#WLeague 2016-2017: 200 Goals

There were 200 goals scored in the 2016-2017 W-League Competition.

191 in the regular season

9 in the Knockout games

Goals By Time Interval

Regular Season

In six time intervals (15 minutes each):

Knockout Games

The Canberra v Melbourne City game involved two periods of extra time. Melbourne City scored in the second period of extra time.

Goal Scoring Data

I have compiled details about the goals scored in a Google Sheet.

My record includes an ordered list of when each goal was scored. There are hyperlinks to the official W-League website to each game within this table to assist with ant secondary data analysis.

 

 

Week Goal Scorer Team Opponents Time
1 11 Caitlin Doeglas Perth WSW 1
12 162 Natasha Dowie Victory City 1
5 57 Katrina Gorry Brisbane City 2
6 68 Jada Whyman Victory WSW 2
7 86 Marianna Tabain City WSW 3
11 124 Tameka Butt Brisbane WSW 3
6 63 Katrina Gorry Brisbane Adelaide 4
5 58 Jessica Fishlock City Brisbane 6
11 125 Tameka Butt Brisbane WSW 6
12 138 Ashley Sykes Canberra Perth 6
1 10 Marianna Tabain City Newcastle 8
2 22 Megan Oyster Newcastle Victory 8
8 90 Jenna Kingsley Newcastle WSW 8
12 159 Summer O’Brien Newcastle Brisbane 9
8 91 Katherine Stengel WSW Newcastle 10
13 173 Rosie Sutton Perth Adelaide 10
6 64 Tameka Butt Brisbane Adelaide 11
1 4 Stella Rigon Adelaide Victory 13
2 20 Leena Khamis Sydney WSW 13
7 80 Nicky Flannery Canberra Brisbane 13
2 23 Arin Gilliland Newcastle Victory 14
9 101 Adriana Jones Adelaide Newcastle 14
10 117 Nicola Bolger Sydney Canberra 15
13 174 Danielle Colaprico Adelaide Perth 15
1 5 Natasha Dowie Victory Adelaide 16
5 55 Rosie Sutton Perth Victory 16
3 30 Alanna Kennedy Sydney Victory 17
5 52 Kyah Simon Sydney Newcastle 17
12 147 Sarah Willacy WSW Adelaide 17
2 17 Laura Alleway City Canberra 18
6 72 Samantha Kerr Perth Sydney 18
7 73 Natasha Dowie Victory Newcastle 18
7 87 Kendall Fletcher WSW City 18
10 108 Laura Spiranovic Victory Brisbane 18
13 169 Marianna Tabain City Brisbane 18
4 36 Michelle Heyman Canberra Newcastle 19
12 139 Ashley Sykes Canberra Perth 19
12 148 Racheal Quigley Adelaide WSW 19
1 6 Adriana Jones Adelaide Victory 20
11 127 Ashley Sykes Canberra Adelaide 20
1 12 Rosie Sutton Perth WSW 22
9 102 Jenna Kingsley Newcastle Adelaide 22
9 105 Katherine Stengel WSW Sydney 23
SF 192 Kyah Simon Sydney Perth 23
2 24 Emma Stanbury Newcastle Victory 25
5 59 Erica Tymrak City Brisbane 25
10 115 Jessica Fishlock City Adelaide 25
10 118 Remy Siemsen Sydney Canberra 25
12 140 Stephanie Ochs Canberra Perth 25
5 48 Ellie Brush Canberra Adelaide 26
4 34 Clare Polkinghorne Brisbane WSW 28
7 81 Stephanie Ochs Canberra Brisbane 28
12 149 Adriana Jones Adelaide WSW 28
2 21 Leena Khamis Sydney WSW 29
SF 193 Vanessa Di Bernardo Perth Sydney 29
