Last week I wrote about Australia’s medal prospects at the Commonwealth Games in Glasgow.
This is my selective record of medals won at the Games.
At the end of Day 4:
This morning, I received a link to an article in The Atlantic.
It was written by Chris Koentges and was published earlier this year (19 February).
It is a fascinating account of coaching. The focus is Urpo Ylönen (Upi), a Finnish ice hockey goaltending coach.
Urpo is seventy-one years old. Chris discusses Upi’s development as a coach in a way that has powerful messages for all coaches.
I enjoyed learning about Upi’s early days playing ice ball in the street in unstructured play and thought this positioned him well to be one of those ice hockey goalkeeper’s without a face mask. The absence of a mask encouraged Upi to try to catch the puck as often as possible rather than blocking it. Chris noted of Upi:
He was the goaltender for Finland for 14 years.
Upi was invited by the Finnish Ice Hockey Association to develop an national goaltender coaching system. Each region in Finland has a goalkeeper coach and Upi’s model has been adopted in other countries. His approach is based on the goaltender staying between the net and the puck.
The puck handler wants to outwait you. Wants you to lose mobility, to fall for any one of an endless series of fakes, so that you go down on the ice and abandon your form. The job gets more complicated when you add a second, third, fourth, and fifth skater to the attack. The goalie needs to keep track of each body, all the while focusing on the one with the puck. Some of these skaters will drift into seams beyond your field of vision; others will plant themselves right in front of you to screen your view entirely.
Upi emphasises the importance of the goaltender skating and catching the puck or deflecting it at an angle away from the goal crease.
Chris illustrated Upi’s coaching with a discussion of Miikka Sakari Kiprusoff. Miika worked with Upi from the age of 12. His training prioritised hand-eye coordination … “controlling the puck and feeling it”. They played badminton to emphasise footwork and lateral movement (and subsequently wrestling).
Upi worked with Miikka and other goaltenders on mind set. “We catch the puck from everywhere—and it might even come to your head. You can take it with your head. You don’t close your eyes, you don’t be afraid.”
This mind set is focussed on long-term flourishing and encourages a “more patient and inclusive style of youth coaching in Finland”.
I think Chris makes a profound point about coaching when he writes of Upi:
Eventually, I realized his deepest purpose: to ease the fears of any human being who would subject himself to such a calling (goaltending).
I am delighted I received an alert (from John Kessel) to this article. I do think there are important cultural issues embedded in Upi’s coaching. But I believe there are some very important generic issues here too.
I am particularly interested in Upi’s understanding of the essence of goaltending and his identification of first principles.
I sensed from Chris’s article that Upi had remarkable observation skill that seemed to amplify his inner calm. He knows goaltending and locates it into a much wider awareness of game playing.
Perhaps I am attracted to his coaching because i grew up with unstructured play too. Street football in a rough laneway gave me confidence in proprioception as well as perception … and possibly my own passion for coaching.
I followed up on Chris’s writing and found this image of Terry Sawchuck:
The photograph was taken in 1966. A make-up artist and doctor recreated the 400+ stitches he had received in 16 years of goaltending.
This convinced me of Upi’s mission (even in an era of protective masks) to ease fears whilst being able to coach a very different approach to goaltending.
There is lots to learn here.
Terry Sawchuck (Ralph Morse, Getty Images)
I have had an opportunity to write a number of articles for The Conversation in recent months.
These include an item about the upcoming Commonwealth Games in Glasgow.
I wrote about the 2014 World Cup in Brazil too.
The football posts included:
Panorama Maracana Stadium (Jimmy Baikovicius, CC BY-SA 2.0)
An alert from OLDaily on 16 July sent me off to look at personal (rather than personalised) learning environments.
Stephen Downes has shared two recent presentations that explore personal learning. As usual with Stephen’s presentations, I was fascinated by his synthesis of ideas.
