

5. Simulated Results after trade
Side Out Percentage
Winning Percentage (by set)
Point Differential
Specify that these are players from team 18 and team 14

6. Conclusion
Simulation seems to be a viable method for assessing hypothetical player fit
Next Steps:
Refine simulations using conditional on previous touch rather than marginal probabilities
Evaluate multiple player pairings via simulation to determine which player types best fit together