8 92 Katherine Stengel WSW Newcastle 30
9 98 Natasha Dowie Victory Canberra 30
12 163 Christine Nairn Victory City 31
5 49 Racheal Quigley Adelaide Canberra 32
4 37 Stephanie Ochs Canberra Newcastle 33
12 141 Grace Maher Canberra Perth 33
3 31 Remy Siemsen Sydney Victory 35
7 75 Samantha Kerr Perth Adelaide 35
9 106 Alanna Kennedy Sydney WSW 35
4 35 Tameka Butt Brisbane WSW 38
1 13 Joanne Burgess WSW Perth 39
14 178 Ashley Sykes Canberra Victory 40
4 46 Remy Siemsen Sydney Adelaide 42
10 109 Amy Chapman Brisbane Victory 42
13 168 Katherine Stengel WSW Canberra 43
10 110 Natasha Dowie Victory Brisbane 45
12 142 Ashley Sykes Canberra Perth 45
12 160 Tameka Butt Brisbane Newcastle 45
F 199 Jessica Fishlock City Perth 45+1
5 53 Jennifer Hoy Newcastle Sydney 46
9 103 Alexandra Chidiac Adelaide Newcastle 46
10 113 Jenna Kingsley Newcastle Perth 46
12 143 Ashley Sykes Canberra Perth 46
14 184 Servet Uzunlar Adelaide Sydney 46
8 93 Rosie Sutton Perth City 47
3 26 Jennifer Hoy Newcastle Perth 48
5 54 Nicola Bolger Sydney Newcastle 48
14 179 Yukari Kinga Canberra Victory 48
6 60 Jenna McCormick Canberra City 49
6 65 Allira Toby Brisbane Adelaide 49
7 88 Kendall Fletcher WSW City 49
12 144 Hayley Raso Canberra Perth 49
12 150 Sofia Huerta Adelaide WSW 49
14 180 Christine Nairn Victory Canberra 49
14 185 Adriana Jones Adelaide Sydney 49
1 14 Helen Petinos WSW Perth 50
7 82 Ashley Sykes Canberra Brisbane 50
11 133 Samantha Kerr Perth Victory 50
9 99 Nicky Flannery Canberra Victory 52
9 104 Jenna Kingsley Newcastle Adelaide 52
10 119 Kyah Simon Sydney Canberra 52
11 134 Arianna Romero Victory Perth 52
1 1 Caitlin Foord Sydney Brisbane 53
4 38 Michelle Heyman Canberra Newcastle 53
6 61 Beverly Goebel City Canberra 53
7 83 Nicky Flannery Canberra Brisbane 53
3 33 Ashley Sykes Canberra Brisbane 54
5 50 Ellie Brush Adelaide Canberra 54
8 89 Princess Ibini Sydney Brisbane 54
14 186 Remy Siemsen Sydney Adelaide 55
1 2 Allira Toby Brisbane Sydney 56
7 84 Ashley Sykes Canberra Brisbane 56
10 120 Remy Siemsen Sydney Canberra 56
14 176 Erika Tymrak City Newcastle 56
12 145 Vanessa Di Bernardo Perth Canberra 57
13 164 Arin Gilliland Newcastle Sydney 57
14 187 Remy Siemsen Sydney Adelaide 58
SF 194 Alanna Kennedy Perth Sydney 58
3 27 Katherine Stengel WSW Adelaide 59
6 66 Adriana Jones Adelaide Brisbane 59
11 135 Alyssa Mautz Victory Perth 59
12 151 Erica Halloway WSW Adelaide 59
8 94 Jessica Fishlock City Perth 60
14 188 Emily Hodgson Adelaide Sydney 60
3 28 Adriana Jones Adelaide WSW 61
6 69 Sarah Yatim WSW Victory 61
7 76 Rosie Sutton Perth Adelaide 62
13 170 Rebekah Stott City Brisbane 62
1 7 Natasha Dowie Victory Adelaide 63
4 39 Rhali Dobson Newcastle Canberra 63
4 43 Jessica Fishlock City Victory 64
5 56 Vanessa Di Bernardo Perth Victory 64
4 40 Grace Maher Canberra Newcastle 65