In Beyond Free (8 July 2014), Stephen points to “a world of free and open resources” that include:
This is an abstract of the talk:
As the concept of ‘open learning’ has grown it has posed an increasing challenge to educational institutions. First admissions were open, then educational resources were open and now whole courses are open. Proponents moreover are demanding not only that open learning be free of charge, but also that the resources and materials be open source – free for reuse by students and educators for their own purposes. This formed the basis for the original design of the Massive Open Online Course as a connected environment in which participants created and reused resources. In such a learning environment, the provision of education moves beyond the programmed delivery of instructional resources and tasks. Education is no longer ‘delivered’ (for free or otherwise) and instruction is no longer ‘designed’ in the traditional sense. Institutions are no longer at the centre of the ecosystem; their value propositions are challenged and new roles for professors and researchers must be found if they are to survive. In this talk Stephen Downes outlines the steps educational institutions must take to remain relevant: embracing the free and open sharing of knowledge and learning, underlining their key role as public institutions, and engagement in the lives and workplaces of people in the community.
In Beyond Institutions (9 July 2014) Stephen emphasises the self-organisation of personal learning. It is what I think Stephen calls elsewhere, prosuming (students produce and consume their own education. They access experts and learning resources directly, and organize these themselves. They form their own communities, work at their own pace, and share extensively with each other).
This is an abstract for the presentation:
In a networked world people become less and less dependent on institutional learning begin to and begin to create their own learning. This creates challenges for institutions, but it also creates challenges for students. In the past, personal learning has been represented as a form of autodidacticism where students either read books at random in the library or at best studied programmed education texts and videos. Today personalized learning is supported using adaptive learning and interactive digital resources. Neither offers what we would call a complete learning experience, as we know there is a social and supportive dimension that must be included. The challenge is to design learning systems that are supportive without asserting control, providing access to a wide range of resources from multiple institutions, but in addition, scaffolding frameworks, access to social and professional networks and support though personal and mobile computing devices, devices and tools, and in workplace systems generally. In this talk Stephen Downes discusses developments in a personal learning infrastructure and outlines how professionals, as both teachers and learners, can take advantage of them.
I finished my reading with a look at some of Alan Levine’s work cited in one of Stephen’s slides.
I thought this was a great way to finish this skywriting journey. It underscores for me how self-organising, personal learning can flourish through the connections we make as learners.
I tracked the teams in this year’s Super 15 Rugby competition in relation to their 2013 ranking at the end of the regular season.
The 2014 looked like this:
The legend for this approach:
The Waratahs defeated six higher ranked teams from 2013 on their way to topping the ladder in 2014. The Highlanders overcame their lowly finish in 2013 with seven wins against higher ranked teams. The Force and the Hurricanes recorded seven wins over higher ranked teams too.
The Reds and the Cheetahs did not retain their 2013 ranking, losing nine times and eight times respectively to lower ranked teams in 2014.
I am hopeful that the Commons will become a virtual meeting place for sport as well as a delightful physical space bringing together all forms of sport.
Twitpic sources acknowledge in captions under photographs.
Andrew Barr (Mandi Semple, CC BY 4.0)
Last month, I looked at a number of predictions about performance at the 2014 FIFA World Cup.
On 11 June 2014, Bing announced a number of World Cup services that included Bing predicts.
Starting today, if you search for “World Cup Predictions”, or any group matches (both preliminary as well as later in the single elimination rounds) we will display the chances of each respective team to win.
Bing models evaluate the interaction of:
Google used the Google Cloud Platform (including Google Cloud Dataflow to import all the data and Google BigQuery to analyse data, build a statistical model and use machine learning to predict outcomes of each match).
The Google prediction approach used data supplied by Opta. These data enabled Google “to examine how activity in previous games predicted performance in subsequent ones”. These data were combined with “a power ranking of relative team strength” and “a metric to stand in for home team advantage based on fan enthusiasm and the number of fans who had traveled to Brazil”.
On 11 July Google announced ahead of the Final “we’re not only ready to make our prediction, but we’re doing something a little extra for you data geeks out there. We’re giving you the keys to our prediction model so you can make your own model and run your own predictions”.
