Anomaly and Event Detection for

Unsupervised Athlete Performance DataJim O’ Donoghue, Mark Roantree, Bryan Cullen, Niall Moyna,

Conor O’ Sullivan, Andrew McCarren

Experiments and Results

This work was funded by In-MINDD EU FP7 Project, Grant Agreement Number 304979 and Science Foundation Ireland grant number SFI/12/RC/2289

jim.odonoghue@insight-centre.org

Introduction

One of the first steps in data preparation and

mining is anomaly detection, where clear outliers

as well as events or changes in the pattern of the

data are identified before subsequent analyses.

Background

A major aim of sports scientists is to evaluate the

characteristics of high-performing athletes. Our

dataset included data from sensor vests worn by

10 Gaelic football players over the course of 17

competitive games, resulting in an excess of 2

million values, a subset of which was analysed.

Gaelic football involves repeated, short duration,

high intensity. A primary goal was evaluate the

physical and fitness characteristics of the players

and compare these characteristics across each

playing position, with a subsequent goal of

inferring when players are approaching optimal

performance level.

Problem

Sensor vests allowed for the automatic

generation and collection of vast amounts of

player data. As a result of this automation,

datasets such as these often contain many

outliers and are unsupervised in nature leading to

difficulty in finding these outliers and fitting

descriptive and predictive algorithms without

substantial manual effort.

Unsupervised Anomaly Detection

P1: Boundary detection

Domain experts define clearly erroneous ranges

for measures and if points are detected outside

these ranges they are excluded from futures

analyses.

Conclusion

A software architecture consisting of Layer,

Learner and Node components which allows for

the easy implementation and application of

different deep learning algorithms to clinical study

data, with a view to the architecture becoming

data source agnostic.

P2: Univariate outlier detection

Based on Chauvnets method

P3: Principal Components Transformation

Extract the eigenvalues and eigenvectors of the

data and transform the data into it’s principal

components using the correlation matrix.

P4: Principal Components Classification

Set the degree of variance