7 77 Sofia Huerta Adelaide Perth 65
10 121 Yukari Kinga Canberra Sydney 65
11 128 Sofia Huerta Adelaide Canberra 65
12 152 Adriana Jones Adelaide WSW 65
10 122 Francisca Ordega Sydney Canberra 66
1 3 Katrina Gorry Brisbane Sydney 67
7 78 Samantha Kerr Perth Adelaide 67
11 131 Caitlin Foord Sydney City 67
13 171 Katrina Gorry Brisbane City 67
1 15 Samantha Kerr Perth WSW 68
14 189 Adriana Jones Adelaide Sydney 68
10 114 Samantha Kerr Perth Newcastle 69
12 153 Racheal Quigley Adelaide WSW 69
10 116 Racheal Quigley Adelaide City 70
10 111 Natasha Dowie Victory Brisbane 71
8 95 Aivi Luik City Perth 72
F 200 Beverly Goebel City Perth 72
1 8 Monica Adelaide Victory 74
4 44 Stephanie Catley City Victory 74
6 67 Sofia Huerta Adelaide Brisbane 74
13 165 Arin Gilliland Newcastle Sydney 74
11 126 Jada Whyman Brisbane WSW 75
11 132 Jessica Fishlock City Sydney 75
12 154 Alexandra Chidiac Adelaide WSW 75
4 45 Jessica Fishlock City Victory 76
8 96 Samantha Kerr Perth City 77
14 181 Grace Maher Canberra Victory 77
SF 195 Vanessa Di Bernardo Perth Sydney 77
12 155 Sofia Huerta Adelaide WSW 78
13 166 Arin Gilliland Newcastle Sydney 78
11 129 Ashley Sykes Canberra Adelaide 79
12 146 Samantha Kerr Perth Canberra 79
12 156 Sofia Huerta Adelaide WSW 79
SF 196 Rosie Sutton Perth Sydney 79
2 18 Melina Ayres City Canberra 80
11 136 Vanessa Di Bernardo Perth Victory 80
1 9 Selin Kuralay Victory Adelaide 81
10 123 Leena Khamis Sydney Canberra 81
11 130 Sofia Huerta Adelaide Canberra 81
5 51 Hayley Raso Canberra Adelaide 82
7 79 Vanessa Di Bernardo Perth Adelaide 82
1 16 Samantha Kerr Perth WSW 83
3 32 Natasha Dowie Victory Sydney 83
6 62 Karly Roestbakken Canberra City 83
12 157 Adriana Jones Adelaide WSW 83
4 47 Emily Hodgson Sydney Adelaide 84
6 70 Kariah White Victory WSW 84
7 74 Jennifer Hoy Newcastle Victory 84
8 97 Samantha Kerr Perth City 84
9 107 Kyah Simon Sydney WSW 84
14 175 Danielle Colaprico Adelaide Perth 84
3 29 Paige Nielsen WSW Adelaide 85
11 137 Rosie Sutton Perth Victory 85
14 190 Sofia Huerta Adelaide Sydney 87
4 41 Emma Stanbury Newcastle Canberra 88
9 100 Ashley Sykes Canberra Victory 89
12 158 Ally Ladas Adelaide WSW 89
6 71 Katherine Stengel WSW Victory 90+1
2 25 Jenna Kingsley Newcastle Victory 90+2
4 42 Nicky Flannery Canberra Newcastle 90+2
7 85 Emily Gielnik Brisbane Canberra 90+2
12 161 Jennifer Hoy Newcastle Brisbane 90+2
13 172 Rebekah Stott City Brisbane 90+2
14 182 Ashley Sykes Canberra Victory 90+2
14 191 Caitlin Doeglas Perth WSW 90+2
10 112 Natasha Dowie Victory Brisbane 90+3
13 167 Kyah Simon Sydney Newcastle 90+3
14 177 Amy Jackson City Newcastle 90+3
SF 197 Shawn Billam Perth Sydney 90+3
2 19 Ellie Brush Canberra City 90+4
14 183 Yukari Kinga Canberra Victory 90+4
SF 198 Jessica Fishlock City Canberra 107

