Three Minute Thesis 2017 - Sport Performance Analysis using Player GPS Traces
Channel: Anj Simmons
Date: 06/07/2017

Transcript:

Elite sports coaches are always looking for new insights into their team's performance.

You may have heard about "sabermetrics" in baseball, originally developed in the 1970's, and popularised in the book and film "Moneyball". In essence, it's about making decisions based upon quantitative performance metrics rather than relying on age old coaching "hunches" or coaching "intuitions" about the game.

With the rise of low cost position tracking devices, we could be on the verge of a new revolution in sports performance analysis. Specifically, in Australia, all AFL players now wear GPS tracking devices (on their back) during each match.

Currently, these tracking devices are being used by sports performance analysts to analyse individual players. Things like, how far did the player run?, or what was their maximum speed?.

But teamwork is about more than one player. What if we could use these devices to quantify the team dynamics as a whole? Which formations lead to goals?, how do these formations change over time, and are any players out of formation?

Initially, I tried training an machine learning classifier on AFL matches played at the MCG to learn which formations are most likely to score a goal. Unfortunately there weren't enough game events from the MCG alone to discover interesting patterns (all it learned was that, unsurprisingly, the team is more likely to score when close to the goal posts).

The difficulty is that we are dealing with very little labeled data - even though AFL is considered a fast paced sport, a typical team only scores about 20-30 times over the course of any given sports match.

Thus, my research has turned toward how we can integrate data from many different sports fields into a common reference frame to permit comparisons. It isn't as simple as it sounds - in AFL each sport field has a different shape, size and orientation. Although I have already partially solved this problem months ago by writing a pipeline of messy custom scripts to munge my specific data sources together, I am trying to model this process in a generalised way that will allow sports performance analysts (my domain experts) to assemble a pipeline for their specific game and dataset.

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