Graduate Certificate in Sport Analytics at the University of Canberra

3b4d8237f7217c1737fa633774867e8cJocelyn Mara has created a Graduate Certificate in Sports Analytics at the University of Canberra.

The University website has this link to the course.

I am excited by this development. Jocelyn will add a distinctive voice and approach to the sharing of insights into the analysis of performance.

I was fortunate to meet her during her undergraduate study at the University and then watched with admiration as she completed an Honours’ project in performance analysis, became a performance analysis scholar at the Australian Institute of Sport and received her PhD.

In addition to her research interests, Jocelyn has also explored the possibilities of creating open educational resources.

With her permission I would like to share some news of the graduate certificate.

Jocelyn writes:

I will be encouraging a Bring Your Own Software approach to the course, as I will be using Open Source software such as RStudio and LongoMatch. Students will have access to Tableau. I will also be using Excel quite a lot throughout the course.

I preparing a MOOC to run on the Canvas Network which will be a 4-week taster of the entire Graduate Certificate (one week for each unit). This will commence in January 2017.

I am delighted with the open aspects of the course. Jocelyn is discussing how her approach might fit in with Roland Goecke‘s work at the University of Canberra to offer a Masters in Data Science with a Sport Analytics strand.

This is the content of the Graduate Certificate course in Sports Analytics:

Unit 1: Performance Analysis in Sport

1.1 Identifying Performance Indicators

1.2 Designing Observational Systems and Collecting Data

1.3 Data Analysis and Interpretation

1.4 Feedback and Communication

Activities

  • Collecting sports data
  • Analysing data
  • Visualising data
  • Online quiz
  • Match Analysis assessment

Unit 2: Athlete Monitoring

2.1 Player tracking

2.2 Monitoring athletes with self-report systems

2.3 Training load and injury

2.4 Performance testing

Activities

  • Analysing player tracking data
  • Analysing RPE and well-being data
  • Monitoring training load
  • Analysing performance testing data
  • Online quiz
  • Athlete monitoring assignment

Unit 3: Applied Data Analysis in Sport

3.1 Data management and transformation

3.2 Determining associations

3.3 Predicting outcomes

3.4 Determining differences

3.5 Data visualisation

Unit 4: Sport Informatics and Analytics

4.1 Introductions

4.2 Pattern recognition

4.3 Performance monitoring

4.4 Audiences and messages

Activities

Formative ePortfolio to document engagement with unit 4.

I am hopeful that many of the resources I have been aggregating and curating will be supportive of Jocelyn’s work, particularly with unit 4 and this WikiEducator resource.

I hope this course is of interest to the sport industry. One of my ideas is that we support people who are in sport by offering flexible and open learning opportunities. I acknowledge too that some people might like a fee-for service structured attention opportunity that aligns them closely with a university and provides blended learning experiences.

I think that Jocelyn’s work can articulate with other institutions and communities of practice as each of decides how we continue to learn.

Dr Ron

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Ron Smith received his PhD at a University of Canberra Awards Ceremony last week.

By a wonderful symmetry, the Ceremony was held at the Australian Institute of Sport (AIS) Arena. Ron was a coach at the AIS from 1982 to 1996.

I think the picture sums up graduation delight.

The title of Ron’s thesis is An Investigation into Goal Scoring Patterns in Association Football.

His abstract is:

This thesis investigates goal scoring in professional association football. There has been a vibrant debate in the research literature about how goals are scored. Researchers have discussed the location of the scorer on the field of play, the number of touches of the ball taken, the type of pass, the number of passes in the sequence preceding each goal, and when in a game goals are scored. There has been a growing interest in identifying the most successful area of the field where the final pass leading to a goal was made and has led to debate about one area in particular, Zone 14. The quantification of the number of passes preceding goals has fueled debate about the tactical success of ‘possession based’ football and ‘Direct Play’. Approximately 90% of goals in association football are scored within 23 yards of the goal and the majority of these with less than five passes.

This research presented here analyzed goal scoring in Open Play to determine if the most successful method of gaining entry into the scoring area was from ‘Passing the ball behind opponents or to a player level with the last defender’, compared with ‘Crossing’ the ball and any ‘Other Methods’ that were not included in the other two categories. This new approach maps 7 areas of the field, rather than the 18 used in the extant literature, to record where the final pass was made in each category. It is argued that the use of 7 areas sensitive to the offside law yields a much better analysis of performance. Data were recorded about in which ‘third’ of the field possession was regained and the number of passes in each sequence. The thesis presents new operational definitions for the quantification of lost possession. It is argued that these definitions provide a more accurate account of events preceding goals specifically in relation to what the literature has regarded as ‘zero’ pass goals. Data for this study were gathered from three seasons of the English Premier League and the Australian ‘A’ League and three tournaments of FIFA World Cups and UEFA European Championships. A total of 3,175 goals in Open Play were analyzed. These data enabled comparisons to be made within and between league football and international tournaments. Goals were captured and coded with Sportscode Elite software. Data were analyzed with SPSS software V.19.

The results presented here report that the most successful method of scoring in all international tournaments and in 4 of the 6 league competitions was from ‘Passing the ball behind opponents’; the vast majority coming from an area identified as Zone 14+, the area between the half way line and the penalty area. The majority of goals were scored with 5 passes or less and from regained possessions in the middle third of the field in every competition. The least successful category for scoring in 11 of the 12 competitions was from ‘Crosses’. The evidence from this research provides coaches with the most effective of three strategies to score goals in professional association football while leaving them to decide how best to implement these strategies with the players at their disposal.

After the conferral, in the floodlights again.

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The Joy of Learning

James Simpson graduated with Honours at the University of Canberra’s conferral ceremony this week.

This is a picture he shared

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I was fortunate to be on James’s supervision panel. I felt like this about his work for the year of his research.

James looked at the impact of rule change on water polo performance. He had an opportunity to attend a junior world championships in Greece to collect his primary data. He analysed games from an old rules world chamionship in 2014 as a benchmark for his investigation.

James concluded:

There is evidence of differences in tactical behaviour as a result of the player reduction rule in elite junior women’s water polo and that this investigation could form part of an exciting machine learning exploration to better inform tactical behaviour in water polo.

James has been recruited by the Malaysian Institute of Sport and I hope he will be able to explore his interest in machine learning there. My year with him encouraged me to think about a taxonomy of performance and the role evidence about new forms of game play might play in coach learning.

It enable me to share a joy in learning too.