Photo Credit

Before the Final (Westfield W-League on Twitter)

Champions (Westfield W-League on Twitter)

An Introduction to Analytic Narratives for Coaches and Students

A photograph of Aboriginal Whalers at Eden, NSW.

Background

I received an alert to a paper today that has sent me off to revisit Donald Polkinghorne‘s and Philippe Mongin‘s discussion of narrative and the process of historical analysis … and to contemplate pedagogy.

The paper that started my journey today is titled ‘The Cooperation of Humans and Killer Whales (Orcinus orca): The Application of a Simple Fuzzy Rule-Based Model to a Historical System‘. The authors of the paper are Emery Coppola, Ryan Jones, Jack Owens and Ferenc Szidarovszky.

They present:

an historical model application that is pedagogical in nature, in that it presents the methodology for constructing a simple fuzzy model for a vague  but complicated social cooperative network along with example model-simulation results.

Their paper has an immediate empirical appeal for me as they discuss activities in a geographical area four hours to the south of my home in New South Wales.

Once I was hooked by the accident of geography, I became intrigued by their approach to bring together fragmentary data sources to create a model.

I believe their paper, and its connections with others interested in ‘narrative knowing’ and ‘analytic narratives’, raises important issues for the discussion of sport analytics.

A Narrative of Cooperation

A picture of a whale hunt at Eden, NSW.

In their paper, Emery, Ryan, Jack and Ferenc study “a complicated social cooperative network in Twofold Bay, southeastern Australia, over a century until 1930″. They note “Surviving sources document that pods of killer whales or orcas worked cooperatively with human bay whalers” to pursue and kill baleen whales.

Jack has been involved in the field of geographically-integrated history since the late 1960s. The Twofold Bay research provides an excellent opportunity to pursue this kind of history project.

The research has to deal with some fundamental issues about data. “The whalers are long dead, and there is no systematic collection of records from which we can draw”. There are subsequent studies of “orca behavior in different regions of the world done over the past 40 years” and there is “significant research on Australia’s Aboriginal peoples”.

Despite these constraints, their goal was to develop a fuzzy rule-based model that predicts the likelihood of the success of the social network in killing a whale”. In this case study “prediction means that the model will simulate the outcome of a whale hunt for each event in our narrative”.

They share the process of developing their fuzzy rule-based model and report:Our attempt to represent a complicated social network with a simple rule structure falls far short of plausibility. At the same time, our initial efforts, however modest, compel the historian/modeler to formulate a set of linguistic rules that quantify often highly vague variables and conditions, qualitative and/or quantitative in nature, in an attempt to represent and simulate a complicated system of interest.

I liked their exploration of the pedagogical issues in their research. I liked too their reflection on their practice:

As we learn from our initial models and accumulate more data, information, and understanding, we can formulate and test new models against the surviving record, allowing us to consider alternative hypotheses and to see more clearly what additional information we need to acquire … in an attempt to explain better this fascinating cooperation between orcas and human whalers for at least a century to hunt successfully large baleen whales. (My emphasis.)

As I read the concluding paragraphs in their paper, I was struck by the generic issues Emery, Ryan, Jack and Ferenc raise:

  • Imperfect information
  • Flawed understanding of processes that are often so complicated that no model will ever accurately capture the underlying dynamics
  • Narrative sharing
  • Acceptable prediction accuracy
  • Models as a first step to forming a theoretical or heuristic framework for analysis
  • Refining and improving understanding through additional data collection, model development, and testing.

These issues are central to a scholarship that embraces “new forms of research organization and rapidly evolving types of information management and analysis” (Owens, 2010).   They connected me with Donald Polkinghorne and Phillipe Mongin.

