Predictive modeling of football injuries
The goal of this thesis is to investigate the potential of predictive modelling for football injuries.
This work was conducted in close collaboration with Tottenham Hotspurs FC (THFC), the PGA
European tour and the participation of Wolverhampton Wanderers (WW).
Three investigations were conducted:
1. Predicting the recovery time of football injuries using the UEFA injury
recordings: The UEFA recordings is a common standard for recording injuries in
professional football. For this investigation, three datasets of UEFA injury recordings
were available: one from THFC, one from WW and one that was constructed by
merging both. Poisson, negative binomial and ordinal regression were used to model
the recovery time after an injury and assess the significance of various injury-related
covariates. Then, different machine learning algorithms (support vector machines,
Gaussian processes, neural networks, random forests, naïve Bayes and k-nearest
neighbours) were used in order to build a predictive model. The performance of the
machine learning models is then improved by using feature selection conducted
through correlation-based subset feature selection and random forests.
2. Predicting injuries in professional football using exposure records: The
relationship between exposure (in training hours and match hours) in professional
football athletes and injury incidence was studied. A common problem in football is
understanding how the training schedule of an athlete can affect the chance of him
getting injured. The task was to predict the number of days a player can train before he
gets injured. The dataset consisted of the exposure records of professional footballers
in Tottenham Hotspur Football Club from the season 2012-2013. The problem was
approached by a Gaussian process model equipped with a dynamic time warping kernel
that allowed the calculation of the similarity of exposure records of different lengths.
3. Predicting intrinsic injury incidence using in-training GPS measurements: A
significant percentage of football injuries can be attributed to overtraining and fatigue.
GPS data collected during training sessions might provide indicators of fatigue, or
might be used to detect very intense training sessions which can lead to overtraining.
This research used GPS data gathered during training sessions of the first team of
THFC, in order to predict whether an injury would take place during a week. The data
consisted of 69 variables in total. Two different binary classification approaches were
followed and a variety of algorithms were applied (supervised principal component
analysis, random forests, naïve Bayes, support vector machines, Gaussian process,
neural networks, ridge logistic regression and k-nearest neighbours). Supervised
principal component analysis shows the best results, while it also allows the extraction
of components that reduce the total number of variables to 3 or 4 components which
correlate with injury incidence.
The first investigation contributes the following to the field:
• It provides models based on the UEFA injury recordings, a standard used by many
clubs, which makes it easier to replicate and apply the results.
It investigates which variables seem to be more highly related to the prediction of
recovery after an injury.
• It provides a comparison of models for predicting the time to return to play after injury.
The second investigation contributes the following to the field:
• It provides a model that can be used to predict the time when the first injury of the
season will take place.
• It provides a kernel that can be utilized by a Gaussian process in order to measure the
similarity of training and match schedules, even if the time series involved are of
different lengths.
The third investigation contributes the following to the field:
• It provides a model to predict injury on a given week based on GPS data gathered from
training sessions.
• It provides components, extracted through supervised principal component analysis,
that correlate with injury incidence and can be used to summarize the large number of
GPS variables in a parsimonious way.
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