We’ve put everything on GitHub. You’ll find the IPython notebook containing all of the code (using pandas and statsmodels) to build the same machine learning models that we’ve used to predict the games so far. We’ve packaged it all up in a Docker container so that you can run your own Google Compute Engine instance to crunch the data. For the most up-to-date step-by-step instructions, check out the readme on GitHub.
The Google Platform blog has a very open evaluation of its predictions at the World Cup. They tipped a French defeat of Germany in the Quarter Final game.
World Cup teams are especially difficult to model because they play so few games together. … If data is the lifeblood of a good model, we suffered for want of more information.
we know that in the same environment, others fared better in their predictions (h/t Cortana; their model relies more on what betting markets are saying, whereas ours is an inductive model derived from game-play data).
This does identify the fundamental issue for predictions at tournaments rather than in a season of competition. How can a system be sufficiently dynamic to respond to short-term events?
My approach is very basic. For this World Cup I followed the World Football Elo Ratings. This led me inevitably to a Germany victory but only after the semi final.
13 of the 16 games in the Round of 16 followed the Elo ratings. The real surprise for me was Costa Rica’s progress. They were ranked 89 points below Greece on Elo ratings. Belgium overcame an Elo Ratings deficit of 8 points to defeat the USA. The Brazil v Germany game was a most remarkable overturn of the 67 points difference in their Elo ratings pre-tournament.
I did not do my own Elo calculations during the tournament. This would have given me a much more dynamic model. I did not check the betting odds either but I do understand the importance of this market on agile prediction.
As usual with my observations, I decided that the outcome of any one game was independent of referee, player selection and environmental conditions.
Wherever the Elo Ratings rule was broken in this World Cup it did give me a great opportunity to look at much more granular data.
… and to go back to many of the pre-tournament predictions to look at their robustness.
Bing Predictions (Frame Grab)
Google (Frame Grab)
Cortana Windows Phone (Tom Warren)
I have tracked these 171 goals in fifteen minute blocks during the 64 Matches played (I thought this would make the inclusion of two periods of extra time in the knockout stage much neater). There were goals scored in 57 of these Matches.
The final goal tally does not include goals scored in penalty shoot outs.
136 goals were scored in the 48 Group games.
35 goals were scored in the 16 games in the Knockout stage.
There were four penalty shoot outs.
Goals scored in ‘normal’ time:
Goals scored in extra time in the Knockout games:
The scorers of the 171 goals in scoring order were:
Group Round 1
Group Round 2
Group Round 3
Frame Grab http://www.fifa.com/worldcup/index.html
A month ago, I wrote a post for The Conversation here in Australia. It was titled World Cup 2014 Predictions: who will take the title?
I indicated that many of the predictions pre-World Cup pointed to a Brazil v Argentina World Cup Final.
Germany’s success against Brazil gave me a new favourite to consider.
I wrote about the relative merits of Germany and Argentina in another post for The Conversation.
This evening I have read Simon Gleave and Infostrada’s take on the Final. They observe:
The 2014 World Cup final is impossible to call on the basis of the Infostrada Sports Forecast model which assesses Argentina’s chance as 49.97% and Germany’s as 50.03% to be World Champion. These odds fittingly make the World Cup final the most closely matched fixture of the 64 played in this tournament.
— Infostrada Sports (@InfostradaLive) July 13, 2014
Germany and Argentina were ranked third and fourth respectively in the World Football Elo Ratings coming into the World Cup. They were separated by just 57 ratings points.
Germany is one of nine teams at the World Cup that has scored first against a higher Elo Ratings team and won (in their semi-final against Brazil). I see this as an important indicator of performance potential.
My feeling is that the greater experience of the German team (three players with over 100 international caps) is a very telling factor. I think the 260 international caps difference between the two starting teams in their respective semi-finals is an important consideration. I am mindful too that Germany beat Argentina in the quarter-final of the 2010 World Cup (there are 9 German players from that game and 8 Argentinian players in the 2014 Final).
The converging of the teams for me comes about if Lionel Messi and Javier Mascherano combine to create a game on Argentina’s terms.
On balance, I do think this is Germany’s Final.
I am staying up to see the outcome early morning Australian time (after a warm up with a mountain stage of the Tour de France).