Sharing Stories With Practitioners

A picture of the fins of two orca whales

After reading Emery, Ryan, Jack and Ferenc’s paper, I thought about how I might share with coaches some of the take-aways from their “flawed understanding”.

I wondered too how I might share the pursuit of heuristic frameworks with students as they develop their understanding of analytics.   Donald Polkinghorne’s (1988) exploration of narrative knowing places significant emphasis on the importance of “having research strategies that can work with the narratives people use to understand”. 

Most of the coaches with whom I work are able to locate themselves within an historical context in sport, in terms of the sport in general and in terms of their own career paths.

I think they would find the story of the orcas as fascinating as I do.

My hope is that this might lead to conversations about understanding and transforming performance. I think I might be very selective about what I share and would gloss over the fuzzy-logic part of the story.   I think the orcas would be a great lead in for students too but the context of our conversations would enable me to explore what constitutes fuzzy logic and its potential to model behaviour.

With both groups, coaches and students, I would be mindful of Donald’s observation:

History’s function is to describe the events of the real world as they have actually happened and to explain why they have happened. … Historical narrative is supposed to be factual – that is, it is supposed to be made up of true sentences that represent actual past events. The sentences of historical discourse are expected to pass a correspondence test based on the evidence of the traces of events left in documents. (1988:57) (My emphasis.)

I take our ability to develop actionable insights to be informed by a rigour in how we collect and analyse data that can be fragmented and partial as well as comprehensive.

Could He Have Won?

Farmland in Belgium that was the site of the Battle of Waterloo

 

I enjoy returning to Philippe Mongin’s 2009 paper, A Game-Theoretic Analysis of the Waterloo Campaign and Some Comments on the Analytic Narrative Project.

In the paper Philippe presents a game-theoretic model of Napoleon’s last campaign, which ended dramatically on 18 June 1815 at Waterloo. It looks in particular at the decision Napoleon made “on 17 June 1815 to detach part of his army against the Prussians he had defeated, though not destroyed, on 16 June at Ligny”.

In his discussion of events in the Waterloo Campaign, Philippe observes:

At three key moments – June 17, around mid-day on June 18, and in the final hours of this same day – Napoleon could have departed from the line of events that his previous decisions had set in motion, and he did not (2009:15).

Philippe is able to include much more detailed data in his analytic narrative compared to the orca paper. His discussion of the process of constructing an analytic narrative provides an explicit opportunity to explore how history might have been redefined and to think critically about ‘the culture of the unique’.

In 2016, Philippe revisited the process of constructing an analytic narrative. He notes that “the transformations that standard narratives incur to become analytic narratives bears some relation to the transformations they incur to become computational narratives” (13:11).

I take the essence of this tranformation to be the understanding that “analytic narratives are narrative texts, which include, among their parts in non-narrative form, the statements of formal models and their consequence” (13:9).

Philippe used this approach in his study of Waterloo, Emery, Ryan, Jack and Ferenc did too in their use of models within a case study with much less documented evidence.

Narratives and Audiences

A picture of Mongolian wrestlers and their coaches.

My aim in discussing analytic narratives is to open conversations about evidence and models.

It is an attempt to extend the epistemic reach of sport analytics in the connections we make with coaches and students.

I am attracted to the qualitative nature of analytic narratives but am mindful that they provide an excellent platform for engagement with quantitative models. Emery, Ryan, Jack and Ferenc used fuzzy logic with fragmented historical accounts; Philippe used game-theoretic tools with an extensive textual record.

I am hopeful that the epistemic reach of sport analytics can be enriched by a pedagogical leap too. Jack Owens (2010), in his work on a Masters course at  Idaho State University, developed a capstone internship experience that allowed tutors to ‘coach’ students “in ways they can interact more effectively with others”.

As Donald suggests, narrative will be at the heart of vibrant interaction with practitioners. Imagine where a story that starts “Did I ever tell you about Old Tom?” or “How could you snatch defeat from the jaws of victory?” might lead us.

