Beach Volleyball Team Optimization

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Beach Volleyball Team Optimization

Presentation transcript:

Beach Volleyball Team Optimization Student: Matt Oehler Mentor: Gilbert Fellingham phd Institution: Brigham Young UNiversity JSM 2018 Contact: mjoehler94@gmail.com

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.

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.

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

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

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