Photo Credits

The Aboriginal whalers of Eden (ABC South East NSW)

Return of the killer whales of Eden (Australian Geographic)

Orcas (Ed Dunens, CC BY 2.0)

Waterloo, Belgium (cjlvp, CC BY-NC-ND 2.0)

P1140782 (WhatsAllThisThen, CC BY-NC-ND 2.0)

References

Coppola, E., Jones, R., Owens, J. & Szidarovszky, F. (2015). The Cooperation of Humans and Killer Whales (Orcinus orca): The Application of a Simple Fuzzy Rule-Based Model to a Historical System. NOAH LLC and the Geographically-Integrated History Lab (ISU).
Mongin, P. (2016). What Are Analytic Narratives? Proceedings 7th Workshop on Computational Models of Narrative. B. Miller et al. (eds), pp. 13:1–13:13. Dagstuhl.
Mongin, P. (2009). A Game-Theoretic Analysis of the Waterloo Campaign and Some Comments on the Analytic Narrative Project. Paris: Groupe HEC.
Owens, J. B. (2010). Graduate Education in Geographically-Integrated History: A Personal Account. Ann Arbor, MI: MPublishing, University of Michigan Library.
Polkinghorne, D. E. (1988). Narrative knowing and the human sciences. New York: State University of New York Press.

Completeness?

News of Steve Pearce’s tree project in Tasmania set me off thinking about how we record sport performance.

There is a home page for the project.

The page includes this information:

In 2016 the team spent over 8 weeks in the Styx Valley, 100 km to the west of  Hobart, filming, photographing and climbing giant Eucalyptus regnans trees.

The camera rig for photographing the tree portrait took two weeks to install due to the huge size of these trees, the wide distances they were apart and the frequent bad weather events. 

Despite this, the team have managed to create an incredible portrait of a 84 meter high tree.

We have also produced some stunning photos and videos of the forest, capturing many beautiful photos from a bird’s eye view using drones.

The picture shared here is comprised of 87 photographs that took “more than 3 weeks of full time editing to assemble”.

The team (there were eight members) note “Of the 67 days in the field we had 12 successful mornings of weather and only 5 mornings of suitable fog.”

They add “There is so much more to the story of this photograph and it possibilities”.

The possibilities include a shared virtual reality experience.

This is a link for use on a mobile phone.

The team write of their consideration of virtual reality:

During the Tasmanian Tree Project we experimented with the idea of creating a virtual reality tour of the tree. This presented us with a number of technical and financial challenges. Our biggest consideration amongst these was the setting. A tree offers no secure sturdy platform to shoot such images with a typical DLSR setup which requires great precision. 

A mobile delivery option would mean we could take advantage of the accelerometers on a mobile device providing a infinitely more dynamic experience and opening up the possibility of using more affordable VR headsets. We also wanted very much for the experience to be one that everyone could download for free and take home from the museum. This mobile delivery would allow to effectively transform the viewer into a advocate for these grand trees when showing it to a friend.

There is a 3D model of the tree too.

Throughout my reading about the project, I was fascinated by the desire to share the process and outputs of the project. I was struck too by the roles team members played in data capture.

  • Creative director
  • Project co-ordinator
  • Research climber
  • Lead climber, POV cameras
  • Forestry scientist
  • Support climber and rigging
  • Filmaker and producer

I thought too about the invisible work that teams do. In this project:

The camera rig for photographing the tree portrait took two weeks to install due to the huge size of these trees, the wide distances they were apart and the frequent bad weather events.

All of which left me thinking about aspirations for completeness in the observation, analysis and recording of performance … that might enable us to appreciate the beauty of performance.

The Tree Project Team share this perspective on their work:

We feel that the simple yet profoundly striking vision of seeing a tree for for first time can break down all preconceptions. We believe that this also allows everyone an opportunity to grasp further complexity and deeper ecological concepts.

I can see how this might guide our work in sport too. We might even start conversations about the ecological validity of our analysis process.

Photo Credit

The Tree Project, Steven Pearce.